A Study on Impact of Customer Review on Online Purchase Decision with Amazon

a International Journal of Engineering, Business and Management (IJEBM)
ISSN: 2456-7817
[Vol-9, Issue-3, Jul-Sep, 2025]
Issue DOI: https://dx.doi.org/10.22161/ijebm.9.3
Article Issue DOI: https://dx.doi.org/10.22161/ijebm.9.3.5
Int. j. eng. bus. manag.
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A Study on Impact of Customer Review on Online
Purchase Decision with Amazon
Umamaheswararao Gobbilla1
, Japa Varun Teja2
1
Associate Professor, Department Of MBA, CMR Institute of Technology, Hyderabad, Telangana, India.
orcid.org/0000-0002-4467-4600.
2
Student of MBA, CMR Institute of Technology, Hyderabad, Telangana, India.
Received: 22 Jun 2025; Received in revised form: 14 Jul 2025; Accepted: 17 Jul 2025; Available online: 21 Jul 2025
©2025 The Author(s). Published by AI Publications. This is an open-access article under the CC BY license
(https://creativecommons.org/licenses/by/4.0/)
Abstract— This study investigates the influence of online customer reviews on consumer purchase decisions
on Amazon within the Indian market. Employing a descriptive, quantitative approach, data were collected
through structured questionnaires from 170 respondents, focusing on review attributes such as sentiment,
credibility, volume, regency, and multimedia content. The findings reveal that a majority of consumers
(95.3%) read reviews prior to purchasing, with positive reviews exerting the strongest influence on purchase
intent. Most respondents trust Amazon reviews (52.4%) and consider reviews with images or videos more
impactful than text alone. Additionally, nearly 82% reported changing their purchase decisions based on
reviews, highlighting the significant role of online feedback in shaping consumer behavior. These insights
underscore the importance for businesses to strategically manage and leverage online reviews to enhance
trust, drive sales, and foster long-term customer engagement in the competitive e-commerce landscape.
Keywords— Customer Review, Online Purchase Decision, Consumer Behaviour, E-commerce, Online
Shopping, Product Ratings, User-Generated Content, Trust and Credibility, Buying Intentions, Review
Quality, Review Quantity, Purchase Influence, Digital Marketplace, Customer Feedback.
I. INTRODUCTION
In the past, when the peoples was wanted to purchase the
goods and services, they want to visit several stores to do.
But technology enabled both the behaviour of online
purchasing and the idea of customer reviews on websites
became more widespread. In order to maintain the status of
the company, online reviews became more crucial.
Customers who have used or purchased a particular product
from a foolish provide online reviews. Online reviews have
spawned a brand-new medium foe marketing and
communication that fills the void between informal
recommendations and the kind of critical criticism that can
transform a company. The value of online reviews is
actually amazing because they increase purchases. Today’s
customer can easily share his experience and opinion about
a specific product or service with an infinite number of other
consumers around the world through feedback and online
reviews due to internet, which has empowered him to
transform himself from a passive to an active and informed
consumer. From electronic word-of-mouth, potential
customers of that specific product or service use these
reviews or feedbacks. In actuality, there are online review
communities where everyone may share and hear various
viewpoints, and people can reconsider their opinions after
hearing from others.
The influence of online customer reviews on other
consumer’s purchasing decisions is growing. The purposes,
functions and characteristics of the many online platforms
for product reviews, such as blogs, retail websites, social
media, video platforms and independent reviewing
platforms, differ. Word-of-mouth has become more
widespread as a consequence of the development of the
internet, allowing people to more quickly access the ideas,
assessments and experiences of others. This phenomenon is
sometimes referred to as electronic word-of-mouth
communication. As a result, there are more opportunities
than ever for people to gather and share information about
products. Instead of relying solely on face-to-face WOM,

