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Explaining consumer shopping channel preferences

in retail categories

Hassen Kajee

Research assignment presented in partial fulfilment

of the requirements for the degree of

Master of Business Administration

at Stellenbosch University

Supervisor: Dr Marietjie Theron-Wepener

Degree of confidentiality: A

Declaration

I, Hassen Kajee, declare that the entire body of work contained in this research assignment is my

own, original work; that I am the sole author thereof (save to the extent explicitly otherwise stated),

that reproduction and publication thereof by Stellenbosch University will not infringe any third party

rights and that I have not previously in its entirety or in part submitted it for obtaining any

qualification.

H Kajee

19738943

Copyright © 2013 Stellenbosch University All rights reserved

Acknowledgements

I would like to acknowledge my wife for her patience and support through this project. I would also

like to acknowledge my two amazing children who allowed me the opportunity to dedicate the

required time to complete this project. A big thank you to my supervisor, Dr Marietjie Theron-

Wepener, for giving me direction, being patient and making me believe I can get this done.

Abstract

The proposed research aims to understand the shopping behaviour and attitudes of consumers

towards categories within the retail sector based on preference towards an online or offline

medium of purchase. The shopping categories used in this study covered Groceries,

Shoes/Clothing, Electronics, Gifts and Homeware with online attributes of price, delivery and

convenience being measured. The target audience are individuals already purchasing online. The

members of the target audience are from different genders, age groups and family sizes, and their

relationship statuses differ. The study also investigated age and gender factors in online

purchasing.

The research looked at the current state of online and mall purchasing in South Africa and

emerging markets in order to gain a better understanding of the South African consumer and a

directive towards the evolution of shopping in the country.

An online survey was used to collect data from individuals already purchasing online. The survey

was distributed via an online survey platform. The database was sourced from two online

platforms.

There are gender differences in shopping channel preference within the categories of Electronics,

Gifts and Homeware. Consumers are adopting omni-channel shopping behaviour where offline is

the primary channel and online the supplementary channel.

Online security was not a primary concern compared to the attributes of convenience, delivery and

price. Consumers differed in their attitudes to the retail attributes of convenience, delivery and price

based on the category in question. Electronics and Gifts had more favourable ratings.

Key words

Attitude, convenience, malls, offline shopping, omni-channel, online shopping, retail categories.

Table of contents

Declaration 2

Acknowledgements 3

Abstract 4

List of tables 6

List of figures 7

CHAPTER 1 INTRODUCTION 9

1.1 INTRODUCTION 9

1.2 BACKGROUND 9

1.3 PROBLEM STATEMENT 12

1.4 RESEARCH QUESTIONS 12

1.5 CHAPTER OUTLINE 13

1.6 SUMMARY 13

CHAPTER 2 LITERATURE REVIEW 14

2.1 INTRODUCTION 14

2.2 LITERATURE REVIEW 14

2.3 SUMMARY 29

CHAPTER 3 RESEARCH METHODOLOGY 30

3.1 INTRODUCTION 30

3.2 THE POPULATION AND SAMPLE 30

3.3 THE QUESTIONNAIRE DESIGN 30

3.4 DATA COLLECTION 32

3.5 DATA ANALYSIS 33

3.6 SUMMARY 34

CHAPTER 4 FINDINGS 35

4.1 INTRODUCTION 35

4.2 MAIN FINDINGS 37

4.3 SUMMARY 49

CHAPTER 5 SUMMARY, CONCLUSION AND RECOMMENDATIONS 51

5.1 INTRODUCTION 51

5.2 SUMMARY OF MAIN FINDINGS 51

5.3 RECOMMENDATIONS FOR FUTURE RESEARCH 52

5.4 RECOMMENDATIONS 52

5.5 CONCLUSION 52

REFERENCES 54

APPENDIX A – Statistical Tables 60

APPENDIX B - Questionairre 125

List of tables

Table 2.1: A typology of consumer value: mall shopping vs online shopping 8

Table 2.2: Attributes of e-service quality 9

Table 3.1: Demographic questions 23

Table 3.2: Purchasing behaviour online (measured in percentages) 23

Table 3.3: Online attributes related to shopping categories 24

Table 3.4: Online attributes related to the success of online shopping 24

Table 4.1: Summarised findings on the Anova and hypothesis tests for gender 29

Table 4.2: Descriptive analysis of gender channel usage 30

Table 4.3: Summarised findings on the Anova and hypothesis tests for family status 30

Table 4.4: Descriptive analysis of family status channel usage 31

Table 4.5: Hypothesis test for online channel usage 32

Table 4.6: Hypothesis test for online versus malls 33

Table 4.7: Anova test for category channel bias 34

Table 4.8: Descriptive statistics for attribute analysis between categories 35

Table 4.9: Friedman Anova sum of ranks for delivery 36

Table 4.10: Anova test for delivery attribute between categories 36

Table 4.11: Friedman Anova sum of ranks for convenience 36

Table 4.12: Wilcoxon test for price attributes of Shoes/Clothes and Electronics 37

Table 4.13: Spearman correlation table between age and online purchases 38

Table 4.14: Spearman correlation table between age and mall purchases 39

Table 4.15: Anova table for online security and family status 40

Table 4.16: Age and propensity to pay delivery charges 40

Table 4.17: Anova and Cronbach reliability table for attitude to the future of online

shopping 41

List of figures

Figure 2.1: Theory of Reasoned Action 10

Figure 4.1: Gender responses 27

Figure 4.2: Family status responses 28

Figure 4.3: Age percentages 28

Abbreviations and acronyms

DOI Diffusion of Innovation (theory)

MRT Media Richness Theory

OSAM Online Shopping Acceptance Model

PBC Perceived Behavioural Control

SACSC South African Council of Shopping Centres

TPB Theory of Planned Behaviour

TRA Theory of Reasoned Action

UK United Kingdom

USA United States of America

CHAPTER 1

INTRODUCTION

1.1 INTRODUCTION

The proposed research aims to understand the shopping behaviour and attitudes of consumers

towards shopping categories within the retail sector based on their preferences towards online or

offline mediums of purchase. The retail categories relate to Groceries, Shoes/Clothing, Gifts,

Electronics and Homeware, with the online attributes of price, delivery and convenience being

measured. The target audience consists of individuals already purchasing online. The members of

the target audience are from different genders, age groups and family set-ups, and their

relationship status differs.

The research will focus on the current state of online and mall purchasing in South Africa and in

emerging markets in order to gain a better understanding of South African consumers and the

evolution of shopping in the country.

In order to describe the current state of e-commerce in South Africa, the literature review of this

research assignment is based on a wide range of online articles, reports and journals. An online

questionnaire was used to collect primary data on consumer preferences towards online and offline

mediums of purchase.

1.2 BACKGROUND

According to the 2015 PwC report Prospects in the retail and consumer goods sector in ten sub-

Saharan countries, consumers in South Africa are under pressure from high debt, high utility costs

and high unemployment (PwC, 2015). The Consumer Confidence Index dropped to a multi-year

low as further economic deterioration is expected. Consumers were being forced to adjust

consumption patterns towards lower priced products, foregoing discretionary purchases. The

report further mentioned that the higher income strata are resilient to the economic downturn, with

consumers becoming more demanding and placing a premium on convenience and to a lesser

extent on online purchasing. Urbanisation was on the increase with 64.8% of the total South

African population urbanised in 2015 (PwC, 2015). In addition, the number of economically active

South Africans will increase as 48.6% of the South African population is under 25 years of age

(PwC, 2015).

According to a report by the South African Council of Shopping Centres (SACSC) – Urbanisation

and the impact of future shopping centre development in Africa and South Africa – urbanisation will

be responsible for a R70 billion increase in retail spend by 2021/2025, with a further demand of 1.5

million to 2 million square meters of additional retail space (SACSC, 2014). As a result, the number

of malls in South Africa has grown to 1 785 with consistent investment in progressive expansion of

this industry (BusinessTech, 2016).

A.T. Kearney’s report on the Global Retail Development Index in 2013 described South Africa as a

well-developed retail market, beyond the mature stage (A.T. Kearney, 2013). This means that

competition within the market is fierce, real estate is expensive and consumer are discretionary

spenders. South Africa was also benchmarked against First World countries, such as Australia,

with South Africa reporting an estimated 1 700 shopping centres versus Australia with 1 452

shopping centres (A.T. Kearney, 2013).

In contrast, the retail sector in the United States of America (USA) has reached a mature stage.

Malls in the suburbs are being “boarded up and decaying”, and big anchor tenants are starting to

move out (Peterson, 2014). Within the next 20 years, it is expected that half of America’s shopping

malls will start closing down, with many staying alive mainly as a result of strong anchor tenants

(Peterson, 2014). Malls in the urban middle class areas in the USA are decaying as a result of

people moving to the cities and online shopping taking a 6% bite out of the retail sector (Uberti,

2014).

Worldwide, mall explosions occur during phases of suburban growth. This means that as people

move to the suburbs, investment in shopping malls increases significantly and mall development

booms with developers keen to maximise profits. Suburban growth occurred in the mid-1980s for

post-war USA and it is currently happening in South Africa. In South Africa in particular, the fall of

apartheid and rise of the middle class with spending power are leading to increased suburban

growth and shopping mall development. Currently, South Africa with its population of 55 million

people has an estimated 1 700 shopping malls while the USA with a population of 318.9 million

has roughly 1 000 active malls.

Retail sector growth in South Africa has seen the country possibly over-capitalise on malls with

signs of saturation. According to an article posted on the SA Commercial Prop News website,

South Africa is facing a mall oversupply despite consumer pressure (SA Commercial Prop News,

2016). South African consumers are cash strapped, and the continuous growth of malls means that

trade will be transferred from one mall to another mall (SA Commercial Prop News, 2016).

It is possible that the closing down of malls in a First World country such as the USA could be an

indication of what the future could bear for a Third World emerging economy like South Africa once

the retail sector reaches saturation and the new tech savvy generation takes over.

1.2.1 The growth of ecommerce in South Africa

In the USA, over 64% of consumers own a smartphone, with 80% of these consumers using their

smartphones to do online shopping (Sosa, 2017). Cyber Monday has become the largest online

sales day in the USA, with shoppers spending $3 billion online, with 30% of those sales made via

mobile sales (Sosa, 2017). In 2016, mobile commerce was responsible for 25% of ecommerce

sales in the USA (Sosa, 2017).

In South Africa, too, online shopping is increasing concurrent to the rapid expansion of bricks-and-

mortar shopping facilities. Currently, South Africa represents the largest ecommerce player on the

African continent, with sales growing by 30% annually (PayU, 2017). In addition, South Africans

are becoming more confident with using online channels, with 20.6% saying they have never

shopped online, 19.4% saying they only shop online, and 32% saying they often shop online

(PayU, 2017). Secure payment gateways are adding to consumer confidence and increased online

shopping (PayU, 2017).

According to Eshopworld’s South Africa eCommerce Insights, in 2017, the country had 18.43

million ecommerce users, with an additional 6.36 million users expected to be shopping online by

2021: “Four years from now, these 24,79 million eCommerce users will spend an average of

189.47 USD online” (Eshopworld, 2017). Currently, Electronics & Media is the leading product

category in South Africa, accounting for US$964.2 million of the market share, followed

by Furniture & Appliances, which generates US$553.7 million in sales. By 2021, Electronics &

Media is expected to still be the most purchased online category, with an estimated value of

US$1.38 billion, followed by Furniture & Appliances with an expected worth of US$1.07 billion

(Eshopworld, 2017).

In 2015, 40% of internet users worldwide have bought goods online, which amounted to roughly 1

billion online users (Fichardt, 2015). The boom was driven by the advent of better payment

gateways. Further analysis showed that, worldwide, business-to-consumer ecommerce sales also

increased, reaching $1.92 trillion in 2016 (Fichardt, 2015). However, Fichardt (2015) pointed out

that South African consumers still lacked trust in online transactions. In this context, companies

such as Naspers are doing their best to strengthen the general view of secure payment via their

payment portals (Fichardt, 2015).

South African penetration in the online sector lies at 40% (World Wide Worx, 2017). Reshaad Sha,

Chief Strategy Officer and Executive Director of Dark Fibre Africa, said in this context: “Finally

reaching the point where we can say every second adult South African is connected to the Internet

is a major landmark, because Internet access is becoming synonymous with economic access

(World Wide Worx, 2017).

A 2017 report by Qwerty found that 75% of all website traffic in South Africa comes from mobile

devices, with an ecommerce user base of 17.1 million people. The report mentioned that most

individuals use a desktop for ecommerce purchasing with mobile usage being social media driven

(Qwerty Digital, 2017).

According to a BidorBuy blog, online shopping in South Africa is growing thanks to advancements in

technology (like fast internet), the prevalence of smartphones (which has made online shopping easy

and convenient), the increase in the variety of products that are available online, and the expansion of

delivery options and shipping destinations (BidorBuy, 2017). However, this growth is hampered by

“the erratic nature of South African postal services” and the fact that internet access is still not

available to all in the country (BidorBuy, 2017). The development of Braamfontein in Johannesburg

into a WiFi city is a sign that South Africa is on its way to becoming a well-developed tech-savvy

society within the African context (Shezi, 2015).

To generate new growth opportunities and to reach customers beyond their bricks-and-mortar

stores, retailers increasing need to cater for tech-savvy customers. The closure of the 159-year-old

department store chain Stuttafords has highlighted important questions about how industries need

to develop digital business models in order to stay relevant, and how they need to “leverage

technology for the benefit of their customers” (Govender, 2017).

The South African media and internet company Naspers seems to have faith in the online business

as it has invested R960 million into the online retail store Takealot.com (Planting, 2017). No one

knows what South Africa’s potential is, but “we see the gap … as our opportunity” (Planting, 2017).

As Planting (2017) said, “In South Africa’s favour is its youthful demographics and mobile

adoption”.

1.3 PROBLEM STATEMENT

Are developers overspending on the development of malls and underspending on digital

innovation? This research assignment seeks to understand where online shopping is positioned in

the mind of the South African consumer, and whether there is a movement towards online

channels of purchase within retail categories.

Online shopping in South Africa makes up 1% of total retail (Planting, 2017). With increasing and

significant capital expenditure on malls in the country coupled with the rollout of advanced data

communication lines (LTE, fibre), what shifts are occurring in the combined share of online and

offline purchases based on consumers’ attitudes?

1.4 RESEARCH QUESTIONS

This research assignment aimed to answer the following research questions:

Are consumers adopting a multi-channel approach to shopping?

Does gender and family dynamics have any relationship with channel usage within specific

shopping categories?

What categories of shopping (Groceries, Clothes, Electronics, Shoes and Gifts) are biased

towards online purchasing?


towards bricks-and-mortar shops?

Do any of the attributes of convenience, delivery, returns and price differ within categories

of shopping?

Do consumers find online shopping safe?

Is there a generational bias towards online shopping?

Which factors or attributes will increase online shopping?

Are South African consumers already omni-channel shoppers?

1.5 CHAPTER OUTLINE

1.6 SUMMARY

This research assignment seeks to understand the shopping behaviour and attitudes of consumers

towards shopping categories within the retail sector based on their preference towards online or

offline mediums of purchase. This chapter provided an overview of the adoption and growth of both

shopping malls and online retail in South Africa in order to ascertain whether consumers are

evolving into omni-channel shoppers. Understanding the evolving retail channels and consumer

choices within categories of shopping will lead to more innovative and resilient retail strategies.

CHAPTER 2

LITERATURE REVIEW

2.1 INTRODUCTION

The purpose of the literature review was to gain a better understanding of the research

phenomenon, which in this case is to explain consumer shopping channel preferences (for online

versus bricks-and-mortar stores) in certain retail categories (Groceries, Shoes/Clothing,

Electronics, Gifts and Homeware).

Shopping is defined as an exchange of goods for payment. Although shopping transactions can

occur on multiple channels, this research assignment focused on the bricks-and-mortar channel

and online channel.

The researcher made use of various resources including journal articles and academic textbooks

for the literature review.

2.2 LITERATURE REVIEW

2.2.1 Online shopping and omni-channel shopping environments

A multitude of factors impact consumer purchase choices. These factors include channel choice.

Channel choice has a significant relationship with age, gender and previous purchases, information

and experience within the channel. Younger individuals prefer to use online shopping channels.

The use of social media as part of the marketing strategies of these channels significantly

improved sales via online and mobile channels. Consumers who use mobile and online channels

are deal-seeking consumers. They are high-volume buyers and mostly purchase online between

09:00 and 18:00 (Park & Lee, 2017: 9).

Cao and Li (2015: 96) looked at how consumers evolved as they experience shopping channels. It

was found that as offline consumers purchase online, they compare the online and offline

environments. However, as consumers get accustomed to the online channel, they start comparing

online stores across the same chains and start forming a brand affinity. The research was

conducted on retail stores that had an online store. It was found that when there was no

congruency in assortment between the offline and online stores, the chains lost consumers to

competitors online. Consumer loyalty is therefore based primarily on the variables of experience,

trust and congruency with the product offering (Cao & Li, 2015: 96).

Three specific categories of product types are search goods, experience goods and credence

goods. High search-ability of information is associated with search goods. These are in the form of

reviews, product information and previous consumer experiences. There is confidence in

purchasing search goods on different e-tailers, whereas experienced high-value goods, where

information was not easily available, focused on the prestigious e-tailers for purchase. Credence

goods (vitamins, etc.) followed a similar route, but lacked importance or high risk. The research

proved that consumers choose specific channel choices for specific product categories. This points

to consumers trusting a brand or e-tailer before they purchase high-value items (Korgaonkar,

Silverblatt & Girard, 2006).

