Syntax Literate: Jurnal
Ilmiah Indonesia p–ISSN: 2541-0849 e-ISSN: 2548-1398
Vol. 7, No. 10, Oktober
2022
THE
INFLUENCES OF PERCEIVED FOOD QUALITY, PRICE FAINESS, AND SATISFACTION ON
CUSTOMERS REVISIT INTENTIONS TOWARDS CONSUME ORGANIC FOOD RESTAURANTS IN
YOGYAKARTA
Rakha Fathin Prakoso1*,
Raden Roro Ratna Roostika2
Faculty of
Business and Economics, Universitas Islam Indonesia, Indonesia
E-mail: 1*[email protected],
2[email protected]
Abstract
When COVID-19 began to attack various
countries, people began to realize how important it is to eat healthy food.
People started looking for healthy food even when they ate at restaurants. This
study aims to conduct research on people's interest in buying organic food in
restaurants in Yogyakarta using two theories: Theory of Planned Behavior and
Theory of Consumer Behavior. The aim is to find out whether there is a positive
relationship between Perceived Food Quality to Price Fairness, and Customer
Satisfaction, Price Fairness to Customer Satisfaction, and Satisfaction to
Revisit Intention, to restaurants in Yogyakarta. A total of 250 people
participated in the study by filling out questionnaires, and data processing
programs using SMART PLS. The results showed that there was a positive
relationship between the following variables: perception of food quality with price
fairness, perception of food quality with customer satisfaction, price fairness
with customer satisfaction, and customer satisfaction with the intention of
visiting again.
Keywords: COVID-19, Organic food,
Restaurant study.
Introduction
At
the beginning of 2020, the Covid-19 pandemic broke out around the world. The
effects that occur are also numerous. Country's economic decline, restrictions
on exchanges, especially human health. Maintaining a healthy body is very
important. A healthy body can prevent various diseases and viruses. There are
many ways to keep your body healthy, one of which is by choosing foods that are
nutritious, and high in protein. Organic foods are foods that have many
benefits for the body because they contain antioxidants, improve heart disease,
reduce pesticides, and are toxin-free, antibiotic-resistant, and naturally
nutritious.
Organic food is an alternative for
consumers who want to avoid the health risks of chemical contamination (Nelson
et al., 2019). In addition to health and safety benefits, organic food is also
good for the environment. It can also improve animal welfare and promote
biodiversity (Lindstrom et al., 2020). Many people perceive organic production
and products to be greener, healthier, and tastier than conventionally produced
food (Gustavsen & Hegnes, 2020). According to traditional consumer opinion,
organic foods are more environmentally friendly (Teng & Lu, 2016),
naturally purer, and therefore healthier (Ditlevsen et al., 2019).
In recent years, consumers have
witnessed a growing interest in organically produced foods, as they are seen as
a healthy and environmentally friendly alternative (Kushwah, Dhir, Sagar &
Gupta, 2019; Yadav, 2016). The development of the organic sector has been
driven by growing interest from consumers and retailers who have played a role
in stimulating growth, promoting products, expanding assortments, and helping
farmers switch (Ozguven 2012). The demand for organic foods is motivating food
manufacturers to offer more organic foods, and consumers are purchasing these
products in both physical and virtual stores for home consumption. Scientists
believe there are several reasons for the growing interest in organic foods,
but the most important reason is that naturally grown foods are better for the
environment and consumers' personal and family health (Kushwah, Dhir, Sagar,
& Gupta, 2019; Shin & Mattila, 2019; Tandon et al. 2020).
Eating out at restaurants has become
a popular pastime in recent decades. These collective changes in diet have
raised concerns about dietary quality among consumers. Eating out is associated
with higher caloric intake, lower vegetable intake, and higher meat intake
(Lachat et al., 2012). In other words, eating out at restaurants is typically
less healthy and less sustainable than eating out at home.
Organic restaurants, on the other
hand, are good options for organic food consumers to eat out (Konuk, 2019).