Gobbilla and Teja A Study on Impact of Customer Review on Online Purchase Decision with Amazon
people are now able to share information via a variety of
online channels, including blogs, discussion forum, chat
rooms, new groups and online reviews. Despite this, there
is now more information available than ever before that
could affect and support consumer decision-making.
In the era of digital commerce, online shopping has
transformed the way consumer make purchasing decisions.
With the absence of physical interaction with products,
potential buyers increasingly rely on the experiences and
opinions of others to guide their choices. Customer reviews
therefore have emerged as a powerful tool in shaping
consumer behaviour and influencing decisions.
These reviews not only provide first-hand insights into
product quality and performance but also build trust and
transparency businesses and consumers. This study aims to
explore the extent to which customer reviews impact online
purchase decisions, examining factors such as review
credibility, volume, sentiment and relevance.
Understanding this relationship is crucial for businesses
seeking to enhance customer engagement, improve product
offerings and drive sales in a competitive online
marketplace.
For e-commerce sites, online customer review has the
power to act as digital word-of-mouth, greatly affecting
users purchasing decisions. Our online testimonials have
weaved a transparent and trustworthy that influences
decisions in this way. Positive feedback, such as
compliments from friends, boost self-esteem by
highlighting the advantages of the product and dispelling
any misgiving. Conversely, unfavourable evaluations act as
warning signs, reveal defects, and discourage impulsive
purchases. In addition to the star rating, the thorough
evaluations also include invaluable details about the user
experience that paint a picture of the functionality, fit and
cost effectiveness of the product. The success or failure of
an e-commerce service in the area of customer pleasure and
trust is determined by the collective wisdom of the others,
which is ensured by this kind of access for customer who
are thinking about accepting a product.
In today’s digital world, customer reviews are a major factor
influencing buying decisions rather than a minor
component. Through comprehension of review’s
implications, subtleties, and ethical implication, brands may
effectively utilize them to establish credibility, enhance
their products and finally, attain enduring prosperity.
Continued focus, dedication to openness and a readiness to
pick up on and modify based on the insightful feedback that
customers offer are all necessary for this journey. One thing
will always be the same as the world changes: consumer
reviews will always have a significant influence on the
decisions we make, and companies who recognize and
value their impact will be well positioned to prosper in this
paced market. Purchase intention can be used to predict
clients’ subjective propensity to make purchases.
The impact of various online reviews has been investigated,
as well as how internet reviews affect buy intent and
valence, similarity, quality and other characteristics.
Customers’ views and decisions are influenced differently
by different types of conflicting comments according to
research on contradictory statements found in online
comments. The value of reviews is influenced by review
quality and peripheral cues, and this has a positive effect on
receivers’ purchase intent.
India’s online shopping scene has developed into a dynamic
experience that is tailored to each customer’s unique needs
and preference. Trends like AI-powered personalized
recommendations, voice assistant integration for hands-free
shopping, the use of regional languages in interfaces for
easier access, hyper local delivery services for speedier
fulfilment, and seamless Omni channel experiences
connecting online and offline shopping have all contributed
to this development. These trends highlight how the Indian
e-commerce market is always changing in terms of both
customer preference and technology breakthroughs.
II. LITERATURE REVIEW
1. Tao Chen et al. (June 2022)-Conducted an eye-
tracking study to investigate how online reviews
influence consumer purchasing decisions. They found
that negative comments, particularly for female
consumers, significantly shape purchase intentions.
2. Semila Fernandes et al. (February 2022)-
Emphasized the importance of online ratings,
particularly in emerging markets like India, where
consumers heavily rely on reviews to make purchasing
decisions. Their study aimed to design a scale to assess
how online reviews influence consumer behaviour,
identifying factors such as source credibility, quantity,
language, and topic relevance.
3. Efthymios Constantinides and Nina Isabel
Holleschovsky (February 2022) -Highlighted the
transformation brought about by Web 2.0 technologies,
leading to the emergence of social electronic word-of-
mouth (eWOM) and the significance of online product
reviews in influencing consumer decisions.
4. Tao chen, Premaratne sa maranayake, xiong ying
cen, meng Qi and yichen lan (2022)-Online product
reviews on consumer choices, with a specific focus on
gender differences. Findings reveal a notable tendency
for consumers especially females, to pay significantly
more attention to negative comments than positive
ones.

5. Furthermore, Guo et al. (2020)-Showed that pleasant
online customer reviews lead to a higher purchase
likelihood compared to unpleasant ones. They also
found that perceived credibility and perceived
diagnosticity have a significant influence on purchase
decisions, but only in the context of unpleasant online
customer reviews. These studies suggest that online
product reviews will influence consumer behaviour but
the overall effect will be influenced by many factors
6. Likewise, Boardman and Mccormick (2021)-Found
that consumer attention and behaviour differ across
web pages throughout the shopping journey depending
on its content, function, and consumer’s goal.
7. Bettina von Helversen et al. (January 2018)-
Examined the influence of consumer reviews on online
purchasing decisions among older and younger adults,
highlighting differences in decision-making processes
between the two age groups.
8. Bettina von Halverson, Katarzyna Abramczuk,
Wiesaw Kope, and Radoslaw Nielek (2018)-
Discussed how product features, average consumer
ratings, and single, highly affective positive or negative
consumer reviews affected hypothetical online
shopping decisions of younger and older persons. They
discovered that average consumer ratings have a
significant impact on pupils, while older persons placed
less value on consumer data like positive, affective
reviews. This illustrates how customer behaviour varies
by age. Positive reviews, picture reviews, extra
reviews, cumulative reviews, and description rating are
among the elements of online reviews that are
impacting consumer purchase behaviour.
9. According to research by Fei L. Weisstein, Lei Song,
Peter Andersen, and Ying Zhu (2017)-Examined the
moderating impact of buying intentions when
examining the effects of adverse reviews on customer
pricing perception and subsequent purchase behaviour.
The findings of their study indicate that more adverse
evaluations with a purchase aim than without are to
blame for the bigger negative effects on consumers'
purchasing decisions. This study adds to our
understanding of unfavourable online reviews and
consumer goals literature while also providing online
retailers some useful takeaways.
10. According to research by Zan Mo, Yan-Fei Li, and
Peng Fan from (2015)-The outcomes do not depend
on the positive or negative ratings, logistical score, or
service score. As a result, in order to give customers
incentives, sellers can create favourable and thorough
reviews during the sales process.
11. According to research by Prabha Kiran and
Vasantha S. (2015)-Customers' perceptions of risk can
be significantly reduced, which can motivate them to
make purchases when they buy online. Buyers'
comments and opinions help future customers make
informed decisions about what to buy, but they also
help businesses improve the quality of their goods and
services. Social media significantly affect customer
behaviour through online reviews and advertisements,
search results, user comments, and online marketing
initiatives.
12. According to study by Simona Vinerean, Iuliana
Cetina, Luigi Dumitrescu, and Mihai Tichindelean
from (2013)-Peer communication through social
media, in particular, has a significant impact on how
consumers make decisions. Different factors have an
impact on how consumers of different ages behave
when making purchases.
13. Ma, Y. J., & Lee, H. H. (2012)-Discuss how online
reviews affect consumers' purchase decisions and what
motivates them to participate in online reviews. Their
study suggests that online marketers should consider
streamlining user evaluations and offering some
guidance for composing them. Consumers should
adhere to certain standards while writing reviews
because these reviews have an impact on customers'
purchase decisions. The homogeneity of evaluations
can be improved because consumers rely on them to
learn more about goods and services.
14. Miao Sun et al. Jang et al. (June 2012)-Investigated
how consumers use product reviews in the purchase
decision process, finding that reviews play a more
significant role in consideration set formation
compared to the choice stage.
15. Ghose and Ipeirotiss, (2010)-The effect of the level of
detail in a product review, and the level of reviewer
agreement with it on the credibility of a review, and
consumers’ purchase intentions for search and
experience products (Jiménez and Mendoza, 2013). For
example, by means of text mining, Ghose and Ipeirotiss
(2010) concluded that the use of product reviews is
influenced by textual features, such as subjectivity,
informality, readability, and linguistic accuracy.
III. RESEARCH GAP
Lack of Integrated Framework on Review Attributes:
While multiple studies examine various elements of online
reviews—such as language, credibility, emotional tone,
quantity, and format (text/image)—there is no
comprehensive model or framework integrating these
variables to assess their combined influence on consumer
purchasing behaviour.

