21PGDM-BHU081_Rounak Kar.pdf

.IMPULSIVE BUYING BEHAVIOUR OF
CONSUMERS IN THE FAST FASHION
APPAREL INDUSTRY
A Capstone Research Project Report
Submitted to
International Management Institute
Bhubaneswar
IN PARTIAL FULFILMENT FOR THE AWARD OF
POST GRADUATE DIPLOMA
In
MANAGEMENT
By
ROUNAK KAR
21PGDM-BHU081
2023

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Table of Contents
Sl No. Topics Page No.
1 Acknowledgement 3
2 Student’s Declaration 4
3 Certificate from faculty Guide 5
4 Abstract 6
5
Introduction
5.1. The fast Fashion Industry
5.2. Impulsive Buying Behaviour
5.3. Use of Impulse Buying by Marketers
7
7
8
8-9
6 Literature Review 10-11
7
Research Project
7.1. Research Objective
7.2. Research Methodology
7.3. Research Framework
7.4. Reliability Analysis
7.5. Response Analysis
7.6. Factor Analysis
7.7. Discriminant Analysis
7.8. Logistic Regression
7.9 Confusion Matrix
7.10 Relation between demographic factors and impulse
buying decision
12
12
12-13
13-14
14-15
15-16
17-19
19-21
21-24
25
26-27
8 Findings of the Study 28-29
9 Limitations of the Study 30
10 Conclusion 31-32
11 References & Bibliography 33-35

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Acknowledgement
I am delighted and feel fulfilled and satisfied to present a Capstone Research Report on
“Impulsive Buying Behaviour of Consumers in the Fast Fashion Apparel Industry” a part
of my two-year Post Graduation Diploma in Management. This report of the study is a
synthesis of both the theoretical and practical elements that I learnt throughout the two-year
programme, which includes rigorous research, public interaction, gathering data and
information and presenting it.
Due to the extensive and profound nature of my topic, I could not have finished this project
without the assistance and direction of numerous individuals who provided me with continuous
feedback, recommendations, guidance, and instruction on how to advance. Their contributions
served as a solid foundation for the project. I would like to express my gratitude to Prof. Rahul
Gupta Choudhury and Prof. Aritra Pan.
Lastly, I would like to extend my thanks to my family and friends who acted as my support
system throughout this period and indirectly contributed to my project in numerous ways.
Although it's impossible to list all their names, they also deserve significant recognition and
gratitude.

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Student’s Declaration
I, Rounak Kar with Institute Roll.no 21PGDM-BHU081, affirm that the Capstone project
report entitled "Impulsive Buying Behaviour of Consumers in the Fast Fashion Apparel
Industry" is solely an original work by me and was done under the guidance of Prof. Rahul
Gupta Choudhury and Prof. Aritra Pan.
Signature:
Name: Rounak Kar
Date: 12.02.2023
Place: International Management Institute, Bhubaneswar

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CERTIFICATE
This is to certify that the Capstone Project titled “Impulsive Buying Behaviour of Consumers
in the Fast Fashion Apparel Industry” submitted to the International Management Institute,
Bhubaneswar in partial fulfilment of the requirements of the Post Graduate Diploma in
Management is an original work done by Mr Rounak Kar at International Management
Institute, Bhubaneswar under my supervision and guidance.
To the best of my knowledge, this report has not formed the basis for the award of any previous
degree/diploma to any candidate of any university/ Institute.
Date: 10/03/2023
Name and Signature
(Supervisor)
IMI, Bhubaneswar

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Abstract
Impulsive Buying behaviour of consumers is a complex and dynamic concept in the field of
consumer behaviour. In the changing market and environment, the cognitive mind of people is
constantly changing and needs exploratory research to explore different factors that influence
and trigger impulsive buying decisions. In today's post-modern world, shopping has taken on
a social and recreational aspect, leading to a decrease in the number of deliberate purchases.
Consequently, impulsive buying has become prevalent and is now considered a socially
acceptable phenomenon in both advanced and developing economies. The fast Fashion
industry is changing drastically and it changes every day. Fashion sense and changing
environment lead to a change in consumer perception and we need to explore that change from
time to time. With the change in the business model, emergence of business ethics and
corporate social responsibility, there will be a change in consumer perception and business
practices. Hence, the purpose of this research is to achieve the above.
Due to its heterogeneous and multi-layered structure, customer behaviour is the marketing issue
that is the most difficult to understand. Impulsive purchasing is one such particular customer
behaviour that is observed in the marketplace. When a consumer has an intense, spontaneous
need to purchase a product offer right away without fully considering the implications of doing
so, they are said to be engaging in impulse purchasing.
This project explores the concepts of Impulsive Buying behaviour of consumers in the context
of the Fast Fashion Apparel Industry. The objective is to examine several underlying factors
that influence consumers’ impulsive buying behaviour.
Literature Review resulted in several factors- Social Media Influencers, Social Media Trends,
Hedonic Motivation, Mood and situation, customer preferences, Promotion & Price. In
conducting an Exploratory Factor Analysis, a total of twenty variables were divided into three
factors- Social Media, Customer Preference & Stimuli, Promotion & Pricing. Hedonic
Motivation, Friends & Family influence were rejected in the factor analysis.
After conducting a Discriminant Analysis, we found that Social Media factors and Customer
Preferences & Stimuli positively trigger impulse buying in the fast fashion industry, whereas
Promotion & Price does not influence the impulse buying behaviour of the customer.

