The American Journal of Management and Economics Innovations
42
https://www.theamericanjournals.com/index.php/tajmei
TYPE
Original Research
PAGE NO.
42-50
10.37547/tajmei/Volume07Issue07-05
OPEN ACCESS
SUBMITED
17 June 2025
ACCEPTED
24 June 2025
PUBLISHED
11 July 2025
VOLUME
Vol.07 Issue 07 2025
CITATION
Mohammad Naim Kakar, Dr. Ali Ahmad, Mujtaba Amin, & Amanullah
Niazai. (2025). The Study of Determinant Factors of Customer
Satisfiction with Industrial Products in Helmand Province, Afghanistan.
The American Journal of Management and Economics Innovations,
7(07), 42
–
50. https://doi.org/10.37547/tajmei/Volume07Issue07-05
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
The Study of Determinant
Factors of Customer
Satisfiction with Industrial
Products in Helmand
Province, Afghanistan
Mohammad Naim Kakar
Department of BBA, Faculty of Economics, Helmand University,
Afghanistan
Dr. Ali Ahmad
Department of Agricultural Economics, Faculty of Agriculture,
Helmand University , Afghanistan
Mujtaba Amin
BBA Department, Faculty of Economics, Bost University,
Helmand,Afghanistan
Amanullah Niazai
Department of BBA, Faculty of Economics, Bost University,
Helmand, Afghanistan
Abstract:
This research aims to evaluate customer
satisfaction with industrial products in Helmand province,
Afghanistan, and identify which dimensions and factors of the
marketing mix have the most significant impact on customer
satisfaction. Sixty-five questionnaires were gathered from
customers who visited industrial production companies within
the past three days to collect data. The collected data was
analysed using SPSS 26.0 and OLS and correlation techniques.
The findings indicate that all dimensions of marketing (7Ps)
have a positive and significant relationship with customer
satisfaction. Among the variables, price was identified as the
most influential factor affecting customer satisfaction
compared to other variables. Based on the model, the
obtained R Square is 0.451, which means that the independent
variables can explain 45.1% of the variance in the dependent
variable (customer satisfaction). Overall, the study's results
show that all independent variables significantly impact the
dependent variable
Keywords
: Marketing, Customer Satisfaction, Marketing
Max, Industrial Products.
The American Journal of Management and Economics Innovations
43
https://www.theamericanjournals.com/index.php/tajmei
The American Journal of Management and Economics Innovations
INTRODUCTION
Helmand, one of the 34 provinces of Afghanistan, is
located in the southern part of the country. It is the
largest province, covering an area of 58,584 square
kilometers (approximately 20,000 square miles). The
Helmand River irrigation system project provides water
to nearly 150,000 hectares of land. However, the
northern part of the province lacks direct access to river
water. It relies on groundwater and natural springs for
irrigation, which flow through traditional underground
systems known as "karez". In the north, deep wells are
also dug.
Due to its extensive water system, developed around 40
years ago with assistance from the United States,
Helmand has a strong agricultural history. Crops such as
wheat, corn, barley, and mung beans are cultivated
where irrigation facilities are available. The climate
supports double cropping (one field, two seasons),
making both summer and winter cultivation feasible.
Due to the limited number of processing factories,
industrial crops such as cotton, sesame, and soybeans
are grown on a smaller scale.
Despite this, industrial enterprises and companies in
Helmand produce goods that are consumed in
international markets. A prime example is the Helmand
woodworking and carving factory. Additionally, the
province has industrial production in cotton processing,
vegetable oil production, handicrafts, and aluminum
manufacturing,
with
products
consumed
both
domestically and abroad. Vegetable production
generally meets local needs, with a small surplus sold in
local markets (Ahmad et al., 2017).
Marketing is not merely an attempt to sell products but
a scientific and creative process to identify customer
needs and achieve their satisfaction. It plays a
fundamental role in the advancement of commerce and
social well-being. The concept has evolved from focusing
solely on selling to a broader emphasis on meeting
customer and societal needs. Marketing is a
management
philosophy
based
on
customer
satisfaction, organizational integration, and profit
generation (Efendi et al., 2023).
Customer satisfaction is critical for companies and
organizations providing financial, communication, or
other services. It compares expected services/products
and the actual ones received. When delivered services
and products match expectations, customers are
satisfied; if they fall short, customers are dissatisfied.
Common causes of customer dissatisfaction include
(MARY LOUIS TEMBA, 2013):
1.
A gap between expectations and reality
2.
