Measuring Ease of Use Aspects of Shopee Usage Behavior during

Pandemic using PLS-SEM Approach

Michael Christian

, Lasmery R. M. Girsang

and Henilia Yulita

Faculty of Social and Humanities, Universitas Bunda Mulia, Jl. Lodan Raya No. 2 Ancol, Jakarta, Indonesia

Keywords: Attitude, Ease of Use, e-Commerce.

Abstract: The emergence of various types and brands of e-commerce which is also followed by intense competition has

made Indonesia one of the countries with the most attractive e-commerce market globally. To become a

market leader like Shopee needs skills to analyse the complexity and unpredictability of young users' behavior.

Studies have revealed that the ease of e-commerce platforms provides user satisfaction, although some explain

the opposite since users are increasingly technology literate. This study aims to explain the behavior of e-

commerce users (Attitude towards using and Actual system usage) as measured by the Perceived ease of use.

The fussiness of young users especially in big cities like Jakarta underlies the sample of this study. Since the

population size is unknown, this study uses 100 samples by multiplying the number of indicators which are

then collected randomly. The PLS-SEM approach was used as an analytical tool. This study reveals that the

ease-of-use factor significantly still affects usage behavior which then affects further decision-making. The

tastes of young consumers that tend to change rapidly, supported by technological developments and the

online community determine purchase intentions. Therefore, this factor is worthy enough to be a suggestion

for further research.

1 INTRODUCTION

The competitive map of e-commerce in recent years

shows an interesting position. Based on research data

published by iPrice, e-commerce competition in the

first two quarters of this year compared to last year's

period as in table 1 shows an interesting change in

position from various aspects such as web visitors,

ranking on the AppStore, Play Store, and the number

of followers in the media. social. In the first and

second quarter of last year, Tokopedia was in the top

position compared to its competitors Shopee. Over

time with the strategies implemented by each e-

commerce, Shopee managed to shift Tokopedia's

"strength" to the top during the first two quarters of

this year.

The data in the table above shows that Shopee has

a strong strategy to form communities on several

social media while simultaneously increasing and

maintaining the number of app downloads, although

from the aspect of web visitors it is still not as high as

https://orcid.org/0000-0001-8892-5400

https://orcid.org/0000-0003-4149-4409

https://orcid.org/0000-0002-3782-2273

Tokopedia's achievement in the same period. As a

new player in the world of e-commerce, Shopee based

on these data also shows the importance of rapid

awareness of Shopee's presence for the public and an

online value proposition with strong differentiation

compared to its competitors. In addition, it can also

be explained that Shopee emphasizes the strategy of

using applications rather than the web. Shopee views

the mobility and users of the technology-literate young

generation as important. Socialization strategies, for

example in the form of advertisements, can create

awareness for users (Indriyarti & Christian, 2020).

Several studies have explained that there is a

connection between existing system experiences

(actual system usage). Actual use can be

characterized by an individual's readiness to use a

system (Rauniar, Rawski, Yang, & Johnson, 2014).

The actual use that is part of the Technology

Acceptance Model (TAM) is determined by the

factors of benefit and ease of use (Jongchul & Sung-

Joon, 2014).

192

Christian, M., Girsang, L. and Yulita, H.

Measuring Ease of Use Aspects of Shopee Usage Behavior during Pandemic using PLS-SEM Approach.

DOI: 10.5220/0010750000003112

In Proceedings of the 1st International Conference on Emerging Issues in Humanity Studies and Social Sciences (ICE-HUMS 2021), pages 192-197

ISBN: 978-989-758-604-0

Table 1: e-Commerce Competition in Indonesia.

e-Commerce Period

Position on

the App Store

Number of

Followers on

social media

iOS PS

Tokopedia

2019

2 2

TW=192.100

IG=1.148.500

FB=6.049.900

2020

2 3

TW=387.800

IG=1.672.100

FB=6.378.200

2019

2 2

TW=205.070

IG=1.263.800

FB=6.135.250

2020

2 3

TW=445.100

IG=1.780.500

FB=6.377.800

Shopee

2019

1 1

TW=69.300

IG=2.164.100

FB=14.409.600

2020

1 1

TW=210.300

IG=4.215.000

FB=16.793.400

2019

1 1

TW=85.440

IG=2.616.800

FB=14.804.290

2020

1 1

TW=320.800

IG=4.851.200

FB=17.841.400

TW=Twitter; IG=Instagram; FB=Facebook; Q=Quarter; PS=Play Store

The TAM concept itself has been widely used in

various aspects of technology adoption, including one

in the culinary business (Christian, 2019a). This is

because this concept is useful for organizations to

understand the adoption of a technology (Jokonya,

2015).

