Kristiningsih, Food Hoarding Intention during Covid-19 in Indonesia …

P-ISSN: 1978-2853

E-ISSN: 2302-8890


MATRIK: JURNAL MANAJEMEN, STRATEGI BISNIS DAN KEWIRAUSAHAAN

Homepage: https://ojs.unud.ac.id/index.php/jmbk/index

Vol. 16 No. 1, Februari (2022), 19-35

Food Hoarding Intention saat Pandemi Covid-19 di Indonesia: Peran Peraturan Pemerintah sebagai Variabel Moderasi

Kristiningsih1), Santirianingrum Soebandhi2)

1,2 Universitas Wijaya Kusuma Surabaya

email: [email protected]


SINTA 2


DOI : https://doi.org/10.24843/MATRIK:JMBK.2022.v16.i01.p02

ABSTRAK

Merebaknya Covid-19 di awal tahun 2020 telah menimbulkan kecemasan masyarakat karena ketidakpastian akan dampak dari wabah ini. Di awal wabah ini, sering kita jumpai perilaku pembelian masyarakat yang tidak seperti biasa. Salah satunya adalah dengan menimbun bahan pangan atau membeli bahan kebutuhan pokok dalam jumlah banyak sebagai antisipasi dari dampak yang mungkin terjadi selama pandemi Covid-19. Oleh karenanya, penelitian ini bertujuan untuk menganalisis pengaruh risk perception dan tiga komponen Theory of Planned Behavior (TPB), yaitu: sikap, norma subyektif, dan perceived behavioral control terhadap niat individu untuk menimbun bahan kebutuhan pokok guna memahami respon individu terhadap kondisi pandemi. Penelitian ini juga menambahkan peraturan pemerintah terkait dengan Pembatasan Sosial Berskala Besar (PSBB) sebagai variabel moderasi. Penelitian ini melibatkan 194 responden. Data yang diperoleh selanjutnya diolah dengan menggunakan metode PLS-SEM. Temuan penelitian ini menunjukkan bahwa food hoarding intention dipengaruhi oleh risk perception dan ketiga komponen TPB. Sedangkan PSBB tidak memoderasi hubungan ketiga komponen TPB pada food hoarding intention.

Kata kunci: food hoarding intention, Indonesia, pandemi covid-19, theory of planned behavior, PSBB

Food Hoarding Intention during Covid-19 in Indonesia: the Role of Government Regulation as Moderating Variable

ABSTRACT

The outbreak of Covid-19 in early 2020 has raised public anxiety because of the uncertainty about the impact of this outbreak. At the beginning of this outbreak, we often encountered unusual purchasing behavior. One of them is by hoarding food or buying basic needs in large quantities in anticipation of the possible impacts during the Covid-19 pandemic. Therefore, this study aims to analyze the effect of risk perception and the three components of Theory of Planned Behavior (TPB), namely: attitudes, subjective norms, and perceived behavioral control on individual intentions to hoard basic needs in order to understand individual responses to pandemic conditions. This study also adds government regulations related to Large-Scale Social Restrictions

(PSBB) as a moderating variable. This study involved 194 respondents. The data obtained were then processed using the PLS-SEM method. The findings of this study indicate that food hoarding intention is influenced by risk perception and the three TPB components. Meanwhile, PSBB did not moderate the relationship between the three TPB components on food hoarding intention.

Keyword: food hoarding intention, Indonesia, the covid-19 pandemic, theory of planned behavior, PSBB

INTRODUCTION

The rapid spread of the corona virus and its ability to infect the entire world distinguishes it from previous outbreaks such as SARS, avian influenza, or Ebola. The Covid-19 outbreak, which began in 2019 in the Chinese city of Wuhan, has caused socioeconomic upheaval all over the world (Bae & Chang, 2021; Long & Khoi, 2020). In contrast to natural disasters such as earthquakes, hurricanes, and floods, which cause more physical/infrastructure losses, pandemics can cause long-term damage and have far-reaching consequences such as economic crises, job losses, and psychological instability (Bae & Chang, 2021; Djalante et al., 2020). According to research, recovering socially and economically from a pandemic can take more than a decade (United Nations, 2020).