Poor service quality
3.
Unprofessional staff behaviour
4.
Inadequate
or
untrustworthy
physical
environment
5.
High costs or long distances
6.
A mismatch between advertising and reality
Research Problem
Today’s markets are highly competitive, and marketing
is a key factor for survival in such environments. Around
the world, organizations use various marketing
strategies to boost product sales and ensure customer
satisfaction. One such method is the 7Ps marketing mix
(Pramesty et al., 2022). Helmand province, companies
and enterprises produce industrial products that reach
international markets. A carving and woodworking
factory is a notable example, alongside cotton
processing, vegetable oils, and handicrafts, which are
marketed domestically and abroad. However, no precise
data regarding customer satisfaction with these
products is available. Product quality, pricing, packaging,
and market access influence customer satisfaction.
While numerous global studies have been conducted on
this topic with varying results, increased focus on
marketing has only elevated its significance. Considering
the value of marketing, Helmand’s industrial companies
utilize various marketing methods to improve sales and
meet customer expectations. However, whether
customers are satisfied with the province's existing
industrial products remains unclear. To address this
ambiguity, this research uses the 7Ps framework to
evaluate customer satisfaction with Helmand’s existing
industrial products.
Research Objectives
•
To measure the impact of product quality on
customer satisfaction.
•
To examine the effect of price appropriateness
on customer purchase decisions.
•
To assess the distribution system and the impact
of location on accessibility.
•
To evaluate the effect of marketing strategies
on customer satisfaction.
The American Journal of Management and Economics Innovations
44
https://www.theamericanjournals.com/index.php/tajmei
The American Journal of Management and Economics Innovations
•
To analyse the impact of hiring professional staff
on customer satisfaction.
•
To assess the quality and efficiency of service
delivery processes.
•
To determine the influence of packaging and
physical evidence on customer purchase behaviour.
METHODOLOGY
Research Design
The research design is cross-sectional, as we collect data
about the variables at a specific time. We chose this
design because we gather information from a defined
population of industrial production company customers
within a fixed timeframe. There is no need for follow-up
with the study participants. Another reason for this
design choice is that the variables are measured so that
no manipulation or change is introduced to them
(Muslih, 2022).
Research Approach
This study employs a deductive research approach. In
this approach, we build hypotheses based on existing
theories. The deductive method moves from a general
understanding to a specific case, meaning the
researcher begins with a theory and then narrows it
down to testable hypotheses (Muslih, 2022).
Research Strategy
This study uses the quantitative method to analyze
customer data. The rationale behind using a descriptive
design is that descriptive studies aim to answer "What"
and "How much" questions (Muslih, 2022).
Research Area and Participants
The University of BOST conducted this study during a
three-day exhibition organized to market agricultural
and industrial products in Helmand Province. The data
was collected using structured questionnaires from
visitors attending the exhibition and later analyzed.
Sampling Method
This research uses the opportunity sampling method. In
this method, customers are actively selected at the
researcher's discretion. This sampling method is chosen
because it is easy to access and convenient (Rahman et
al., 2018).
Dependent Variable
•
Customer Satisfaction
Independent Variables
•
Product
•
Price
•
Place
•
People
•
Promotion
•
Process
•
Physical Evidence
RESULT
The
reliability
test determines whether
the
questionnaire can produce consistent results. In other
words, it assesses whether data collected multiple times
using the same questionnaire yields similar outcomes.
According to standard criteria, the Cronbach's Alpha
value should equal or exceed 0.7. If this condition is met,
it can be concluded that the data and the questionnaire
are reliable (Vebiyanti et al., 2024).
Table 1: Reliability Statistics
Cronbach's Alpha
Cronbach's Alpha Based on
Standardized Items
N of Items
.835
.846
8
Kaiser-Meyer-Olkin (KMO) and Bartlett's Test
These are statistical tests used to evaluate data adequacy for factor analysis. A KMO value greater than 0.5 is
considered acceptable, while a value above 0.8 is considered very good (Ambo, 2022).
Table 2: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.701
Bartlett's Test of Sphericity
Approx. Chi-Square
208.782
The American Journal of Management and Economics Innovations
45
https://www.theamericanjournals.com/index.php/tajmei
The American Journal of Management and Economics Innovations
df
24
Sig.
.000
Source: Researcher's analysis
The table shows that the
KMO value
is 0.701
, greater
than 0.5. This indicates that the sample size is adequate
and acceptable for factor analysis.
Regarding
Bartlett’s Test, the Sig value is 0.000;
according to the rule, this value should be less than 0.05.
