Marketing Research Project Report
Submitted in partial fulfillment of the requirements
of
MARKETING RESEARCH COURSE
Guide: Professor Anubhav Mishra
“Retail crowding and consumer decision making”
Submitted by
GROUP 3
Hitesh Fataniya: 2022018
Roy Richard Williams: 2022036
Shraddhay: 2022044
Vedant Deshmukh: 2022103
Mitali Jain: 2022129
Prayag Pandya: 2022189
GOA INSTITUTE OF MANAGEMENT
SANQUELIM, GOA
2022-24
1 September 2023
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ABSTRACT
This research investigates the impact of crowded retail environments on consumer buying
decisions through five key constructs: shopping motivation, navigation and accessibility,
affective responses, buying convenience, and critical buying purchase. By examining these
factors, we uncover the complex interplay between consumer behaviour and crowding. Our
findings reveal how motivations, emotional responses, and navigational challenges influence
buying decisions. Additionally, we analyse the convenience of purchasing and how crowding
affects decisions regarding essential or high-priority items. Through a comprehensive
questionnaire, this study sheds light on the intricate relationship between these constructs and
consumer choices, providing valuable insights for retailers to optimize the retail environment
and enhance the shopping experience.
Keywords: retail crowding, consumer decision, purchase decision, consumer behaviour, spatial
density, human density, satisfaction, the urge to buy, shopping motivation, navigation and
accessibility, affective responses, buying convenience, critical buying purchase, consumer
behaviour, retail strategies, shopping experience.
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Table of Contents
INTRODUCTION ..................................................................................................................... 4
LITERATURE REVIEW ........................................................................................................... 5
Objectives of the Study .......................................................................................................... 6
RESEARCH METHODOLOGY............................................................................................... 7
Data Collection and Processing Technique ............................................................................ 8
QUALITATIVE ANALYSIS ................................................................................................... 12
In-depth Interviews .............................................................................................................. 12
QUANTITATIVE ANALYSIS ................................................................................................ 13
Constructs ............................................................................................................................ 13
Descriptive Statistical Analysis............................................................................................ 14
Normality Test of Data Set............................................................................................... 15
Factor Analysis..................................................................................................................... 16
Reliability Analysis .............................................................................................................. 19
Inferential Analysis .............................................................................................................. 20
Independent t-test ............................................................................................................. 20
One-way ANOVA ............................................................................................................ 22
N Way ANOVA ................................................................................................................ 24
One-way MANOVA ........................................................................................................ 26
N- way MANOVA ........................................................................................................... 29
Regression Analysis ............................................................................................................. 31
Multiple Linear Regression.............................................................................................. 31
Discriminant Analysis .......................................................................................................... 35
Cluster Analysis ................................................................................................................... 38
Hierarchical Cluster Analysis .......................................................................................... 38
K Means Clustering ......................................................................................................... 39
Two-step Cluster Analysis ............................................................................................... 42
Structural Equation Modelling (Conceptual Model) ............................................................ 43
Confirmatory Factor Analysis .......................................................................................... 43
CONCLUSION ........................................................................................................................ 47
REFERENCES ........................................................................................................................ 48
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INTRODUCTION
Retail crowding is a common phenomenon that can have a significant impact on
consumer decision-making. Previous research has shown that retail crowding can lead to
decreased shopping satisfaction, increased stress levels, and shorter shopping times (Lee et al.,
2007; Park & Zhang, 2018). In addition, retail crowding can also affect the way consumers
perceive the store's image (Paden, 1993).
The purpose of this research report is to investigate the impact of retail crowding on
consumer decision-making. The report will review the relevant literature, present the findings
of a new study, and discuss the implications of the findings for retailers.
The literature review will discuss the definition of retail crowding, the antecedents and
consequences of retail crowding, and how retailers can manage retail crowding. The new study
will examine the impact of retail crowding on consumer shopping satisfaction, stress levels,
and shopping times. The study will also examine the impact of retail crowding on the way
consumers perceive the store's image.
The findings of the new study will be discussed in the context of the previous research.
The report will also discuss the implications of the findings for retailers. The report will
conclude with a summary of the key findings and recommendations for future research.
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LITERATURE REVIEW
In the dynamic world of retail shopping, various factors contribute to the constantly
changing consumer behaviour. One such factor of increasing interest to researchers and
retailers alike is the concept of retail crowding and its impact on consumer decision-making.
Retail crowding refers to the number of shoppers within a retail outlet/setting, which can either
improve or degrade the consumer's shopping experience and influence their purchasing
decisions.
