Corresponding Author: M.
Syafrudin Yusuf
E-mail: [email protected]
INTRODUCTION
The expansion of the Indonesian economy, particularly the
financial and banking industry sectors, has been impacted by economic
globalization, which has altered how businesses behave. The national banking
system must immediately adapt to carry out its duties and obligations to the
community due to the swift development of the national or global economy, which
is accompanied by challenges of increasing importance. The role of banking has
a significant impact on a nation's financial operations. One may say that banks
are the lifeblood of a nation's economy. A gauge of a country's growth can also
be found in the development of one of its banks. The importance of controlling
a country increases with its level of development. In addition to being a part
of a nation's financial system and payment system, banks are also a part of the
global financial and payment systems in the modern era of globalization (Regaer et
al., 2016).
Along with the many competitors, to maintain
the existence of a Bank, good performance is needed from various aspects such
as strategy, product innovation, and competitive ability. The corporate
strategy explains why a company is essential in the marketplace by defining its
approach to creating superior customer value. It represents an organizational
commitment to pursuing multiple choices about how to compete. Strategy can help
companies to gain legitimacy and improve performance. The company's ability to perform
better is influenced by a unique organizational structure, proven systems and
processes aligned with its resources (Wibowo &
Handika, 2017). One strategy that the company can improve
is its marketing strategy (Kartawinata
& Wardhana, 2015).
Marketing strategy includes the choice of
market share and customers, the identification of customer needs for products, and
designing and integrating marketing programs to create, deliver, and
communicate product offerings. The marketing strategy also includes realizing
the goals the company wants to achieve in the future and the means used to
achieve them (Morgan et
al., 2019). The company's primary objective is to
establish a distinct brand that captures the attention of potential customers
and devoted customers offline and online while representing the company's
beliefs and characteristics. When these two worlds connect, the organization
can achieve its full potential in financial and nonfinancial rewards (Schwarzl
& Grabowska, 2015). Customer Relationship Management (CRM) has
evolved from a mere point of customer data collection to an enterprise-wide
support and integrated process for the entire customer relationship process.
The business management process is changing from services and data management
to creating business information systems as strategic business management
tools. Overall, the business model has become more customer-oriented through an
e-business model based on a network of information systems related to
prospective customer data in the future. Through this new business model, the
same products are sold with the support of more detailed customer information
that can drive business success and add a sustainable competitive advantage (Alawiyah
& Humairoh, 2017). Due to these circumstances, engagement
marketing, or the company's conscious effort to motivate, empower and measure
the customer's contribution to its marketing function outside its core,
transactional economy. Customer relationship management uses client information
to help clients with post-purchase assistance and client retention (Harmeling et
al., 2017).
One of the most important research issues of
the day is customer participation or involvement. It can be described as the
psychological process that a customer goes through before developing loyalty.
Second, the expression of customer behaviour toward the brand or business
results from the motivating factor but does not include actual transactions.
Third, the degree of zeal, commitment, engrossment, and interaction are
indicators of psychological condition. An offline and online environment can be
used for customer involvement (Greve, 2014). A specific indirect brand contact's level
of cognitive, emotional, and behavioural activity is utilized to gauge the
client's level of motivating, brand-related, context-dependent state of mind. Cognitive
process (e.g. level of concentration and brand attachment). Emotional
involvement Personality characteristics, such as the degree of brand-related
inspiration and pride (e.g. level of energy used in interacting with the
focused brand) (Hollebeek,
2011). (Absorption represents cognitive aspects, Dedication
represents emotions and Vigor, and interaction reflects behavioural aspects (Patterson
& Robots, 2015; Raeisi & Lingjie, 2017). Customers become emotionally invested in a
company's brand and are more interested in its goods and services when engaged (Bansal &
Chaudhary, 2020). This study aims to examine the effect of
the Customer Engagement dimension on Marketing. This research has implications
for providing information in the form of analysis results that private banking
companies can use to improve the marketing team's performance through customer
engagement.
METHOD
This
study uses quantitative methods. In this work, a statistical analytic tool
called structural equation modelling (SEM) was applied. There will be various
relationships between the latent variables in this study, which include several
latent variables, dimensions, and indicators. SEM effectively enables path
analysis using latent variables (Arya
Pering, 2020). Lisrel is used to analyze the
relationship between latent variables, measured by a set of observed variables,
to understand customer behaviour. The number of samples in this study was
determined using the solving formula.
n states the
number of samples, N represents the total population, and e represents the
significant allowance due to errors in the study. Researchers use a 5%
allowance. Four hundred samples have been counted, representing the population
of private bank customers in the Jabodetabek area. The sample in this study was
selected by random sampling technique. The estimation method used for this
study uses the Maximum Likelihood (ML) estimation method. Data for measurement
using the ML method is usually continuous (Ramadiani,
2010). The data collection technique was carried out by the survey
method. The instrument for data collection is a questionnaire distributed
online to 400 private bank customers in the Jabodetabek area. The solving
formula carried out the determination of the research sample. The variables in
this study are Absorption, Dedication, Vigor, Interaction, Customer Engagement,
Marketing Performance, Nonfinancial Performance and Financial Performance. The
conceptual framework of this research can be seen in Figure 1.
