As
many buying decision are made in retail stores, it is interesting to determine
which factors, such as noise, lights, music, colours, visual communication have
a significant influence on customers’ buying behaviour in a supermarket.

Objective:

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The
aim of this study was to examine the role of the various environmental factors
in the supermarket as well as how servicescape influences customers’ buying
behavior.

Methodology:

The
questionnaire is customized to collect data on facets in regards to the role of
servicescape and customers’ buying behaviour in the supermarket. A supermarket
chain- Dmart was used for this purpose. The collected data was analysed with
the means of factor analysis and multiple regression methods in order to obtain
factors that can influence customers’ behaviour in the supermarket.

Findings and Conclusion:

The
conducted study resulted that lighting, noise, colours, signs and symbols as
well as space conditions such as layout and equipment are the factors that
generate emotionally pleasant environment in the supermarket. Thus, these
factors influence customers’ moods, attitudes or certain beliefs about the
supermarket. also, customers may feel happier, more satisfied or relaxed in the
supermarket, which lead to arousal – stimulation or excitement. As well as, the
environmental factors explain the approach behaviour such as exploring the
supermarket, spending more time on browsing the products which, consequently,
refer to an increased number of items bought.

Keywords- Servicescape, Service environment, Customers’ buying
behavior outcomes.

 

 

1 Introduction

 

1.1  Background

The
Indian retail industry has emerged as one of the most dynamic and fast-paced
industries due to the entry of several new players. It accounts for over 10 per
cent of the country’s Gross Domestic Product (GDP) and around 8 per cent of the
employment. India is the world’s fifth-largest global destination in the retail
space.

Indian Retail Industry has immense potential as India has the
second largest population with affluent middle class, rapid urbanisation and
solid growth of internet.

India’s retail market is expected to grow at a Compound Annual
Growth Rate (CAGR) of 10 per cent to US$ 1.6 trillion by 2026 from US$ 641
billion in 2016. While the overall retail market is expected to grow at 12 per
cent per annum, modern trade would expand twice as fast at 20 per cent per
annum and traditional trade at 10 per cent. Indian retail market is divided
into “Organised Retail Market” which is valued at $60 billion which is only 9
per cent of the total sector and “Unorganised Retail Market constitutes the
rest 91 per cent of the sector.India’s Business to Business (B2B) e-commerce market is expected
to reach US$ 700 billion by 2020. Online retail is expected to be at par with
the physical stores in the next five years.India’s total potential of Business to Consumer (B2C) is
estimated to be US$ 26 billion, of which $3 billion can be achieved in the next
three years from 16 product categories, according to a study by Federation of
Indian Chambers of Commerce and Industry (FICCI) and Indian Institute of
Foreign Trade (IIFT).

India has replaced China as the
most promising markets for retail expansion, supported by expanding economy, coupled
with booming consumption rates, urbanizing population and growing middle class.Supermarkets
are not just buildings filled with products on the shelves. The supermarket
consists as well of a certain atmosphere inside the store. There are many
factors such as noise, lighting, music, colour, layout or visual communication
that can be taken into consideration in order to build an environment as
customer-friendly as possible. Since the suitable environment of the
supermarket can influence customers to spend more time inside the store, this
can lead to an increased number of items bought. Therefore, many marketers are
trying to adapt supermarkets’ interiors to increase customer satisfaction,
loyalty, and by doing so, to increase the sales volume. 1.2 Problem Since
servicescape has been identified as an important factor in shaping the
consumer’s experience in a retail setting. In a retail store majority of
activities happening are services from the time customer enters and exits the
store.The
environment of the supermarket has big potential to be a powerful and an
effective marketing tool if marketers would better understand how to utilize
it. Since marketers and retailers want their customers to spend more time in
the supermarket, it is necessary to create a relaxed and comfortable
environment. What is more, the environmental factors are considered to be
crucial elements that determine success for the marketers. Even if one has not
been in a particular supermarket, one’s first impression may have a great
influence on buying intentions (Nguyen & Leblanc, 2002). Moreover, since
buying decisions made in the supermarket can be influenced by the environmental
factors, retailers and marketers should take as much advantage as possible
(Converse & Spencer, 1942).Every
year retailers spend large amounts of money to build, design and refurbish
stores. However, marketers are unable to inspect the appropriate mix of the
environmental factors/servicescape that attracts customers’ in supermarkets and
make them to spend more time in supermarkets.1.3
ScopeSince
servicescape is a marketing tool through which marketers attract customers and
give them extra utility. Its really important to understand which environment
factor influence the most, so marketers can get appropriate mix of environment
factors and spend accordingly.This
research study is going to help the marketers to understand appropriate mix of
environment factors, get the maximum return on their investment as well as help
them to attract and satisfy maximum number of customers. 