Behavior in use is affected by the ease of using an

existing technology (Mustafa, Harun, & Endin,

2014). Likewise with research conducted by Teo &

Noyes (2011) explained that in the field of education,

the behavior of teachers in using technology if the

factor of ease of using the technology is formed.

Learning systems that tend to use technology often

emphasize that the ease of use factor affects student

behavior at the National Taiwan University of

Science (NTUST) in using the Blackboard Learning

System (Lin, Persada, & Nadlifatin, 2014). The same

thing is in the case of the International Education

College campus management information system

emphasizing that the attitude to use is formed from

the ease of using a system called the Campus

Management System (Shaffiei, Mokhsin, Hamidi, &

Yusof, 2011).

Various aspects can form a factor of ease of use

that allows individuals to continue to use the

technology. This explains that one can determine

behavior by the ease-of-use factor. This is in line with

research on student behavior in Southern Thailand in

using Facebook which is affected by the ease of use

as social media (Teo & Jarupunphol, 2015). In other

matters, such as the use of internet banking, it also

explains that the desire to use internet banking for

users in Curitiba, South Brazil is affected by the ease

of use factor (Pires, Costa Filho, & Cunha, 2011).

Other fields such as food, convenience factor also

affect the behavior of using QR Codes in a food

tracking system (Kim & Woo, 2016). Even in a larger

scope, the ease of use factor has a significant effect

on the use of e-government services for the public in

Abu Dhabi (Dahi & Ezziane, 2015). Things like user-

friendly design and use can make up the convenience

factor (Huang, Ho, Liu, & Lin, 2015). The same thing

is also explained that online purchases using e-

commerce are affected by this factor (Hajiha,

Shahriari, & Vakilian, 2014).

The factor of ease of use of technology can affect

individual satisfaction in using this technology (Xu &

Gan, 2010), although these results are still

contradictory to other studies. Using an e-learning

system with a blackboard system, student satisfaction

in the United Arab Emirates (UAE) is not determined

by the ease of use factor (Al-hawari & Mouakket,

2010). This allows the must factor using the system

in learning. This result is also directly proportional to

research in the hospitality aspect where the desire to

book hotels online for business travelers is not

affected by comments that are easily searched for as

reviews (Memarzadeh, Blum, & Adams, 2016).

However, there are still several studies that state that

the use of a form of technology is not affected by the

ease of use (Yadav, Sharma, & Tarhini, 2016);

Chong, 2013; Lee, Park, Chung, & Blakeney, 2011).

Based on the explanation presented, the hypothesis in

this study is as follows:

Hypothesis 1: Ease of Use Factors Affect User Behavior

The usage attitude factor affects the desire to make

online purchases using e-commerce (Hajiha et al.,

2014). The results of this study are inversely

proportional to research conducted by Teo &

Jarupunphol, (2015) which explains that in the use of

social media such as Facebook, actual use is not

affected by student behavior as users. This can

Measuring Ease of Use Aspects of Shopee Usage Behavior during Pandemic using PLS-SEM Approach

193

explain that social media factors that are commonly

used or that have been widely used do not

significantly affect their actual use. This also means

that new technologies that have not been wide will

provide a certain level of difficulty in their use. The

contradictory results are also explained by Tyas &

Darma, (2017) where technology adoption is not

affected by the attitude of its use. Behavior in use can

provide good or not assessment results (Huang et al.,

2015; Kanchanatanee, Suwanno, & Jarernvongrayab,

2014). Based on these explanations, the hypothesis in

this study is as follows:

Hypothesis 2: Usage Behavior Factors Affect Actual Use

The originality of this research lies in the use of two

variables of consumer behavior, namely the behavior

in shaping the desire for decision making and the

actual behavior that is taken in the decision. This is

important in analysing actions of desire and actions

of use, especially in using Shopee as one of the e-

commerce media in Indonesia. Online purchasing is

an interesting thing to study in line with the fast-

changing consumer tastes and the dynamic

competition in this sector. In addition, conditions

such as during the Covid-19 pandemic will allow it to

provide a different picture of consumer behavior

compared to conditions before the pandemic.

2 METHODS AND MATERIALS

This quantitative research uses Structural Equation

Modeling (SEM) - Partial Least Square (PLS) using

SMART PLS 3.0. PLS-SEM can explain the analysis

of structural models that are causal effects (Al Idrus,

Ahmar, & Abdussakir, 2018; Christian et al., 2021)

with a relatively small number of samples (Wong,

2013). Based on data from iPrice Insight (iPrice,

2021), the number of Shopee web visitors in

Indonesia until 2020 was 129,320,800 visitors.

However, there is no valid and specific publication

about the number of young Shopee users in Jakarta.