This format, therefore, allows restaurateurs to gain a competitive advantage by
differentiating themselves from their competitors with health claims. Organic
foods are generally expensive concerning the additional production costs. For
this reason, organic menu prices in restaurants are inevitably higher than
conventional ones. Previous studies have shown that price is a significant
barrier to organic food consumption (Marian et al., 2014; Hughner et al.,
2007). It is important to understand whether consumers are willing to pay extra
to purchase healthy foods. Consumer responses to a willingness to pay extra to
purchase different types of health and wellness foods (Tabassum, Ali, 2020).
Drawing from the information presented in the preceding problem, the
researcher establishes the definitive objectives of this study, which
encompass: (1) elucidating the potential positive impact of perceived food
quality on perceptions of price fairness, (2) examining the potential positive
correlations between perceived food quality and customer satisfaction, (3)
investigating whether perceptions of price fairness yield positive effects on
customer satisfaction, and (4) delving into the potential positive influence of
customer satisfaction on the intention of customers to revisit.
This research helps social commerce
companies understand that restaurants need to offer fresh and delicious menus.
The attractive food presentation on the menu should not be overlooked. The
example is serving organic food decorated with aesthetic tableware can visually
enhance your menu. This also improves the perceived quality of organic foods.
In addition, organic restaurants can put the nutritional value of organic foods
on their menus to promote food health. Similarly, you can put a brochure on the
table with information about the standards of organic food production to
increase your customers' knowledge of organic food. This may improve the
customer's perceived quality rating.
Questionnaires will be distributed
in Yogyakarta regions for this research, with the goal of decreasing the scope
of the researcher and therefore make data gathering more easily.
The population is defined as a group
of people as the object that shares a common characteristic as specified by the
researcher’s sampling criteria. The population will be asked questions based on
their name, age, educational background, monthly income, and occupation. To
protect privacy rights, identities will not be released in this research.
Sample research is certain objects
chosen to represent the whole population. The population in this research is
the customers of organic food restaurants in Yogyakarta. Moreover, the amount
of research samples is 250 people.
The information utilized in this
research are primary. Primary data is data obtained firsthand from the object
of the research by employing a measurement or data retrieval tool directly on
the subject as the source of the information sought. In this research, the data
were collected by using primary quantitative data collection to test the
hypothesis. Moreover, it will be distributed to 250 respondents. Whereas, the
secondary data is collected from the supported journal to assist this research.
Further, the secondary data used in this research were collected from previous
literature reviews and relevant journals.
Chapter 4 will describe the results of data
processing. Some of the issues discussed in this chapter are the
characteristics of the respondents, the results of the descriptive analysis,
and the inferential statistics using SmartPLS and their discussion. The results
of data processing were used as the basis for accepting or rejecting
hypothesis. The number of respondents used in the analysis for this study was
250. Data were collected using an online Google Forms questionnaire.
A. Descriptive Analysis
Table
1
Descriptive Table
|
Average |
Median |
Minimum |
Maximum |
Standard Deviation |
|
|
PFQ 1 |
5.15 |
5 |
2 |
6 |
0.917 |
|
PFQ 2 |
4.67 |
5 |
1 |
6 |
1.065 |
|
PFQ 3 |
5.16 |
5 |
3 |
6 |
0.882 |
|
PFQ 4 |
4.98 |
5 |
2 |
6 |
1.024 |
|
PF 1 |
5.60 |
6 |
2 |
6 |
0.710 |
|
PF 2 |
5.54 |
6 |
3 |
6 |
0.770 |
|
PF 3 |
5.50 |
6 |
3 |
6 |
0.806 |
|
S 1 |
5.21 |
5 |
2 |
6 |
0.804 |
|
S 2 |
5.36 |
5 |
3 |
6 |
0.702 |
|
S 3 |
5.02 |
5 |
2 |
6 |
0.912 |
|
RI 1 |
5.17 |
5 |
2 |
6 |
0.914 |
|
RI 2 |
5.05 |
5 |
2 |
6 |
0.854 |
|
RI 3 |
5.15 |
5 |
3 |
6 |
0.747 |
B. Model Evaluation
1.
Outer Model (Measurement Model)
The assessment of the measuring model is examined through
various measures such as Convergent Authenticity, Discriminant Authenticity,
and Dependability. The measuring model is computed by utilizing the PLS
Algorithm.
a.
