FACTORS AFFECTING THE BEHAVIOR OF DRY AGRICULTURE PRODUCTS ONLINE -BUYING CONSUMERS IN HO CHI MINH CITY

Abstract

E-commerce is a growing industry, businesses are constantly innovating to improve business quality every day. In the current epidemic situation, food-related items, especially agricultural products, are being purchased by consumers online quite a lot. Knowing this, our team created the study with the aim of understanding the factors that influence consumers' buying behavior of dry agricultural products online. We create a survey with 480 samples, the survey subjects are consumers living and working in Ho Chi Minh City and some in neighboring countries. After synthesis, the collected data will be processed by SPSS. The research results show that the factors affecting the online buying behavior of dry agricultural products of consumers include: purchase intention, subjective norm, perceived usefulness and perceived risk. From the results, the study makes some conclusions and opinions for businesses and retailers on e-commerce platforms to have appropriate business strategies and methods to bring the most effectiveness during the current COVID-19 pandemic.

Full text

International Symposium on Sustainable Development in Transition Economies (ISSDTE 2022)
482 © 2022 Industrial University of Ho Chi Minh City
ICATSD2F.538
FACTORS AFFECTING THE BEHAVIOR OF DRY AGRICULTURE PRODUCTS
ONLINE - BUYING CONSUMERS IN HO CHI MINH CITY
NGUYEN THI PHUONG GIANG*, LE HUU HUNG, NGUYEN BINH PHUONG DUY
Faculty of Commerce and Tourism, Industrial University of Ho Chi Minh City
*nguyenthiphuonggiang@iuh.edu.vn
Abstract. E-commerce is a growing industry, businesses are constantly innovating to improve business
quality every day. In the current epidemic situation, food-related items, especially agricultural products, are
being purchased by consumers online quite a lot. Knowing this, our team created the study with the aim of
understanding the factors that influence consumers' buying behavior of dry agricultural products online.
We create a survey with 480 samples, the survey subjects are consumers living and working in Ho Chi
Minh City and some in neighboring countries. After synthesis, the collected data will be processed by SPSS.
The research results show that the factors affecting the online buying behavior of dry agricultural products
of consumers include: purchase intention, subjective norm, perceived usefulness and perceived risk. From
the results, the study makes some conclusions and opinions for businesses and retailers on e-commerce
platforms to have appropriate business strategies and methods to bring the most effectiveness during the
current COVID-19 pandemic.
Keywords. dried agricultural products, subjective norms, perceived usefulness, attitude, trust, perceived
product quality, perceived risk, online purchase intention, online shopping behavior.
1 INTRODUCTION
In recent decades, thanks to the explosion of the internet as well as the rapid development of e-commerce,
online shopping has become familiar to the majority of Vietnamese people. The means of accessing the
internet are becoming more and more popular, people can more easily access to choose goods through
phones, laptops, desktop computers or other internet-connected devices in anywhere, no longer constrained
by time, place or travel. Online shopping is becoming increasingly dominant, especially during the current
COVID-19 pandemic.
A series of online shopping websites such as Shopee, Lazada, Tiki, Sendo... have made shopping more
convenient for Vietnamese consumers. However, under the fierce competition of the e-commerce market
has also created pressure for businesses. Therefore, in order for businesses to have more bases, develop
orientations and attract the attention of customers to bring about increased consumption of goods through
the website. Businesses need to capture information as well as the factors that influence consumers to buy.
This study aims to determine the factors affecting the online purchase of dried agricultural products by
consumers in Ho Chi Minh City during the difficult time of the COVID-19 pandemic. From there, the
researchers proceed to build a customer model based on the premise and results of data analysis. Proposing
solutions to help businesses and online sales websites take new steps and adjust business policies to attract
more and more customers to make transactions.
2 LITERATURE REVIEW
TPB (The theory of planned behavior)
The Theory of Planning Behavior - TPB initiated by Icek Ajzen in 1991 has brought many advantages in
predicting and explaining the behavior of individuals under certain circumstances. The theory of planned
behavior is one of the most widely cited and applied theories of behavior theory and is widely applied in
many different fields of research. TPB is a theory that demonstrates the relationship between a person's
beliefs and behavior. In which beliefs are divided into 3 types: behavioral beliefs, normative beliefs and
self-control beliefs.