A study by Bell et al. (1999) reflected that moderate levels of crowding can stimulate a
positive shopping experience by creating a sense of excitement and urgency. However,
excessive crowding can lead to discomfort and negatively affect decision-making, causing
consumers to cancel their shopping intent or make impulsive decisions. Also, as per Kim and
Kim (2019) moderately crowded environments, consumers perceived higher value in their
purchases, likely due to the excitement associated with the shopping experience.
Consumer responses to crowding are not uniform; they depend on individual factors.
Grewal et al. (2003) examined the role of personal factors in crowding tolerance. They
discovered that consumers with higher levels of social anxiety were more likely to perceive
crowding negatively, leading to reduced exploration and decision avoidance. On the contrary,
consumers with higher levels of extraversion were more likely to embrace the social aspects of
crowding.
Using observational data, Donovan et al. (1994) analysed the relationship between store
layout and consumer crowding perception. Store layout can significantly impact crowding
perception. They found that stores with crowded layouts, characterized by narrow aisles and
cluttered displays, increased the perception of crowding, leading to decreased shopping
enjoyment and decision avoidance. Conversely, stores with open layouts mitigated the negative
effects of crowding, enhancing the shopping experience.
With the advent of technology, retailers have implemented various strategies to manage
crowding. A study by Huang et al. (2020) examined the impact of in-store digital signage on
crowding perception. The findings indicated that strategically placed digital signage could
reduce the perception of crowding by diverting consumers' attention and providing helpful
information. This, in turn, positively influenced their decision-making process and overall
shopping experience.
The literature on retail crowding and consumer decision-making demonstrates that the
effects of crowding are multifaceted and context-dependent. Moderate levels of crowding can
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enhance the shopping experience, increasing perceived shopping value and excitement.
However, excessive crowding can lead to discomfort, negatively influencing consumers'
decision-making processes and purchase intentions. Personal factors, such as social anxiety
and extraversion, also play a role in shaping how individuals respond to crowding.
In conclusion, retail crowding is a complex phenomenon that significantly influences
consumer decision-making. While moderate levels of crowding can enhance the shopping
experience, excessive crowding can lead to negative perceptions and impact purchasing
decisions. Individual factors, store layout, and technology all play crucial roles in mediating
the effects of crowding. Retailers must carefully consider these factors in their store design and
management strategies to create an optimal shopping environment that positively influences
consumer decision-making. Further research in this field can provide valuable insights into the
ever-evolving dynamics of retail and consumer behaviour.
Objectives of the Study
● To understand retail crowding and consumer behaviour.
● To analyse the impact of retail crowding on consumer buying decisions.
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RESEARCH METHODOLOGY
This research aims to investigate the impact of retail crowding on customer buying
decisions through a mixed-methods approach, encompassing both qualitative and quantitative
research techniques. The methodology involves a systematic process to comprehensively
understand how retail crowding influences customer behaviour.
The initial phase involves an extensive review of existing literature on retail crowding,
customer behaviour, and the relationship between the two. This step provides a theoretical
foundation and identifies research gaps, guiding the subsequent research design.
In-depth interviews are conducted with a diverse group of customers to gain insights
into their experiences and perceptions of retail crowding. Participants are selected based on
factors such as age, gender, shopping frequency, and preferences. Open-ended questions are
used to explore participants' emotions, thoughts, and motivations behind their buying decisions
within crowded retail environments. These interviews offer rich qualitative data, allowing for
an in-depth exploration of the phenomenon.
A structured questionnaire is designed based on the findings from the qualitative phase
and the literature review. The survey captures a larger sample of customers' perspectives on
retail crowding and its impact on their buying decisions. The questionnaire includes both
closed-ended and Likert-scale questions to gather quantitative data on customer preferences,
behaviours, and attitudes.
To analyse the qualitative data collected from in-depth interviews, the transcribed
interviews are coded and categorized to identify recurring themes and patterns related to retail
crowding and its influence on customer buying decisions. These themes help provide a deeper
understanding of the emotional and cognitive aspects of customer behaviour.
The quantitative data gathered from the survey questionnaire is analysed using
descriptive and inferential statistical methods. The Statistical Package for the Social Sciences
(SPSS) software is utilized to perform analyses such as frequency distributions, correlations,
and regression analyses. These analyses help quantify the relationships between variables and
provide a broader view of customer preferences and behaviours in crowded retail
environments.
