Consumer buying behaviour towards e-commerce platform

Project by-
Kajal Singh
Maitreyee Mukherjee
Radhika Khandelwal
Souranil Bhattacharjee
Contents
1. Background Of the study:.............................................................................................................4
1. Major brands and market share:..............................................................................................6
2. Products & Services:.................................................................................................................7
3. Consumer buying process-........................................................................................................9
4. RECOMMENDER SYSTEM........................................................................................................10
5. VIRTUAL ASSISTANT................................................................................................................11
6. IMAGE SEARCH OPTION..........................................................................................................11
7. Need Of The Study:.....................................................................................................................12
2. Literature Review:.......................................................................................................................12
1. Research Gap:.........................................................................................................................14
2. Research Questions:................................................................................................................15
3. Significance Of the study:.......................................................................................................15
4. Project Title:................................................................................................................................15
5. Objective Of The Study:..............................................................................................................15
6. Hypothesis...................................................................................................................................16
7. Research Methodology:..............................................................................................................17
1. Research design:.....................................................................................................................17
2. Sampling design:.....................................................................................................................17
3. Scaling Techniques Used:........................................................................................................17
4. Data Collection Method:.........................................................................................................17
5. Data analysis tools and techniques:-......................................................................................17
6. Secondary Research Objective:...............................................................................................17
7. Data Analysis:..............................................................................................................................19
1. Correlation Regression Analysis:............................................................................................19
2. Chi-Square Test-..........................................................................................................................23
1. Frequency Table-.....................................................................................................................24
3. Factor Analysis:...........................................................................................................................26
8. Key Findings:...................................................................................................................................32
9. Suggestion.......................................................................................................................................33
10. Conclusion and managerial implication-......................................................................................33
11. Reference:.....................................................................................................................................34
12. Annexure:......................................................................................................................................35
13. Key Learnings:...............................................................................................................................37
 1. Background Of the study:
E commerce Industry is the future of modern trade and all brick and mortar shops in near
future. A lot of companies are shifting their operations from conventional stores to
ecommerce due to rapid changes in lifestyle of people, change in socio economic culture,
technological advancement and many more factors. Consumer preferences are rapidly
changing. From marketing and customer acquisition to checkout and fulfilment, shoppers are
expecting a seamless and personalized experience. Convenience and immediacy have become
key to removing friction from the customer journey as researchers ll. As social commerce and
e commerce continues to grow, consumers expect a targeted click message on where they can
purchase goods with the click of a button.

Worldwide ecommerce sales topped $3.5 trillion USD an increase of approximately 18%
from the year before $6.5 billion is expected to grow in 2023.

 Revenue in the ecommerce market amounts to US$40,845m in 2020.

 Revenue is expected to show an annual growth rate (CAGR 2020-2024) of 12.8%,
resulting in a market volume of US$66,199m by 2024.
 The market's largest segment is Fashion with a market volume of US$16,553m in
2020.
 User penetration is 53.2% in 2020 and is expected to hit 67.3% by 2024.
 The average revenue per user (ARPU) currently amounts to US$55.65.
 In India, ecommerce industry is largely dominated by two players-US based Amazon
and Walmart owned Flipkart. There are also many other players in this e-commerce industry.
Key drivers of growth:
1. people have increased their standards of living because of which there is an increase in
annual household income.
2. Foreign Investors are investing and funding ecommerce sector because of which there is
increase in this industry.
3. There is decrease in communication cost, because of large population subscribed to
internet broadband and 3G.
4.There are increased use of Smartphone, I-pad and tablets promote growth of ecommerce
plus most of the spending comes from mobile devices.
India has the second-largest internet user base, which exceeds 450 million todays. Of these,
approximately 70 million people are estimated to have more than three to four years of online
experience, which makes them comfortable with engaging in e-commerce. As more people
join the internet economy (internet penetration is expected to almost double to 60% by 2022)
and continue to get accustomed to their new online lives it is expected that ecommerce will
increase to 100 billion USD by 2022.
Market size of e-commerce industry across industry from 2014-2027, with forecasts
until 2027

Market Size
250

200

150

100

50

0
2014 2015 2017 2018 2020 2021 2025 2027

E-commerce as share of India's GDP in 2016 and 2017 with a forecast for
2018
 GDP Share
1.00%

0.90%

0.80%

0.70%

0.60%

0.50%

0.40%

0.30%

0.20%

0.10%

0.00%
2016 2017 2018

DIGITAL INDIA 2019

Population 1.361 B
Mobile subscriptions 1.190 B
Internet users 560.0 M
Social media users 310.0 M
Mobile social users 290.0 M

TOP 9 INDIAN E-COMMERCE PLATFORM

Companies Estimate monthly traffic
Amazon 365.5 Million visit
Flipkart 221.5 Million visit
Snapdeal 83.5 million visit
Indiamart 42.8 Million visit
Bookmyshow 43.4 Million visit
Myntra 27.8 Million visit
Firstcry 16.1 Million visit

1. Major brands and market share:

1) Amazon:
Amazon established in the year 1994 on 5 th July by Jeff Bezos. They initially started
with online books store but later expanded to sell electronics, software, video
games, apparel, furniture, food, toy. In 2017 amazon acquired whole food market for
 US$14 million. The total revenue of amazon in 2019 was US$280.522 billion. Amazon
owns 40 subsidiaries including Zappos, Shopbop etc.
2) Flipkart:
Flipkart is a Bengaluru based largest Indian ecommerce company. Sachin Bansal
and binny Bansal jointly founded the company in 2007. Flipkart focuses on the sales
of books, electronics, fashion, home appliances etc.in 2017 flipkart market share was
39.5% in ecommerce industry.in august 2018, US based Walmart a retail chain
purchased the flipkart at 77% controlling stake in Flipkart for US$ 16 billion. The total
revenue in 2019 was 43,615 crores.
 3) BookMyShow:
BookMyShow is the premier show ticketing portal and retailer in India. Since its
launch in 2007, BookMyShow has expanded its sales to millions of tickets for movies,
sporting events, plays and more every month. With the success of the company,
BookMyShow has expanded to others, opening subsidiaries in Indonesia, the United
Arab Emirates, Sri Lanka, and the West Indies. Revenue of BookMyShow is 594.2
crore in 2019.