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Subjective norms refer specifically to how the person we care about feels when we perform a particular
behavior. According to this theory, if the people we care about are inclined to buy dry produce online, we
will also be inclined to buy dried produce online.
TAM (The technology acceptance model)
Technology Acceptance Model- TAM as well as TPB was extended and developed from the Theory of
Reasoned Action (TRA) of Fishbein & Ajzen (1975).
TAM predicts behavior using information technology (Davis, 1986). In the TAM model, behavioral
intention (BI) is influenced by attitude (A) and perceived usefulness (U). Meanwhile, behavioral intentions
in the TRA model are influenced by attitudes and subjective norms. Perceived usefulness will influence
behavioral intention to use. For example, if it is more profitable for you to buy dry produce online than
buying it in person, you would choose to buy dried produce online.
2.1 Subjective norms (SN)
According to Ajzen and Fishbein (1980), subjective norm is an important factor determining the influence
of society on behavioral intention. But according to Ajzan & Driver (1980) defines that subjective norms
are perceived as perceived pressures imposed by others such as neighbours, friends, colleagues... those who
perform the action behavior, and that action directly or indirectly affects the respondent's behavior.
Subjective norms refer to the perception of ‘‘The person most people who are important to him think he
should or should not perform the behavior in question’. Khalil and Michael (2008), Lin 2007 reported that
friends, family members and co-workers are subjective criteria that have a positive influence on individuals
buying online. At the same time, there are many studies showing that subjective norms directly affect the
purchase intention of users.
Previous research has shown that the country of origin of a product can have an impact on consumers'
willingness to buy such a product (Kleinetal, 1998; Sharmaetal, 1995). In the present study, the normative
effect of consumer nationalism is superseded by another factor, namely the subjective norm attached to the
purchase of Danish products due to limited domestic production institutions in Kuwait (Meyer et al. 2007).
Previous research has found subjective norms to play a more important role than attitude in predicting
consumer intention behavior in collectivist societies (Lee and Green, 1991). From the above bases, the
proposed hypothesis is:
H1: Subjective norms have a positive effect on consumer purchase intention
H2: Subjective norms have a positive effect on consumers' online buying behavior of agricultural products
2.2 Perceived usefulness (PU)
Perceived usefulness refers to the degree to which a person believes that a particular technology they are
using will increase their task performance (Davis, 1989; Liao, To, & Liu, 2013). . In a study by Massoud
Moslehpour and colleagues conducted and published in 2018, it was shown that consumers in different
countries have the expectation to seek out the benefits of their own purchases goods over the internet.
Besides, the study of Ha et al. (2020) proves that the shopping intention of consumers is directly affected
by the perceived usefulness that shopping brings to them. Research by Shafique Ur Rehman, et al (2019)
has concluded that perceived usefulness, ease of use, attitudes, subjective norms and perceived behavioral
control have a positive influence and significantly on consumer purchase intention. Purchase intention
(CPI) mediates between all five independent constructs and online shopping behavior (OSB). So the
proposed hypothesis is:
H3: Perceived usefulness has a positive effect on consumers' intention to buy agricultural products online
H4: Perceived usefulness has a positive influence on consumers' online agricultural buying behaviors
2.3 Attitude (ATT)
Attitudes are learned and developed over a certain period of time and are often difficult to change but can
be influenced by psychological motivations of satisfaction (Lien and Cao, 2014). More specifically,
attitudes change over time as individuals learn new concepts about the idea or object they are evaluating
(Shaouf et al., 2016). According to Allport (1935), attitude is an important factor determining an individual's
disposition and has a positive relationship with behavior. It is defined as the degree to which an individual
makes a positive or negative assessment of a behavior (Fishbein and Ajzen, 1977). In the context of online
shopping, consumers will develop a positive attitude towards buying products online when they find that