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Introduction
5.1 The Fast Fashion Industry
Fast fashion is a business strategy utilized by the apparel industry, involving the rapid
production of trendy and fashionable clothing items to meet the high demand in retail
outlets. This model is commonly referred to as "fast fashion".
The rise of fast fashion is largely attributed to the adoption of more efficient supply chains,
innovative manufacturing techniques, and the availability of low-cost labour from
outsourcing companies in South, Southeast, and East Asia, where women comprise the
majority of the garment workforce. However, the fast fashion industry often relies on
exploitative labour practices, which disproportionately affect women due to their
prevalence in the garment industry.
The biggest Fast Fashion Apparel industry players are Zara, H&M, Shein, Primark, and
Forever 21.
Fast fashion firms prioritize the quick production and release of trendy items, utilizing
efficient supply chain practices to ensure affordable and speedy conception,
manufacturing, and sale of these fashion items to consumers at discounted prices. Fast
Fashion means quick response to consumer demand, and manufacturing as per the latest
Fashion Trends. once the trend is over manufacturing is also stopped. Players compete to
bring in new fashion trends. These sellers commonly cause things to be out of stock
because they manufacture and sell goods in small quantities, and have excess
manufacturing capacity on hand. This will help retailers smooth, swift and significant
changes to manufacturing. The reason for this is that a fast fashion model like Zara's can
swiftly update designs, leading to short product cycles where a garment does not lie on the
store's shelf for lengthy periods, giving the retailer a feeling of exclusivity and increasing
the attraction of an item.
The fast fashion industry has had both positive and negative impacts on the fashion
industry and society. On the positive side, it has made fashion more affordable and
accessible to a wider range of consumers and has created more job opportunities,
particularly in developing countries.

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However, the fast fashion industry has also been criticized for its negative impact on the
environment, as it relies on the overproduction and waste of clothing items. The industry's
fast-paced production cycle also encourages consumers to buy more and dispose of
clothing items quickly, leading to a culture of disposable fashion and contributing to the
global waste problem. The fast fashion industry has also been criticized for relying on
exploitative labour practices, particularly in developing countries, where workers are often
paid low wages and work in poor conditions.
Overall, the fast fashion industry has both positive and negative impacts on the global
fashion industry and society, and it is important to consider both when evaluating its role
in the fashion industry.
5.2 Impulsive Buying Behaviour
“Impulsive buying behaviour is a sudden, compelling, hedonically complex purchasing
behaviour in which the rapidity of the impulse purchase decision process precludes
thoughtful, deliberate consideration of all information and choice alternatives.” (Bayley &
Nancorrow, 1998; Rook 1987; Thompson, Locander,&Pollio, 1990; Weinberg &Gottwa,
1982). Cultural factors have a significant impact on consumers' impulsive buying
behaviour. One particular theory that can provide insights into this behaviour is the theory
of individualism and collectivism.
“Impulse buying is defined as an unplanned purchase that is characterized by: (1)
Relatively rapid decision-making, and (2) a subjective bias in favour of immediate
possession” (Rook & Gardner, 1993; Rook, 1987; Rook & Hoch, 1985). When compared
to deliberate and pre-planned purchase behaviour, impulsive buying is described as more
alluring, less intentional, and more tempting. “Highly impulsive buyers are likely to be
unreflective in their thinking, to be emotionally attracted to the object, and to desire
immediate gratification” (Hoch & Loewenstein, 1991; Thompson et al., 1990).
Several studies suggest that the most common factor influencing impulsive buying
behaviour is the consumers’ mood and emotional state.
“Consumers tend to self-gift” (Mick, DeMoss, & Faber, 1992), and “impulse purchases
highlight the significance of self-expression and perceived social image in purchasing
decisions”, (Dittmar et al., 1995). Bellenger et al. (1978) found out that shoppers under 35
were more likely to make impulsive purchases. The young generation scores higher on

