Vol. 5, No. 10, October 2024
E-ISSN: 2723 - 6692
P-ISSN:2723- 6595
http://jiss.publikasiindonesia.id/
Journal of Indonesian Social Sciences, Vol. 5, No. 10, October 2024 2733
The Effect of Viral Marketing and Bandwagon Effect on Decision
to Use Social Media Threads in Bekasi
(Case Study on Social Media Threads Users in Bekasi)
Carlos Tanuarto, Shenda Aprilia Christanti
Universitas Darma Persada, Indonesia
Email: [email protected], [email protected]
Corespondence: [email protected]
*
KEYWORDS
ABSTRACT
Viral Marketing; Bandwagon
Effect; Usage Decision
The purpose of this research was to analyze the effect of Viral
Marketing, the Bandwagon Effect, on the Decision to Use Social
Media Threads in the Bekasi Region. Data was collected by
distributing questionnaires to 100 respondents who used Social
Media Threads in the Bekasi area for at least 2 months. Sampling
was carried out using non-probability sampling techniques and
using purposive sampling. The analytical tool used is multiple
linear regression; classical assumption tests (normality test,
multicollinearity test, and heteroscedasticity test) are carried out
as requirements that must be met in multiple linear regression
analysis. Hypothesis testing simultaneously using the (F) test and
partially using the (T) test. The results of this study indicate that
viral marketing and bandwagon effect variables simultaneously
affect customer satisfaction; this is evidenced by the F test with an
Fhitung> Ftabel value (59.866> 3.09) and an amount of 0.552
(55.2%), viral marketing and bandwagon effect variables can be
explained influenced by usage decisions while other variables
outside this model influence the remaining 44.8%. While viral
marketing partially / partially has an influence on usage decisions,
this is proven by the T test with a value of Thitung> Ttabel of
(5.423> 1.985) and has an influence of 0.231 (23.1%). And the
bandwagon effect partially / partially has an influence on usage
decisions; this is evidenced by the T-test with a Thitung> Ttable
value of (9.859> 1.985) and has an influence of 0.498 (49.8%).
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Introduction
The rapid development of the Internet in communication and information brings various
benefits that can reach various aspects of life. One of them is interacting with and expanding
relationships through social media. Social media is a medium that facilitates its users' interaction
with each other, socializing with each other, and doing things online without being limited by space
and time.
According to a source from Data Indonesia.id, sourced from a survey conducted by We Are
Social, reported that the number of social media users in Indonesia in 2022 experienced a
significant increase of 12.35% from the previous year (Mahdi, 2022). Furthermore, the same source
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states that over 191 million people in January 2022 were active social media users in Indonesia. As
shown in figure 1 below:
Figure 1. Number of Active Social Media Users in Indonesia
Source: DataIndonesia.id, 2022
Figure 1 above shows statistics on the increase in the number of active social media users in
Indonesia from 2014 to 2022, and it can be seen that every year, active social media users in
Indonesia continue to increase, according to sources from KilasBandungNews.com, sourced from
the West Java Provincial Government in 2019. The number of active social media users in Indonesia
at that time was 150 million active users, and 16.4 million of them were in the West Java region,
with Bandung City being the most prominent social media user, followed by Bekasi City and Bogor
City (Hadisaputro, 2019). Bekasi City is the third largest contributor to social media users in the
West Java region, with 1,674,400 users (Hadisaputro, 2019).
Viral marketing has a concept similar to E-WOM (Electronic Word of Mouth), which is the use
of word-of-mouth marketing but the internet version, in the sense that it can be spread through
other social media, such as Facebook, Instagram, YouTube, Twitter, Google and so on (Muliajaya,
2019). Social media has a homepage feature or main display when opening it where everyone who
is friends with the sender can see what is uploaded, both known and unknown people. Indirectly,
the more people who see each upload, which can be news, video clips, text messages, or images, it
will be possible for it to go viral, and viral news about a product will attract the attention of
everyone to buy, try, or use it (Muliajaya, 2019).