Limited Exploration of Cultural and Regional Contexts
(Especially India): Only a few studies (e.g., Semila
Fernandes et al.) touch upon the emerging markets like
India. There is a gap in understanding how cultural or
regional factors (such as trust levels, consumer literacy,
digital penetration) moderate the effect of online reviews
in the Indian context.
Inadequate Focus on Product Categories and Industry-
Specific Impact: The reviewed literature generally
discusses online reviews in a broad or hypothetical
manner, without distinguishing between product
categories (e.g., electronics vs. furniture vs. fashion) or
industry-specific nuances.
Demographic Diversity Beyond Age and Gender
Underexplored: Although some studies examine the
impact of age and gender (e.g., Tao Chen et al., Bettina von
Helversen et al.), there is limited investigation into how
other demographic factors like income level, education,
or urban vs. rural location influence responses to online
reviews.
Neglect of Multimedia Reviews and Their Effectiveness:
While textual reviews are widely studied, visual elements
such as photo or video reviews, which are increasingly
common in e-commerce platforms, are largely ignored in
the existing literature.
Temporal Dynamics and Review Freshness: The effect of
review recency or freshness (i.e., how recent a review is)
on consumer decisions remains understudied, though this
may significantly impact credibility and relevance.
Lack of Longitudinal Studies: Most studies employ cross-
sectional data. There is a lack of longitudinal research to
track changes in consumer perception and behaviour
over time due to evolving online review ecosystems.
Role of AI-Generated or Fake Reviews: With the growing
concern over fake or AI-generated reviews, there is
limited research on how consumers detect, interpret, and
respond to potentially inauthentic reviews.
Behavioral Outcomes beyond Purchase Intent: Most
studies focus solely on purchase intentions. There is a need
to explore other behavioural outcomes such as brand
loyalty, product returns, word-of-mouth, and post-
purchase satisfaction.
Psychological and Emotional Triggers in Reviews:
Though some research acknowledges the emotional tone of
reviews, specific psychological mechanisms (e.g., fear of
missing out, trust, cognitive dissonance) through which
online reviews affect decision-making are not deeply
explored.
Statement of the Problem:
Despite the increasing significance of online reviews in
shaping consumer purchasing behaviour, existing research
remains fragmented and insufficiently integrated. Various
studies have examined individual elements of online
reviews—such as language, source credibility, emotional
tone, quantity, and review format—but there is no
comprehensive framework that encapsulates their
combined influence on consumer decision-making.
Furthermore, the Indian consumer landscape,
characterized by diverse cultural norms, digital literacy
levels, and regional disparities, remains underexplored,
particularly in the context of how these factors moderate the
impact of online reviews.
Additionally, current literature tends to treat online reviews
generically, without adequate consideration of industry-
specific or product category variations. While
demographic factors such as age and gender have been
partially studied, other crucial dimensions like income,
education, and urban–rural differences have received
limited attention. Moreover, the growing prevalence of
multimedia reviews (photos and videos) on e-commerce
platforms and the increasing risk of AI-generated or fake
reviews have not been adequately addressed in existing
studies.
The temporal dimension of reviews (i.e., freshness or
recency), longitudinal effects of evolving review
ecosystems, and behavioural outcomes beyond purchase
intent (e.g., brand loyalty, product returns, and post-
purchase satisfaction) also remain largely overlooked.
Finally, there is a lack of deep exploration into the
psychological and emotional mechanisms—such as trust,
cognitive dissonance, or fear of missing out—that mediate
the influence of online reviews on consumer behaviour.
These gaps point to the pressing need for a holistic,
contextually grounded, and multi-dimensional study that
addresses these deficiencies in current research.
Need For the Study:
In the rapidly growing digital marketplace, customer
reviews have emerged as a crucial determinant of online
purchase decisions. With platforms like Amazon becoming
dominant players in e-commerce, consumers increasingly
rely on user-generated content—especially reviews—to
assess product quality, compare alternatives, and reduce
perceived purchase risks. Unlike traditional word-of-mouth,
these reviews are publicly accessible, persistent, and often
accompanied by rating systems and multimedia content.
Despite the prevalence of reviews on Amazon, there is a
lack of in-depth understanding of how different aspects of
these reviews—such as sentiment, credibility, quantity,
recency, and format (textual or visual)—influence