The attributes of search ability, information and purchase are primary drivers of the technological

purchasing decision. Consumers have access to many devices and mediums and they understand

the specific roles that each medium plays within the purchasing decision of a channel. A consumer

might search for information online and purchase offline, or might compare prices offline and

purchase online or on mobile. The research showed that consumers focus on mobile as a search

mechanism and desktop and offline as purchase mechanisms. It was also found that hedonic

reasons for shopping were higher within a bricks-and-mortar setting than desktop or mobile

settings with utilitarian reasons being equal for both the desktop and bricks-and-mortar settings.

The findings clearly identified online mobile as a search and purchase channel, with online

ecommerce and offline being the major purchasing platforms. Consumers can move in and out of

each channel which offers them the purchase utility they require. This points towards multi-channel

shoppers who adopt many different channels (Korgaonkar, Silverblatt & Girard, 2006).

Colour, touch, feel and pictures are sensory items which affect channel choice. The choice of

channel that a consumer chooses for a purchase is a function of the level of perceived media

richness. Customers who shop in a mall or store have physical interaction with the environment

which is highly visual and personal, compared to online and mobile environments. Online adopts

an informative approach with media richness, while mobile due to the nature of the screen size has

a low level of media richness (Maity & Dass, 2014).

Media Richness Theory (MRT) is usually used in the context of media choice. Maity and Dass

(2014) have identified four types of media along this richness continuum, namely text, audio, video

and face-to-face communications. Face-to-face is the richest medium as it allows mutual feedback

and simultaneously conveys a variety of cues (e.g. tonal, facial or emotional). Text-based

interaction (e.g. texting through mobile devices or browsing information through text-only cell

phone browsers) is less “rich” than audio, video or face-to-face interaction (e.g. in-store

interactions or communications using Apple iPhone's Facetime feature). Media that are highest

and lowest in media richness anchor the two ends of the continuum, which is online. A higher

media richness setting will require more research from consumers (e.g. in-store), whereas medium

media richness (e-commerce – highly informative) to lower media richness (mobile) requires less

research and cognitive costs from consumers (Maity & Dass, 2014).

Consumers may evaluate their consumption experiences in terms of specific attributes such as

product selection which may then be related to higher-order performance dimensions such as

website design. The dimensions are associated with e-service quality, which in turn is associated

with key outcomes such as customer satisfaction, customer behavioural intentions (e.g.

repurchase intentions) and customer behaviour (e.g. word-of-mouth referral) (Blut, Chowdhry,

Mittal & Brock, 2015).

Extrinsic value pertains to a means-end relationship where some thing or event is a means in

accomplishing some further purpose, thereby referring to the convenience and time-saving factor

of online, and malls as a shopping utilitarian environment. Intrinsic value characterises an

experience appreciated for its own sake, which references sound, sight, touch and feel. This feeds

into consumer value typology which is characterised by the dimensions of extrinsic and intrinsic,

active versus reactive, and self versus other (Holbrook, 1999). Refer to Table 2.1 below which

references the consumer value typology.

Table 2.1: A typology of consumer value: mall shopping vs online shopping

Source: Holbrook, 1999.

In such a conceptual approach, it is critical to understand (i) the strength of relationships among

the different components and overall e-service quality, (ii) overall e-service quality and its

outcomes such as customer satisfaction, repurchase intentions, and word of-mouth, and (iii)

different factors that can moderate these associations (Blut et al., 2015). Table 2.2 below lists

attributes of e-service quality.

Table 2.2: Attributes of e-service quality

Source: Blut, Chowdhry, Mittal & Brock, 2015.

Consumers have to adapt to new innovations. The initial purchase risk when doing the purchase

online is very high. However, over time, the product gains momentum and diffuses (or spreads)

through a specific population or social system. Developed by Rogers (1995), this is called the

Diffusion of Innovation (DOI) theory, which focuses on the adoption of new innovations based on

the relative advantages, compatibility, trialability and observability.

Online shopping in South Africa has gone through the diffusion of innovations, where trust has

been built across online brands and consumers are now becoming accustomed to the ecommerce

setting. Kamarulzaman (2011) explained that Malaysian shoppers followed the Diffusion of

Innovation theory when adopting the internet. Her research also found that consumers initially used

the internet for information purposes, with the purchase taking place in the bricks-and-mortar

setting, before they became online purchasing consumers.

Relative advantage is positively related to adoption as compared to other perceived adoption

characteristics. It represents the degree to which an innovation is being perceived as better than

the idea it supersedes. The relative advantages appear to be significant in terms of the diffusion of

internet shopping innovation (Rogers, 1995). However, the reasons for adoption vary depending on

the types and nature of products, time, price, promotions and needs during the course of the

buying process (Kamarulzaman, 2011).

Complexity is the degree to which an innovation is perceived as relatively difficult to understand

and use. If the degree of difficulty is high, then adoption will be low (Rogers, 1995). Most

consumers agreed that no additional skills were required for executing internet shopping, as it was

not complicated at all. For them, basic computer knowledge should be sufficient to shop online.

However, for first- time users, familiarity with browsing and searching on the internet is important

(Kamarulzaman, 2011).

Once compatibility has been achieved, adoption becomes the focal point. Compatibility, which is

positively related to adoption, refers to the degree to which an innovation is perceived as

consistent with existing values, past experiences, and the needs of potential adopters (Rogers,

1995).

Consumer acceptance of innovation is based on their belief in the usefulness of the technology.

Online shopping is going through this phase where its usefulness is being experienced with limited

effort to shop, and with individuals starting to adopt and use online shopping systems. The

technology acceptance model describes the constructs of perceived usefulness and perceived

ease of use. Both these constructs relate to the salient beliefs under the assertion of information

technology. Perceived usefulness postulates the probability of the activity increasing a consumer’s

performance or engagement with information and/or information systems alike. Perceived ease of

use, in contrast with usefulness, refers to the degree to which the information and/or information

system’s use will be free of effort or require limited effort from the consumer (Davis, Bagozzi &

Warshaw, 1989).

Figure 2.1: Theory of Reasoned Action

Source: Davis, Bagozzi & Warshaw, 1989.

The Theory of Reasoned Action (TRA) focuses on the consumer’s attitude and subjective norms.

Within the notion of purchase behaviour in online and offline settings, consumer cognitive and

behavioural aspects towards the channels are analysed, with a focus on specific attributes within

the utilitarian and hedonic aspects of both channels. This helps to understand specific consumer

behavioural traits. Consumers’ subjective norms are biased by age and gender where influence

could be social, family driven or gender specific. The Theory of Reasoned Action considers a

consumer's behaviour as determined by the consumer's behavioural intention, where behavioural

intention refers to an attitude towards the behaviour and subjective norm. The TRA predicts an

individual’s intention to behave in a certain way based on a consumer's attitude towards that

behaviour. Also, a consumer's intention to behave in a certain way may be influenced by the

normative social beliefs of the consumer (Hansen, Jensen & Solgaard, 2004).

Consumers may perceive obstacles in online shopping activities, as it requires certain skills to

navigate a computer and conduct online searches. This refers to consumers’ online behaviour.

Consumers also form negative or positive cognitive beliefs towards an action such as online

searching and shopping, thereby creating an attitude towards the action. The theory of planned

behaviour (TPB) predicts intention to perform a behaviour by the consumer’s attitude towards that

behaviour rather than by consumer’s attitude towards a product or service. Also, a consumer’s

intention to perform a certain behaviour may be influenced by the normative social beliefs held by

the consumer. In comparison with the Theory of Reasoned Action (TRA), the Theory of Planned

Behaviour (TPB) adds Perceived Behavioural Control (PBC) as a determinant of behavioural

intention (Hansen, Jensen & Solgaard, 2004).

Torben Hansen (2008) explained the Schwartz Theory of Values based on self-interest, activism,

openness and conservation where a set of values are created. The more consumers find things in

their favour, the more they see the action as favourable, which affects their attitude towards that

item. Hansen’s study (2008) found that there is a strong correlation between attitude and behaviour

towards online grocery shopping, with attitude being the most important predictor of action.

Consumers who are hesitant about online shopping would need a message that portrays the

compatibility of this method with the traditional form of shopping, whereas consumers who are

already using online shopping are seen as consumers who value control, therefore enhancing their

attitude towards this portal.

Hansen (2008) looked at the impact of values on the attitude and behaviour of consumers who

shop online for groceries. The study divided consumers into three groups depending on their use

of the internet for online grocery shopping and tested the resulting values that affected the

consumers’ decision to shop online. The foundation Theory of Planned Behaviour (TPB) highlights

the consumer’s behavioural intention based on the attitude of the consumer. This tests the

consumer’s attitude towards a certain behaviour versus the consumer’s attitude towards a product,

which builds upon the consumer’s perception towards online grocery shopping. The beliefs that are

formed become part of the consumer’s attitude, catalysed by the social value of the action versus

the individual value. This phenomenon is possibly occurring within the environment of malls in

South Africa, where the perceived value of shopping at malls and being part of a growing mall

community is seen as a value offering to the consumer at the social value level.

Shankar, Smith and Rangaswamy (2003) found that customer satisfaction is higher in the online

medium, which includes offering rewards, coupons and more product information. Making data

easily accessible and rewarding frequent users enhance loyalty. Their study showed that

consumers become more loyal when the online and offline environments are merged. According to

their study, the online environment leads to more sales in the offline environment, and a significant

increase in brand loyalty (Shankar, Smith & Rangaswamy, 2003).

Kamarulzaman (2011) investigated the effect of motivation and the adoption of e-shopping in the

United Kingdom (UK) and Malaysia. It was found that a key determinant of e-shopping is price.

Consumers expected lower prices on e-platforms than at bricks-and-mortar retailers. It was also

found that shoppers did not like the role of retail sales people because of pressurised decisions.

Consumers were found to be similar in the UK and Malaysia in terms of the attributes, with uptake

mostly slower in Malaysia. The key attributes were pricing, convenience, control/privacy, security,

no complexity, website atmospherics, personalisation, trialibilty, returns, and observability due to

peer adoption.

Kacen, Hess and Chiang (2013) investigated the impact of value on consumers when they choose

a shopping platform. Traditional channels still maintain a dominant stance in total retail value. With

online shopping starting to become more popular, the question is whether consumers accept

online equally to traditional stores. The argument put forth is one of transactional economics where

consumers will choose the seller that minimises the total transaction cost. The online sector has

costs associated with delivery, handling, post-service costs and search costs. These perceived

costs are reduced when shopping at a traditional store.

Time pressure is seen as an economic cost, which is high when shopping in a traditional

environment. The study of Kacen, Hess and Chiang (2013) was done using six products which can

be purchased either online or offline, with the population consisting of students and non-students

ranging between 28 and 82 years of age with majority being web users. The study found that

people only shopped online if prices were lower. Individuals felt books was the best product to buy

online. The study further showed that social interaction was an unimportant attribute and therefore

not a defining notion towards the traditional form of shopping, which is in stark contrast to El-Adly

and Eid (2016) who introduced social interaction as key to the hedonic attribute of shopping.

Online stores suffer on attributes such as handling, delivery, returns and social interaction. This

also needs to be tested within the South African context.

Pauwels and Neslin (2015) found that merging offline sales with an online non-transacting option

created more revenue for the stores on the sensory items, giving customers more time to analyse

how these products form part of their consumption requirements. It was also found that consumers

who live close by substitute the entertainment of browsing instore with browsing on the internet for

products, therefore reducing the number of feet instore (Pauwels & Neslin, 2015). In contrast,

Johnson, Kim, Mun and Lee (2015) found that store loyalty is directly linked to the store attributes.

They concluded that to create a loyal customer, a store needs to enhance its visual offering in

order to keep customers shopping (Johnson, Kim, Mun & Lee, 2015). The entertainment value

offered by browsing online versus browsing in a physical store highlights the need to further

investigate, among others, the online store layout and the possibility of creating atmospherics in

the online store layout.

Kukar-Kinney, Ridgway and Monroe (2009) discussed preferences to shop online versus offline,

and the tendency to buy compulsively. The study found that consumers who were compulsive

buyers preferred the internet because they wanted to avoid social interaction and have an

immediate positive feeling. It was found that these shoppers experienced shame and guilt towards

their impulsive nature, and therefore they stayed away from social interaction due to the negative

effect of the habit.

Perea y Monsuwe, Dellaert and De Ruyter (2004) mentioned that attitude to online shopping is a

function of the ease of use, convenience, enjoyment, customer traits, product characteristics and

trust. Korgaonkar, Silverblatt and Girard (2006) found that search products was the key attribute for

consumers to shop online. The reason was the ease of retrieving information on the product. Their

study also found that there was not a great deal of loyalty to specific e-tailers as products were

highly searchable and comparable. The important factor differentiating one e-tailer from the next

was the level of security and privacy. Another important aspect was the ability to cancel an order

and the way the merchandise was displayed. Customers ranked merchandise assortment higher

for prestigious e-tailers than for discount retailers, which was based on product type more than e-

tailer loyalty (Korgaonkar, Silverblatt and Girard, 2006).

González-Benito, Martos-Partal and San Martín (2015) looked at the power of brands on an online

purchase decision as a substitution for touch and the lack of tangibility. Consumers who

experienced brands within a bricks-and-mortar setting were more likely to purchase that specific

item online as the brand was associated with trust. The trust aspect of brands is what eventually

will create an evolution in the online environment for purchases of similar kinds of products. When

making an online purchasing decision, intangibility is limited to specific products where consumers

who are purchasing garments focus on the size, fit, the quality of the material and the feel of the

item (Cho & Workman, 2011). The degree of tangibility or lack thereof in an online setting is not

more powerful than the trust that the brand creates with the consumer. However, this does become

more challenging when focusing on food items (González-Benito, Martos-Partal & San Martín,

2015).

Consumers who make online purchases complain more than those who make purchases offline.

The internet has created a social system that allows individuals to be heard much louder,

highlighting a power shift to the consumer. Complaints and returns channels within the online

environment must be well setup and easy to use (Lee & Cude, 2011). Hasan (2010) suggested

that online shopping is more attractive to males than to females. It was found that females find

online shopping boring, and they were more aware of the risks associated with online shopping.

Hasan (2010) suggested the use of social media discussions and blogs to create more awareness

of the benefits of online shopping for females. This links directly to the discussion around

segmentation based on the hedonic and utilitarian aspects of mall shopping, where females are

more hedonic and mall orientated, and males are less hedonic and more online orientated. De

Klerk and Lubbe (2006) found that females are extremely perceptive to aesthetics as a gender

segment towards apparel. This finding speaks to the instinctive nature of females to be more

sensitive to colour and beauty. Therefore, in order to attract female shoppers, aesthetics and

colour should play an important role in both the online and offline environments.

Jackson, Stoel and Brantley (2011) explored the difference between males and females in terms of

shopping value where females are more drawn to atmospherics and emotion in their shopping

experience, which means that the variety offered by malls can add to this emotional attraction. The

experiential benefits gained from shopping include that of value, seen from a point of quantitative,

pricing and utility value, versus the qualitative aspects of the benefits, with quality measured from

the consumer’s perspective.

Kim, Park and Lee (2017) looked at new consumption patterns emerging as technology becomes

more advanced. Consumers are able to move between online and offline more easily. Consumers

mostly research offline and purchase at cheaper rates online. The innovative hybrid system of buy

online and pick up in store enables consumers to find the product online and pick it up at a store,

leading to an outcome of immediate gratification.

Product perceived risk is divided into high and low product involvement. High product involvement

relates to high economic risk of poor choices (such as computers and electronics) versus low

product involvement which has a low economic risk of poor choices (such as groceries and

clothing). High product involvement requires a significant amount of information and research.

Consumers purchasing high-involvement products will travel distances to touch, feel and test,

thereby making sure the economic risk is reduced. The research concluded that consumers who

purchase high-involvement products online prefer a pickup in-store option which allows the touch

and feel that lacks online (Kim, Park & Lee, 2017).

Blut, Chowdhry, Mittal and Brock (2015) concluded that using the means-end chain theory – which

says that specific attributes of web design, which is hedonic in nature, coupled with a functional

and systematic fulfilment process and customer service – is strongly associated with e-service

quality. Security was not seen as an important attribute. This suggests that online security is taken

as a norm. E-service quality is industry-specific and country-specific based on maturity of online

adoption. Quality of service and brand creates trust which leads to repeat purchases. The research

was conducted with a focus on products sold by Amazon.com and Alibaba.

Hasan (2010) discussed the Theory of Reasoned Action in terms of attitude. He proposed the

notion of attitude to learn and gain experience over time, with positive or negative outcomes. The

attitude construct looks at cognitive, affective and behavioural components. The findings point to

men having a higher acceptance of online shopping based on the attitudinal aspects of cognitive,

affective and behavioural components. Females are focused on stimulus and touch. Outcomes

pointed to more stimulation and the playful design of websites and experiences. This also relates

to the maturity of online shopping within a specific country.

Tse and Yim (2002) looked at how access to more information, security and the ability to make

decisive and well-informed purchase choices warrant consumers to buy books online. The

research concluded that online was the preferred channel for book purchases based on research

ability and the information gathering that can be done. The only difference between online and

offline is the ability to touch.