2. Products & Services:

The e-commerce industry is more like a virtual marketplace. In 2019, E-Commerce Sales
Accounted for 14.1 Percent of All Retail Sales Worldwide Considering that more than a tenth
of all retail sales are made onlineThere are various products and services associated to it.
They business is divided into some categories according to the nature of business-

1. Retail- This business model works just like an offline retail store but in this case it’s a
virtual store. The model involves selling the product directly to the end customer
without involving any intermediaries. This model can sell various goods starting from
basic groceries to apparels and electronics goods. In this model companies usually
access the shopping option in their own Website. Like in shop.bigbazar.com one can
buy daily essential groceries and like this Samsung official Website also offers
trending gadgets of Samsung and one can easily purchase it from those Websites.

2. Wholesale- This business model is also replica of physical wholesale marketplace.

Just like in marketplaces this online wholesale e-commerce also supply products to
small businesses or to end customers in bulk. The discounting depends on the
quantity. This business model is very beneficial to small businesses or to customers
who often have seasonal demand of bilk materials. Rather going to a congested
marketplace the buyers can enjoy almost same amount of discounts sitting in home
only. In this virtual market place buyers can also negotiate the deal talking to the
seller and can have effective interaction. Websites like indiamart, alibaba, salehoo
follows this business model.
3. Drop shipping: This concept is very popular since long back but there was no formal
name to it. In marketplace it’s a very common practice that retailers perform
sometime. When any customer ask for a product which wasn’t not there in his stock
but to retain the customer he promises to arrange the product somehow in next
date. Then the retailer contact it’s distributor and if distributor also fails to deliver
the order then the company contacted a drop shipper who hasn’t any link with that
wholesaler. The drop shipper arrange that particular product from another
wholesaler in discount and sell it further to retailer after keeping its desired margin.
This practice became quite easy in this era because of e-commerce. This concept is
quite popular as the drop shipper don’t need maintain any inventory and according
to the demand it can sell the products to customers. Dropshift direct, sunrise
wholesale, wholesale 2B are top drop shipping Websites. Top companies like
Amazon also allows drop shipping but it should be listed in amazon’s FBA program.

4. Crowd funding: This is the popular practice among e commerce channels. This
Websites first collect money from consumers in advance of a product being available
in order to raise the startup capital necessary to bring it to market. Most of the small
startups usually follow this techniques. The companies actually want to reduce their
inventory cost and that’s why according to demand they produce and supply the
goods. Many of the times the e commerce companies even doesn’t allow cash on
delivery options to orders. Due to lack of capital the amount paid by customers
utilized by the company to manufacture goods and then delivered to customers. Due
to this whole process the companies usually took subsequently large amount of time
to deliver the product to end customers. Club factory was a popular example of
crowd funding. Many of the customers has experience the delay of delivery even
the company use to send messages after confirming the orders like how the product
is manufactured and all about the shipment processes.

5. Subscription- This is one of the trending model that e-commerce is following these
days. The model talks about automatic recurring purchase of a product or service on
a regular basis until the subscriber chooses to cancel. The one of the successfully
implemented example if researchers talk about then that will be amazon prime. The
 prime service required extra penny to avail its services and it’s a subscription based
model after a certain period it will stop the add benefits and the user again need to
purchase that service. Amazon prime offers additional discounts to its customers and
that’s why people are attracted to these kind of model. The automobile giant
Porsche also introduce this kind of service where they follow a subscription based
model where customers can rent car monthly basis by paying some amount and can
also switch between different models of brands in every week.

6. Physical products:
Any tangible good requires inventory keeping and deliver orders by physical shipping
to customers as sales are made. This model is very common model for all e
commerce company. Every company is following this trend, starting from Amazon,
flipkart all the top brand who have enough working capital and want to serve
customers readily after getting the order. The customers experience immense
satisfaction getting the order within 4-7 days after placing the order depending on
type of product. The company can keep the inventory for a longer period of time
because of having huge working capital to hold the inventory and thus able to
deliver the goods in proper time frame.

7. Digital products:
This model includes digital goods, templates, and courses, or media that one can
download and that need to be purchased for consumption or licensed for use.
Amazon kindle is a type of digital product also one example like udemy, course era
also sell courses as a product this also includes in digital product.

Services:

With the products there are various services associated with product. The service can be
experience like effortless delivery of the product, return policies, grievance handling, on
time delivery etc.

3. Consumer buying process-

Consumer buying process is a journey of customers from identifying the problem to
evaluating the products these whole process involve 5 steps-
i. Problem Identification/Need recognition- This is the initial stage of customer
journey in this stage customers either identify the problems that can be solved
by that particular product or there might be some latent need of the customers
which currently is not getting fulfilled with existing product that’s why the
customer is looking for another option.

ii. Information Search – This is one of the important stage for both the buyer and
seller. In this stage buyers rigorously grasp every information they get from
internet, advertisements or peer groups. Companies should focus to strengthen
its IMC strategies and achieve effective brand communication. Buyers in this
stage gathered all informations and selects some evoked set of brand to research
about more.

iii. Evaluation Of Alternatives: This stage is most crucial for sellers in this case buyer
can choose their brand and also can completely eject it from evolved set. This
stage customer usually has a bunch of alternatives in hand and they choose the
best alternatives for them after filtering it with perceive value of brand in the
mind of customers. In this stage the company need to make sure that there
should be no negative word of mouth prevailing with existing customers. The
peer group has more influence to make decision in this stage so the word of
mouth marketing need to be considered.

iv. Selecting the best Alternative: Now after the evaluation the customer alone not
able to take decision about the product. In this stage customers choose best
option from the available alternative with the help of mainly peer groups. So in
this stage buyers should be very active to wipe out all the negative comment and
do rigorous promotion to create value and influence customer to buy that
particular product.

v. Review Of Product: Buyer might have a perception that after the product
purchase they are free from every liability but no this stage is equally important
as previous one. Because after purchasing product customer review is very
important and that should be positive one because only positive review can drive
it’s sales with more and more customers. So the company should implement the
after sales service option to enhance it’s product offerings.
4. RECOMMENDER SYSTEM
“31% of e commerce revenues researchers generated from personalized product
recommendations “-Barillance.com 2014

“Already, 35% of what consumers purchases on Amazon and 75% of what they watch on
Netflix come from product recommendation based on such algorithms”-McKinsey