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measures of impulsivity and has less self-control as compared to the people belonging to
the older generation.
5.3 Use of Impulse Buying by Marketers
Marketers use impulse buying as a marketing technique to motivate customers to make
unplanned purchases by generating a feeling of urgency or excitement. This is often
achieved through promotional tactics, limited-time offers, or advertising.
Marketers frequently utilize the strategy of placing low-cost products in highly visible areas
of a store, such as a checkout counter, to promote impulse buying. The aim is to attract
customers who make buying decisions spontaneously, and the products are chosen
accordingly.
Marketers and merchants always seek ways to increase their customers' share of wallet,
measured in incremental sales, in this day of growing customer expectations and fierce
competition. Thus, to optimise it, marketers nowadays concentrate on the preferences,
requirements, and aspirations of the clients. Advertising and promotional campaigns often
use persuasive language, such as phrases like "while supplies last" or "limited time offer,"
to create a sense of urgency in customers. Additionally, marketers collaborate with social
media influencers to create visually appealing and attention-grabbing advertisements on
various social media platforms. Due to the widespread characteristics of consumer
behaviour associated with it as well as its potential in the marketing industry, marketers are
very concerned with the study around impulse buying.
In general, impulse buying is an effective approach that marketers utilize to enhance their
sales and revenue. They create a sense of urgency and excitement around their products,
which persuades customers to purchase items that they may not have planned to buy
otherwise. As a result, the company can generate greater profits.
Simply said, impulse shopping refers to a spontaneous, immediate purchase made without
any prior planning to fulfil a specified job or purchase from a particular product category.
Due to the widespread characteristics of consumer behaviour associated with it as well as
its potential in the marketing industry, marketers are very concerned with the study around
impulse buying.

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Literature Review
The conceptualization of impulsive purchase and formulation of the questionnaire in this
research is based on several past studies.
External Stimuli- Peers and Family, Promotion, Store characteristics, merchandising, hopping
channels; Internal Stimuli- Emotion, hedonism, fashion, impulsiveness, self-identity,
enjoyment variety seeking; Situational and Product Related factors- Time, Money, type of
products and characteristics; Demographics, Socio-Cultural factors- Gender, Age, Income,
education, cultures, Socioeconomic are the factors that influence the impulsive buying
behaviour of consumers. (G. Muruganantham & Ravi Shankar Bhakat 2013)
Applebaum (1951) suggested that the consumer's reaction to sales promotion stimuli might
lead to impulsive buying. Wolman (1973) defines an Impulse as unintentional and develops as
a result of coming into contact with certain stimuli. Kroeber–Reil (1980) also argues that
Impulse purchasing is a reactive habit that frequently involves a quick reaction to stimuli.
Kotler (1974) explains that marketers’ environmental and situational manipulation leads to
consumers making impulsive buying decisions. As per Rook (1987); Rook & Hoch (1985)
consumers struggle the hardest to control the want to buy an item right after seeing it, and the
thing itself is the source of the drive to buy.
Some research explains that the product is itself the stimuli, while other researchers explain
that other factors can trigger impulsive buying decisions. Hirschman and Holbrook (1982)
suggest that the experience of things by their tastes, sounds, tactile sensations, and visual
representations is one part of hedonic consumption. Childers et al. (2001) suggest that there is
reason to anticipate that consumers will like utilising the new media for online shopping if they
feel that the sensory information provided by interactive media is sufficient. Danthu and Garcia
(1999) say that online shoppers are more impulsive because of the stimuli they are exposed to.
The impulse buying behaviour of the consumer is primarily affected by the Mood of the
consumer. (Gardner and Rook 1988; Rook 1987), in-store browsing and positive & negative
effects (Beatty and Ferrell 1998). Rook and Gardener (1993) proposed that three mood
dimensions influence consumers’ impulsive behaviour, they are pleasure, arousal, and
dominance.

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Influencer marketing on social media and the internet is altering what marketing
communication means (Jiménez-Castillo and Sanchez-Fernandez, 2019). Because Indians
spend a lot of time on social media sites today (Statistica Global Consumer Survey, 2019),
people who have a fear of losing out on experiences due to seeing others' experiences have the
propensity to behave impulsively and make impulse purchases. “Usage of social media is
supported by low self-esteem, internet addiction, loneliness and anxiety. It is also used as a
mechanism of negative coping” (Zheng et al., 2020).
“The influence of social media (for impulsive buying), hedonic happiness (for compulsive
buying) and the pandemic COVID (for both) have emerged as the strongest predictors of
impulsive buying” (Kavita Kshatriya and Priyanka Sharad Shah, 2021).
Factors influencing impulsive buying behaviour will change with time, and hence there is a
need to study the same due to recent development in retailing and huge cultural differences
when compared to developed economies (Manilall Dhurup 2014).
Impulse purchasing seems to be a useful strategy for getting out of a bad mood, whether it be
unhappy, annoyed, or bored. However, post-impulse buy attitudes are not always upbeat.
People with fluctuating moods go through a larger variety of emotions and can be more inclined
to feel down after making impulsive purchases. (Meryl Paula Gardner & Dennis W. Rook
1988).
Consumers' emotions (pleasure and arousal) might be encouraged by a well-designed website
interface (visual appeal), resulting in their making spontaneous purchases. Impulse Buying
influences consumers’ product and emotional dissonance, influencing return intention (Wen-
Kuo Chen, Chien-Wen Chen & Yu-Chun Lin 2020). Positive impulse purchasing is encouraged
by customers' impulsive buying habits and pre-purchase attitudes. The role of satisfaction as a
partial mediator between the pre-and post-purchase moods (Leyla Ozer & Beyza Gultekin
2015). Male customers and low-income earners have been found to have a higher relationship
between impulsive purchases and post-purchase dissonance. The bank-card payment shows a
strong correlation with impulsive purchasing, but not sales advertising (Mahmoud Abdel &
Hamid Saleh 2012).

