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Asian Business Research Journal 
Vol. 10, No. 9, 7-15, 2025 
ISSN: 2576-6759 
DOI: 10.55220/2576-6759.556 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
Factors Influencing the Adoption of Natural Language Processing of Commercial 
Banks in Vietnam 
 
Dinh The Hung1

 

Nguyen Danh Quang2 

Tran Minh Tuan3 

Nguyen Khoa Nghi4  

 
 

 
 
 

1School of Accounting and Auditing, National Economics University, Vietnam. 
2BIFA 8D, School of Accounting and Auditing, National Economics University, Vietnam. 
3HNUE High School for Gifted Students, Vietnam. 
4Nguyen Sieu School, Vietnam. 
Email: hungdt@neu.edu.vn  
Email: danhquang2311@gmail.com  
Email: mittuantran@gmail.com  
Email: khoanghi.cgd@gmail.com  
( Corresponding Author) 

 
Abstract 

In the digital age, the adoption of natural language processing (NLP) has emerged as an inevitable 
trend in the banking sector, supporting the reduction of time and costs, optimizing data 
management, and enhancing customer experience. However, the adoption of NLP in commercial 
banking activities in Vietnam faces significant challenges. The study utilized a multivariate 
regression model and SPSS software to examine the influence of six factors: Compatibility, 
Technical Complexity, Business Orientation, Human Resources, Legal Corridor, and Market 
Uncertainty. The results showed that three factors, namely Technical Complexity, Business 
Orientation, and Legal Corridor, affect the adoption of NLP in commercial banks in Vietnam. 
Based on these findings, the research team proposed several recommendations and solutions to 
accelerate the adoption of NLP in Vietnam. 

 
Keywords: Artificial intelligence (AI), Digital transformation, Natural language processing (NLP). 

 
1. Introduction 

The fourth industrial revolution is currently unfolding on a global scale, marked by the advent of 
transformative technologies such as artificial intelligence (AI). These technologies are playing pivotal roles across 
various sectors, with the finance and banking industry being particularly affected. Banks in Vietnam, as well as 
globally, have begun to integrate Natural Language Processing (NLP) into several management-related functions. 
This integration aids banks in achieving operational stability and identifying solutions to enhance profitability. 
However, the full potential of NLP remains underutilized due to several constraints that impact its adoption within 
the banking sector.  

In response to this, our research team proposes a model to examine the factors affecting the adoption of 
Natural Language Processing (NLP) in commercial banks in Vietnam. Analyzing this model is crucial, as the 
research findings will provide valuable insights and recommendations for bank managers and policymakers, aimed 
at improving the implementation and effectiveness of NLP in commercial banks throughout Vietnam. 

 

2. Literature Review 
Fridgen et al. (2022) evaluated the applicability of AI in the retail banking sector in Germany. Through the 

participation of 23 IT experts with deep knowledge of AI, this study applied the Technology-Organization-
Environment (TOE) framework to analyze factors influencing the integration of AI into banking systems. The 
results indicated that not only do the factors within the TOE model affect individual processes, but they also 
influence in various ways throughout the digital technology adoption process in banking. From this, the research 
team identified 12 factors that most significantly affect the adoption of AI in this sector, including: (1) The bank's 
relative advantage, (2) The availability of facilities and technology, (3) The safety and security of the technology 
used, (4) The efficiency of the bank's investment in technology, (5) The consensus of senior management, (6) 
Resource factors within the bank, (7) The expertise of the human resources, (8) The ability to restructure the 
management apparatus, (9) The size of the bank, (10) Competitive pressure from other organizations, (11) Legal 
factors when applying technology in banking, and (12) The trend of using AI across the industry. 

Van Phuoc (2022) research focused on the factors influencing the adoption of artificial intelligence in Vietnam. 
Using the structural equation method and based on 193 responses from businesses across Vietnam, this study 
indicated that management capacity is the most significant factor affecting the use of AI technology in businesses. 

mailto:hungdt@neu.edu.vn
mailto:danhquang2311@gmail.com
mailto:mittuantran@gmail.com
mailto:khoanghi.cgd@gmail.com
https://doi.org/10.55220/2576-6759.556


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Conversely, organizational size and competitive pressure do not play a significant role in the process of AI adoption 
in businesses. Specifically, the study concluded the following important points: (1) Technical compatibility, (2) 
Relative advantage, (3) Management support, (4) Management capacity, (5) Organizational readiness, (6) 
Government involvement, (7) Market uncertainty, (8) Supplier partnership, all positively impact the intention to 
integrate AI technology in organizations in Vietnam. Organizational size and competitive pressure do not 
significantly affect the intention to adopt AI technology in organizations in Vietnam. 