Convergent Validity
A valid indicator is characterized by a loading factor that is positive
and exceeds 0.7. This factor quantifies the significance of each indicator or
item in measuring the corresponding variable. Indicators with high loading
factors are indicative of a stronger (dominant) variable measure. Table 7
displays the loading factor values.
Table
2
Convergent Validity Test
|
Variable |
Indicator |
Loading Factor |
Description |
|
Perceived Food Quality |
PFQ 1 |
0.732 |
Valid |
|
PFQ 2 |
0.780 |
Valid |
|
|
PFQ 3 |
0.711 |
Valid |
|
|
PFQ 4 |
0.814 |
Valid |
|
|
Price Fairness |
PF 1 |
0.887 |
Valid |
|
PF 2 |
0.933 |
Valid |
|
|
PF 3 |
0.952 |
Valid |
|
|
Customer Satisfaction |
S 1 |
0.886 |
Valid |
|
|
S 2 |
0.942 |
Valid |
|
|
S 3 |
0.883 |
Valid |
Source: Smart PLS Output Result
As the data presented in Table 7, it is evident that all the indicators
have a loading factor value of over 0.7. Therefore, it can be concluded that
these indicators are reliable and can be used as an effective measure of latent
variables.
b.
Discriminant Validity
The assessment of a model's validity is done through the examination of
its discriminant validity. This is determined by analyzing the cross loading
value which displays the extent of the correlation between the construct and
its indicators, as well as the indicators of other constructs. To ensure a
reliable cross-loading value, it should exceed 7 or be compared to the square
root value of the average variance extracted (AVE) of each construct and its correlation
with other constructs within the model. If the AVE root value surpasses the
correlation value between the construct and other constructs in the model, then
the model is considered to possess a strong discriminant validity value.
Table
3
Forrmell-Lacker Criterion
Value
|
Variable |
Customer Satisfaction |
Perceived Food Quality |
Price Fairness |
Revisit Intention |
|
Customer Satisfaction |
0.904 |
|
|
|
|
Perceived Food Quality |
0.678 |
0.760 |
|
|
|
Price Fairness |
0.582 |
0.577 |
0.925 |
|
|
Revisit Intention |
0.698 |
0.682 |
0.613 |
0.884 |
Source: Smart PLS Output Result
Cross-Loading Value
|
Indicators |
Customer Satisfaction on |
Perceive Food Quality |
Price Fairness |
Revisit Intention |
|
|
PF 1 |
0.490 |
0.447 |
0.887 |
0.515 |
|
|
PF 2 |
0.551 |
0.605 |
0.933 |
0.618 |
|
|
PF 3 |
0.572 |
0.540 |
0.952 |
0.564 |
|
|
PFQ 1 |
0.480 |
0.732 |
0.414 |
0.477 |
|
|
PFQ 2 |
0.538 |
0.780 |
0.463 |
0.526 |
|
|
PFQ 3 |
0.352 |
0.711 |
0.332 |
0.393 |
|
|
PFQ 4 |
0.639 |
0.814 |
0.514 |
0.635 |
|
|
R 1 |
0.664 |
0.611 |
0.610 |
0.920 |
|
|
R 2 |
0.613 |
0.621 |
0.469 |
0.892 |
|
|
R 3 |
0.568 |
0.577 |
0.544 |
0.838 |
|
|
S 1 |
0.886 |
0.602 |
0.506 |
0.587 |
|
|
S 2 |
0.942 |
0.550 |
0.513 |
0.631 |
|
|
S 3 |
0.883 |
0.678 |
0.555 |
0.668 |
Source: Smart PLS Output Result
Based on Tables 8 and 9, the cross-loading value on each item has a
value of> 0.70, and also on each item has the greatest value when associated
with its latent variable compared to when associated with other latent
variables. This shows that each manifest variable in this study has accurately
explained its latent variable and proves that the discriminant validity of all
items is valid.
c.
Reliability
Reliability in PLS uses Cronbach alpha and Composite reliability values.
