p–ISSN: 2723 - 6609 e-ISSN: 2745-5254
Vol. 5, No. 9 September 2024 http://jist.publikasiindonesia.id/
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 9, September 2024 3454
The Influence of Heuristic and Herding Behavior on
Investment Decisions through Fomo on Retail Investors in
Indonesia
Mery Oktori Uly Binu
Universitas Katolik Widya Mandala Surabaya, Indonesia
Email:
*Correspondence
ABSTRACT
Keywords: heuristic
behavior, herding
behavior, FOMO,
investment decisions,
retail investors.
Retail investors in Indonesia are often influenced by
psychological biases in making investment decisions. These
biases, including heuristic and herding behaviors, often
create a fear of missing out on key moments (FoMO) that
impact investment decisions. This study aims to analyze the
influence of heuristic behavior (representativeness bias,
availability bias, anchoring bias) and herding behavior on
investment decisions with FoMO mediation. The research
used a survey method with 109 retail investor respondents
and data analysis using smartPLS. The results show that
herding behavior significantly affects FoMO, and FoMO
also has a significant impact on investment decisions. In
contrast, heuristic behavior has no significant influence on
FoMO. These findings reinforce FoMO's role in the
investment decision-making process among retail investors.
Based on the results of data analysis from the hypothesis test,
it can be concluded that heuristic behavior
(representativeness bias, availability bias, and anchoring
bias) does not affect the FoMO of retail investors in
Indonesia. So any change in heuristic behavior
(representativeness bias, availability bias, and anchoring
bias) does not affect the FoMO of retail investors in
Indonesia.
Introduction
Investment decisions are one of the important aspects in the world of finance, where
investors are faced with a variety of asset options to invest capital in the hope of making
profits in the future. Globally, investment decision-making is often influenced by a
variety of psychological factors, including heuristic behavior and herding behavior, which
have been a concern in the study of financial behavior. The phenomenon of Fear of
Missing Out (FoMO), which is also developing in the context of social media and digital
information, further strengthens the effects of these two behaviors, especially among
retail investors who generally have limited access to complete market information. (ul
Abdin et al., 2017).
The Influence of Heuristic and Herding Behavior on Investment Decisions through Fomo on
Retail Investors in Indonesia
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 9, September 2024 3455
Global issues related to heuristic and herding behavior in investment decision-
making have been discussed extensively in various literature. Heuristics, which are
decision-making processes that use rules of thumb or shortcuts, often lead investors to
irrational decisions. (Akbar et al., 2016). This phenomenon is further exacerbated by
biases such as representativeness, availability, and anchoring bias. Representativeness
bias occurs when investors make decisions based on stereotypes or experiences that are
considered similar to the current situation, even though the fundamental data is not
supportive. (Aisafitri & Yusriyah, 2021). Availability bias occurs when investors rely too
much on the information that is most easily remembered or accessible, while anchoring
bias occurs when investors place too much weight on the initial information received,
regardless of the validity of the information. (Agustin & Mawardi, 2014).
In addition, herding behavior, where investors follow trends or decisions of other
investors without conducting in-depth analysis, is also a global issue. Herding behavior
is prevalent in the capital market, where investors often feel more comfortable following
the majority, especially in situations of market uncertainty (Zahera & Bansal, 2018).
Previous studies have shown that herding behavior can lead to a bubble or crash in the
stock market, as irrational collective decisions can significantly affect asset prices. The
main factor that triggers problems in investment decision-making among retail investors
is the limited access to accurate and relevant information. Retail investors tend to rely on
social media or easily accessible sources of information, which are often not based on
fundamental analysis. (Wijaya, 2019). The influence of social media, especially through
investment influencers, amplifies the FOMO effect, where investors are worried about
missing out on lucrative investment opportunities if they do not act immediately on the
information circulating. This FoMO phenomenon is increasingly relevant in the digital
era, where the rapid and wide flow of information makes investors feel pressured to stay
connected and follow market trends. (Areiqat et al., 2019).