Ruby Roy Dholakia (1999) quantified shopping as a household chore as well as a pleasurable

activity. She examined shopping as a gendered activity where a ratio of 2:1 females to male shop

within a bricks-and-mortar setting. Shopping is seen as creating hedonic value while completing

utilitarian chores. The hedonic aspects are still strong within the bricks-and-mortar setting, but as

time becomes constrained and women enter the working environment, online shopping will

become more relevant. Men who are family orientated are also found to be taking part in shopping

activities, but with a utilitarian motivation.

Zhou, Dai and Zhang (2007) discussed the Online Shopping Acceptance Model (OSAM) where

males are more open to online shopping than females. Men tend to buy electronics, hardware and

software while women focus on clothing and food items. Women are far more touch and

emotionally orientated and need to be able to see and experience the product. Women generally

do a lot of the food and clothes shopping due to the emotional hedonic and fun nature of the

shopping environments and the opportunity to meet people there. Income plays a role in online

shopping as individuals with more income tend to shop more. This is also evident from the general

nature of online products, being mostly services and non-essential items such as toys, gifts,

clothes and electronics. The cost of owning a computer and purchasing data also plays a role in

the demographic that has access to the online environment. Perceived risks associated with online

shopping is the intangible nature of the purchase, financial risk, delivery time, information risk,

product risk and privacy risk. As consumers get more accustomed to the internet and trust is built

with brands, product risk decreases (Zhou, Dai & Zhang, 2007).

Shwu-ing Wu (2003) found that a large number of individuals who shop online are higher income

earners, with a mean age of 36. These online shoppers are predominantly males with focus

attributes such as convenience, time and delivery. This highlights the notion that online shoppers

are individuals who can afford the cost of data and equipment costs (phones/computers)

associated with browsing and shopping. According to Wu (2003), efficient delivery plays a key role

in making sure consumers’ positive experience of online shopping is maintained. Research

conducted by Korgaonkar, Silverblatt and Girard showed that experience within a specific sector

will affect the outcome of a repurchase decision and the channel used for the repurchase

(Korgaonkar, Silverblatt & Girard, 2006).

Hedonic value is related to the social interaction within the shopping environment. This includes

the touch, social factor and environment. It was found that grocery shoppers going to bricks-and-

mortar shops are highly utilitarian in nature, and they usually want a sense of accomplishment

thereafter (Olsen & Skallerud, 2011). Online grocery shoppers do not focus on the money saving

potential of online grocery shopping. Their online focus is mainly time saving in nature. Consumers

shop at conventional stores to save money, with hypermarkets seen as high on assortment but low

on savings. Online shopping seemed to be very low in hedonic value with a high focus on

individuals who view sustainability as core to their values to save the environment (Cervellon,

Sylvie & Ngobo, 2015).

Consumers are situationally driven when choosing a channel. These situational variables relate to

store distance, time, convenience, social interaction and clarity of website layout. It was found that

consumers who purchased items such as books, IT products, T-shirts and airline tickets had used

multiple offline and online channels. This shows that consumers move between channels, where

the channel does not matter but the situational variable in the channel choice does. Factors that

increase or decrease channel choice relate to store tidiness or the clarity of the website. An untidy

store will make consumers turn to online. This points to a reduction in the hedonic aspect of

shopping where online shopping becomes more utilitarian in nature. The intangibility of online

products and distance to store have the same effect on the consumer. Reducting intangibility by

focusing on increasing the sight aspects with larger and more pictures would help reduce

intangibility online. Factors that affect the offline sector is the limited business hours. Consumers in

this context will purchase items online after business hours, and they prefer the search and

information aspects of online shopping (Chocarro, Cortiñas & Villanueva, 2013).

Compulsive shopping is also a key determinant of channel choice where compulsive buyers opt for

the online rather than the physical store environment. Compulsive consumers have 24-hour access

to shopping online, without being unobserved, and they experience positive feelings when

shopping online (Kukar-Kinney, Ridgway & Monroe, 2009).

The main focus of bricks-and-mortar shops is instant gratification, touch and hedonic motivations

whereas online shopping creates convenience, comparison shopping and reduced travel.

Consumers may utilise both channels, one as a primary and the other as a supplementary

shopping channel. The option of channel choice is inherent within a developed online/offline sector

where online growth is faster or equal to the offline growth in sales. This occurs beyond the stage

of technological adoption where consumers are mature within the channel choice and have

adopted a combined buying pattern (Chu, Arce-Urriza, Cebollada-Calvo & Chintagunta, 2010).

Verhoef, Kannan and Inman (2015) discussed the notion of the online and offline environments as

multi-channel to omni-channel retailing taking into consideration the evolution of smart phones and

social interaction. This harnesses all aspects of communication, logistics and the supply chain to

amass a greater and more involved communication channel as well as a more well-connected

system of retail. The idea of bricks-and-mortar stores combined with various online channels and

social media interactivity creates a view of online retail integrated with offline retail, with online

channels that support the traditional channels (Verhoef, Kannan & Inman, 2015).

2.2.2 Bricks-and mortar shopping environments

Dubihlela and Dubihlela (2014) tested the effect of mall image attributes on consumers. They

tested merchandising, atmosphere, accessibility, entertainment and in-mall convenience as key

attributes for retail success. They also found that South African shoppers were more extrinsic in

their motivations for shopping with a task orientation. Their study concluded that developers should

focus on design and convenience for a successful mall experience.

Johnson, Kim, Mun and Lee (2015) found that store loyalty is directly linked to the store attributes.

They explained that in order to create loyal customers, a store needs to enhance its visual offering

in order to keep customers shopping. The study also found that consumers enjoy the experience of

theatre, where they can meet the designer. Other attributes such as location, product and facilities

did not affect customer enjoyment as these were seen as necessary as a result of intense

competition in the market. This study does relate to a First World economy, highlighting the

contrast between South African consumers who are very product and location driven versus First

World consumers who are experience driven. Arslan, Sezer and Isigicok (2010) found that younger

consumers in Turkey focused on comfort, social interaction, a secure environment, accessibility

and leisure as key determinants of mall attributes. The younger consumers enjoyed the size of the

mall, with the main focus on the entertainment and experiential effects of the mall.

Farrag, El Sayed and Belk (2010) conducted research to understand the motives for mall shopping

in Egypt. The merging of entertainment with shopping has created a whole new experience, which

combines consumption with shopping as a fun activity. The consumer might enter the mall with a

single motive. However, as a result of the atmospherics and surroundings, latent motives for

shopping at the mall emerge. The shoppers are distributed into three groups, namely family

focused, hedonists and strivers. These three groups are further identified by their shopping

motives, which are convenience, safety, identity, entertainment and sales. It was found that the

majority of shoppers in Egypt are strivers who do not have excess income to spend on wants.

Therefore, they do a lot of window shopping, and use the mall as a place to hang out. This is in

stark contrast to shopping malls in the USA where the majority of the individuals shopping are

older and within the bracket that can afford the mall prices (Kirtland 1996). The types of shoppers

frequenting malls are also based on the economic and cultural composition of the malls. In Kuala

Lumpur, malls are seen as bringing modernity to those who want to look good, feel good and live a

modern lifestyle (Nurani, 2003). However, in the UK, malls are seen as places for the family to

shop, and for the elderly to stroll (Miller, Jackson, Thrift, Holbrook & Rowlands, 1998). Although

many malls are homogenous in their design, appeal, products, motives and stores, culture and

history differentiate the value of the experience gained from the mall.

Varman and Belk (2012) looked at malls from an identity point of view, where young consumers

from India transform their Third World identities within a Westernised environment. They found that

the young consumers feed off the elitism of malls to overcome inferiority, and challenge the

Western way in order to gain a sense of respect and acceptance. This study goes into the realm of

consumer identity based on the historical and globalisation aspects of consumption. The influence

or utilitarian effects of mall shopping differ from country to country as can be seen from a study by

Kuruvilla and Joshi (2010) where they looked at the demographics affecting attitude and purchases

in malls in India. India is a fast developing economy with a large population and, as such, a study

was undertaken to understand the people who shop at these malls.

Jackson, Stoel and Brantley (2011) found that female consumers in the USA focused mainly on the

utility function of a mall. However, the Indian study by Kuruvilla and Joshi (2010) found that it was

mostly male consumers who focused on the utility of the malls. In India, the males are the heads of

the households and earned the biggest portion of household salary. It was found that 60% of

shopping is spent window shopping and browsing, with a significant amount of the additional time

spent at the food court and entertainment areas, which translates into malls being used for

experiential means. The attributes that were chosen as important included parking, ambiance,

refreshments and safety. It was also found that mall attributes weighed stronger with the more

serious shoppers than with the shoppers doing window shopping. It was found that the more

serious shoppers were invested in the atmospherics as part of their purchase decisions.

Jackson, Stoel and Brantley (2011) looked at how different generational cohorts and genders

experienced malls and how this impacted their consumer behaviour. Generational cohorts in

developing economies might perceive certain aspects of the mall experience with a value impact

differently from their counterparts in developed economies. Based on this, it is clear that millennials

in South Africa might not necessarily share the perceptions of millennials in First World economies.

In the South African environment, apartheid saw many consumers segmented according to colour,

with less opportunity to participate. This can have an effect on young people seeking the

experience of elitism and acceptance through retail and branding, creating a booming market for

malls. This is clearly seen in the increasing number of malls being built in countries such as South

Africa.

Taking a holistic view, El-Adly and Eid (2016) believed that malls offer value beyond utilitarian and

hedonic attributes. Malls added self-gratification, transaction, epistemic, social interaction and time

convenience value.

El-Adly and Eid (2015) mentioned that the mall is not only a place for hedonic value, but also a

place of relaxation and destress that improves the shopper’s wellbeing. During this self-

gratification, other dimensions are fulfilled, which pertains to the utilitarian aspect of shopping.

El-Adly and Eid (2016) referred to the three dimensions of a mall, namely recreation, interior and

staff. According to their study, the atmospherics affect the perceived value that the mall will create

within the three dimensions. This underlines the notion that customers are becoming more holistic

as these dimensions will become common ground.

Olsen and Skallerud (2011) found that the hedonic value of a shopping all is related to personal

interaction and product value, whereas utilitarian value is based on the physical aspects of

assortment, etc.

Jeanne van Eeden (2006) studied the social structure surrounding shopping, and found that

shopping malls have sustained the viewpoint that women are the main shoppers. Shopping malls

are often designed to resemble a cocoon or a womb, which speaks to the view that shopping is

female orientated with a strong hedonic character.

Scott, Bloch and Ridgway (1990) found that customers who are shopping with the motive of

experiencing the shopping environment will find more utility than those who are do not have

experience as a motive. The customers who purposefully came to shop or see sights or hear

sounds would experience these attributes more clearly than those who do not come for those

reasons. Also, the focused shoppers were more likely to purchase a product versus those visiting

the mall without a clear plan of action.

Karim, Kumar and Rahman (2013) found that hedonic values such as joy, escape and adventure

delve deeper into the intrinsic value of shopping, and found that different segments experience

different levels of value. They found that males are less hedonic than females.

Jacob Miller (2013) discussed the need to have retail therapy at the mall but not the shop. He

looked at the mall as a commodified urban space, with its own governance at the centre. Miller

believed there is a need for customers to come to a place that is more affective by the very means

of its design and the social demographics that form part of the shopping mall environment.

El-Adly (2007) segmented consumers into three categories, namely relaxed shoppers, demanding

shoppers and pragmatic shoppers. These segmented shoppers demand different aspects from the

shopping experience. The study found that these segmented shoppers find the following six

attributes attractive in a mall: comfort, entertainment, diversity, mall attractiveness and sales

incentives. El-Adly’s research (2007) was limited to university students.

Wong, Lu and Yuan (2001) mentioned that consumers are becoming more selective in the malls

they patronise based on the availability of merchandise, stores, location and variety. This speaks to

the emotional and convenience aspects of shopping malls.

Keng, Huang, Zheng and Hsu (2007) discussed the effect of service encounters on consumers.

Service is viewed from a perspective of retail service as well as atmospherics. Their study looked

at experience related to service. According to them, customers enjoy the environment to a level of

playfulness. When this happens, they come back more often and they increase their purchases.

They looked at the experience in terms of a value orientation, taking into account the extrinsic,

intrinsic, active, reactive, self and other values. The intrinsic effects of shopping refer to ambience,

entertainment and experience, which leads to the extrinsic effects, which is convenience, one-stop

shopping, quality and price. These experiences form part of the service offering which predicts the

customer behavioural intention.

Oteng-Ababio and Arthur (2015) examined the re-emergence of shopping malls within the

landscape of Ghana, looking at the effects that malls have on urbanisation and the informal retail

sector. Malls are seen to be part of rapid globalisation that works towards transformation of the

urban environment as well as the growth of the informal sector in Africa. Many of the malls that

have sprung up in Ghana have attracted a vibrant informal trade outside these malls, creating a co-

existence between formal and informal trade and impacting different demographics. It was found

that majority of the youth visit the malls mostly for the entertainment activities as the malls provide

a safer environment, creating a conducive environment for social interaction. It was also found that

people visit the mall to conduct informal business meetings. The success of the malls in Ghana is

determined by, among others, their architectural appeal, their ability to create an environment that

is conducive to a longer stay, and the attraction of retailers as the heart of the mall.

Ugur Yavas (2003) found that consumers in the USA preferred malls that do not have too much

traffic, that are easy to walk through without too many kiosks, and that have a mix of shops with

price appeal instead of too many expensive high-end shops. Kim, Park and Lee (2017) countered

this by saying that stores should focus on the enjoyment of shopping in order to maintain customer

loyalty. The experience is the focal factor to adjust consumer’s attitudes towards stores. Wong, Lu

and Yuan (2001) found that consumers choose malls that have a wide variety of goods that meet

their specific preferences. A SCATTR model was used which tested the dimensions of location,

quality, variety, popularity, facilities and sales incentives, finding that consumers are quite sensitive

to quality and variety with sales and incentives following closely behind. This corresponds with the

findings of Ugur Yavas (2003) where consumers frequent malls with a mix of variety and sales

incentives.

Kim (2002) discussed the changing world of shopping where consumers are more information

savvy and require more information to make purchase decisions. Consumers are divided into

Holbrook’s consumer value segments of efficiency, excellence, play and aesthetics (Holbrook,

1999). This divides consumers based on what they value as a shopping experience. Internet

shoppers value convenience, speed and information while mall shoppers would add smell and

taste to their experience.

2.3 SUMMARY

In this chapter, a literature review was undertaken to gain a better understanding of the online and

offline shopping environments in South Africa and elsewhere in the world. This provided insight

into the reasons why some consumers prefer bricks-and-mortar shops while others prefer the

convenience of online purchasing or even switching between online and offline. Understanding

consumer preferences can play an important role in creating strategies for offline and online retail.

CHAPTER 3

RESEARCH METHODOLOGY

3.1 INTRODUCTION

This chapter examines the research methodology used as a map to gain a better understanding of

consumer shopping channel preferences in certain retail categories. Primary data for this

quantitative study was gathered by means of an electronic questionnaire, which contained

questions relating to consumers’ gender, family dynamics, demographics and shopping behaviour.

This chapter therefore describes the methodology used for gathering and interpreting the data.

3.2 THE POPULATION AND SAMPLE

Non-random sampling was used because not all malls and online shoppers have been

approached. The population consisted of individuals who are already purchasing online without

bias towards age, gender or family orientation. The sampling method used was non-random

sampling with a population consisting of 872 individuals whose names were obtained from two

online shopping platforms, namely www.shopbook.co.za and www.iamcooking.co.za. Stellenbosch

University’s survey system was used where individuals were sent an email to access the survey. A

response rate of 20% was expected based on the informal nature of the survey, with a minimum of

125 responses being accepted for data analysis.

3.3 THE QUESTIONNAIRE DESIGN

The research methodology and questionnaire were designed to ascertain how much of shopping is

done online within the retail categories of Groceries, Shoes/Clothes, Electronics, Gifts and

Homeware. Consumers were asked questions relating to how much (measured in percentages) of

their purchases within the shopping categories were done online. Further, respondents were given

an opportunity to rate attributes of online shopping pertaining to convenience, price and delivery.

The questionnaire consisted of four sections. The first section looked at demographic questions

related to age, gender and family status. The second section looked at the percentage of online

shopping behaviour within retail categories. The third section focused on the utilitarian attributes of

online shopping – such as cost, delivery, security, returns and convenience. The last section

looked at items which will increase the online purchase behaviour.

Section 1 of the questionnaire

The first three questions focused on demographics. Demographics such as gender, age and family

status are expected to play a key role in understanding consumer behaviour. The objective of this

study is to find out whether shopping behaviour is affected by generational cohorts, gender and

family orientation in online and offline shopping channels.

Table 3.1: Demographic questions

Source: Hassen Kajee research questionnaire.


The second section of the questionnaire focused on the percentage of shopping behaviour within

shopping categories online. These chosen values which were presented in percentage format to

represent the amount of shopping consumers did online with the opposite value being true for their

shopping behaviour within a mall setting. Refer to Table 3.2 below.

Table 3.2: Purchasing behaviour online (measured in percentages)



The third section focused on consumer attitudes towards attributes of online shopping. A 5-point

Likert scale was used with 5 representing Strongly agree and 1 Strongly disagree. The values were

added up to calculate consumers’ attitudes to each category.

Table 3.3: Online attributes related to shopping categories



The fourth section analysed factors that will cause consumers to shift towards online shopping.

Refer to Table 3.4 below.

Table 3.4: Online attributes related to the success of online shopping


3.4 DATA COLLECTION

Data was collected by means of an electronic survey. A questionnaire was emailed to a target

audience of 872 individuals whose names were obtained from two online shopping platforms. The

target audience for the specific survey was people who shopped both online and offline, hence the

choice of the online medium for the survey.