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internet-connected devices or tools can be used easily. Therefore, we propose the following research
hypothesis:
H5: Attitude has a positive effect on consumers' intention to buy dry agricultural products online
2.4 Trust (TRU)
Trust plays an important role in creating satisfied outcomes in online shopping (Aiken et al. 2007). The
theory of planned behavior (Ajzen 2011) states that beliefs influence customers' attitudes and risk
perceptions towards online shopping, which in turn affects purchase intention in the shopping environment
online. When customers perceive that adopting online shopping will bring them valuable experiences both
functionally and emotionally, they may rate the online store as trustworthy and choose to buy. For online
shopping, customers cannot touch, see or check the quality of the product, so trust plays an extremely
important role in e-commerce. Loketkrawee and Bhatiasevi (2018) found that website trust is positively
associated with attitude towards online grocery shopping, while Alagoz and Hekimoglu (2012) found that
online trust improve attitudes towards online food ordering. In addition, Mortimer et al. (2016) suggested
that trust has a positive effect on behavioral intentions of occasional online grocery customers. Authors like
Naresh et al. (2015) and Keh and Shieh (2001) suggest that trust in online shopping and websites plays an
important role in consumers' decision to buy food online. Therefore, our proposed research hypothesis is:
H6: Trust has a positive effect on consumers' intention to buy dry agricultural products online
2.5 Perceived product quality (PPQ)
Perceived product quality is the individual value assessed about a product to provide fitness for
consumption based on relevant quality attributes (Steenkamp, 1990; Kim & Lee, 2016). Steenkamp (1990)
studied attributes that can affect perceived product quality and found that product characteristics are internal
and external features of the product. Product externals are attributes associated with the product but not part
of the product including price, brand name, and packaging. The internal markings of a product represent
product-related attributes such as ingredients, freshness, and nutrition. With these arguments in mind,
Chung et al. (2006) did an empirical test and found that price, brand, packaging, taste, nutrition and
freshness are the determinants of perceived product quality. They also suggest that perceived product
quality can be a motivator for consumers to make purchasing decisions.
Perceived product quality can enhance consumer purchase intention. Specifically, the study by Dodds et al.
(1991) and Tsiotsou (2006) found that the higher the perceived product quality, the greater the consumer's
intention to purchase the product. Subsequently, some scholars asserted a positive relationship between
perceived product quality and consumer behavior in the food market (Truong & Nguyen, 2020; Wang,
2013). Therefore, product quality is perceived as the most important determinant of consumers' willingness
to shop online (Rosillo-Diaz et al., 2019; Wells et al., 2011). The impact of perceived quality on consumer
purchase intention is demonstrated in the study of Jianhua Wang et al., (2020), the study of Ha et al. (2019)
and Ying-qing CH et al. (2018). Therefore, we propose the following hypothesis:
H7: Product quality has a positive effect on consumers' intention to buy dry agricultural products.
2.6 Perceived risk (PR)
Perceived risk is defined by Dowling and Staelin (1994) as “a consumer's perception of uncertainty and
adverse consequences of purchasing a product or service”. According to Bettman, J. R. (1973) Risk can be
divided into 2 types: Inherent risk is the potential risk that a product may have; Treated risk is the degree
of conflict that a product category can have when a buyer selects a brand from a product category during
his or her normal purchasing process. There are many studies on perceived risk that have shown that
perceived risk has a negative impact on consumers' online shopping behavior (Adnan, 2014). Research by
(Bhatti, 2018; Chakraborty, 2016) has shown that product risk negatively affects online shopping behavior.
It is also the factor that is considered to have the strongest influence on online shopping intention (HA,
Ngoc Thang, et al., 2021). In addition, privacy risks also include financial losses because consumers think
they can be scammed and lose money through online shopping (Crespo, del Bosque, & de los Salmones
Sanchez, 2009).
The perceived risk will give the client a feeling of uncertainty about the future. This uncertainty will directly
affect the purchase intention of consumers. Due to the fact that network security is uncertain, consumers
may worry about the legal use of personal and financial information. Over the past decades, perceived risk

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has been identified as an important factor driving consumer acceptance of online shopping, and online
shopping risk can be classified as economic risk, performance risk, psychological risk, and time risk
(Forsythe and Shi, 2003; Huang et al., 2014). So the hypothesis that we propose is:
H8: Perceived risk has a negative effect on consumers' intention to buy dry agricultural products online.
H9: Perceived risk has a negative effect on consumers' online buying behavior of dry agricultural products.
2.7 Online Purchase Intention (OPI) and Online Shopping Behavior (OSB)
According to (Ajzen, 1991) Intention is assumed to capture the motivational factors affecting a behavior;
they are indications of the extent to which people are willing to perform a certain behavior. Purchase
intention is defined as an individual's planning to purchase goods or services in the future (Limbu et al.,
2012). Intentions are antecedents of behavior and they are powerful predictors of behavior (Ajzen, 1991).
Similar in the study of Orapin Laohapensang, 2009; Bhatti et al. (2020); Bhatti, A., & Ur Rahman, S. (2019)
also suggested that intention is an important factor to predict online shopping behavior. In the study of (Yi
JinLim, et al., 2016; Jamil and Mat (2011)) Purchase intention significantly positively affects online
shopping behavior. The stronger the intention, the greater the probability that an action will be performed
(Ajzen, 2001; Azjen, 1991). In the Theory of Reasoned Action (TRA) by Ajzen & Fishben (1980) and The
theory of planned behavior (TPB) by Ajzen (1991), it is stated that "purchasing behavior is determined by
his or her intention to perform the behavior”. Behavior is influenced by behavioral intention, attitude toward
behavior, behavioral control, and also subjective norm (Ajzen and Fishbein, 1980). In the study (Bhatti,
2018) suggested that future studies should study buying intention as mediators with online shopping
behavior. With previous studies, the group proposed the following hypothesis:
H10: Purchase intention has a positive influence on consumers' online buying behavior of dry agricultural
products.
Figure 1: Suggested author model
Sources compiled by the author himself
Subjective norms
Perceived usefulness
Attitude
Trust
Perceived product
quality
Perceived risk
Online Purchase
Intention
Online Shopping
Behavior
H2
H1
H4
H3
H5
H6
H7
H8
H9
H10

