Consumer Preference Analysis of using Shopee Application with
Conjoint Method
Mutia Ryantika, and Rahmat Hidayat
1
Department of Business Management, Politeknik Negeri Batam, Jl.Ahmad Yani, Batam, Indonesia
Keywords: Consumer Preferences, Conjoint Analysis, E-commerce.
Abstract: This study examines the Analysis of Consumer Preferences in Using Shopee Applications with Conjoint
Method. The population in this study are Shopee application users as an online shopping platform in Batam
city in particular, with a total sample of 300 respondents. The sampling technique in this study uses the
purposive sampling method with a formula that refers to the theory of sample size from Hair et al (2010). In
this study data were collected through a questionnaire distributed online to respondents. Analysis of the data
used is conjoint analysis. The results of this study are the highest level of consumer preference for the concept
of using Shopee application with conjoint method in general is the attribute of product features with the
delivery charge subsidy feature as the highest preference and ShopeePayLater feature as the lowest preference.
Product quality attribute occupies the second level with products at affordable prices as the highest preference
and safety in transactions as the lowest preference. Product design attribute occupies the last level with easy-
to-use application as the highest preference and attractive visual design as the lowest preference.
1 INTRODUCTION
Kotler & Armstrong (2012) explains that e-commerce
is a set of technologies, applications, and business
processes are dynamic linking businesses, consumers
and the public through electronic transactions and the
exchange of goods, services, and electronic
information. E-commerce (Electronic Commerce)
also known as electronic commerce or trade via the
Internet, refers to the buying and selling of goods or
services using the Internet, and transfer money and
data for this transaction. The advent of e-commerce
platforms is a significant form of retail therapy and
online shopping has emerged as a different trend.
Customer shopping preferences is driven by
convenience and range of products offered by the
business operator of e-commerce. Large segments of
customers prefer shopping through e-commerce
portals because the scope is wider and can be
accessed anytime and anywhere as long as they have
a supported internet network. Katawetawaraks &
Wang (2013) explained that the ease of marketing
communication provided by the online market or e-
commerce greatly affected consumers' decisions in
choosing to shop online.
There are several considerations when consumers
decide to buy a product. A product attribute has a role
in shaping consumer preferences and will have an
impact on consumers' decisions to buy a product. This
matter supported by research conducted by Zamhir
(2014) which states that product attributes have a
positive and significant effect on consumer
preferences. Kotler and Armstrong (2012) explains
that there are three important elements in product
attributes, namely: the quality of the product (product
quality), product features (product features) and
product design (product design).
Based on data from eMarketer, the number of e-
commerce transactions in Indonesia continues to
increase every year, starting from 2014, as can be
seen in Figure 1.2 below. In 2014, Indonesia's e-
commerce transactions hit a figure of Rp. 25.1
trillion. And increased in 2016 to Rp 69.8 trillion,
with the rupiah exchange rate of Rp 13,200 per US
dollar. Until 2018, the number of Indonesian e-
commerce transactions have reached more than Rp
140 trillion.
Below are the Ten Countries with the Fastest
Growth in E-commerce released by Databoks in
2019:
Ryantika, M. and Hidayat, R.
Consumer Preference Analysis of using Shopee Application with Conjoint Method.
DOI: 10.5220/0010354100290035
In Proceedings of the 2nd International Conference on Applied Economics and Social Science (ICAESS 2020) - Shaping a Better Future Through Sustainable Technology, pages 29-35
ISBN: 978-989-758-517-3
Copyright
c
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
29
Figure 1: Ten countries with the fastest growth in e-
commerce.
Shopee is an electronic commerce platform
headquartered in Singapore and under the SEA Group
which was founded in 2009 by Forrest Li. Shopee was
first launched in Singapore in 2015, then expanded its
reach to Malaysia, Thailand, Taiwan, Indonesia,
Vietnam and the Philippines. Shopee was first started
as a market for customers (C2C) but has switched to
the C2C and Business for Customers (B2B) hybrid
models since the launch of Shopee Mall which is an
online shop platform for distribution of well-known
store brands.
In 2017, Shopee recorded 80 million app
downloads. Even in Malaysia, Shopee became the
third (3) most visited e-commerce portal in the 4th
quarter of 2017. Based on a survey conducted by
TheAsianParent I, Shopee is the most visited platform
in Indonesia with a percentage of 73%. Along with
the intense competition in the marketplace in
Indonesia, Shopee was able to rank in the top 5 in
Indonesia
According to a report by iPrice, Shopee has taken
the first position of most visited e-commerce platform
in Southeast Asia. Shopee defeated Lazada in the
second place and Tokopedia in the third place, with
an average total of 184.4 million visits. Based on a
research the was hell by App Annie and iPrice Group,
Shopee has experienced an increase in total visits of
5%, mainly driven by increased visits in Thailand and
Indonesia. This is as a proof that Shopee was able to
maintain its growth momentum from the previous
quarter even though Q1 2019 was considered a non-
peak period.