The database of shoppers was supplied by the online shopping sites www.shopbook.co.za and

www.iamcooking.co.za. The individuals who purchase on these platforms purchase both

perishable and non-perishable goods and are online savvy. All individuals who are on the

databases of the companies have to register their details on the site and confirm via confirmation

emails to their email addresses. The target audience was South African focused, and was

scattered all around the country.

The target response rate was set at 150 with a final number of 125 completed responses. A slow

initial response rate was expected. Hence, reminders for survey completion were sent out.

The survey questionnaire was developed to garner interest based on asking individuals questions

about their purchasing behaviour when shopping. The questions were simple and utilised a

percentage of total shopping within categories. Questions about the respondents’ current

experience with online shopping were also included.

The target audience consisted of individuals accessing the web from their desktops, laptops, cell

phones and tablets.

3.5 DATA ANALYSIS

The data was exported into an Excel spreadsheet. All incomplete responses were removed. These

were also denoted as outliers. All columns were given names and variable descriptors were

denoted as V1, V2, etc.

The questions in Section 2 of the questionnaire asked respondents to quantify what percentage

within a category of their total spend was online. The difference was denoted as the portion

purchased at the malls. The malls dataset was added in the data.

A variable of attitude was created which averaged each category relating to questions on

Groceries, Electronics, Gifts, Shoes/Clothing and Homeware. This attitude construct calculated the

average of the responses on the attributes of convenience, returns, safety, delivery and price.

The analysis was focused on answering the research questions below:

Are consumers adopting a multi-channel approach to shopping?

Does gender and family dynamics have any relationship with channel usage within specific

shopping categories?


towards online purchasing?


towards bricks-and-mortar shops?

Do any of the attributes of convenience, delivery, returns and price differ within categories

of shopping?

Do consumers find online shopping safe?

Is there a generational bias towards online shopping?

Which factors or attributes will increase online shopping?

Are South African consumers already omni-channel shoppers?

3.6 SUMMARY

Shopping and purchasing will always find roots in innovation based on new technology and

evolving generational needs. As we evolve, so do our needs. The advancement of innovative

shopping solutions with strong and growing infrastructure leads us towards an innovative multi-

channel shopping environment. This questionnaire has been designed to determine the current

multi-channel behaviour of consumers within today’s technological environment. The questionnaire

consisted of four sections:

Section 1, the demographic section, explored the respondents’ age, family orientation

(single, couple, couple with kids) and gender.

Section 2 focused on the percentage of shopping behaviour of online consumers within the

specific categories of Groceries, Clothes, Shoes, Electronics, Gifts and Homeware.

Section 3 used a Likert scale to ascertain specific online attributes which averaged into a

construct of attitudes per retail category. These attitudes were tested for reliability as a

construct.

Section 4 analysed the growth factors that make online shopping more appealing.

CHAPTER 4

FINDINGS

4.1 INTRODUCTION

This chapter presents the results of the quantitative research undertaken in this research

assignment. This body of knowledge represents an analysis of consumer purchasing behaviour

within the online sector and mall sector in South Africa. The aim is to extend the body of

knowledge on the online and mall sectors, as well as to understand consumers’ sentiments

towards the various channels by extending the questions to shopping categories.

Each research question will be statistically tested by means of descriptive and inferential analysis.

The aim is to ascertain whether the null hypothesis can be accepted or rejected.

4.1.1 Statistical quality control

After completion of the surveys by the respondents, the following process was undertaken to

produce high-quality data: A datasheet was prepared where columns represented the variables

and descriptors, and rows represented the data entered by the respondents. The data was cleaned

by removing all the outliers, which in this case represented incomplete data responses. Incomplete

data represented data that did not have all questions answered.

4.1.2 Sample descriptive statistics

For the complete set of sample descriptive statistics, refer to Appendix A.

Respondents per gender segment

Due to the simple non-random sampling method, the eventual responses was based on a much

higher female versus male response rate. In total, 87 females responded versus 38 males (see

Figure 4.1 below). There was no further bias to picking, removing or adding specific gender

responses. The process was approached to be as random as possible.

Figure 4.1: Gender responses

Respondents per family segment

The majority of respondents were couples. The response rate of 70% female shows that most of

the 50% that relate to couples are the female partners who are doing most of the online shopping.

See Figure 4.2 below:

Figure 4.2: Family status responses

Respondents per age category

The pseudo-random nature shows that majority of the online shoppers within the sample are

between the ages of 31 and 40, as illustrated in Figure 4.3 below.

Figure 4.3: Age percentages

4.2 MAIN FINDINGS

4.2.1 Gender and channel usage

To test the gender and family dynamic channel usage, a statistical analysis referred to as analysis

of variance, or Anova, was used. Consumers were divided into two groups, namely males and

females. The test focused on analysing whether there was a difference in gender preferences

towards the categories of Groceries, Shoes/Clothing, Electronics, Gifts and Homeware. As

illustrated in Table 4.1, differences in gender preferences within the online channel have been

noted. Rejection of the null hypothesis occurs within Electronics, Gifts and Homeware.

Table 4.1: Summarised findings on the Anova and hypothesis tests for gender

Anova Data Normally

Distributed

P Value Gender

Online

P Value

Gender

Offline

Ho : Men Online =

Women Online

H1 : Men Online <>

Women Online

Ho : Men Offline =

Women Offline

H1 : Men Offline <>

Women Offline

Groceries Yes 0.0702 0.0702 Accept Reject Accept Reject

Shoes/Clothes Yes 0.14018 0.14018 Accept Reject Accept Reject

Electronics Yes 0.000448 0.000448 Reject Accept Reject Accept

Gifts Yes 0.010638 0.010638 Reject Accept Reject Accept

Homewear Yes 0.008306 0.008306 Reject Accept Reject Accept

Source: Hassen Kajee DATA 20171207.sta.

4.2.2 Gender and online channel usage

Gender does not differ for the shopping categories of Groceries and Shoes/Clothing. It is quite

clear that men and women have equal preferences to buy online. This is shown by the acceptance

of the null hypothesis with p-values of 0.07 (Groceries) and 0.14018 (Shoes/Clothes).

Men did not prefer online shopping for Groceries and Shoes/Clothes more than women preferred

online shopping for Groceries and Shoes/Clothes. The analysis shows no gender bias within the

categories of Groceries and Shoes/Clothes.

Genders differed within the categories of Electronics, Gifts and Homeware. Males preferred the

online environment more than females. There was a rejection of the null hypothesis for these

categories where all the p-values were below the significance level of 0.05 (Table 4.2). This

showed no equality of variances between the means of the males and females. Men preferred the

online environment for Electronics, Gifts and Homeware significantly more than females.

Table 4.2: Descriptive analysis of gender channel usage

Males Online Females Online Males Offline Females Offline

Groceries 7.4% 12.4% 92.6% 87.6%

Shoes/Clothes 11.1% 22.3% 88.9% 77.7%

Electronics 37.4% 23.7% 62.6% 76.3%

Gifts 30.1% 27.6% 69.9% 72.4%

Homewear 28.4% 23.6% 71.6% 76.4%


The shopping categories Groceries and Shoes/Clothes still dominate the offline environment, with

an average of 90% of males and 84% of females utilising the offline channel for groceries, shoes

and clothing.

There is a clear channel differentiation within Electronics, Gifts and Homeware where males and

females are above the 20% mark in purchase behaviour towards the online environment with the

offline environment recording under 80% for those purchases. This shows a marked difference

within shopping categories, where the preferences within categories differ towards a channel.

4.2.3 Family status and channel usage

An Anova test was conducted to test the utilisation of the online channel by specific family status

groups. Consumers were divided into three groups, namely singles, couples and couples with kids.

The test focused on analysing whether there was a difference in family status preferences towards

the categories of Groceries, Shoes/Clothing, Electronics and Homeware. As illustrated in Table 4.3

below, none of the categories has a rejection of the null hypothesis.

Table 4.3: Summarised findings on the Anova and hypothesis tests for family status

Anova

Data Normally

Distributed

P Value

Family Status

Online

P Value

Family Status

Offline

Ho: = Single = Couple

= Couple + Kids

H1: = Single <> Couple

<> Couple + Kids

Ho: = Single = Couple

= Couple + Kids

H1: = Single <>

Couple <> Couple +

Kids

Groceries Yes 0.16152 0.16152 Accept Reject Accept Reject

Shoes/Clothes Yes 0.79593 0.79593 Accept Reject Accept Reject

Electronics Yes 0.93745 0.93745 Accept Reject Accept Reject

Gifts Yes 0.79506 0.79506 Accept Reject Accept Reject

Homewear Yes 0.34346 0.34346 Accept Reject Accept Reject

Online Offline


It can be concluded that there is no difference in the preferences of singles, couples and couples

with kids to shop online. This conclusion is represented by the acceptance of the null hypothesis

(H0: Single = Couple = Couple+Kids).

Table 4.4: Descriptive analysis of family status channel usage

Singles

Online

Couples

Online

Couple + Kids

Online

Singles

Offline

Couples

Offline

Couple + Kids

Offline

Groceries 8.20% 9.60% 16.55% 91.80% 90.40% 83.45%

Shoes/Clothes 21.00% 19.00% 16.50% 79.00% 81.00% 83.50%

Electronics 26.40% 28.70% 27.50% 73.60% 71.30% 72.50%

Gifts 31.10% 27.00% 28.20% 68.90% 73.00% 71.80%

Homewear 30.50% 22.20% 24.80% 69.50% 77.80% 75.20%

Average 23.44% 21.30% 22.71% 76.56% 78.70% 77.29%


The summarised descriptive analysis above (Table 4.4) shows that there is no significant

differences between the shopping categories and family status. What can be seen is that a

significant amount of shopping is taking place at malls. Malls are the primary shopping channel

with online being the secondary channel. This is also representative of shoppers who are multi-

channel shoppers within the family status category.

4.2.4 Categories online – channel usage

A single-factor Anova test was done to ascertain whether online channel usage is equal between

the category groups. This analysis was done to answer the question of category bias toward a

specific channel. A hypothesis test was done that shows equality between the groups, and the

alternative showing no equality. See Table 4.5 below.

Table 4.5: Hypothesis test for online channel usage

Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

6GroceriesOnline 125 13.6 0.1088 0.034519

7Clothes/ShoesOnline 125 23.6 0.1888 0.05568

8ElectronicsOnline 125 34.8 0.2784 0.085739

9GiftsOnline 125 35.6 0.2848 0.07888

10HomewearOnline 125 31.4 0.2512 0.070906

ANOVA

Source of Variation SS df MS F P-value F crit

Between Groups 2.73664 4 0.68416 10.50215 0.000000 2.386303

Within Groups 40.38976 620 0.065145

Total 43.1264 624


H0: V6 = V7 = V8 = V9 = V10

H1: V6<>V7<>V8<>V9<>V10

The p-value recorded is below 0.05, therefore rejecting Ho, which hypothesised equality between

the category groups where the preference to buy online is equal. Acceptance of H1 is evidence

that there is a significant difference within categories of the online channel based on the low p-

value.

This makes it clear that consumers shop differently within categories of the online channel. There

exists a gender bias within the categories of Electronics, Gifts and Homeware. Males prefer the

online channel more than females within these categories. This is also true for malls where

females prefer the mall environment for the shopping categories of Electronics, Gifts and

Homeware.

4.2.5 Channel usage – malls versus online

When looking at the usage of online and offline shopping channels, it is clear with significance that

malls are still the main source of shopping. An average of 70%+ of all shopping is still taking place

at malls.

An Anova test was conducted to record the variances between the online and offline channels,

where the hypothesis tested whether online shopping is equal to offline shopping. The results

pointed to p-values of less than 0.05, which shows there is still a significant number of individuals

choosing the mall environment for shopping, with 74% of spend at malls and 26% of spend online.

See Table 4.6 below.

H0: Online shopping = Mall shopping

H1: Online shopping <> Mall shopping

Table 4.6: Hypothesis test for online versus malls

SUMMARY


11Total SpendOnline 125 32.6 0.2608 0.042725

11Total SpendMalls 125 92.4 0.7392 0.042725

ANOVA


Between Groups 14.30416 1 14.30416 334.7948 0.00 3.879228

Within Groups 10.59584 248 0.042725

Total 24.9 249


Consumers are multi-channel shoppers. There is a sharing of expenditure between channels. The

primary channel is the offline environment (malls) while the supplementary channel is the online

environment (internet). The average adoption of online purchases is at 26% of total spend. This

clearly represents a large portion of the total shopping basket. The analysis shows that malls are

still the primary channel. However, there is a clear indication that consumers are adopting multiple

channels to fill their shopping baskets. It can therefore be concluded that consumers use both

primary shopping channels (malls) and secondary shopping channels (online) for their purchases.

4.2.6 Category channel biases – online

To understand category biases within the online channel, Anova tests of variances have been

conducted to compare the category purchasing behaviour of the online channel to the total

purchasing behaviour of the online channel. The testing focused on two specific categories,

namely Groceries and Electronics.

Table 4.7: Anova test for category channel bias

Anova: Single Factor For Electronics

SUMMARY


8ElectronicsOnline125 34.8 0.2784 0.085739

11Total SpendOnline125 32.6 0.2608 0.042725

ANOVA

Source of VariationSS df MS F P-value F crit

Between Groups0.01936 1 0.01936 0.301406 0.583496 3.879228

Within Groups15.9296 248 0.064232

Total 15.94896 249

Anova: Single Factor for Groceries

SUMMARY


6GroceriesOnline 125 13.6 0.1088 0.034519

11Total SpendOnline 125 32.6 0.2608 0.042725

ANOVA


Between Groups 1.444 1 1.444 37.38808 0.00 3.879228

Within Groups 9.57824 248 0.038622

Total 11.02224 249


Groceries:

H0: Groceries online >= Total spend online

H1: Groceries online < Total spend online

Within the category of Groceries, the null hypothesis represented a point where groceries was

equal to or higher than the total online spend. The p-value was below 0.05, therefore rejecting the

null hypothesis and accepting the alternative that the consumers preferred not to buy groceries

online. There is a clear indication that consumers prefer the physical shopping environment for

grocery shopping.

Electronics:

H0: Electronics online >= Total spend online

H1: Electronics online < Total spend online

Within the category of Electronics, the null hypothesis represented a point where Groceries was

equal to or higher than the total online spend. It was found that the p-value is above 0.05, therefore

accepting the null hypothesis.

Consumers prefer shopping online for items that are non-perishable, which includes Electronics,

Gifts and Homeware.

4.2.7 Attribute analysis within categories

An Anova test, descriptive statistics and a Friedman Anova sum of ranks test were conducted to

ascertain whether the attributes of price, convenience and delivery between the category groups

are equal. Refer to Table 4.8 below in this regard.

Table 4.8: Descriptive statistics for attribute analysis between categories

Descriptive Statistics (DATA 20171207.sta)

Variable

Valid N Mean Median Minimum Maximum LowerQuartile

UpperQuartile

Std.Dev.

13I will pay Delivery Charges



14Groceries Online is Convenient

21Buying Shoes+clothes online is more convenient

26Buying elec+gifts online is more convenient

17Clothes/Shoes is Cheaper online

22Electronics/Gifts Cheaper online

125 2.392000 2.000000 1.000000 5.000000 2.000000 3.000000 1.135185

125 2.832000 3.000000 1.000000 5.000000 2.000000 4.000000 1.105237

125 3.088000 3.000000 1.000000 5.000000 2.000000 4.000000 1.062681

125 3.248000 3.000000 1.000000 5.000000 3.000000 4.000000 1.147620

125 3.440000 4.000000 1.000000 5.000000 3.000000 4.000000 0.928057

125 3.800000 4.000000 1.000000 5.000000 3.000000 4.000000 0.915811

125 3.136000 3.000000 1.000000 5.000000 2.000000 4.000000 0.961719

125 3.440000 3.000000 1.000000 5.000000 3.000000 4.000000 0.919327


A descriptive analysis shows that individuals do not want to pay delivery charges for groceries with

a mean of 2.392, but are more neutral towards paying delivery charges for shoes and electronics

with a higher mean of 2.9+.

The data points to a neutrality towards groceries being convenient online, but a more conclusive

agreeability that shoes, clothing, electronics and gifts are convenient online with means of 3.4+.

As can be seen, price plays an important role, with neutrality for clothes and shoes, but more

agreeability for electronics with a mean of 3.4.

A Friedman Anova sum of ranks test was also conducted. Below are the tables.

Table 4.9: Friedman Anova sum of ranks for delivery

Friedman ANOVA and Kendall Coeff. ofConcordance (DATA 20171207.sta)ANOVA Chi Sqr. (N = 125, df = 2) = 40.49240 p =.00000Coeff. of Concordance = .16197 Aver. rank r =.15521

Variable

AverageRank

Sum ofRanks

Mean Std.Dev.




1.664000 208.0000 2.392000 1.135185

2.020000 252.5000 2.832000 1.105237

2.316000 289.5000 3.088000 1.062681


According to the above data (Table 4.9), individuals are more willing to pay delivery charges for

Shoes/Clothes, Electronics and Gifts with average ranks of 2 (Shoes/Clothing) and 2.3

(Electronics), and less willing to pay delivery charges for Groceries with an average rank of 1.7.