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3 RESEARCH METHOD
The research process was carried out with two methods: qualitative research and quantitative research.
Qualitative research aims to explore, adjust, supplement and complete the questionnaire. The study consists
of 2 phases. Stage 1: Researching theoretical bases, concepts, related models to build models and scales.
Stage 2: Group discussion. From that result, the research team adjusted the scale and the official
questionnaire.
Quantitative research method: Phase 1: Randomly survey 140 samples by online form to check and then
adjust the questionnaire accordingly. Phase 2: Quantitative research was carried out with a total sample of
557 through an online survey, however 77 questionnaires were rejected and only 480 questionnaires were
valid. Data were entered for analysis using SPSS software. Finally, the research team discussed the research
results, then offered some managerial implications to improve the effectiveness of business strategies for
retailers and online businesses in the Ho Chi Minh City. The detailed observed variables will be presented
in Appendix 1.
In terms of gender, the percentage of women accounted for the majority of the population with 64.79% and
the male gender was 35.21%. In terms of age, the age group 18-25 accounted for the highest proportion
with 92.08%, the age group 26-35 participated in the survey accounted for nearly 7.29% and the age group
36-45 had the lowest participation rate, accounting for about approx 0.63%. Therefore, the majority of
customers who buy dry agricultural products online who participated in the survey are young people.
Regarding income, income under 5 million dong accounts for the highest proportion at 63.75%. The second
highest proportion is income from 5 -10 million with 29.38%. Next is the income from 10 -20 million
accounted for 5.42% and the lowest is the income over 20 million accounted for 1.46%.
In terms of qualifications, the percentage of survey participants with a university degree is the majority of
the sample with 91.25%. Other qualifications accounted for a small percentage, college accounted for
2.71%, graduate accounted for 2.71%, high school accounted for 3.1% and finally the smallest proportion
was intermediate level with 0.2% of the research samples.
4 RESULTS
Cronbach's Alpha test
From the analysis results of Cronbach's Alpha, it shows that the reliability of all 8 scales with Cronbach's
Alpha index ranges from 0.795 - 0.948. According to many previous studies, when the Cronbach's Alpha
index is from 0.8 to close to 1, it is a good scale (Nunnally and Bernstein, 1994). The Cronbach Alpha index
of 0.7 to 0.8 is a usable scale (Peterson, 1994). Therefore, we conclude that the proposed scales include:
SN, PU, PR, TRU, ATT, PPQ, OPI all meet the standards and have statistical significance. Based on the
results of Cronbach's Alpha analysis, the correlation coefficients of the observed variables are all greater
than or equal to 0.479.
Table 1: Cronbach's Alpha results for 8 coal measurements
No.
1
The Scale
Number of observed
variables
Coefficient Cronbach's
Alpha
1
Subjective norms (SN)
3
0.918
2
Perceived usefulness (PU)
3
0.822
3
Attitude (ATT)
5
0.795
4
Trust (TRU)
4
0.942
5
Perceived product quality (PPQ)
4
0.918
6
Perceived risk (PR)
5
0.942

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7
Online Purchase Intention (OPI)
3
0.877
8
Online Shopping Behavior (OSB)
5
0.948
Explore factor analysis (EFA)
After analyzing Cronbach's Alpha, 4 independent variables of the research model and 1 dependent variable
with 22 observed variables were kept unchanged and included in EFA exploratory factor analysis.
According to Hair et al. (2010), the criteria when analyzing EFA are: The KMO index with a value between
0.5 and 1 is suitable for exploratory factor analysis. After EFA analysis of all 6 observed variables, the
following results are obtained:
The KMO and Bartlett tests in factor analysis showed that KMO=0.897, satisfying the requirements
(0.5<KMO<1), showing that the data is suitable for exploratory factor analysis. And it gives Sig =0 < 0.05,
which proves that the factors are linearly correlated with each other in the population. Extracted variance =
80,570 >50%, with this result showing 6 explanatory factors and 80.570% variation of observed data.
Eigenvalue= 1,054 >1, extraction coefficient means good information. Thus, after analyzing EFA
exploratory factors, the results of the rotation component matrix show that the observed variables converge
into groups of factors consistent with the theoretical basis and the proposed model. The results of running
the first EFA analysis are summarized in Appendix 2a.
After conducting EFA analysis for the dependent variable OSB and OPI, the results are summarized and
presented in detail in Appendix 2b and 2c. The two-variable EFA analysis of buying behavior and intention
has the following results: KMOs all meet the requirements (0.5<KMO<1), showing that the data is suitable
for exploratory factor analysis. Eigenvalues are all >1. And having Sig =0 < 0.5 shows that the factors are
linearly correlated with each other in the population. Therefore, the original research model (Figure 1) will
be retained.
Regression analysis
1st regression analysis
To evaluate the impact of the factors affecting the online buying behavior of dry agricultural products of
consumers, the study conducted regression analysis with dependent variable (OSB) and 6 independent
variables (PPQ, SN, PU, ATT, PR, TRU). The results of linear regression analysis are summarized and
presented in the following tables:
Table 2: Model summary table of factors affecting consumers' intention to buy dry agricultural products online
Model
R
R
Square
Adjusted R
Square
Std. Error of the
Estimate
Durbin-Watson
1
.775a
.600
.595
.65644
1.839
a. Predictors: (Constant), PPQ, SN, PU, ATT, PR, TRU
b. Dependent Variable: OPI
The coefficient of determination R2 indicates how much (%) of the dependent variable is explained by the
independent variable. Table 2, the results of the regression analysis show that the coefficient of
determination R2 = 0.600 (≠0) ie 60% of this index means that the variation of the OSB variable is explained
by the variation of 6 factors (PPQ, SN, PU, ATT, PR, TRU), the remaining 40% belongs to other random
factors and errors. In this model, Adjusted R Square = 0.595 (59.5%) This index can help determine the fit
of the model more accurately and safely. The Durbin - Watson statistical value is 1,839 in the range (1,3)
from which it can be inferred that the data has no first-order correlation phenomenon, the data meets the
requirements.