Recommender systems are used by E-commerce sites, as a serious business tools that
are shaping the world of E-commerce. Many of the largest commerce sites are using
recommender systems to help their customers find products to purchase. The main idea
behind the recommendation systems for eCommerce is to build relationship with the
products (items), users (visitors/customers) and make decision to select the most
appropriate product to a specific user. For this, Recommendation systems use machine
learning algorithms. During a learning phase (which you might see the term cold start),
the system builds the model which is actually an abstraction of the relationship with the
items and users. Basically, the Recommender systems are used by E-commerce sites to
suggest products to their customers. The products are recommended on the basis of
Innovation, Attributes, Value Proposition, Effortless Purchase, Personalization

Innovation mainly includes technological advancement, big data analysis, IOT, Artificial
Intelligence which affects the recommender system

Recommender system is also affected by various attributes that includes knowledge-

based recommendation, content-based recommendation. Item attributes are classified
as either extrinsic or intrinsic. Extrinsic features cannot be easily identified by analyzing
the contents automatically. Intrinsic features on the other hand are easily obtainable
from the contents. Sometimes, features of items are obtained from the description of
the items themselves in addition to analyzing the item. This is evident in cases such as
news articles or pages.

Recommender system is also affected by Value proposition given by product which is

measured by the review of product These systems utilize data on customers’ past
purchases, ratings, and browsing patterns, and product information to suggest
“recommended items” that is related to a given “item of interest”. The
recommendations generated by these systems can be based on either user to-user
 collaborative filtering, where the suggestions are functions of the purchases of
customers considered to be similar to the current buyer, or on item-to-item
collaborative filtering, where the suggestions are made based on the relatedness of
items

Effortless purchase also affects the recommender system which includes the user
interaction, interactive model

Recommender system is also affected by Personalization which is one of the elements

that may cause improve the interaction between people and computers and offer
possibilities for establishing long-term customer relations. Now more than ever, the
promise of electronic commerce and online shopping will depend to a great extent upon
the interface and how people interact with the computer and the online shopping

experience.

5. VIRTUAL ASSISTANT
E-commerce Virtual Assistants are a valuable support for customers browsing products in
your store. They let you inexpensively improve customer service, improving user experience
and site usability E-commerce virtual assistants are bots that use machine learning and
natural language processing to perform various tasks, including understanding user queries,
providing them with relevant information, or even creating product descriptions.  e-
commerce virtual assistant, focus on its usefulness, effectiveness, and most of all make the
experience enjoyable. The main works of virtual assistant are

 Customer Service. To be effective, you need to keep your clients cheerful

 Order Processing. Handling orders is definitely a standout amongst the essential
assignments

 Returns/Exchanges
 Manage Product Data & Inventory
 Maintenance.

Where virtual assistant is affected by customer responsiveness, different influencing factor,

service delivery, trust building and customer experience.
And Customer responsiveness measured on the basis of easy interaction among customers
and good grievance handling which affects the virtual assistant.
Different influencing factor and good service delivery which can be achieve by the correct
product information, customer information and AI chat box.
trust building which is built by having real time data, transparency and customer experience
also affects the virtual assistant which all are the important factor that influence consumer
buying at e commerce platform

6. IMAGE SEARCH OPTION

Google Images is considered one of the largest mediums for online searching, second only
to Google.com. This data presents a significant opportunity for companies to capitalize on
channel image searching toward ecommerce success. If a company produces quality images,
understands Google’s algorithms, and makes it easy for customers to go from an image to
their ecommerce store, it can win big where Google Images allows consumers to search for
items by taking photos and doing a reverse image search to find pictures that relate to the
item. Companies can use this to their advantage by uploading a file of every product they
have to their online store
It is mostly affected by search effort which should involves less effort and can be achieve
when less time is consumed and genuine data is there and also by user ability which is
measured by un interrupted search and more clarity
It can also be affected by search complexity, information unavailability and interactivenes.

7. Need Of The Study:

In present scenario Ecommerce Websites are playing a very vital role in the online business.
It is one of the best & cheapest intermediate for reaching out to new customers in the
online market, if ecommerce implemented effectively, it also offers a smart way of doing
online business & expanding it more.

In this paper researchers have tried to portrait a clear view of the E-commerce and how
does consumers associate with them in day to day life. In this study researchers have figured
out what are the key factors which are affecting the consumer buying behaviour in an online
platform and what factors are going in favour of the e-commerce websites and others which
are not restricting them to buy in such platforms.

In our study researchers have found that the switching percentage varies majority of the

respondent are in the age group of 18-25 i.e. 74.9 percent, 17.9 percent are in the age
group of 25-35 the minority respondents are of the age group 36-45 i.e. 7.2%. So it can
clearly be seen that youth people are adapting the changes rapidly than any other group. As
the data suggest almost 44% of the respondents are students and almost 61% of them
usually buy electronic goods from online.

Researchers have also tried to understand the key components of E-commerce platforms
like image search options, virtual assistance and recommender system how does this factors
work and do they have any impact in the consumer buying behavior process.

