Vol. 5, No. 10, October 2024
E-ISSN: 2723 - 6692
P-ISSN:2723- 6595
http://jiss.publikasiindonesia.id/
Journal of Indonesian Social Sciences, Vol. 5, No. 10, October 2024 2692
KEYWORDS
ABSTRACT
Determinants of Consumer
Decisions; Promotion
Attractiveness; Competitive
Price
Digital technology, such as e-commerce, has changed the way people
live by providing convenience, speed, comfort and efficiency. Freight
forwarding services are vital in online transactions to deliver goods
from sellers to buyers. The purpose of this study was conducted to
determine the effect of customer orientation, promotional
attractiveness, and competitive pricing on marketing performance.
This research method uses the causality method which aims to test
how much the cause-and-effect relationship between the
independent variable and the dependent variable is. The population
of this study were consumers totaling 13,577, from 2018-2023, and
a sample of 100 respondents. The data analysis technique uses
quantitative data analysis and uses a range scale measurement. The
results of simultaneous testing (F test) indicate that customer
orientation, promotional attractiveness, and competitive prices
have a significant effect on marketing performance. The results of
persial testing (t test) show that customer orientation, promotional
attractiveness, and competitive prices have a significant effect on
marketing performance.
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Introduction
In recent years, digital technology has dramatically reshaped consumer behaviour, particularly
through e-commerce platforms that provide ease, speed, and convenience in daily transactions
(Radzikhovska, 2021; Suresh & Latha, 2021). Research indicates that customer orientation,
promotional appeal, and competitive pricing significantly influence marketing performance in digital
marketplaces (Dewi & Nuzuli, 2017; Karina & Sari, 2022). As a crucial component in these digital
transactions, freight forwarding services have adapted to these changes, providing online tracking,
flexible delivery options, and 24/7 availability (Kotler & Armstrong, 2021; Zulaicha & Irawati, 2016).
The rise of courier services like J&T Express in Indonesia highlights a competitive shift driven by this
factor.
In Indonesia, the award is given to brands that have achieved the TOP title and have outstanding
performance in the Indonesian market. This award is given based on the assessment obtained from a
nationwide survey conducted by Frontier. Since its inception until the end of 2020, it has involved
more than 100,000 respondents in fifteen major cities, recorded more than 500 product categories
Influence Analysis of The Factors Determining Consumer
Decisions on The Marketing Performance of J&T Express in
Pontianak
Andry Lindi Lim
Universitas Widya Dharma Pontianak, Indonesia
Email: andry_lim@widyadharma.ac.id
Correspondence: andry_lim@widyadharma.ac.id*
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Journal of Indonesian Social Sciences, Vol. 5, No. 10, October 2024 2693
and produced more than 2,000 brands that received the title. Top Brand Index ( TBI ) is an award
given to the best brands based on research on Indonesian consumers and is a barometer for
measuring the success of a product brand in the market and brands that get a minimum award of
10.00 per cent and according to survey results are in the top three positions, then the brand will hold
the title of Top Brand Award brand.
Figure 1. Top Brand Index Category of Courier Services in Indonesia
2020-2023
Source: (Top Bran Award, 2023)
Table 1. Consumer Princes Index per Month by Expenditure Group
(2018=100) in Pontianak Municipality, 2022
Month
Food Beverages and
Tobacco
Clothing a Footwear
Housing, Water,
Electricity and
Household Fuels
(1)
(2)
(3)
(4)
January
112,80
104,75
103,74
February
112,67
104,75
104,13
March
113,06
104,75
105,09
April
114,90
105,00
106,47
May
116,36
105,00
106,67
June
116,22
105,00
106,67
July
115,49
105,00
106,99
August
114,93
105,01
107,75
September
116,19
105,02
108,19
October
115,31
104,86
108,35
Novemver
116,35
104,89
108,35
December
117,38
105,21
108,46
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Journal of Indonesian Social Sciences, Vol. 5, No. 10, October 2024 2694
Table 2. Consumer Princes Index per Month by Expenditure Group
(2018=100) in Pontianak Municipality, 2022
Month
Furnishing, Household,
Equipment and
Routine Household
Maintenance
Health
Transport
(1)
(5)
(6)
(7)
January
108,17
122,35
105,11
February
108,49
122,35
106,51
March
109,69
122,13
107,03
April
112,68
121,17
107,96
May
112,88
121,10
109,57
June
113,19
121,23
109,59
July
113,25
121,23
110,51
August
113,39
120,95
111,76
September
114,07
120,78
121,47
47October
114,28
120,94
122,26
Novemver
114,37
120,95
122,23
December
114,58
121,39
123,76