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Table 3: ANOVA factors affecting the relationship quality intention to buy dry agricultural products online
Sum of Squares
df
Mean Square
F
Sig.
Regression
306.358
6
51.060
118.490
.000b
Residual
203.825
473
.431
Total
510.183
479
a. Dependent Variable: OPI
b. Predictors: (Constant), PPQ, SN, PU, ATT, PR, TRU
From Table 3 it is shown that F = 118,490 and significant Sig level. = 0.000 (sig. ≤ 0.05), which means that
the regression model fits the collected data and the included variables have statistical significance with 5%.
Table 4: Regression weight table of factors affecting the intention to buy dry agricultural products online
B
Std.
Error
Beta
t
Sig
Toleran
ce
VIF
Hypotheses
Result
(Constant)
-.673
.301
-2.240
.026
SN
.211
.038
.216
5.527
.000
.554
1.804
H1
CN
PU
.414
.041
.309
10.177
.000
.919
1.088
H3
CN
PR
-.204
.040
-.208
-5.113
.000
.510
1.961
H8
CN
ATT
.426
.042
.311
10.061
.000
.886
1.128
H5
CN
TRU
.124
.041
.130
3.002
.003
.450
2.223
H6
CN
PPQ
.097
.037
.080
2.626
.009
.909
1.100
H7
CN
a. Dependent Variable: OPI
As the results presented in Table 4, we see the Sig index. of all variables PPQ, SN, PU, ATT, PR, TRU are
less than 0.05, so it can be concluded that the above variables have an influence on consumers' intention to
buy dry agricultural products online and these variables have has a positive effect on the dependent variable
of intention to buy dry agricultural products online due to the positive Beta coefficient. From the analysis
results, we have the regression model as:
OPI = 0.216 SN + 0.309 PU - 0.208 PR +0.311 ATT + 0.130 TRU + 0.080 PPQ
2nd regression analysis
The results of the second regression, to test the research hypothesis about the relationship between the
independent variables (SN, PU, PR, OPI) and the dependent variable OSB, the results are detailed in the
table below:
Table 5: The table summarizes the model of factors affecting the online buying behavior of dry agricultural products
of consumers
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
Durbin-Watson
1
.741a
.548
.545
.72734
2.040