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Research Project
“Establishing the factors that trigger consumers’ impulsive buying behaviour in
the Fast Fashion Apparel Industry”
7.1 Research Objective:
• Determining various factors and motivations that trigger/influence Impulse Buying
Behaviour of consumers in the Apparel Industry.
• Examining the emotions and actions that occur after consumers make an impulsive
purchase.
• Investigating whether demographic characteristics have an impact on customers' tendency
to engage in impulse buying behaviour.
7.2 Research Methodology:
This study primarily focuses on the factors and motivation that influence and triggers impulsive
buying behaviour of consumers in the Fast Fashion Apparel Industry.
§ Conducted Primary Research with the help of a questionnaire (Saamarth Gandhi 2020),
taking several factors from the literature review, with the help of Google Forms. The
literature review suggests that impulse behaviour is a complex and dynamic topic in the
field of Consumer Behaviour and needs to be explored every now and then. The rationale
here is to attain the same and understand what kind of factors (Hedonic, Website Quality,
Trust and ethical values, Situational variables, Variety seeking) (Manoj Bansal &, Satinder
Kumar, 2018).
o Used Exploratory factor analysis to reduce the number of variables and club these
variables with high correlation into groups.
o The dependent variable is dichotomous (categorical), ran a discriminant Analysis to
understand how the factors/group is influencing the dependent variable (Whether the
consumer makes impulsive purchases).
o There was not enough variance in the data to determine the relevance of the
Discriminant Model. Hence ran a Logistic Regression to understand which variables

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are able to influence or trigger impulsive buying behaviour of the consumer in the fast
fashion apparel industry.
§ Conducted Secondary research with the help of a Literature review (Reports, Past Studies
& Research Papers and articles) to conceptualise the topic.
7.3 Research Framework:
The google form was shared with students and friends across different Business schools,
Undergraduate Colleges and Schools; professionals in organisations and colleagues. The
sample size was 140. The sampling method used is Convenient Sampling.
Demographic Questions:
1. Age
2. Gender
3. Education Qualification
4. Income per annum
5. Occupation
Dependent Variable (Dichotomous Question):
Do you make Impulse Buying decisions when you come across Fashion Products?
Independent Variables-
• Strongly Disagree/Strongly Agree:
1. I often buy Things Spontaneously
2. If I see something I like, I often end up buying it
3. I tend to buy impulsively when I’m in a Happy mood.
4. I tend to buy impulsively when I’m in a Sad mood
5. Friends and Family often influence my impulse buying behaviour.
• Least Important/Most Important:
1. Overall Shopping Environment (user interface)
2. Promotional Schemes and Ongoing Discounts
3. Low Prices
4. Visual Merchandising

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5. Social Media Trend
6. Influencers
• Likert Scale:
1. How likely are you to repurchase an apparel item which you have previously
purchased impulsively?
2. Do you think the emergence of online shopping platforms and applications has
enhanced your impulsive shopping behaviour?
3. Convenience
4. Variety
5. 24*7 Availability
6. Heavy Advertising
7. Delivery Speed
• Other Questions:
1. On the basis of your past apparel-based impulse purchases, which of the following
best describes your Post Purchase Feeling? – a. Regret b. Happiness c. Guilt d.
Excitement
2. How often do you shop online for fashion products? – a. Once a month b. Twice a
month c. Once a week d. Twice a week
3. What changes your frequency of Shopping for fashion Products the most? –
a. Season b. Festival c. Occasions and Functions d. Event (Professional/Business)
4. Which of the following product categories are you more likely to shop impulsively?
(Rank the Categories) – a. Cosmetics b. Clothing c. Footwear d. Accessories
7.4 Reliability Analysis:
Before running further analysis, the fact that the data collected is reliable as per the questions
asked for further analysis has to be established. Hence, a reliability analysis was run to check
the Reliability score.
To interpret the result, Cronbach’s alpha (α) score is checked. The score 0.7 ≤ α < 0.8
established the data as acceptable; the score 0.8 ≤ α < 0.9 established the data as Good; the
score α ≥ 0.9 established the data as Very Good (Excellent) and the score 0.6 ≤ α < 0.7
established the data as Doubtful, the score 0.5 ≤ α < 0.6 established the data as bad and the

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score α < 0.5 established the data as Not Acceptable
(Wiwi Siswaningsih, Harry Firman, Zackiyah
Assadi & A-Khoirunnisa, 2017)
The Cronbach’s α score in this study is .869 (0.8 ≤ α
< 0.9). Thus, the question formed is reliable as per
the data collected.
It is safe to run an analysis with the data.
7.5 Response Analysis:
Age:
• Below 18 - 2
• 18-25 - 107
• 26-34 - 29
• 34-44 - 2
• 45-60 - 1
• Above 60 - 0
Gender:
• Male - 84
• Female - 57
Education Qualification:
• High School - 0
• Higher Secondary - 4
• UG - 30
• PG - 103
• PhD - 3
• B.ed - 1
Income per annum:
• 0-100000 - 91