 1.4
Objectives1-To
determine which environment factors influence customers’ buying behaviour in
the supermarket.2-To
provide the appropriate mix of environment factors to marketers.  

 2. Methodology2.1
Research DesignThe
questionnaire is customize to determine which environment factors influence the
customers’ buying behavior and to find appropriate mix of environment factors.  Questionnaire
was made on the close ended questions, which were base on Likert scale.2.1.1 Quantitative
research method the
quantitative method was chosen, the quantitative research method is widely used
while examining customers’ attitudes and opinions. Since we wanted to
investigate which environment factors influence the customers’ buying behavior
the quantitative research method was justified to be used in this study. Also,
one of the main advantages of using quantitative research is its clarity, since
numbers can be easier to understand and interpret in comparison with hundreds
of coding categories.2.1.2
Descriptive research It is a Descriptive research study that aims to determine
which environment factors influence the customers’ buying behavior and to find
appropriate mix of environment factors.2.2
Sampling Size and MethodA
sample of 245 customers is selected from Dmart. Primary data will be collected
by a pre-tested questionnaire.In this study, judgemental
non-probability sampling was used. In the judgemental sampling, the units were
selected based on the researchers’ judgement about which units would be the
most suitable, useful or representative in the research.  2.3 Data
Collection and MethodPrimary data was collected based on a
survey, a structured questionnaire created to determine which environment factors
influence the customers’ buying behavior and to find appropriate mix of
environment factors.  Questionnaire
was made on the close ended questions, which were base on Likert scale. 2.3.1
QuestionnaireThe questionnaire was composed
of four types of questions. Questions were related to: (1) ambient conditions, (2) space/function,
(3) signs, symbols
and artifacts, (4) customers’ behaviour in the
supermarket.These four types of questions
contain seven environment factors, (1) Lightning and colours, (2) Signs and
symbols, (3) Customers’ behaviour in Dmart, (4) Space/function and noise, (5)
Design and music (6) Products and browsing, (7) Bright colour and dark
lightning Questions in the first section
were asked in order to understand what respondents think about colours,
lighting and the noise level in Dmart. also, respondents also had to give their
opinion whether they would like to hear music in Dmart. Questions in the second section
were asked in order to understand whether customers are satisfied with the layout
of Dmart. Questions in the third section
were asked in order to analyze respondents’ opinion when it comes to, billing
desk, directional signs and other types of communication, displayed in Dmart.Questions in the forth section
were asked in order to analyze general customers’ attitude towards Dmart. Likert scale allows the
respondent to choose a degree of disagreement or agreement with each of the
statements. In the research, 7-point Likert scale was adopted. Respondents were
asked to indicate in each statement a degree they agree or disagree with
particular statements from strongly disagree (=1) to strongly agree (=7).         