Table 4.10: Anova test for delivery attribute between categories

Anova: Single Factor

SUMMARY


13I will pay Delivery Charges(Groceries) 125 451 3.608 1.288645

18I will pay Delivery Charges(Shoes_Clothing) 125 354 2.832 1.221548

23I will pay Delivery Charges (Electronics) 125 386 3.088 1.12929

ANOVA


Between Groups 39.088 2 19.544 16.10998 0.000 3.019987

Within Groups 451.296 372 1.213161

Total 490.384 374


H0: 13l=18l=23l

H1: 13l<>18l<>23l

A p-value of less than 0.05 rejects the null hypothesis, therefore showing significant differences

between the attributes of delivery charge. The conclusion is that consumers are more open to

paying delivery for electronics.

Table 4.11: Friedman Anova sum of ranks for convenience


Variable

AverageRank

Sum ofRanks

Mean Std.Dev.




1.804000 225.5000 3.248000 1.147620

1.940000 242.5000 3.440000 0.928057

2.256000 282.0000 3.800000 0.915811


Consumers strongly believed that Electronics was the shopping category most convenient in terms

of online shopping. The average ranks for Groceries and Shoes/Clothes were close at 1.8 and 1.9

respectively, as illustrated in Table 4.11.

Table 4.12: Wilcoxon test for price attributes of Shoes/Clothes and Electronics

Wilcoxon Matched Pairs Test (DATA20171207.sta)Marked tests are significant at p <.05000

Pair of Variables

ValidN

T Z p-value

17Clothes/Shoes is Cheaper online & 22Electronics/Gifts Cheaper online 61 480.5000 3.339991 0.000838


H0: Clothes/Shoes cheaper = Electronics/Gifts cheaper

H1: Clothes/Shoes cheaper <> Electronics/Gifts cheaper

A Wilcoxon match was done for price as this study compared the categories of Shoes/Clothes and

Electronics. A p-value of less than 0.05 rejects the null hypothesis. Hence, there is a clear

difference in the consumers’ perception of price between the categories of Shoes/Clothes and

Electronics.

The category attribute differences between the online and offline channels are clear. Also,

consumers perceived price, delivery and convenience differently across the shopping categories.

Electronics and Gifts are the preferred online categories with Groceries less preferred within the

online environment.

4.2.8 Age orientation between the online channel and malls

A Spearman correlation, as shown in Table 4.13, was used to test the relationship between age

and purchasing electronics online. The test focuses on plotting the ranks of age and percentage of

electronics online. The outcome is a correlation co-efficient of -0.19, which lies between 1 and -1. A

hypothesis test was also done.

Table 4.13: Spearman correlation table between age and online purchases

Age:8ElectronicsOnline: r = -0.1911, p = 0.0328

Spearman r = -0.19 p=0.03

<30 31-40 41-50 51-60 >60

Age

-20%

0%

20%

40%

60%

80%

100%

120%

8E

lectr

onic

sO

nlin

e


H0: All ages purchase electronics online.

H1: There is a significant difference in age and online purchase for electronics and gifts.

This test analyses the relationship that age has on the propensity to purchase electronics online.

The correlation, as described above, is a negative correlation, which shows that as age increases,

the propensity to use online channels to purchase electronic items decreases. A p-value of lower

than 0.05 is significant, therefore rejecting the null hypothesis. There is a significant difference in

age and online purchase behaviour for Electronics and Gifts.

There is a clear distinction between online shopping and age. As can be seen on the right-hand

side of the graph (see Table 4.13), the amount of online shopping decreases as age increases.

Online purchasing is highly technology intensive.

Table 4.14: Spearman correlation table between age and mall purchases

Age:8ElectronicsMalls: r = 0.1911, p = 0.0328

Spearman r = 0.19 p=0.03

<30 31-40 41-50 51-60 >60

Age

-20%

0%

20%

40%

60%

80%

100%

120%

8E

lectr

onic

sM

alls


The above graph (Table 4.14) analyses age in terms of electronics purchases in malls, and clearly

shows a positive relationship for increasing age and propensity to shop at malls. As age increases,

the need to shop at malls increases.

4.2.9 Online shopping is safe

One of the key determinants of the success of online growth is the perception of security.

Consumers rated their perception of online security on a Likert scale, and the observations were

analysed using an Anova table. A p-value higher than 0.05 denotes equality within the variances.

The data within the family groups was analysed to ascertain whether online purchasing is safe.

See Table 4.15 below.

Table 4.15: Anova table for online security and family status


Based on these results (see Table 4.15) consumers do feel that online shopping in South Africa is

secure.

4.2.10 Delivery charges

Table 4.16: Age and propensity to pay delivery charges


The above table (Table 4.16) confirms that consumers are opposed to delivery charges when it

comes to online shopping. There is consensus across the population demographic with a p-value

greater than 0.05 on gender, age and family orientation.

4.2.11 The future of online shopping

An Anova test was conducted to test the attitudes to the future of online shopping. Consumers

were divided into three groups, namely singles, couples and couples with kids. The test focused on

analysing whether there was a difference in family status preferences towards the future of online

shopping. The p-value above 0.05 implies that there is an acceptance of a successful future for

online shopping based on better delivery times, lower prices and improved return processes.

Table 4.17: Anova and Cronbach reliability table for attitude to the future of online shopping


The Cronbach reliability table above (Table 4.17) confirms the acceptability of the construct attitude

towards the future of online shopping. The 0.85 Alpha confirms a positive correlation. The

descriptive analysis points out that consumers will increase their online purchasing if the attributes

of efficient delivery, lower prices and easier return processes can be realised. This also confirms

that online shopping is continuously evolving based on the strong agreeability of the sample

towards the attributes of delivery, price and returns.

4.3 SUMMARY

Consumers are shifting their shopping behaviour towards multi-channel purchasing behaviour

within certain retail categories. The primary channel is still bricks-and-mortar malls with online

shopping on the increase. Non-perishable items such as electronics, gifts, clothes, shoes and

homeware items are taking a larger percentage of sales away from the bricks-and-mortar

environment. The respondents from the chosen sample reported that 30% of their electronics

shopping is done online. The same applies to 19% of their shopping for shoes and clothing, 28% of

their shopping for gifts, 25% of their shopping for homeware and 11% of their shopping for

groceries. There was no significant difference when it comes to online channel choice between

males and females, and family orientation between singles, couples and couples with kids.

Consumers’ attitudes to the online attributes of price, delivery, convenience and returns also

differed within categories. Consumers were neutral in terms of the Shoes/Clothing shopping

category. This denotes growing trust in online shopping for shoes and clothes. Electronics was

biased towards males who agreed with the attributes of convenience, delivery and price. Females

were neutral, denoting increasing trust in the Electronics category. There was broad consensus

that the attributes of convenience, returns, delivery and good online content would lead to a further

increase in online purchasing activity.

CHAPTER 5

SUMMARY, CONCLUSION AND RECOMMENDATIONS

5.1 INTRODUCTION

This chapter presents a summary of the findings and key recommendations for the future of the

online purchasing environment.

5.2 SUMMARY OF MAIN FINDINGS

Consumers in South Africa are using multiple channels to conduct their shopping. A descriptive

analysis of the results from this study showed an average of 23% for online channel usage and

77% for offline channel usage (malls). There is a clear propensity to shop in the offline sector, with

malls as the primary channel and online shopping as the supplementary channel. Rejection of the

null hypothesis bolstered the argument that malls are the preferred channel for shopping.

Females and males shared their preference to shop both online and offline for groceries, shoes

and clothes. There was a clear difference with electronics, gifts and homeware, where a rejection

of equal preference occurred. Females do not prefer to buy electronics, gifts and homeware online

while their male counterparts do.

There was no specific drive towards family dynamic relating to purchase decisions in terms of the

online channel. Also, there was no difference in the preferences of singles, couples and couples

with kids in terms of shopping online. Offline categories assumed the same relationship. Equality of

variances is a clear sign that consumers are multi-channel shoppers.

Within the online channel, electronics, gifts and clothing are the highest purchased categories.

Within the offline channel, groceries are the highest purchased items. A test was done to check the

level of each category against the total of the category. Online shopping for groceries was least

preferred while online shopping for electronics, gifts and homeware was the most preferred.

Each category was tested for the attributes of convenience, delivery and price. There was a clear

difference between the attributes for each online category. Consumers rated the same attributes

differently within the categories. Electronics and Gifts showed a positive overall attitude towards

each attribute, whereas Groceries and Shoes/Clothing showed more neutrality. This clearly shows

that a significant amount of work still needs to be done in terms of convenience, delivery and price

for consumers to be more positive about online shopping.

Attitudes towards the future of online shopping were tested, which showed that consumers would

prefer better delivery times and lower prices online.

Consumers agreed that the online environment in South Africa is safe. There was full acceptance

of the hypothesis. Generational biases would play a role in the safety aspect of online shopping.

There was a generational bias towards online shopping. A negative correlation was observed in

terms of online shopping and age. As the age increases, the propensity to shop online decreases.

There is high acceptance of online shopping at younger ages.

Consumers are adopting a multi-channel approach to shopping within retail categories. The bricks-

and-mortar channel is still the primary channel, with data proving that consumers prefer to shop

within a multi-channel environment. This qualifies the online channel in South Africa as a strong

secondary shopping channel or supplementary channel.

5.3 RECOMMENDATIONS FOR FUTURE RESEARCH

The sample for the study focused on individuals who purchased goods online. The study should be

expanded to include shoppers in malls. This will give a more holistic view of current South African

shoppers.

In this study, the retail categories Groceries, Shoes/Clothes, Electronics, Gifts and Homeware

were analysed. The purchasing behaviour of buying non-perishables online is on the increase

without significant changes in consumer preferences across gender and family dynamic. Further

analysis should be done on each retail category to determine how online purchasing within the

categories can be enhanced. Online grocery shopping amounted to 11% of total expenditure while

online shopping for electronics amounted to 30%. Understanding the attributes that will increase

grocery shopping online is a key priority.

5.4 RECOMMENDATIONS

Aggressive expenditure on bricks-and-mortar shops must be followed by the digitisation of these

bricks-and-mortar environments. The omni-channel environment will create more convenience and

choice for customers.

The study clearly showed that consumers are multi-channel shoppers who use malls as their

primary purchase channel and online shopping as their secondary purchase channel. The future of

retail would see both channels coming together into an omni-channel environment.

Online and offline warehouses can centralise their distribution channels under one roof. Every mall

reframed would be a culmination of many small warehouses, which can be linked to the cloud

environment. Malls and stores will be able to create a live cloud of their products for sale.

Consumers today are becoming accustomed to the idea of being able to purchase specific

categories of products using different channels. Items such as Gifts, Clothing, Shoes and

Electronics are ideally suited for a digital environment, while items such as Groceries and

Convenience Food are better suited for the bricks-and-mortar channel.

5.5 CONCLUSION

Two factors that have a significant impact on shopping behaviour today are impulse purchasing

and immediate gratification. Impulse purchasing lends itself to a smart digital selling strategy where

consumers are lured into purchasing items based on the attraction of price points and the

opportunity cost of purchasing immediately due to increased prices at a later stage. Immediate

gratification is one of the key reasons why people frequent malls. However, due to the constraints

of time and environment, customers are drawn into a no-time-now mind-set, and push the

purchasing decision to a later time.

Convenience and ease of use are important for consumers. Added to this is excellent customer

service. Consumers no longer want to wait three to five days for the delivery of the items they have

ordered online. Shopping malls, once reframed, can become pickup points for online orders,

mitigating the long waiting periods. This will allow purchasing and footfall to increase in malls.

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41-62.

APPENDIX A

Statistical Findings

Q1 ANOVA (DATA 20171207.sta)

6GROCERIESONLINE | GENDER

Normal Prob. Plot; Raw Residuals


Dependent variable: 6GroceriesOnline

(Analysis sample)

-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Gender; LS Means

Gender; LS Means

Current effect: F(1, 123)=1.9656, p=0.16 Mann-Whitney U p=0.21

Effective hypothesis decomposition

Vertical bars denote 0.95 confidence intervals

M F

Gender

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

6G

roceriesO

nlin

e

Levene's Test for Homogeneity of Variances (DATA 20171207.sta)

Levene's Test for Homogeneity of Variances(DATA 20171207.sta)Effect: GenderDegrees of freedom for all F's: 1, 123MS

EffectMS

ErrorF p

6GroceriesOnline 0.048135 0.014429 3.336095 0.070200

Gender; LS Means (DATA 20171207.sta)

Gender; LS Means (DATA 20171207.sta)Current effect: F(1, 123)=1.9656, p=.16344Effective hypothesis decomposition

Cell No.

Gender 6GroceriesOnlineMean

6GroceriesOnlineStd.Err.

6GroceriesOnline-95.00%

6GroceriesOnline+95.00%

N

1

2

M 0.073684 0.030023 0.014256 0.133112 38

F 0.124138 0.019842 0.084862 0.163414 87



Effect

Level ofFactor

N 6GroceriesOnlineMean

6GroceriesOnlineStd.Dev.

6GroceriesOnlineStd.Err



Total

Gender

Gender

125 0.108800 0.185792 0.016618 0.075909 0.141691

M 38 0.073684 0.157144 0.025492 0.022032 0.125336

F 87 0.124138 0.195867 0.020999 0.082393 0.165883

Mean of men smaller then mean of the women. Men and women do not differ with groceries online

Man white

6GROCERIESMALLS | GENDER



Dependent variable: 6GroceriesMalls

(Analysis sample)

-0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3

Residual

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Gender; LS Means

Gender; LS Means




M F

Gender

0.82

0.84

0.86

0.88

0.90

0.92

0.94

0.96

0.98

1.00

1.02

6G

roceriesM

alls

Levene’s Test for Homogeneity of Variances (DATA 20171207.sta)


EffectMS

ErrorF p

6GroceriesMalls 0.048135 0.014429 3.336095 0.070200



Cell No.

Gender 6GroceriesMallsMean

6GroceriesMallsStd.Err.

6GroceriesMalls-95.00%

6GroceriesMalls+95.00%

N

1

2

M 0.926316 0.030023 0.866888 0.985744 38

F 0.875862 0.019842 0.836586 0.915138 87



Effect

Level ofFactor

N 6GroceriesMallsMean

6GroceriesMallsStd.Dev.

6GroceriesMallsStd.Err



Total

Gender

Gender

125 0.891200 0.185792 0.016618 0.858309 0.924091

M 38 0.926316 0.157144 0.025492 0.874664 0.977968

F 87 0.875862 0.195867 0.020999 0.834117 0.917607

7CLOTHES/SHOESONLINE | GENDER



Dependent variable: 7Clothes/ShoesOnline

(Analysis sample)

-0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Gender; LS Means

Gender; LS Means




M F

Gender

0.00

0.05

0.10

0.15

0.20

0.25

0.30

7C

loth

es/S

hoesO

nlin

e

Levene’s Test for Homogeneity of Variances (DATA 20171207.sta)


EffectMS

ErrorF p

7Clothes/ShoesOnline 0.055301 0.025087 2.204348 0.140180



Cell No.

Gender 7Clothes/ShoesOnlineMean

7Clothes/ShoesOnlineStd.Err.

7Clothes/ShoesOnline-95.00%

7Clothes/ShoesOnline+95.00%

N

1

2

M 0.110526 0.037491 0.036314 0.184738 38

F 0.222989 0.024778 0.173942 0.272035 87



Effect

Level ofFactor

N 7Clothes/ShoesOnlineMean

7Clothes/ShoesOnlineStd.Dev.

7Clothes/ShoesOnlineStd.Err



Total

Gender

Gender

125 0.188800 0.235966 0.021105 0.147026 0.230574

M 38 0.110526 0.165692 0.026879 0.056065 0.164988

F 87 0.222989 0.254129 0.027246 0.168826 0.277151

Men whitney and anova says there is a difference between men and women

7CLOTHES/SHOESMALLS | GENDER



Dependent variable: 7Clothes/ShoesMalls

(Analysis sample)

-0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Gender; LS Means

Gender; LS Means




M F

Gender

0.70

0.75

0.80

0.85

0.90

0.95

1.00

7C

loth

es/S

hoesM

alls



EffectMS

ErrorF p

7Clothes/ShoesMalls 0.055301 0.025087 2.204348 0.140180



Cell No.

Gender 7Clothes/ShoesMallsMean

7Clothes/ShoesMallsStd.Err.

7Clothes/ShoesMalls-95.00%

7Clothes/ShoesMalls+95.00%

N

1

2

M 0.889474 0.037491 0.815262 0.963686 38

F 0.777011 0.024778 0.727965 0.826058 87



Effect

Level ofFactor

N 7Clothes/ShoesMallsMean

7Clothes/ShoesMallsStd.Dev.

7Clothes/ShoesMallsStd.Err



Total

Gender

Gender

125 0.811200 0.235966 0.021105 0.769426 0.852974

M 38 0.889474 0.165692 0.026879 0.835012 0.943935

F 87 0.777011 0.254129 0.027246 0.722849 0.831174

8ELECTRONICSONLINE | GENDER



Dependent variable: 8ElectronicsOnline

(Analysis sample)

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Gender; LS Means

Gender; LS Means




M F

Gender

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

8E

lectr

onic

sO

nlin

e



EffectMS

ErrorF p

8ElectronicsOnline 0.368742 0.028330 13.01586 0.000448

Gender; Weighted Means


Current effect: F(1, 123)=6.0153, p=.01559



M F

Gender

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

8E

lectr

onic

sO

nlin

e

Games-Howell post hoc

1t

2df

3p

1:2 2.15318306 54.0554774 0.0357834625



Cell No.

Gender 8ElectronicsOnlineMean

8ElectronicsOnlineStd.Err.