The research team of Horani et al. (2023) experimented with data from 512 senior IT/IS managers in public 
and private organizations in Jordan. The results indicated that several factors such as (1) Relative advantage, (2) 
Senior management support, (3) Cost efficiency, and (4) Compatibility positively influence the intention to adopt 
AI-based technologies. Additionally, factors such as (5) Legal frameworks and (6) Technical complexity of the 
technology were found to negatively affect the intention to adopt. These results complement previous studies and 
clarify the importance of technical compatibility in integrating AI into an organization's technological 
infrastructure. 
 

3. Literature Review 
3.1. Literature Review of Natural Language Processing  
3.1.1. Natural Language Processing (NLP) 

Natural Language Processing (NLP) is a subfield of computer science within the broader domain of artificial 
intelligence (AI) that aims to endow computers with the ability to comprehend text and speech similarly to humans 
(Machiraju & Moxdi, 2017). NLP models function by identifying relationships between linguistic elements, such as 
letters, words, and sentences within text datasets. This is a complex process requiring stringent technical 
specifications, encompassing multiple stages and diverse methodologies. Data preprocessing, feature extraction, 
and modeling are among the steps involved (DeepLearning.AI, 2023). Over time, NLP has undergone significant 
advancements driven by improvements in hardware, computer software, and linguistic theories (Qiu et al., 2020). 
The integration of computational linguistics with methodologies such as statistics, machine learning, deep learning, 
and deep neural networks has enabled NLP to decode and reconstruct natural language structures to achieve 
specific objectives. These objectives include information extraction, transforming unstructured text into structured 
formats, syntactic processing, semantic understanding, and identifying relationships between concepts. The 
research conducted by Klein et al. (2020), Lindvall et al. (2018), Robert and Cornwell (2013), and Velupillai et al. 
(2018) has elucidated these advancements. NLP is not only beneficial across various scientific disciplines but also 
applicable for multiple purposes such as language analysis, information retrieval, text translation, constructing 
conversational bots, text classification, sentiment analysis, and numerous other applications as indicated by 
Guamán et al. (2017) and Lázaro et al. (2024). 
 

3.1.2. Adoption of Natural Language Processing in Banking 
Firstly, in its capacity as a tool for reading, searching, and retrieving information, NLP aids in resume 

evaluation by integrating with the K-nearest neighbors (KNN) algorithm. This integration facilitates the filtering 
of resumes, extraction of key keywords, and classification of candidates based on their profiles to match them with 
appropriate positions (Elets BFSI, 2023). NLP also plays a crucial role in supporting the compliance processes of 
banks globally. Labeling unstructured data simplifies the search for digital document sets, enabling compliance 
agencies to assess adherence to standards and regulations (Reshma, 2018). NLP techniques can also be applied to 
scan documents, identify key regulatory entities, extract metadata, and interpret the main regulatory objectives 
outlined in the texts. By automating a substantial portion of the process, NLP not only mitigates the risk of human 
error but also reduces the likelihood of regulatory violations stemming from human perception and emotion. This 
allows financial institutions to comply with increasingly stringent regulations efficiently and optimize workflow 
(International Banker, 2021). Moreover, NLP is utilized as a search tool to advance financial markets. Financial 
institutions store vast volumes of documents in their databases. A search tool powered by NLP facilitates the 
retrieval of elements and concepts within these documents to gather valuable investment information. The system 
then displays a summary of the most pertinent information for search queries from financial company employees on 
the search tool interface (Reshma, 2018). 