The findings from both qualitative and quantitative analyses are synthesized to draw
comprehensive conclusions. The insights gained from the qualitative phase add depth to the
statistical results obtained in the quantitative phase. The implications of the research findings
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are discussed in the context of existing literature and contribute to a more holistic
understanding of the impact of retail crowding on customer buying decisions.
Data Collection and Processing Technique
Because our study is grounded on both primary and secondary data, it was critical for
us to choose the participants in our study group, the best combination of interviewees, the best
set of interview questions, and the final alignment of the design with the research question.
The sample size for IDI was 30, whereas the sample size for administering survey
questionnaire for quantitative research was 114. The sample was chosen at random, but a
variety of characteristics, including age, gender, occupation, and others, were taken into
consideration.
For quantitative research, the following table shows the questionnaire asked during the survey.
Sr.
Questionnaire
No.
Strongly
Disagree
Neutral
Agree
Disagree
Strongly
Agree
Do you visit the crowded store
1
if there are great discounts or
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
deals available?
Are you determined to buy
2
items that you need, regardless
of the crowd?
When the store is crowded, do
3
you prioritize finding specific
items over the discomfort
caused by the crowd?
Do you find yourself buying
4
more things when you are in a
crowded store?
Do you find yourself spending
5
more time in a store when it is
crowded?
Do you find yourself browsing
6
through the shelves more when
you are in a crowded store?
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Do you find yourself more
7
likely to buy something if it is
on display in a prominent
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
location in a crowded store?
Do you tend to make purchase
8
decisions quickly in crowded
stores to minimize your time
there?
Does the fast pace of decision-
9
making in crowded stores
affect your confidence in your
choices?
How likely do you regret
10
making a purchase from a
crowded store?
When you entered the store,
11
did you feel that it was too
crowded for your comfort?
Is your emotional state largely
12
affected by how crowded the
store is?
Do crowded environments
13
make you feel stressed and
anxious during shopping?
Does your emotional reaction
to crowded environments
14
significantly impact your
overall enjoyment of
shopping?
In a crowded retail store, how
15
likely are you to ask for help
from a salesperson?
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Least
Somewh
Neutral
Likely
Most
likely
at likely
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
likely
How likely do you compare the
16
product with its alternative or
substitute in a crowded retail
store?
How likely are you to stick to
17
the shopping list and purchase
only the items that you intend
to buy?
Does a long billing queue
18
make you change your
shopping decision?
Does a crowded retail store and
19
long billing queue make you
change the shopping store?
Apart from these we also asked about gender, Occupation, Frequency of visiting retail stores
and Age in the above-mentioned survey.
For qualitative research IDIs, the following questions were asked in the in-depth interviews to
conduct a qualitative analysis.
i. How frequently do you do offline shopping from retail stores?
ii. Have you ever experienced a high crowd at the place you visited?
iii. What is your reaction after seeing the crowded store? Is it positive or negative?
iv. Have you ever encountered situations where retail crowding affected your willingness
to make purchases or even caused you to leave the store without buying anything?
v. In crowded retail environments, do you tend to stick to your original shopping list, or
do you make spontaneous purchases that you hadn't planned for?
vi. How does retail crowding influence your ability to browse and compare products?
vii. Does it lead to impulse purchases or more considered decisions?
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viii. Have you ever felt overwhelmed or anxious in a crowded retail setting? How does
this emotional response influence your shopping decisions?
ix. If you are buying clothes from the shop then with the higher crowd you feel a lack of
uniqueness in the product? Or it will attract you to shop from that store?
x. How do you rate your overall experience of buying at retail place with a heavy crowd?
xi. What will you prefer personally, visiting stores offering lower priced products with
heavy crowds or vice-versa?
Considering the above questions as the baseline for the interview, the discussion led in detail
based on the responses to the above questions
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QUALITATIVE ANALYSIS
Quantitative research was carried out to break down the concept to make it more
comprehensive and understand the latent factors of a larger quantity of people participating in
the activity of shopping the regularly. A hypothesis model was created and the data was
processed using SPSS to provide conclusions to the research. Various factors came up as the
main constructs to build our variables and bundle them further to test and interpret.
In-depth Interviews
During the analysis of the in-depth interviews, we came across a variety of opinions. Most
of the replies had a common underlying meaning, but also, a few responses were outliers and
gave us significant insights apart from the consensus.
When asked about the frequency of offline shopping from retail stores, half of them
mentioned that they only visit physical stores around twice a month that too when they can't
find what they need online. Few respondents indicate that they frequently (around once a week)
shop in physical stores because they enjoy the experience of browsing products in person. The
remaining only prefer online shopping and rarely go to physical stores.