Figure 1. Conceptual Framework
Data analysis in this study was carried out in 2 stages;
the first stage was checking each variable's validity and construct reliability.
The second stage is hypothesis testing. The hypotheses in this study include
the following:
H1������ :
Customer Engagement has a positive effect on Marketing Performance
H2������ :
Absorption has a positive effect on Customer Engagement
H3������ :
Dedication has a positive effect on Customer Engagement
H4������ : Vigor
has a positive effect on Customer Engagement
H5������ :
Interaction has a positive effect on Customer Engagement
H6������ :
Marketing Performance has a positive effect on Nonfinancial Performance
H7������ : Marketing Performance has a positive effect on Financial
Performance
RESULTS AND DISCUSSION
Model Fit Test
A model fit
test is carried out to assess whether the data collected is consistent and fits
the model. If the model does not match the data, it is necessary to find the
cause in the model and find a way to modify the model to obtain a better data
fit (Ramadiani,
2010). Figure 2 shows the results of the lisrel
output in the measurement of model fit.
Figure 2. Output-Model Fit
If the model
matches the data, it is correct and suitable according to the goodness of fit.
Each estimate is reviewed by referring to the goodness of fit (GOF), which can
be seen in table 1.
Table 1. Goodness of Fit
|
Indicator
|
Criteria Goodness of Fit
|
The goodness of Fit Test Results
|
Conclusion
|
|
RMSEA
|
 �0.08
|
0.067
|
Goodness of Fit
|
|
NFI
|
 0.9
|
0.99
|
Goodness of Fit
|
|
NNFI
|
0.9
|
0.98
|
Goodness of Fit
|
|
CFI
|
0.9
|
0.99
|
Goodness of Fit
|
|
IFI
|
0.9
|
0.99
|
Goodness of Fit
|
|
RFI
|
0.9
|
0.98
|
Goodness of Fit
|
|
RMR
|
0.05
|
0.036
|
Goodness of Fit
|
|
GFI
|
0.9
|
0.98
|
Goodness of Fit
|
|
AGFA
|
0.9
|
0.95
|
Goodness of Fit
|
Table 1 shows
nine indicators to see the fit of the model. Each indicator has criteria. The
results showed that all indicator criteria were met, and it could be concluded
that the tested data were consistent and matched the model.
Validity and Reliability Test
If the overall model fit test has been successful, the evaluation of the
measurement model's applicability can proceed. Each concept is subjected to a
separate examination, which looks at the reliability and validity of the
construct. Checking the value of the factor load or Coefficient in the model is
the first step in the validity evaluation. The assessment of the factor load of
each variable must be > 0.70 to prove that the indicator variables are
significantly related and can represent the underlying building concept. In
other words, the designed model has a pretty good validity to the construct.
Figure 3. Output Score Loading Factor and Error
Measurement
Figure 3 shows the
results of the score loading factor and error measurement of latent variables.
The latent variable is declared if it has a common loading value > 0.5. The
results of the validity test on each latent variable in this study can be seen
in table 2.
Table 2. Validity and Reliability
Test
|
Variable
|
std loading
|
error
|
CR0.7
|
VE0.5
|
Result
|
|
AB
|
0.87
|
0.24
|
0.8
|
0.4
|
Valid and Reliable
|
|
DED
|
0.98
|
0.04
|
1.0
|
0.5
|
Valid and Good reliability
|
|
VIG
|
0.98
|
0.03
|
1.0
|
0.5
|
Valid and Good reliability
|
|
INT
|
0.82
|
0.32
|
0.7
|
0.4
|
Valid and Reliable
|
|
NP
|
1.12
|
0.25
|
0.8
|
0.5
|
Valid and Good reliability
|
|
FP
|
0.85
|
0.28
|
0.8
|
0.4
|
Valid and Reliable
|
The reliability test was carried out to show good measurement consistency
in our model. The construct reliability and variance extracted values should be
0.70 and 0.50. The CFA test in this study contained 3-factor loads whose values
were <0.50, namely the variables AB (Absorption), INT (Interaction) and FP
(Financial Performance). The researcher does not delete it because it refers to
Wijanto (2008), which indicates that the linked variable can be regarded as not
to be deleted if the common factor load value is less than 0.50 but still 0.30.
But if the common factor load value is <0.30, the related variable can be
removed from the model (Rakafathia et
al., 2015). Table 2 shows that all variables are valid
based on the standard loading value (std. Loading) above 0.70. The value of
construct reliability on all variables also shows the result of 0.70. It can be
concluded that all variables are valid and reliable.