It is declared reliable if the Composite reliability value is above 0.7 and the
Cronbach's alpha value is recommended above 0.6. The following Table 10 is the
value of Cronbach alpha and Composite reliability:
Table 5
Reliability Test
|
Variable |
Cronbach’s Alpha |
Composite Reliability |
Average Extracted Variance (AVE) |
|
Customer Satisfaction |
0.888 |
0.931 |
0.818 |
|
Perceived Food Quality |
0.758 |
0.845 |
0.578 |
|
Price Fairness |
0.915 |
0.946 |
0.855 |
|
Revisit Intention |
0.860 |
0.915 |
0.781 |
Source: Smart PLS Output Result
Based on Table 10 above, it can be seen that the composite reliability
value of all research variables is> 0.7 and Cronbach Alpha> 0.6. These
results indicate that each variable has met the composite reliability and Cronbach
alpha so it can be concluded that all variables have a high level of
reliability. Therefore further analysis can be carried out by checking the
goodness of fit of the model by evaluating the inner model.
2.
Inner Model Evaluation
(Structural Model)
After testing the outer model, the next step is to test the
inner model. Inner model or structural model testing is carried out to see the
relationship between constructs, significance values, and R-square of the
research model.
Evaluation of the PLS structural model begins by looking at the R-square
of each dependent latent variable. Table 11 is the result of the R-square
estimate using PLS.
Table 6
R-Square Test Result
|
Variable |
R-Square |
R-Square Adjusted |
|
Customer Satisfaction |
0.548 |
0.542 |
|
Price Fairness |
0.333 |
0.330 |
|
Revisit Intention |
0.487 |
0.485 |
Source: Smart PLS Output Result
Based on Table 6 the adjusted R-Square value of the Customer
Satisfaction variable is 0.542, this value means that the Customer Satisfaction
variable can be explained by the Perceived Food Quality, Price Fairness, and
Perceived Value variables by 54.2%. The remaining 45.8% can be explained by
other variables not contained in this study.
While the adjusted R-Square value of the Perceived Value variable is
0.548, which means that the Perceived Value variable can be explained by the
Perceived Food Quality and Price Fairness variables by 54.8% and the remaining
45.2% can be explained by other variables that are not included in this study.
b. Predictive Relevance ( Q2 )
Predictive relevance is a test conducted to find out how good the
observation value produced using the blindfolding procedure is by looking at
the Q square value. If the Q square value> 0 then it can be said to have a
good observation value, while if the Q square value < 0 then it can be
stated that the observation value is not good. Q-Square predictive relevance
for structural models measures how well the observed values are generated by
the model and its parameter estimates.
Table 7
Predictive Relevance
|
Variable |
Q² (1-SSE/SSO) |
Description |
|
Customer Satisfaction |
0.437 |
Has a Predictive Relevance Value |
|
Price Fairness |
0.275 |
Has a Predictive Relevance Value |
|
Revisit Intention |
0.375 |
Has a Predictive Relevance Value |
Source: Smart PLS Output Result
Based on the data presented in Table 12 above, it can be seen that the Q
square value on the dependent variable is> 0. By looking at this value, it
can be concluded that this study has a good/good observation value because the
Q square value> 0 (zero).
c. Goodness of Fit
To meet the GoF model criteria, the RMS Theta or
Root Mean Square Theta value is <0.102, the SRMR or Standardized Root Mean
Square value is <0.10 or <0.08 and the NFI value is >0.9, the
following are the results of the Goodness of Fit (GoF) model test:
Table 8
Goodness of Fit Test
Result
|
Criteria |
Saturated Model |
Estimated Model |
|
SRMR |
0.072 |
0.135 |
|
d_ULS |
0.982 |
3.482 |
|
d_G |
0.748 |
0.992 |
|
Chi-Square |
1037.098 |
1263.711 |
|
NFI |
0.758 |
0.705 |
|
Rms Theta |
0.211 |
|
Source: Smart PLS Output Result
Following the GoF model output above, the RMS Theta or Root Mean Square
Theta value is 0.211> 0.102 and the NFI value is 0.758 <0.9, so based on
these two model assessments, it does not meet the GoF model criteria. However,
based on the SRMR or Standardized Root Mean Square value, the value is 0.072
<0.10. So it can be concluded that the model fits the data.