The frequent store visitors responded that they have encountered crowded stores several
times. While the encounter for online shoppers seems to be low as they usually visit during offpeak hours to avoid crowds.
When asked about the reaction and emotion that the respondents felt seeing the crowded
store, there was a mixed bag of responses. It was perceived negatively that it was chaotic and
overwhelming. On the contrary, some associated them with popularity and good deals while
other responses were that respondents expected this crowd in retail.
Respondents acted differently in terms of their willingness to make purchases to the retail
crowding. Either they left stores due to the crowd finding it difficult to shop comfortably or
they remain focused on their shopping goals regardless of the crowd. For some, it didn't
significantly impact their purchase decisions.
In crowded retail environments, respondents made unplanned purchases due to the
excitement of the environment. While some stuck to their shopping list to avoid getting sidetracked by the crowd. An outlier opinion was that their behaviour varies depending on their
mood and the product.
When asked about retail crowding’s influence on their ability to browse and compare
products, respondents mentioned that it becomes challenging to browse and compare products
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thoroughly when the store is crowded. The other pool indicated that they've become
accustomed to navigating crowds while shopping.
When asked if they feel a lack of uniqueness in the product in crowded stores, there were
polar responses. some respondents express that a high crowd might make them feel that the
clothes are less unique due to many people buying them. While other groups found crowded
stores attractive, associating them with trendy items.
Lastly, about the preference, some vouched for visiting less crowded stores, even if prices are
slightly higher, to avoid the hassle. While some were willing to tolerate crowds if the prices
were significantly lower. Also, a less taken opinion was considering their preference
situational, opting for crowded stores for essentials and quieter ones for leisure shopping.
QUANTITATIVE ANALYSIS
Constructs
Shopping Motivation: This construct delves into the underlying motivations that drive
consumers to visit retail stores, even in crowded environments. It explores whether consumers
are prompted by specific items they seek, enticing incentives, or the desire to engage in the
overall shopping experience. Understanding shopping motivation helps unravel the factors that
lead consumers to navigate through crowded spaces to fulfil their goals.
Navigation and Accessibility: This construct examines how the presence of a crowd
affects consumers' ability to navigate a retail environment and access desired products. It
considers challenges related to product visibility, movement, and efficient completion of
shopping lists. By investigating navigation and accessibility, the study sheds light on how
crowding impacts the practical aspects of shopping and influences consumer behaviour.
Affective Responses: Affective responses focus on the emotional reactions triggered by
crowded retail settings. This construct explores the range of feelings consumers experience,
from stress and frustration to excitement and enjoyment, when exposed to crowding.
Understanding affective responses provides insights into the emotional dimensions of shopping
in crowded environments and their subsequent effects on buying decisions.
Buying Convenience: Buying convenience assesses the ease with which consumers can
interact with products, and displays, and make decisions in crowded retail spaces. It examines
whether crowding enhances or hampers the convenience of the purchasing process, influencing
factors such as browsing, decision-making, and product interaction.
Critical Buying Purchase: This construct centres on essential or high-priority purchases
consumers make, even in the face of crowding. It investigates how the urgency and importance
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of certain items influence consumer decisions. By understanding the impact of crowding on
critical buying purchases, the research provides insights into the resilience of these decisions
amid crowded retail environments.
Descriptive Statistical Analysis
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Normality Test of Data Set
Descriptive statistics are used to describe the fundamental characteristics of the data under
examination. The quantitative data are described using measures of central tendency and
dispersion. The normality test is a crucial step in choosing the measures of central tendency
and statistical techniques for data analysis for continuous data. Parametric tests are used to
compare the groups when our data have a normal distribution; otherwise, nonparametric
approaches are used.
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Here, under the Shapiro-Wilk test significant values are too low (<0.1), which rejects the null
hypothesis of data being normally distributed. However, as sample size is only 114 which is
too small to check normality, will continue to perform parametric tests.
Factor Analysis
The actor analysis was performed on all variables to reduce it in pre-selected constructs.
Here, the Kaiser-Meyer-Olkin test gave the value of 0.672. This shows that there is a significant
amount of information overlap among the variables due to the presence of a strong partial
correlation. So, it makes sense to run a factor analysis. Bartlett’s test of Sphericity is used to
test the null hypothesis that the correlation matrix is an identity matrix. The variables are
unrelated if you have an identity correlation matrix, which makes factor analysis a poor choice.
A significant value (often less than 0.05) in this case .00 demonstrates that the correlation
matrix is in fact not an identity matrix, as shown in the table above, rejecting the null
hypothesis.