Expanding on its role as a "reader" capable of searching and retrieving documents, NLP also functions as a 
statistician, including quantifying large volumes of text and analyzing them to identify emerging signals. For 
instance, current voice analysis tools can "listen" to analysts' conference calls to detect the tone and sentiment 
behind what company leaders present, thus summarizing information and providing insights for equity analysis 
(Pereira & Shroff, 2022). NLP is also an effective technology in supporting the identification, creation, and analysis 
of eXtensible Business Reporting Language (XBRL) and other classification standards (e.g., Environment-Social-
Governance standards) to enhance standardization in information disclosure, tailored to the specific characteristics 
of various industries. NLP can combine the analysis of structured data from numerical reports with unstructured 
data in financial reports by leveraging XBRL classification and running automated tests to verify internal 
consistency, compliance with minimum requirements, and alignment with economic trends and stakeholder 
expectations. Standardization, comparability, and interoperability can be achieved through NLP analysis, aiding 
drafters in understanding stakeholder expectations (Faccia et al., 2021). Utilizing NLP tools for text analysis, 
unstructured data sources frequently used by investors can be converted into a single enhanced format, specifically 
optimized for financial applications. This intelligent format facilitates the generation of impactful data analyses by 
making structured data readable and visualizable, thereby enhancing the efficiency and accuracy of data-driven 
decisions. The text analysis functionality of NLP is applied in various financial operations with distinct 
characteristics (Reshma, 2018). Sentiment analysis is one of the most common objectives of text analysis and is also 
a crucial factor in forecasting stock and financial markets (Poria et al., 2016). By combining sentiment analysis 
capabilities and leveraging NLP's intelligent documents, financial companies can identify the most sought-after 
services, key customer challenges, and their perceptions of the company, and monitor market reactions to 



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significant events. The results obtained can be used to create personalized incentives, evaluate customer feedback, 
and improve product and service quality (Staff GBAF Publications Ltd., 2023). 

Text analysis is also primarily utilized for fraud detection, risk management, investment evaluation, and alpha 
generation in the financial domain. NLP can analyze large volumes of transaction data and account activity to 
identify transaction patterns based on the type of transaction, amount thresholds, channels, etc., of potentially 
fraudulent activities. In this way, NLP generates alerts and triggers preventive measures to mitigate losses for 
customers and organizations. Furthermore, NLP is applied in the financial sector to analyze loan applications, 
evaluate financial and credit reports, measure customer reliability, and automate loan underwriting processes. 
Additionally, NLP can scrutinize business plans to assess the borrower's consistency and attitude based on the 
wording and expressions in the documents. Integrating NLP in financial institutions helps optimize evaluation 
processes, minimize manual work, and enhance decision accuracy. NLP techniques also play a significant role in 
analyzing company profiles, earnings reports, and articles to assess investment opportunities and construct risk 
models. For portfolio managers, NLP aids in making intelligent decisions and managing risk effectively (Elets 
BFSI, 2023). NLP also achieves the goal of content enrichment in the financial sector by identifying and 
distinguishing the most engaging thought leadership blogs compared to competitors, while providing a 
personalized customer experience through content tailored to potential customers (Reshma, 2018). 

Lastly, with customer support and chatbot functionalities, NLP can analyze and predict to handle voice and 
text commands, quickly responding to queries and assisting in various financial transactions, effectively meeting 
customer needs (Staff GBAF Publications Ltd., 2023; Elets BFSI, 2023). Chatbots are not only tools for providing 
24/7 customer support on simple issues such as money transfers, setting up recurring payments, checking bank 
statements, and detecting customer spending habits but also have the capability to use multi-context scenarios and 
conduct natural dialogues (Neto & Fernández, 2019). This application allows customers to access information and 
explore additional services without visiting bank branches; instead, they can interact through an online messaging 
system from laptops or smartphones. The implementation of chatbots brings numerous benefits to the banking 
industry, including enhancing customer experience, reducing response time, and increasing customer satisfaction, 
as has been implemented by most major banks (Barnes, 2024; Elets BFSI, 2023). 