Source: Pontianak City in Figures 2023, Pontianak Municipality in Figures 202, 323-324 (2023)
Table 1. Growth Rate of Gross Domestic Product at 2010 Constant
Market Prices by Industry in Pontianak Municipality (Percent), 2019-2022
Business / industry report
2019
2020
2021*
2022**
(1)
(3)
(4)
(5)
(6)
A
Agriculture, Forestry and Fishing
4,04
5.75
1.57
-1.13
B
Mining and Quarrying
C
Processing industry
7,72
-2.17
4.74
0.56
D
Electricity and gas procurement
5,36
25.56
3.69
3.53
E
Water supply; waste management, Waste,
and Recycling
2,47
8.54
5.38
5.14
F
Construction
0,92
-4.21
5.57
1,06
G
Wholesale and Retail Trade; Automobile
and Motorcycle Repair
1,75
-13.50
4.97
12.09
H
Transportation and Trade
6,46
-14.36
-4.77
16.41
I
Provision of Food and Beverage
Accommodation
6,52
-20.11
9.89
9.15
J
Information and Communication
10,23
17.19
7.13
6.88
K
Financial and Insurance Services
-2,56
0.97
3.50
0.08
L
Real Estate
1,54
0.69
1,03
0.05
M, N
Company Services
6,17
-5.16
0.47
11.90
O
Government Administration, Defense, and
Compulsory Social Security
8,91
5.26
-1.08
-2.59
P
Education Services
3,39
-8.90
4.99
3.05
Q
Health and Social Services
8,42
50.50
38.11
6.41
R,ST,U
Other Services
8,47
-13.36
0,59
9.26
Gross Regional Domestic Product
4,14
-3,96
4,60
4,98
Notes: * Provisional figures
** Very provisional figures
Source: BPS, Various Census, Survey, and Other Sources/BPS-Statistics Indonesia, Various Census, Survey, and Other Sources
(BPS, 2024)
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Journal of Indonesian Social Sciences, Vol. 5, No. 10, October 2024 2695
J&T Express has more advantages than other express services. So, it can be concluded that there
is a gap in consumer interest in choosing the two shipping service companies. A customer-oriented
strategy is also expected to identify consumer needs and information about market competitors and
determine product success. With this information, business actors can understand their potential
customers, what consumers want now and in the future, and what market conditions are like in
competition. The collection of this information will later be used to evaluate what steps the company
will take to implement strategies based on market conditions. In addition to customer orientation,
promotional attractiveness is important because it can influence consumer decisions to buy a product.
Many business owners carry out promotions to communicate the products offered to
consumers, believing in being able to persuade new consumers to make transactions by consuming
the products offered. The information contained in a promotion can affect both a person's knowledge
and impression. Through promotional activities by entrepreneurs, consumers can find out the
products offered and the benefits these products provide to consumers (Santika, 2021). A good
promotional attraction can help to achieve the target that has been set and will help the company to
compete with other companies. Therefore, to get consumers, business actors must be able to provide
good promotional attractiveness and by consumer perceptions so that consumers feel satisfied with
the promotions provided. This can also provide added value for business actors.
To balance customer orientation and promotional attractiveness, companies must set
competitive prices (Arief et al., 2023; Kassemeier et al., 2022). The role of price will be very important,
especially in conditions of increasingly sharp competition and the development of increasingly
limited demand. In other words, competitive pricing affects the company's ability to compete with
other companies and the company's ability to influence consumers to buy a product. With competitive
prices, business actors can outperform their competitors because price is sensitive to consumers.
Price is one of the factors driving purchasing behaviour, which will increase sales volume and is a
determinant of improving marketing performance.
In order to survive and excel in competition, business actors must be able to pay close attention
to their marketing performance. Good marketing performance is expressed in three main values,
namely sales value, sales growth, and a large portion. Sales value shows how many rupiah or units of
products the company has successfully sold to consumers or customers, while sales growth shows
how much the sales of the same product have increased compared to a certain unit of time, and a large
portion shows how much the contribution of the product handled can dominate the market for similar
products compared to competitors. Suppose business actors can apply these three things wisely. In
that case, marketing performance will increase because the increase in performance in a company is
measured by how much impact the application of company strategies to create products and
recognize consumer needs has on the company's sales level.