According to (Al Ibrahim, 2018), psychological biases that affect irrational
sufficiency are heuristic behavior and herding behavior. The effect of heuristic behavior
on investors will affect investment decision-making. It is also mentioned that it is
necessary to develop various instruments to evaluate and specifically measure the effects
of heuristic behavior on investors. The concept of heuristics is very important to help
cognitive efforts to be effective and efficient in the time and resources that investors have
in the investment decision-making process. (Dangol & Manandhar, 2020).
The impact of the above factors is the increase in irrational investment decisions
among retail investors. Heuristic and herding behavior, reinforced by FoMO, leads to
investment behaviors that are based on emotions rather than rational analysis. As a result,
investors are more vulnerable to the risk of large losses, especially in situations where the
market experiences sharp fluctuations. Investors who make decisions based on heuristic
biases tend to overestimate potential profits or underestimate existing risks (Caldwell &
Dolvin, 2012). On the other hand, investors who behave herding are often caught up in a
cycle of unstable market trends, which is a big risk in the long run. Furthermore, heuristic
behavior, which includes representativeness bias, availability bias, and anchoring bias, is
Mery Oktori Uly Binu
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 9, September 2024 3456
a complex phenomenon in financial psychology (Dale, 2015). Representativeness bias,
for example, describes an investor's tendency to make decisions based on stereotypes or
past experiences that are considered similar, even though they may not be relevant to the
current situation. Availability bias describes an investor's reliance on the most memorable
or recent information while anchoring bias reflects a tendency to rely too much on initial
information obtained, regardless of whether the information is accurate or not. On the
other hand, herding behavior, both intentional and unintentional, reflects a phenomenon
in which investors follow the collective actions of other market participants, without
paying attention to relevant fundamental information.
(Azhari & Damingun, 2021), the Influence of Heuristics and Herding Behavior on
Residential Property Investment Decision-making. Journal of Indonesia Economics and
Business, 15(1), 101-115".
This study focuses on the influence of heuristic and herding behavior in property
investment decision-making in Surabaya. The main findings show that representativeness
bias and anchoring bias influence investment decisions while herding does not have a
significant influence. However, this study did not examine the role of FoMO as a
mediating variable.
(Addinpujoartanto & Darmawan, 2020). The Effect of Overconfidence, Regret
Aversion, Loss Aversion, and Herding Bias on Investment Decisions in Indonesia.
Journal of Economic and Business Research",
This study explores behavioral biases such as overconfidence, regret aversion, loss
aversion, and herding bias in the context of investment decisions in Indonesia. The results
show that all of these biases significantly influence investment decisions, but FoMO is
not part of the variables studied. The urgency of this research is increasingly relevant in
the digital information era, where the rapid and massive flow of information can
significantly affect investor behavior. Many retail investors do not fully understand how
psychological biases such as heuristics and herding can affect their investment decisions.
This research is expected to provide a deeper understanding of this phenomenon so that
it can help retail investors make more rational and informed decisions. On the other hand,
with the increasing use of social media in the world of investment, understanding the role
of FoMO in the investment decision-making process is becoming increasingly important.
This study has several novelty elements that distinguish it from previous studies:
1. Geographical and Demographic Context: Unlike previous studies that focused heavily
on other countries (such as India), this study focuses on retail investors in Indonesia.
Market conditions, investment culture, and social dynamics in Indonesia provide a
unique context that has not been studied in depth.
2. FoMO as a Mediating Variable: Previous research, such as those conducted by Gupta
& Shrivastava (2021), has examined the role of FoMO, but it is mostly limited to the
stock market or a specific market. This study examines how FoMO mediates the
influence of heuristics and herding in the context of retail investment, expanding the
application of this theory in Indonesia.
The Influence of Heuristic and Herding Behavior on Investment Decisions through Fomo on
Retail Investors in Indonesia
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 9, September 2024 3457
3. Combining Heuristics and Herding with FoMO: Most previous studies have separated
between heuristic and herding studies, or focused on one or the other. This research
makes a new contribution by studying the interaction between heuristic, herding, and
FoMO behaviors simultaneously, something that has not been widely done in
Indonesia's literature.