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Initial commonalities are the percentage of variance in each variable that the other variables
account for in correlation analysis. Extraction commonalities are calculations of the variance
in each variable that the factors in the factor solution are expected to account for. Small
numbers signify variables that do not match the factor solution well and may need to be
removed from the study. Since our model's values range from moderate to high (0.55 and
above), we can use all of the variables.
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As can be seen in the above table first 6 factors have the eigenvalues more than one and they
explain about 66% of the variability in the present 17 variables. So, basically, 17 variables can
be explained by 6 factors. The same can be seen from the Scree plot below, that 6 factors have
eigenvalue more than 1.
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Using the Varimax method of rotation and suppressing small coefficients below the absolute
value of 0.5 we get the above table. At the same time, the reason for selecting 0.5 values is to
avoid the cross-loading of the variable in other factors. So, with the result, we can confirm that
all variables are suppressed in 6 factors. In this project, these factors are named as follow.
Component-1: Affective response (AR)
Component 2: Accessibility of product (AP)
Component 3: Shopping motivation (SM)
Component 4: Buying convenience (BC)
Component 5: Purchase Regrets (PR)
Component 6: Critical Purchase (CP)
Reliability Analysis
With the use of reliability analysis, it is ascertained how closely connected questionnaire items
are to one another, obtain a general index of the scale's repeatability or internal consistency,
and spot problematic items that should be removed from the scale. Here, the reliability analysis
is performed on each set of questions under one construct and the value of Cronbach’s Alpha
is noted.
Affective response (AR)
Accessibility of product (AP)
Shopping motivation (SM)
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Buying convenience (BC)
Purchase Regrets (PR)
Critical Purchase (CP)
From the value of Cronbach's Alpha in this analysis we are going to consider only 5 factors
whole values are above 0.6. So, we are not going to consider Critical Purchase (CP) as a factor.
As the other 5 factors have larger Alpha values near 0.6 or above those are the final 5 Factors
(AR, AP, SM, BC and PR) to be considered.
Inferential Analysis
Independent t-test
Case 1: significance interval of 95%
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H0: There is no significant difference in the shopping motivation of males and females in
crowded stores
H1: There is significant difference in the shopping motivation of males and females in crowded
stores
Here if we see the significance level is P( 0.529) > 0.05, we can say there is no significant
difference in variances of the shopping motivation of males and females in crowded stores
Case II: Confidence interval of 90%
H0: There is no significant difference in the affective response of males and females in crowded
stores
H1: There is significant difference in the affective response of males and females in crowded
stores
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Here if we see the significance level is P(0.806) > 0.05, there is no significant difference in the
variances of the affective response of males and females in crowded stores
One-way ANOVA
Impact of frequency of store visits on shopping motivation level
H0: If p>0.1, we accept H0 - There is no significant difference between frequency of visit and
shopping motivation level
H1: If p<0.1, we reject H0 – There is a significant difference between the frequency of visits
and shopping motivation level)
In the test of homogeneity of Variances, p>0.1, which tells us that the variances within each
age group are not statistically different from each other (this can be seen through the values in
the above table which are very close to each other).
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The significance of 0.26 indicates that statistical difference does not exist between the
frequency of visits on shopping motivation level.
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In the Tukey’s we do pairwise comparison of means of the variables. In this case we are
checking impact of frequency of store visit on shopping motivations level. Checking the
significance for each comparison of means, we observe that none of the difference are
significant. This implies that H0 will be accepted and no difference in means of shopping
motivation level
N Way ANOVA
Impact of gender and frequency of store visit on buying convenience- Gender& frequency IV,
buying convenience DV
H0: If p > 0.1, we accept H0 – There is no significant difference between gender and frequency
of visit on buying convenience
H1: If p < 0.1, we reject H0 – There is a significant difference between gender and frequency
of visit on buying convenience
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For Gen*Freq, p > 0.1 – there is no statistical difference in the buying convenience between
both gender and frequency of visits. Similarly, from the significance values for Gender (p =
0.371) and frequency (p = 0.779), we observe that there is no statistical difference between
gender and frequency separately on buying convenience.
.
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Based on observed means. The error term is Mean Square (Error) = 1.105. As the significance
values for the frequency of visits are > 0.1, there is no statistical difference between all the five
frequencies of visits. Thus, we can conclude that there is no effect of any frequency of visit on
buying convenience
One-way MANOVA
Part - 1
Difference of impact between affective response and buying convenience on age – Affective
response, buying convenience (DV) & Age (IV)
H0: If p > 0.1, we accept H0 – There is no significant difference between affective response
and buying convenience on age
H1: If p < 0.1, we reject H0 – There is a significant difference between affective response and
buying convenience on age
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Here, F (8,216), p > 0.1, Wilk’s lambda = 0.892, Partial Eta Squared = 0.056.