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• 100000-200000 - 13
• 200000-500000 - 15
• 500000-1000000 - 12
• more than 1000000 - 10
Occupation:
• Student - 98
• Professional - 32
• Self-Employed - 8
• Homemaker - 0
• Government Employee. - 1
• Business Owner - 1
• School teacher - 1
Impulse Buyer:
• Yes - 49
• No - 92
Post Purchase Behaviour:
• Regret - 12
• Happiness - 69
• Guilt - 14
• Excitement. - 43
Events:
• Season - 26
• Festival - 35
• Occasions and Functions - 67
• Event (Professional/Business) – 13

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7.6 Factor Analysis:
Ran an Exploratory Factor analysis with all the independent variables. In SPSS, used
“Dimension Reduction” to run the factor analysis.
• Under “Descriptives”, checked the “initial solution”, “coefficients” and “KMO and
Bartlett’s test of sphericity”.
• Used Maximum Likelihood, Promax with a Kappa value of 2.
• Remove all possible cross-loadings by using the factor loading of 0.4.
Interpretation-
We have to see Bartlett’s Test of Sphericity-
H0: There is no Multicollinearity
H1: There is Multicollinearity
The p-value (sig.) is 0.000 which is less than 0.05. Hence, we can reject the H0. The collinearity
is good.
From the same table, we will have to see the Kaiser-Meyer-Olkin score-
To establish the fact that there is an adequate amount of collinearity in the data, the KMO score
should be more than 0.5. In this case, the KMO score is 0.844 (>0.5). We can safely conclude
that there is adequate collinearity in the data.
We will have to check the Pattern Matrix (Rotated Component Matrix) to check the
relationship between factors and variables -

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Out of a total of eighteen variables Delivery
Speed, Online Shopping platform, Sad
Mood, Friends and Family and Repurchase
are rejected from the groups.
The most relevant labels to the factors are as
follows:
Promotion and attractive
pricing attract consumers
towards the products. The
Retail and online shopping
experience also adds to the
Attractiveness.
Consumers are more inclined
towards the availability and
variety of product offerings.
Hence the second factor is
labelled as Variety Seeking.
A happy mood is situational and can lead to an impulsive purchasing decision. Liking a product
in a certain situation makes an impulse buyer purchase the product, irrespective of the cost
price or whatever situation he or she might be in. Hence the third factor is labelled as
Situational.
Social Media trends, influencer endorsement and heavy advertisements lead to an increase in
traction on the website which might finally lead to impulse buying. Hence the third factor is
labelled as Trendiness.

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We will have to check the Variance explained from the Total Variance Explained table.
The cumulative percentage of the sum of squared factor loading is 50.115%. This means
50.115% variance in the variable is explained by the total factor solution.
7.7 Discriminant Analysis:
Ran Discriminant Analysis to understand how these four factors are influencing the impulsive
buying decision of the consumers. In SPSS, used “Classify- Discriminant” to run the analysis.
• Under “Statistics”, checked the “Means”, “Univariate ANOVAs”, “Box’s M and
“Unstandardized”.
• Used Stepwise method. Under “Method”, checked “Wilks’ Lambda”, and set the entry F
value as 1.15 and the removal at 1.
• Under “Classify” selected “Compute from group sizes”, and checked “Casewise Results”,
“Summary Table” and “Combined-groups”.
• Under “Save”, checked “Predicted group membership”, “discriminant scores” and
“Probabilities of group membership”.
Interpretation-
We have to see Wilks Lambda-
The Wilks’ Lambda value is .805. The lower the value, the better it is. It indicates a greater
discriminatory ability of the function where the functions listed are equal across groups.

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A higher Wilks’ Lambda infers that the group means are equal, whereas a lower Wilks’ Lambda
value infers that the group means are not equal.
The Lambda (λ) value, in this case, is high (.805). Hence, the group means are equal. The
groups are poorly distributed. 80.5 % of the variance in the data is not accounted for group
membership.
At the same, the p-value (Sig.) is 0.000 (p < 0.05)
H0: There is a high variance in the groups
H1: There is a low variance in the groups
The p-value is < 0.05, hence the null hypothesis is rejected. There is a low variance in the
group.
We have to see the Canonical Discriminant Function Co-efficients” table to get the
Unstandardized Discriminant Co-efficients. It is used for creating the model.
Model:
DF= 0.000 – 0.359 (Attractiveness) + 1.138 (Situational) +
0.385 (Trendiness)
In the “Structural Matrix” table we get the Discriminant Loading.
Situational Factors that include mood, and spontaneity
have the highest impact on the Impulsive purchase
behaviour of consumers.
Variety Seeking cannot discriminate. This factor is not
being able to identify the differences between people
making impulse purchase decisions and people not
making impulse purchase.