This means that there is a strong relationship among the
variables. (Ringle & Sarstedt, 2021)
Tests for Normality
Since there are two types of statistical tests for data
analysis
(Parametric and Non-parametric)
, we need to
determine which type of test is suitable by checking the
data distribution. This involves:
1.
Skewness and Kurtosis analysis
2.
Q-Q plots analysis
Skewness and Kurtosis Analysis
Skewness
indicates the deviation of the data from the
mean.
Kurtosis
shows the relative height of the
distribution. For a
normal distribution
, skewness should
be around
±3
, and kurtosis should be between
±5
.
(Jammalamadaka et al., 2021).
Table 3: Skewness and Kurtosis Analysis
Source: Researcher's analysis
product
price
place
promotion
Phy.
Evid.
people
process
cs
N
Valid
66
66
66
66
66
66
66
66
Missing
0
0
0
0
0
0
0
0
Skewness
1.521
-.100
.172
-.241
-.695
-.340
-.214
2.278
Kurtosis
3.623
-2.340
-2.384
-4.540
-.325
-1.102
-.399
3.779
Table 4: Correlations Test
product
price
place
promotio
n
Phy. Evid.
people
process
cs
product
Pearson
Correlation
1
price
Pearson
Correlation
.311
1
place
Pearson
Correlation
.382
.604
1
promotio
n
Pearson
Correlation
.547
.300
.470
1
Phy. Evid.
Pearson
Correlation
.256
.556
.322
.282
1
people
Pearson
Correlation
.189
.697
.376
.571
.501
1
process
Pearson
Correlation
.103
.611
.521
.221
.342
.404
1
cs
Pearson
Correlation
.445
.422
.433
.387
.331
.490
.307
1
The American Journal of Management and Economics Innovations
46
https://www.theamericanjournals.com/index.php/tajmei
The American Journal of Management and Economics Innovations
Source: Researcher's analysis
The data presented in Table 4 shows that the
independent variable Product has a moderate positive
correlation with the dependent variable Customer
Satisfaction, reflecting a correlation value of (p = .445).
The independent variable Price also exhibits a moderate
positive correlation with Customer Satisfaction,
indicated by a correlation value of (p = .422). The
independent variable Place demonstrates a moderate
positive relationship with Customer Satisfaction, with a
correlation value of (p = .433). The independent variable
Promotion is associated with a weak positive
relationship with Customer Satisfaction, presenting a
correlation value of (p = .387). The independent variable
Physical Evidence shows a weak positive correlation
with Customer Satisfaction, with a correlation value of
(p: .331). The independent variable People has a
moderate positive relationship with Customer
Satisfaction, resulting in a correlation value of (p: .490).
Finally, the independent variable Process reveals a weak
positive correlation with Customer Satisfaction, as
shown by a correlation value of (p: .307).
Table 5: Regression Analysis
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
1
.587
a
.451
.367
.59643
Source: Researcher's analysis
a. Predictors: (Constant), process, product, physical.
evidence, promotion, price, place, people
It is very important to explain the R Square for Multiple
Regression. According to the model in the table above,
the Adjusted R Square is 0.451. If expressed as a
percentage, it becomes 45.1%. The model can explain
45.1%of the dependent variable (DV) variation.
ANOVA
The goal is to answer the
Null Hypothesis (H0)
using the
Statistical F-test
. In the table above, the important point
is the
F-test significance
(Sig/Significance or p-value),
which shows the result (Sig = 0.000). Since this value is
less than
0.05
or 5%, we can confidently reject
H0
(Null
Hypothesis). At the same time, we can confirm
H1
(Alternative Hypothesis), meaning the model has
explanatory power, and there is a relationship between
the variables
Table 6. ANOVA
a
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
161.341
7
40.335
73.770
.000
b
Residual
90.217
165
.547
Total
251.558
169
a. Dependent Variable: cs
b. Predictors: (Constant), process, promotion, people, product, physical.evidance, price, place
Source: Researcher's analysis
Table 7. Coefficients
Model
Coefficients
t
Sig.
B
Std. Error
1
(Constant)
2.214
.138
3.762
.000
product
.422
.024
2.838
.001
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
The American Journal of Management and Economics Innovations
47
https://www.theamericanjournals.com/index.php/tajmei
The American Journal of Management and Economics Innovations
price
.501
.020
4.575
.000
place
.397
.109
1.816
.000
promotion
.337
.074
2.207
.002
Phy. Evid.