Confirmatory
Factor Analysis Test
Confirmatory factor analysis is used to empirically verify or confirm a
model (measurement of the model) or a number of manifest variables. The goal of
the confirmation factor analysis is to construct a test measurement model, not
a model, based on theoretical research. This is a model measurement test on all
variables without any effect lines simultaneously (Wiryanto et
al., 2019). The CFA test output on the Absorption,
Dedication, Vigor and Interaction dimensions can be seen in Figure 4.
Figure 4. CFA Customer Engagement Dimension
Hypothesis Test
The last stage is to test the hypothesis.
Hypothesis testing is done by comparing the value of t. The hypothesis can be
accepted if the T value 1.96. At the same time, the hypothesis is rejected if
the T value obtained is 1.96. The T value between variables can be seen in
Figure 5, while the standard Coefficient can be seen in Figure 5.
Figure 5. Output-T Value
Figure 6. Output Standart Coefficient
Figure 5
shows if the T value between variables meets the criteria above 1.96. The
description of the T value obtained can be seen in the following table:
Table 3. Hypothesis Test
|
Hypothesis
|
Relationship between variables
|
T value
|
Standard Coefficient
|
Result
|
|
H1
|
CE --> MP
|
1.97
|
0.1
|
|
H1 is
accepted because the T value  1.96
|
|
|
H2
|
AB --> CE
|
15.59
|
0.66
|
H2 is accepted because the T
value 1.96
|
|
H3
|
DED --> CE
|
26.69
|
0.99
|
H3 is accepted because the T
value≥ 1.96
|
|
H4
|
VIG --> CE
|
28.07
|
0.99
|
H4 is accepted because the T value1.96
|
|
H5
|
INT --> CE
|
20.67
|
0.83
|
H5 is accepted because the T
value1.96
|
|
H6
|
MP --> NP
|
8.8
|
1.13
|
H6 is accepted because the T
value1.96
|
|
H7
|
MP --> FP
|
8
|
0.85
|
H7 is accepted because the T
value1.96
|
Table 3 shows
the results of hypothesis testing between variables. There are seven proposed
hypotheses with the following details:
H1������ :
Customer Engagement has a positive effect on Marketing Performance
H2������ :
Absorption has a positive effect on Customer Engagement
H3������ :
Dedication has a positive effect on Customer Engagement
H4������ :
Vigor has a positive effect on Customer Engagement
H5������ :
Interaction has a positive effect on Customer Engagement
H6������ :
Marketing Performance has a positive effect on Nonfinancial Performance
H7������ :
Marketing Performance has a positive effect on Financial Performance
The t value
obtained from the analysis results shows 1.96. It can be concluded that all
proposed hypotheses 1 to 7 are accepted.
Customer engagement has become one of the essential strategies that
customers must consider company managers in recent years for the company's
success. Customer engagement with a business is a fundamental process for
running that business. Customer engagement can provide valuable insights by
creating influential relationships between customers, brands and organizations.
The four customer engagement aspects in this study favourably impact customer
engagement. The first dimension is absorption, which is the psychological state
of the customer that is directly related to how to interact with the company.
The second dimension is Dedication. Dedication is a sense of pride and loyalty
that customers have for the brand. The third dimension of Vigor is a great
sense of customer enthusiasm in the form of tremendous customer interest and
interest in the brand. The fourth dimension of interaction is a form of
customer participation in a brand. Strong customer engagement and service
innovation are advantageous, according to a study by �Raeisi &
Lingjie (2017), demonstrating a strong relationship between
stakeholders and the company.
����������� Customer
engagement can create long-term relationships between customers and the company
(Handayaningrum,
2019). Customer engagement can be seen from
consumers who feel accepted, listened to and cared for through social
relationships even though they do not make purchases (Ridanasti, 2021). Customer engagement is essential in
marketing strategy because it is directly related to customer loyalty.
Relationship marketing practices focus on increasing customer attractiveness to
the company's products and building further relationships with these customers
to achieve the desired economic goals (Vinerean
& Opreana, 2021). The perceived business benefits of the
company are not only in the form of financial benefits. Long-term consequences
like market share, customer happiness, customer loyalty/retention, brand
equity, and innovation are also influenced by nonfinancial profits (Gao, 2010). Customers can contribute to the company's
innovation process, create shared value, and work together to develop
competitive strategies (Bijmolt et
al., 2010).
CONCLUSION
Based on the data
analysis that has been done, it can be concluded that the four dimensions of
customer engagement, namely Absorption, Dedication, Vigor, and Interaction have
a significant effect on customer engagement. Customer engagement has a
significant effect on marketing performance. Marketing performance has a
significant effect on the company's financial and non-financial profits. This
research has the meaning of being able to provide information in the form of
analysis results that can be used by private banking companies to improve the
performance of the marketing team through customer engagement.
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