Disease outbreaks are widely acknowledged to cause changes in individual behavior. People's consumption habits have shifted as a result of their concerns about the virus's impact. Some countries even demonstrated unusual consumption habits, such as stockpiling toilet paper (Baddeley, 2020; Carroll, 2020; Dexter & Sakkal, 2020). Another thing we can see at the start of the pandemic is that many people buy large quantities of food and necessities to meet the needs of themselves and their families. For example, people in Indonesia's major cities, such as Jakarta and Surabaya, rush to buy large amounts of food and medicine supplies, following the President of the Republic of Indonesia's announcement about the first two cases of Covid-19 (Loasana, 2020; Putra, 2020; Snapcart, 2020). This is understandable given that fears about the outbreak's impact can lead to overreactions in order to defend themselves (Bavel et al., 2020). Hoarding is defined as an attempt to collect goods in large quantities due to a perceived scarcity of certain goods' supply (Long & Khoi, 2020; Sterman & Dogan, 2015). Because of the uncertainty caused by the Covid-19 outbreak, this behavior is an attempt to create a sense of security. When panic spreads, people lose their ability to think rationally. They always want to buy more than they need, despite the government's assurances that food supplies will always be available to everyone (Arafat et al., 2020; Dholakia, 2020; Long & Khoi, 2020; Wilkens, 2020).

The Theory of Planned Behavior (TPB) is widely used in various disciplines to predict individual behavior. Attitude, subjective norms, and perceived behavioral control, according to this theory, are strong predictors of behavioral intentions, which can then influence a person's actual behavior. A

person's positive or negative assessment of a particular phenomenon or action is referred to as their attitude. Subjective norms are social pressures that either prevent or encourage people to do certain things. Perceived behavioral control (PBC) refers to a person's belief that he or she will be able to control the situation and manage the resources required to carry out specific actions (Ajzen, 2008). However, the Covid-19 pandemic has revealed some intriguing behaviors that are difficult to explain using conventional economic theory, challenge existing norms, and highlight the need to predict individuals' future behavioral intentions (Baddeley, 2020; Bae & Chang, 2020). As a result, understanding individual responses to pandemic conditions is critical. Researchers have attempted to include additional variables in TPB in order to increase TPB's predictive power (Bae & Chang, 2020). Given the magnitude of the pandemic's impact, we expanded the TPB in this study by including the risk perception variable to the outbreak's spread and the government regulation on Large-Scale Social Restrictions (PSBB) as moderating variables to determine whether the regulation strengthens or weakens people's intention to stockpile food (foodhording intentions).

Hoarding is frequently motivated by a desire to reduce the risk of losing access to certain products that may no longer be available as a result of rising demand or rising prices (Power, Doherty, Pybus, & Pickett, 2020; Sterman & Dogan, 2015). When the Covid-19 pandemic struck, health products and commodities became extremely scarce due to an unexpected increase in demand, while raw materials and labor were restricted due to activity restrictions imposed to prevent disease spread (Long & Khoi, 2020). Hoarding can be explained in two ways: operation and behavior (Sterman & Dogan, 2015). The operational aspect is concerned with resources to overcome material and labor shortages, as well as logistics system overloads (Choi, Chiu, & Chan, 2016). Whereas behavioral factors explain consumer hoarding based on personal experience and sentiment decisions, these decisions are frequently driven by psychological factors such as concerns about the scarcity of goods; anxiety about an individual's economic capacity not being able to meet a shortage situation; or panic caused by herd behavior (Deng, Wang, & Yousefpour, 2017; Long & Khoi, 2020).

As we all know, the Covid-19 pandemic has forced many people to stock up on necessities such as food. This is done to protect themselves from the consequences or risks associated with the Covid-19 outbreak. Concerns about government policies related to efforts to mitigate Covid-19 through Large-Scale Social Restrictions (PSBB), which can limit their activities outside the home, drive the intention to stockpile food in addition to concerns about scarcity of goods (Humas Kemenkes, 2020; Sterman & Dogan, 2015). Anxiety or concern about a person's potential risks is a strong predictor of human behavior (Bae & Chang, 2021; Johnson & Tversky, 1983; Williams & Noyes, 2007). This risk perception is determined by a person's assessment of the severity of an event and the potential negative consequences (Bourassa, Doraty, Berdahl, Fried, & Bell, 2016; Sheu & Kuo, 2020; Sjöberg, Moen, & Rundmo, 2004). Previous research has demonstrated that perceived risk can influence attitudes, which in turn can

influence behavioral intentions (Ajzen, 1991; Quintal, Lee, & Soutar, 2010). In addition, perceived risk was discovered to be a significant predictor of subjective norms and perceived behavioral control (Lee, 2009). In general, a low perceived risk level will result in a positive attitude toward a behavior, increased belief that their family or friends will support the behavior (subjective norm), and increased perception of their own ability to perform the desired action (perceived behavioral control) (Bae & Chang, 2021; Jarvenpaa, Tractinsky, & Saarinen, 1999). However, because this study discusses the purchase of large quantities of food in anticipation of the impact of the Covid-19 outbreak, it formulates the following hypothesis:

H1: Risk perception (RP) has a positive effect on attitude (AT)

H2: Risk perception (RP) has a positive effect on subjective norms (SN)

H3: Risk perception (RP) has a positive effect on perceived behavioral control (PBC)

Behavior intention, which is influenced by attitudes, subjective norms, and perceived behavioral control, can predict a person's actual behavior (Ajzen, 2008). Hoyer, MacInnis, and Pieters (2013, p. 128) defined attitude as an overall evaluation that expresses a person's likes or dislikes toward an object, problem, person, or action. Kristiningsih, Hartini, and Usman (2020) described consumer attitude as a combination of three elements: cognitive information, affective information, and information about consumers' past behavior and future intentions. In other words, attitudes are composed of thoughts or beliefs, feelings, and behaviors or intentions toward specific things, which are typically goods or services. Consumers have an opinion about what they like and dislike. Consumers' attitudes toward objects are learned tendencies to evaluate objects by consistently supporting or opposing them. A person's attitude toward hoarding groceries may be motivated by a desire to reduce risk. According to the literature on self-interest orientation, humans are born with an innate tendency to be selfish, which motivates them to pursue self-interest while avoiding risk. In unusual events such as the Covid-19 outbreak, these characteristics encourage a person to rationalize hoarding behavior (Dreu & Nauta, 2009; Sheu & Kuo, 2020).

Subjective norm (SN) refers to the belief that a person's decision is influenced by social norms, i.e. that the behavior is approved and supported by another person or group of people (Bavel et al., 2020; Ham, Jeger, & Ivković, 2015). The purchase of food in large quantities has both positive and negative consequences in society. On the one hand, this is understandable for a variety of reasons, such as reducing activities outside the home so that they can buy in large quantities directly, or there is uncertainty about the long-term availability of these foodstuffs because many people do the same thing. As a result, there is a scarcity of supplies everywhere, causing harm to others.

Furthermore, perceived behavioral control (PBC) refers to an individual's perception of his ability to control certain behaviors (Ajzen, 1991). PBC is based 22

on beliefs about having access to the resources and opportunities needed to successfully perform specific behaviors (eg, I have the funds and the opportunity to buy large quantities of groceries) (Conner, 2001). The Covid-19 outbreak has infected the entire country, causing widespread panic. This causes them to behave erratically and possibly purchase more than they require. If they have the ability to do so, this is a possibility.

Individuals are more likely to engage in food hoarding if they believe that it will result in certain outcomes that they believe will benefit them, if they believe that other people think they should do food hoarding, and if they believe they have the resources and opportunities required for food hoarding. Based on the preceding discussion, this study proposes the following hypothesis:

H4: Attitude (AT) has a positive effect on food hording intention (FHI)

H5: Subjective norms (SN) have a positive effect on food hording intention (FHI)

H6: Perceived behavioral control (PBC) has a positive effect on food hording intention (FHI)

The spread of the Covid-19 pandemic in Indonesia has prompted the government to enact policies restricting residents' activities in affected areas. This policy is known as Large-Scale Social Restrictions (PSBB), and it is governed by Minister of Health Regulation No. 9 of 2020. The goal of this policy is to break the chain of transmission of Covid-19. Although supermarkets, markets, food needs, basic needs, and so on are still permitted, there are activity restrictions and health protocols that must be followed (Humas Kemenkes, 2020). However, the implementation of PSBB has some unintended consequences. On the one hand, entrepreneurs and the community expressed concern that the PSBB would stifle a variety of industries and livelihoods (Sari, 2020). On the other hand, the use of shopping apps, particularly those selling daily necessities, increased by 300% (Lubis, 2020). As a result, the following hypothesis is proposed in this study:

H7a: Government regulation (GR) moderates the relationship between attitude (AT) and food hoarding intention (FHI)

H7b: Government regulation (GR) moderates the relationship between subjective norms (SN) and food hoarding intention (FHI)