1. Literature Review:
The modern century is the augmentation of internet, digital platforms are giving solutions
from entertainment to basic needs which consists of staple food and groceries shopping and
apparel selection. The virtual retailer’s new retail format has emerged and obligated the
existing retailers to consider retailing’s e-tailing model. With favoured demographics such as
a fairly young population, rising income levels, access to latest technology, a plethora of
young entrepreneurs and a huge market potential is giving a boost to the “Brick to click to
mobile” growth story(Singh, 2016) . Starting from incubation of e-commerce trust factor
plays a very important role to shop from online. Regardless of the country trust in online
transaction is one of the main hindrance in e commerce development(Uresearchersmi &
Khan, 2018). Shift in purchase behaviour from traditional/modern trade towards E-
commerce observed among consumers since early 2000. The change in behaviour
shoresearchersd that online hold more positive attitudes towards buying online. (Bridges &
Goldsmith, 2000). The switching percentage varies majority of the respondent are in the age
group of 18-25 i.e. 74.9 percent, 17.9 percent are in the age group of 25-35 the minority
respondents are of the age group 36-45 i.e. 7.2%. So it can clearly be seen that youth people
are adapting the changes rapidly than any other group. As the data suggest almost 44% of
the respondents are students and almost 61% of them usually buy electronic goods from
online. The trends are indicating the perception of online buyers(Gupta & Jain, n.d.). From
findings attitude can also do a better job than demographics of explaining differences in
consumer behaviour. There are several factors influence the ways customer tends to buy
online and develop a habit for their online purchases. Some variables like customer
demographics, psychographic, shopping orientation, web store qualities, online privacy &
security, trust & risk, attitude to online advertisement and vendor’s trust these are
somewhat influencing consumer to purchase from ecommerce.(Swarnakar et al., 2016) .
Out of these four primary drivers of customers channel choice, namely trust and risk,
privacy and security, customer’s shopping orientation and web quality. But in contrary if
they trust the website, they are prone to buy more from that website. Contrary to popular
belief, the sense of privacy did not seem to affect the consumer behavior. Respondents did
not seem very worried about giving their personal data online such as addresses, provided
that they could buy using the Cash on Delivery (CoD) method for purchase youth people
mainly driven by some of the factors like trust, convenience, time, product variety and
privacy out of these trust is the major driver(Bashir et al., 2015). .These variables are
primary drivers to affect online shopping experience but over the year researcher also has
found some drivers that usually affects consumer purchase intention in offline store has
upgraded gradually to e commerce platform also, one of the factors is electric word of
mouth. Along with the primary drivers Online WOM activities are becoming increasingly
important to customers retailers (Kamtarin, 2012).Comparing traditional WOM, online
WOM is more useful due to its speed, convenience, one-to-many reach, and its absence of
face-to-face pressure. Along with the all factors quick and convenient delivery of goods
(logistics). The future is linked to the drones, which might change traditional logistics and
existing postal services.(Jusoh & Ling, 2012) . The technological advancement gradually
driving the e-commerce purchase, Most of the time people use internet for communication
purpose i.e. for e-mail, chatting, social networking etc. but people also use internet for
gathering information and shopping purpose now a days. Past online purchase frequency
and future online shopping intentions is observed implying that customers who purchased
more products via internet in past will continue to make online purchases in future too it’s
like a trend and companies are very keen to learn about that(Gautam Buddha University,
India et al., 2015). The need of articulating past purchase behaviour of a customer and
predicting the future purchase pattern the need of “recommender system” first came in
picture. Recommendation systems are one solution to the need for customization of
companies to serve multiple needs. The system will gather information of customer
according to their past purchase trend and personal information to predict the future
purchase trend and this way the system will not only use purchasing data as input, but also
the customer reactions to the recommendations, which is the most basic measure of its
effectiveness(Prassas et al., 2001). Statistical tests have indicated that use of recommender
agents positively and significantly influenced the decision quality of participants in a
simulated shopping session. Recommender system significantly reduce the shopping time
and search effort(Huseynov et al., 2016). information search, recommendation system,
dynamic pricing and customer services has high significant effect on the intention with
recommendation system having a strongest influence followed by information search,
dynamic pricing and lastly customer service. Information search had highest influence to
customer behavior, followed by recommendation system and dynamic pricing(Le & Liaw,
2017). Here comes one more variable of e commerce influence that is “information search”.
Behavioral factors and user’s interactions with technology that is search engine's capability
play an important role in the determination, and possibly reduction, of search costs and
increase user satisfaction(Consumer Search Behavior in Online Shopping Environments - IEEE
Conference Publication, n.d.). Image search is more effective search option than keywors
search in case of user’s interaction it also get improved in image search as it’s more
interactive than normal keywords search. Most of the researcher has faces the trust issues
among customer while talking about consumer buying perceptions towards e commerce
platform. The interaction of service and customer is one of the most important thing and it
should be as natural as retail outlet to drive trust towards different customers. Interaction
quality as an important factor in adoption of new technologies.so from the result it was
founded that interaction quality is the most important factor of quality which builds trust in
users, and as a result they intend to use the VAS(Nasirian et al., 2017)’s.

1. Research Gap:
Reviewing 16 research papers researchers have found a gap that people are talking about
the factors like consumer demographics, risk association, trust association etc which is no
doubt the key determinant of consumer buying behaviour in retail store and as well as in
Ecommerce. But what about the factors that are only present in E commerce platform and
striving to differentiate itself from brick and mortar stores? The gap is no one is considering
that the technological component of E commerce platform making it different from retail
store and there can be association of recommender system, virtual assistance, image
search options effecting consumer buying behaviour towards modern Ecommerce platform.
Recommender systems are designed to benefit both buyers and sellers — it saves
customers the time and effort required through pages of different products available in
digital markets while businesses can use it to understand customer preferences, build
brands and increase sales, but what are the factors and how does these factors help or
affect consumer buying behaviour, same goes for the Virtual assistance. Companies need to
be aware that the impact of AI and voice-activation doesn’t stop with marketing, sales will
also be impacted by the rise in use of voice-activated virtual assistants such as Amazon’s
Alexa, Apple’s Siri, and Google Home that combine both of these technologies.

According to some research paper researchers found that, people who shop on their phones
find photos to be the key feature. 63% of consumers say that images are more important
than product descriptions, while 53% believe that visuals are more significant than ratings or
reviews. Visuals noticeably influence consumer behavior based on research.

2. Research Questions:
1. What are the demographic characters of consumer that influence their behavior
towards online platform?
2. What are the major drivers of modern e-commerce that influence consumer buying
behavior?
3. Do recommender system has any effect to drive buying behavior in e commerce
platform?
4. Do virtual assistance has any effect to influence buying behavior in e commerce
platform?
5. Do image search has any effect to influence buying behavior in e commerce
platform?

3. Significance Of the study:

The study has the objective to provide meaningful informations to modern ecommerce
companies. In 2020 the digitisation rapidly replacing manual work with IoT where people
gradually depending on machine to machine communication to get error free result.
Machine learning has resulted to several changes from predictive analytics to Artificial
Intelligence and the number is increasing gradually. E-commerce currently cannibalising 10%
of retail purchase and from that figure one can imply that its still in incubation stage. In
2020 some major drivers should be added to increase that percentage to a certain extend.
In this paper the researcher will talk about the modern influencing factors that effective
consumer buying behaviour towards e-commerce platform.

4. Project Title:
Factors affecting consumer buying behaviour towards modern E-commerce platform.

Dependent Variable: Consumer Buying Behaviour.