consumer behaviour. Furthermore, in the Indian context,
where consumers show varying levels of digital literacy,
trust in online sources, and buying power, the impact of
customer reviews may differ significantly across
demographic and psychographic segments.
This study is therefore necessary to:
• Identify which review attributes most significantly
affect consumer buying behaviour on Amazon.
• Understand consumer perceptions and trust levels
regarding customer reviews.
• Bridge the knowledge gap by exploring this
phenomenon in an India-specific and platform-
specific (Amazon) context.
• Provide actionable insights for marketers, sellers,
and platform designers to enhance review systems
and optimize consumer experience and sales
conversions.
By focusing on Amazon—one of the world’s leading e-
commerce platforms—this study aims to contribute
meaningful findings that reflect real-world consumer
behaviour patterns, supporting both academic inquiry and
practical strategy development in the online retail space.
Objectives of the Study:
• To analysis the influence of customer reviews on
consumer trust and confidence in online shopping.
• To investigate the significance of reviews volume
and rating scores in online purchase decision.
• To explore the moderating effect of demographic
factor on the influence of customer reviews.
• To provide recommendation for businesses on how
to leverage customer reviews to enhance online
sales and customer satisfaction.
Scope of the Study:
The following study focuses on understanding how
customer reviews influence online purchasing decisions
specifically on the Amazon platform. The scope of the
research encompasses the following dimensions:
1. Platform Specific Focus – Amazon: The study is
confined to Amazon, one of the leading e-
commerce platforms in India and globally. It will
examine how reviews on Amazon—both text-
based and multimedia—affect the decision-
making process of customers.
2. Consumer Behavior Analysis: The study
explores the behavioural responses of consumers
to various review attributes such as star ratings,
number of reviews, review recency, sentiment
(positive/negative), language clarity, reviewer
credibility, and review format (text, image, or
video).
3. Demographic Considerations: The research will
consider the influence of demographic factors such
as age, gender, income, education level, and
geographic location (urban/rural) on how
customers interpret and act upon reviews.
4. Product Categories: The scope includes a
selection of major product categories on Amazon
(e.g., electronics, apparel, home appliances,
books) to determine whether the impact of reviews
varies across different types of products.
5. Geographical Scope: The study primarily focuses
on Indian consumers, capturing diverse regional
and cultural attitudes toward online reviews and
trust in digital platforms.
6. Time Frame: The research is based on current
consumer trends and perceptions, providing a
snapshot of the impact of customer reviews during
the present e-commerce landscape.
7. Limitations on Seller Perspectives: The study is
limited to consumer-side analysis and does not
explore how sellers manage, respond to, or
influence reviews.
The study provides a comprehensive analysis of the role of
customer reviews in influencing online purchase decisions
on Amazon, offering insights that are both practically
relevant for marketers and academically significant for
future research.
Hypotheses of the Study:
H1: There is a significant relationship between the overall
customer review rating and consumers' online purchase
decisions on Amazon.
H2: The sentiment of customer reviews (positive or
negative) significantly influences consumers’ purchase
intentions on Amazon.
H3: The credibility of the reviewer (e.g., verified
purchase, helpful votes) positively impacts consumer trust
and purchase decisions.
H4: The quantity of customer reviews available for a
product significantly affects the likelihood of purchase on
Amazon.
H5: The recency of reviews (i.e., how recent they are) has
a significant influence on the consumer's purchase decision.
H6: Multimedia reviews (reviews with images or videos)
have a greater influence on purchase decisions than text-
only reviews.