Based on above description, the researcher were
interested in knowing how consumer preferences are
in using Shopee application as a platform of online
shopping by using the theory of product attributes by
Kotler and Armstrong (2012) , namely the product
quality, product features and product designs by
conducting a research which is entitled "Analysis of
Consumer Preferences in Using Shopee Application
with Conjoint Method".
Your paper will be part of the conference proceedings
therefore we ask that authors follow the guidelines
explained in this example and in the file
«FormatContentsForAuthors.pdf» also on the zip file,
in order to achieve the highest quality possible
(Smith, 1998).
Be advised that papers in a technically unsuitable
form will be returned for retyping. After returned the
manuscript must be appropriately modified.
2 LITERATURE REVIEW
2.1 Theoretical Review
Kotler and Keller (2016) explained that consumer
behaviour is the study of how individuals, groups and
organizations choose, buy, use, and dispose of goods,
services, ideas or experiences to meet their needs and
desires. Consumers are faced with a myriad of
marketing and other external stimuli every day where
marketers must consider the characteristics of
consumers and consumer psychology to successfully
position a product or service. Consumer
characteristics include; cultural, social and personal
factors and consumer psychology including
motivation, perception, learning and memory.
According to Kotler (2009) the consumer
purchasing decision process is a systematic stage to
see how consumers make the decision to buy a
product or service. The consumer purchasing decision
process intervenes between marketing strategies and
results. That is, the outcome of an organization's
marketing strategy is determined by its interaction
with the consumer decision making process.
According to Kotler and Armstrong (2014)
product attributes can be defined as something that is
able to provide benefits offered by products and can
add value to customers. Developing a product
involves determining the benefits that the product will
offer. Consumers can evaluate these attributes in
terms of their own values, beliefs, and past
experiences.
Indarto (2011) defines consumer preferences as
the subjective tastes of individual consumers,
measured by their satisfaction with these items after
they buy them. This satisfaction is often referred to as
utility. Whereas Frank (2011) assumes that consumer
references enable consumers to rank various bundles
of goods according to the level of utility, or total
satisfaction from consuming goods or services.
ICAESS 2020 - The International Conference on Applied Economics and Social Science
30
2.2 Research Accomplished
Research from Wibisono and Indrawati, Ph.D. (2019)
under the title “Analysis of Consumer Preferences in
Using Flight Ticket Provider Applications & Online
Hotel Booking” and the result of this research is
product quality in the form of ease of use is the
preference which holds the highest position for
consumers, while speed in transactions is the quality
of products that are the lowest preference for
consumers. Product features in the form of airplane
ticket and hotel booking are the highest preferences
for consumers, while e-ticket is the product feature
that is the lowest preference for consumers. Product
style and design in the form of user-friendly
appearance is the highest preference for consumers,
while attractive appearance is the style and product
design that is the lowest preference for consumers.
Research from Anugraheni and Kusdiartini
(2018) under the title “Consumer Preference for
Social Media in Finding and Buying Products
Online”. The result of this research is that the security
attribute is the attribute that has the highest
preference, followed by the ease attribute as the
second preference, then the feature attribute as the
third preference, and the follower attribute is the
attribute with the lowest preference.
Research from Liu et al (2018) under the title “A
study on Chinese consumer preferences for food
traceability information using best-worst scaling” and
the result of this research is this study call for direct
involvement of the Chinese government in the food
safety information sharing system as follows. First,
with diverse consumer preferences, various types of
information that can be tracked must be recorded into
an information sharing platform depending on the
type of food. Second, the government can promote
the gradual development of the platform based on
priority consumer preferences. Third, new
technology must be applied to ensure the reliability of
traceable information. Finally, local preferences in
terms of how consumers receive and understand
information must be considered.
Research from Batavio, Amani, and Tripiawan,
(2017) under the title “Consumer Preferences in
Using Bukalapak Website Services Using the
Conjoint Method” and the result of this research is
consumer preferences in the use of Bukalapak site
services in the city of Bandung Bukalapak site are
ranked from the highest importance level, namely:
Payment method
Display Website
Product
Features
Research from Ghassani and Wardhana (2016)
under the title “Analysis of the Factors Forming Go-
Jek Consumer Preference in the City of Jakarta” and
the result of this research is There are six initial
factors that become consumers' preferences in using
Go-Jek services in Jakarta, including: practicality,
tariffs, speed, security, trustworthiness, comfort and
also formed a new factor called Go-Jek's competitive
advantage factor and the most dominant factor. The
consumer's preference for using Go-Jek services is
comfort factor.