Independent Variable: Recommender system, Virtual assistance, image search option.

5. Objective of The Study: There are 3 objectives of this research paper.

Ob 1: To analyse is there any association of consumer buying behaviour with respect to

recommender system of Website, presence of virtual assistance and availability of image
search options of that Website.

Ob 2: To analyse the effect of demographic factors which is mainly driving the buying nature
of consumer.

Ob3: To draw a conclusion with research findings which can help Ecommerce companies
making decision towards the technological advancement and which factor should get more
attention determining consumer buying pattern in future.

6. Hypothesis:

Hypothesis 1-

H0= There is no association between consumer buying behaviour and presence of

recommender system of the e commerce platform.

H1= There is association between consumer buying behaviour and presence of

recommender system of the e-commerce platform.

Hypothesis 2-

H0= There is no association between consumer buying behaviour and presence of virtual
assistant of the e commerce platform.
H1= There is association between consumer buying behaviour and presence of virtual
assistant of the e commerce platform.

Hypothesis 3-

H0= There is no association between consumer buying behaviour and presence of image
search result of platform.

H1= There is association between consumer buying behaviour and presence of image search
result of e-commerce platform.

Hypothesis 4-

H0= There is no association between consumer age with buying behaviour in e commerce
platform.

H1= There is an association between consumer age and buying behaviour in e commerce
platform.

Hypothesis 5-

H0= There is no association between consumer income with buying behaviour in e-

commerce platform.

H1= There is an association between consumer income with buying behaviour in e-

commerce platform.

Hypothesis 6-

H0= There is no association between consumer occupation with buying behaviour in e-

commerce platform.

H1= There is an association between consumer income with buying behaviour in e-

commerce platform.

Hypothesis 7-

H0= There is no association between consumer gender with buying behaviour in e-

commerce platform.
H1= There is an association between consumer gender with buying behaviour in e-
commerce platform.

7. Research Methodology:
1. Research design: Descriptive quantitative research
2. Sampling design: In this study researchers have identified 16 attributes and the
according to that 81 sample size has decided for this study and according to the
pandemic situation convenience sampling technique has followed.
3. Scaling Techniques Used: Nominal scale has used to label the categorical data
and for metric data Likert scale has used by researchers.
4. Data Collection Method: A survey has floated among respondents using
google form to collect data.
5. Data analysis tools and techniques:- SPSS as data analysis tools and chi
square test and correlation-regression test as primary analysis tool and factor
analysis as advanced analysis tool.

6. Secondary Research Objective:

Management
Opportunity/ Model
Problem Situational Analysis Development Specification required

Tecchnological advancement, big data analytics,

Innovation IoT, Artificial Intelligence
knowledge based recommendation, content
Attributes based recommendation
Effect Of Value Proposition List of alternatives, review based top selection
recommender Effortless purchase user inteference, interactive model
system Personalisation 33% Past purchase record, Future recoomendation

Customer Responsiveness Easy interaction, good grievance handling

Influencing Factor Purchase influence, help in dicision making

product information, customer information, AI
Service delivery chat box
Effect of virtual Trust building Real time data, transparency
assitant. Customer experience 33% improvement, enhancement
Search effort Less time consuming, genuine data.
User ability uninterrupted search, more clarity
Search complexity New product, language barrier
Effect of image Information Unavailability least popular item, difficulty in information chase
search option Interactiveness 33% Better decision making, great interactive model

7. Data Analysis:
In this project researchers have used two basic data analysis techniques correlation
regression analysis and chi square and another advanced technique that is factor analysis-

1. Correlation Regression Analysis:

Significance Of bivariate correlation test-
1. Pearson correlation used to define association between two metric data.
2. Analysis of partial correlation.
3. Analysis of positive/negative/ no relationship between the dependent and
independent variable.
4. Measure the degree of association between dependent and independent
variable.

A bivariate correlation test has performed using SPSS tool and following result has
obtained-
Analysis-

1. From the above table between so many pairs researchers only need to define
association between sales and other independent variables.
2. In Pearson correlation the value always range from -1 to +1 and any value greater
than 0.7 means there is a good association between two variables.
3. In above table predictive recommendation and effortless purchase, has very strong
association. So increasing effectiveness prediction by recommender system will
increase effortless purchase of customers
4. Transparency in information, list of alternatives and influencing factor by virtual
assistance has moderate association with effortless purchase. Increasing these factors
will increase range of effortless purchase but not in that significant amount.
Significance Of regression analysis:

1. To analyse possible effect on dependent variable cause by independent variable.

2. Coefficient of Determination value determine the “goodness of fit” or cause effect
relationship between variables.
3. Y= A +BX this formula defines the linear relationship which researchers need to
establish in this case.
4. The value of b determine the positive/negative relationship and also the degree of
association between two variables.

A linear regression analysis test has performed using SPSS tool and following result has
obtained-

Model Summaryb

Model R R Square Adjusted R Std. Error of the Durbin-Watson

Square Estimate

1 .812a .659 .586 .49935 2.016

a. Predictors: (Constant), TechnologicalAdvancement, Serviceimprovement,

HighlyInteractive, Preference, Goodgrievancehandling, ListOfAlternatives,
Hasslefreeness, Additionalfeature, Transperacyofinformation, Easyinteraction,
Languagebarrier, Lesstimeconsuming, Predictiverecommendation, Influencingfactor
b. Dependent Variable: Effortlesspurchase

Analysis-
The value of R2 0.659 means the independent variables i.e technological advancement,
service improvement, highly interactive, preference, good grievance handling, list of
alternatives, hasslefreeness, additional feature, transparency of information, easy
interaction, language barrier, lesstimeconsuming, predictive recommendation, influencing
factor all that cause 65.9% variation in dependent variable that is sale.
Now researchers need to again find out the significance of R2 value-

ANOVAa

Model Sum of Squares df Mean Square F Sig.