Figure 2. List of Twitter Trending Topics on July 6, 2023
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Source: www.Kompas.com, 2023
In Figure 2, which explains the successful intentional and unplanned viral marketing strategy
of Threads, the viral effect is seen to arouse the enthusiasm of X (Twitter) users to switch to try
Threads and influence a massive increase in Threads users on the same day of its launch. Therefore,
viral marketing strategies can increase business efficiency and productivity (Riani & Samatan,
2020). Regarding social media, viral marketing helps increase the number of users and keeps people
talking. Viral on Twitter is not only experienced by threads; research conducted by (Rahmawati et
al., 2023)) examined the Oreo product in collaboration with Blackpink. Before the product was
officially launched, it had become a viral topic on Twitter, with 101 thousand tweets.
The results of previous research conducted by Tricahyono et al. (2019) and Ningsih (2023)
show that viral marketing has a relationship with usage decisions and has a positive effect on usage
decisions. However, research (Gina, 2018) shows that viral marketing has a positive effect on usage
decisions. Gina, 2018), Saktiendi et al. (2022), and Rika (Riani & Samatan, 2020) show that viral
marketing does not affect usage decisions.
According to Engel (Arta & Azizah, 2020), the decision to use or use is a consumer decision
regarding what to use, where, when, and how the use will be carried out. Herdani and Ratnasari's
research, cited from (Utami & Trifiyanto, 2022), stated that usage decisions are identical to
consumer purchasing decisions. Also, Susanto research (Al-Jurfri & Pradana, 2021) showed that the
decision process for using a product or service is the same as buying a product or service.
When the general public widely favors a product or activity or is trending (viral), a
consumption behavior will encourage someone to consume the same goods or products, called the
bandwagon effect. (Srigustini & Aisyah, 2021). The bandwagon effect is a phenomenon that arises
from the tendency to follow a trend, especially when other people do it. The bandwagon effect can
occur when the decision of the majority influences a person, it can also occur as a request and
invitation from someone closest to us. (Ramadhanie, 2021a).
Figure 3. Pattern of The Bandwagon Effect Phenomenon
Source: www.Medium.com, 2019
Figure 3 explains the pattern of the bandwagon effect phenomenon in three phases:
awareness, mainstream, and fade out (Omkar, 2019). In the context of Threads, Figure 1.4 shows the
pattern of the bandwagon effect where in the first phase, namely at the time of its launch, Threads
became a worldwide concern, and in the media, the second phase, when news about Threads went
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viral and became a trending topic that aroused the enthusiasm of the general public, then was talked
about by the general public which eventually led to the bandwagon effect also to want to use the
application, and in the last phase, where at one moment Threads managed to be at its peak, namely
on July 7 Threads by reaching 49.3 million users. However, after that, Threads tended to experience
a decline in users and slowly normalized its growth. Results from research (Ramadhanie,
2021)stated that the bandwagon effect significantly influences usage intention and research
(Ramadhanie, 2021). Yudistira (2022)shows that the bandwagon effect significantly influences
purchasing decisions.
Viral marketing and bandwagon effect have a close relationship and influence each other in
the context of social media marketing. Viral marketing is a marketing strategy that utilizes the
power of engaging and widely shareable content to reach a larger audience in a short period. When
viral content captures users' attention, it tends to create a bandwagon effect, where individuals feel
compelled to jump on the bandwagon and adopt a trending product or service simply because many
others are doing so as well. This effect is even more substantial in a social media environment,
where users often view the actions and opinions of others as an indicator of the validity or
popularity of a product. In this case, viral marketing can trigger the bandwagon effect. Conversely,
the bandwagon effect can amplify the impact of viral marketing by increasing the exposure and
appeal of shared content.
This research aims to analyze and understand the influence of viral marketing and the
bandwagon effect on the decision to use Threads social media in the Bekasi area. This research aims
to explore how much impact viral marketing content has on user behavior in choosing to participate
in the Threads platform, as well as to assess the role of the bandwagon effect in influencing such
decisions. In addition, this research also aims to provide insights into the dynamics of user behavior
on social media, particularly in the local context, and how these two concepts can be leveraged by
businesses to design more effective marketing strategies. By identifying the relationship between
viral marketing and the bandwagon effect, it is hoped that this research can contribute to the
development of digital marketing theory as well as practical recommendations for stakeholders in
the Bekasi region.
Research Methods
This study employed a quantitative approach with a survey design, focusing on collecting data
from users of Threads social media in the Bekasi area. The data was collected through a structured
questionnaire to measure three key variables: viral marketing, bandwagon effect, and decision to
use Threads.
The sampling technique used was non-probability sampling, specifically purposive sampling.
