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UDC 332; DOI 10.18551/rjoas.2022-02.01
THE IMPACT OF HEALTH AWARENESS, FOOD SAFETY ATTENTION, AND ATTITUDE FACTORS TOWARDS CONSUMER PURCHASE INTEREST OF FOOD PRODUCTS
POST-RISE OF COVID-19
Pariasa Imaniar Ilmi*, Muchlisyiyah Jhauharotul, Hardana Andrean Eka
Faculty of Agriculture, University of Brawijaya, Malang, Indonesia *E-mail: pariasa@ub.ac.id
ABSTRACT
This study purposes to analyze the relationship between the factors that shape consumer decisions in purchasing food products after the Covid 19 incident in June 2021. Consumers currently have high attention to the food products they consume. The Attention to Food Safety variable has a positive and highly significant effect on the Attitude and Purchase Intention variables, which are also consumer concerns about food safety which increase during the Covid-19 event. Attitude variable has a positive and highly significant effect on the Purchase Intention variable, interest can reflect a person's willingness to take a certain action. Consumers pay more for good quality products and services for the food products they consume.
KEY WORDS
Health awareness, attitude, purchase intention.
The World Health Organization (WHO) has determined that COVID-19 is a pandemic; this has resulted in an increase in the number of infected COVID-19 cases and has spread to various countries. 144,000 people in Indonesia (Wolddometers & Aljazeera News, 2021). One of the ways of handling the outbreak in an area is by establishing quarantine at that location. Almost all activities have been suspended, and this policy is known as a lockdown. Lockdown can help prevent the spread of the Covid-19 virus to an area, so that people who are in an area are expected to be able to avoid a fast-spreading outbreak (Yunus & Rezki, 2020). This condition causes people to no longer be able to walk freely outside their homes during the quarantine period, so most people are active at home. The World Health Organization (WHO) has determined that COVID-19 is a pandemic; this has resulted in an increase in the number of COVID-19 cases. Infected quickly and has spread to various countries. This virus has expanded to reach 4.25 million cases and 144,000 people died in Indonesia (Wolddometers & Aljazeera News, 2021). One of the ways of handling the outbreak in an area is by establishing quarantine at that location. Almost all activities have been suspended, and this policy is known as a lockdown. Lockdown can help prevent the spread of the Covid-19 virus to an area, so that people who are in an area are expected to be able to avoid a fast-spreading outbreak (Yunus & Rezki, 2020). This condition causes people to no longer be able to walk freely outside their homes during the quarantine period, so most people are active at home.
Policies that have been set by the local government can affect the daily activities of the community, especially in fulfilling their food needs. People in this case began to limit themselves to leave the house, except for important things. This can change people's behavior as consumers of food products due to the Covid-19 outbreak, people prefer to buy their needs online. Digital shopping has increased; this is in line with the government's implementation of work and school from home. Since the spread of the Covid-19 virus in Indonesia, several shipping services companies have recorded an increase in shipments of goods reaching 80% (Maggioni et al., 2019).
Expedition services in fast delivery of packages experienced an increase in deliveries, especially for food commodities and necessities, such as food products, vegetables, fruits, medical devices, and chemicals for cleaning fluids (Wesley, LeHew, & Woodside, 2006). Thus, it is important for producers and marketers of various food products to understand how
the consumer decision-making process during the COVID-19 pandemic is because it is very useful for obtaining information about potential consumers (Lim & Hahn, 2019).
Information related to potential consumers, it is very important for MSME actors to understand how changes in consumer health behavior and awareness in consuming food needs because of the Covid-19 pandemic which is still ongoing today. Excellent process and service strategies make consumers loyal to the products and services produced by MSMEs. It can be said that buying interest is a consumer statement that reflects the purchase plan of several products with a certain brand. This is very much needed by marketers to find out consumers' buying interest in a product.
Therefore, this study aims to analyze the relationship between the factors that shape consumer decisions in purchasing food products after the spike in Covid 19 cases in June 2021. Thus, it is important for producers and marketers of food products to understand what kind of service consumers expect during this period. This outbreak, especially in online marketing. It is hoped that the results of this research will be very useful in providing study material in the form of fundamental ideas that are relevant to current conditions in online marketing of food products in order to strengthen the local economy and business.