4. Focus on Social Media Behavior and Psychological Influence: With the growing use
of social media by retail investors, this study is becoming relevant by looking at how
social media-driven FoMO can influence investment decisions. This is a new area in
investment studies in Indonesia that has not been explored much.
This study aims to analyze the influence of heuristic behavior (representativeness
bias, availability bias, and anchoring bias) on FoMO, analyze the influence of herding
behavior on FoMO, and analyze the influence of FoMO on the investment decisions of
retail investors in Indonesia. By using the smartPLS analysis method, this study is
expected to provide empirical evidence regarding the relationship between these
variables, as well as enrich the literature on investment decision-making among retail
investors.
The benefits of this research are divided into two, namely academic benefits and
practical benefits. Academically, this research contributes to the development of financial
psychology theories, especially related to heuristic bias, herding behavior, and the FoMO
phenomenon in the context of investment decision-making. Practically, the results of this
study are expected to help retail investors in Indonesia to better understand the factors
that affect their investment decisions, so that they can make more rational decisions and
avoid adverse psychological biases.
Method
This study is quantitative research with a causal design that aims to analyze the
relationship between Heuristic Behavior, Herding Behavior, FoMO (Fear of Missing
Out), and Investment Decisions. The focus of this research is on retail investors in
Indonesia, to understand how these psychological behaviors affect investment decisions.
The methodology used in this study is designed in detail and structured to answer the
research objectives and ensure the accuracy and reliability of the results obtained.
Location and Time of Research
This research was conducted in Indonesia, with the main target of retail investors
who actively use social media and are involved in investment activities. The data
collection was carried out for three months, which provided enough time to collect
responses from various regions in Indonesia. This timeframe ensures that the research
captures investment behavior that takes place in real-time, which is influenced by market
dynamics and trends that are developing on social media.
Population and Sample
The population in this study includes retail investors in Indonesia, i.e. individuals
who make investment decisions on their behalf without institutional support. The research
sample consisted of 109 respondents, who were selected using the purposive sampling
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technique. This technique was chosen so that the research participants met certain criteria:
(1) they were over 17 years old, (2) they were active in using social media, and (3) they
had experience in making investment decisions. These criteria are important for the focus
of research on FoMO, which is often triggered by activity on social media. The sample
size of 109 respondents was considered adequate to meet the statistical requirements in
the analysis of this study.
Data Collection Methods
The main instrument used in data collection is a structured questionnaire distributed
through Google Forms. This questionnaire is designed to gain insight into the
psychological behavior of retail investors, especially as it relates to Heuristic Behavior
and Herding Behavior, as well as how these behaviors contribute to investment decisions
mediated by FoMO. The questionnaire was divided into several sections, each focusing
on a specific variable in the study. To ensure clarity and reliability, this questionnaire has
been tested first on a small group of respondents to identify ambiguities or errors in the
questions asked.
Research Instruments
The main instrument in this study is a questionnaire with a Likert scale, ranging
from 1 (Strongly Disagree) to 5 (Strongly Agree). This scale was chosen because of its
simplicity and effectiveness in capturing the attitudes and opinions of respondents. The
questionnaire includes items designed to measure Heuristic Behavior, Herding Behavior,
FoMO, and Investment Decisions. These items are adapted from scales that have been
validated in previous studies to ensure that these measurements are valid and reliable.
Data Analysis Techniques
The data obtained from the questionnaire was analyzed using Partial Least Squares
Structural Equation Modeling (PLS-SEM) through SmartPLS software. PLS-SEM was
chosen because of its ability to handle complex models as well as its relatively small
sample size. This analysis is divided into two main stages: the measurement model (outer
model) and the structural model (inner model).
Evaluation of the Measurement Model (Outer Model): At this stage, the validity of
convergence and discrimination is tested. The validity of convergence is measured by
looking at the factor loading value of each item, with a value of > 0.7 indicating good
validity. Meanwhile, the validity of discrimination was measured using the Average
Variance Extracted (AVE) method, where a > value of 0.5 was considered adequate. In
addition, the reliability of the composite is calculated to ensure the internal consistency
of the scale used.