Regression 31.765 14 2.269 9.099 .000b

1 Residual 16.457 66 .249

Total 48.222 80

a. Dependent Variable: Effortlesspurchase

b. Predictors: (Constant), TechnologicalAdvancement, Serviceimprovement, HighlyInteractive,
Preference, Goodgrievancehandling, ListOfAlternatives, Hasslefreeness, Additionalfeature,
Transperacyofinformation, Easyinteraction, Languagebarrier, Lesstimeconsuming,
Predictiverecommendation, Influencingfactor

Analysis-
From ANOVA test the p value researchers got 0.000 which is less than level of significance
0.05. So the value of R2 is significant.
Now researchers can proceed for co efficient table to form the linear equation-

Coefficientsa

Model Unstandardized Coefficients Standardized t Sig.

Coefficients

B Std. Error Beta

(Constant) .290 .519 .558 .578

Additionalfeature .095 .092 .088 1.035 .304

HighlyInteractive .008 .100 .007 .078 .938

Predictiverecommendation .478 .105 .471 4.537 .000

ListOfAlternatives .167 .077 .196 2.168 .034

Easyinteraction .003 .088 .003 .032 .975

Goodgrievancehandling .056 .098 .055 .569 .571

1 Influencingfactor .055 .094 .061 .589 .558

Transperacyofinformation .231 .098 .224 2.358 .021

Serviceimprovement .057 .082 .073 .686 .495

Lesstimeconsuming -.131 .087 -.155 -1.509 .136

Hasslefreeness .197 .092 .201 2.132 .037

Languagebarrier -.110 .088 -.125 -1.252 .215

Preference -.100 .069 -.124 -1.447 .153

TechnologicalAdvancement -.082 .101 -.081 -.807 .423

a. Dependent Variable: Effortlesspurchase

Analysis-
1. The linear equation will be Y= 0.29+0.095(additional feature)+ 0.08(Highly
interactive)+ 0.478(predictive recommendation)+0.167 (list of alternatives)
+0.003(easy interaction)+0.056 (good grievance handling)+0.055(influencing factor)
+0.231(transparency of information)+0.057(service improvement)-0.131( less time
consuming)+ 0.197(Hassle freeness)-0.110(language barrier)-0.100(preference)-
0.082(technological advancement).
 2. Predictive recommendation has strong influence on effortless purchase than any
other variables and list of alternatives, transparency of information and
hasslefreness has significant impact ( level of significance < 0.05) on effortless
purchase.

2. Chi-Square Test-

Significance of chi-square test-

i. To find association between categorical data or between categorical or metric
data.
ii. In this research researchers want to find association between customer’s age,
income, occupation, gender with dependent variable effortless purchase.
Chi-square test has been performed using SPSS data analysis tool and results are
shown below-

Chi-Square Tests

Value df Asymp. Sig. (2-

sided)
a
Pearson Chi-Square 84.452 8 .000
Likelihood Ratio 14.230 8 .076
Linear-by-Linear Association 5.237 1 .022
N of Valid Cases 81

a. 9 cells (60.0%) have expected count less than 5. The minimum expected
count is .01.

This result is obtain by considering gender as independent variable and effortless purchase
as dependent variable and at 5% level of significance it supports alternate hypothesis that is
there is an association between gender and effortless purchase. Effortlessly purchase by
customers depends on their gender.

Chi-Square Tests

Value df Asymp. Sig. (2-

sided)

Pearson Chi-Square 21.686a 8 .006

Likelihood Ratio 9.333 8 .315
Linear-by-Linear Association .779 1 .377
N of Valid Cases 81

a. 10 cells (66.7%) have expected count less than 5. The minimum

expected count is .05.
This result is obtain by considering occupation as independent variable and effortless
purchase as dependent variable and at 5% level of significance it supports alternate
hypothesis that is there is an association between occupation of respondents with effortless
purchase. That is effortlessly purchase depends on customer’s occupation whether they
student or employed or business owners.

Chi-Square Tests

Value df Asymp. Sig. (2-

sided)
a
Pearson Chi-Square 87.818 16 .000
Likelihood Ratio 17.191 16 .373
Linear-by-Linear Association 5.708 1 .017
N of Valid Cases 81

a. 22 cells (88.0%) have expected count less than 5. The minimum

expected count is .01.

This result is obtain by considering age as independent variable and effortless purchase as
dependent variable and at 5% level of significance it supports alternate hypothesis that is
there is an association between age of respondents with effortless purchase. That is
effortlessly purchase depends on customer’s age whether they are generation z, generation
Y generation X or baby boomers.

Chi-Square Tests

Value df Asymp. Sig. (2-

sided)
Pearson Chi-Square 93.733a 16 .000
Likelihood Ratio 20.314 16 .206
Linear-by-Linear Association 5.413 1 .020
N of Valid Cases 81

a. 20 cells (80.0%) have expected count less than 5. The minimum

expected count is .01.

This result is obtain by considering income as independent variable and effortless purchase
as dependent variable and at 5% level of significance it supports alternate hypothesis that is
there is an association between income of respondents with effortless purchase. That is
effortlessly purchase depends on customer’s income whether they belongs to high, medium
or low income bracket.

1. Frequency Table-
Now after establishing association with every categorical variables with dependent variable
it’s time to check which bracket of respondents are more active or have more effect to the
dependent variable that is effortless purchase researchers need to do frequency analysis
how frequently a value has appeared and how it affected dependent variable.
 Gender

Frequency Percent Valid Percent Cumulative

Percent

Male 37 45.7 45.7 45.7

Female 43 53.1 53.1 98.8

Valid
3.00 1 1.2 1.2 100.0

Total 81 100.0 100.0

From the frequency table it can be seen that female respondents which is 53.1% has more
association than male respondents 45.7% where as others are 1.2%.

Occupation

Frequency Percent Valid Percent Cumulative

Percent

Student 51 63.0 63.0 63.0

Business Owner 4 4.9 4.9 67.9

Valid
Employed 26 32.1 32.1 100.0

Total 81 100.0 100.0

From this frequency table in total respondents students are more inclined towards effortless
purchase which is 63% followed by employed people where the percentage is 32.1% and
business owner has low association that is 4.9%.

Age

Frequency Percent Valid Percent Cumulative

Percent

18-30 71 87.7 87.7 87.7

31-45 7 8.6 8.6 96.3

46-60 1 1.2 1.2 97.5

Valid
More Than 60 1 1.2 1.2 98.8

below 18 1 1.2 1.2 100.0

Total 81 100.0 100.0

From this frequency table of age it can be seen that generation Y that is 18-30 years people
has more association with online purchase that is 87.7% and that is also supports the
previous findings of occupation where students are seen to have more inclination towards
online purchase. This number is followed by next age bracket 31-45 years and the
percentage is 8.6% where rest of the age groups that are 46-60 years (gen x), more than 60
years (baby boomers) and below 18 (generation Z) has 1.2% frequency percentage
respectively.