8ElectronicsOnline-95.00%

8ElectronicsOnline+95.00%

N

1

2

M 0.373684 0.046568 0.281505 0.465863 38

F 0.236782 0.030777 0.175861 0.297702 87



Effect

Level ofFactor

N 8ElectronicsOnlineMean

8ElectronicsOnlineStd.Dev.

8ElectronicsOnlineStd.Err



Total

Gender

Gender

125 0.278400 0.292813 0.026190 0.226563 0.330237

M 38 0.373684 0.354648 0.057532 0.257114 0.490254

F 87 0.236782 0.252483 0.027069 0.182970 0.290593

8ELECTRONICSMALLS | GENDER



Dependent variable: 8ElectronicsMalls

(Analysis sample)

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Gender; LS Means

Gender; LS Means




M F

Gender

0.50

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

8E

lectr

onic

sM

alls



EffectMS

ErrorF p

8ElectronicsMalls 0.368742 0.028330 13.01586 0.000448



Current effect: F(1, 123)=6.0153, p=.01559



M F

Gender

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.80

0.85

8E

lectr

onic

sM

alls


1t

2df

3p

1:2 2.15318306 54.0554774 0.0357834625



Cell No.

Gender 8ElectronicsMallsMean

8ElectronicsMallsStd.Err.

8ElectronicsMalls-95.00%

8ElectronicsMalls+95.00%

N

1

2

M 0.626316 0.046568 0.534137 0.718495 38

F 0.763218 0.030777 0.702298 0.824139 87



Effect

Level ofFactor

N 8ElectronicsMallsMean

8ElectronicsMallsStd.Dev.

8ElectronicsMallsStd.Err



Total

Gender

Gender

125 0.721600 0.292813 0.026190 0.669763 0.773437

M 38 0.626316 0.354648 0.057532 0.509746 0.742886

F 87 0.763218 0.252483 0.027069 0.709407 0.817030

9GIFTSONLINE | GENDER



Dependent variable: 9GiftsOnline

(Analysis sample)

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Gender; LS Means

Gender; LS Means

Current effect: F(1, 123)=.28817, p=0.59 Mann-Whitney U p=0.87



M F

Gender

0.18

0.20

0.22

0.24

0.26

0.28

0.30

0.32

0.34

0.36

0.38

0.40

0.42

9G

iftsO

nlin

e



EffectMS

ErrorF p

9GiftsOnline 0.155820 0.023157 6.728849 0.010638


Gender; LS Means (DATA 20171207.sta)Current effect: F(1, 123)=.28817, p=.59236Effective hypothesis decomposition

Cell No.

Gender 9GiftsOnlineMean

9GiftsOnlineStd.Err.

9GiftsOnline-95.00%

9GiftsOnline+95.00%

N

1

2

M 0.305263 0.045692 0.214818 0.395708 38

F 0.275862 0.030198 0.216088 0.335637 87



Effect

Level ofFactor

N 9GiftsOnlineMean

9GiftsOnlineStd.Dev.

9GiftsOnlineStd.Err

9GiftsOnline-95.00%

9GiftsOnline+95.00%

Total

Gender

Gender

125 0.284800 0.280856 0.025121 0.235079 0.334521

M 38 0.305263 0.340863 0.055295 0.193224 0.417302

F 87 0.275862 0.251953 0.027012 0.222164 0.329561

9GIFTSMALLS | GENDER



Dependent variable: 9GiftsMalls

(Analysis sample)

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6

Residual

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Gender; LS Means

Gender; LS Means




M F

Gender

0.58

0.60

0.62

0.64

0.66

0.68

0.70

0.72

0.74

0.76

0.78

0.80

0.82

9G

iftsM

alls



EffectMS

ErrorF p

9GiftsMalls 0.155820 0.023157 6.728849 0.010638



Cell No.

Gender 9GiftsMallsMean

9GiftsMallsStd.Err.

9GiftsMalls-95.00%

9GiftsMalls+95.00%

N

1

2

M 0.694737 0.045692 0.604292 0.785182 38

F 0.724138 0.030198 0.664363 0.783912 87



Effect

Level ofFactor

N 9GiftsMallsMean

9GiftsMallsStd.Dev.

9GiftsMallsStd.Err

9GiftsMalls-95.00%

9GiftsMalls+95.00%

Total

Gender

Gender

125 0.715200 0.280856 0.025121 0.665479 0.764921

M 38 0.694737 0.340863 0.055295 0.582698 0.806776

F 87 0.724138 0.251953 0.027012 0.670439 0.777836

10HOMEWEARONLINE | GENDER



Dependent variable: 10Homew earOnline

(Analysis sample)

-0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Gender; LS Means

Gender; LS Means




M F

Gender

0.16

0.18

0.20

0.22

0.24

0.26

0.28

0.30

0.32

0.34

0.36

0.38

0.40

10H

om

ew

earO

nlin

e



EffectMS

ErrorF p

10HomewearOnline 0.178374 0.024783 7.197303 0.008306



Current effect: F(1, 123)=.83797, p=.36177



M F

Gender

0.15

0.20

0.25

0.30

0.35

0.40

0.45

10H

om

ew

earO

nlin

e


1t

2df

3p

1:2 0.806258902 54.3702273 0.423606179



Cell No.

Gender 10HomewearOnlineMean

10HomewearOnlineStd.Err.

10HomewearOnline-95.00%

10HomewearOnline+95.00%

N

1

2

M 0.284211 0.043225 0.198650 0.369771 38

F 0.236782 0.028567 0.180235 0.293328 87



Effect

Level ofFactor

N 10HomewearOnlineMean

10HomewearOnlineStd.Dev.

10HomewearOnlineStd.Err



Total

Gender

Gender

125 0.251200 0.266281 0.023817 0.204060 0.298340

M 38 0.284211 0.327584 0.053141 0.176536 0.391885

F 87 0.236782 0.235320 0.025229 0.186628 0.286935

10HOMEWEARMALLS | GENDER



Dependent variable: 10Homew earMalls

(Analysis sample)

-0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Gender; LS Means

Gender; LS Means




M F

Gender

0.60

0.62

0.64

0.66

0.68

0.70

0.72

0.74

0.76

0.78

0.80

0.82

0.84

10H

om

ew

earM

alls



EffectMS

ErrorF p

10HomewearMalls 0.178374 0.024783 7.197303 0.008306






M F

Gender

0.55

0.60

0.65

0.70

0.75

0.80

0.85

10H

om

ew

earM

alls


1t

2df

3p

1:2 0.806258902 54.3702273 0.423606179



Cell No.

Gender 10HomewearMallsMean

10HomewearMallsStd.Err.

10HomewearMalls-95.00%

10HomewearMalls+95.00%

N

1

2

M 0.715789 0.043225 0.630229 0.801350 38

F 0.763218 0.028567 0.706672 0.819765 87



Effect

Level ofFactor

N 10HomewearMallsMean

10HomewearMallsStd.Dev.

10HomewearMallsStd.Err



Total

Gender

Gender

125 0.748800 0.266281 0.023817 0.701660 0.795940

M 38 0.715789 0.327584 0.053141 0.608115 0.823464

F 87 0.763218 0.235320 0.025229 0.713065 0.813372

6GROCERIESONLINE | FAMILY STATUS



Dependent variable: 6GroceriesOnline

(Analysis sample)

-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Family Status; LS Means


Current effect: F(2, 122)=1.8507, p=0.16 Kruskal-Wallis p=0.18



Single Couple Couple+Kids

Family Status

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

6G

roceriesO

nlin

e

Non parametric, 3 or more treatments.kruskal wallis


Levene's Test for Homogeneity of Variances(DATA 20171207.sta)Effect: "Family Status"Degrees of freedom for all F's: 2, 122MS

EffectMS

ErrorF p

6GroceriesOnline 0.028337 0.014466 1.958903 0.145423

Width of the confidence intervals, are they widely different, variation is the same for comparability. If there is non homogeneity

LSD test; variable 6GroceriesOnline (DATA 20171207.sta)

LSD test; variable 6GroceriesOnline (DATA20171207.sta)Probabilities for Post Hoc TestsError: Between MS = .03405, df = 122.00

Cell No.

Family Status {1}.08235

{2}.09677

{3}.16552

1

2

3

Single 0.714840 0.077083

Couple 0.714840 0.100312

Couple+Kids 0.077083 0.100312

Tell if the means are different, where are the differences

Family Status; LS Means (DATA 20171207.sta)

Family Status; LS Means (DATA 20171207.sta)Current effect: F(2, 122)=1.8507, p=.16152Effective hypothesis decomposition

Cell No.

Family Status 6GroceriesOnlineMean

6GroceriesOnlineStd.Err.



N

1

2

3

Single 0.082353 0.031647 0.019705 0.145001 34

Couple 0.096774 0.023435 0.050381 0.143167 62

Couple+Kids 0.165517 0.034266 0.097683 0.233351 29



Effect

Level ofFactor

N 6GroceriesOnlineMean

6GroceriesOnlineStd.Dev.

6GroceriesOnlineStd.Err



Total

Family Status

Family Status

Family Status

125 0.108800 0.185792 0.016618 0.075909 0.141691

Single 34 0.082353 0.140282 0.024058 0.033406 0.131300

Couple 62 0.096774 0.183739 0.023335 0.050113 0.143435

Couple+Kids 29 0.165517 0.227213 0.042192 0.079090 0.251944

6GROCERIESMALLS | FAMILY STATUS



Dependent variable: 6GroceriesMalls

(Analysis sample)

-0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99







Family Status

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1.05

6G

roceriesM

alls



EffectMS

ErrorF p

6GroceriesMalls 0.028337 0.014466 1.958903 0.145423

LSD test; variable 6GroceriesMalls (DATA 20171207.sta)

LSD test; variable 6GroceriesMalls (DATA20171207.sta)Probabilities for Post Hoc TestsError: Between MS = .03405, df = 122.00

Cell No.


{2}.90323

{3}.83448

1

2

3

Single 0.714840 0.077083

Couple 0.714840 0.100312

Couple+Kids 0.077083 0.100312



Cell No.

Family Status 6GroceriesMallsMean

6GroceriesMallsStd.Err.



N

1

2

3

Single 0.917647 0.031647 0.854999 0.980295 34

Couple 0.903226 0.023435 0.856833 0.949619 62

Couple+Kids 0.834483 0.034266 0.766649 0.902317 29



Effect

Level ofFactor

N 6GroceriesMallsMean

6GroceriesMallsStd.Dev.

6GroceriesMallsStd.Err



Total

Family Status

Family Status

Family Status

125 0.891200 0.185792 0.016618 0.858309 0.924091

Single 34 0.917647 0.140282 0.024058 0.868700 0.966594

Couple 62 0.903226 0.183739 0.023335 0.856565 0.949887

Couple+Kids 29 0.834483 0.227213 0.042192 0.748056 0.920910

7CLOTHES/SHOESONLINE | FAMILY STATUS



Dependent variable: 7Clothes/ShoesOnline

(Analysis sample)

-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99



Current effect: F(2, 122)=.22867, p=0.80 Kruskal-Wallis p=0.46




Family Status

0.05

0.10

0.15

0.20

0.25

0.30

0.35

7C

loth

es/S

hoesO

nlin

e



EffectMS

ErrorF p

7Clothes/ShoesOnline 0.028896 0.028733 1.005670 0.368811

LSD test; variable 7Clothes/ShoesOnline (DATA 20171207.sta)

LSD test; variable 7Clothes/ShoesOnline (DATA20171207.sta)Probabilities for Post Hoc TestsError: Between MS = .05638, df = 122.00

Cell No.


{2}.19032

{3}.16552

1

2

3

Single 0.759315 0.502523

Couple 0.759315 0.643220

Couple+Kids 0.502523 0.643220


Family Status; LS Means (DATA 20171207.sta)Current effect: F(2, 122)=.22867, p=.79593Effective hypothesis decomposition

Cell No.

Family Status 7Clothes/ShoesOnlineMean

7Clothes/ShoesOnlineStd.Err.



N

1

2

3

Single 0.205882 0.040722 0.125269 0.286496 34

Couple 0.190323 0.030156 0.130626 0.250019 62

Couple+Kids 0.165517 0.044093 0.078231 0.252804 29



Effect

Level ofFactor

N 7Clothes/ShoesOnlineMean

7Clothes/ShoesOnlineStd.Dev.

7Clothes/ShoesOnlineStd.Err



Total

Family Status

Family Status

Family Status

125 0.188800 0.235966 0.021105 0.147026 0.230574

Single 34 0.205882 0.253391 0.043456 0.117470 0.294294

Couple 62 0.190323 0.218572 0.027759 0.134816 0.245829

Couple+Kids 29 0.165517 0.256732 0.047674 0.067862 0.263173

7CLOTHES/SHOESMALLS | FAMILY STATUS



Dependent variable: 7Clothes/ShoesMalls

(Analysis sample)

-0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99







Family Status

0.65

0.70

0.75

0.80

0.85

0.90

0.95

7C

loth

es/S

hoesM

alls



EffectMS

ErrorF p

7Clothes/ShoesMalls 0.028896 0.028733 1.005670 0.368811

LSD test; variable 7Clothes/ShoesMalls (DATA 20171207.sta)

LSD test; variable 7Clothes/ShoesMalls (DATA20171207.sta)Probabilities for Post Hoc TestsError: Between MS = .05638, df = 122.00

Cell No.


{2}.80968

{3}.83448

1

2

3

Single 0.759315 0.502523

Couple 0.759315 0.643220

Couple+Kids 0.502523 0.643220



Cell No.

Family Status 7Clothes/ShoesMallsMean

7Clothes/ShoesMallsStd.Err.



N

1

2

3

Single 0.794118 0.040722 0.713504 0.874731 34

Couple 0.809677 0.030156 0.749981 0.869374 62

Couple+Kids 0.834483 0.044093 0.747196 0.921769 29



Effect

Level ofFactor

N 7Clothes/ShoesMallsMean

7Clothes/ShoesMallsStd.Dev.

7Clothes/ShoesMallsStd.Err



Total

Family Status

Family Status

Family Status

125 0.811200 0.235966 0.021105 0.769426 0.852974

Single 34 0.794118 0.253391 0.043456 0.705706 0.882530

Couple 62 0.809677 0.218572 0.027759 0.754171 0.865184

Couple+Kids 29 0.834483 0.256732 0.047674 0.736827 0.932138

8ELECTRONICSONLINE | FAMILY STATUS



Dependent variable: 8ElectronicsOnline

(Analysis sample)

-0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99







Family Status

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

8E

lectr

onic

sO

nlin

e



EffectMS

ErrorF p

8ElectronicsOnline 0.031809 0.030417 1.045763 0.354552

LSD test; variable 8ElectronicsOnline (DATA 20171207.sta)

LSD test; variable 8ElectronicsOnline (DATA20171207.sta)Probabilities for Post Hoc TestsError: Between MS = .08705, df = 122.00

Cell No.


{2}.28710

{3}.27586

1

2

3

Single 0.722743 0.881338

Couple 0.722743 0.865875

Couple+Kids 0.881338 0.865875



Cell No.

Family Status 8ElectronicsOnlineMean

8ElectronicsOnlineStd.Err.



N

1

2

3

Single 0.264706 0.050600 0.164538 0.364874 34

Couple 0.287097 0.037471 0.212919 0.361274 62

Couple+Kids 0.275862 0.054789 0.167402 0.384322 29



Effect

Level ofFactor

N 8ElectronicsOnlineMean

8ElectronicsOnlineStd.Dev.

8ElectronicsOnlineStd.Err



Total

Family Status

Family Status

Family Status

125 0.278400 0.292813 0.026190 0.226563 0.330237

Single 34 0.264706 0.290123 0.049756 0.163477 0.365934

Couple 62 0.287097 0.314921 0.039995 0.207122 0.367072

Couple+Kids 29 0.275862 0.253060 0.046992 0.179603 0.372121

8ELECTRONICSMALLS | FAMILY STATUS



Dependent variable: 8ElectronicsMalls

(Analysis sample)

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4

Residual

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99







Family Status

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

8E

lectr

onic

sM

alls



EffectMS

ErrorF p

8ElectronicsMalls 0.031809 0.030417 1.045763 0.354552

LSD test; variable 8ElectronicsMalls (DATA 20171207.sta)

LSD test; variable 8ElectronicsMalls (DATA20171207.sta)Probabilities for Post Hoc TestsError: Between MS = .08705, df = 122.00

Cell No.


{2}.71290

{3}.72414

1

2

3

Single 0.722743 0.881338

Couple 0.722743 0.865875

Couple+Kids 0.881338 0.865875



Cell No.

Family Status 8ElectronicsMallsMean

8ElectronicsMallsStd.Err.



N

1

2

3

Single 0.735294 0.050600 0.635126 0.835462 34

Couple 0.712903 0.037471 0.638726 0.787081 62

Couple+Kids 0.724138 0.054789 0.615678 0.832598 29



Effect

Level ofFactor

N 8ElectronicsMallsMean

8ElectronicsMallsStd.Dev.

8ElectronicsMallsStd.Err



Total

Family Status

Family Status

Family Status

125 0.721600 0.292813 0.026190 0.669763 0.773437

Single 34 0.735294 0.290123 0.049756 0.634066 0.836523

Couple 62 0.712903 0.314921 0.039995 0.632928 0.792878

Couple+Kids 29 0.724138 0.253060 0.046992 0.627879 0.820397

9GIFTSONLINE | FAMILY STATUS



Dependent variable: 9GiftsOnline

(Analysis sample)

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99







Family Status

0.15

0.20

0.25

0.30

0.35

0.40

0.45

9G

iftsO

nlin

e



EffectMS

ErrorF p

9GiftsOnline 0.029216 0.024563 1.189405 0.307907

LSD test; variable 9GiftsOnline (DATA 20171207.sta)

LSD test; variable 9GiftsOnline (DATA 20171207.sta)Probabilities for Post Hoc TestsError: Between MS = .07987, df = 122.00

Cell No.