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In the “Eigenvalues” table we have to check the canonical Co-relation.
Since there are 2 categories in the dependent variable, the eigenvalue won’t be taken into
account. The Canonical Correlation is 0.442. Squaring of which will give 19.53.
Hence, 19.53% of the variance in the discriminant data is explained in the model.
In the “Classification Results” table, we have to check the Hit Ratio.
There are 2 categories in the dependent variable. The Hit Ratio must be more than 62.5%. The
Hit ratio of this model is 71.2% (> 62.5%). Hence, 71.2 % of the original grouped cases were
correctly classified. The model is good in terms of prediction accuracy.
7.8 Logistic Regression:
Ran Logistic regression with the Factors that are included in the discriminant function to
understand what is the exact association of these factors with the impulse purchasing decision
of consumers.
Divided the data into test data and training data.
There are 83 training data. There are 56 Test Data.
Training data is used for Model creation and Test data is used for Validation.

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To run logistic regression, the three factors- 'Spontaneously’, ‘Attachment’, ‘Happy mood’,
‘Shopping Environment’, ‘Promotional and Discounts’, ‘Low Prices’, ‘Visual
Merchandising’, ‘Social Media Trend’, ‘Influencers’, ‘Convenience’, ‘Variety’, ‘24*7
Availability’, ‘Heavy Advertising’ that are included in the discriminant analysis has to be
loaded into the test and training data.
X_train = train_df[['Spontaneously', 'Attachment', 'Happy mood', 'Shopping Environment',
'Promotional and Discounts', 'Low Prices', 'Visual Merchandising', 'Social Media Trend',
'Influencers', 'Convenience’, 'Variety’, '24*7 Availability', 'Heavy Advertising']]
y_train = train_df[["Impulse Buying"]]
X_test = test_df[['Spontaneously', 'Attachment', 'Happy mood', 'Shopping Environment',
'Promotional and Discounts', 'Low Prices', 'Visual Merchandising', 'Social Media Trend',
'Influencers', 'Convenience’, 'Variety’, '24*7 Availability', 'Heavy Advertising']]
y_test = test_df[["Impulse Buying"]]
Ran Logistic Regression-
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
logreg.fit(X_train,y_train)
Balanced Accuracy: 0.7428571428571429
Balanced Accuracy is a performance measurement used for classification models that
calculates the average of sensitivity and specificity. It offers an inclusive assessment of how
effectively the model is categorizing various classes, providing a comprehensive measure of
its performance.
The Balanced Accuracy score is a numerical value that varies between 0 and 1, with 1
representing an ideal classification accuracy and 0 representing a model that is no better than a
random chance. A score greater than 0.5 suggests that the model is performing better than
random chance, while a score less than 0.5 indicates that the model's performance is subpar.
The Balanced Accuracy of this model is 74.28%, and the model’s performance is good.
The Accuracy of the Model is 0.7678, which is 76.78%. It can be concluded that the model
formed is a good model which can predict with an accuracy of 76.78%.

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The Coefficients of the model are:
Intercept -0.384
Spontaneously 0.256
Attachment 0.212
Happy mood 0.624
Shopping Environment 0.061
Promotional and Discounts -0.281
Low Prices -0.002
Visual Merchandising -0.412
Social Media Trend -0.142
Influencers 0.450
Convenience -0.346
Variety 0.296
24*7 Availability -0.236
Heavy Advertising 0.054
Model:
Logit= -0.384 + 0.256*(Spontaneously) + 0.212*(Attachment) + 0.624*(Happy mood) +
0.061*(Shopping Environment) - 0.281*(Promotional and Discounts) - 0.002*(Low Prices) -
0.412*(Visual Merchandising) - 0.142*(Social Media Trend) + 0.450*(Influencers) -
0.346*(Convenience) + 0.296*(Variety) - 0.236* (24*7 Availability) + 0.054*(Heavy
Advertising)
Odd Ratio:
Spontaneously 1.291
Attachment 1.236
Happy mood 1.867
Shopping Environment 1.062
Promotional and Discounts 0.755
Low Prices 0.998
Visual Merchandising 0.662
Social Media Trend 0.867
Influencers 1.567
Convenience 0.707
Variety 1.344
24*7 Availability 0.789
Heavy Advertising 1.055
Odds Model: [eintercept
(eᵝ1)IV1
(eᵝ2)IV2
(eᵝ3)IV3
(eᵝ4)IV4
………(eᵝn)IVn
]
(0.681)Intercept
(1.291)Spontaneously
(1.236)Attachment
(1.867)Happy mood
(1.062)Shopping Environment