METHODS OF RESEARCH
The approach used in this study is a quantitative approach with explanatory research methods. This research was conducted in Malang Raya. The location determination is based on the high spike in cases in East Java in June 2021 due to the impact of community mobility after the Religious Holidays. The time of the research was carried out in June November 2021. The sampling technique used was purposive sampling. According to Siregar (2013), the determination of the sample whose population is unknown can use the Issac Michael approach. The formula used is as follows:
.p.q n -
e2
Where: n = Sample number; z| = The value of the normal table to the level of significance;
p = Population propotion; q = 1 - p; e = Margin of error.
The proportion of the population used is 0.5 which is the highest standard error for a fixed sample size, 95% significance level and 10% margin of error. Due to the 95% significance level, Z a/2 is 1.96. So, the minimum number of samples to be studied in the research conducted is as follows:
(1,96)2.0,5.0,5
n = --—r;- « 100
0,12
Based on these calculations, obtained a sample of 96.04 so that it is rounded up to 100 respondents. The criteria for selecting the sample required and obtained by filling out the questionnaire in this study were 17 years old, respondents who live in Malang Raya, and respondents who intend to purchase food online. Data collection techniques by conducting structured interviews using a questionnaire tool.
The data analysis technique in this study, namely SEM-PLS. Data analysis in this study was carried out through SmartPLS. SEM-PLS is an inferential data analysis technique used in this study to test a hypothesis in a study. According to Sholihin et al. (2020), SEM-PLS is a multivariate statistical method that can test the relationship between variables with reflective and formative measurements. SEM-PLS is generally used for testing a structural equation model that involves the relationship between variables that are many and complex but oneway (recursive). The analytical tool used is the SmartPLS software. There are two models in the analysis technique using SEM-PLS, namely the outer model (measurement model) and the inner model (structural model). According to Solimun et al., (2020), the outer model is a
specification of the relationship between latent variables and their indicators. The reflective indicator model and the formative indicator model are two types of models in the outer model. This study uses a reflective indicator model. The equation for the reflective indicator model can be written as follows (Solimun et al., 2020).
x = Ax% + sx y = Ay-q + £y
Where: x = Exogenous (<f); y = Endogeneous Ax, Ay = Matriks loading; ex, ey = Measurement error (noise).
Evaluation of the measurement model was carried out to determine the validity and reliability of the research data used. The tests carried out are convergent validity and discriminant validity, composite reliability, and Cronbach's Alpha. The evaluation of the structural model is carried out as an effort to support the theoretical model in the research conducted (Avkiran & Ringle, 2018). It can also be said that the evaluation of the structural model is carried out to see the relationship between exogenous variables and endogenous variables. The equation model in the evaluation of the structural model is as follows:
= Pf + ^ßjiti + Vi
Where is an endogenous variable and is an exogenous variable, while is a constant in the regression model, is the regression coefficient. And is error (Tenenhaus et al., 2005 in Avkiran & Ringle, 2018). Evaluation of the structural model is done by testing R-square, path coefficients, t-statistics (bootstrapping), and predictive relevance.
