Corresponding Author: Edward Leonard Parsaoran
E-mail: [email protected]
INTRODUCTION
The
Indonesian pharmaceutical industry currently still has the potential to grow (Nugroho,
2017). This is due to population growth, increased
public health awareness, an increasing economy, and improved health access and
budget (Nasrullah,
2018). This research helps industry players to
obtain information and understand market desires to increase sales.
In the first half of 2022, the Chemical, Pharmaceutical, and
Traditional Medicine Industry rose 4.3% compared to the first quarter of 2021. Meanwhile,
in the second quarter of 2022, construction experienced minus 3.4% compared to
the second quarter of 2021. Graph 1.1 shows the industry's
annual growth.
Figure 1. Data on the Growth of
the Chemical, Pharmaceutical,
and Traditional Medicine
Industries, 2011 � 2022
Source: Industry Research Data,
2022
Competition in the pharmaceutical
industry is very tough, making business people compete to strengthen all their
potential and resources in order to be able to provide something new and
increase competitiveness against similar companies (Windha & Andriati, 2023).
The Ministry of Health noted that
Indonesia had 30,199 pharmacies in 2021. Seeing the trend in the last decade,
the number of pharmacies in Indonesia tends to increase. The highest increase
in the number of pharmacies was 19.81% in 2013, from 17,613 units to 21,103
units. By region, West Java has the highest number of pharmacies in Indonesia,
with 4,874 units in 2021. East Java is in second place with 4,250 pharmacies,
and Central Java has 3,768 units. Meanwhile, DKI Jakarta and Banten have 2,055
pharmacies and 1,632 units, respectively.
Business competition between Pharmacy
brands to gain a large share in Indonesia is unavoidable. Below is the Top Brand Index data for Phase 2 Pharmacies in
Indonesia in 2021 and 2022.
Figure 1. Pharmacy Top Brands
Source: Top Brand Award (2022)
Looking at the picture above of
the four big players in the retail community Pharmacy in Indonesia, we see that
there has been a significant increase in the K 24 Pharmacy, which in 2021 was
23.9%, up in 2022 it was 28.3%. Researchers try to dig deeper into the
phenomena that occur in pharmacies, especially around East Bekasi, related to
the causes of the increase in the number of customers, increasing value, and
even winning the market in terms of the variable aspects of price, service
quality, consumer confidence, and purchasing decisions.
In previous research, namely the
Effect of Price and Consumer Confidence on Purchase Decisions through Market
Place Shopee, such as, one of the conclusions from the research results is that
consumer trust has a positive and significant influence on purchasing decisions
(Ofori et al., 2020). Other research results, namely one of the conclusions of the
research results is that price has a significant positive influence on
purchasing decisions mediated by consumer trust (Pratama & Santoso, 2018).
Furthermore, service quality is
the consumer's perception of the company's serviceability in realizing consumer
expectations and satisfaction (Wulandari & Suwitho, 2017). The pharmacy's quality of service is also a motivation for
customers to make purchasing decisions. Customer service can be in the form of
employee friendliness, smiles in serving, and competence in serving consumers (Maramis et al., 2022). One of the results of previous research is that individual and
simultaneous service quality has a positive effect on the purchasing decision
process at Starbucks coffee outlets in Senayan City (Budiyanto, 2019).
In the increasingly dynamic world
of the Pharmacy business, consumer trust is very important to determine
purchasing decisions. Consumer confidence in a product is influenced by price,
product quality, availability, service quality, and store/pharmacy image. Trust
will also affect consumer attitudes towards purchasing or reusing a product (Faizal & Nurjanah, 2019).
Purchasing decisions are the
consumer's final stage in considering the factors and reasons before purchasing
a product or service (Kholidah & Arifiyanto, 2020). The purchasing decision process consists of recognizing needs,
seeking information, evaluating alternative products, purchasing decisions, and
post-purchase behavior (Kotler, 2016).
The phenomenon of a significant
increase in sales at the K 24 Pharmacy in 2022 and the phenomenon of consumer
behavior patterns in buying drugs at pharmacies and from the results of
previous research, the purpose of this study was to identify and analyze the
behavior of consumers buying drugs in the East Bekasi area. Examining the
effect of price and service quality mediated by consumer trust on pharmacy drug
purchasing decisions.
METHODS
The research
design is a quantitative method with a causal explanation type to explain the
relationship between research variables and the influence of independent
variables on the dependent variable and to test hypotheses. With this causal
explanation research, it will be known and analyzed how much influence price
and service quality have on drug purchasing decisions at pharmacies mediated by
consumer trust.