H7c: Government regulation (GR) moderates the relationship between perceived behavioral control (PBC) and food hoarding intention (FHI)

Figure 1. Conceptual Framework

RESEARCH METHODS

This study's population consisted of all consumers who went grocery shopping during the Covid-19 pandemic. Food shopping activities are limited to purchasing necessities that can be stored for an extended period. The shopping period was from April to July 2020, when the PSBB was implemented. The areas chosen as samples were PSBB-implemented areas. Purposive sampling was used to select the sample, which has the following characteristics: respondents are decision makers for purchasing staple products in the family, aged 17 and up, and live in areas where the PSBB policy is implemented. To avoid direct contact with respondents, data was collected online using a Google form. 194 of the 200 respondents who completed the questionnaire were eligible to proceed to the next stage. The questionnaire results were then processed and analyzed using the PLS-SEM method. The questionnaire is divided into two sections. The first section contains the respondent profile, and the second section contains the questionnaire questions related to the constructs studied.

In this study, risk perception (RP) is defined as a consumer's assessment of the likelihood and severity of a COVID-19 pandemic outbreak with potentially negative consequences (Bourassa et al., 2016; Sjöberg et al., 2004). This construct is measured using three Sheu and Kuo indicators Sheu and Kuo (2020). Furthermore, attitude (AT) refers to consumers' proclivity to respond positively or negatively to basic needs shopping activities (Ajzen, 2008). Attitude indicators are made up of three parts: affective, cognitive, and conative (Grimm, 2005; Rosenberg & Hovland, 1960). Subjective norm (SN) refers to the belief that a person or group of people will approve and support consumer behavior in purchasing basic necessities during the Covid-19 pandemic (Ajzen, 2008). The three indicators used to assess SN are based on Ham et al. (2015). Perceived behavior control (PBC) refers to the consumer's perception of their ability to control their shopping behavior for basic needs during the Covid-19 pandemic (Ajzen, 1991). PBC indicators were adapted from Albert Bandura's self-efficacy model (Ajzen, 1991). Consumer perception of the government's policy of Large-Scale Social Restrictions (PSBB) to limit certain activities to residents of an area

affected by the Covid-19 pandemic is referred to as government regulation (GR). This variable is measured using categorical measurements, namely strict PSBB rules for limiting basic needs shopping activities and loose PSBB rules for limiting basic needs shopping activities. Food hoarding intention (FHI) is a consumer behavior that is motivated by the desire to stockpile these materials during the COVID-19 pandemic (Power et al., 2020). Food hoarding intention indicators were adapted from several studies, including Deng et al. (2017), Long and Khoi (2020), and Power et al. (2020)

RESULTS AND DISCUSSION

The quality of research instruments must be standardized, and they must meet the criteria of validity and reliability testing techniques. The validity test is used to ensure that the respondents understand the question items used in this study. In testing indicators with data analysis techniques using partial least squares (PLS), the validity of an indicator is determined by the outer loading score. In general, the outer model or measurement model tests indicators against latent variables, or measures how well the indicator can explain the latent variables. In testing the validity of a research instrument using data analysis techniques from partial least squares (PLS), we need to measure convergent validity and the average variance extracted (AVE) value.

According to Table 1, all indicators have met the requirements of convergent validity, namely a loading factor greater than 0.50 and all indicators are significant with a p value less than 0.05. As a result, the indicators used to measure all research variables have been declared valid and can be used for further investigation. Furthermore, for a research indicator to be valid, the Average Variance Extracted (AVE) value must be greater than 0.50. Table 2 shows that the six constructs met the criteria, namely above 0.5 with the highest value of 0.795, indicating that all indicators in this study are valid.

The next test is reliability testing, which demonstrates the consistency and stability of measuring instruments or research instruments when measuring a concept or construct. Reliability testing is always conducted concurrently with construct validity testing. A valid construct is unquestionably reliable; however, a reliable construct is not always valid. The reliability of research instruments can be measured using two methods: composite reliability and Cronbach's alpha (Sholihin & Ratmono, 2013). A research instrument is considered reliable if it has a composite reliability value greater than 0.70 (Wiyono, 2011) and Cronbach's alpha greater than 0.60 (Abdillah & Hartono, 2015). Table 3 shows that the six constructs have a composite reliability value greater than 0.6, with the Perceived behavioral control construct having the lowest value of 0.771. Furthermore, the Cronbach's alpha values for the six constructs measured in this study are all greater than 0.6, with the exception of government regulation and perceived behavioral control, which have moderate reliability (0.5). As a result, it is possible to conclude that all construct indicators in this study are reliable.