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a. Predictors: (Constant), OPI, PU, PR, SN
b. Dependent Variable: OSB
The coefficient of determination R2 = 0.548 (≠0) ie 60% of this index means that the variation of the OSB
variable is explained by the variation of 6 factors (PPQ, SN, PU, ATT, PR, TRU)), the remaining 40%
belongs to other random factors and errors. In this model, Adjusted R Square = 0.595 (59.5%) This index
can help determine the fit of the model more accurately and safely. The Durbin - Watson statistical value
is 1,839 in the range (1,3) from which it can be inferred that the data has no first-order correlation
phenomenon, the data meets the requirements.
Table 6: ANOVA factors affecting the quality of the relationship between buying behavior of dry agricultural
products online
Sum of Squares
df
Mean Square
F
Sig.
Regression
305.190
4
76.298
144.222
.000b
Residual
251.289
475
.529
Total
556.480
479
a. Dependent Variable: OSB
b. Predictors: (Constant), OPI, PU, PR, SN
ANOVA factors affecting the quality of the relationship intention to buy dry agricultural products online.
From Table 6 it is shown that F = 144.222 and significant Sig level. = 0.000 (sig. ≤ 0.05), which means that
the regression model fits the collected data and the included variables have statistical significance with 5%.
Table 7: Regression weight table of factors affecting the online purchase of dry agricultural products
B
Std. Error
Beta
t
Sig
Tolerance
VIF
Hypotheses
Result
(Constant)
1.916
.266
7.203
.000
SN
.195
.041
.191
4.773
.000
.593
1.687
H2
CN
PU
.175
.050
.125
3.532
.000
.756
1.323
H4
CN
PR
-.305
.041
-.298
-7.474
.000
.598
1.671
H9
CN
OPI
.361
.045
.346
8.011
.000
.511
1.958
H10
CN
a. Dependent Variable: OSB
From the above results table, it shows that the highest VIF index is 1,958, less than 2. Therefore, we can
conclude that the multicollinearity in this model is small. Sig of factors SN, PU, PR, OPI are all less than
0.05, so they all have influence on OSB variable. In which, the normalized β of the variables SN=0.191,
PU= 0.125, OPI= 0.346 shows that these three variables have a positive impact on the dependent variable
OSB. On the contrary, PR variable has normalized β = -0.298, so PR variable has a negative impact on OPI
variable. From the analysis results, we have the following regression model:
OSB = 0.191*SN + 0.125*PU + 0.041*PR -0.298 PR + 0.346 OPI
Test the hypotheses of the model

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There are 10 proposed hypotheses. At the first regression for the Dependent variable of online purchase
intention, the hypotheses H1, H3, H5, H7, H8, H9 all have sig numbers. <0.05. Therefore, it can be
concluded that these hypotheses are accepted. The remaining hypotheses (H2, H4, H6, H10) are tested in
the second regression for the dependent variable online buying behavior. The results show that these
variables have Sig. < 0.05, so all these hypotheses are accepted.
Purchase intention and perceived usefulness online shopping behavior shows a relationship with each other,
in which purchase intention has the strongest influence, consistent with previous studies Lim, et al., (2016);
Jamil and Mat (2011); Orapin, (2009). Therefore, this is a factor that needs to be considered to increase
consumer’s intention.
Subjective norm has a positive influence on online shopping behavior, a hypothesis supported with the
findings of the study of Shafique Ur Rehman, et al., (2019), Meyer et al., (2007), Lee and Green (1991)
however the effect was not significant, consistent with the results of the study Lim, et al., (2016). Perceived
risk is also a factor that has a negative impact on consumers' online shopping behavior. This finding is
consistent with previous work (Adnan, 2014).
Besides, the subjective norm variables have a positive influence on purchase intention similar to the studies
Khalil and Michael (2008), Lin (2007), Shafique Ur Rehman et al., (2019). Online purchase intention
variable is negatively impacted by perceived risk, which is consistent with previous research (HA, Ngoc
Thang, et al., 2021). Perceived usefulness can increase consumers' intention to shop online, this result is
implied by the study of Ha et al. (2020) and Shafique Ur Rehman et al. (2019).
The results also suggest that attitude is related to online purchase intention. In the previous study Xue
WANG, Jun ZHANG, (2020); Shafique Ur Rehman et al.(2019); Lin (2007 ); Bigne-Alcaniz et al.(2008 )
gave similar results. Sumit Chaturvedi et al., (2016); Naresh et al. (2015) research shows that trust
significantly affects purchase intention. Perceived quality also positively affects consumer purchase
intention. This is demonstrated in the study of Jianhua Wang et al. (2020). Research by Ha et al. (2019) and
by Ying-qing CH et al. (2018) also gave similar results.
5 DISCUSSION
The results of the regression analysis show that the online buying behavior of dry agricultural products of
consumers in Ho Chi Minh City is influenced by 4 factors: subjective norm, perceived usefulness, perceived
risk, and online purchase intention. These factors explain more than 74% of the variance of the dependent
variable. In which, the perceived risk factor has a negative impact on the online purchase of dried
agricultural products in Ho Chi Minh City, the higher this factor, the lower the intention to buy dry
agricultural products online of consumers. The research results also show that there are 6 factors in the
theoretical model affecting the intention to buy dry agricultural products online of consumers in Ho Chi
Minh City, including attitude, perceived usefulness, subjective norms, perceived risk, trust, perceived
product quality with an explanation level of nearly 78% of the variance of behavioral intention. The higher
these factors, the higher the intention to buy dry agricultural products of consumers. In contrast, perceived
risk negatively affects consumers' intention to buy dry agricultural products online in Ho Chi Minh City.
Attitudes were once seen as the starting point of all human behavioral intentions, along with perceived
usefulness contributing to more continuum of behavior. This study once again shows the important
mediating role of behavioral intention in increasing users' online buying behavior. Users' attitudes about
buying dried agricultural products online are gradually changing, accessing wholesale markets, or specialty
agricultural products in regions is no longer too difficult with connection protocol online connection. With
the appearance of more sellers on e-commerce platforms that are increasingly censored, users have felt that
online shopping for dry agricultural products is a good, wise idea and they love to do it there. Users feel
that buying dry agricultural products is no longer difficult no matter where they are, writing preferential
prices inherited from inventory optimization in flash sales programs. The search for dry agricultural
products also needs to become simpler, helping people save time and thereby increase their sense of
usefulness.
Subjective norms still prove important in studies of buying behavior. Online shopping shows an intuitive
connection of user reviews. A miniature social network contributes not only to connect buyers and sellers,
but also to be users to each other. Marketing is an extremely important platform that e-commerce sites want
to attract attention from users. If you do this well, the number of previous users will become a source of