 3 Data AnalysisTo analyze the data, two
multivariate techniques were conducted. These techniques are suitable for
analyzing data when there is more than one measurement of each element and the
variables are analyzed simultaneously, factor analysis and multiple regression
were conducted. 3.1
Factor AnalysisFactor
analysis is a technique that is used to reduce and summarize  a large number of variables into fewer
numbers of factors.  This technique extracts maximum common variance from
all variables and puts them into a common score.  As an index of all
variables, we can use this score for further analysis.  Factor analysis is
part of general linear model (GLM) and this method also assumes several
assumptions: there is linear relationship, there is no multicollinearity, it
includes relevant variables into analysis, and there is true correlation
between variables and factors.The
variables that were investigated are following: ‘ambient conditions’, ‘space/function’ and ‘signs, symbols and artifacts’. 3.1.1
Principle Component AnalysisPrinciple
component analysis method was used. This is the most common method used by
researchers.  PCA starts extracting the maximum variance and puts them
into the first factor.  After that, it removes that variance explained by
the first factors and then starts extracting maximum variance for the second
factor.  This process goes to the last factor.3.2
Multiple RegressionMultiple
regression is an extension of simple linear regression. It is used when we want
to predict the value of a variable based on the value of two or more other
variables. The variable we want to predict is called the dependent variable (or
sometimes, the outcome, target or criterion variable). The variables we are
using to predict the value of the dependent variable are called the independent
variables (or sometimes, the predictor, explanatory or regressor variables).Multiple
regression is a general as well as an flexible data analytic system. According
to Cohen, Cohen, West and Aiken (2003), it can be conducted when a quantitative
variable (a dependent variable) is to be studied in relationship to any factors
of interest (independent variables). In this case the dependent variable was ‘customers’ behaviour in Dmart’, and the rest of the variables were
independent variables. Multiple
regression analysis was conducted to see the impact of the servicescape
variables and customers’ behaviour in Dmart. It is usually used to dertermine
how much of the variance in dependent variable (‘customers’ behaviour in Dmart’) can be explained by independent
variables (‘lighting
and colours’, ‘signs and symbols’, ‘space/function and noise’). Since only four factors were retained
after conducting factor analysis, three of them were used as independent
variables and one as the dependent.

 4 Results and Discussions4.1
Descriptive Statistics Descriptive statistics is a way
to summarize numerical data to make it easier to interpret, also, it includes
mean and standard deviation. The 7-point Likert scale was used in the
questionnaire, where 1 means ‘strongly
disagree’ and 7 ‘strongly agree’. In
this case, the statement ‘The
lighting in Dmart is appropriate’
has a mean of 5.68. It indicates, that the respondents agreed and had a
positive opinion towards this question. On the other hand, ‘I think that Dmart has too many
products for the size of the store’
has a mean of 2.6 which points out that the majority of the respondents rather
disagreed with this statement. Standard deviation measures the
spread of a set of observations. The larger the standard deviation was, the
more spread out the observations. A low score means that the responses were
concentrated, hence, the respondents had the same opinion about a statement and
scored similar. For example, ‘I
am satisfied with the general impression of Dmart’ has the standard deviation of 1.132. and, the higher
standard deviation means that the respondents had different opinion towards the
same statement. For example, the statement ‘I would prefer if Dmart would play music’, formed the standard deviation of
2.165.  4.2 Factor
Analysis To
measure the reliability of factors, the Cronbach ?s Alpha was used. The scores
of the Cronbach ?s Alpha for ‘lightning
and colours’, ‘signs and symbols’, ‘customers’ behaviour in Dmart’, ‘space/function and noise’ factors formed high reliability that
leads to acceptable scales (respectively 0.890; 0.867; 0.823; 0.764). It can be
concluded that the higher the respondents scored the questions about ‘lightning and colours’, ‘signs and symbols’, ‘customers’
behaviour in Dmart’, ‘space/function and noise’, the higher their intensions to stay
or explore Dmart. A part from that, the scores of the Cronbach ?s Alpha for ‘design and music’, ‘products and browsing’ and ‘bright
colours and dark lightning’
formed low reliability which leads to unacceptable scales (respectively 0.19;
0.417; 0.462). Cronbach’s
Alphas for all factors The
Cronbach’s Alphas for only the first four factors are scored above 0.6 which
stands for a reliable scale. This indicates a strong internal consistency among
the items included in a particular factor. Essentially, this can be interpret
that respondents who selected a low score for one item tended to select low
scores for the other items included in the factor, and conversely, the
respondents who tented to select a high score for one item tended to select
high scores for the others within the same factor. Hence, knowing the score for
one item, it is possible to predict with some accuracy the possible scores for
the other items within the same factor. The rest factors have a low reliability
as the Cronbach’s Alphas are scored respectively 0.187; 0,425; and 0,452 –
thus, they do not create a reliable scale. which means, that the ability to
predict scores from one item is not possible. so, only the first four factors
will be retained for further analysis.  

