The Effect of Digital Marketing Communication in Opening Letter of Credit at Bank Bni Bandung

This article explores the impact of digital marketing communication on the opening of letters of credit at Bank BNI, a leading bank in Indonesia. Digital marketing communication, the use of digital channels to communicate with customers and promote products, has become increasingly popular among businesses, including in the banking industry. The study aims to investigate the relationship between digital marketing communication and the number of letters of credit opened at Bank BNI. The article provides a literature review on banking, marketing, digital marketing, digital marketing communication, and letters of credit. The research methodology will include data collection, sources of data, methods of data collection, sampling strategy, and data analysis techniques. The findings of this study could provide valuable insights into the effectiveness of digital marketing in the banking industry, which can be used to develop more effective marketing strategies to attract and retain customers.


I. INTRODUCTION
Digital marketing communication has become increasingly popular among businesses in recent years. With the growth of technology, digital marketing has opened up new opportunities for companies to reach their target audiences more effectively and efficiently. Digital marketing is the use of digital channels, such as search engines, social media, email, and mobile devices, to promote a product or service and connect with a target audience" (Chaffey & Ellis-Chadwick, 2019). One of the areas where digital marketing has had a significant impact is in the banking industry, specifically in opening letters of credit.
Bank BNI is a leading bank in Indonesia. According to a report by Euromonitor International, Bank BNI has been recognized as the second-largest bank in terms of total assets in Indonesia in 2020 (Euromonitor International, 2021). Bank has embraced digital marketing as a means of communication with its customers. Through its website, social media platforms, and mobile application, Bank BNI has been able to communicate more effectively with its customers and provide them with a better user experience. However, the impact of digital marketing communication on the opening of letters of credit at Bank BNI has not been fully explored.
The opening of letters of credit is an essential aspect of banking services, particularly for international trade, understanding the impact of digital marketing communication on the opening of letters of credit at Bank BNI can provide valuable insights into the effectiveness of digital marketing in Moreover, digital marketing communication enables businesses to track and analyze customer behavior and preferences, allowing them to create personalized marketing messages that resonate with their target audience.

d. Letter of Credit
According to Al-Amaren, Ismail, and Nor (2020), a letter of credit is a financial instrument commonly used in international trade transactions to reduce the risk for both the buyer and the seller. It is a guarantee issued by a bank that ensures the seller will be paid for their goods or services, provided that they meet the terms and conditions specified in the letter of credit.. It serves as a type of payment assurance for both the buyer and the seller, ensuring that the buyer will receive the goods or services they ordered and that the seller will receive payment for those goods or services. In the context of Bank BNI, a letter of credit would refer to a specific product or service offered by the bank in which it issues letters of credit for its customers engaging in international trade or other transactions that require payment guarantees.

III. RESEARCH METHODOLOGY
According to Kadam and Bhalerao (2020), research method is the process of deciding the overall research approach that includes the type of data to be collected, sources of data, methods of data collection, sampling strategy, and data analysis techniques In this study, the researcher used descriptive research design.Fraenkel and Wallen (2019) define descriptive quantitative research as a method that involves collecting and analyzing data in a way that aims to provide a rich, detailed, and comprehensive description of the phenomenon being studied. This method is used to explore complex issues, gather information about participants' perspectives, and understand their experiences, feelings, and thoughts. In line with Fraenkel and Wallen, Ary et al. (2020) stated that it involves collecting data through observations, interviews, and other qualitative methods such as questionnaires.

IV. RESULTS AND DISCUSSIONS Path Analysis Results Using Smart PLS-SEM Measurement Model Results (Outer Model)
The measurement model relates the latent variable to the observable variable. In this research, four latent variables are assessed using twenty-two indicators. Based on the Partial Least Square estimating approach, a complete path model diagram is generated, as illustrated in the picture below: Validity test a. Convergent Validity The following are the findings of the convergence validity test, which contain the loading factor and AVE value for each study variable.
The following are the findings of a convergent validity test conducted using the SmartPLS version 3.2.9 software. 0.826 0.000 Valid (Source: Researcher Analyses using SmartPLS 3.2.9 Software) For convergent validity testing, outer loading or cross-loading factor values are used. According to (Ghozali, 2014), convergent validity is deemed satisfactory if the outer loading indicator is more than 0.70. However, the outer loading value between 0.5 and 0.6 is deemed adequate to fulfill the convergent validity criterion. Table 4.6 reveals all indications have an outside loading of higher of 0.5 and between 0.5 and 0.9. This indicates that the indicator has been deemed valid for research purposes and may be utilized for further study.
Testing the AVE value for each research variable is another way for validating the research variables. If the AVE value is more than 0.5, the items in a variable have enough convergent validity. The following is a convergent validity test with AVE, as shown in Table 4.22:

Discriminant Validity
The cross-loading value is used to evaluate discriminant validity. An indicator is said to have discriminant validity if the value of the cross-loading indicator on the variable is the highest among other variables. Table 4.23 contains the cross-loading value for each indicator: Apart from comparing AVE values and their relationships, discriminant validity may also be determined by examining cross-loading values. c.