Table 1. Output of Combined Loading and Cross Loading

riskper

attitud

Sub norm

PBC

govreg

FHI

gov* att

gov*sn

gov* pbc

Type as define

SE

p-value

Rp1

0.667

0.088

-0.080

-0.031

-0.046

-0.102

0.082

-0.340

0.370

Reflect

0,050

<0.001

Rp2

0.822

-0.221

0.147

0.012

-0.057

-0.012

-0.181

0.097

-0.100

Reflect

0.049

<0.001

Rp3

0.770

0.160

-0.087

0.014

0.100

0.101

0.121

0.192

-0.214

Reflect

0,049

0.001

Att1

0.187

0.843

0.224

0.029

-0.087

-0.090

-0.022

0.309

-0.353

Reflect

0,049

<0.001

Att2

-0.099

0.910

-0.107

-0.028

0.082

-0.040

-0.074

-0.099

0.144

Reflect

0,050

<0.001

Att3

-0.073

0.921

-0.100

0.001

-0.002

0.122

0.094

-0.184

0.181

Reflect

0,050

<0.001

Sn1

-0.025

0.230

0.876

-0.085

0.020

0.001

-0.031

-0.182

0.139

Reflect

0,050

<0.001

Sn2

-0.043

0.136

0.918

-0.071

0.100

0.005

-0.015

0.178

-0.186

Reflect

0,049

<0.001

Sn3

0.080

-0.425

0.769

0.182

-0.142

-0.007

0.053

-0.005

0.064

Reflect

0,049

<0.001

Pbc1

-0.207

0.202

-0.067

0.784

-0.195

0.021

0.291

-0.323

0.386

Reflect

0,050

<0.001

Pbc2

0.043

0.163

-0.000

0.738

0.143

0.138

-0.269

0.495

-0.689

Reflect

0,049

<0.001

Pbc3

0.199

-0.424

0.080

0.657

0.072

-0.179

-0.045

-0.170

0.314

Reflect

0,049

<0.001

Gov1

0.120

0.231

-0.131

0.088

0.833

-0.262

0.186

-0.666

0.713

Reflect

0,049

<0.001

Gov2

-0.120

-0.231

0.131

-0.088

0.833

0.262

-0.186

0.666

-0.713

Reflect

0,049

<0.001

Fhi1

0.005

-0.498

0.058

-0.049

-0.082

0.689

0.039

0.086

-0.146

Reflect

0,048

<0.001

Fhi2

-0.074

0.260

-0.167

-0.061

0.161

0.863

0.009

0.361

-0.426

Reflect

0,049

<0.001

Fhi3

-0.028

0.148

-0.013

0.140

0.014

0.870

0.044

-0.255

0.364

Reflect

0,049

<0.001

Fhi4

0.099

-0.012

0.135

-0.042

-0.111

0.856

-0.086

-0.175

0.177

Reflect

0,048

<0.001

GOV

-0.000

-0.000

0.000

-0.000

0.000

-0.000

1.000

0.000

-0.000

Reflect

0,048

<0.001

*att

GOV

-0.000

-0.000

0.000

0.000

-0.000

0.000

-0.000

1.000

-0.000

Reflect

0,048

<0.001

*sn

GOV

-0.000

-0.000

0.000

-0.000

-0.000

0.000

-0.000

0.000

1.000

Reflect

0,048

<0.001

*Pbc

Source: Data processed

Table 2. Average Variance Extracted (AVE)

RISKPER

ATTITUD

SUBNORM

PBC

GOVREG

FHI

GOVREG*

GOVREG *

GOVREG*

0.571

0.795

0.734

0.53 0

0.694

0.677

1.000

1.000

1.000

Sumber: Data diolah

Table 3. Output Laten Variable Coefficients for Composite Reliability and

Cronbach’s Alpha

RISKPER ATTITUD

SUBNORM  PBC  GOVREG  FHI  GOVREG*  GOVREG*  GOVREG*

Composite      0.799      0.921

reliability

Cronbach’s      0.621       0.870

alpha

Avg. Var.        0.571       0.795

extract

Full collin VIF   1.410       2.957

Q-squared                  0.116

0.892         0.771    0.820       0.893    1.000        1.000          1.000

0.816         0.554    0.560       0.838    1.000        1.000          1.000

0.734         0.530    0.694       0.677    1.000        1.000          1.000

2.986         1.268    1.414       1.875    1.413        3.415          3.656

0.088         0.088                0.486

Source: Data processed

The next step is to test the goodness of the model. This test is used to determine the goodness of the research model (model fit). Table 4 shows the results of the model fit and quality indices test results.