The 2nd International Conference on Advanced Technology & Sustainable Development (ICATSD 2022)
© 2022 Industrial University of Ho Chi Minh City 491
spreading product reviews. Of course, to do this, product quality also plays a very important role in
promoting behavioral intention.
The quality of products that users perceive can be viewed from the perspective of products that demonstrate
sufficient relevant information and documents. This will be partly certified by e-commerce platforms. The
censorship of information about documents, origin, and food safety certification is increasingly under
scrutiny. Certified "mall" brands are often more interested by users, due to the endorsement of famous
brands and genuine sales. The characteristics of the food supply chain, after all, the most important thing is
still the quality of the product, the recognition of quality is not only from the point of view of nutritional
value.
Perceived risk is a deterrent to users' purchase intention and behavior. This is even the third strongest factor
affecting buying behavior, so minimizing perceived risk should be considered. Like every other product,
users expressed concern that online payments often contain many risks. This implies developing and
partnering with online payment providers with trusted security protocols such as e-wallets or still allowing
payment-on-delivery options. In addition, the security of user information, information about online
payment cards should also be concerned. Creating trust is also an important factor in increasing user
intention to buy behavior. Reliability can be considered in terms of products, the quality of information
about the products that the website provides, the information about the conditions for buying or receiving
promotions is done openly and transparently.
6 CONCLUSION
Researchers can refer to the model of this study to develop more different research directions on consumer
purchase intention and behavior in the context of online business growing strongly in Vietnam. The research
results have both theoretical and practical significances, helping businesses capture the intention and buying
behavior of consumers, and promote the effectiveness of online business strategies. Thereby, improving the
position and competitiveness of enterprises in the market.
The limitation of this study is that it only focuses on understanding the influence of subjective norm factors,
perceived risks, perceived usefulness, perceived product quality, attitudes, and trust on intention and
behavior online purchase of dried agricultural products by consumers in Ho Chi Minh City, aged 18-25. In
fact, there are many other factors that also influence consumers' buying behavior of dry agricultural
products online but have not been analyzed in this study. The second limitation is that the study only studies
the intention and buying behavior of individuals, not to mention the intention and buying behavior of the
organization's customers. Because the factors affecting the intention and buying behavior of individual
customers and organizational customers are certain differences. Thirdly, in terms of data processing
methods, the study only uses Cronbach's Alpha methods, exploratory factor analysis methods, correlation
analysis methods and linear regression methods to test the reliability. reliability of the scales. However, it
is recommended to use the linear structural model SEM to test the relationship between the factors in a
research model to give the most accurate results. The fourth limitation is that the research is only practical
at the present time, it may no longer be relevant in the future. Finally, the research period is relatively short,
so the number of research samples is not really large. Therefore, the representativeness of the whole is still
limited. Future studies may increase the observational sample size and consider additional factors that
hinder the online purchase of dried agricultural products by consumers in Ho Chi Minh City and
neighboring countries of Vietnam.
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492 © 2022 Industrial University of Ho Chi Minh City
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APPENDIX
Appendix 1: Summary of observed variables
Factor
Scale
Code
Source
Subjective
norms
People who are important to me will encourage me
to buy dry farm produce online
SN1
Pavlou và Fygenson (2006);
MD Clemes, C. Gan và J.
Zhang (2014)
Most people, important to me, want me to buy dry
produce online
SN2
Song-Lin-Wơng et al. (2018)