In the banking sector, the application of NLP is becoming increasingly prevalent, not only for extracting 
structured information from unstructured content but also for synthesizing natural language. Financial 
applications of NLP must meet specific requirements such as using time-split data to prevent "leakage" of future 
information into the past, along with achieving high accuracy and low latency. In contrast, other fields like 
healthcare and education may have different requirements. Although the focus has been on fully automated NLP 
applications in finance, it is important to consider human-performed NLP applications, such as computer-assisted 

interactive trading (İrsoy et al., 2019) or computer-assisted drafting of research reports (Chen et al., 2020). This 
highlights that the combination of human and technology is essential to achieving optimal results in these fields. 
Therefore, continued discussion and research in the field of NLP in finance are crucial to ensure sustainable 
progress and meet the practical needs of the industry (Capponi & Lehalle, 2023). 
 

3.1.3. Status of Natural Language Processing Adoption in Vietnamese Commercial Banks 
Commercial banks in Vietnam are in the nascent stages of utilizing NLP, a component of AI, with significant 

developmental potential. In practice, in Vietnam, 41% of financial institutions have adopted NLP to personalize 
their marketing strategies for their customer segments. This trend is expected to continue, with 45% of 
respondents in a survey by Finastra in Vietnam focusing on improving customer service through AI (Barnes, 
2024). As of January 2024, 15 out of 43 commercial banks have implemented chatbots. Among these, the majority 
have deployed chatbots on the Facebook platform (14/17), with some also integrating chatbots on websites (5/17) 

and mobile applications (7/17) (Vũ et al., 2024). Some banks invest in self-developing technology, while others opt 
for outsourcing technology development. Many banks choose outsourcing to rapidly access technology and provide 
customers with professional experience, despite the inherent advantages and disadvantages of each approach 

(Hương & Bình, 2022). 
Notably, the adoption rate of AI chatbots by 34.9% of commercial banks in Vietnam, compared to only 8% in 

the United States according to Shevlin (2021), signifies the robust commitment and determination in applying 
advanced technology. It can be affirmed that Vietnamese banks have made substantial investments in technology 
systems to adapt to changing consumer trends, demands for financial services, and the rapid evolution of new 
technological waves. Modern technology not only enhances operational efficiency and reduces costs for banks but 
also ensures safer and more transparent transactions (Thu, 2021). 
 

3.2. Theoretical Background 
The study of factors influencing the application of natural language processing (NLP) technology is based on 

the following theoretical frameworks: 
 

3.2.1. Theory of Reasoned Action - TRA 
The Theory of Reasoned Action (TRA) is a model in social psychology and human behavior aimed at 

explaining and predicting human behavior based on their motivations and thoughts. TRA posits that human 
behavior is contingent upon the close link between attitude and outcome. It asserts that an individual's behavior in 
performing a specific action directly arises from their behavioral intention (Fishbein & Ajzen, 1975). 
 

3.2.2. Theory of Planned Behavior - TPB 
Building on the principles of TRA, Ajzen (1991) extended the model by incorporating the independent variable 

"perceived behavioral control," which considers the perceived ease or difficulty in performing the desired behavior. 
Behavioral intention is a crucial predictor of human behavior. According to the TPB, behavioral intention is formed 
by three main factors: attitude toward the behavior, subjective norms, and perceived behavioral control. Attitude 
toward the behavior refers to the degree to which a person values or disvalues a behavior. Subjective norms refer to 



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a person's perception of what others think they should do. Perceived behavioral control refers to the extent to 
which a person believes they can perform a particular behavior. The more positive the attitude towards the 
behavior, the more supportive the subjective norms, and the fewer the perceived barriers, the stronger the 
behavioral intention (Ajzen, 1991). 
 

3.2.3. Technology Acceptance Model - TAM 
The Technology Acceptance Model (TAM), introduced by Davis et al. (1989) and based on the TRA, is used to 

explain and predict behavior related to technology acceptance and usage. The core of this model focuses on 
describing the impact of technical factors on individual decisions regarding the acceptance and intended use of 
technology. TAM aims to explain the general determinants of computer acceptance, leading to an understanding of 
user behavior with computer technologies on a broad scale. The model indicates that when users interact with new 
technology, key factors influencing their decision to use it include perceived usefulness (PU) and perceived ease of 
use (PEU). TAM is often employed in studies involving human-computer interaction and information technology 
in general, asserting that PU and PEU are crucial antecedents of the behavioral intention to use IT (Davis et al., 
1989). 
 