The
population in question is residents or people over 17 years domiciled in the
East Bekasi Region. The sampling technique uses a non-probability sample design
with purposive sampling, in which each population is not given equal rights to
be used as a sample but uses certain requirements according to the researcher's
wishes. The selected sample is people who live in the East Bekasi Region and
have bought medicine at the East Bekasi Regional Pharmacy. The number of
samples determined was 138 respondents.
The data used
is primary data with data collection techniques through distributing
questionnaires and using a five-point Likert scale measurement (1 � 5). Score 1
for Strongly Disagree (STS), Score 2 for Disagree (TS), Score 3 for Neutral
(N), Score 4 for Agree (S), Score 5 for Strongly Agree (SS). In the Likert
Scale, the variables measured are perceptions, attitudes, and views of a
person, which are then translated into variable indicators as a reference for
compiling question items.
The data analysis method uses
Structural Equation Modeling (SEM) using the Smart-Partial Least Square
(SmartPLS) software version 3.3.7 PLS (Partial Least Square). Measurements were
made on the Outer Model and Inner Model.
RESULTS AND DISCUSSION
Characteristics
of respondents
The number of
samples that met the criteria was 138 respondents in gender, age, and type of
work. Table 1 shows that female respondents
are more dominant, namely 108 people with a percentage of 78.26%, and male
respondents as many as 30 people or 21.74%.
Table 1. Correspondent
characteristics
|
Respondent Criteria
|
Amount
|
Percentage
|
Respondent Criteria
|
Amount
|
Percentage
|
|
Man
|
30
|
21.74%
|
Woman
|
108
|
78.26%
|
|
Age 17 - 25 Years
|
4
|
2.90%
|
Age 17 - 25 Years
|
15
|
10.87%
|
|
Employee
|
1
|
|
Employee
|
9
|
|
|
Student
/ Student
|
3
|
|
Housewife
|
1
|
|
|
Age 26 - 50 Years
|
17
|
12.32%
|
Student
/ Student
|
5
|
|
|
Employee
|
14
|
|
Age 26 - 50 Years
|
60
|
43.48%
|
|
Businessman
|
3
|
|
Employee
|
32
|
|
|
Age > 50 Years
|
9
|
6.52%
|
Housewife
|
19
|
|
|
Employee
|
6
|
|
Businessman
|
9
|
|
|
Businessman
|
2
|
|
Age > 50 Years
|
33
|
23.91%
|
|
Pension
|
1
|
|
Employee
|
14
|
|
|
|
|
Housewife
|
15
|
|
|
Total (Lk and Pr)
|
138
|
100.00%
|
Businessman
|
4
|
|
Outer
Model Measurement
The
measurement of the outer model describes the latent variable constituent
indicators used in the study. The outer model is measured to test its validity
and reliability. In testing the validity of the Convergent validity and
Discriminant validity methods can be used (average variance extracted through
correlation techniques, Fornell lacker criterion, and cross loading). While in
reliability testing, it can be used by measuring Composite reliability and
Cronbach's alpha.
Validity test
Convergent
Validity
According to Chin (Ghozali, 2014), an
indicator is stated to have good validation if the value is > 0.70, while it
is considered good enough if the loading factor value is 0.50 to 0.60 and if
there is a loading factor the value is <0.60 then it will be dropped of
models.
Table 2. Outer loading Matrix
|
Price
|
Service quality
|
Consumer Trust
|
Buying decision
|
|
H1
|
0.769
|
|
|
|
|
H2
|
0.772
|
|
|
|
|
H3
|
0.871
|
|
|
|
|
H4
|
0.875
|
|
|
|
|
KL1
|
|
0.874
|
|
|
|
KL2
|
|
0.868
|
|
|
|
KL3
|
|
0.880
|
|
|
|
KL4
|
|
0.841
|
|
|
|
KK1
|
|
|
0.873
|
|
|
KK2
|
|
|
0896
|
|
|
KK3
|
|
|
0.857
|
|
|
KK4
|
|
|
0.888
|
|
|
Opel
|
|
|
|
0.736
|
|
KPe2
|
|
|
|
0.873
|
|
KPE3
|
|
|
|
0.858
|
|
KPE4
|
|
|
|
0.874
|
|
KPe5
|
|
|
|
0.715
|
Based on the data in Table 2, it
can be seen that all indicators are perfectly extracted and have a loading
factor value of > 0.7. This shows that the indicators used can explain the
construct well.