Structural Model Evaluation (Inner Model): The structural model tests the causal
relationship between latent variables (Heuristic Behavior, Herding Behavior, FoMO, and
Investment Decisions). The strength and significance of this relationship were measured
using path coefficients, t-statistics, and p-values. The p-value < 0.05 is considered
significant, which indicates that the independent variable has a significant influence on
the dependent variable.
Variables and Operational Definitions
The Influence of Heuristic and Herding Behavior on Investment Decisions through Fomo on
Retail Investors in Indonesia
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 9, September 2024 3459
Heuristic Behavior: Refers to quick decision-making based on cognitive biases or
mental shortcuts that investors use in situations of uncertainty. In this study, heuristic
behavior includes representativeness bias, availability bias, and anchoring bias. Each is
measured through statements that assess how investors use limited information or
experience in making decisions.
Herding Behavior: Describes an investor's tendency to follow the actions of others
without conducting independent analysis. This variable is measured through statements
that assess how often investors follow other people's investment decisions.
FoMO (Fear of Missing Out): This variable measures the psychological pressure
that investors feel when they feel left behind from the investment opportunities that others
are making. The statements that measure FoMO focus on investors' emotional reactions
to social media content related to investment opportunities.
Investment Decision: This is an outcome variable that reflects the investor's final
decision regarding their investment. Statements that measure investment decisions focus
on how informed and confident investors are in making investment decisions.
Results and Discussion
Respondents by Age Type
Based on the data collected, 4 respondents did not meet the criteria, and 150
respondents met the criteria from 154 receptors. Below is a diagram of respondents by
age.
Figure 1 Diagram Based on Respondent Age
Respondents Who Have Social Media.
Based on data collected from 154 respondents, all of them have social media. Below
is a diagram of respondents who have social media.
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Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 9, September 2024 3460
Figure 2 Diagram that Has Social Media
Respondents Who Have Invested
Based on the data collected, 45 respondents had never invested and 109
respondents had invested out of the 154 respondents. Below is a diagram of respondents
based on who has invested and who has never invested.
Figure 3 Diagram of Respondents Who Have Invested
Measurement Model or Outer Model.
1. Convergent Validity.
To test convergent validity, the value of outer loading or loading factor is used.
An indicator is declared to meet convergent validity in the good category if the outer
loading value > 0.6 according to Chin in Ghozali (2014). The following are the outer
loading values of each indicator on the research variables:
Table 1 Loading Factor Phase I
Instruments
Outer Loadings
Information
RB1 <-
Representativness
0.743
Valid
RB2 <-
Representativness
0.763
Valid
RB3 <-
Representativness
0.643
Valid
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Retail Investors in Indonesia
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AB1 <- Availability
0.774
Valid
AB2 <- Availability
-0.147
Invalid
AB3 <- Availability
0.694
Valid
AB4 <- Availability
0.779
Valid
AB5 <- Availability
0.750
Valid
ACB1 <- Anchoring
0.720
Valid
ACB2 <- Anchoring
0.696
Valid
ACB3 <- Anchoring
0.826
Valid
FOMO1 <- FoMO
0.713
Valid
FOMO2 <- FoMO
0.809
Valid
FOMO3 <- FoMO
0.762
Valid
FOMO4 <- FoMO
0.673
Valid
HB1 <- Herding
Behavior
0.876
Valid
HB2 <- Herding
Behavior
0.875
Valid
HB3 <- Herding
Behavior
0.771
Valid
HB4 <- Herding
Behavior
0.790
Valid
KI1 <- Investment
Decision
0.785
Valid
KI2 <- Investment
Decision
0.826
Valid
KI3 <- Investment
Decision
0.821
Valid
KI4 <- Investment
Decision
0.752
Valid
Of the indicators used, some are invalid or the value is < 0.60 so invalid indicators
are deleted and continued with repeated tests.