24
(0.755)Promotional and Discounts
(0.998)Low Prices
(0.662)Visual Merchandising
(0.867)Social Media Trend
(1.567)Influencers
(0.707)Convenience
(1.344)Variety
(0.789)24*7 Availability
(1.055)Heavy Advertising
From the Odds Ratio, we infer that:
• One Unit increase in Spontaneity would increase the odds of consumers making an
impulsive/unplanned buying decision by 1.291 times.
• One Unit increase in Attachment would increase the odds of consumers making an
• One Unit increase in Happy mood would increase the odds of consumers making an
• One Unit increase in Shopping Environment would increase the odds of consumers
making an impulsive/unplanned buying decision by 1.062 times.
• One Unit increase in Promotional and Discounts would increase the odds of consumers
making an impulsive/unplanned buying decision by 0.755 times.
• One Unit increase in Low Prices would increase the odds of consumers making an
• One Unit increase in Visual Merchandising would increase the odds of consumers making
an impulsive/unplanned buying decision by 0.662 times.
• One Unit increase in Social Media Trend would increase the odds of consumers making
an impulsive/unplanned buying decision by 0.867 times.
• One Unit increase in Influencers would increase the odds of consumers making an
• One Unit increase in Convenience would increase the odds of consumers making an
• One Unit increase in Variety would increase the odds of consumers making an
• One Unit increase in 24*7 Availability would increase the odds of consumers making an
• One Unit increase in Heavy Advertising would increase the odds of consumers making an

25
7.9 Confusion Matrix:
A confusion matrix is a tool used to evaluate the performance of a machine learning model by
comparing its predicted outcomes with the actual values of a classification problem.
The matrix provides a tabulated summary of these predictions, allowing us to determine the
model's effectiveness by identifying metrics such as true positives, true negatives, false
positives, and false negatives. These metrics assist in pinpointing areas of strength and
weakness in the model, thereby providing valuable insights into where improvements may be
necessary.
True Positive (TP)- 32
True Negative (TN)- 12
False Positive (FP)- 9
False Negative (FN)- 3
The Accuracy of the Model is 0.81, which is 81% It can be concluded that the model formed
is a good model which can predict with an accuracy of 81%.
(TP + TN) / (TP + TN + FP + FN) = (32 + 12) / (32 + 12 + 9 + 3) = 0.81 or 81%
Specificity- 57% prediction of consumers who does not make an impulsive buying decision is
correct. TN / (TN + FP) = 12 / (12 + 9) = 0.57 or 57%
Sensitivity (Recall)- Out of all the consumers who make an impulse buying decision, the
model is being able to correctly identify 91% of them.
TP / (TP + FN) = 32 / (32 + 3) = 0.91 or 91%
Precision- Out of all the positive predictions made by the model, 78% of them are correct.
TP / (TP + FP) = 32 / (32 + 9) = 0.78 or 78%

26
7.10 Relation between demographic factors and impulse buying decision:
As per the literature review, consumer demographics might influence consumers’ impulsive
buying behaviour. There is a need to establish the fact whether there is any association between
two variables, that is a demographic variable and the impulsive buying decision.
Running a chi-square test between Age, Annual Income, gender, education qualification,
Occupation & impulsive buying decision to conclude, whether the demographics have any
association or not.
Chi-Square Test:
1. Age-
H0: There is no association between age and
impulsive buying decisions.
H1: There is an association between age and
impulsive buying decisions.
The p-value (Sig.) is 0.746 which is more than 0.05 significant level (p-value > 0.05). Hence,
we accept the null hypothesis. There is no association between age and the impulsive buying
decisions of consumers.
2. Education Qualification-
H0: There is no association between education
qualification and impulsive buying decisions.
H1: There is an association between education
qualification and impulsive buying decisions.
we accept the null hypothesis. There is no association between education qualification and the
impulsive buying decisions of consumers.

27
3. Gender-
H0: There is no association between gender
and impulsive buying decisions.
H1: There is an association between gender
The p-value (Sig.) is 0.002 which is less than
0.05 significant level (p-value < 0.05). Hence, we reject the null hypothesis. There is an
association between gender and the impulsive buying decisions of consumers.
4. Income-
H0: There is no association between income
H1: There is an association between income
we accept the null hypothesis. There is no association between income and the impulsive
buying decisions of consumers.
5. Occupation-
H0: There is no association between
occupation and impulsive buying decisions.
H1: There is an association between
occupation and impulsive buying decisions.
we accept the null hypothesis. There is no association between occupation and the impulsive
buying decisions of consumers.

28
Findings and Managerial Implications
1. Fast fashion product categories are Accessories, Footwear, Clothing and Cosmetics.
Consumer preferences for these
categories are almost equally
distributed. The impulse buying
decision depends on the mood,
emotional and product stimuli, and
the trend in the market. Hence there
is no specific product category that is
preferred by the consumers.
2. As per the study, the count of satisfaction (Happiness and Excitement) is very high. The
number of consumers having a post-
purchase dissonance is very low
compared to those who are satisfied.
3. Out of 139 respondents, 95 that is the majority of respondents, think that online shopping
portals and platforms have highly influenced their impulse buying behaviour.
4. As per the study, “Variety seeking” does not influence or trigger the Impulsive buying
decision of the consumers. The variety of products in the fast fashion apparel industry does
not matter because the stocks come in and get out of stock due to the high demand for
trending apparel. Once the trend is over, the production of a certain variety is stopped there