RESULT AND DISCUSSION
The analysis of the SEM-PLS model using WarpPLS 7.0 was carried out with two evaluation models, namely the outer model to analyze the validity and reliability of the research instrument and the inner model to examine the Goodness of Fit before testing the hypothesis. The following are the results of the evaluation of the model from the results of the processed research data. Hair et al., (2013) suggest that the value that must be possessed by the loading factor of each indicator to be accepted is 0.7. If the loading factor value is 0.7 then the indicator can be said to be valid. The following are the results of the evaluation of the convergent validity test:
Table 1 - Evaluation of Convergent Validity with Loading Factor Value
Variable Indicator Value of Loading Factor
HS (Health Awareness) HS1 Concerns about the health of food products HS2 Attention to physical and mental development HS3 Medical examination HS4 Attention to food intake 0.896 0.841 0.792 0.868
FSC (Attention to Food Safety)
FSC1 Pay attention to details of raw materials / product content FSC2 Pay attention to food product processing FSC3 Pay attention to the packaging and presentation of food products 0.873 0.893 0.907
A (Attitude) A1 Food product quality A2 Food product safety A3 Food product guarantee A4 Implementation of health protocol A5 MSME Credibility 0.861 0.737 0.832 0.784 0.821
PI (Purchase Intention) PI1 Repurchase the product PI2 Willing to pay more for product quality PI3 Willing to pay more for the quality of service 0.828 0.932 0.898
Based on Table 1, each indicator has a loading factor value of 0.7 which is in the range of 0.737 to 0.932, so it can be concluded that all indicators are said to be valid or acceptable. In addition to the loading factor value, the convergent validity test is also seen from the Average Variance Extracted (AVE) test. According to Hair et al., (2013) the AVE value must be > 0.5 which means that the construct is able to explain more than half of the indicator variance. Here are the AVE values:
Table 2 - Evaluation of Convergent Validity with Average Variance Extracted (AVE)
n/n Criteria Health Awareness Attention to Food Attitude Purchase
(HS) Safety (FSC) (A) Intention (PI)
Average Variances Extracted (AVE) >0.5 0.723 0.794 0.653 0.787
Based on Table 2, all constructs have an AVE value > 0.5 which is in the range of 0.653 to 0.749, so it can be concluded that all constructs have met the requirements of the AVE criteria.
Discriminant validity is a test that aims to determine that each latent variable is different from other latent variables or to determine the extent to which a construct is different from other constructs. Discriminant validity can be seen from the value of the loading and cross loading indicators. If the indicator loading value of each indicator on the relevant variable is greater than the cross loading on other latent variables, it can be said to have met the criteria for discriminant validity (Solimun et al., 2017). The following are the loading and cross loading indicator values:
Table 3 - Evaluation of Discriminant Validity with Loading and Cross Loading Indicator Values
n/n Health Awareness Attention to Food Safety Attitude Purchase Intention P-value
(HS) (FSC) (A) (PI)
HS1 0.896 0.055 0.074 -0.041 <0.001
HS2 0.841 -0.122 0.063 -0.049 <0.001
HS3 0.792 -0.206 -0.124 0.105 <0.001
HS4 0.868 0.249 -0.024 -0.006 <0.001
FSC1 -0.156 0.873 0.059 0.103 <0.001
FSC2 0.095 0.893 -0.006 -0.054 <0.001
FSC3 0.057 0.907 -0.052 0.046 <0.001
A1 -0.369 0.206 0.861 -0.017 <0.001
A2 -0.461 0.298 0.737 -0.247 <0.001
A3 0.270 -0.179 0.832 -0.164 <0.001
A4 0.201 -0.026 0.784 0.219 <0.001
A5 0.336 -0.277 0.821 0.197 <0.001
PI1 0.107 0.072 0.017 0.828 <0.001
PI2 0.019 -0.033 0.059 0.932 <0.001
PI3 -0.118 -0.032 -0.077 0.898 <0.001
Based on Table 3, the value of each loading indicator in each construct is greater than the cross-loading value of the related construct. So it can be concluded that it has met the criteria for discriminant validity and can be said to be valid. In addition, the p-value of all indicators has also met the criteria of significant value, namely p <0.05 so it can be said that each loading factor value is significant.
Another method is to look at the discriminant validity of all indicators together by comparing the value of the square root of AVE with the correlation coefficient between the relevant latent variables and other latent variables. If the value of the square root of AVE is greater than the correlation coefficient with other constructs, it can be said to have good discriminant validity (Solimun et al., 2017). The following is the square root of AVE value:
Based on Table 4, the value of the square root of AVE in each construct is greater than the value of the correlation between constructs, so it can be concluded that each latent variable in this study has met the criteria of discriminant validity or has good discriminant validity.