Incomemonthly

Frequency Percent Valid Percent Cumulative

Percent

0-10000 48 59.3 59.3 59.3

11000-30000 18 22.2 22.2 81.5

31000-60000 11 13.6 13.6 95.1

Valid
61000-100000 3 3.7 3.7 98.8

More Than 100000 1 1.2 1.2 100.0

Total 81 100.0 100.0

From the income table it’s seen that large number of respondent 59.3%belongs low income
bracket that is 0-10000 and it again supports more inclination of students towards online
shopping. This percentage followed by middle income group which has frequency 35.8% and
high income group has frequency of 4.9%. This can also be infer like majority of the
generation Y people belongs to the first two income groups and 81.5% respondents belongs
to that income groups only.
So, the observation from frequency distribution is generation Y students or employed
people with low to middle income bracket prefers to do effortless purchase in e commerce
platforms.

3. Factor Analysis:
Prerequisite Checkbox for Factor Analysis:
☐ The data should be metric and continuous.
☐ There should be proper sampling adequacy that is the data should be adequate to do
factor analysis.
The first check box is right as the data has collecting using proper scaling technique. For this
study data has collected through Likert scale questionnaire so the data is metric and
continuous.
Now the researcher needs to evaluate adequacy of data or test is this sample appropriate to
perform factor analysis or not.

Step-1
To find the sampling adequacy Kaiser-Meyer-Olkin test need to be performed and also
Bartlett’s test of sphericity will check the mutual correlation between the variables.
In the above case researchers use SPSS as a statistical tool to perform KMO and Bartlett’s
test of sphericity:

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .834

Approx. Chi-Square 483.639

Bartlett's Test of Sphericity df 105

Sig. .000

Analysis: From the above table it’s observed that the sampling adequacy is greater than
the threshold point 0.5 that is 0.834. As the data with KMO value greater than 0.5 is
appropriate for factor analysis. So, the above data is suitable to perform factor analysis.
The Bartlett’s test of sphericity whether the variables are orthogonal or not, here in 5% level
of significance if the variables are not perfectly orthogonal that is they correlate significantly
with each other then only the data reduction technique will be able to compress the data
set in meaningful way. In this case the significant value is 0.000 that is less than 0.05, so the
factor analysis technique can compress the data in a meaningful way.

Step-2
The next step is total variance explained, here a table represents total variance explained by
each factor-

Analysis:
In this table the total variance explained by each factor on the basis of eigenvalue.
Researchers only can consider eigenvalue greater than 1 because those values not
considered to be stable and researchers will end up with more or equal number of factors as
the number of variables so there will be no point of doing factor analysis with such type of
data.
So, here researchers extracted three factors with percentage attribution of 38.21%, 10.47%
and 9.11% that is in total 57.81% variance attributed by this factors and these are most
important for further analysis.

Step-3
The scree plot represent graph of eigenvalue vs number of factors. This is also an extraction
technique like total variance explained and the eigenvalues more than 1 need to be
considered.

Analysis:
In the plot it can be seen that there are 3 factors with eigenvalue more than 1. So,
the analysis from principal component analysis has confirmed with this graphical
representation that there are 4 main factors which researchers will consider in further
steps.

Step-4
The next step is Rotated Component Analysis which represents the factor loading. The
factor loading is the simple correlation between factors and variables. The main purpose of
these rotation using varimax is to bring the variables compressed to near the orthogonal
that is close to 0, -1 or +1 or identity matrix. The preferable value of factor loading is
considered to be >0.5 but it is mostly preferred to consider the value>0.7 according to the
data.
Analysis: According to our data researchers will consider the values > 0.7. In factor 1 there
are three variables with value more than 0.7 those are technological advancement, easy
interaction and influencing factor. In factor 2 there are two variables with factor loading
>0.7, those are service improvement and less time consuming. In factor 3 there are two
variables with factor loading >0.7 those are additional feature, highly interactive.

Step-5
In next step the communality will define how much each variables attributed to the define
factors.

Communalities

Initial Extraction

TechnologicalAdvancement 1.000 .529

Additionalfeature 1.000 .586
HighlyInteractive 1.000 .661
Predictiverecommendation 1.000 .649
ListOfAlternatives 1.000 .433
Effortlesspurchase 1.000 .588
Easyinteraction 1.000 .640
Goodgrievancehandling 1.000 .496
Influencingfactor 1.000 .605
Transperacyofinformation 1.000 .488
Serviceimprovement 1.000 .760
Lesstimeconsuming 1.000 .731
Hasslefreeness 1.000 .479
NewProduct 1.000 .574
Languagebarrier 1.000 .453

Extraction Method: Principal Component Analysis.

The proportion of variance explained by summing up the square of three factors with
respect to each variables.

Step-6
This step involves factor labelling based on factor loading value more than 0.7.

Rotated Component Matrixa

Component

1 2 3

TechnologicalAdvancement .711 .000 .154

Additionalfeature .111 .052 .756
HighlyInteractive .015 .037 .812
Predictiverecommendation .597 .184 .508
ListOfAlternatives .564 .315 .125
Effortlesspurchase .544 .260 .474
Easyinteraction .796 -.074 -.034
Goodgrievancehandling .635 .278 -.130
Influencingfactor .703 .236 .233
Transperacyofinformation .545 .296 .322
Serviceimprovement .005 .869 .073
Lesstimeconsuming .125 .846 -.004
Hasslefreeness .372 .517 .271
NewProduct .485 .574 .097
Languagebarrier .493 .384 .249

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 4 iterations.

Analysis:
This steps involves labelling of the data to common factors-
Factor 1: (F1): Smooth Processing= (Technological advancement, easy interaction,
influencing factor).
Factor 2: (F2): Effective service delivery= (Service improvement, less time consuming).
 Smooth Processing vs Effective Service Delivery
1

0.8

0.6
Factor 4: (F4): Innovative
Technology= (Additional
0.4 feature, highly
interactive)
0.2

0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
-0.2
Step-7
This step involves
drawing perceptual maps with attributes.