.406
.081
3.484
.002
people
.441
.121
3.837
.004
process
.421
.089
4.210
.001
Source: Researcher's analysis
The results obtained from the regression test indicate
the following:
•
The product demonstrates a coefficient (B) of
0.422 and a significance value (Sig) of .001,
indicating a positive and significant correlation
with customer satisfaction. Thus, the researcher
dismisses the null hypothesis and accepts the
alternative hypothesis, which asserts that the
product has a positive and significant effect on
customer satisfaction.
•
The price exhibits a coefficient (B) of 0.501 and
a significance value (Sig) of .000, reflecting a
positive and significant connection with
customer satisfaction. As a result, the
researcher rejects the null hypothesis and
embraces the alternative hypothesis, stating
that price positively and significantly influences
customer satisfaction.
•
The place has a coefficient (B) of 0.397 and a
significance value (Sig) of .000, revealing a
positive and significant association with
customer satisfaction. Consequently, the
researcher discards the null hypothesis and
endorses the alternative hypothesis, which
claims that place positively and significantly
affects customer satisfaction.
•
The promotion shows a coefficient (B) of 0.337
and a significance value (Sig) of .002, signifying a
positive and significant relationship with
customer
satisfaction.
Therefore,
the
researcher rejects the null hypothesis and
supports the alternative hypothesis, indicating
that promotion positively and significantly
impacts customer satisfaction.
•
The physical evidence carries a coefficient (B) of
0.406 and a significance value (Sig) of .002,
illustrating a positive and significant link with
customer
satisfaction.
Accordingly,
the
researcher rejects the null hypothesis and
accepts the alternative hypothesis, asserting
that
physical
evidence
positively
and
significantly affects customer satisfaction.
•
The people aspect has a coefficient (B) of 0.441
and a significance value (Sig) of .004,
demonstrating a positive and significant
relationship with customer satisfaction. Thus,
the researcher dismisses the null hypothesis and
accepts the alternative hypothesis, emphasizing
that people positively and significantly impact
customer satisfaction.
The process presents a coefficient (B) of 0.421 and a
significance value (Sig) of .001, showing a positive and
significant relationship with customer satisfaction.
Therefore, the researcher rejects the null hypothesis
and accepts the alternative hypothesis, affirming that
the process positively and significantly influences
customer satisfaction
DISCUSSION
The research findings suggest that the marketing mix
has a positive effect on customer satisfaction. Each
element used to evaluate customer satisfaction within
the marketing realm
—
product, price, place, people,
promotion, physical evidence, and process
—
has a
favorable influence on customer satisfaction.
The results indicate that all aspects of marketing show a
significant and positive correlation with customer
satisfaction. This outcome is consistent with the
research conducted by Emmanuel et al. (2013).
The findings demonstrate that the product presents a
Beta = 0.422 and Sig. = 0.001, signifying a noteworthy
and positive effect on customer satisfaction.
Consequently, the researcher dismisses the null
hypothesis in favor of the alternative hypothesis. This
result aligns with the investigation by Saidani &
Sudiarditha (2019).
The study also shows that price has a Beta = 0.501 and
Sig. = 0.000, indicating a significant and positive impact
on customer satisfaction. Therefore, the researcher
The American Journal of Management and Economics Innovations
48
https://www.theamericanjournals.com/index.php/tajmei
The American Journal of Management and Economics Innovations
rejects the null hypothesis and accepts the alternative
hypothesis. Similar findings were reported in the
research by Saidani & Sudiarditha (2019).
The results reveal that physical evidence has a Beta =
0.406 and Sig. = 0.002, demonstrating a significant and
positive effect on customer satisfaction. Thus, the
researcher rejects the null hypothesis in favor of the
alternative hypothesis. This outcome is supported by the
study conducted by Al-Fadly (2022).
Furthermore, the findings indicate that people have a
Beta = 0.441 and Sig. = 0.004, showcasing a significant
and positive influence on customer satisfaction. Hence,
the researcher dismisses the null hypothesis and accepts
the alternative hypothesis. This conclusion is also
reinforced by the research carried out by Al-Fadly
(2022).
Finally, the results show that the process has a Beta =
0.421 and Sig. = 0.001, confirming a significant and
positive impact on customer satisfaction. Therefore, the
researcher rejects the null hypothesis and embraces the
alternative hypothesis. This result concurs with the
findings reported by Al-Fadly (2022).