Table 4 - Evaluation of Discriminant Validity with Square Root of AVE
n/n Health Awareness (HS) Attention to Food Safety (FSC) Attitude (A) Purchase Intention (PI)
Health Awareness (HS) Attention to Food Safety (FSC) Attitude (A) Purchase Intention (PI) 0.850 0.819 0.819 0.891 0.700 0.711 0.435 0.425
0.700 0.435 0.711 0.425 0.808 0.564 0.564 0.887
Reliability test is a measurement that can show the extent to which the questionnaire is able to measure variables stably and consistently. The reliability test was carried out by looking at the composite reliability value and Cronbach's alpha value of each construct. If the composite reliability value of a construct is 0.7, it can be said to have good composite reliability or has been reliable. While the criteria that are often used for Cronbach's alpha values are if it has a value > 0.6 then the questionnaire can be said to be reliable (Solimun et al., 2020). The following is the value of Composite Reliability and Cronbach's Alpha:
Table 5 - Evaluation of Composite Reliability and Cronbach's Alpha
Variable Criteria Composite Reliability Criteria Cronbach's Alpha
Health Awareness (HS) >0,7 0.912 >0,6 0.871
Attention to Food Safety (FSC) >0,7 0.920 >0,6 0.870
Attitude (A) >0,7 0.904 >0,6 0.866
Purchase Intention (PI) >0,7 0.917 >0,6 0.863
Based on Table 5, each variable has a composite reliability value range of 0.904 to 0.920 which has met the criteria, namely 0.7. Then for the value of Cronbach's alpha each variable has a value range of 0.863 to 0.871. This value has also met the criteria, namely > 0.6. So, it can be concluded that the latent variable has good or reliable reliability.
Evaluation of the structural model (inner model) is used to see the relationship or influence between the variables in the model. Tests on the structural model can be seen from the Full Collinearity Assessment (VIF), path coefficient, R-quared (R2), Q-Squared (Q2), Effect Size, and Goodness of Fit (Hair et al., 2013). The test has the aim of knowing the effect between variables. The results of the evaluation of the structural model (inner model) are as follows:
Full Collinearity Assessment (VIF) evaluation is used to determine the level of collinearity between variables. According to Solimun et al., (2017) the criteria for the VIF value is 5. If the VIF value exceeds this value, it is stated that there is a collinearity problem in the study. Here are the VIF values:
Table 6 - Full Collinearity Assessment (VIF)
Variable Criteria Full collin. VIF
Health Awareness (HS) <5 3.331
Attention to Food Safety (FSC) <5 3.429
Attitude (A) <5 2.590
Purchase Intention (PI) <5 1.437
Based on Table 6, the value of all variables 5 is with a range of values from 1.437 to 3.429. It can be said that the variable has a good VIF value. So it can be concluded that all of these variables do not have collinearity problems.
The path coefficient is used to determine the effect between variables. According to Hair et al., (2013) the criteria for a good path coefficient are if it has a value of <0.1 then the relationship between the variables is weak. If it is close to 1 then it has a strong or positive relationship. The results of the path coefficients in this study are as follows:
1^=0.57
Figure 1 - Path Coefficient
Based on Figure 1, the path coefficient on the construct has a positive and significant effect. The five constructs have a value of 0.34; 0.32; 0.43; 0.18 and 0.50 on the path coefficient and has a p-value of 0.03.
Evaluation of R-Squared (R2) is used to determine the effect between exogenous and endogenous variables or to see how many exogenous variables can explain endogenous variables. The greater the value of R2 in a model, the higher the influence of the exogenous variables on the endogenous variables. According to Hair et al., (2013), if the R2 value ranges from 0-1 then the relationship between these variables can be said to have high accuracy. Another opinion states that there are three criteria, namely >0.67 having a strong model, >0.33 having a moderate model, and >0.19 having a weak model (Suhan & Achar, 2016). The following is the value of R2:
Table 7 - Value of R-Squared (R2)
Variable Criteria R-Squared (R2)
Health Awareness (HS) Attention to Food Safety (FSC) Attitude (A) Purchase Intention (PI) >0.67 = strong >0.33 = moderate >0.19 = weak 0.548 0.571
Based on Table 7, the R2 value obtained by the attitude variable (A) is 0.548 and the purchase interest variable (PI) is 0.571, which means that both values fall into the moderate category, namely > 0.33.