Table 2 Loading Factor Phase II
Instruments
Outer Loadings
Information
RB1 <- Representativness
0.743
Valid
RB2 <- Representativness
0.763
Valid
RB3 <- Representativness
0.643
Valid
AB1 <- Availability
0.774
Valid
AB3 <- Availability
0.694
Valid
AB4 <- Availability
0.778
Valid
AB5 <- Availability
0.750
Valid
ACB1 <- Anchoring
0.720
Valid
ACB2 <- Anchoring
0.696
Valid
ACB3 <- Anchoring
0.826
Valid
FOMO1 <- FoMO
0.713
Valid
FOMO2 <- FoMO
0.809
Valid
FOMO3 <- FoMO
0.762
Valid
FOMO4 <- FoMO
0.673
Valid
HB1 <- Herding Behavior
0.876
Valid
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HB2 <- Herding Behavior
0.875
Valid
HB3 <- Herding Behavior
0.771
Valid
HB4 <- Herding Behavior
0.790
Valid
KI1 <- Investment Decision
0.785
Valid
KI2 <- Investment Decision
0.826
Valid
KI3 <- Investment Decision
0.821
Valid
KI4 <- Investment Decision
0.752
Valid
Based on the loading factor phase II, all indicators used have values that are by the
outer loading, which is above 0.60 so that they are considered to meet the requirements
of convergent validity (Gozhali, 2014) so that the indicators are declared feasible or valid
to be used for research and can be used for further analysis.
2. Discriminant Validity.
In this section, the results of the discriminant validity test will be described. The
discriminant validity test uses a cross-loading value. An indicator is declared to meet the
discriminant validity if the cross-loading value of the indicator on the variable is the
largest compared to other variables. The following is presented with a cross-loading
table:
Table 3 Cross Loading
Vari
abel
Anchoring
Avail
abilit
y
FoMO
Herding
Behavio
r
Kepu
tusan
Inves
tasi
Represent
ativeness
RB1
0.460
0.544
0.265
0.339
0.563
0.743
RB2
0.476
0.511
0.298
0.447
0.242
0.763
RB3
0.319
0.293
0.251
0.079
0.405
0.643
AB1
0.552
0.774
0.337
0.435
0.492
0.588
AB3
0.569
0.694
0.247
0.454
0.389
0.407
AB4
0.389
0.778
0.240
0.378
0.598
0.449
AB5
0.563
0.750
0.239
0.292
0.565
0.412
ACB
1
0.720
0.428
0.295
0.123
0.333
0.291
ACB
2
0.696
0.674
0.327
0.465
0.553
0.583
ACB
3
0.826
0.437
0.298
0.354
0.183
0.424
FOM
O1
0.327
0.270
0.713
0.523
0.101
0.276
FOM
O2
0.368
0.353
0.809
0.526
0.198
0.407
FOM
O3
0.352
0.284
0.762
0.474
0.385
0.294
FOM
O4
0.109
0.119
0.673
0.314
0.236
0.079
HB1
0.421
0.540
0.557
0.876
0.269
0.368
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Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 9, September 2024 3463
HB2
0.311
0.427
0.531
0.875
0.192
0.328
HB3
0.442
0.482
0.480
0.771
0.237
0.329
HB4
0.251
0.294
0.528
0.790
0.225
0.349
KI1
0.355
0.448
0.283
0.212
0.785
0.347
KI2
0.385
0.612
0.260
0.234
0.826
0.408
KI3
0.406
0.612
0.196
0.279
0.821
0.578
KI4
0.409
0.504
0.244
0.171
0.752
0.466
Based on Table 3, it can be interpreted as follows:
1. Each measurement indicator of the representativity bias variable (RB1, RB2, and RB3)
has a high correlation compared to the correlation of other variables
2. Each measurement indicator of the availability bias variable (AB1, AB3, AB4, and
AB5) has a higher correlation compared to the correlation of other variables.
3. Each measurement indicator of the anchoring bias variable (ACB1, ACB2, and ACB3)
had a higher correlation compared to the correlation of other variables.
4. Each measurement indicator of the FoMO variable (FOMO1, FOMO2, FOMO3, and
FOMO4) had a higher correlation compared to the correlation of other variables.
5. Each measurement indicator of the herding behavior variable (HB1, HB2, HB3, and
HB4) had a higher correlation compared to the correlation of other variables.
6. Each measurement indicator of the investment decision variables (KI1, KI2, KI3, and
KI4) has a higher correlation compared to the correlation of other variables.