•
Since, value of p (0.128) > 0.1, we observe there is no significant difference between
affective response and buying convenience on age.
•
Since there is no significance, we do not perform any further tests.
Part – 2
Difference of impact between affective response and shopping motivation on frequency of
shopping – Affective response, shopping motivation (DV) & Age (IV)
H0: If p>0.1, we accept H0: There is no significant difference between affective response and
shopping motivation on frequency of buying from crowded store.
H1: If p<0.1, we reject H0 – There is significant difference between affective response and
shopping motivation on frequency of buying from crowded store.
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Here, F (8,216), p > 0.1, Wilk’s lambda = 0.865, Partial Eta Squared = 0.07.
•
Since, value of p (0.044) < 0.1, we observe that there is significant difference between
affective response and shopping motivation towards frequency of buying from crowded
store.
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•
From the table of test between-subject effects, we have the significance between
frequency and affective response of 0.311 which is more than 0.05. Therefore, we
conclude that frequency of buying has significant effect on affective response in
crowded retail space.
•
However, we have the significance between frequency and shopping motivation of
0.026 which is less than 0.05. Therefore, we can conclude that frequency of buying has
no significant effect on shopping motivation.
N- way MANOVA
H0: there is no significant difference between the buying convenience, affective response on
age & frequency of shopping in crowded area
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H1: there is significant difference between the buying convenience, affective response on age
& frequency of shopping in crowded area
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Here, F (14,194), p > 0.1, Wilk’s lambda = 0.842, Partial Eta Squared = 0.083.
•
Since, value of p (0.194) > 0.1, we observe there is no significant difference between
the buying convenience, affective response on age & frequency of shopping in crowded
area.
Regression Analysis
Multiple Linear Regression
Multiple regression is performed to understand the impact of two or more independent
variables on the dependent variables. Regression is also called a predicting or forecasting
method.
H0: There is no statistically significant relationship between Shopping motivation (SM),
Accessibility of products (AP), Affective response (AR) and buying convenience (BC) on the
Purchase regret (PR) in a crowded retail store.
H1: There is a statistically significant relationship between Shopping motivation (SM),
Accessibility of products (AP), Affective response (AR) and buying convenience (BC) on the
Purchase regret (PR) in a crowded retail store.
Dependent variable: Purchase regret (PR)
Independent variable: Shopping motivation (SM), Accessibility of products (AP), Affective
response (AR) and buying convenience (BC)
Assumptions:
i.
Both DV and IV must be measured on the metric scale
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ii.
There must be a linear relationship between DV and IV
iii.
Normality of the data
iv.
There must be no significant outliers
v.
Residuals must not be correlated
vi.
Independence of observations (co-relation less than 0.5)
vii.
Homoscedasticity (Homogeneity of variance)
From the correlation table, it can be seen that there is no high correlation present between the
two IVs. So, all IVs can be taken for regression analysis as they are going to impact DV
individually. Ultimately, we can say that the absence of multicollinearity allowed us to use all
selected IVs.
From the ANOVA output table it is shown that the significance value P= 0.000 which is less
than 0.1, means null the hypothesis (H0) was rejected. So, there is a relationship between said
IVs and DVs. Also, the F value is 6.963 which is higher than the (1.64)^2 value, which indicates
rejection of the null hypothesis.
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The above table indicates the results of R, R square, Adjusted R Square, and std error.
The R-value is the multiple correlation coefficient, here an R-value of 0.451 is a good level of
prediction of the dependent variable. The R square explains the proportion of variance in DV
due to IV. R square of 20.4% explains the variance in DV. However, in multiple regression
Adjusted R square must be near to value of the R square. Here we can see adjusted R square is
0.174 which is near 0.204.
Also, the value of Durbin Watson is 2.055, which is in the range of 1.5 to 3, indicating
the absence of autocorrelation.
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From the histogram and scatterplot of residuals, it can be observed, that the error is normally
distributed and spread with equidistance indicating homogeneity in nature. This proves
Homoscedasticity in variance.
•
Based on the value of Tolerance (>0.1) and VIF (<10) multicollinearity is not a problem.
•
The above table coefficients provide regression coefficients and test the significance of
each IV in impacting DV.