H7: There is a significant difference in the impact of
customer reviews on purchase decisions across different
demographic segments (e.g., age, gender, income,
education).
H8: The impact of customer reviews varies across
product categories (e.g., electronics, clothingband home
appliances) on Amazon.
H9: Consumers’ perceived trust in Amazon’s review
system significantly moderates the relationship between
reviews and their purchase decisions.
H10: Consumers who frequently rely on customer reviews
are more likely to exhibit brand loyalty and post-
purchase satisfaction.
Limitations of the Study:
The study aims to provide valuable insights into the
influence of customer reviews on online purchase decisions
via Amazon, it is subject to several limitations:
1. Platform-Specific Focus: The study is confined
exclusively to Amazon, and the findings may not
be generalizable to other e-commerce platforms
such as Flipkart, Myntra, or international
platforms like eBay or Walmart.
2. Geographical Limitation: The research primarily
focuses on Indian consumers, which may limit the
applicability of the results to consumers from other
countries with different online shopping behaviors
and cultural contexts.
3. Self-Reported Data Bias: Data collected through
surveys or questionnaires may be influenced by
respondents' perceptions, memory recall, or social
desirability bias, which can affect the accuracy of
the findings.
4. Limited Product Categories: The study may only
include a few product categories (e.g., electronics,
fashion, home appliances), and the impact of
customer reviews on other categories might differ.
5. Dynamic Nature of Online Reviews: Online
reviews are constantly changing, and consumer
behavior is influenced by trends, promotions, and
seasonal factors. Hence, the findings represent a
snapshot in time and may not account for
evolving patterns.
6. Exclusion of Seller Perspectives: The study
focuses solely on the consumer perspective and
does not consider how sellers respond to or
manage customer reviews, which could influence
purchase decisions indirectly.
7. Limited Consideration of Fake or Manipulated
Reviews: Although the issue of fake or AI-
generated reviews is acknowledged, the study may
not be able to comprehensively distinguish or
evaluate their specific impact on consumer
behavior.
8. Technological Limitations: Advanced techniques
such as eye-tracking, behavioral analytics, or
sentiment mining are not employed, which could
have offered deeper insights into consumer
responses to reviews.
9. Sample Representation: If the study sample is not
adequately diverse in terms of demographics or
geographic location, sampling bias may affect the
representativeness and generalizability of the
findings.
These limitations should be taken into account
when interpreting the results, and future research
can address these gaps to develop a more
comprehensive understanding of the topic.
IV. RESEARCH METHODOLOGY
1. Research Design: The study adopts a descriptive and
quantitative research design to analyze the impact of
customer reviews on consumers’online purchase decisions.
The aim is to understand the relationship between various
review attributes (e.g., sentiment, credibility, quantity) and
consumer behavior on Amazon.
2. Data Collection Method:
• Primary Data:
Data will be collected using a structured
questionnaire distributed to Amazon users
through online surveys (Google Forms, email, and
social media).
• Secondary Data:
Relevant secondary data will be gathered from
academic journals, market research reports,
and Amazon review samples to support the
literature review and theoretical framework.
3. Sampling Method:
• Sampling Technique:
Non-probability convenience sampling will be
used to select respondents who have prior
experience purchasing products on Amazon.
• Sample Size:
A sample of 170 respondents will be targeted to
ensure adequate representation and statistical
validity.

4. Target Population:
• Individuals aged 18 and above who have made at
least one purchase on Amazon in the past 6
months.
• The study will cover a diverse group based on age,
gender, education, income, and region within
India.
5. Research Instrument: A structured questionnaire will
be designed with closed-ended questions using a 5-point
Likert scale to measure factors such as:
• Review sentiment (positive/negative)
• Review credibility (verified buyer, helpful votes)
• Review quantity and recency
• Multimedia review influence (images/videos)
• Impact on trust, purchase intent, and satisfaction
6. Data Analysis Techniques: The collected data will be
analyzed using statistical tools such as: Chi- Square Test
7. Duration of Study: The research is expected to be
conducted over a period of 6–8 weeks, including
questionnaire design, data collection, analysis, and
interpretation.
8. Ethical Considerations:
• Participation will be voluntary and anonymous.
• Respondents will be informed about the purpose
of the study and their right to withdraw at any
time.
• Data will be used solely for academic purposes.
V. DATAANALYSIS & INTERPRETATION
1.Age
Age No. of responses Percentage
Below 20
years
50 29.40%
21-30 years 105 61.80%
31-40 years 14 8.20%
41-50 years 1 0.60%
51 above 0 0.00%
Total 170 100%
Table no.1
Graph no.1
Interpretation: The above chart shows the most of the
responses from the 21-30 i.e:105 responses (62.80%), the
next responses is the age of below 20 years i.e: 50 responses
(29.40%), next responses is the age of 31-40 i.e:14
responses (8.20%) and very less responses from the age of
41-50 i.e: 1 responses (0.60%). No responses are aged 51
above. Through this we can know that the most responses
are the age of 21-30 years.
2. Gender:
Gender No. of responses Percentage
Male 76 44.70%
Female 94 55.30%
Other 0 0
Total 170 100%
Table no.2
Graph no.2
Interpretation: The above chart shows that the most of the
responses are female i.e: 94 responses (55.30%). The males
29%
62%
8%
1%
0% Below 20 years
21-30 years
31-40 years
41-50 years
51 above
45%
55%
0%
Male
Female
Other