Table 4. Model Fit and Quality Indices

Measurement

Value

Criteria

Average path coeficient (APC)

0.232, P<0.001

Accept if p value <0.05

Average R squared (ARS)

0.179, P=0.003

Accept if p value <0.05

Average adjusted R squared (AARS)

0.171, P=0.004

Accept if <0.05

Average bolck VIF (AVIF)

1.940

Aceept if p value <5

Average full colinearity VIF

2.266

Accept if <5

Tenenhaus GOF

0.373

Low>0.1, med>0,25, high> 0.36

Symson’s paradox ratio (SPR)

0.778

Accept if>0,7

R squared contribution ratio (RSSR)

0.960

Accept if>0,9

Statistical supresion ratio (SSR)

1.000

Accept if >0.7

Rsquared contributuion ratio (RSCR)

0.978

Accept if > 0.7

Non linier bivariat causality direction ratio

0.667

Accept if>=0.7

Source: Data processed

Except for non-linear bivariate causality (= 0.667), all criteria in Table 4 are met. However, the results are still close to the acceptance criteria, so it is considered quite good. As a result, the test of the model developed in this study yielded results that satisfied the goodness of fit requirements. This means that the model is capable of predicting actual field conditions.

The following step is hypothesis testing, which aims to test the relationship between latent variables in the research model, with the results visible from the estimated path coefficients and level of significance (p-value). The output image of the estimation results is shown below to demonstrate the relationship between latent variables.

Figure 2. Estimated Output

Source: Data processed


OOVMO I (R)ZI J

p>0.02 (PwOlZ)

P«-O.ZI .I^M∣


The p-value can be used to determine whether the hypothesis test is supported or rejected. If the p-value is less than 0.05, then the proposed hypothesis can be supported. The following table shows the estimation results of path coefficients to test the strength of the influence between variables and explain the relationship between variables:

Table 5. Path Coefficients Estimation Results

Hipotesis

Path coefficients

p-value

Decision

Riskperception – attitude

0.332

<0.001

significant

Riskperception – subjectivenorm

0.289

<0.001

significant

risk perception - perceived behavioral control

0.293

<0.001

significant

attitude – FHI

0.374

<0.001

significant

Subjective norms – FHI

0.285

<0.001

significant

Perceived behavioral control – FHI

-0.282

<0.001

significant

govreg*attitude

-0.106

0.068

not significant

govreg*subjective norm

-0.103

0.072

not significant

govreg*PBC

0.024

0.366

not significant

Source: Data processed

The results of this study indicate that all hypotheses are supported except for the hypothesis related to the moderating role of government regulations (PSBB). The following is a detailed discussion of the study's findings. First, with a path coefficient of 0.332 and a positive sign, the results show that risk perception has a significant effect on attitude. This can be explained by the fact that during a health crisis, people develop a risk perception of the situation. A person's perception of risk is determined by his assessment of the threat of an outbreak (Wen, Sun, Li, He, & Tsai, 2019). As a result, the higher their perceptions of the risks that this pandemic may pose, the more favorable their attitude toward the intention in hoarding food as a form of self-protection behavior in an effort to minimize risk and provide a sense of security (Sheu & Kuo, 2020). By stockpiling necessities, one can reduce visits to stores that limit contact with other people in order to avoid disease transmission or product shortages due to rising demand for staple foods (Yuen, Wang, Ma, & Li, 2020).

Second, the study's findings show that the p-value of risk perception on subjective norm has a significance value <0.001 or less than 0.05. Based on these findings, it is possible to conclude that risk perception has a significant effect on subjective norms, with a path coefficient value of 0.289 (positive), implying that the second hypothesis that risk perception has a positive effect on subjective norms is supported. This finding suggests that the higher the perceived risk, the higher the assessment of the prevailing subjective norms, or in other words, the higher the belief that their family or friends will support the behavior.