3.2.4. Unified Theory of Acceptance and Use of Technology - UTAUT 
The Unified Theory of Acceptance and Use of Technology (UTAUT) is a significant theory in the field of 

technology usage behavior research, developed by Venkatesh et al. (2003). It is based on various models and 
theories, including the Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Technology 
Acceptance Model (TAM, TAM2), Motivational Model (MM), combined TAM and TPB, Model of PC Utilization 
(MPCU), Innovation Diffusion Theory (IDT), and Social Cognitive Theory (SCT). 

UTAUT provides a useful tool for managers and researchers to evaluate the potential success of new 
technology adoption within an organization or community. Due to its utility, the model has been widely applied in 
various fields, including information systems, e-commerce, healthcare, and education. Introducing new technology 
can often be challenging due to user resistance, especially among those reluctant to change. To address this issue, 
based on UTAUT, managers can design specific interventions, such as training programs or marketing campaigns, 
to create a more favorable environment for users, helping them to accept and effectively use new technology. This 
is particularly important in promoting transformational development within organizations and society (Venkatesh 
et al., 2003). 
 

4. Research Methodology 
4.1. Sample Selection 

This study employs a quantitative approach to evaluate the impact of factors identified through qualitative 
research on the use of Natural Language Processing (NLP) by commercial banks in Vietnam. The research utilizes 
descriptive statistics, factor analysis, correlation analysis, and multivariate regression analysis 

Initially, the authors will employ descriptive statistical analysis to collect, synthesize, and analyze primary and 
secondary data to achieve the study’s objectives. This tool is used to provide a comprehensive description of the 
relationships between factors influencing the use of Natural Language Processing (NLP) in commercial banks in 
Vietnam. Subsequently, factor analysis assists the authors in assessing the reliability of the measurement scale and 
testing the exploratory components. Cronbach’s Alpha reliability coefficient is used to evaluate the quality of the 
scale and determine the appropriateness of the observed variables and scale in the research model using collected 
survey data. Exploratory Factor Analysis (EFA) will be utilized to assess the convergence of the observed variables 
and identify the impacting factors. Correlation analysis helps in examining the relationships between influencing 
factors and the affected factor. Thereafter, the authors use multivariate regression analysis to examine the 
interactions between the affected factor and influencing factors (Binh, 2023). 

Furthermore, the mediating variable must satisfy three conditions: the independent variable explains the 
variance of the mediating variable, the mediating variable explains the variance of the dependent variable, and the 
presence of the mediating variable reduces the relationship between the independent and dependent variables 
(Binh, 2023). 
 

4.2. Research Model and Hypothesis Proposals 
4.2.1. Research Frame 

The research team chose this topic in the hope of creating a useful reference document, providing suggestions 
for bank administrators and lawmakers to improve the quality of NLP implementation in commercial banks in 
Vietnam. The team consulted concepts, theories, and results from similar researchs. From the collected data, the 
team creates a hypothesis and research model, including independent variables and dependent variables. These 
hypotheses and models are built on basic theories and previous research in similar fields. Then, the team conducted 
a quantitative survey to choose the appropriate scale for the variables in the official research model. Based on that, 
the team conducted preliminary quantitative research and formal quantitative research. Finally, the SPSS is used to 
process and analyze survey data and evaluate research results. Therefore, give conclusions and propose solutions, 
while also pointing out limitations and suggesting analytical directions for future research. 
 

4.2.2. Research Hypotheses 
Based on foundational theories: Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), 

Technology Acceptance Model (TAM), Unified Theory and Acceptance of Technology Use (UTAUT) along with 
the actual situation in the Vietnamese market, the research team selected 06 factors affecting the adoption of 
Natural Language Processing (NLP) in commercial banks in Vietnam.   
 
 



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4.2.2.1. Compatibility Factor (TT) 

Nowadays, with the trend of applying artificial intelligence being considered the destination of most businesses 
globally, commercial banks in Vietnam are still racing with technology to keep up with the development of the 
industry. world. Having the ability to adapt and expedience will be a lever to help banks be more competitive 
compared to other banks (Deepalakshmi, 2019). In particular, the compatibility of artificial intelligence technology 
with available digital platforms will determine the risk of investing in new technology development. After 
considering the above factors, the research team hypothesized as follows:  

Hypothesis H1: Technology compatibility positively affects the adoption of NLP in commercial banks in 
Vietnam. 
 