where we got i.e: 76 responses (44.70%) . Through this we
can know that the most responses are the females.
3. Occupation:
Occupation No. of responses Percentage
Student 123 72.40%
Employee 29 17.10%
Business Owner 12 7.10%
Homemaker 6 4%
Other 0 0.00%
Total 170 100%
Table no.3
Graph no.3
Interpretation: The above chart shows the most of the
responses are students with the 123 responses (72.40%)
next responses are employee with the responses are 29
(17.10 %) next responses are business owner with the
responses of 12 (7.10%) next responses are the Homemaker
with the responses are 6 (4%). No responses are the other
occupation. Most of the responses are the students.
4. Income:
Income No. of responses Percentage
Below 10,000 110 64.70%
10k-20k 18 10.60%
20k-30k 29 17.10%
30k-40k 7 4.10%
40k to above 6 3.50%
Total 170 100%
Table no.4
Graph no.4
Interpretation: The above chart shows the most of
responses income below 10,000 with the responses are 110
(64.70%) next is the 20k-30k with responses is 29 (17.10%)
, next responses is the 10k-20k responses are 18(10.60%) ,
next is 30k-40k with responses are 7 (4.10%) and the last
responses is 40k to above responses are 6 (3.50%).
5. How Often Do You Shop Online?
Online Shopping No. of Responses Percentage
Daily 6 3.50%
Weekly 38 22.40%
Monthly 51 30%
Rarely 67 39.40%
Other 8 4.70%
Total 170 100%
Table no.5
Graph no.5
Interpretation: The above chart shows the most responses
are rarely with the responses are 67 (39.40%) , next is the
72%
17%
7%4%
0% Student
Employee
Business Owner
Homemaker
Other
65%
11%
17%
4%
3% Below 10,000
10k-20k
20k-30k
30k-40k
40k to above
4%
22%
30%
39%
5%
Daily
Weekly
Monthly
Rarely
Other

monthly with the responses are 51(30%) , next weekly with
the responses are 38(22.40%) , next is other with the
responses are 8 (4.70%) and the last is daily with the
responses are 6 (3.50%). The most of responses is having
rarely.
6. Do You Read Customer Reviews Before Purchasing
A Product Line?
Customer Reviews No. of responses Percentage
Yes 162 95.30%
No 8 4.70%
Other 0 0%
Total 170 100%
Table no.6
Graph no.6
are 162 (95.30%) are read reviews before purchasing
product line and 8 (4.70%) responses are not read reviews
before purchasing product line. No responses for the other.
7. What Type Of Reviews Influences Your Purchase
Decision The Most?
Reviews influences No. of responses Percentage
Positive 144 84.70%
Negative 21 12.40%
Other 5 2.90%
Total 170 100%
Table no.7
Graph no.7
are positive 144(84.70%) are reviews influences, next
responses is negative 21 (12.40%) are reviews influences
and other responses is 5 (2.90%) are reviews influences.
8. How Much Do Customer Reviews Affect Your Final
Purchase Decision?
Reviews effect on final
purchase decision
No. of
responses
Percent
age
very much 62 36.5%
Somewhat 62 36.5%
Neutral 36 21.1%
Not at all 7 4.1%
Other 3 1.8%
Total 170 100%
Table no.8
Graph no.8
are very much and somewhat are equal responses are 62
(36.50%), next responses is neutral 36(21.1%), next
responses is not at all with the responses are 7 (4.1%) and
other responses is 3 (1.8%).
95%
5%
0%
Yes
No
Other
85%
12%
3%
Positive
Negative
Other
37%
36%
21%
4%
2% very much
Somewhat
Neutral
Not at all
Other

9. Which Platform Do You Trust The Most For
Customer Reviews?
Which platform do you
trust
No. of
responses
Percentag
e
Amazon 89 52.4%
Flipkart 47 27.6%
Google reviews 8 4.7%
Social Media 14 8.2%
Other 12 7.1%
Total 170 100%
Table no.9
Graph no.9
are amazon with the responses are 89 (52.4%) , next is
Flipkart with the responses are 47 (27.6%) , next is social
media with the responses are 14 (8.2%) , next other with the
responses are 12 (7.1%) and last is google reviews with the
responses are 8 (4.7%).
10. How Many Reviews Do You Usually Read Before
Making A Purchase?
How many reviews read
before purchase
No. of
responses
Percent
age
1 - 5 reviews 43 25.30%
6 - 10 reviews 76 44.70%
11 -15 reviews 28 16.50%
16 - 20 reviews 8 4.70%
More than 20 reviews 15 8.80%
Total 170 100%
Table no.10
Graph no.10
are 6-10 reviews with the responses are 76 (44.7%), next is
1-5 reviews with the responses are 43 (25.3%), next is 11-
15 reviews with the responses are 28 (16.5%), next review
is more than 20 reviews with the responses are 15 (8.8%)
and last is 16-20 reviews with the responses are 8 (4.7%).
11. What Factors In A Reviews Impact Your Decision
The Most?
Factors No. of responses Percentage
Star rating 50 29.40%
Detailed text reviews 50 29.40%
Verified buyer status 18 10.60%
Review photos, videos 45 26.50%
Other 7 4.10%
Total 170 100%
Table no.11
Graph no.11
52%
28%
5%
8%
7% Amazon
Flipkart
Google reviews
Social Media
Other
25%
45%
16%
5%
9%
1 - 5 reviews
6 - 10 reviews
11 -15 reviews
16 - 20 reviews
More than 20
reviews
29%
29%
11%
27%
4%
Star rating
Detailed text
reviews
Verified buyer
status
Review photos,
videos
Other