Individuals are constituents of society. As a result, their decisions may be influenced by the larger group's attitudes, opinions, and beliefs (Wang, Liu, & Zhang, 2019). Although purchasing necessities in large quantities can reduce market supply (Kennett‐Hensel, Sneath, & Lacey, 2012), misinformation and the spread of rumors such as out of stock situations can affect individuals during

times of anxiety and panic (Yuen et al., 2020). Seeing that others are doing the same thing can lead to individuals imitating this behavior and believing that hoarding necessities is acceptable (Frank & Schvaneveldt, 2016; Yangui & Hajtaïeb El Aoud, 2015; Zheng, Shou, & Yang, 2020).

Third, the path coefficient value of risk perception on perceived behavioral control is 0.293 (positive), indicating that the third hypothesis, which explains how risk perception affects perceived behavioral control, is acceptable. According to these findings, the greater consumers' perceptions of the risks posed by the Covid-19 pandemic, the greater their perceptions of their ability to control situations and perform certain behaviors.

Humans have an innate desire to control their environment in order to survive (Kemp, Kennett-Hensel, & Williams, 2014). The spread of the Covid-19 outbreak causes public anxiety and reduces individual perceptions of their ability to cope with potential risks. This can cause behavior to be triggered in order to overcome problems or obstacles encountered during the pandemic (coping behavior) (Sneath, Lacey, & Kennett-Hensel, 2009). When sources of discomfort (such as disease outbreaks) are uncontrollable, people tend to exert more control over other domains (Yuen et al., 2020). In this case, the behavioral tendency to hoard groceries can be viewed as a way out of the situation in order to regain control (Ballantine, Zafar, & Parsons, 2014).

Fourth, the three TPB components (attitudes, subjective norms, and perceived behavioral control) all have a positive effect on a person's intention to hoard necessities (food hoarding intention). This is consistent with many previous TPB findings, despite the fact that they were applied in different contexts (e.g., Bae and Chang (2021), Long and Khoi (2020), Soebandhi, Hartini, and Gunawan (2020)). The evaluation of the behavior influences the individual's intention to engage in food hoarding. The greater a person's positive attitude, the greater the desire to stockpile necessities. FHI is also influenced by their social group's recognition. In the sense that the greater a person's belief that FHI's behavior will be tolerated by the community, the greater the desire to stockpile necessities. Furthermore, the perception of being able to control the situation during a pandemic (such as access to basic supplies, financial availability, etc) can increase the proclivity to engage in food hoarding.

Fifth, our findings indicate that the Indonesian government's regulation (PSBB) does not moderate the relationship between attitudes, subjective norms, and perceived behavioral control on FHI. As a result, H7a-c is not supported. The rapid spread of the Covid-19 outbreak, which has affected the entire world, has caused concern in the community. Although the government has implemented PSBB in a number of regions to prevent the spread of the Covid-19 outbreak and urges people not to make panic purchases (Puspita, 2020), this has no effect on the relationship between the three components of the TPB and FHI. This result can be motivated by an assessment of the impact of the epidemic on themselves and their families, encouraging people to purchase necessities in greater quantities than usual. Concerns about the scarcity of food due to increased

demand at the start of the pandemic are another reason why someone might consider food hoarding. Furthermore, even though the PSBB is being implemented, the growing number of online buying and selling platforms makes it easier for individuals to purchase basic commodities online (Meodia, 2020).

CONCLUSION

This study employs an expanded TPB to improve the TPB's predictive power by including risk perceptions of the Covid-19 outbreak as well as government regulations (PSBB). According to the findings, PSBB does not moderate the relationship between the three components of TPB on food hoarding intention, whereas the other hypotheses are supported.

The managerial implication of this study is that government regulation has shown no effect on food hoarding intentions; therefore, the government must implement strong and accurate communication about the Covid-19 outbreak, food sources, and other important commodities in order to reduce anxiety and excessive purchasing of goods.

This study supports the Theory of Planned Behavior, which states that attitudes accompanied by subjective norms and perceived behavioral control influence behavioral intention to do food hoarding; the presence of risk perception variables supports the expansion of this model.

There are several limitations to this study as well. First, this study was conducted at the beginning of the pandemic. The intention to do food hoarding during a pandemic may differ when the pandemic is still in its early stages versus current situation, when people have begun to become accustomed to and adapt to pandemic conditions. Subsequent research can collect additional data over multiple time periods to compare intention in food hording at the start of the pandemic to the end of 2021. Second, because the study's findings are based on research populations in Indonesia's major cities, the research model needs to be validated in a variety of contexts.

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