Discriminate Validity
Discriminant validation is carried
out to ensure that the observed variable correlation relationship with the construct
is higher than with other constructs (Hair et al., 2014). The loading
factor value indicates the variable correlation relationship. The following
measurements can identify the discriminate validity method:
AVE (Average Variance Extracted)
Discriminate validity can be used
to measure the validity level of latent variables. The following is an analysis
of the research results of AVE data:
Table 3. AVE Analysis Data
|
|
Cronbach's alpha
|
Composite reliability (rho_a)
|
Composite reliability (rho_c)
|
Average Variance Extracted (AVE)
|
|
Price
|
0.842
|
0.864
|
0893
|
0.678
|
|
Service
quality
|
0.889
|
0896
|
0.923
|
0.749
|
|
Consumer
Trust
|
0.902
|
0.903
|
0931
|
0.772
|
|
Buying
decision
|
0.871
|
0.883
|
0.907
|
0.663
|
Based on
the data in Table 3, it can be seen that all variables are valid because they
have an AVE > 0.5
Fornell Larcker Criterion
The Fornell-Larcker criterion is a
discriminant validity test based on Fornell-Larcker calculations (Ramadhina & Frianto, 2023). Similar to the correlation technique, the Fornell criterion
calculation compares the relationship between the two latent variables using
the Fornell Larcker calculation with the square root of the average variance
extracted. Following are the results of the analysis of discriminant validity
testing with the Fornell Larcker criterion.
Table 4. Data analysis of the Fornell Larcker Criterion test.
|
|
Price
|
Consumer
Trust
|
Buying
decision
|
Service
quality
|
|
Price
|
0.823
|
|
|
|
|
Consumer Trust
|
0.736
|
0879
|
|
|
|
Buying decision
|
0.637
|
0.755
|
0814
|
|
|
Service quality
|
0.770
|
0.746
|
0.637
|
0.886
|
Based on the data in Table 4, it
can be seen that all variables are valid because the AVE square root value of
each variable is greater than the correlation value of the other variables.
Cross loading
The cross-loading value of the
construct measurement indicates the discriminant Validity measurement model.
The cross-loading value shows the correlation between each construct and its
indicators and indicators from other constructs. If the correlation between the
constructs and the indicators is greater than between the indicators and the
other constructs, it is said to have good discriminant validity. The following
are the results of the cross-loading value of this study.
Table 5. Cross-loading analysis
data
|
Price
|
Service quality
|
Consumer Trust
|
Buying decision
|
|
H1
|
0.769
|
0.476
|
0.539
|
0.453
|
|
H2
|
0.772
|
0.489
|
0.476
|
0.460
|
|
H3
|
0.871
|
0.620
|
0.649
|
0.575
|
|
H4
|
0.875
|
0.684
|
0.717
|
0.586
|
|
KL1
|
0.699
|
0.874
|
0.686
|
0.536
|
|
KL2
|
0.642
|
0.868
|
0.590
|
0.525
|
|
KL3
|
0.563
|
0.880
|
0.713
|
0.598
|
|
KL4
|
0.514
|
0.841
|
0.573
|
0.540
|
|
KK1
|
0.636
|
0.713
|
0.873
|
0.680
|
|
KK2
|
0.692
|
0.654
|
0896
|
0.670
|
|
KK3
|
0.621
|
0.585
|
0.857
|
0.647
|
|
KK4
|
0.635
|
0.666
|
0.888
|
0.657
|
|
kpel
|
0.393
|
0.419
|
0.519
|
0.736
|
|
KPe2
|
0.580
|
0.553
|
0.655
|
0.873
|
|
KPE3
|
0.545
|
0.554
|
0.656
|
0.858
|
|
KPE4
|
0.592
|
0.563
|
0.688
|
0.874
|
|
KPe5
|
0.455
|
0.490
|
0.536
|
0.715
|
Based on the data in Table 5, it
can be seen that all indicators are valid because the loading value of the
construct is greater than the loading value of the construct to the others.
Reliability Test
The
reliability test is carried out to measure the internal consistency of a
measuring instrument. Reliability shows a measuring instrument's accuracy
(accuracy) and consistency. High
reliability describes the reliability of the latent variables in the construct.
Reliability test conducted by Cronbach's Alpha and Composite Reliability (CR).
Research data obtained as follows:
Table 6.