Since the population in this study (the number of

young Shopee users in Jakarta) is unknown, this study

uses the approach popularized by Hair, Black, Babin,

& Anderson, (2014) where the number of samples

from an unknown population can be determined by

multiplying the number of indicators (14 items) by 5

to 10. Based on this, the sample of this study is 100

Shopee users (who purchased in the last 1 year) in

Jakarta as research samples were collected randomly.

This study used a questionnaire instrument with a

range of 5 Likert scales (1=very disagree to 5=very

agree) with several tests, namely validity, reliability,

determination, and hypothesis testing for data

analysis.

The indicators in this study adapt the

measurements made by Kim & Woo (2016), Teo &

Jarupunphol (2015), Yadav et al., (2016), and Chong,

(2013). Indicators on the Actual System Usage

variable make purchases in this marketplace, buy in

the marketplace even though it is not a priority, open

this marketplace website/application every day and at

leisure, give satisfaction in making purchases.

Furthermore, the indicators on Attitude Towards

Using consist of being happy with this marketplace,

providing comfort when buying in this marketplace,

providing value for the products provided. The

Perceived Ease of Use variable consists of indicators

of easy access, ease of purchase, a layout that is easy

to understand, ease of features available, can solve

user problems in searching for products, ease of

customer service.

3 RESULTS AND DISCUSSION

Respondents in this study consisted of 57 men (57%)

and 43 women (43%). These results explain that the

respondents in this study were dominated by male

respondents. The initial testing process generates

numbers to determine whether the existing construct

is valid or not. In the Perceived Ease of Use variable,

of the four items, there are two items (PU5=0,197 and

PU6 = 0,110) that have a number below 0,7 which

means they do not meet the requirements (>0,70) so

these two items must be eliminated. Furthermore, in

the Actual System Usage variable of five items, there

is one item (ACT2=0,676) which is below 0,7, so this

item must also be eliminated because it does not meet

the specified conditions. After the elimination

process on items that do not meet the requirements, a

retest process is carried out to determine validity and

reliability. Based on the results in Figure 1 which

shows the discriminant validity test, where the

numbers on all items are above 0,70, which means

that all items are valid (valid). In the Perceived Ease

of Use variable, there are four items with all numbers

above 0,70, namely PU1=0,811; PU2=0,792;

PU3=0,799 and PU4=0,804. The next variable,

namely Attitude Towards Using, consists of three

items whose scores are also above 0,70

(ATT1=0,907; ATT2=0,908; ATT3=0,885). The last

variable, namely Actual System Usage, consists of

four items (ACT1=0,808; ACT3=0,829;

ACT4=0,870; ACT5=0,879), all of which are above

0,70.

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Figure 1: Outer Loading.

Average Extracted Variance (AVE) in table 2

explains the convergent validity where the numbers

on all items are above 0,5 so that from these results it

can be explained that the average value of the

extracted variance on the indicators can represent the

variables used. In the Perceived Ease of Use, the

number on the AVE is 0,642 or above 0,5 so it can be

said that the Perceived Ease of Use variable is valid.

The next variable is Attitude Towards Using, where

the number on the AVE shows 0,810 which means

that this variable is valid. The next variable, namely

Actual System Usage, shows 0,718 or is above 0,5

and explains that this variable is valid.

Table 2: Validity Test.

Variable/Item Outer

AVE Results

Perceived

Ease of Use

PU1 0,811

0,642

valid

PU2 0,792 valid

PU3 0,799 valid

PU4 0,804 valid

Attitude

Towards Using

ATT1 0,907

0,810

valid

ATT2 0,908 valid

ATT3 0,885 valid

Actual System

Usage

ACT1 0,808

0,718

valid

ACT3 0,829 valid

ACT4 0,870 valid

ACT5 0,879 valid

The reliability test can be seen in table 3 which

consists of the results of Composite Reliability (CR)

and Cronbach's alpha (CA). The variable Perceived

Ease of Use shows 0,878 which is above 0,7 which is

the number of reliability requirements on CR. Based

on these results it can be explained that this variable

is reliable. The next variable, namely Attitude

Towards Using, also shows a number above 0,7

which is 0,928. These results explain that this variable

is reliable. The last variable, namely Actual System

Usage, has a result of 0,910 which is above 0,7 which

explains that this variable is reliable. In the results

section of Cronbach's Alpha, the Perceived Ease of

Use variable shows 0,836 or above 0,7 so it can be

explained that this variable is also variable. In the

Attitude Towards Using variable, the results show

0,883 (>0,7) which explains that this variable is

reliable. The third variable, namely Actual System

Usage, shows the number on CA of 0,872 (> 0,7) also

explains that this variable is reliable. Based on all the

results that have been presented, it can be explained

that all variables in this study are reliable.

Table 3: Reliability Test.