CONCLUSION
The product exhibits a weak positive correlation with
customer satisfaction, with a significance level of (Sig =
0.001), i.e., *(p < .001, r = .422). The price demonstrates
a moderate positive correlation with customer
satisfaction, with a significance level of (Sig = 0.001), i.e.,
(p < .001, r = 0.434). The place shows a moderate
positive correlation with customer satisfaction, with a
significance level of (Sig = 0.000), i.e., (p < .000, r =
0.397). Promotion reflects a weak positive correlation
with customer satisfaction, with a significance level of
(Sig = 0.002), i.e., (p < .002, r = 0.406). Physical evidence
reveals a moderate positive correlation with customer
satisfaction, with a significance level of (Sig = 0.004), i.e.,
(p < .004, r = 0.441). People present a moderate positive
correlation with customer satisfaction, with a
significance level of (Sig = 0.001), i.e., (p < .001, r =
0.421). The process indicates a moderate positive
correlation with customer satisfaction, with a
significance level of (Sig = 0.001), i.e., (p < .001, r =
0.407). According to the model, the R Square value
stands at 0.451, which converts to 45.1% when
expressed as a percentage. This suggests that the model
can account for 45.1% of the variation in the dependent
variable (DV). Additionally, the ANOVA test result
indicates a significance value of (Sig = 0.000), which is
below the 0.05 (5%) threshold. Hence, we can
confidently reject the null hypothesis (H0), which posits
that the model lacks explanatory power and any
relationships among the variables. Simultaneously, we
can accept the alternative hypothesis (H1) and deduce
that the model possesses explanatory power and that
relationships among the variables do exist.
Recommendations
As recommendations, many comments and suggestions
can be made about this study because this paper found
that customer satisfaction has a positive and significant
relationship with marketing mix.
First and foremost, for customer satisfaction, we need
to understand the importance of implementing
marketing mix strategies. A good marketing mix strategy
can increase product sales and increase revenue levels.
Secondly, every organization should consider the
location of production because it plays an important role
in customer satisfaction so that the company's products
can be easily and safely obtained.
Thirdly, price has a positive relationship with customer
satisfaction, so a company should be very careful and
work hard in setting the best and most reasonable price
for customer satisfaction.
Fourth, professional employees have a positive
relationship with customer satisfaction, and customers
can be satisfied only if the company employs
professional and efficient people to ensure good
production quality and delivery.
At the End Ultimately, all manufacturing companies
should increase their product sales by implementing and
implementing marketing mix strategies. Through this,
they should not only increase market share, but also
satisfy their customers and offer products to their
customers according to their needs.
Recommendations for further research
The results of the study showed that in this study, the
variables could explain 45.1 percent of the variation in
the independent variable. Therefore, future researchers
should try to use those variables to explain the
remaining 54.9 percent of the variation.
REFERENCES
1.
Ahmad, A., Kumar, D., Mujadidi, N., Rafi, B., & Khan
Wasifhy, M. (2017). Impact Factor: RJIF 5.34
www.alleducationjournal.com Volume 2; Issue 5. In
The American Journal of Management and Economics Innovations
49
https://www.theamericanjournals.com/index.php/tajmei
The American Journal of Management and Economics Innovations
International Journal of Advanced Education and
Research
2.
Al-fadly, A., (2022). Differnces Between Supplier and
Customer Experiences of Marketing Mix in the
Construction industry.(8)
3.
Ambo, A. A. (2022).
Exploratory Factor Analysis
Output.docxDescriptive statistics, KMO & Bartlett’s
test, total variance explained and communalities
.
https://doi.org/10.6084/m9.figshare.20092661.v2
4.
Arlina, U., Sujianto, A. E., & Mutohar, P. M. (2024).
Marketing Mix Strategy (7P) in Increasing
Community Interest at Man 1 Trenggalek.
INTERNATIONAL JOURNAL OF SOCIAL SCIENCE AND
EDUCATION
RESEARCH
STUDIES
.
https://doi.org/10.55677/ijssers/v04i5y2024-07
5.
Efendi, A. Z., Winarto, P. S., & Kuswati, K. (2023a).
Customer satisfaction of goat kids using Customer
Satisfaction Index and Importance Performance
Analysis (case study in goat production farm of East
Java Livestock Agency).
BIO Web of Conferences
,
81
.
https://doi.org/10.1051/bioconf/20238100014
6.
Efendi, A. Z., Winarto, P. S., & Kuswati, K. (2023b).
Customer satisfaction of goat kids using Customer
Satisfaction Index and Importance Performance
Analysis (case study in goat production farm of East
Java Livestock Agency).