Based on the results obtained, it can be stated that the indicators used in this study
have good discriminant validity in compiling their respective variables.
In addition to observing the cross-loading value, discriminant validity can also be
known through other methods, namely by looking at the average variant extracted (AVE)
value for each indicator, the value must be > 0.5 for a good model (Gozhali, 2014).
Table 4 Average Variant Extracted (AVE)
Variable
AVE
Anchoring
0.562
Availability
0.562
FoMO
0.549
Herding Behavior
0.688
Investment Decision
0.634
Representativeness
0.515
Based on Table 4 of the AVE values from the variables anchoring bias, availability
bias, FoMO, herding behavior, representativeness bias, and investment decisions, the
measurement items used have a value of ≥0.50, so the conditions for good discriminant
validity are met.
3. Composite Reliability.
Composite Reliability is the part used to test the reliability value of indicators on
a variable. A variable can be declared to meet composite reliability if it has a composite
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reliability value > 0.6. (Gozhali, 2014). The following is the composite reliability value
of each variable used in this study:
Table 5 Composite Reliability
Variabel
Composite Reliability
Anchoring
0.793
Availability
0.837
FoMO
0.829
Herding Behavior
0.898
Investment Decision
0.874
Representativness
0.760
Based on Table 5, the overall composite reliability, namely the variables anchoring bias,
availability bias, FoMO, herding behavior, investment decisions, and representativeness bias have
an acceptable level of reliability or each measurement indicator that measures overall is consistent
or reliable.
Structural Model or Inner Model
1. Model Goodness and Fit Evaluation
Based on data processing in the PLS4.0 Program, r-square was obtained as follows:
Table 6 R-Square
Variable
R-square
FoMO
0.439
Investment Decision
0.099
From the results of Table 6, the influence of anchoring bias, availability bias,
representativeness bias, and herding behavior variables on the FoMO variable was
43.9%, including moderate influence (Gozhali, 2014).
In addition, there is an f-square used in the evaluation of the goodness and fit of
the model, the f-square is used to test the influence of variables at the structural level. F-
square (0.02 low), (0.15 moderate), (0.35 high).
Table 7 F-Square
Variable
FoMO
Investment Decision
Representativenes
s
0.014
Anchoring
0.036
Availability
0.018
Herding Behavior
0.425
FoMO
0.110
Based on Table 7, the influence of anchoring bias variables, representativity bias,
and availability bias on FoMO variables is included in the low influence. Meanwhile,
the herding behavior variable has a high influence on the FoMO variable because it has
a value of 0.425 and the influence of the FoMO variable on the investment decision
variable is included in the low to moderate influence because it has a value of 0.110. In
addition, the model match table is presented in the following table:
Tabel 8 Kecocokan Model (Model Fit)
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Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 9, September 2024 3465
Perkiraan model
SUMMER
0.165
d_ULS
6.907
d_G
1.504
Chi-square
798.330
NFI
0.452
Based on the table above, the SRMR value is 0.165 > 0.10 is good because the indigo is
greater than 0.10. So that the goodness of fit is good. The proposed model matches or is
close to the empirical data. The estimated results of the model correlation matrix are
close to the empirical data correlation matrix.
2. Uji Hipotesis
Based on the data processing that has been carried out, the results can be used to
answer the hypothesis in this study. The hypothesis test in this study was carried out by
looking at t-statistics and p-values. The research hypothesis can be declared acceptable
if the p-values < 0.05 (Yamin and Heri, 2011). The following are the results of the
hypothesis test obtained in this study through the inner model.
Tabel 9 Pengujian Hipotesis
Variabel
Koefisie
n
T
statistik
P
values
Representativness -> FoMO
0.120
0.853
0.394
Anchoring -> FoMO
0.205
1.644
0.100
Availability -> FoMO
-0.160
1.301
0.193
Herding Behavior -> FoMO
0.580
6.445
0.000
FoMO ->Keputusan Investasi
0.315
3.766
0.000
Based on the test results in Table 4.9, it can be interpreted that:
1. The representativeness bias does not affect FoMO by (0.120) with t-statistic (0.858 <
1.96) or p-value (0.394 > 0.05), so any change in representativeness bias does not
affect FoMO.