Research from Sinaga, Safitri, and Rusgiyono
(2015) under the title Analysis of Consumer
Preferences of Airline Service Users for the
Semarang-Jakarta Route with Choice-Based Conjoint
Method (Full Profile). The result of this research is
that the price attribute is the highest preference which
is very influential in the decisions of airline service
consumers. Then followed by airport tax, as the
second preference, class attribute occupied the third
preference and facility attribute as the fourth or
lowest preference.
Research from Chang et al (2012), under the title
“Consumer Preferences for Service Recovery
Options After Delivery Delay When Shopping
Online” and the result of this research is the greatest
importance is in the compensation attribute. Then
followed by the second response speed response
attribute. Attributes Apology occupies the position of
third importance and is followed by contact attribute
as the last attribute interest.
2.3 Conceptual Framework
Figure 2: Conceptual framework.
This study used product attributes as variables that
would later determine consumer preferences. Where
in the product attributes there are 3 indicators:
product quality, product features, and product design.
Product attributes have a role in shaping consumer
preferences. Where in the purchasing decision stage,
Consumer Preference Analysis of using Shopee Application with Conjoint Method
31
preferences are at the evaluation stage and will have
an impact on consumers' decisions to buy a product.
3 RESEARCH METHOD
3.1 Research Design
This study used quantitative analysis methods.
According to Sugiyono (2011), quantitative research
studies are used to measure problems by producing
numerical data or data that can be converted into
statistics that can be used. This research is descriptive
research. According to Sugiyono (2015) descriptive
research is research used to describe the
characteristics of a population or phenomenon that is
being studied. In this study, researchers wanted to
find out how consumer preferences in using the
Shopee application in particular in the city of Batam.
3.2 Population and Sample
Shopee application users in the city of Batam are the
population in this study with 300 respondents. The
sampling method used in this study is nonprobability
sampling. Then the sampling technique used was
purposive sampling. According to Sugiyono (2017)
purposive sampling is a technique for determining
research samples with certain considerations aimed at
making the data obtained later more representative.
3.3 Data Collection Techniques
This study uses a questionnaire as a tool or data
collection technique. In general, the technique in
scoring used in the research questionnaire is a Likert
scale technique. According to Sugiyono (2013) Likert
scale is used to measure the attitudes, opinions and
perceptions of a person or group of people about
social phenomena. Sugiyono (2013) also suggested
that the measurement scale can be: nominal scale,
ordinal scale, interval scale, and ratio scale, from the
measurement scale, nominal, ordinal, interval, and
ratio data will be obtained.
3.4 Operational Variables
This study is using product attributes as the
operational variables and using the elements of
product attributes by Kotler and Armstrong (2012) as
indicators of product. Because the product attribute
theory by Kotler and Armstrong (2012) is considered
too general for this study, the levels of each product
attribute indicator will be developed according to the
object under study which is Shopee, by referring to
the most advanced research and development of the
Shopee application itself, namely:
Table 1: Operational variables.
Attribute Level
Product Quality Security in transactions
Complete product information
Ease of transaction
Products at affordable prices
Product Features Delivery charge subsidy feature
ShopeePay feature
ShopeePayLater feature
Easy Payment Methods
Product Design Easy-to-use applications
Simple application interface
An attractive visual application design
3.5 Data Analysis Methods
Conjoint analysis is a technique specifically used to
understand how consumers' desires or preferences for
a product or service by measuring the level of
usefulness and the relative importance of various
attributes of a product. This analysis is very useful to
help design new product characteristics, create new
product concepts, help determine price levels and
predict sales levels. This analysis is a multivariate
analysis used specifically to understand how
respondents build preferences for a product or
service.
4 RESULT AND DISCUSSION
4.1 Model Description
The discrete model shows that the level of factors is
categorical and no assumptions are made for the
relationship between factors and values or ranking.
Following are the number of levels and relationships
of each attribute studied:
ICAESS 2020 - The International Conference on Applied Economics and Social Science
32
Table 2: Model description.