Table 2. J&T Express in Pontianak Number of Consumers and Consumer Growth 2018-2023
Year
Total
Consumer
Consumer Growth
Δ Growth
Δ %
2018
10.951
-
-
2019
11.183
232
2,12
2020
11.511
328
2,93
2021
12.082
571
4,96
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Journal of Indonesian Social Sciences, Vol. 5, No. 10, October 2024 2696
2022
12.678
596
4,93
2023
13.577
899
7,09
Prior studies have focused on isolated factors affecting marketing performance, such as
customer orientation alone (Azizah & Maftukhah, 2017) or promotional attractiveness as a separate
component (Meiliani & Ferdinand, 2016). However, limited research has examined how these factors
interact and jointly impact marketing performance in a competitive context within the courier service
industry. This study distinguishes itself by adopting a holistic approach to analyze the combined effect
of customer orientation, promotional attractiveness, and competitive pricing on J&T Express's
marketing performance in Pontianak. This multi-faceted examination provides a comprehensive view
of how these factors work synergistically, contributing to a more nuanced understanding of consumer
behaviour in Indonesia’s courier service sector.
This research is particularly relevant due to the increased reliance on courier services for e-
commerce and marketplace transactions in Indonesia. With growing consumer demand and intense
market competition, understanding how these key marketing factors impact consumer decision-
making and company performance is crucial for strategic development in the logistics industry. The
insights from this study will be valuable for practitioners and business leaders in enhancing customer
satisfaction, competitive positioning, and promotional effectiveness.
Although past research has explored the influence of these variables individually, this study's
novelty lies in its integrated approach. By concurrently examining customer orientation, promotional
appeal, and competitive pricing, this study provides a distinctive perspective on how these factors
collectively contribute to marketing success. This holistic view fills a gap in the existing literature and
offers actionable insights that courier service providers can apply to sustain competitive advantage
in a rapidly evolving market.
Materials and Methods
This study applies a causality research method to examine the cause-and-effect relationship
between the independent variables (customer orientation, promotional attractiveness, and
competitive pricing) and the dependent variable (marketing performance). In this context, causality
is used to determine how each independent variable influences the improvement of marketing
performance in the courier service industry, specifically for J&T Express in Pontianak. This method is
suitable for explaining the relationships among variables and provides a deeper understanding of the
factors contributing to marketing performance.
The population for this study includes all J&T Express customers in Pontianak from 2018 to
2023, totalling 13,577 individuals. The sample is selected using purposive sampling, with a sample
size of 100 respondents deemed representative of the population. This sampling approach ensures
sufficient representation, allowing for the generalization of findings to a broader population.
Data collection was conducted through a closed-ended questionnaire, where respondents were
asked to rate their perceptions of customer orientation, promotional attractiveness, and competitive
pricing using a Likert scale. The collected data was analyzed using quantitative analysis methods,
including regression analysis, to identify and measure the relationships among the variables studied.
Data analysis involved instrument validity and reliability tests to ensure the reliability and accuracy
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of the findings. Additionally, hypothesis testing was performed using the F-test and t-test to assess
the significance of each variable’s impact on marketing performance.
Results and Discussions
Respondents' Responses to Customer Orientation
Table 5. Respondents' Answer Index for Customer Orientation Variables
Customer
Orientation
Value Weight
Total
Indicator
1
2
3
4
5
6
7
8
9
10
Commitment to
customer
satisfaction
0
0
0
0
0
8
27
37
22
6
100
Value Weight
0
0
0
0
0
48
189
296
198
60
902
Percentage of
Answers (%)
0
0
0
0
0
8,00
27,00
37,00
22,00
6,00
100
Respondent
Answer Index (%)
0
0
0
0
0
4,80
18,90
29,60
19,80
6,00
79,10
Collect customer
needs
information
0
0
0
0
0
8
30
46
13
3
100
Value Weight
0
0
0
0
0
48
210
368
117
30
901
Percentage of
Answers (%)
0
0
0
0
0
8,00
30,00
46,00
13,00
30,00
100
Respondent
Answer Index (%)
0
0
0
0
0
4,80
21,00
36,80
11,70
3,00
77,30
How to satisfy
customers
0
0
0
0
0
4
28
39
22
7
100
Value Weight
0
0
0
0
0
24
196
312
198
70
921
Percentage of
Answers (%)
0
0
0
0
0
4,00
28,00
39,00
22,00
7,00
100
Respondent
Answer Index (%)
0
0
0
0
0
2,40
19,60
31,20
19,80
7,00
80,00
Knowing
customer
complaints
0
0
0
0
0
10
30
35
16
9
100
Value Weight
0
0
0
0
0
60
210
280
144
90
898
Percentage of
Answers (%)
0
0
0
0
0
10,00
30,00
35,00
16,00
9,00
100
Respondent
Answer Index (%)
0
0
0
0
0
6,00
21,00
28,00
14,40
9,00
78,40
Special attention
to customers
0
0
0
0
3
12
36
29
10
10
100
Value Weight
0
0
0
0
15
72
252
232
90
100
911
Percentage of
Answers (%)
0
0
0
0
3,00
12,00
36,00
29,00
10,00
10,00
100
Respondent
Answer Index (%)
0
0
0
0
1,50
7,20
25,20
23,20
9,00
10,00
76,10
Average Respondent Answer Index
78,18
Conclusions: On average, respondents gave high perception scores for customer orientation.