Intrepretation: The above chart shows the most responses
are star rating and detailed text reviews are equal with the
responses are 50 (29.4%) , next is reviews photos , videos
with the responses are 45 (26.5%) , next is verified buyer
status with the responses are 18 (10.6%) and the other with
12. Do You Think Online Customer Reviews Are
Genuine And Trustworthy?
Reviews are genuine and
trustworthy
No. of
responses
Percenta
ge
Strongly agree 35 20.60%
Agree 78 45.90%
Neutral 44 25.90%
Disagree 7 4.10%
Other 6 3.50%
Total 170 100%
Table no.12
Graph no.12
are agree with the responses are 78 (45.9%) , next is neutral
with the responses are 44 (25.9%) , next is strongly agree
with the responses are 35 (20.6%) , next is disagree with the
responses are 7 (4.1%) and the other with the responses are
6 (3.5%).
13. Have You Ever Bought A Product Based On Fake
Or Misleading Reviews?
Fake or misleading
reviews
No. of
responses
Percentag
e
Yes 84 49.40%
No 82 48.20%
Other 4 2.40%
Total 170 100%
Table no.13
Graph no.13
are yes with the responses are 84 (49.4%), next is no with
the responses are 82 (48.2%) and other responses are 4
(2.4%).
14. What Do You Do When You Suspect Fake Reviews
On A Product?
Fake reviews No. of
responses
Percent
age
Ignore them and proceed with
the purchase
55 32.40
%
Search for more reviews from
other platforms
101 59.40
%
Other 14 8.20%
Total 170 100%
Table no.14
Graph no.14
are search for more reviews from other platforms with the
responses are 101 (59.4%) , next response is ignore them
and proceed with the purchase with the responses are 55
(32.4%) and other with responses are 14 (8.2%).
15. Have You Ever Changed Your Decision After
Reading Reviews?
Changed decision after
reading reviews
No. of
responses
Percent
age
Yes 139 81.80%
No 29 17.10%
Other 2 1.20%
Total 170 100%
Table no.15
21%
46%
26%
4%
3% Strongly agree
Agree
Neutal
Disagree
Other
50%
48%
2%
Yes
No
Other
32%
60%
8%
Ignore them and
proceed with the
purchase
Search for more
reviews from other
platforms
Other

Graph no.15
are changed their decision after reading reviews with the
response are 139 (81.8%) , next 29 responses are not
changed their decision after reading reviews and other with
the response are 2 ( 1.2%).
16. Do You Prefer Product With A Mix Of Positive And
Negative Reviews Over Those With Only Positive
Reviews?
Positive and Negative No. of responses Percentage
Yes 127 74.70%
No 31 18.20%
Other 12 7.10%
Total 170 100%
Table no.16
Graph no.16
are yes with the responses are 127 (74.7%), next is no with
the responses are 31 (18.2%) and other with the responses
are 12 (7.1%).
17. Do Video Reviews Like (Youtube , Instagram Etc.)
Influence You More Than Text Reviews ?
Video reviews No. of responses Percentage
Yes 132 77.60%
No 33 19.40%
Other 5 2.90%
Total 170 100%
Table no.17
Graph no.17
are Yes with the responses are 132 (77.6%), next is No with
the responses are 33 (19.4%) and other responses are 5
(2.9%).
18. Have You Ever Left A Review After Purchasing A
Product Online?
Left a review after
purchasing
No. of
responses
Percenta
ge
Yes 112 65.90%
No 54 31.80%
Other 4 2.40%
Total 170 100%
Table no.18
Graph no.18
are Yes with the responses are 122 (65.9%), next is no
with the responses are 54 (31.8%) and other with the
19. What Motivates You To Leave A Reviews?
Motivates you to leave a
review
No. of
responses
Percenta
ge
Good experience 98 57.60%
Bad experience 21 12.40%
82%
17%
1%
Yes
No
Other
75%
18%
7%
Yes
No
Other
78%
19%
3%
Yes
No
Other
66%
32%
2%
Yes
No
Other