C. Hypothesis Testing Result
Testing the structural relationship model is to explain the
relationship between the variables in the study. Structural model testing is
done through tests using PLS software. The basis used in testing the hypothesis
directly is the image output and the value contained in the path coefficient
output. The basis used to test the hypothesis directly is if the p-value
<0.05 (significance level = 5%), then it is stated that there is a
significant effect of exogenous variables on endogenous variables. The
following is a complete explanation of hypothesis testing:
Table 9
Hypothesis Testing
|
Variable |
Original Sample (O) |
Average Sample (M) |
Standard Deviation (STDEV) |
T Statistics (|O/STDE|) |
P Values |
|
Perceived Food Quality>Price Fairness |
0.577 |
0.576 |
0.051 |
11.304 |
0.000 |
|
Perceived Food Quality>Customer Satisfaction |
0.411 |
0.409 |
0.055 |
7.472 |
0.000 |
|
Price Fairness>Customer Satisfaction |
0.161 |
0.165 |
0.065 |
2.457 |
0.014 |
|
Customer Satisfaction > Revisit Intention |
0.698 |
0.699 |
0.038 |
18.128 |
0.000 |
Source: Smart PLS Output Result
In PLS, statistical testing of each hypothesized
relationship is carried out using simulation. In this case, it is done with the
bootstrapping method on the sample. The following are the results of the PLS
bootstrapping analysis as follows:
1.
The Effect of Perceived Food Quality on Price
Fairness
The results of testing the first hypothesis,
namely the effect of Perceived Food Quality on Price Fairness, show a coefficient
value of 0.577, a p-value of 0.000, and a t-statistic of 11.304. The p-value of
0.000 is less than 0.05 and the t-statistic value of 11.304 is more than the
t-table of 1.960. These results indicate that Perceived Food Quality affects
Price Fairness. So the hypothesis that stated Perceived Food Quality has a Positive Effect on Price Fairness is
accepted.
2.
The Effect of Perceived Food Quality on Customer
Satisfaction
The results of testing the third hypothesis,
namely the effect of Perceived Food Quality on Customer Satisfaction, show a
coefficient value of 0.411, a p-value of 0.000, and a t-statistic of 7,472. The
p-value of 0.000 is less than 0.05 and the t-statistic value of 7.472 is more
than the t-table of 1.960. These results indicate that Perceived Food Quality
affects Customer Satisfaction. So the hypothesis that Perceived Food Quality has a Positive Effect on Customer Satisfaction
is accepted.
3.
The Effect of Price Fairness on Customer
Satisfaction
The results of testing the fifth hypothesis,
namely the effect of Price Fairness on Customer Satisfaction, show a
coefficient value of 0.161, a p-value of 0.014, and a t-statistic of 2.457. The
p-value of 0.014 is less than 0.05 and the t-statistic value of 2.457 is more
than the t-table of 1.960. These results indicate that Price Fairness has an
Effect on Customer Satisfaction. So the hypothesis that stated Price Fairness has a Positive Effect on
Customer Satisfaction is accepted.
4.
The Effect of Customer Satisfaction on Revisit
Intention
The results of testing the seventh hypothesis,
namely the effect of Customer Satisfaction on Revisit Intention, show a
coefficient value of 0.698, a p-value of 0.000, and a t-statistic of 18,128.
The p-value of 0.000 is less than 0.05 and the t-statistic value of 18.128 is more
than the t-table of 1.960. These results indicate that Customer Satisfaction
Affects Revisit Intention. So that the hypothesis that states Customer Satisfaction Has a Positive Effect
on Revisit Intention is accepted.
Based on the results of the research
and discussion that has been explained in the previous chapters, the items in
this study were declared valid in validity testing, the items in this study
were declared valid. These items are perceived food quality, price fairness,
satisfaction, and revisit intention. Meanwhile, in reliability testing, these
items were declared eligible as research instruments. The results of this
research indicate that the variables in this research are very influential and
related to organic food restaurants. Perceived food quality is the first
attraction for customers to come to organic food restaurants. Price fairness
provides the second answer that the price set by the restaurant is acceptable
to the customer. Satisfaction makes customers feel that their choice to go to
an organic food restaurant is not wrong because the perceived food quality,
price, and taste are very satisfying. Revisit intention indicates that the
customer will continue to visit the restaurant at the next opportunity because
the customer is satisfied.
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