The 2nd International Conference on Advanced Technology & Sustainable Development (ICATSD 2022)
© 2022 Industrial University of Ho Chi Minh City 493
People whose views I appreciate will love that I
buy dry produce online
SN3
Song-Lin-Wơng et al. (2018)
Perceived
usefulness
Online shopping helps me buy dry agricultural
products that I can't buy where I live
PU1
Ha Ngoc Thang (2015)
Buy dry agricultural products online will be
cheaper
PU2
Ha Ngoc Thang (2015)
Online store improves search performance and
buys dry agricultural products
PU3
NathaliePeña-García(2020)
Attitude
Buying dry produce on online stores is a good idea
ATT1
Radka Bauerová, Martin
Klepek (2018)
Buying dry farm produce on online stores is a wise
idea
ATT2
Hsiu-Fen Lin (2007)
I like to buy dried agricultural products on online
stores
ATT3
Hsiu-Fen Lin (2007)
Trust
The dry agricultural products on the website are
reliable
TRU1
Chen, Shu -Hui and Lee, Kuan
-Ping (2008)
I believe in the information about the products that
the website provides
TRU2
AnilBilgihan (2016)
Conditions of purchase of dried agricultural
products are specified in the website
TRU3
Chen, Shu -Hui and Lee, Kuan
-Ping (2008)
Website ensures customer privacy
TRU4
Chen, Shu -Hui and Lee, Kuan
-Ping (2008)
Perceived
product
quality
I agree that dry agricultural products with safety
certificates will ensure nutritional value
PPQ1
Jianhua Wang, Junying Tao,
May Chu (2020)
The wide range of dry agricultural products makes
it easy for me to compare and choose good quality
dry agricultural products
PPQ2
Xue Wang, Jun Zhang (2020)
I am often impressed with dry agricultural
products that are well described in terms of overall
quality
PPQ3
I usually buy dried agricultural products online at
famous brand stores
PPQ4
Perceived
risk
Buying goods on e-stores carries a higher risk of
payment
RRNT1
Wu, I.-L., Chiu, M.-L., &
Chen, K.-W. (2020)
I find online shopping for dry agricultural products
to be very risky
RRNT2
Mr. Vinay Kumar & Dr.
Ujwala Dange (2014)
I am concerned that the online retailer will
disclose my personal information to others
RRNT3

International Symposium on Sustainable Development in Transition Economies (ISSDTE 2022)
494 © 2022 Industrial University of Ho Chi Minh City
Products may not be delivered to me within the
time promised by online retailers
RRNT4
I may receive a defective or unwanted product
when I shop online
RRNT5
Online
Purchase
Intention
I think I will buy dry agricultural products online
in the near future
OPI1
Shafique Ur Rehman et al.
(2019)
I will continue to buy dried agricultural products
through online stores
OPI2
Peña García et al. (2020);
Hausnam & Siepe (2009)
I will recommend to friends and colleagues to buy
dried agricultural products online
OPI3
Kim et al. (2016)
Online
Shopping
Behavior
Using the Internet to buy dry agricultural products
online is easy
OSB1
Shafique Ur Rehman1 et al.
(2019)
I buy dry farm produce online because I don't have
to leave the house to shop
OSB2
I buy dry agricultural products online because I
can get detailed product information online
OSB3
I buy dry farm produce online because I have more
options
OSB4
Buy dry agricultural products online with many
facilities for easy price comparison
OSB5
Hypothesis: 8; Number of variables observed: 30
Appendix 2 Table a. EFA for 6 independent variables
Rotated Component Matrix a
Component
1
2
3
4
5
6
RRNT5
.860
RRNT3
.846
RRNT4
.845
RRNT2
.830
RRNT1
.806
PPQ1
.896
PPQ4
.882
PPQ3
.882
PPQ2
.881

The 2nd International Conference on Advanced Technology & Sustainable Development (ICATSD 2022)
© 2022 Industrial University of Ho Chi Minh City 495
TRU2
.832
TRU1
.802
TRU4
.801
TRU3
.797
SN3
.852
SN2
.827
SN1
.806
PU2
.888
PU1
.841
PU3
.814
ATT1
.843
ATT2
.826
ATT3
.809
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy
.897
Bartlett's Test of Sphericity
Approx. Chi-Square
8213.077
df
231
Sig.
.000
Source: Compiled from the results of running EFA analysis on SPSS
Table b. EFA for OPI dependent variable
Component Matrix
OPI2
0.903
OPI1
0.901
OPI3
0.884
KMO
0.741
Bartlett’s (sig.)
0.00
Method wrong deduction
80.321%
Eigenvalue
2.410

International Symposium on Sustainable Development in Transition Economies (ISSDTE 2022)
496 © 2022 Industrial University of Ho Chi Minh City
Source: Compiled from the results of running EFA analysis on SPSS
Table c. EFA for OSB dependent variable
Component Matrix
OSB1
0.930
OSB4
0.919
OSB2
0.909
OSB5
0.901
OSB3
0.894
KMO
0.911
Bartlett’s (sig.)
0.000
Method wrong deduction
82.922
Eigenvalue
4.146
Source: Compiled from the results of running EFA analysis on SPSS