2. Achoring bias did not affect FoMO by (0.205) with t-statistic (1.664 < 1.96) or p-value
(0.100 > 0.05). So any change in anchoring bias does not influence FoMO.
3. Availability bias does not affect FoMO by (-0.160) with t-statistic (0.301 < 1.96) or p-
value (0.193 > 0.05) so any change in availability bias does not affect FoMO.
4. Herding behavior has a significant effect on FoMO of (0.580) with t-statistic (6.445 >
1.96) or p-value (0.000 < 0.05) so every change in herding behavior affects FoMO.
5. FoMO has a significant influence on investment decisions of (0.315) with t-statistic
(3.766 > 1.96) or p-value (0.000 < 0.05) so every change in FoMO influences
investment decisions.
Table 10 Results of Hypothesis Test Analysis
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Hypothesi
s
P Values
Information
H1a: Representativeness bias affects the FoMo of retail
investors in Indonesia
0.394
Rejected
H1b: Availability bias affects FoMO
retail investors in Indonesia
0.100
Rejected
H1c: Anchoring bias affects FoMO
retail investors in Indonesia
0.193
Rejected
H2: Herding behavior affects FoMO
retail investors in Indonesia.
0.000
Accepted
H3: FoMO influences the investment decisions
of retail investors in Indonesia.
0.000
Accepted
Heuristic
Heuristic explains that the human cognitive system basically has a limited
capacity, so to reduce the work of the cognitive system unconsciously, individuals use
shortcut rules to simplify the information-processing process (Vitmiasih et al., 2021). In
making a decision, heuristic behavior is usually used to make it easier for investors to
make decisions, but investors who experience FoMO have a lot of information that they
receive through social media (Pratiwi, 2020) so the process of simplifying information
does not apply to investors who experience FoMO so that it can be concluded that
heuristic behavior (representativeness bias, availability bias, and anchoring bias) does
not affect FoMO, a retail investor in Indonesia. Some of the heuristic biases used in this
study are as follows:
The influence of Representativeness bias on FoMo retail investors in Indonesia.
The results of the data analysis of the research on representativeness bias do not
affect FoMO (0.394 > 0.05), based on these results, H1a is rejected because any change
in representativeness bias does not affect the FoMO of retail investors in Indonesia.
According to Pompian (2006), representativeness bias shows that humans tend to be
based on decisions about similarities or stereotypes. Retail investors in Indonesia tend not
to make decisions based on the existence of equations so what is on social media makes
investors not affected quickly. Some investors have the belief that success in investing in
the present tends to continue in the future, a tendency like this is known as a stereotype
(Sherfin, 2020). Investors who have a stereotype like this will find it very difficult to be
influenced by information found on social media because investors have a strong stance
not to be fast and not afraid of missing out on information that is trending on social media.
Most of the investors in this study chose to agree that they made investment
decisions based on the past and made in the present and the future, so that trending news
or widely circulated on social media does not affect investment decisions. This is because
of the pattern of events that occur repeatedly that investors believe can happen again, but
this expectation is unreasonable because it is not balanced with maximum technical
analysis efforts (Vitmiasih et al., 2021).
The Influence of Heuristic and Herding Behavior on Investment Decisions through Fomo on
Retail Investors in Indonesia
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 9, September 2024 3467
The influence of Availability bias affects FoMO retail investors in Indonesia.
Availability bias has no effect on FoMO with a p-value (0.193 > 0.05), based on
these results, H1b is rejected, because any change in availability bias does not affect
FoMO retail investors in Indonesia. Availability bias is characterized by quick and easy
information-based decision-making, while FoMO is characterized by fear because of not
knowing information or events and a desire to continue to connect with what others are
doing (Przybylski et al, 2013). The desire to continue to connect with others through
social media can provide easy information. The information obtained too quickly and
easily can make investors unable to choose the appropriate investment opportunity so
they can make a mistake in judgment cause a loss of potential profits and reduce market
efficiency (Shah et al, 2018). Meanwhile, in this study, investors choose to agree that in
making investment decisions, investors will choose the type or product they are familiar
with even though on social media there are many investment product recommendations
from influencers or experienced investors. In this case, investors do not consider or pay
attention to the available information in making investment decisions and do not take
advantage of all available information (Anggia et al., 2022).