Source: Processed data, 2024
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Journal of Indonesian Social Sciences, Vol. 5, No. 10, October 2024 2698
Table 6. Analysis of Respondents' Answers to Customer Orientation Variables
Indicator
Average
Answer Index
(%)
Research Findings
1. Commitment to
customer
satisfaction
78,18
- Some respondents apply the commitment of not making
customers wait long.
- Some respondents maintain product quality, ease of
access & convenience for customers.
2. Collect customer
needs
information
- Some respondents asked directly.
- There are also those who do not collect information
because if the customer needs it, they will buy it.
3. How to satisfy
customers
- Some respondents provided friendly and professional
services.
- Some respondents provided bonuses.
4. Knowing
customer
complaints
- Some respondents established communication channels.
- Some respondents know about customer complaints, for
example by monitoring social media.
5. Special attention
to customers
- Provide more value to loyal customers.
- Some respondents pay special attention by giving a
warm welcome.
Source: Processed data, 2024
Respondents' Responses to Promotion Attractiveness
Table 7. Respondent's Answer Index on Promotion Attractiveness
Promotion
Attractiveness
Value Weight
Total
Indicator
1
2
3
4
5
6
7
8
9
10
Uniqueness of
promotional
activity concept
0
0
0
0
1
10
18
36
21
14
100
Value Weight
0
0
0
0
5
60
126
288
189
140
808
Percentage of
Answers (%)
0
0
0
0
1,00
10,00
18,00
36,00
21,00
14,00
100
Respondent Answer
Index (%)
0
0
0
0
0,50
6,00
12,60
28,80
18,90
14,00
80,80
Attractive
promotional
attributes
0
0
0
0
1
8
24
29
29
9
100
Value Weight
0
0
0
0
5
48
168
232
261
90
804
Percentage of
Answers (%)
0
0
0
0
1,00
8,00
24,00
29,00
29,00
9,00
100
Respondent Answer
Index (%)
0
0
0
0
0,50
4,80
16,80
23,20
26,10
9,00
80,40
Povocative
promotion
0
0
0
0
0
11
30
31
17
11
100
Value Weight
0
0
0
0
0
66
210
248
153
110
787
Percentage of
Answers (%)
0
0
0
0
0
11,00
30,00
31,00
17,00
11,00
100
Respondent Answer
Index (%)
0
0
0
0
0
6,60
21,00
24,80
15,30
11,00
78,70
Average Respondent Answer Index
79,97
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Journal of Indonesian Social Sciences, Vol. 5, No. 10, October 2024 2699
Conclusions: On average, respondents gave high perception scores for promotional attractiveness.
Source: Processed data, 2024
Table 8. Analysis of Respondents' Answers to the Promotion Attractiveness Variable
Indicator
Average Answer
Index (%)
Research Findings
1. Uniqueness of
promotional activity
concept
79,97
- Some respondents said they use promotions in the
form of advertisements that are different from
competitors through social media such as Instagram
and Facebook.
- Some respondents invited famous influencers to use
and review their products in person.
2. Attractive
promotional
attributes
- Some respondents distributed fliers as promotional
media.
- Some respondents used banners as promotional
media.
3. Provocative
promotion
- Some respondents provide lucky draws, discounted
prices or discounts at certain times.
- Some respondents provide bundles or promotional
packages at a lower price than buying them separately.