Received incentives 16 9.40%
Free product 15 8.80%
Other 10 5.90%
Just to help 10 5.90%
Total 170 100%
Table no.19
Graph no.19
are good experience with the response are 98 (57.6%) , bad
experience with the responses are 21 (12.4%) , received
incentives with the responses are 16 (9.4%) , free products
with the responses are 15 (8.8%) and just to help and other
responses are equal with the responses are 10 (5.9%).
20. Do You Recommend Product To Others Based On
Online Reviews?
Recommend product No. of responses Percentage
Yes 134 78.40%
No 33 19.40%
Other 3 1.80%
Total 170 100%
Table no.20
Graph no.20
are yes with the responses are 134 (78.4%), No with the
responses are 33 (19.4%) and other responses are 3 (1.8%).
21. How Much Do Online Review Influence Your Trust
In A Brand?
Trust in a brand No. of responses Percentage
Very much 64 37.60%
Somewhat 69 40.60%
Neutral 30 17.60%
Not at all 4 2.40%
Other 3 1.80%
Total 170 100%
Table no.21
Graph no.21
are somewhat with the responses are 69 (40.6) , very much
with the responses are 64 (37.6%) , neutral with the
response are 30 (17.6%) , not at all with the responses are 4
(2.4%) and other with the response are 3 (1.8%).
VI. STATISTICALANALYSIS
H0: Customer reviews have no significant influence on
H1: Customer reviews have a significant influence on
58%
12%
9%
9%
6%6%
Good experience
Bad experience
Received
incentives
Free product
Other
Just to help
79%
19%
2%
Yes
No
Other
38%
40%
18%
2%
2%
Very much
Somewhat
Neutal
Not at all
Other

Since the table value is less than calculated value, H0 is
reject and H1 is accept. So there is significant impact of
customer review on online purchase decision with amazon.
VII. FINDINGS
• Females are the majority of the respondents (55.3%),
while males account for (44.7%).
• The most of the peoples are respondents rarely make
purchases (39.4%). In the online platforms.
• A Hague majority (95.3%) read reviews before
purchasing products.
• Reviews are mostly positively influential (84.7%),
while only a small percentage report a negative
influence.
• Amazon (52.4%) and Flipkart (27.6%) are the top
platforms for checking reviews.
• Most respondents look at 6–10 reviews (44.7%) while
before purchasing.
• Responses are nearly even between those who believe
fake reviews exist (49.4%) and those who don’t
(48.2%).
• Star ratings and detailed text reviews are equally
preferred (29.4%), followed closely by photos/videos
(26.5%).
• Responses are nearly even between those who believe
fake reviews exist (49.4%) and those who don’t
(48.2%).
• 65.9% believe they have posted genuine reviews.
• 78.4% have not regretted following reviews.
• Most respondents are somewhat (40.6%) or very
much (37.6%) influenced by online reviews.
VIII. SUGGESTIONS
1. Since females form the majority of respondents
(55.3%), platforms and sellers can tailor marketing
strategies and campaigns with content and product
suggestions that resonate more with female
consumers, especially in categories like fashion,
personal care, and household items.
2. Encourage Purchase Frequency with 39.4% rarely
making purchases, introduce. Limited-time deals
or flash sales, personalized product
recommendations, Loyalty programs or reward
points. These can increase engagement and
convert occasional buyers into regular customers.
3. Highlight Positive Review Impact, as 84.7% are
positively influenced by reviews, encourage happy
customers to leave feedback by, Prompting
reviews after purchases, Offering small incentives
like coupons for honest reviews.
4. Optimize Presence on Amazon & Flipkart. Since
Amazon and Flipkart are primary review
platforms, businesses should: Actively manage
reviews on these sites, quickly respond to negative
reviews to build credibility, Use A+ content and
verified purchase responses for transparency.
5. Encourage Mid-range Review volume. Since most
users check 6–10 reviews, ensure: products have a
minimum of 10 detailed and recent reviews,
promote “most helpful” reviews to appear at the
top.
6. Diversify Review content, since users value star
ratings, text, and visuals, sellers should:
Encourage users to upload images and videos with
their reviews, allow filtering reviews by type (text-
only, photo, video).
7. Promote genuine feedback, since 65.9% believe
they write genuine reviews, reinforce this by:
Avoiding over-incentivization, Encouraging
honest experiences (positive or negative).
8. Showcase Trust Outcomes, with 78.4% not
regretting trusting reviews, brands can: Feature
real review testimonials in ads or landing pages,
Include review summaries like “most buyers found
this useful” or “X% made a repeat purchase.
IX. CONCLUSION
The most of the consumer are read reviews before making
an online purchase, also find them positively influential .In
demographic most of the responses is young adults only,
mainly female and also students. Amazon and Flipkart are
the most trusted platforms for checking reviews, indicating
that business should focus their review management efforts
on these platforms. Most respondents prefer a moderate
volume of reviews (6-10) and value both star ratings and
detailed text equally, with a strong interest also in photos
and videos. Responses are nearly even between those who
believe fake reviews exist (49.4%) and those who don’t

(48.2%), this suggest a need for platforms to improve
transparency and verification. A large portion of consumer
are changed their purchasing decision after reading reviews,
this confirms that the reviews role is influencing and
validating consumer behavior. The majority of consumers
are believe they write genuine reviews, showing a
willingness among consumers to contribute honestly.
Purchase frequency is low because of consumers are doing
rarely on online shopping, suggesting potential for growth
in regular purchasing through engagement strategies.
BIBLIOGRAPHY
[1] Tao Chen et al. (June 2022) The Impact of Online Reviews
on Consumers’ Purchasing Decisions: Evidence From an
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