•
The p-value of Shopping motivation (0.086) is less than the p-value cutoff of 0.1 and
hence we can conclude that the Shopping motivation is statistically significant and
influences the DV of regret of a making purchase from a crowded store. But the beta
value is negative which shows a negative correlation between IV and DV. So, with an
increase in shopping motivation regret of making a purchase decreases.
•
The p-value of Accessibility of the product in the store (0.035) is less than the p-value
cutoff of 0.1 and hence we can conclude that the Accessibility of the product in-store is
statistically significant influencing the DV of regret of making a purchase from a
crowded store.
•
The p-value of the Affective response (0.006) is less than the p-value cut-off of 0.1 and
hence we can conclude that the Affective response is statistically significant in
influencing the DV of regret of making a purchase from a crowded store.
•
The p-value of Buying convenience (0.029) is less than the p-value cut-off of 0.1 and
hence we can conclude that Buying convenience is statistically significant influencing
the DV of regret of making a purchase from a crowded store.
•
From the above table regression equation can be written as:
Y=1.260 - 0.163 (SM) + 0.197 (AP) + 0.289 (AR) + 0.194 (BC)
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Discriminant Analysis
The classification functions are used to assign cases to groups. The coefficient for
BC_AVG is larger for the “1” classification function, which means that customers who have
greater buying convenience are less likely to be satisfied with their shopping experience and
more likely to change stores.
The within-groups correlation matrix shows the correlations between the predictors.
The Largest value of BC_AVG & AR_AVG (0.421), implies they have the strongest
correlation between greater Buying Convenience & Affective Response amongst other
variables.
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Comparing mean values for variables, after categorizing depending on the Decision to
change store, we see no significant difference.
Log determinants are a measure of the variability of the groups. Larger log
determinants correspond to more variable groups. Large differences in log determinants
indicate groups that have different covariance matrices. Nearly equal values of log
determinants indicate that co-variances are nearly equal.
H0: There is no significant difference between covariances across groups
H1: There is a significant difference between covariances across groups
Box's M tests the assumption of equality of covariances across groups. Since the
value 0.346 is not significant (>0.1), it states that there is no significant difference in
covariances across groups.
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The tests of equality of group mean measure each independent variable's potential
before the model is created. Each test displays the results of a one-way ANOVA for the
independent variable using the grouping variable as the factor. If the significance value is
greater than 0.10, the variable probably does not contribute to the model. According to the
results in this table, it is less likely that Shopping Motivation doesn’t contribute to our model.
Coefficients with large absolute values correspond to variables with greater discriminating
ability, hence BC_AVG (Convenience) has greater discriminating ability as the function
coefficient is 1.102.
The eigenvalues table provides information about the relative efficacy of each discriminant
function. R 2 =0.787
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Wilks' lambda is a measure of how well each function separates cases into groups. It
is equal to the proportion of the total variance in the discriminant scores not explained by
differences among the groups. Smaller values of Wilks' lambda indicate the greater
discriminatory ability of the function. The associated chi-square statistic tests the hypothesis
that the means of the functions listed are equal across groups.
The classification table shows the practical results of using the discriminant model. Of
the cases used to create the model, 72 of the 78 people who previously made the decision to
change the store are classified correctly. 34 of the 36 non-changers are classified correctly.
Overall, 93% of the cases are classified correctly.
Since Box’s M is not significant, the results are kept the same.
Overall, 92.98% [(34+72)/ (36+78)] have been classified correctly.
Cluster Analysis
Hierarchical Cluster Analysis
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A visual representation of the cluster solution is a dendrogram. Variables are listed
vertically along the left axis. The distance between clusters when they are linked is displayed
on the horizontal axis. Here 19-25 we can see a gap, which indicates two clusters.
The agglomeration Schedule is a numerical summary of the solution. At first in stage
2, clusters 10 and 11 are combined because of the closest distance. Here we can see that in
coefficients, the maximum jump is found in stages 3 to 4. So, the ideal cluster number should
be 4.
K Means Clustering
Initially, the cluster centres of every variable in this cluster are shown.
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There were 20 iterations but due to no change in cluster centres after 13, it stopped. The
minimum distance between initial centres is 8.832. If we had reached the maximum iterations
and still no results would have been obtained, we would have to increase the number of
iterations to get the output.
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The distance between different clusters is shown. Usually, cluster 1 and 2 has the
minimum distance which shows similarity. Here cluster 3 is the most dissimilar from cluster 4.
Cluster 3 is almost similar to another cluster.
Variables with large F values provide the greatest separation between clusters. In this
case, a long billing queue has the maximum effect on the clustering depending on variables and
shows the highest difference from the cluster.