4.2.2.2. Trust Factor (TC) 

Nowadays an ever-changing environment, with massive volumes of data, has accelerated the processing speed 
and precision of modern innovations. Experts must continually update algorithms, structures, and technological 
networks to match this development requirement. However, this has increased the technical complexity of those 
technologies, particularly in AI and Natural Language Processing (NLP), both of which rely heavily on data. Data 
and information are used as sources to generate important insights. In Vietnam, the adoption of artificial 
intelligence to specific activities and operations in organizations is still relatively new, resulting in issues such as 
lack of maturity, technological ability, and specialists, as well as long development durations and expensive prices. 
As a result, firms tend to delay internal adoption of a complex technology until they have accumulated sufficient 
technical knowledge to successfully deploy and operate it (Attewell, 1992). Therefore, the research team 
hypothesized as follows: 

Hypothesis H2: Technical complexity negatively affects the adoption of natural language processing (NLP) in 
commercial banks in Vietnam. 
 
4.2.2.3. Bank Orientation Factor (DH) 

Business orientation is always considered as a guideline for the human resources team to be able to operate the 
business seamlessly and organized. Therefore, the top apparatus' decision plays a significant role in the company's 
development orientation. As members with senior roles in the apparatus, they are always affected by factors such as 
risk, development potential and competitiveness with other financial entities. However, the banks considered to 
have a smart strategic orientation are those that know how to develop in the direction of innovation, initiative, and 
good risk management. These are also factors that weigh heavily on the decision to apply NLP in the banking 
organization. From there, the team posed the following hypothesis: 

Hypothesis H7: The bank's orientation affects the adoption of NLP in commercial banks in Vietnam. 
 
4.2.2.4. Human Resources Factor (NL) 

 Human resources play a critical role in the implementation of Natural Language Processing (NLP) in banks. 
In the current era of digital transformation, technology and artificial intelligence are widely used by most banks to 
optimize management and operations. Therefore, banks need to have a highly skilled and well-qualified workforce 
to utilize Natural Language Processing (NLP) proficiently. Based on this, the research team proposes the following 
hypothesis: 

 Hypothesis H4: Human resources positively influenced the adoption of Natural Language Processing (NLP) in 
commercial banks in Vietnam. 
 
4.2.2.5. Legal Corridor Factor (PL) 

Currently, in Vietnam, there are no legal documents specifically applied to the use of Natural Language 
Processing (NLP) in the commercial banking sector. With the current regulations, banks have been proactive in 
innovating and applying technical solutions that are compatible with the legal corridor while ensuring safety, 
controlling risks, and facilitating customers in the context of digitalizing services and instantaneous online 
transactions with a global reach. However, when the legal framework still has limitations, banks have faced 
difficulties in recording accounting transactions and in providing services to customers. Based on these 
observations, the research team proposes the following hypothesis: 

 Hypothesis H5: The legal corridor positively influences the adoption of Natural Language Processing (NLP) 
in commercial banks in Vietnam. 
 
4.2.2.6. Market Uncertainty Factor (KCC) 

Recently, the socio-economic landscape has been marked by market uncertainty, influenced by various factors 
such as the COVID-19 pandemic, geopolitical tensions, trade wars, and energy crises. This volatility poses 
challenges for businesses in terms of data collection, risk management, and accurate predictions. As a result, the 
utilization of AI in general, and NLP in particular, with adaptable and correct approaches to address these issues, 
becomes crucial. Banks and other financial institutions also believe that embracing new technology at a faster pace 
than their competitors will ensure they maintain a competitive edge and play a pivotal role in operational efficiency. 
Furthermore, it has been observed that the COVID-19 pandemic, a factor contributing to market uncertainty, has 
paved the way for the swift penetration of AI in businesses, highlighting its proficiency like never before. Hence, 
the research team proposes the following hypothesis: 

Hypothesis H6: Market uncertainty positively affects the adoption of NLP in commercial banks in Vietnam. 
 