{2}.27097

{3}.28276

1

2

3

Single 0.500041 0.685431

Couple 0.500041 0.853186

Couple+Kids 0.685431 0.853186



Cell No.

Family Status 9GiftsOnlineMean

9GiftsOnlineStd.Err.

9GiftsOnline-95.00%

9GiftsOnline+95.00%

N

1

2

3

Single 0.311765 0.048468 0.215817 0.407713 34

Couple 0.270968 0.035892 0.199915 0.342020 62

Couple+Kids 0.282759 0.052481 0.178868 0.386649 29



Effect

Level ofFactor

N 9GiftsOnlineMean

9GiftsOnlineStd.Dev.

9GiftsOnlineStd.Err

9GiftsOnline-95.00%

9GiftsOnline+95.00%

Total

Family Status

Family Status

Family Status

125 0.284800 0.280856 0.025121 0.235079 0.334521

Single 34 0.311765 0.266020 0.045622 0.218946 0.404583

Couple 62 0.270968 0.305355 0.038780 0.193422 0.348513

Couple+Kids 29 0.282759 0.247947 0.046043 0.188445 0.377073

9GIFTSMALLS | FAMILY STATUS



Dependent variable: 9GiftsMalls

(Analysis sample)

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99







Family Status

0.55

0.60

0.65

0.70

0.75

0.80

0.85

9G

iftsM

alls



EffectMS

ErrorF p

9GiftsMalls 0.029216 0.024563 1.189405 0.307907

LSD test; variable 9GiftsMalls (DATA 20171207.sta)

LSD test; variable 9GiftsMalls (DATA 20171207.sta)Probabilities for Post Hoc TestsError: Between MS = .07987, df = 122.00

Cell No.


{2}.72903

{3}.71724

1

2

3

Single 0.500041 0.685431

Couple 0.500041 0.853186

Couple+Kids 0.685431 0.853186



Cell No.

Family Status 9GiftsMallsMean

9GiftsMallsStd.Err.

9GiftsMalls-95.00%

9GiftsMalls+95.00%

N

1

2

3

Single 0.688235 0.048468 0.592287 0.784183 34

Couple 0.729032 0.035892 0.657980 0.800085 62

Couple+Kids 0.717241 0.052481 0.613351 0.821132 29



Effect

Level ofFactor

N 9GiftsMallsMean

9GiftsMallsStd.Dev.

9GiftsMallsStd.Err

9GiftsMalls-95.00%

9GiftsMalls+95.00%

Total

Family Status

Family Status

Family Status

125 0.715200 0.280856 0.025121 0.665479 0.764921

Single 34 0.688235 0.266020 0.045622 0.595417 0.781054

Couple 62 0.729032 0.305355 0.038780 0.651487 0.806578

Couple+Kids 29 0.717241 0.247947 0.046043 0.622927 0.811555

10HOMEWEARONLINE | FAMILY STATUS



Dependent variable: 10Homew earOnline

(Analysis sample)

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99







Family Status

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

10H

om

ew

earO

nlin

e



EffectMS

ErrorF p

10HomewearOnline 0.006460 0.026834 0.240730 0.786426

LSD test; variable 10HomewearOnline (DATA 20171207.sta)

LSD test; variable 10HomewearOnline (DATA20171207.sta)Probabilities for Post Hoc TestsError: Between MS = .07082, df = 122.00

Cell No.


{2}.22258

{3}.24828

1

2

3

Single 0.144990 0.393461

Couple 0.144990 0.668535

Couple+Kids 0.393461 0.668535



Cell No.

Family Status 10HomewearOnlineMean

10HomewearOnlineStd.Err.



N

1

2

3

Single 0.305882 0.045638 0.215537 0.396228 34

Couple 0.222581 0.033796 0.155677 0.289484 62

Couple+Kids 0.248276 0.049416 0.150452 0.346100 29



Effect

Level ofFactor

N 10HomewearOnlineMean

10HomewearOnlineStd.Dev.

10HomewearOnlineStd.Err



Total

Family Status

Family Status

Family Status

125 0.251200 0.266281 0.023817 0.204060 0.298340

Single 34 0.305882 0.256953 0.044067 0.216227 0.395538

Couple 62 0.222581 0.278414 0.035359 0.151877 0.293285

Couple+Kids 29 0.248276 0.248741 0.046190 0.153660 0.342892

10HOMEWEARMALLS | FAMILY STATUS



Dependent variable: 10Homew earMalls

(Analysis sample)

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99







Family Status

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

10H

om

ew

earM

alls



EffectMS

ErrorF p

10HomewearMalls 0.006460 0.026834 0.240730 0.786426

LSD test; variable 10HomewearMalls (DATA 20171207.sta)

LSD test; variable 10HomewearMalls (DATA20171207.sta)Probabilities for Post Hoc TestsError: Between MS = .07082, df = 122.00

Cell No.


{2}.77742

{3}.75172

1

2

3

Single 0.144990 0.393461

Couple 0.144990 0.668535

Couple+Kids 0.393461 0.668535



Cell No.

Family Status 10HomewearMallsMean

10HomewearMallsStd.Err.



N

1

2

3

Single 0.694118 0.045638 0.603772 0.784463 34

Couple 0.777419 0.033796 0.710516 0.844323 62

Couple+Kids 0.751724 0.049416 0.653900 0.849548 29



Effect

Level ofFactor

N 10HomewearMallsMean

10HomewearMallsStd.Dev.

10HomewearMallsStd.Err



Total

Family Status

Family Status

Family Status

125 0.748800 0.266281 0.023817 0.701660 0.795940

Single 34 0.694118 0.256953 0.044067 0.604462 0.783773

Couple 62 0.777419 0.278414 0.035359 0.706715 0.848123

Couple+Kids 29 0.751724 0.248741 0.046190 0.657108 0.846340

Q1 2D Scatterplots (DATA 20171207.sta) INTERPRET THE SPEARMAN CORRELATION IN EACH CASE

AGE VS 6GROCERIESONLINE

Age:6GroceriesOnline: r = -0.0181, p = 0.8412


<30 31-40 41-50 51-60 >60

Age

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

6G

roceriesO

nlin

e

Age does influence

AGE VS 6GROCERIESMALLS

Age:6GroceriesMalls: r = 0.0181, p = 0.8412


<30 31-40 41-50 51-60 >60

Age

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

110%

6G

roceriesM

alls

AGE VS 7CLOTHES/SHOESONLINE

Age:7Clothes/ShoesOnline: r = -0.1633, p = 0.0688


<30 31-40 41-50 51-60 >60

Age

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

7C

loth

es/S

hoesO

nlin

e

There is an indication

AGE VS 7CLOTHES/SHOESMALLS

Age:7Clothes/ShoesMalls: r = 0.1633, p = 0.0688


<30 31-40 41-50 51-60 >60

Age

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

110%

7C

loth

es/S

hoesM

alls

AGE VS 8ELECTRONICSONLINE

Age:8ElectronicsOnline: r = -0.1911, p = 0.0328


<30 31-40 41-50 51-60 >60

Age

-20%

0%

20%

40%

60%

80%

100%

120%

8E

lectr

onic

sO

nlin

e

Correlation is 0.19, -0.19 – significant – older you are the less you are buying electronics online

AGE VS 8ELECTRONICSMALLS

Age:8ElectronicsMalls: r = 0.1911, p = 0.0328


<30 31-40 41-50 51-60 >60

Age

-20%

0%

20%

40%

60%

80%

100%

120%

8E

lectr

onic

sM

alls

AGE VS 9GIFTSONLINE

Age:9GiftsOnline: r = -0.2461, p = 0.0057


<30 31-40 41-50 51-60 >60

Age

-20%

0%

20%

40%

60%

80%

100%

120%

9G

iftsO

nlin

e

AGE VS 9GIFTSMALLS

Age:9GiftsMalls: r = 0.2461, p = 0.0057


<30 31-40 41-50 51-60 >60

Age

-20%

0%

20%

40%

60%

80%

100%

120%

9G

iftsM

alls

AGE VS 10HOMEWEARONLINE

Age:10Homew earOnline: r = -0.2343, p = 0.0086


<30 31-40 41-50 51-60 >60

Age

-20%

0%

20%

40%

60%

80%

100%

120%

10H

om

ew

earO

nlin

e

AGE VS 10HOMEWEARMALLS

Age:10Homew earMalls: r = 0.2343, p = 0.0086


<30 31-40 41-50 51-60 >60

Age

-20%

0%

20%

40%

60%

80%

100%

120%

10H

om

ew

earM

alls

Q2 WITH CONTINGENCY TABLES Basic Statistics/Tables (DATA 20171207.sta)

GENDER | 12ONLINE IS SAFE

2-Way Summary Table: Observed Frequencies (DATA 20171207.sta)

Marked cells have counts > 10. Chi-square(df=4)=7.15, p=.12806

Gender

12Online is safe1

12Online is safe2

12Online is safe3

12Online is safe4

12Online is safe5

RowTotals

M

Row %

F

Row %

Totals

0 1 6 18 13 38

0.00% 2.63% 15.79% 47.37% 34.21%

1 5 23 45 13 87

1.15% 5.75% 26.44% 51.72% 14.94%

1 6 29 63 26 125

Another picture of , the larger chi square, the more the difference between gender

Categorized Histogram: Gender x 12Online is safe

Categorized Histogram: Gender x 12Online is safe

Chi-square(df=4)=7.15, p=.12806

No o

f obs

Gender: M

3%

16%

47%

34%

1 2 3 4 5

12Online is safe

0

5

10

15

20

25

30

35

40

45

50

Gender: F

1%

6%

26%

52%

15%

1 2 3 4 5

12Online is safe

AGE | 12ONLINE IS SAFE



Age

12Online is safe1

12Online is safe2

12Online is safe3

12Online is safe4

12Online is safe5

RowTotals

<30

Row %

31-40

Row %

41-50

Row %

51-60

Row %

>60

Row %

Totals

0 1 6 13 5 25

0.00% 4.00% 24.00% 52.00% 20.00%

0 2 12 23 14 51

0.00% 3.92% 23.53% 45.10% 27.45%

0 1 7 15 3 26

0.00% 3.85% 26.92% 57.69% 11.54%

1 2 3 11 3 20

5.00% 10.00% 15.00% 55.00% 15.00%

0 0 1 1 1 3

0.00% 0.00% 33.33% 33.33% 33.33%

1 6 29 63 26 125

Categorized Histogram: Age x 12Online is safe

Categorized Histogram: Age x 12Online is safe

Chi-square(df=16)=9.59, p=.88702

No o

f obs

Age: <30

4%

24%

52%

20%

1 2 3 4 5

12Online is safe

02468

101214161820222426

Age: 31-40

4%

24%

45%

27%

1 2 3 4 5

12Online is safe

Age: 41-50

4%

27%

58%

12%

1 2 3 4 5

12Online is safe

Age: 51-60

5%10%

15%

55%

15%

1 2 3 4 5

12Online is safe

02468

101214161820222426

Age: >60

33% 33% 33%

1 2 3 4 5

12Online is safe

FAMILY STATUS | 12ONLINE IS SAFE



Family Status

12Online is safe1

12Online is safe2

12Online is safe3

12Online is safe4

12Online is safe5

RowTotals

Single

Row %

Couple

Row %

Couple+Kids

Row %

Totals

0 1 7 20 6 34

0.00% 2.94% 20.59% 58.82% 17.65%

1 5 16 24 16 62

1.61% 8.06% 25.81% 38.71% 25.81%

0 0 6 19 4 29

0.00% 0.00% 20.69% 65.52% 13.79%

1 6 29 63 26 125

Categorized Histogram: Family Status x 12Online is safe

Categorized Histogram: Family Status x 12Online is safe

Chi-square(df=8)=11.10, p=.19622

No o

f obs

Family Status: Single

3%

21%

59%

18%

1 2 3 4 5

12Online is saf e

0

4

8

12

16

20

24

Family Status: Couple

2%

8%

26%

39%

26%

1 2 3 4 5

12Online is saf e

Family Status: Couple+Kids

21%

66%

14%

1 2 3 4 5

12Online is saf e

0

4

8

12

16

20

24


12ONLINE IS SAFE | GENDER



Dependent variable: 12Online is safe

(Analysis sample)

-3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Residual

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Gender; LS Means

Gender; LS Means




M F

Gender

3.4

3.5

3.6

3.7

3.8

3.9

4.0

4.1

4.2

4.3

4.4

4.5

12O

nlin

e is

safe



EffectMS

ErrorF p

12Online is safe 0.086407 0.251515 0.343547 0.558863



Cell No.

Gender 12Online is safeMean

12Online is safeStd.Err.

12Online is safe-95.00%

12Online is safe+95.00%

N

1

2

M 4.131579 0.131856 3.870579 4.392579 38

F 3.735632 0.087143 3.563139 3.908126 87



Effect

Level ofFactor

N 12Online is safeMean

12Online is safeStd.Dev.

12Online is safeStd.Err



Total

Gender

Gender

125 3.856000 0.829924 0.074231 3.709077 4.002923

M 38 4.131579 0.777072 0.126058 3.876162 4.386996

F 87 3.735632 0.827714 0.088740 3.559222 3.912042

Anova, picture – men feels online is safe – significantly different.

12ONLINE IS SAFE | AGE




(Analysis sample)

-3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Residual

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Age; LS Means

Age; LS Means




<30 31-40 41-50 51-60

Age

3.1

3.2

3.3

3.4

3.5

3.6

3.7

3.8

3.9

4.0

4.1

4.2

4.3

4.4

12O

nlin

e is

safe


Levene's Test for Homogeneity of Variances(DATA 20171207.sta)Effect: AgeDegrees of freedom for all F's: 3, 118MS

EffectMS

ErrorF p

12Online is safe 0.256447 0.291906 0.878528 0.454418

LSD test; variable 12Online is safe (DATA 20171207.sta)

LSD test; variable 12Online is safe (DATA 20171207.sta)Probabilities for Post Hoc TestsError: Between MS = .69260, df = 118.00

Cell No.

Age {1}3.8800

{2}3.9608

{3}3.7692

{4}3.6500

1

2

3

4

<30 0.691652 0.635544 0.358813

31-40 0.691652 0.341451 0.159576

41-50 0.635544 0.341451 0.630917

51-60 0.358813 0.159576 0.630917

Age; LS Means (DATA 20171207.sta)

Age; LS Means (DATA 20171207.sta)Current effect: F(3, 118)=.77837, p=.50831Effective hypothesis decomposition

Cell No.

Age 12Online is safeMean




N

1

2

3

4

<30 3.880000 0.166445 3.550393 4.209607 25

31-40 3.960784 0.116535 3.730013 4.191555 51

41-50 3.769231 0.163213 3.446024 4.092437 26

51-60 3.650000 0.186092 3.281488 4.018512 20



Effect

Level ofFactor






Total

Age

Age

Age

Age

122 3.852459 0.829937 0.075139 3.703702 4.001216

<30 25 3.880000 0.781025 0.156205 3.557609 4.202391

31-40 51 3.960784 0.823669 0.115337 3.729124 4.192445

41-50 26 3.769231 0.710363 0.139314 3.482309 4.056153

51-60 20 3.650000 1.039990 0.232549 3.163270 4.136730

12ONLINE IS SAFE | FAMILY STATUS




(Analysis sample)

-3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Residual

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99



EffectMS

ErrorF p

12Online is safe 1.953255 0.279544 6.987285 0.001340

Family Status; Weighted Means

Family Status; Weighted Means





Family Status

3.4

3.5

3.6

3.7

3.8

3.9

4.0

4.1

4.2

4.3

12O

nlin

e is

safe


LSD test; variable 12Online is safe (DATA20171207.sta)Probabilities for Post Hoc TestsError: Between MS = .69567, df = 122.00

Cell No.

Family Status {1}3.9118

{2}3.7903

{3}3.9310

1

2

3

Single 0.765645 0.992465

Couple 0.765645 0.674557

Couple+Kids 0.992465 0.674557



Cell No.

Family Status 12Online is safeMean




N

1

2

3

Single 3.911765 0.143041 3.628600 4.194929 34

Couple 3.790323 0.105927 3.580630 4.000015 62

Couple+Kids 3.931034 0.154882 3.624429 4.237640 29



Effect

Level ofFactor






Total

Family Status

Family Status

Family Status

125 3.856000 0.829924 0.074231 3.709077 4.002923

Single 34 3.911765 0.712131 0.122129 3.663291 4.160239

Couple 62 3.790323 0.977402 0.124130 3.542109 4.038536

Couple+Kids 29 3.931034 0.593479 0.110206 3.705287 4.156782


13I WILL PAY DELIVERY CHARGES | GENDER



Dependent variable: 13I w ill pay Delivery Charges

(Analysis sample)

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Gender; LS Means

Gender; LS Means




M F

Gender

1.9

2.0

2.1

2.2

2.3

2.4

2.5

2.6

2.7

2.8

2.9

3.0

3.1

13I w

ill p

ay D

eliv

ery

Charg

es



EffectMS

ErrorF p

13I will pay Delivery Charges 1.156710 0.365009 3.168990 0.077518



Cell No.