4.2 The Result of Overall Utility Value
Analysis of All Attributes
The utility value explains explaining the respondents'
choices of the attribute levels used in this study,
namely product quality, product features, and product
design. A utility value that is positive states that the
respondent chooses the level of an attribute, while a
utility value that is negative states that the respondent
does not choose or reject the level of an attribute. If
the value of the attribute is zero, then at the level of
the attribute each other is neutral or it can be said that
the level of the attribute does not affect the
respondent. The overall utility value of attributes
could be seen in Table 3. below:
Table 3: The result of overall utility value analysis of all
attributes.
From Table 3. it can be seen that in general
respondents choose the level of products at affordable
prices on product quality attributes, delivery charge
subsidy features on product feature attributes, and
easy-to-use application on product design attributes
as the level of attributes preferred or chosen by
respondents where these level attributes determine
consumer preferences in using the Shopee application
as an online shopping platform.
Then, in the following order are the levels of
attributes that are preferred or chosen by the
respondent as a whole from the highest utility values
as the most liked levels to the lowest utility values as
the most disliked levels:
Table 4: Ranking attributes level based on overall utility
value.
4.3 the Result of Overall Importance
Value Analysis of All Attributes
Table 5: The result of overall importance value analysis of
all attributes.
Consumer Preference Analysis of using Shopee Application with Conjoint Method
33
Overall, product feature attributes have the highest
importance in determining consumer preferences
with importance value 46,878. Furthermore, product
quality attributes have the second order of importance
with an importance value of 30,781. while product
design attributes occupy the last importance level
position with importance value 22,341.
4.4 Ideal Alternative Profile
The ideal alternative profile for consumers in using
the Shopee application of each product attribute based
on their level of importance and attention to its utility
value can be seen in Table 6. below:
Table 6: Ideal alternative profile.
From Table 8. above it can be seen that the
product feature attribute occupies the first position
with an important value of 46,878 and delivery charge
subsidy feature as the most preferred level with a
utility value of 0.205. The product quality attribute
ranks second with an importance value of 30,78 and
products at affordable prices as the most preferred
level with a utility value of 0.011. And product design
attributes occupy the last position with an importance
value of 22,341 and easy-to-use as the most preferred
level with an importance value of 0.034.
4.5 Prediction Accuracy Test
Measurement of correlation both Pearson and the
predictive accuracy test for conjoint results is a
testing process to find out whether the prediction that
has been made has a high accuracy. In this prediction
accuracy test, Pearson and Kendall will measure
output correlation. The measurement will reveal how
strong the relationship between estimates and actual
is or how high the predictive accuracy is (Sari et al.,
2010). Measurement of correlation both Pearson and
Kendall produce relatively strong numbers that are
above 0.5. This proves the existence of a strong
relationship between the actual value, or there is a
high predictive accuracy of the conjoint process. This
can be seen from the correlation values in the table
below:
Table 7: Correlation value.
From Table 7. above it can be seen that the correlation
value on Pearson and Kendall for 300 respondents
each has a correlation value of 0.984 for Pearson and
0.891 for Kendall. The guidelines used for
significance tests with nth respondents, with n
constitute respondents 1 to 300 are:
Ho: There is no strong correlation between the
estimation variable and nth respondent
H1: There is a strong correlation between the
estimation variable and nth respondent
If the probability (significance)> 0.05 then Ho is
accepted. If the probability (significance) <0.05 then
Ho is rejected. Based on Pearson and Kendall's
calculations, both of them significantly below 0.05
(0,000 and 0,000), so Ho is rejected. This shows that
there is a strong correlation between the results of the
conjoint with the opinions of respondents. That way
it can be concluded in general that respondents really
want a product with an affordable price, delivery
charge subsidy features and application that is easy to
use in using the Shopee application to shop online.
5 CONCLUSIONS
Based on the results of research on the analysis of
consumer preferences in using the Shopee application
ICAESS 2020 - The International Conference on Applied Economics and Social Science
34
with the conjoint method, the conclusion is product
feature attribute is the highest level with the delivery
charge subsidy feature as the highest preference and
the ShopeePayLater feature which is the lowest
preference. Product quality attribute occupies the
second level with products with affordable prices as
the highest preference and security in transactions as
the lowest preference. Product design attribute
occupies the third or lowest level with easy-to-use
applications as the highest preference and attractive
visual design as the lowest preference.
Based on the characteristics of respondents in
general, there is no difference in the structure of
consumer preferences for product attributes in terms
of importance or importance. Where the product
feature attribute is an attribute that is the main
concern of respondents in using the Shopee
application followed by product quality attributes in
the second position, and product design attributes in
the last position.