Variable CR CA Results

Perceived Ease of Use 0,878 0,836 reliable

Attitude Towards Using 0,928 0,883 reliable

Actual System Usage 0,910 0,872 reliable

The R-Square results in table 4 explain the

evaluation of the structural model in this study. Based

on the results on the R-Square, it can be explained that

the number in the Attitude Towards Using variable

which is 0,054 means that this variable is affected by

the independent variable, namely Perceived Ease of

Use of 5,4% so that other independent variables can

affect by 94,6%. The next variable, namely Actual

System Usage, shows an R-Square number of 0,245

which means that this variable is affected by the

Attitude Towards Using variable of 24,5% where

there are 75,5% of other independent variables that

are not used in this study.

Table 4: R-square.

Variable R-square

Attitude Towards Using 0,054

Actual System Usage 0,245

The Total Effect results in table 5 show the t-

statistic number of Perceived Ease of Use to Attitude

Towards Using of 7,685 or greater than 1,96 with the

original sample result of 0,494. In the results of the p-

value, the number on Perceived Ease of Use to

Attitude Towards Using is 0,000 or less than 0,05.

Table 5: Total Effect.

Path Total effect Results

Perceived ease of use -

Attitude Towards Using

Original

sample= 0,494

(+)

Attitude Towards Using

- Actual System Usage

Original

sample= 0,233

(+)

Perceived ease of use -

Attitude Towards Using

t-statistic =

7,685

>1,96 (accept

hypothesis 1)

Attitude Towards Using

- Actual System Usage

t-statistic =

2,538

>1,96 (accept

hypothesis 2)

Perceived ease of use -

Attitude Towards Using

p-value =

0,000

<0,005 (accept

hypothesis 1)

Attitude Towards Using

- Actual System Usage

p-value =

0,001

<0,005 (accept

hypothesis 2)

Measuring Ease of Use Aspects of Shopee Usage Behavior during Pandemic using PLS-SEM Approach

195

Based on these results, it can be explained that

Attitude Towards Using is affected by Perceived

Ease of Use so that it is also explained that

Hypothesis 1 is accepted. The many features and

product variants available at Shopee should be

followed by ease of use. This also means that the

functional factors in the embedded technology must

also support the aspects of effectiveness and

efficiency (Christian, 2018). The variety and

complexity of the arrangement of features/products

on Shopee's display must be considered so as not to

cause confusion or dislike of their use. New users or

users who are not accustomed to using Shopee must

also be considered from the complexity and

complexity of using existing features so as not to

cause stress when using it as a form of technology

(Christian, Purwanto, & Wibowo, 2020). The results

on Hypothesis 1 also support research conducted by

Kim & Woo, (2016), Teo & Jarupunphol (2015),

and Huang et al. (2015). However, these results are

not in line with research conducted by Yadav,

Sharma, & Tarhini (2016), Chong (2013), and Lee,

Park, Chung, & Blakeney (2011).

The t-statistic Attitude Towards Using to Actual

System Usage is 2,538 or greater than 1,96 with the

Original Sample result of 0,233. In the results of the

p-value, the number on Attitude Towards Using to

Actual System Usage is 0,011 or less than 0,05.

Based on these results, it can be explained that

Actual System Usage is affected by Attitude

Towards Using so that it is also explained that

Hypothesis 2 is accepted. Aspects such as comfort,

the feeling of pleasure when buying, and the value

that Shopee users get are very decisive in purchasing

decisions even those that are impulsive, or purchases

made with planning. The advertisements on Shopee

also need to be regulated and designed in such a way

that they are not disturbing and are still informative

so that they can form the intention to take the next

action (Christian, 2017). The inconvenience of

making a purchase will make users leave Shopee and

reluctant to make a purchase. The design and display

arrangement that is not cluttered can be one of the

factors that determine the pleasure and comfort of

looking to make a purchase (Christian, 2019b). The

results on Hypothesis 2 are in line with the results of

research conducted (Huang et al., 2015;

Kanchanatanee, Suwanno, & Jarernvongrayab,

2014). However, these results are inversely

proportional to the results of research conducted by

Tyas & Darma, (2017).

4 CONCLUSIONS

Based on the results and discussion described above, it

can be explained that the Attitude Towards Using

factor in the use of Shopee is affected by the Perceived

Ease of Use factor that is felt by users in this study.

Furthermore, the Actual System Usage factor in

Shopee according to the user experience is affected by

the Attitude Towards Using factor that is felt by the

user himself. Based on the conclusions and discussion

above, this study suggests that efforts or strategies are

still needed to increase awareness of Shopee's presence

for the public, both as users and for those who have not

used it. Socialization strategies such as advertisements

in various forms and both on-page and off-page media

can be a supporting factor in increasing the number of

followers, especially on Twitter and increasing the

number of visits on the Shopee web.

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