Composite Reliability and Cronbach's alpha
|
|
Cronbach's alpha
|
Composite reliability (rho_a)
|
Composite reliability (rho_c)
|
Average Variance Extracted (AVE)
|
|
Price
|
0.842
|
0.864
|
0893
|
0.678
|
|
Service
quality
|
0.889
|
0896
|
0.923
|
0.749
|
|
Consumer
Trust
|
0.902
|
0.903
|
0931
|
0.772
|
|
Buying
decision
|
0.871
|
0.883
|
0.907
|
0.663
|
Based on
the data in Table 6, all reliable variables are valid because the CR value is
> 0.7 or the Cronbach's alpha value is > 0.6.
Inner Model Measurement
The measurement of the inner model
is to analyze the relationship between exogenous and endogenous variables,
which are described in a conceptual framework (Marliana, 2020). Development of an inner model based on concepts and theories.
From the measurement results of
the researchers, the Path Coefficient Matrix data was obtained as follows:
Table 7. Path Coefficient Matrix
|
|
Price
|
Service
quality
|
Consumer
Trust
|
Buying
decision
|
|
Price
|
|
|
0.418
|
|
|
Service Quality
|
|
|
0.453
|
|
|
Consumer Trust
|
|
|
|
0.755
|
|
Buying decision
|
|
|
|
|
Based on the data in Table 7, it can be seen that:
Consumer Trust ���������� =
0.418 (Price) + 0.453 (Quality of service)
Purchase Decision ������ =
0.755 (Consumer Trust)
Inner model analysis can be done
using the R-Square, Q-Square, and VIF indicators (Variance Inflation Factor)
with explanations and results as follows:
R-Square
value (R2)
R-Square (R2) indicates how much
the independent variable can explain the dependent variable, whose value ranges
from zero to one. Suppose the value of R2 is close to number one. In that case,
it means that the independent variable can properly explain all the information
needed to predict the variation of the dependent variable. Moreover, vice
versa, if the value of R2 is close to zero, it indicates that the ability of
the independent variable to predict variations in the dependent variable is
increasingly limited. The R2 value of the research results can be seen as
follows:
Table 8. R-Square
|
|
R-square
|
|
Consumer Trust
|
0.646
|
|
Buying decision
|
0.570
|
The R2 value
of the consumer trust variable is 0.646, which indicates that the effect of the
price and service quality variables on consumer trust is 64.6%. The R2 value of
the purchasing decision variable is 0.570. which indicates that the influence
of consumer trust variables on customer decisions is 57.0%.
-Square value (Q2)
-Square
value (Q2) was obtained using the blindfolding analysis method. Q2 can explain
the predictive relationship between the dependent and independent variables.
The threshold value for the 𝑄 � test is 0.35 with a large effect, 0.15 with a
moderate effect, and 0.02 with a small effect. Following are the results of the
Q-Square in the study:
Table
9. Value Q2 predictive relevance
|
|
Q 2 predicts
|
RSME
|
MAE
|
|
Consumer
Trust
|
0.622
|
0.636
|
0.476
|
|
Buying
decision
|
0.448
|
0.764
|
0.566
|
Based on the data in Table 9, Value Q2, the endogenous latent variable of
consumer trust is 0.622. The endogenous variable of the purchase decision is
0.448, the value of Q2; the two endogenous latent variables are > 0; it is
stated that the model already has predictive relevance.
Variance Inflation Factor (VIF)
VIF is an indicator that can see
the symptoms of multicollinearity of the independent and dependent variables (Anwar & Satrio, 2015). The multivariance assumption requires that there is no
multicollinearity between the independent variables. The VIF value that can
explain that the independent variable does not have multicollinearity symptoms
is VIF < 5. Following are the results of the VIF test in this study.
Table 10. VIF Test Results
|
|
VIF
|
|
Price ->
Consumer Confidence
|
1961
|
|
Service
Quality -> Consumer Trust
|
1961
|
|
Consumer
Confidence -> Purchase Decision
|
1,000
|
Based on
the data in Table 10, there is no multicollinearity problem because the VIF
value < 5
Hypothesis testing
Hypothesis
testing is shown by the original sample
value (O) with the aim that the direction of the relationship between
variables can be known. The t-statistics (T)
and p-values (P) indicate the
significance level of the relationship between variables. The relationship
between variables is declared positive if the
original sample value leads to a positive number of one (+1). The
relationship between variables is declared negative if the original sample value leads to a number close to a negative one
(-1) (Hair Jr. et al., 2017). The
variable relationship has a positive and significant effect if the t-statistics > 1.96 or the p-value is smaller than the standard
significance. The hypothesis test data for the direct variable relationship are
as follows:
Table 11.