Source: Processed data, 2024
Respondents' Responses to Competitive Prices
Table 9. Respondents' Answer Index for Competitive Price Variables
Competitive Price
Value Weight
Total
Indicator
1
2
3
4
5
6
7
8
9
10
Price according to
quality
0
0
0
0
2
3
27
40
16
12
100
Value Weight
0
0
0
0
10
18
189
320
144
120
956
Percentage of answers
(%)
0
0
0
0
2,00
3,00
27,00
40,00
16,00
12,00
100
Respondent Answer Index (%)
0
0
0
0
1,00
1,80
18,90
32,00
14,40
12,00
80,10
Price comparison
0
0
0
0
0
14
16
39
23
8
100
Value Weight
0
0
0
0
0
84
112
312
207
80
958
Percentage of answers
(%)
0
0
0
0
0
14,00
16,00
39,00
23,00
8,00
100
Respondent Answer Index (%)
0
0
0
0
0
8,40
11,20
31,20
20,70
8,00
79,50
Price affordability
0
0
0
0
3
6
28
32
17
14
100
Value Weight
15
36
196
256
153
140
951
Percentage of answers
(%)
0
0
0
0
3,00
6,00
28,00
32,00
17,00
14,00
100
Respondent Answer Index (%)
0
0
0
0
1,50
3,60
19,60
25,60
15,30
14,00
79,60
Average Respondent Answer Index
79,73
Conclusions: On average, respondents gave high perception scores for competitive pricing.
Source: Processed data, 2024
Table 10. Analysis of Respondents' Answers to Competitive Price Variables
Indicator
Average Answer
Index (%)
Research Findings
1. Price according to
quality
79,73
- Most respondents stated that the prices set varied and
were in accordance with product quality.
- some respondents set the price proportional to the
quality of the product.
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2. Price comparison
- Some respondents stated that the prices set were the
same as competitors' prices.
- Some respondents set lower prices than competitors.
3. Price affordability
- Most respondents stated that the prices set were
average and of an affordable standard for all groups.
- Some respondents stated that the products sold are of
good quality and at an affordable price.
Source: Processed data, 2024
Respondents' Responses to Marketing Performance
Table 11. Respondents' Answer Index for Marketing Performance Variables
Marketing
Performance
Value Weight
Total
Indicator
1
2
3
4
5
6
7
8
9
10
Sales Volume
0
0
0
0
0
5
25
39
18
13
100
Value Weight
0
0
0
0
0
30
175
312
162
130
961
Percentage of Answers
(%)
0
0
0
0
0
5,00
25,00
39,00
18,00
13,00
100
Respondent Answer Index (%)
0
0
0
0
0
3,00
17,50
31,20
16,20
13,00
80,90
Sales Growth
0
0
0
0
0
6
30
35
20
9
100
Value Weight
0
0
0
0
0
36
210
280
180
90
959
Percentage of Answers
(%)
0
0
0
0
0
6,00
30,00
35,00
20,00
9,00
100
Respondent Answer Index (%)
0
0
0
0
0
3,60
21,00
28,00
18,00
9,00
79,60
Customer Growth
0
0
0
0
0
9
23
37
21
10
100
Value Weight
0
0
0
0
0
54
161
296
189
100
974
Percentage of Answers
(%)
0
0
0
0
0
9,00
23,00
37,00
21,00
10,00
100
Respondent Answer Index (%)
0
0
0
0
0
5,40
16,10
29,60
18,90
10,00
80,00
Average Respondent Answer Index
80,17
Conclusions: On average, respondents gave high perceived scores for marketing performance.
Source: Processed data, 2024
Table 12. Analysis of Respondents' Answers to Marketing Performance Variables
Indicator
Average Answer
Index (%)
Research Findings
1. Sales volume
80,17
- Most respondents experienced an increase in sales
with fairly small-scale gains.
- Some respondents said the targeted products were
well achieved.
2. Sales growth
- Some respondents said the growth in turnover
generated is increasing every year
- Some respondents said their income fluctuates.
3. Customer growth
- Some respondents said the number of customers had
increased due to social media promotions.
- Most of the increase in customers is due to selling
quality products and affordable prices.