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Finally, we can see that out of 114 responses,
•
In cluster 1 - 33
•
In cluster 2 - 29
•
In cluster 3 - 23
•
In clusters 4 - 29
Generally, if in any cluster we find more responses it is advisable to increase the cluster groups.
Two-step Cluster Analysis
The Cluster Sizes view shows the frequency of each cluster. Hovering over a slice in
the pie chart reveals the number of records assigned to the cluster. 35.1% (40) of the records
were assigned to the first cluster, and 64.9% (74) to the second.
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Structural Equation Modelling (Conceptual Model)
Confirmatory Factor Analysis
Notes for Model (Default Model)
Computation of degrees of freedom (Default Model)
Result (Default Model)
Now, we calculate,
Chi-square/Degrees of freedom = 165.705/104 = ~1.6 (which is less than 3)
Hence, we can conclude that the model is fit.
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Standardized Regression Weights: (Group 1 – Default Model)
Here, we can see that the factor loading is greater than 0.7., five of the characters among the
three variables measure more than 0.7. This indicates a strong linear relationship between the
latent variable and the observed indicator i.e. it means that changes in the latent variable have
a substantial impact on the observed indicator.
Regression Weights: (Group 1 – Default Model)
The table shows that the regression weights for the following paths are significant:
Gender → Buying Convenience (BC_AVG): 0.52
Discounts or Deals (Discounts) → Buying Convenience: 0.42
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This means that gender and discounts or deals have a significant impact on buying convenience.
The path coefficient for gender is stronger than the path coefficient for discounts or deals,
which means that gender has a greater impact on buying convenience.
Covariances: (Group 1 – Default Model)
The path coefficients between gender and buying convenience, frequency of store visits
and buying convenience, and discounts or deals and buying convenience are all positive and
significant. This means that all three variables have a significant impact on buying
convenience.
The path coefficient between gender and buying convenience is the strongest, followed
by the path coefficient between discounts or deals and buying convenience. This means that
gender has the greatest impact on buying convenience, followed by discounts or deals.
Baseline Comparisons
The Comparative Fit Index (CFI) ranges from 0 to 1, with higher values indicating better model
fit. In our context, a CFI value greater than 0.8 in baseline comparisons suggests that our model
fits the data well relative to the baseline models.
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CMIN
A CMIN/DF value less than 3 is generally considered an indicator of good model fit. The value
in our analysis is 1.593, which is in the range (1,2). This range is considered an excellent model
fit. It indicates that the model provides a good fit to the data while avoiding overfitting.
RMSEA
RMSEA value of less than 0.05 indicates that the model is an excellent fit. With a value in the
range [0.05, 0.08], the model is considered to be a good fit. The value in our analysis is 0.072
and hence the model is a good fit.
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CONCLUSION
The research undertaken in this study delves into the multifaceted dynamics of retail
crowding and its influence on customer buying decisions. Various quantitative research
methodologies were employed to scrutinize these relationships, ultimately shedding light on
critical insights for retailers and marketers. From factor analysis and reliability analysis, we
realised that the criticality of purchase wasn’t a significant factor. Independent t-test and Oneway ANOVA explored the impact of gender, store visit frequency, and shopping motivation on
customer behaviour in crowded stores, with results indicating no significant differences in most
cases. The discriminant analysis revealed that customers with higher buying convenience were
less likely to be satisfied with their shopping experience and more likely to switch stores. The
analysis also highlighted the strong correlation between buying convenience and affective
response. Both hierarchical and K-means clustering methods identified distinct customer
segments based on various attributes, providing insights into customer preferences and
behaviours. SEM confirmed the goodness of fit of the proposed model, suggesting that gender
and discounts significantly impact buying convenience.
The quantitative analysis conducted in this study offers several vital managerial
implications for retailers and marketers aiming to enhance their operations and customer
satisfaction in crowded retail environments:
•
Retailers should prioritize optimizing buying convenience by improving store layouts
and reducing queues. Investments in technology can streamline the shopping process
•
Creating positive in-store atmospheres and emotional connections with customers can
enhance satisfaction and loyalty.
•
Tailor marketing strategies to specific customer segments identified through cluster
analysis by customising marketing and store layouts based on gender preferences to
improve customer experience.
•
Carefully plan and execute discount strategies to attract and retain customers in
crowded retail settings.
•
Invest in employee training to improve customer service and create a positive
shopping environment.
These recommendations can lead to improved customer satisfaction, loyalty, and profitability
in crowded retail environments.
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