Direct variable hypothesis test data
|
|
Original Sample (O)
|
Sample Means (M)
|
Standard Deviation (STDEV)
|
T statistics (O/STDEV)
|
P Values
|
|
Price ->
Consumer Confidence
|
0.418
|
0.420
|
0.110
|
3,795
|
0.000
|
|
Service
Quality -> Consumer Trust
|
0.453
|
0.451
|
0.102
|
4,424
|
0.000
|
|
Consumer
Confidence -> Purchase Decision
|
0.755
|
0.753
|
0.063
|
12043
|
0.000
|
Based on the data in Table 11, it
can be seen that:
a. t statistics value is 3.795 > 1.96, and the p-value is 0.000
<0.05, which means that the price variable positively impacts consumer
confidence. Hypothesis
� 1 (H1): Price positively impacts consumer confidence in buying drugs at
pharmacies, acceptable.
b. t statistics value is 4.424, > 1.96,
and the p-value is 0.000 <0.05; it is stated that the service quality
variable has a positive, significant impact on consumer confidence.
Hypothesis-2 (H2): Service quality positively and significantly impacts consumer
confidence in buying drugs at pharmacies acceptable.
c. t statistics value is 2.043 > 1.96, and
the p-value is 0.000 <0.05; it is stated that the variable consumer trust
has a positive, significant impact on purchasing decisions. Hypothesis-3 (H3):
Consumer trust positively and significantly impacts drug purchasing decisions
at pharmacies, acceptable.
The data for testing the hypothesis of the indirect (mediation)
variable relationship are as follows.
Table 12.
The results of testing the mediating variable hypothesis
|
|
Original Sample (O)
|
Sample Means (M)
|
Standard Deviation (STDEV)
|
T statistics (O/STDEV)
|
P Values
|
|
Price -> Consumer Confidence ->
Purchase Decision
|
0.316
|
0.317
|
0.089
|
3,543
|
0.000
|
|
Quality of Service -> Consumer Trust
-> Purchase Decision
|
0.342
|
0.340
|
0.086
|
4,004
|
0.000
|
Based on the data in Table 12, it
can be seen that:
t statistics value is 3.543 >
1.96, and the p-value is 0.000 <0.05, so it is stated that the price
variable has a positive impact, significantly mediated by consumer confidence
in purchasing decisions. (H4): Consumer trust mediates prices so that a
positive and significant impact on pharmacy drug purchasing decisions is
acceptable.
t statistics value is 4.004 >
1.96, and the p-value is 0.000 <0.05. so that the service quality variable
has a positive impact, significantly mediated by consumer confidence in
purchasing decisions. (H5): Consumer trust mediates service quality so that a
positive and significant impact on pharmacy drug purchasing decisions is
acceptable.
CONCLUSION
The conclusions from the results of this
study are as follows: 1) Price has a positive and significant impact on
consumer confidence. That is, the price consumers pay to buy drug products is
felt to follow the expected quality and benefits. Hence, consumers tend to try
or buy the drug product again. 2) Service quality has a positive and
significant impact on consumer confidence. That is, with the increase in the
quality of service provided by the pharmacy to its customers, consumer
confidence will continue to increase, and the lower the quality of service
provided to customers by the pharmacy, the lower the level of consumer
confidence. Consumers who believe in a pharmacy's guarantee of good quality
service will become loyal customers, and even these consumers will recommend
other people about this. 3) Consumer trust has a positive and significant
impact on purchasing decisions. That is, the higher consumer trust in a
pharmacy, the easier it will be for consumers to decide to buy drugs at a
pharmacy. Likewise, conversely, the lower consumer trust in a pharmacy, the
more difficult it is for consumers to decide to buy drugs at that pharmacy. 4)
Consumer trust mediates prices so that it has a positive and significant impact
on drug purchasing decisions at pharmacies. This means that prices that are
affordable and supported by consumer confidence in the benefits of drugs that
can provide faster healing will make it easier for consumers to decide to buy
at the pharmacy. 5) Consumer trust mediates service quality so that it has a
positive and significant impact on drug purchasing decisions at pharmacies.
This means that good service quality and supported by consumer confidence in
promises and commitments, as well as product quality assurance, will make it
easier for consumers to decide to buy at the pharmacy.
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