Source: Processed data, 2024
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Research Data Analysis
1. Validity Test
Table 13. Validity Test Results
Variables
Grain
rcount
rtabel
Description
Customer Orientation
X1.1
0,711
0,1966
Valid
X1.2
0,671
0,1966
Valid
X1.3
0,714
0,1966
Valid
X1.4
0,658
0,1966
Valid
X1.5
0,641
0,1966
Valid
Promotion
Attractiveness
X2.1
0,792
0,1966
Valid
X2.2
0,845
0,1966
Valid
X2.3
0,775
0,1966
Valid
Competitive Price
X3.1
0,748
0,1966
Valid
X3.2
0,801
0,1966
Valid
X3.3
0,792
0,1966
Valid
Marketing
Performance
Y1.1
0,768
0,1966
Valid
Y1.2
0,816
0,1966
Valid
Y1.3
0,777
0,1966
Valid
Source: Processed data, 2024
2. Reliability Test
Table 14. Reliability Test Results
Variables
Cronbach's
Alpha
Cronbach's
Alpha Theory
Description
Customer Orientation
0,699
0,60
Reliable
Promotion Attractiveness
0,725
0,60
Reliable
Competitive Price
0,678
0,60
Reliable
Marketing Performance
0,692
0,60
Reliable
Source: Processed data, 2024
Classical Assumption Test
1. Normality Test
a. Residual Normality Test
Table 15. Residual Normality Test
One-Sample Kolmogorov-Smirnov Test
Unstandardized Residual
N
100
Normal Parametersa,b
Mean
,0000000
Std. Deviation
1,88467477
Most Extreme Differences
Absolute
,057
Positive
,057
Negative
-,028
Test Statistic
,057
Asymp. Sig. (2-tailed)
,200c,d
a. Test distribution is Normal.
b. Calculated from data.
c. Lilliefors Significance Correction.
d. This is a lower bound of the true significance.
Source: Processed data, 2024
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Journal of Indonesian Social Sciences, Vol. 5, No. 10, October 2024 2702
b. Histogram Graph Normality Test
Regression Standardized Residual
Figure 2.
Histogram
Dependent Variable: Marketing Performance
Source: Processed data, 2024
c. Normality Probability Plot
Observed Cum Prob
Figure 3.
Normal P-P Plot of Regression Standardized Residuals
Dependent Variable: Marketing Performance
Source: Processed data, 2024
Mean = -7.39E-16
Std.Dev. = 0,985
N = 100
Expected Cum Prob
Frequency
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2. Multicollinearity Test
Table 16. Multicollinearity Test Results
Coefficientsa
Model
Collinearity Statistics
Tolerance
VIF
1
Customer Orientation
,504
1,986
Promotion Attractiveness
,558
1,793
Competitive Price
,537
1,862
a. Dependent Variable: Marketing Performance
Source: Processed data, 2024
3. Heteroscedasticity Test
a. Residual Heteroscedasticity Test
Table 17. Heteroscedasticity Test Results
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
T
Sig.
B
Std. Error
Beta
1
(Constant)
1,462
1,335
1,095
,276
Customer Orientation
-,036
,047
-,110
-,769
,444
Promotion
Attractiveness
,062
,055
,152
1,118
,266
Competitive Price
-,003
,059
-,006
-,045
,965
a. Dependent Variable: AbsRes
Source: Processeddata, 2024
b. Heteroscedasticity Test Scatterplot
Regression Standardized Predicted Value
Figure 4.
Scatterplot
Dependent Variable: Marketing Performance
Regression Studentized Residual
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Source: Processed data, 2024
4. Autocorrelation Test
Table 18. Autocorrelation Test Results
Model Summaryb
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
Durbin-Watson
1
,670a
,449
,432
1,91390
1,649
a. Predictors: (Constant), Competitive Price, Promotion Attraction, Customer Orientation
b. Dependent Variable: Marketing Performance
Source: Processed data, 2024
Table 19. Autocorrelation Decision Making
Hypothesis 0
Decision
If
No positive autocorrelation
Reject
0 < d < dl
No Autocorrelation
No decision
dl ≤ d ≤ du √
No negative autocorrelation
Reject
4 - du < d < 4
No negative autocorrelation
No decision
4 - du ≤ d ≤ 4 - dl
No Autocorrelation, positive and negative
Not rejected
du < dw < 4-du
Source: Processed data, 2024
5. Correlation Test
Table 20. Correlation Test Results
Correlations
Customer
Orientation
Promotion
Attractiveness
Competitive
Price
Marketing
Performance
Customer
Orientation
Pearson Correlation
1
,618**
,636**
,605**
Sig. (2-tailed)
,000
,000
,000
N
100
100
100
100
Promotion
Attractiveness
Pearson Correlation
,618**
1
,583**
,547**
Sig. (2-tailed)
,000
,000
,000
N
100
100
100
100
Competitive
Price
Pearson Correlation
,636**
,583**
1
,575**
Sig. (2-tailed)
,000
,000
,000
N
100
100
100
100
Marketing
Performance
Pearson Correlation
,605**
,547**
,575**
1
Sig. (2-tailed)