BIO Web of Conferences
,
81
.
https://doi.org/10.1051/bioconf/20238100014
7.
Emmanuel, A., Odunlami, I., Dele, A., (2013).
Assessment of Service Marketing as a tool for
Customer Satisfaction in Service Industry in Ado-
Ekiti Nigeria.
European Journal of Humanities and
Social Sciences .
26(1).
8.
Jammalamadaka, S. R., Taufer, E., & Terdik, G.
(2021).
On Multivariate Skewness and Kurtosis
.
83
(2), 607
–
644.
https://doi.org/10.1007/S13171-
9.
Kotler, P., & Keller, K. L. (2012). Marketing
Management. In Agrekon (14th ed., Vol. 11, Issue 1).
Pearson
Education,
Inc.
https://doi.org/10.1080/03031853.1972.952 3871
10.
Maisah, M., Mahdayeni, M., Maryam, M., &
Muhammad Roihan Alhaddad. (2020). Penerapan
7P Sebagai Strategi Pemasaran Pendidikan Tinggi.
Jurnal Ekonomi Manajemen Sistem Informasi, 1(4),
325
–
333.
https://doi.org/10.31933/jemsi.v1i4.116
11.
Muslih,
nisar
ahmad.
(2022).
Research
methodology
.
12.
Pramesty, D. P., Sapar, S., & Aqsa, M. (2022a). The
Effect of the 7P Marketing Mix and Service Quality
on Customer Satisfaction.
Quantitative Economics
and
Management
Studies
,
3
(2),
186
–
192.
https://doi.org/10.35877/454ri.qems928
13.
Pramesty, D. P., Sapar, S., & Aqsa, M. (2022b). The
Effect of the 7P Marketing Mix and Service Quality
on Customer Satisfaction.
Quantitative Economics
and
Management
Studies
,
3
(2),
186
–
192.
https://doi.org/10.35877/454ri.qems928
14.
Rahman, Z. A., Majid, A. A., Aziz, R. A., Ali, S., &
Omar, A. S. (2018).
The Influence of Total Quality
Management Practices towards Employee Job The
Influence of Total Quality Management Practices
towards Employee Job Satisfaction
.
December 2020
.
https://doi.org/10.32861/jssr.412.826.833
15.
Ringle, C. M., & Sarstedt, M. (2021).
Partial Least
Squares Structural Equation Modeling ( PLS-SEM )
Using R
.
16.
Saidani, B., & Sudiarditha, I. K. R. (2019).
Marketing
Mix-7Ps: The Effect on Customer Satisfaction
.
7
(1),
72
–
86.
https://doi.org/10.21009/JPEB.007.1.7
17.
Saidani, B., Sudiardith, K., (2019). Marketing Max -
7ps: Effect on Customer Satisfaction. Journal
Pendidikan Economics& Business.7(17)72-86.
18.
Setiyawan, A. N., Utami, I. W. P., & Saputro, F. W.
(2023).
Pengaruh Marketing Mix 7P Terhadap Minat
Beli Konsumen Di Dodolan Coffee Solo
.
1
(3), 155
–
172.
https://doi.org/10.59061/repit.v1i3.365
19.
Sukotjo, H. (2010). Analisa Marketing Mix7P
(Produk, Price, Promotion, Place, Partisipant,
Process, dan Physical Evidence) terhadap Keputusan
Pembelian Produk. Jurnal Mitra Ekonomi Dan
Manajemen Bisnis, 1(2), 216
–
228
20.
Sutojo, S. (2009). Manajemen pemasaran. PT.
Damar Mulia Pustaka.
21.
Vebiyanti, D., Salsabila, K., Bilhaki, R. R., Faradhilla,
A., & Alyani, F. (2024). Students Mathematical
Critical Thinking Skills: Validity and Reliability with
Winsteps
and
SPSS.
Pattimura
Proceeding
Conference of Science and Technology
,
5
(1), 79
–
88.
https://doi.org/10.30598/ppcst.knmxxiiv5i1p79-88
22.
Wardani, R. D. K., Soedarto, T., & Syah, M. A.
(2024a). Analysis of the Influence of The 7P
Marketing Mix on Customer Satisfaction of Bikla
Coffee, Jember District.
Buletin Penelitian Sosial
Ekonomi Pertanian Fakultas Pertanian Universitas
Haluoleo
,
26
(1),
59
–
70.
https://doi.org/10.37149/bpsosek.v26i1.1282