For smooth processing of all steps from product findings to payment technological
advancement, easy interaction and influencing behaviour is most important whereas for
service delivery this are not so much important.

Effective Service Delivery vs Innovative

Technology
1

0.5

0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
For effective service delivery depends on service improvement and time consume , so it’s
mandatory to follow these two deliver service effectively where in case of innovative
technology these are not improvement but additional features and high interactiveness is
very important.

Innovative Technology vs Smooth Processing

1

0.5

0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Innovative technology has component like additional feature and high interaction but it will
not smoothen the process directly it will obviously have some indirect effect but primarily it
will enhance customer experience.

8. Key Findings:
From data analysis researchers have found some interesting takeaways from this research.

i. The categorical variables that taken for this studies like gender, occupation, age ,
income has association with dependent variable that is effortless purchase that are
has significant association. So from this it can infer that age, income,
occupation, gender are all demographics factors that influence consumer
buying behavior towards e-commerce platform.
ii. the observation from frequency distribution is generation Y students or
employed people with low to middle income bracket prefers to purchase from
e-commerce platforms and it also shows some trend in consumer behavior
towards e-commerce platform.
iii. In case of metric data there are 15 independent variables among that predictive
recommendation, list of alternatives, or elements of recommender system
strongly drive the consumer buying behavior towards e-commerce platforms
than any other variables and transparency of information and hasslefreness or
 the elements of virtual assistance has significant impact on consumer buying
behavior towards e-commerce platform.
iv. Using advance data analysis techniques it has observed that three factors smooth
processing, effective service delivery and innovative technology which are
responsible for almost 58% variance on consumer buying behavior towards
any e commerce platform.

9. Suggestion-
There are some suggestions that e-commerce businesses can take from this study-

i. As students and low to middle income groups influence major sales figure in
online platforms so the managements should take decisions on giving student
discounts or always remain up to date with collections in all aspects from
groceries to technology as generation Y fond of experimenting new things
according to trend.
ii. As predictive recommendation, list of alternatives elements of recommender and
some elements of virtual assistance strongly drive the consumer buying behavior
towards e-commerce platforms , the companies should focuses on all
technological advancements and build a strong AI network of recommender
system and virtual assistance to increase the e commerce purchase.
iii. E –commerce companies should focus on major three factors smooth processing
of information, effective service delivery to customers and innovative technology
incorporation which are responsible for almost 58% variance on consumer buying
behavior towards any e commerce platform to drive consumer purchase in e-
commerce platform.

10. Conclusion and managerial implication-

The results can be implemented as given in suggestion part management should consider
to solve the management objective of this research that is “Factors influencing consumer
buying towards e-commerce platform”. As this study has promised to talk about the
modern drivers that influence the consumer buying behaviour in this era of digitisation
and only consider modern technology related drivers to continue this research. The
aftermath of the research has identified three factors of modern e-commerce drivers that
 are smooth processing of information, effective service delivery to customers and
innovative technology incorporation. This factors surely help companies to identify the
factors that actually influence e-commerce buying behaviour of this era of digitisation
2020.

11. Reference:
Bashir, D. R., Mehboob, I., & Bhatti, W. K. (2015). EFFECTS OF ONLINE SHOPPING TRENDS

ON CONSUMER-BUYING BEHAVIOR: AN EMPIRICAL STUDY OF PAKISTAN. Journal of

Management and Research, 2(2), 1–24. https://doi.org/10.29145/jmr/22/0202001

Bridges, E., & Goldsmith, R. (2000). E-Tailing vs. Retailing: Using Attitudes to Predict Online

Buying Behavior. Quarterly Journal of Electronic Commerce, 1, 245–253.

Gupta, N., & Jain, R. (n.d.). CONSUMER BEHAVIOR TOWARDS E- COMMERCE: ONLINE

SHOPPING. 6.

Huseynov, F., Huseynov, S. Y., & Özkan, S. (2016). The influence of knowledge-based e-

commerce product recommender agents on online consumer decision-making.

Information Development, 32(1), 81–90.

https://doi.org/10.1177/0266666914528929

ieeexplore.ieee.org/document/1385588

Jusoh and Ling https://www.semanticscholar.org/paper/FACTORS-INFLUENCING-

CONSUMERS'-ATTITUDE-TOWARDS-Jusoh-Ling/

Kamtarin, M. (2012). The Effect of Electronic Word of Mouth, Trust and Perceived Value on

Behavioral Intention from the Perspective of Consumers. International Journal of

Academic Research in Economics and Management Sciences, 1(4), 11.

Kanchan Upasana (2016)A STUDY OF ONLINE PURCHASE BEHAVIOUR OF CUSTOMERS IN

INDIA http://ictactjournals.in/ArticleDetails.
Le, T. M., & Liaw, S.-Y. (2017). Effects of Pros and Cons of Applying Big Data Analytics to

Consumers’ Responses in an E-Commerce Context. Sustainability, 9(5), 798.

https://doi.org/10.3390/su9050798

Nasirian et al (2017) AI based voice assistant System : Evaluation from the interaction
and trust perspective .aisel.aisnet.org/amcis2017/AdoptionIT/Presentations/27/

Prassas, G., Pramataris, K. C., Papaemmanouil, O., & Doukidis, G. J. (2001). A recommender

system for online shopping based on past customer behaviour. Proceedings of the

14th BLED Electronic Commerce Conference, BLED, 1, 766–782.

Singh E-Commerce in India –Consumer Buying Behaviour and Growth Trends an Exploratory
Study (2015).index.php/ajms/article/view/1653
Swarnakar, P., Kumar, A., & Kumar, S. (2016). Why generation Y prefers online shopping: A

study of young customers of India. International Journal of Business Forecasting and

Marketing Intelligence, 2(3), 215. https://doi.org/10.1504/IJBFMI.2016.078604

Uresearchersmi, S., & Khan, H. U. (2018). Possible impact of e-commerce strategies on the

utilisation of e-commerce in Nigeria. International Journal of Business Innovation and

Research, 15(2), 231. https://doi.org/10.1504/IJBIR.2018.10009762

12. Annexure:
Questionnaire:
13. Key Learnings:
i. Secondary research and information gathering.
ii. Report writing skills.
iii. Data preprocessing.
iv. Data cleaning.
v. Data analysis.