,000
,000
,000
N
100
100
100
100
**. Correlation is significant at the 0.01 level (2-tailed).
Source: Processed data, 2024
6. Test Coefficient of Determination (R )2
Table 21. Test Results of the Coefficient of Determination (R )2
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
1
,670a
,449
,432
1,91390
a. Predictors: (Constant), Competitive Price, Promotion Attraction, Customer Orientation
Source: Processed data, 2024
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Journal of Indonesian Social Sciences, Vol. 5, No. 10, October 2024 2705
7. Multiple Linear Regression Test
Table 22. Multiple Linear Regression Test
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
T
Sig.
B
Std. Error
Beta
1
(Constant)
5,027
2,203
2,282
,025
Customer Orientation
,229
,077
,318
2,978
,004
Promotion
Attractiveness
,182
,091
,202
1,997
,049
Competitive Price
,238
,097
,255
2,465
,015
a. Dependent Variable: Marketing Performance
Source: Processed data, 2024
8. F test
Table 23. F Test Results
ANOVAa
Model
Sum of Squares
Df
Mean Square
F
Sig.
1
Regression
287,102
3
95,701
26,126
,000b
Residuals
351,648
96
3,663
Total
638,750
99
a. Dependent Variable: Marketing Performance
b. Predictors: (Constant), Competitive Price, Promotion Attractiveness, Customer Orientation
Source: Processed data, 2024
Analysis Discussion of research data results which will discuss the results of hypothesis
testing that has been carried out to determine the effect of customer orientation, promotional
attractiveness, and competitive prices on marketing performance. The following is Table 24:
Table 24. Summary of Classical Assumption Test Results
Relevance
Test Results
Cut Off
Conclusion
1. Normality
Using the Kolmogorov-Smirnoov
test method (K-S test or KS test)
Asymp. Sig (2-
tailed) 0.200
>0,05
The data is normal, because it
meets the normality requirement
of 0.200 greater than 0.05, so it
can be used in this study.
2. Multicollinearity
Tolerance
X1 = 0.504
X2 = 0.558
X3 = 0.537
VIF
X1 = 1.986
X2 = 1.793
X3 = 1.862
Tolerance
>0.10 and VIF
<10.00
The tolerance value of the three
variables is not less than 0.10 and
VIF is more than 10.00, so it can
be concluded that there is no
multicollinearity.
3. Heteroscedasticity
Using the test method
Glajser
Sig Value:
X1 = 0.444
X2 = 0.266
X3 = 0.965
>0,05
There is no heteroscedasticity
problem, because the significant
value of the three variables is
greater than 0.05.
4. Autocorrelation
Using the Durbin-Watson test
method (DW test)
1,6131<
1,649 <
1,7365
(dl ≤ d ≤ du)
There is no autocorrelation
problem, so the data is declared
autocorrelation-free and can be
used.
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5. Correlation Test
Using the method
Pearson Correlation
X1 = 0.000
X2 = 0.000
X3 = 0.000
Sig
<0,05
The correlation is very strong
because it meets the
requirement that the significance
value is smaller than 0.05.
6. Determination Coefficient Test
0,449
45.00 percent of independent
variables.
7. F test
F = 26,126
Sig = 0.000
Fcount >
Ftabel 2.70
Sig < 0.05
The model can be used because
the fcount test is greater than ftabel
and the significant value is less
than 0.05.
Source: Processed data, 2024
9. Test t
Table 25. Results of the t-test
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
5,027
2,203
2,282
,025
Customer Orientation
,229
,077
,318
2,978
,004
Promotion
Attractiveness
,182
,091
,202
1,997
,049
Competitive Price
,238
,097
,255
2,465
,015
a. Dependent Variable: Marketing Performance
Source: Processed data, 2024
The results of normality testing using the Kolmogorov-Smirnov test method obtained
Asymp. Sig (2-tailed) of 0.200 and shows that this value is greater than the significant level of 0.05.