Q-Squared (Q2) evaluation is used to see the relevance and predictive validity of a set of exogenous variables to endogenous variables in a model. There are two criteria in the Q2 assessment, namely if the Q-Squared value> 0 is considered to have good predictive relevance and if the Q-Squared value is <0 then it is considered to have less predictive relevance (Hair et al., 2013). Here are the Q2 values:
Table 8 - Value of Q-Squared (Q2)
Variable Criteria Q-Squared (Q2)
Health Awareness (HS)
Attention to Food Safety (FSC) >0 = Good
Attitude (A) <0 = Bad 0.548
Purchase Intention (PI) 0.389
Based on Table 8, the Q2 value obtained in the attitude variable (A) is 0.548 and the purchase interest variable (PI) is 0.389, which means that both values fall into the good category, namely > 0. So it can be concluded that the research model has a good predictive relevance.
Effect size is used to determine the effect of exogenous variables on endogenous variables. The criterion for the effect size value is that if the value of f 2 > 0.02 is categorized as having a small effect, f 2 > 0.15 has a medium effect, and f 2 > 0.35 has a large influence (Hair et al., 2013). The following is the result of the effect size:
Table 9 - Value of Effect Size
Variable Criteria HS FSC A PI
Health Awareness (HS) Attention to Food Safety (FSC) Attitude (A) Purchase Intention (PI) f2 >0,02 = small f 2 >0,15 = m f 2 >0,35 = besar 0.242 0.175 0.306 0.090 0.306
Based on Table 9, it can be seen that the effect size value of the health awareness variable (HS) is 0.242 which means it has a medium effect on the attitude variable (A), the health awareness variable (HS) is 0.175 which means it has a medium effect on the purchase intention variable (PI), Attention to food safety variable (FSC) is 0.306 which means it has a medium effect on the attitude variable (A), the attention to food safety variable (FSC) is 0.090 which means it has a small effect on the purchase intention variable (PI), and the attitude variable (A). ) of 0.306 which means that it has a medium effect on the purchase interest variable (PI). It can be concluded that the effect size which has a medium value then has a moderate level of relationship or influence. While the value of the effect size is small, it has a weak level of relationship or influence.
Evaluation of Goodness of Fit is an index and measure of the goodness of the relationship between latent variables (inner model) related to its assumptions. The existing criteria are rule of thumb, so they should not apply rigidly and absolutely. If there are one or two indicators of Model Fit and Quality Indices, of course the model can still be used (Solimun et al., 2020). The following are the results of Goodness of Fit:
Table 10 - Value of Goodness of Fit
No Model Fit and Quality Indices Criteria Result
1 Average Path Coefficient (APC) p<0.05 0.353 P<0.001 fulfilled
2 Average R-squared (ARS) p<0.05 0.560 P<0.001 fulfilled
3 Average Adjust R-squared p<0.05 0.549 P<0.001 fulfilled
4 Average Block VIF (AVIF) Accaptable if<5 Ideally<3.3 4.017 fulfilled
5 Average Full Collinearity (AFVIF) Accaptable if<5 Ideally<3.3 2.706 Ideal
6 Tenenhaus GoF (GoF) Small>0.1 Medium>0.25 Large>0.36 0.643 Large
7 Sympson's Paradox Ratio (SPR) Accaptable if>0.7 Ideally=1 1.000 Ideal
8 R-squared Contribution Ratio (RSCR) Accaptable if>0.9 Ideally=1 1.000 Ideal
9 Statistical Suppression Ratio (SSR) Accaptable if>0.7 1.000 fulfilled
10 Nonlinear Bivariate Causality Direction Ratio (NLBCDR) Accaptable if>0.7 1.000 fulfilled
Based on Table 10, the structural model through the Goodness of Fit shows that all indicators in the fit and quality indices model meet the criteria. So from the results of the analysis, it is concluded that the model in the study is said to be good or feasible.