 4.2 Correlations
and multicollinearity It
can be seen that all independent variables show a relationship with the
dependent variable. correlations suppose to be higher than 0.3. In this result
we can see that, ‘lightning
and colours’, ‘signs and symbols’, and ‘space/function and noise’ correlate with ‘customers’ behaviour in Dmart’ (0.501; 0.563; and 0.509) and create a
positive correlation, since all scores are between 0 and 1. All three factors
create large relationships between dependent and independent variables, as they
score above 0.5. Also, we can see that most correlated factor, hence, the
strongest relationship exists between ‘customers’
behaviour in Dmart’ and ‘signs and symbols’.   

 5 ConclusionTo conclude, four
main factors such as ‘lightning
and colours’, ‘signs and symbols’, ‘customers’ behaviour in Dmart’, and ‘space/function and noise’ were obtained. The higher the
customers scored questions in each of those factors, the higher their
intensions to stay or explore Dmart, as well as more satisfaction. The factor ‘lightning and colours’ has the highest reliability which
shows a high degree to which each item correlates with a total score. The ‘lightning and colours’ is followed by the ‘signs and symbols’ component. These
four factors create an emotionally pleasant environment in the supermarket.
This means that those factors influence customers’ mood, attitude or certain
beliefs about the supermarket. As well as make customers feel happy, satisfied
and relaxed in the supermarket which leads to stimulation or excitement. also,
environmental factors mentioned above explain the approach behaviour –
exploring the supermarket, spending more time on browsing the products which
refers to the increase of number items bought as well as increase in sales for
the marketers. Multiple correlation was
conducted based on four components created by factor analysis. three factors
created by factor analysis, namely ‘lightning
and colours’, ‘signs and symbols’, and ‘space/function and noise’ stand for the independent variables,
whereas ‘customers’
behaviour in Dmart’ for the
dependent variable. Multiple correlation showed that all independent variables
(‘lightning and
colours’, ‘signs and symbols’, and ‘space/function and noise’) play an important role to influence
the ‘customers’
purchase, customers satisfaction and their revisit in Dmart.To satisfy customers and
make them stay longer and brought more and more product marketers should focus
of three main factors such
as ‘lightning and
colours’, ‘signs and symbols’, and ‘space/function and noise’.

 5 Limitations of the study1.     
Small sample size which did not allow to
come to concrete observations2.     
Paucity of time3.     
Area constricted to nearby places of
Electronic city, Bangalore. ?4.     
Small sample size in comparison to the
wide spread reach and effect of marketing strategies. ? 

 5 Managerial ImpactIn retail industry servicescape play important role to attract customers
and generate sales. Marketers spend good chunk of amount on servicescape, every
year but appropriate mix of environment factors is still not available.They don’t know what proportion of money, they should spend on
particular environmental factor. Which factor is going to influence customer
the most and make them stay longer with them.”This study is going to help to
discover environmental factors that creates physical environment in the
supermarket and are used in determining customers’ impression of the store.
Since those factors lead to the approach behaviour – such as willingness to
explore the supermarket, spend more time on browsing, which refer to intention
to revisit the supermarket, an increased number of items bought as well as
increase in sales for the marketers; understanding environmental factors allows
both retailers and marketers to make improvements in these areas. Even this
research did not make the attempt to determine what kind of colours customers
prefer or what genre of music they would like to hear in the supermarket, the
findings show extra caution and should be carefully considered when designing
or redesigning the store”.