4.2.3. Proposing Framework   
Research model is presented by the diagram. 



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Figure 1. Research framework on factors affecting the adoption of Natural Language Processing in commercial banks in Vietnam. 

 

4.3. Measurement Method 
This article utilizes a quantitative research method, based on synthesizing previous studies, constructing a 

research model, and testing it through conducting surveys and collecting opinions from experts and professionals 
in the commercial banking sector in Vietnam. The concepts in the research model are measured using a 5-point 
Likert scale, with a total of 25 observed variables and 06 factor components. This scale measures the degree of 
agreement of the participants from "Strongly Disagree" to "Strongly Agree" concerning the related observed 
variables. 

The dependent variable Likert scale "Adoption of Natural Language Processing  in commercial banks in 
Vietnam" - denoted as UDCN, is measured by 6 criteria: 

UDCN 1 - My bank is technologically compatible with the solutions provided by NLP. 
UDCN 2 - I have mastered the operation and use of applications developed by NLP. 
UDCN 3 - My bank is strategically ready for the adoption process of NLP. 
UDCN 4 - My colleagues and I possess sufficient knowledge and experience to handle potential issues when 

using NLP technology 
UDCN 5 - There are adequate policy mechanisms for digital transformation - applying NLP in banking. 
UDCN 6 - My bank prioritizes the development of NLP during times when the market fluctuating. 

 

5. Research Results 
5.1. Analyze and Discuss Research Results 
 

Table 1. Descriptive statistics of variables in the model. 

Descritive Statistics 

  N Minimum Maximum Mean Std. Deviation 

TT 181 1 5 3.99 0.854 
PT 181 1 5 2.25 1.082 
DH 181 1 5 4.29 0.795 
NL 181 1 5 3.91 0.908 
PL 181 1 5 3.95 0.926 
KCC 181 1 5 2.15 0.843 
Valid N (listwise) 181     

Source: SPSS results. 

   
In general, we see that the average value of the factors ranges from 2.15 to 4.29, which shows that the people 

surveyed evaluate the influence of the factors: compatibility, technical complexity. Techniques, business 
orientation, human resources, legal framework and market uncertainty all affect the adoptionof NLP to commercial 
banks in Vietnam. 

Among these, the business orientation factor (DH) has the highest average level of 4.29. The standard 
deviation of this factor is 0.795. This reflects the opinions of the survey group's subjects who highly appreciated the 
influence of business orientation on the adoptionof NLP in commercial banks in Vietnam. The next level of 
influence is compatibility (TT), legal corridor (PL), human resources (NL), technical complexity (PT) and market 
uncertainty with an average level according to the order is 3.99, 3.95, 3.91, 2.25, 2.15. 
 

5.2. Evaluate the Scale with Cronbach's Alpha 
After the team analyzed the information obtained from SPSS, the research team checked the reliability of the 

data. 
 
 



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Table 2. Test results of the scale. 

Factor  
Corrected Item-Total 

Correlation 
Cronbach's Alpha if Item 

Deleted 

Compatibility 

TT1 

a=0.876,N=4 

0.746 0.836 
TT2 0.816 0.807 
TT3 0.720 0.847 
TT4 0.655 0.870 
Technical Complexity 
PT1 

a=0.899,N=4 

0.818 0.854 
PT2 0.791 0.864 
PT3 0.771 0.872 
PT4 0.724 0.888 
Bank Orientation 
DH1 

a=0.895,N=5 

0.683 0.885 
DH2 0.724 0.876 
DH3 0.770 0.865 
DH4 0.765 0.867 
DH5 0.769 0.866 
Human Resources 

NL1 

a=0.901,N=4 

0.742 0.886 

NL2 0.827 0.855 

NL3 0.821 0.857 

NL4 0.731 0.890 

Legal Corridor 

PL1 

a=0.930,N=3 

0.877 0.882 

PL2 0.897 0.864 

PL3 0.799 0.882 

Market Uncertainty 

KCC1 

a=0.955,N=5 

0.818 0.954 

KCC2 0.880 0.944 

KCC3 0.889 0.942 

KCC4 0.889 0.942 

KCC5 0.902 0.940 

  Source: SPSS results. 