This approach ensured the diversity of respondents while minimizing selection bias by targeting
individuals who had actively used Threads social media for at least two months. The number of
respondents was set to 100, determined based on the Cochran formula to ensure a representative
sample size with a suitable margin of error.
The use of multiple linear regression analysis in this study was motivated by the need to
understand how two independent variables—viral marketing and bandwagon effect—
simultaneously impact the decision to use Threads. Multiple linear regression is particularly well-
suited for this research because it helps to determine the degree of influence of each independent
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variable on the dependent variable, thus providing insights into the predictive relationship between
viral marketing strategies, the bandwagon effect, and user decisions. By employing this statistical
method, we aim to understand how these factors interact to shape user behaviors.
Regarding data processing, after collecting the responses, data entry and cleaning were
conducted using SPSS 26. The data was analyzed using regression analysis to determine the
relationships between the variables. Classical assumption tests such as normality, multicollinearity,
and heteroscedasticity tests were also conducted to ensure the validity and reliability of the
regression model. The regression results allowed us to assess the individual and collective impact of
viral marketing and the bandwagon effect on the decision to use Threads.
Results and Discussion
Results
Profile Characteristics of Respondents
Table 1.
Profile Characteristics of Respondents
Gender
No.
Gender
Total
Percentage
1
Male
44
44%
2
Female
56
56%
100
100%
Age
No.
Age
Total
Percentage
1
18 - 20 Years
13
13%
2
21 - 25 Years
84
84%
3
26 - 30 Years
2
2%
4
30 - 34 Years
1
1%
100
100%
Region
No.
Region
Total
Percentage
1
North Bekasi
31
31%
2
East Bekasi
39
39%
3
West Bekasi
19
19%
4
South Bekasi
11
11%
100
100%
Education
No.
Last Education
Total
Percentage
1
SD
0
0%
2
SMP
0
0%
3
SMA/SMK
78
78%
4
Diploma 3
0
0%
5
S1 / Bachelor
Degree 1
22
22%
100
100%
Intensity of Use
No.
Intensity of Use
Total
Percentage
1
3 Times
39
39%
2
4 Times
15
15%
3
5 Times
18
18%
4
6 Times
5
5%
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5
> 6 Times
23
23%
100
100%
Long Usage Time
No.
Length of time
Total
Percentage
1
2 Hours
69
69%
2
3 Hours
15
15%
3
4 Hours
8
8%
4
5 Hours
3
3%
5
> 5 Hours
5
5%
100
100%
No.
Reason for Using
Total
Percentage
1
The need to find
newer social media
and
bored/saturated
with current social
media
50
50%
2
Has unique,
different, and
complete features
compared to other
social media
19
19%
3
Can gather and
socialize with
friends and add
new relationships
16
16%
4
Get
recommendations
from friends,
family,
companions, and
others
13
13%
5
More
2
2%
100
100%
Source: SPSS 26 data processing results, 2024
Based on Table 1, the characteristics of respondents in this study provide a comprehensive
picture of the demographics of Threads social media users in the Bekasi area. Regarding gender,
44% of respondents were male and 56% female, indicating that female users slightly dominated.
Regarding age, most respondents (84%) are between 21-25 years old, indicating that Threads is
more popular among the younger generation, while only a few users are above 26. In terms of
region, the distribution of users is pretty even, with 39% coming from East Bekasi and 31% from
North Bekasi, while West Bekasi and South Bekasi account for 19% and 11%, respectively.
Regarding education, 78% of respondents have a high school/vocational school educational
background, while 22% have completed a Bachelor's degree. Regarding usage intensity, most
respondents (39%) use Threads 3 times a week, with another 23% using it more than 6 times.
Regarding length of usage, 69% of respondents spend around two hours per day on the platform.
Finally, the main reason respondents use Threads is that they are looking for new social media and
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are bored with existing platforms (50%), followed by a desire for unique features (19%) and to
socialize (16%). These characteristics reflect the potential for active and enthusiastic users of
Threads social media and provide valuable insights into understanding their behaviors and
preferences.
Validity Test
The validity test measures whether a questionnaire is valid. In this validity test, the basis for
making decisions is comparing the
count
value with
the label
for degree of freedom (df) = n - 2. In this
case, n is the number of samples, namely 100 respondents. Then (df) = 100-2 = 98. With a
significance level of 5% or 0.05, the l
abel
value is 0.196.