REFERENCES
Anugraheni, D. T., & Kusdiartini, V. (2018). Preferensi
Konsumen Terhadap Media Sosial dalam Mencari dan
Membeli Produk Secara Online. Jurnal Ekonomi dan
Bisnis, 8-17.
Batavio, A., Dr.Ir. Husni Amani, M., & Wawan Tripiawan,
S. (2017). Preferensi Konsumen dalam Menggunakan
Layanan Website Bukalapak Dengan Metode Conjoint.
Jurnal Teknik, 2813-2820.
Chang, D. S., & Wang, T. S. (2012). Consumer Preferences
for Service Recovery Option after Delivery Delay When
Shopping Online. Social Behavior and Personality
Research, 1033-1044.
Databoks. (2019). Sepuluh Negara dengan Pertumbuhan E-
commerce Tercepat available at
https://databoks.katadata.co.id/datapubli
sh/2019/04/25/indonesia-jadi-negara- dengan-
pertumbuhan-e-commerce- tercepat-di-dunia (diakses
pada 10 Oktober 2019).
Frank, R. H. (2011). Microeconomics and Behaviour 8
th
Edition. New York: McGraw Hill International.
Ghozali, I. (2013). Aplikasi Analisis Multivariate dengan
Program IBM SPSS 21, Edisi 7. Semarang: Badan
Penerbit Universitas Diponegoro.
Harish, A. G., & Wardhana, A. (2016). Analisis Faktor-
Faktor Pembentuk Preferensi Konsumen Go-Jek di
Kota Jakarta. Jurnal Manajemen. 2060-2066.
Indrawati. (2017). Perilaku Konsumen Individu Dalam
Mengadopsi Layanan Berbasis Teknologi Informasi &
Komunikasi (Cetakan Kesatu). Bandung: Refika
Aditama.
Kotler, P. (2009). Manajemen Pemasaran 1. Jakarta:
Erlangga.
Kotler, P., & Armstrong, G. (2012). Prinsip-prinsip
Pemasaran. Jakarta: Erlangga.
Kotler, P., & Armstrong, G. (2014). Prinsip-prinsip
Manajemen. Jakarta: Erlangga.
Kotler, P., & Armstrong, G. (2015). Dasar-dasar
Pemasaran Jilid 1. Jakarta: Prenhalindo.
Kotler, P., & Keller, K. L. (2016). Manajemen Pemasaran.
Jakarta: Erlangga.
Liu, C., Li, J., Steele, W., & Fang, X. (2018). A study on
Chinese consumer preferences for food traceability
information using best-worst scaling. 1-16.
Rossi Prasetya, I. (2011). Analisis Preferensi Konsumen
Terhadap Bundling Kartu GSM dengan Smartphone
(Studi Terhadap Bundling Smartphone oleh Telkomsel
dan XL). Fakultas Ekonomi Universitas Indonesia.
Jakarta: Program Studi Magister Manajemen
Pemasaran, Fakultas Ekonomi, Universitas Indonesia.
Setiadi, N. J. (2010). Perilaku Konsumen. Jakarta:
Kencana.
Sinaga, V. D., Safitri, D., & Rusgiyono, A. (2015). Analisis
Preferensi Konsumen Pengguna Jasa Maskapai
Penerbangan Untuk Rute Semarang-Jakarta dengan
Metode Choice-Based Conjoint (Full Profile). Jurnal
Gaussian, 1055-1064.
Sugiyono. (2011). Metode Penelitian Kuantitatif, Kualitatif
dan R&D. Bandung: Alfabeta. Sugiyono. (2013).
Metode Penelitian Pendidikan Pendekatan Kuantitatif,
Kualitatif, dan R&D. Bandung: Alfabeta.
Sugiyono. (2014). Metode Penelitian Pendidikan
Pendekatan Kuantitatif, Kualitatif, dan R&D.
Bandung: Alfabeta.
Sugiyono. (2015). Metode Penelitian Pendidikan.
Bandung: Alfabeta.
Sugiyono. (2017). Metode Penelitian Kuantitatif,
Kualitatif, dan R&D. Bandung: Alfabeta. Sunyoto, D.
(2012). Manajemen Pemasaran. Yogyakarta: Buku
Seru.
Wibisono, A., & Indrawati, P. (2019). Analisis Preferensi
Konsumen Dalam Menggunakan Aplikasi Penyedia
Tiket Pesawat & Booking Hotel Online. Jurnal
Manajemen, 223-230.
Zamhir. (2014). Analisis Preferensi Konsumen terhadap
Produk Air Minum Dalam Kemasan. Universitas
Brawijaya.
Consumer Preference Analysis of using Shopee Application with Conjoint Method
35