Thus, it can be concluded that the data in this study is stated to have a regression model that has a
normal distribution. In the multicolinerity test results of each variable obtained from the tolerance
value. For the customer orientation variable, the tolerance value is 0.504 and the VIF value is 1.986,
then the promotional attractiveness variable gets a tolerance value of 0.558 and a VIF value of
1.793 and the competitive price variable gets a tolerance value of 0.537 and a VIF value of 1.862,
where the value is greater than 0.100 and the VIF value does not exceed 10.00, so a conclusion can
be drawn that there is no multicollinearity problem. The results of the heteroscedasticity test using
the Glejser test method obtained with the calculation results on the customer orientation variable
get a significant value of 0.444 then the promotional attractiveness variable gets a significant value
of 0.266 and the competitive price variable gets a significant value of 0.965 which means that the
value is above 0.05 so it can be concluded that there is no heteroscedasticity problem. The results
of the autocorrelation test can be seen that the Durbin Watson statistical value is 1.649, the sample
used for research is 100, with a significance level of 0.05 or 5%, has 3 independent variables, the
dL result is 1.6131, dU is 1.7364. In order for a variable to be declared not to occur autocorrelation,
it must meet the conditions where dl ≤ d ≤ du. From this description, the results obtained are
1.6131 < 1.649 < 1.7365, so it can be stated that there is no autocorrelation. Correlation test
Pearson Correlation calculation value of the relationship between variables (X) on marketing
performance (Y). The variable customer orientation (X1 ) Pearson correlation calculation value of
0.605 then the promotional attractiveness variable (X2 ) Pearson correlation calculation value of
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Journal of Indonesian Social Sciences, Vol. 5, No. 10, October 2024 2707
0.547 and the value of the competitive price variable (X3 ) Pearson correlation calculation value of
0.575 the value is positive and is in the range of 0.50 - 0.75 which means it shows a strong
correlation relationship and a significance value of 0.000 which means the sig value is smaller than
0.05 indicating a significant relationship to marketing performance. The coefficient of
determination test shows that the R Square value is 0.449 or 45.00 per cent. This shows that
customer orientation (X1), promotional attractiveness (X2 ), and competitive prices (X3 ) contribute
45.00 per cent to the marketing performance variable (Y). Other variables that influence the
remaining 55.00 are not explained in this study. Then, in the ANOVA test results or F test, the Fcount
value is 26.126 with an Ftabel value of 2.7 (Fcount 26.126> Ftabel 2.70). It can be seen that Fcount is much
greater than Ftabel and has a significant level of 0.000, showing a number much smaller than 0.05
(0.000 <0.05). So, it can be concluded that the regression model can be declared feasible for
regression modelling, which, together with the independent variables, have an influence on the
dependent variable.
Table 26. Hypothesis Result
Hypothesis
T test
Sig
Cut Off
Description
H1 : Customer Orientation
(X1 ) has a positive and
significant effect on
Marketing Performance
(Y)
X1 = 2.978
X1 = 0.004
T test >
Ttabel =
1.98498 Sig
> 0.05
H1 Accepted.
Meaning: Customer Orientation
has a positive and significant
effect on Marketing
Performance.
H2 : Promotion
Attractiveness (X2 ) has a
positive and significant
effect on Marketing
Performance (Y)
X2 = 1.997
X2 = 0.049
T test >
Ttabel =
1.98498 Sig
> 0.05
H2 Accepted.
Meaning: Promotion
Attractiveness has a positive
and significant effect on
Marketing Performance
H3 : Competitive Price (X3 )
has a positive and
significant effect on
Marketing Performance
(Y)
X3 = 2.465
X3 = 0.015
T test >
Ttabel =
1.98498 Sig
> 0.05
H3 Accepted.
Meaning: Competitive Price has
a positive and significant effect
on Marketing Performance
Source: Processed data, 2024
Based on Table 26, the research results state that customer orientation positively affects
marketing performance (Karina & Sari, 2022). Other supporting research states that promotional
attractiveness variables positively and significantly affect marketing performance. (This is also in line
with price research, which positively and significantly affects marketing performance
(Puspaningrum, 2020; Zulaicha & Irawati, 2016).
Conclusion
Based on the results of hypothesis testing, the variables of customer orientation, promotional
attractiveness and competitive prices have a positive and significant effect on marketing
performance. Business actors can increase sales by establishing good customer relationships through
good service and understanding customers. Suggestions should be given should business actors be
able to maintain and understand customer orientation, promotional attractiveness, and competitive
prices on marketing performance so that in the future, they can maintain market share and increase
profits and customer growth.
e-ISSN: 2723-6692 p-ISSN: 2723-6595
Journal of Indonesian Social Sciences, Vol. 5, No. 10, October 2024 2708
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