Reliability Test
The reliability test in Partial Least Squares (PLS) may use either Composite Reliability or Cronbach's Alpha. A variable is considered trustworthy if its composite reliability value is more than 0.70, and its Cronbach alpha value is more significant than 0.60 .
The following table summarizes the reliability test results obtained using the SmartPLS software version 3.0.  Table 4.23, the Composite Reliability and Cronbach's Alpha values for each variable are more than 0.70 and 0.60, respectively, indicating that the data has a high degree of reliability. All variables' items included in the study's questionnaire may be inferred as accurate or consistent.

Structural Measurement (Inner Model)
The measurement of the structural model (inner model) has the aim of testing the influence of other latent variables. In PLS, it can be measured using R-Square (R 2 ) and path coefficient. The structural model test was carried out by considering the R 2 value of the endogenous latent construct (dependent) and the t-value of each exogenous (independent) latent variable on the endogenous latent Volume 5, Issue 3, May-June 2023 8 construct from the bootstrapping results. The following is a path diagram of the inner model in this study: According to the explanation of these findings, the variables in the inner model have a positive route coefficient. The bigger the path coefficient value from the independent variable to the dependent variable, the greater the effect of the independent factors on the dependent variable.

b. R-Square (R 2 )
According to Hamdalah (2020), the R-Square value is the endogenous construct's coefficient of determination. According to Chin and Ghozali (2013), the R2 value of 0.67 or higher for endogenous latent variables in the structural model indicates that the influence of exogenous variables on endogenous variables falls into the "good" category. If the result falls between 0.33 and 0.67, it is classified as medium, and if it falls between 0.19 and 0.33, it is classified as weak. Based on R-Square testing, the following conclusions were reached: The R-Square value for the variable measuring the continuance use intention is 0.491, which is in the medium category.

c. Predictive Relevance
Q Square is used to evaluate how well the observed values produced by the model and parameter estimations correspond to the estimated values. If the value of Q Square is less than 0, the model has less predictive significance; however, if it is more than 0, the model has predictive relevance. Using the following formula, the inner model test with predictive relevance is computed: According to the formula applied above, the predictive relevance value is 0.242, which is more than 0 and indicates that the model has a meaningful predictive value.

Hypotheses Test
According to Sugiyono (2019: 220), the research hypothesis is a provisional solution to the formulation of a research topic that must be validated by the acquired data. To test the hypothesis, it is important to compare the t-statistic value (to) with the t-table value (t), where the t-table value in this research is 1.96 and acceptance of the hypothesis is contingent on the following conditions: Software) The following is an explanation of the hypothesis based on Table 4.25 above:

The Effect of Digital Marketing Communication on the Opening Letters of Credit
As the above table shows, the research significance value on hand is 9.666 > 1.96, the significance level is 0.000, less than 0.05, and the path coefficients value is positive 0.701, indicating that the link between Digital Marketing Communication is significant and in the positive direction. Therefore, this research concludes that the level of Digital Marketing Communication impacts the Opening Letter of Credit.

V. CONCLUSION
The study demonstrates good convergent validity, as indicated by the AVE values for Digital Marketing Communication and Opening Letter of Credit exceeding 0.5. The reliability tests confirm the high degree of data reliability, with Composite Reliability and Cronbach's Alpha values surpassing 0.70 and 0.60, respectively, indicating accurate and consistent questionnaire items. The path coefficient analysis reveals a significant positive influence of Digital Marketing Communication on Opening Letters of Credit, with a path coefficient value of 9.785. The discriminant validity analysis supports the conclusion that the indicators used in the research have high discriminant validity. The R-Square value for the variable measuring the continuance use intention is 0.491, indicating a medium level of influence. Furthermore, the predictive relevance value of 0.242 indicates meaningful predictive value in the model. The hypotheses test demonstrates that the link between Digital Marketing Communication and Opening Letter of Credit is significant and positive, leading to the conclusion that the level of Digital Marketing Communication impacts the Opening Letter of Credit.