Gender 13I will pay Delivery ChargesMean

13I will pay Delivery ChargesStd.Err.

13I will pay Delivery Charges-95.00%

13I will pay Delivery Charges+95.00%

N

1

2

M 2.578947 0.183791 2.215144 2.942751 38

F 2.310345 0.121467 2.069909 2.550780 87



Effect

Level ofFactor

N 13I will pay Delivery ChargesMean

13I will pay Delivery ChargesStd.Dev.

13I will pay Delivery ChargesStd.Err



Total

Gender

Gender

125 2.392000 1.135185 0.101534 2.191036 2.592964

M 38 2.578947 1.244047 0.201811 2.170039 2.987855

F 87 2.310345 1.081669 0.115967 2.079810 2.540880

13I WILL PAY DELIVERY CHARGES | AGE




(Analysis sample)

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Age; LS Means

Age; LS Means




<30 31-40 41-50 51-60

Age

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

13I w

ill p

ay D

eliv

ery

Charg

es



EffectMS

ErrorF p


LSD test; variable 13I will pay Delivery Charges (DATA 20171207.sta)

LSD test; variable 13I will pay Delivery Charges (DATA20171207.sta)Probabilities for Post Hoc TestsError: Between MS = 1.2643, df = 118.00

Cell No.

Age {1}2.2000

{2}2.4118

{3}2.6538

{4}2.0500

1

2

3

4

<30 0.442015 0.152241 0.657368

31-40 0.442015 0.373443 0.225097

41-50 0.152241 0.373443 0.073529

51-60 0.657368 0.225097 0.073529


Age; LS Means (DATA 20171207.sta)Current effect: F(3, 118)=1.3034, p=.27665Effective hypothesis decomposition

Cell No.

Age 13I will pay Delivery ChargesMean




N

1

2

3

4

<30 2.200000 0.224882 1.754672 2.645328 25

31-40 2.411765 0.157449 2.099973 2.723557 51

41-50 2.653846 0.220515 2.217166 3.090526 26

51-60 2.050000 0.251426 1.552108 2.547892 20



Effect

Level ofFactor






Total

Age

Age

Age

Age

122 2.360656 1.128632 0.102182 2.158360 2.562951

<30 25 2.200000 1.000000 0.200000 1.787220 2.612780

31-40 51 2.411765 1.151980 0.161310 2.087765 2.735764

41-50 26 2.653846 1.164210 0.228320 2.183612 3.124081

51-60 20 2.050000 1.145931 0.256238 1.513688 2.586312

13I WILL PAY DELIVERY CHARGES | FAMILY STATUS




(Analysis sample)

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99







Family Status

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5

2.6

2.7

2.8

2.9

3.0

13I w

ill p

ay D

eliv

ery

Charg

es



EffectMS

ErrorF p


LSD test; variable 13I will pay Delivery Charges (DATA 20171207.sta)

LSD test; variable 13I will pay Delivery Charges (DATA20171207.sta)Probabilities for Post Hoc TestsError: Between MS = 1.3065, df = 122.00

Cell No.


{2}2.3871

{3}2.4828

1

2

3

Single 0.794838 0.582567

Couple 0.794838 0.710528

Couple+Kids 0.582567 0.710528



Cell No.

Family Status 13I will pay Delivery ChargesMean




N

1

2

3

Single 2.323529 0.196026 1.935476 2.711583 34

Couple 2.387097 0.145164 2.099731 2.674463 62

Couple+Kids 2.482759 0.212253 2.062582 2.902935 29



Effect

Level ofFactor






Total

Family Status

Family Status

Family Status

125 2.392000 1.135185 0.101534 2.191036 2.592964

Single 34 2.323529 1.147344 0.196768 1.923202 2.723857

Couple 62 2.387097 1.192254 0.151416 2.084321 2.689873

Couple+Kids 29 2.482759 1.021927 0.189767 2.094038 2.871479


14GROCERIES ONLINE IS CONVENIENT | GENDER



Dependent variable: 14Groceries Online is Convenient

(Analysis sample)

-3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Gender; LS Means

Gender; LS Means




M F

Gender

2.8

2.9

3.0

3.1

3.2

3.3

3.4

3.5

3.6

3.7

3.8

14G

roceries O

nlin

e is

Convenie

nt



EffectMS

ErrorF p

14Groceries Online is Convenient 1.849821 0.448261 4.126657 0.044367



Cell No.

Gender 14Groceries Online isConvenient

Mean

14Groceries Online isConvenient

Std.Err.


-95.00%

14Groceries Online isConvenient+95.00%

N

1

2

M 3.315789 0.186780 2.946070 3.685509 38

F 3.218391 0.123442 2.974045 3.462737 87



Effect

Level ofFactor

N 14Groceries Online isConvenient

Mean


Std.Dev.


Std.Err


-95.00%


Total

Gender

Gender

125 3.248000 1.147620 0.102646 3.044834 3.451166

M 38 3.315789 1.337712 0.217006 2.876094 3.755485

F 87 3.218391 1.061211 0.113774 2.992216 3.444566

14GROCERIES ONLINE IS CONVENIENT | AGE




(Analysis sample)

-3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99

Age; LS Means

Age; LS Means




<30 31-40 41-50 51-60

Age

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

3.8

4.0

14G

roceries O

nlin

e is

Convenie

nt



EffectMS

ErrorF p


LSD test; variable 14Groceries Online is Convenient (DATA 20171207.sta)

LSD test; variable 14Groceries Online is Convenient (DATA20171207.sta)Probabilities for Post Hoc TestsError: Between MS = 1.3294, df = 118.00

Cell No.

Age {1}3.2800

{2}3.3725

{3}3.3846

{4}2.7500

1

2

3

4

<30 0.742909 0.746568 0.128135

31-40 0.742909 0.965433 0.042924

41-50 0.746568 0.965433 0.066729

51-60 0.128135 0.042924 0.066729


Age; LS Means (DATA 20171207.sta)Current effect: F(3, 118)=1.5691, p=.20055Effective hypothesis decomposition

Cell No.

Age 14Groceries Online isConvenient

Mean


Std.Err.


-95.00%


N

1

2

3

4

<30 3.280000 0.230596 2.823356 3.736644 25

31-40 3.372549 0.161450 3.052834 3.692264 51

41-50 3.384615 0.226118 2.936839 3.832391 26

51-60 2.750000 0.257815 2.239457 3.260543 20



Effect

Level ofFactor


Mean


Std.Dev.


Std.Err


-95.00%


Total

Age

Age

Age

Age

122 3.254098 1.161087 0.105120 3.045986 3.462211

<30 25 3.280000 1.061446 0.212289 2.841857 3.718143

31-40 51 3.372549 1.264291 0.177036 3.016961 3.728137

41-50 26 3.384615 1.022817 0.200591 2.971491 3.797740

51-60 20 2.750000 1.118034 0.250000 2.226744 3.273256

14GROCERIES ONLINE IS CONVENIENT | FAMILY STATUS




(Analysis sample)

-3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

Residual

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Expecte

d N

orm

al V

alu

e

.01

.05

.15

.35

.55

.75

.95

.99







Family Status

2.4

2.6

2.8

3.0

3.2

3.4

3.6

3.8

4.0

4.2

14G

roceries O

nlin

e is

Convenie

nt



EffectMS

ErrorF p


LSD test; variable 14Groceries Online is Convenient (DATA 20171207.sta)

LSD test; variable 14Groceries Online is Convenient(DATA 20171207.sta)Probabilities for Post Hoc TestsError: Between MS = 1.3081, df = 122.00

Cell No.


{2}3.2419

{3}3.5172

1

2

3

Single 0.385601 0.094078

Couple 0.385601 0.286740

Couple+Kids 0.094078 0.286740



Cell No.

Family Status 14Groceries Online isConvenient

Mean


Std.Err.


-95.00%


N

1

2

3

Single 3.029412 0.196143 2.641126 3.417697 34

Couple 3.241935 0.145250 2.954398 3.529473 62

Couple+Kids 3.517241 0.212380 3.096813 3.937669 29



Effect

Level ofFactor


Mean


Std.Dev.


Std.Err


-95.00%


Total

Family Status

Family Status

Family Status

125 3.248000 1.147620 0.102646 3.044834 3.451166

Single 34 3.029412 1.193043 0.204605 2.613139 3.445684

Couple 62 3.241935 1.223881 0.155433 2.931128 3.552743

Couple+Kids 29 3.517241 0.870988 0.161738 3.185935 3.848548

Q3 DESCRIPTIVE STATS (DATA 20171207.sta)

DESCRIPTIVE STATISTICS DIALOG



Variable


UpperQuartile

Std.Dev.

6GroceriesOnline

6GroceriesMalls

7Clothes/ShoesOnline

7Clothes/ShoesMalls

9GiftsOnline

9GiftsMalls

125 0.109 0.000 0.000 0.800 0.000 0.200 0.186

125 0.891 1.000 0.200 1.000 0.800 1.000 0.186

125 0.189 0.200 0.000 0.800 0.000 0.200 0.236

125 0.811 0.800 0.200 1.000 0.800 1.000 0.236

125 0.285 0.200 0.000 1.000 0.000 0.400 0.281

125 0.715 0.800 0.000 1.000 0.600 1.000 0.281

Q3 Basic Statistics/Tables (DATA 20171207.sta)

T-TEST FOR DEPENDENT (CORRELATED) SAMPLES DIALOG

T-test for Dependent Samples (DATA 20171207.sta)

T-test for Dependent Samples (DATA 20171207.sta)Marked differences are significant at p < .05000

Variable

Mean Std.Dv. N Diff. Std.Dv.Diff.

t df p Confidence-95.000%

Confidence+95.000%

6GroceriesOnline


0.108800 0.185792

0.188800 0.235966 125 -0.080000 0.246262 -3.63201 124 0.000410 -0.123596 -0.036404

Box & Whisker Plot

Box & Whisker Plot

6GroceriesOnline vs. 7Clothes/ShoesOnline

Mean

Mean±SE

Mean±1.96*SE

6GroceriesOnline


6%

8%

10%

12%

14%

16%

18%

20%

22%

24%



Variable



Confidence+95.000%


7Clothes/ShoesMalls

0.188800 0.235966

0.811200 0.235966 125 -0.622400 0.471932 -14.7450 124 0.000000 -0.705947 -0.538853

Box & Whisker Plot

Box & Whisker Plot

7Clothes/ShoesOnline vs. 7Clothes/ShoesMalls

Mean

Mean±SE

Mean±1.96*SE


7Clothes/ShoesMalls

10%

20%

30%

40%

50%

60%

70%

80%

90%



Variable



Confidence+95.000%

9GiftsOnline

9GiftsMalls

0.284800 0.280856

0.715200 0.280856 125 -0.430400 0.561712 -8.56671 124 0.000000 -0.529841 -0.330959

Box & Whisker Plot

Box & Whisker Plot

9GiftsOnline vs. 9GiftsMalls

Mean

Mean±SE

Mean±1.96*SE 9GiftsOnline 9GiftsMalls20%

30%

40%

50%

60%

70%

80%

Q3 Nonparametrics (DATA 20171207.sta)

NONPARAMETRIC COMPARISONS OF TWO VARIABLES DIALOG

Wilcoxon Matched Pairs Test (DATA 20171207.sta)


Pair of Variables

ValidN

T Z p-value

6GroceriesOnline & 6GroceriesMalls 125 121.0000 9.403591 0.000000

Box & Whisker Plot

Box & Whisker Plot

Median

25%-75%

Min-Max

6GroceriesOnline

6GroceriesMalls

-20%

0%

20%

40%

60%

80%

100%

120%



Pair of Variables

ValidN

T Z p-value

7Clothes/ShoesOnline & 7Clothes/ShoesMalls 125 451.0000 8.590494 0.000000

Box & Whisker Plot

Box & Whisker Plot

Median

25%-75%

Min-Max


7Clothes/ShoesMalls

-20%

0%

20%

40%

60%

80%

100%

120%



Pair of Variables

ValidN

T Z p-value

9GiftsOnline & 9GiftsMalls 125 1222.000 6.690804 0.000000

Box & Whisker Plot

Box & Whisker Plot

Median

25%-75%

Min-Max 9GiftsOnline 9GiftsMalls-20%

0%

20%

40%

60%

80%

100%

120%

Basic Statistics/Tables (DATA 20171207.sta)

DESCRIPTIVE STATISTICS DIALOG



Variable


UpperQuartile

Std.Dev.







17Clothes/Shoes is Cheaper online

22Electronics/Gifts Cheaper online

125 2.392000 2.000000 1.000000 5.000000 2.000000 3.000000 1.135185

125 2.832000 3.000000 1.000000 5.000000 2.000000 4.000000 1.105237

125 3.088000 3.000000 1.000000 5.000000 2.000000 4.000000 1.062681

125 3.248000 3.000000 1.000000 5.000000 3.000000 4.000000 1.147620

125 3.440000 4.000000 1.000000 5.000000 3.000000 4.000000 0.928057

125 3.800000 4.000000 1.000000 5.000000 3.000000 4.000000 0.915811

125 3.136000 3.000000 1.000000 5.000000 2.000000 4.000000 0.961719

125 3.440000 3.000000 1.000000 5.000000 3.000000 4.000000 0.919327

Nonparametrics (DATA 20171207.sta)

FRIEDMAN ANOVA AND KENDALL'S CONCORDANCE DIALOG

Friedman ANOVA and Kendall Coeff. of Concordance (DATA 20171207.sta)


Variable

AverageRank

Sum ofRanks

Mean Std.Dev.




1.664000 208.0000 2.392000 1.135185

2.020000 252.5000 2.832000 1.105237

2.316000 289.5000 3.088000 1.062681

Box & Whisker Plot

Box & Whisker Plot

Median 25%-75% Min-Max 1

3I

will

pa

y D

eliv

ery

Cha

rge

s

18

I w

ill p

ay D

eliv

ery

Cha

rge

s

23

I w

ill p

ay D

eliv

ery

Cha

rge

s

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

Box & Whisker Plot

Box & Whisker Plot

Mean Mean±SE Mean±SD 1

3I

will

pa

y D

eliv

ery

Cha

rge

s

18

I w

ill p

ay D

eliv

ery

Cha

rge

s

23

I w

ill p

ay D

eliv

ery

Cha

rge

s

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Friedman ANOVA and Kendall Coeff. of Concordance (DATA 20171207.sta)


Variable

AverageRank

Sum ofRanks

Mean Std.Dev.




1.804000 225.5000 3.248000 1.147620

1.940000 242.5000 3.440000 0.928057

2.256000 282.0000 3.800000 0.915811

Box & Whisker Plot

Box & Whisker Plot

Median 25%-75% Min-Max

14

Gro

ceri

es O

nlin

e is C

onve

nie

nt

21

Buyin

g S

ho

es+

clo

the

s o

nlin

e is m

ore

co

nvenie

nt

26

Buyin

g e

lec+

gifts

on

line is m

ore

co

nve

nie

nt0.5

1.5

2.5

3.5

4.5

5.5

Nonparametrics (DATA 20171207.sta)

NONPARAMETRIC COMPARISONS OF TWO VARIABLES DIALOG



Pair of Variables

ValidN

T Z p-value

17Clothes/Shoes is Cheaper online & 14Groceries Online is Convenient 83 1534.000 0.948888 0.342678

Box & Whisker Plot

Box & Whisker Plot

Median 25%-75% Min-Max

17Clothes/Shoes is Cheaper online22Electronics/Gifts Cheaper online

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

APPENDIX B

M F

Gender

<30 31-40 41-50 51-60 >60

Age

Single Couple without kids Couple with kids

Family Status

Purchasing Behavior 1 2 3 4 5 6

How much of your grocery shopping is done online? 0% 20% 40% 60% 80% 100%

How much of your shopping for clothes and shoes is done online? 0% 20% 40% 60% 80% 100%

How much of your electronics shopping is done online? 0% 20% 40% 60% 80% 100%

How much of your shopping of gifts is done online? 0% 20% 40% 60% 80% 100%

How much of your shopping for homeware and home equipment is done online? 0% 20% 40% 60% 80% 100%

How much of total spend is done online? 0% 20% 40% 60% 80% 100%

Groceries Online Strongly agree Agree Neutral Disagree Strongly disagree

Online shopping is safe

Delivery costs deter me from buying groceries online

I buy groceries online because of convenience

The items I purchase online are always in stock

I do not buy groceries online because it is not immediately available

Clothes and shoes Online Strongly agree Agree Neutral Disagree Strongly disagree

Buying clothes and shoes online is cheaper

Delivery costs deter me from buying clothes and shoes online

I always get the right fit when purchasing online

Complicated returns processes deter me from buying clothes and shoes online

I buy clothes and shoes online because of convenience

Electronics and Gifts (E and G's) Strongly agree Agree Neutral Disagree Strongly disagree

Buying electronics and gifts online is cheaper

I do not mind paying delivery costs online for electronics and gifts

I always get the right product when purchasing online

Complicated returns processes deter me from buying electronics and gifts online

I buy electronics and gifts online because of convenience

I will increase my online purchasing if… Strongly agree Agree Neutral Disagree Strongly disagree

Delivery times are faster

It is easy to return products

Prices are lower

The website must be well designed and have great content

Attributes of the future of online

Demographic Questions

Quantifying the amount of shopping done Online vs shopping at Malls/Stores

Attributes of Groceries Online

Attributes of Clothes and Shoes Online

Attributes of Electronics and Gifts Online

explaining consumer shopping channel preferences in retail ... › app › webroot › files... ·...

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