29
is a shift to a different variety. Hence, the 24*7 availability of products is also not possible,
due to the high demand for the products.
5. The “Attractiveness” of the product triggers the impulse buying decisions of the consumers.
Promotion and Discounts, Low Prices, Shopping Environment and Visual Merchandising
help increase the attractiveness of the product.
6. “Situational Attributes” have the highest impact on the impulsive buying behaviour of
consumers. Factors like happy mood, sheer spontaneity or just consideration of the product
attribute irrespective of the price or feasibility of purchasing the product highly influence
or triggers the impulse buying decision of the product.
7. The “Trendiness” of the product triggers impulse purchase decisions. The consumer often
suffers from the fear of missing out. Influencer promotion, shopping haul, and trends
running on social media indite the urge to buy the products. These factors provoke a sense
of emotional appeal.
8. This study model is statistically significant, but the factors cannot fully explain the
dependent variable (Impulsive Buying decision).
9. Gender has a significant association with the impulsive buying decision of the consumer.
Females tend to make more impulse buying decisions (Sigal Tifferet & Ram Herstein
2012).
10. As per the study Age, Education Qualification, Income and Occupation has no association
with impulsive buying decision. Emotional stimuli have more association with impulsive
purchasing, hence age, education, occupation and income have no association.
11. The factors that are formed are correctly Identified. The model is statistically significant
and is good in terms of prediction accuracy.
12. Spontaneity, Attachment, Happy Mood, Shopping Environment, Influencers, Variety and
Heavy Advertising has a positive influence on the Impulsive Buying decision of consumers.
An increase in these factors is likely to make consumers make unplanned purchases.
13. Promotional and Discounts, Low Prices, Visual Merchandising, Social Media Trend,
Convenience, and 24*7 Availability have a negative influence on the Impulsive Buying
decision of consumers.

30
Limitation
1. As Data is limited to 139 Responses.
2. With a response of 109, less than 15 per cent variance in the discriminant data is explained
in the model. But later with 139 responses 19 per cent variance in the discriminant data is
explained. Hence, due to limited responses the variance explained is less.
3. The majority of the respondents are students in a specific age group, income group and
education qualification. There is a possibility that due to this unequal distribution, the
variance in the discriminant data is not being explained properly.
4. Other moderators influence the Mood. Only a happy mood might lead to impulse buying
behaviour, but there are other moderators that influence a happy mood. There have to be
other studies done to understand what influences a happy mood to make impulsive
purchases.
5. The factors are not able to fully explain the dependent variable, there must be other factors
that could influence the impulse buying behaviour of consumers. These factors has to be
identified in further study. This can lead to a better explanation of the variance.
6. This study does not show how income affects the intensity and frequency of impulse
purchases by the consumer.

31
Conclusion
The study explores various concepts of impulsive buying behaviour and the complex
psychology that leads to impulse consumer buying behaviour. The literature review suggests
that the complex field of impulsive consumer buying behaviour needs frequent studies and
research to understand the changing psychology of the consumer with time and demand. This
study focuses on several factors that influence and triggers consumer buying behaviour.
Literature review suggests that women tend to do more impulsive purchases, and this research
backs this point by proving the association of gender with impulsive buying behaviour in the
fast fashion industry. Emotional Stimuli affect the most when it comes to unplanned buying in
the fast fashion industry. When it comes to the fast fashion industry impulsive buyers will
purchase the product if they like it, irrespective of the price. A happy mood can lead to impulse
buyers purchasing fast fashion offerings.
The evolution of Social media and social media platforms has led to breakthrough consumer
acquisition strategies and business models. In this fast fashion apparel industry, social media
plays an important role in influencing consumers around the globe to reach consumers of
different demographics. Social media advertisement and promotion strategies provoke a sense
of emotional appeal- anxiety, happiness, fear, love & affection, sadness, passion and humour.
Social media trends and favourite influencers trigger these emotions, leading to consumers
making impulsive buying decisions. Fast Fashion market players offer a huge variety of
offerings at a particular point in time, creating a trend across social media with the help of
influencers and celebrities. These products get out of stock very quickly. These trends last for
a limited amount of time, and once the trend is over the production of those varieties stops.
This makes the consumer make impulse buying decisions.
Product stimuli also play an important role in influencing the unplanned buying of fast fashion
products. Low price, aggressive discounting, promotions, in-store virtual merchandising and
online (Visuals, flexible payment options, UI/UX) & offline shopping experiences impact the
buying decision of impulse buyers.

32
Model Framework from the study:
Trendiness and Situational Factors are the emotional stimuli and the attractiveness of the
product is the product stimulus. These factors independently or collectively lead to consumers
making impulsive buying decisions.
With the change in the market, the introduction of corporate social responsibility and new
policies regarding disposing of vast amounts of fast fashion industry waste and working
conditions of cheap workers from third-world countries, consumers’ take on the fast fashion
industry will change in near future. As a matter of fact, there has been a worldwide protest
against the industry's malpractices and carbon emissions. This will further lead to a change in
norms and consumer perception and demand. Hence, there is a need to perform frequent studies
in order to understand the market conditions and thus understand business implications that
will further help the market players in strategic planning and decision-making.

33
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