The effect of anchoring bias on FoMO retail investors in Indonesia
Anchoring bias did not affect FoMO with p-values (0.100 > 0.05). Based on these
results, H1c is rejected because any change in heuristic behavior (anchoring bias) does
not affect FoMO retail investors in Indonesia. Anchoring bias is characterized by the
attitude of investors who have determined the value of an investment based on the results
of the last observation of the purchase price (Vijaya, 2014). The set purchase price will
not change even if something good or bad happens in the price change of any investment
product, so the form of anxiety or fear has no effect because from the beginning the value
that will be used to both buy and sell investment products has been determined. When
there is new information obtained from social media, it will further complicate decision-
making (Pompian, 2006). Anchoring bias has the behavior to be more confident in the
first information obtained (Liangga, 2022). If there is a lot of information on social media
that discusses investment products, investors who experience anchoring bias are not
easily influenced because most of the investors in this study agree that they will stick to
the investment profit target that has been set, even though there is a lot of information
circulating on social media.
FoMO's influence on retail investors' investment decisions in Indonesia
FoMO has a significant influence on the investment decision of retail inverters in
Indonesia with a p-value (0.000 < 0.05) based on these results, H3 is accepted because
every change in FoMO influences the investment decision of retail investors in Indonesia.
This happens because the presence of FoMO in retail investors makes them very
vulnerable in making investment decisions. Social media is a medium to be able to obtain
as much information as possible so investors who have anxiety and fear of missing out
on information will open social media to obtain as much information as possible so that
they are always up to date with the surrounding situation. Excessive fear can have an
impact on the low psychological well-being of investors and make investors unable to
control the environment and are unable to establish positive social interaction
relationships with others (Savitri, 2019). FoMO can increase the intensity of social media
use and this increase indicates the occurrence of social media addiction that always wants
Mery Oktori Uly Binu
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 9, September 2024 3468
to connect with other people so that the desire to make investment decisions on
investment products always arises due to often seeing influencers or people around them
who often discuss investment products (Fathadhika, 2019).
Investment decisions made by investors are driven by the feelings of FoMO
investors because they often look at social media so that investors are psychologically
encouraged to follow investors who have more abilities such as influencers who have a
large following on social media. This opinion is in line with the research of
(Addinpujoartanto & Darmawan, 2020) This means that a person tends to make
investment decisions based on actions taken by others. This is because they consider other
people or other investors to have more abilities (Gupta & Ahmed, 2016). This assumption
can be obtained through social media because social media helps investors get very wide
information in making investment decisions (Pratiwi, 2020). So it can be concluded that
investors who experience FoMO have complete information, if investors can filter the
information obtained properly then investors can make more appropriate investment
decisions and in the context of investment, FoMO can have a good influence on investors.
Conclusion
Based on the results of data analysis from the hypothesis test, it can be concluded
that heuristic behavior (representativeness bias, availability bias, and anchoring bias) does
not affect the FoMO of retail investors in Indonesia. So any change in heuristic behavior
(representativeness bias, availability bias, and anchoring bias) does not affect the FoMO
of retail investors in Indonesia. Investors who experience FoMO have a lot of information
about investment products because they often look at social media and this is not in line
with heuristic behavior that has a mindset to simplify the information received to make
investment decisions. Herding's behavior has a significant influence on the FoMO of
retail investors in Indonesia so any change in herding behavior influences the FoMO of
retail investors in Indonesia. Investors who behave herding will tend to look for more
relationships, so the investor tends to find out what others are doing or does not want to
miss information. FoMO has a significant influence on the investment decisions of retail
investors in Indonesia so any changes to FoMO influence the investment decisions of
retail investors in Indonesia. Investors who experience FoMO are investors who have a
lot of information so that the information obtained can make the right investment
decisions.
The Influence of Heuristic and Herding Behavior on Investment Decisions through Fomo on
Retail Investors in Indonesia
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 9, September 2024 3469
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