Based on the results of data processing, the Health Awareness (HS) variable has a positive and significant influence on the Attitude (A) and Purchase Interest (PI) variables, so that H1 and H2 are acceptable. The following is the result of data processing using WrapPLS 7.0:
Table 11 - Analysis of the Effect of Health Awareness on Purchase Attitudes and Interests
Hypothesis Path Correlation Path Coefficient P-Value
H1 Health Awareness ^ Attitude 0.343 <0.01
H2 Health Awareness ^ Purchase Intention 0.316 <0.01
Based on Table 11, the resulting significance value is p-value <0.01 so it can be said that the resulting significance level is strong. This is in accordance with the statement of Solimun et al., (2020) which states that the criteria or hypothesis results are said to be highly significant if the p-value is 0.001. The resulting path coefficient value indicates that Health Awareness has a positive relationship with Purchase Attitudes and Interests, where concern and concern for health during a spike in Covid-19 cases can have an impact on consumer buying attitudes and interests in choosing food products and MSMEs to meet their daily needs.
Health awareness is a concern and concern for getting better and motivated in improving, maintaining, maintaining health and quality of life by implementing a healthy lifestyle (Michaelidou & Hassan, 2008). When there is a spike in Covid-19 cases, consumers are more concerned about health awareness. It can be seen based on research results that consumer today have more concerns and high attention to health and the food products they consume. In addition, consumers also pay more attention to the development of physical and mental conditions and are more routine in conducting health checks. Routines in health checks are carried out by consumers to find out the health developments experienced (Plasek et al, 2020; Sánchez-Bravo et al, 2021).
Consumers realize that the food products they consume can affect their health, so to meet their daily needs they also consider MSMEs to be used as a reference for meeting their needs. Therefore, health awareness is a factor that can shape consumer attitudes (Kutresnaningdian & Albari, 2021). Based on the results of research on consumer attitudes in meeting food needs when there is a spike in Covid-19 cases, they pay more attention to the quality, safety, and guarantee of food products sold by MSMEs. In addition, consumers will also pay attention to the credibility of MSMEs and choose MSMEs that apply health protocols.
Health awareness also affects consumer buying interest in fulfilling food products. Purchase intention is a type of decision making that studies the reasons for buying a certain brand by consumers (Singh et al, 2017; Kyto et al, 2019). Buying interest arises because of a process that occurs in the consumer's mind and is influenced by various factors that depend on needs. The Indonesian Institute of Sciences (LIPI) stated that based on the results of an online survey conducted in September - October 2020, families currently tend to consume healthy foods with the aim of maintaining their immune system during a pandemic. This is in accordance with the results of research that has been carried out, when there is a spike in Covid-19 cases, consumers are willing to pay more for good quality products and services. In addition, consumers will also continue to repurchase products that are considered good even though their conditions have returned to normal.
Based on the results of data processing, the Attention to Food Safety (FSC) variable has a positive and significant influence on the Attitude (A) and Purchase Interest (PI) variables, so H3 and H4 are acceptable. The following is the result of data processing using WrapPLS 7.0:
Table 12 - Analysis of the Effect of Attention on Food Safety on Purchase Attitudes and Interests
Hypothesis Path Correlation Path Coefficient P-Value
H3 Attention to Food Safety ^ Attitude 0.427 <0.01
H4 Attention to Food Safety ^ Purchase Intention 0.176 0.027
Based on Table 12, the significance value produced by H3 is p-value <0.01 which means that the resulting significance level is strong, while H4 is p-value 0.027 which means that it has a significant value. This is in accordance with the statement of Solimun et al., (2020) which states that the criteria or hypothesis results are said to be highly significant if the p-value is 0.001 and the hypothesis is said to be significant if the p-value is 0.05. The resulting path coefficient value indicates that Attention to Food Safety has a positive relationship with Purchase Attitudes and Interests, where consumer concern for the safety of the food they consume during a surge in Covid-19 cases can have an impact on attitudes and interest in purchasing food products and the selection of MSMEs to meet daily needs.