Table 2. Viral Marketing Validity Results
Source: SPSS 26 data processing results, 2024
Table 2 above shows the viral marketing validity test count value obtained from data
processing SPSS version 26. All statement items for the viral marketing variable are declared valid
because the value of rcount> label is 0.196. Thus, it can be concluded that all statement items can be
used in this study.
Table 3.
Bandwagon Effect Validity Test Results
Variables
Statement
Item
RHitung
R
tabel
Description
Viral
Marketing
P1
0.563
0,196
Valid
P2
0.479
Valid
P3
0.684
Valid
P4
0.729
Valid
P5
0.733
Valid
P6
0.735
Valid
P7
0.510
Valid
P8
0.733
Valid
P9
0.731
Valid
Variables
Statement
Item
RHitung
R
tabel
Description
Bandwagon
Effect
P1
0.835
0,196
Valid
P2
0.824
Valid
P3
0.818
Valid
P4
0.754
Valid
P5
0.764
Valid
P6
0.829
Valid
P7
0.844
Valid
P8
0.752
Valid
P9
0.835
Valid
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Source: SPSS 26 data processing results, 2024
In Table 3 above, the bandwagon effect validity test, the
count
value is obtained from data
processing SPSS version 26. It can be seen that all statement items for the bandwagon effect variable
are declared valid because the rcount>
table
value is 0.196. So, it can be concluded that all statement
items can be used in this study.
Table 4.
Usage Decision Validity Test Results
Source: SPSS 26 data processing results, 2024
Table 4 above shows the validity test of the decision to use the
count
value obtained from data
processing SPSS version 26. It can be seen that all statement items for the usage decision variable
are declared valid because the rcount>
table
value is 0.196. So, it can be concluded that all statement
items can be used in this study.
Reliability Test
A reliability test was carried out to check the consistency of the research instrument. The
reliability test uses the Cronbach's alpha (α) formula. A variable is declared reliable if it provides a
Cronbach's alpha (α) value> 0.60.
Table 5
Results of Reliability Test for Viral Marketing Bandwagon Effect Variables and Usage
Decisions
Variables
Cronbach's Alpha
N of
items
Results
Viral Marketing
0,830
9
Reliable
Bandwagon Effect
0,932
9
Reliable
Usage Decision
0,826
9
Reliable
Source: SPSS 26 data processing results, 2024
From the results of Table 5, it can be seen that all variables are reliable or consistent, which
means that if the statement is repeated, relatively the same answer will be obtained. This can be
seen in that each variable has Cronbach's alpha (α)> 0.60.
Variables
Statement
Item
RHitung
R
tabel
Description
Usage Decision
P1
0.723
0,196
Valid
P2
0.680
Valid
P3
0,686
Valid
P4
0,495
Valid
P5
0,529
Valid
P6
0,671
Valid
P7
0,647
Valid
P8
0,707
Valid
P9
0,680
Valid
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Classical Assumption Test
Normality Test
The normality test is used to test the normality of the variables under study and whether the
data is normally distributed or not. Below is a picture of the results of testing the normality of the
data obtained through SPSS version 26 as follows:
Figure 4. Normality Test Results
Source: SPSS 26 data processing results, 2024
From Figure 4 of the p-plot curve above, it can be seen that the data spreads around the
diagonal line and follows the direction of the diagonal line; it can be concluded that the data is
usually distributed, and the regression model has met the normality assumption.
Multicollinearity Test
The multicollinearity test is "A situation where one or more independent variables are
perfectly or almost perfectly correlated." One is to detect the presence or absence of
multicollinearity problems by looking at the Tolerance value and Variance Inflation Factor (VIF). If
the tolerance value is less than 0.10 and the VIF value is greater than 10, multicollinearity occurs.
Meanwhile, there is no multicollinearity if the tolerance value is more significant than 0.10 and the
VIF value is smaller than 10. The multicollinearity test results are in the following table:
Table 6 .
Multicollinearity Test Results
Coefficients
a
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
Collinearity
Statistics
B
Std.
Error
Beta
Tolerance
VIF
1
(Constant)
13,319
2,436
5,468
0,000
Viral
Marketing
0,255
0,074
0,252
3,436
0,001
0,860
1,163
Bandwagon
Effect
0,398
0,048
0,612
8,349
0,000
0,860
1,163
a. Dependent Variable: Usage Decision