Attention to food safety is a form of consumer concern for residues in food produced due to chemical sprays, artificial fertilizer additives and preservatives which are often associated with agricultural methods (Michaelidou & Hassan, 2008). The intensity of consumer attention on food safety increased during the spike in Covid-19 cases. This is in accordance with the results of research that consumers today pay more attention to detailed information on raw materials and content of food products purchased or consumed. In addition, consumers also pay attention to packaging, processing methods and how to present food products that are purchased or consumed.
Consumer attention to food safety that increases when there is a surge in Covid-19 cases can be used to predict consumer attitudes in choosing food products and SMEs (Di Crosta et al, 2021). According to Michaelidou & Hassan (2008) safe eating is food that meets health quality standards. So in accordance with research conducted that consumers will pay attention to the quality, safety, and guarantee of food products sold by SMEs. This is in accordance with the statement of Raewf et al (2021) and Eger et al (2021) that currently consumers tend to prefer to buy raw materials to be processed at home because they think that cooking by them will be safer and hygienic.
Attention to food safety can also affect purchase intention. According to Kutresnaningdian & Albari (2021) and Purwanto et al, 2021) interest is an intermediate variable that causes behavior from an attitude that reflects a person's willingness to take a certain action. Based on the results of research that has been carried out, when there was a spike in Covid-19 cases, consumers had high attention to the safety of what they consumed so they were willing to pay more for better quality products and services (Yang, 2020).
Based on the results of data processing, the Attitude variable (A) has a positive and significant influence on the Purchase Interest (PI) variable, so H5 can be accepted. The following is the result of data processing using WrapPLS 7.0:
Table 13 - Analysis of the Effect of Attitude on Purchase Interest
Hypothesis Path Correlation Path Coefficient P-Value
H5 Attitude ^ Purchase Intention 0.504 <0.01
Based on Table 13, the resulting significance value is p-value <0.01 so it can be said that the resulting significance level is strong. This is in accordance with the statement of Solimun et al., (2020) which states that the criteria or hypothesis results are said to be highly significant if the p-value is 0.001. The resulting path coefficient value indicates that Attitude has a positive relationship with Purchase Intention, where consumer attitudes towards food products and MSMEs during the surge in Covid-19 cases can have an impact on purchase intention to meet their daily needs.
Attitude is a psychological construct of individual readiness to act in a certain way (Voon et al., 2011); (Stanley et al, 2021). Attitudes possessed by consumers can be divided into three components, namely cognitive, affective, and psychomotor. Based on these components, consumers will pay more attention to food products and the selection of MSMEs when there is a spike in Covid-19 cases. In accordance with the results of the study, the attitude taken by consumers in fulfilling their daily needs is that they pay more attention to the quality, safety, and guarantee of food products sold by MSMEs. In addition, consumers will also consider the credibility of MSMEs and choose MSMEs that have implemented health protocols. Health protocols are considered as a form of health awareness.
Consumer attitudes greatly affect consumer behavior in purchasing daily necessities. This is in accordance with the statements of Kutresnaningdian & Albari (2020) and Guney et al (2021) that attitude has a positive influence on purchase intention. Interest can reflect a person's willingness to take a certain action. According to research results, when there is a spike in Covid-19 cases, consumers will be more selective in meeting their daily needs. Consumers are willing to pay more for good quality products and services for the food products they consume. This is in accordance with the statement of Khayyam et al., (2021) that concerns about food safety can make consumers tend to avoid products that are not known to these consumers.
RJOAS, 2(122), February 2022 CONCLUSION
Health awareness has a high positive and significant relationship with Purchase Attitude and Interest. In addition, attention to food safety has a high positive and significant relationship with Purchase Attitude and Interest. And Attitude has a positive relationship with Purchase Interest, where concern and concern for health during a spike in Covid-19 cases can have an impact on consumer attitudes and buying interest in choosing food products and MSMEs to meet their daily needs.
Consumers realize that the food products they consume can affect their health, so they also consider their daily needs. Attention to food safety is a form of consumer concern for residues in food produced due to chemical sprays, artificial fertilizer additives and preservatives so that consumers will be more selective in meeting their daily needs.
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