 
Using Cronbach's Alpha coefficient from the analysis results of SPSS software, we see:  

• All 4 observed variables of the Compatibility factor meet the standards;  

• All 4 observed variables of the Technical Complexity factor meet the standard;  

• All 5 observed variables of the Business Orientation factor meet the standards;  

• All 4 observed variables of the Human Resources factor meet the standards;  

• All 3 observed variables of the Legal Corridor factor meet the standards;  

• All 5 observed variables of the Market Uncertainty factor meet the standards.  
By testing the appropriateness of the EFA factor analysis model, the research team relied on the KMO test and 

the Bartlett test to conclude that using the EFA model is appropriate. At the same time, the scale is also accepted 
through the results of testing the variance of the factors. 
 

5.3. Analyze Regression Models 
Using multivariate regression analysis techniques and the method of entering variables into SPSS software, 

results from the software have helped the research team evaluate some issues of the overall regression model: 

UDCN= β0 + β1TT + β2PT+ β3DH+  β4NL + β5PL +β6KCC  

The important parameter used in testing model fit is the adjusted R2 coefficient. The larger the value of this 
parameter shows the higher the model's fit.   

After considering the effects of the independent variable on the dependent variable, the results from the 
regression weight table helped the research team determine the variables PT, DH, PL and (Constant) variables 
that are statistically significant and variable. NL, KCC and PT have no impact on the dependent variable UDCN. 
 

Table 3. Results of regression. 

Coefficients 

Model Unstandardized Coefficients Standardized Coefficients t Sig. 

B Std. Error Beta 

 

(Constant) 2.961 0.470  6.300 0.000 
NL -0.091 0.067 -0.128 -1.361 0.175 
KCC 0.082 0.092 0.083 0.887 0.376 
DH 0.200 0.081 0.225 2.474 0.014 
PT -0.278 0.098 -0.320 -2.845 0.005 
PL 0.246 0.073 0.308 3.372 0.001 
TT 0.004 0.097 0.004 0.039 0.969 

       Source: SPSS results. 

 



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From there, the regression equation is determined: 
UDCN =  2.961 - 0.278*PT + 0.200*DH + 0.264*PL 

5.4. Discuss Research Results 
The results of research and evaluation show that the process of applying natural language processing 

technology in commercial banks in Vietnam is influenced by different factors in terms of both external and internal 
impacts of businesses, directly impacting the adoption of this technology more widely.  

From the initial hypothesis of 06 variables affecting the adoption of natural language processing technology in 
commercial banks in Vietnam, the results after running multivariate regression showed that only 03 variables 
recorded an impact, expressed in the formal regression equation as follows: 

UDCN =  2.961 - 0.278*PT + 0.200*DH + 0.264*PL 
According to the results after running the regression model, Hypotheses H2, H3 and H5 are accepted while the 

remaining 3 hypotheses including H1, H4, H5 are rejected. The research team assessed the levels of influencing 
factors. The research results show that the factor has a negative impact on Technical Complexity (0.278), followed 
by the remaining two factors that have a positive impact, respectively: the Legal Corridor factor (0.264) and the 
Factor Business Orientation factor (0.200). 

In the 21st century, the Industrial Revolution 4.0 has created leverage for comprehensive transformation in 
many fields, especially the financial sector. This digital transformation trend motivates and requires financial 
organizations and businesses to adapt to new challenges and opportunities. This improved the quality of banks' 
operating processes, but many limitations remain. 

According to the research results, the limitations in applying natural language processing (NLP) in commercial 
banks in Vietnam come from many factors, typically technical complexity. This technology not only requires an 
extremely complex structure of multiple entanglements and layers to operate, but also must be flexible in applying 
models corresponding to each specific task and field. This creates a challenging work environment, as experts need 
to deal with the possibility of algorithmic errors, which are sometimes only discovered when it is too late to fix 
them. Language diversity is also another problem to be faced, especially when NLP is not yet widespread enough in 
Vietnam to have enough data and analysis techniques. In addition, rapid change and growth, along with 
requirements for information security in data banks, also pose more challenges for experts in operating and 
developing NLP systems with depends significantly on the input data. 
 

6. Recommendations 
 As outlined in the research context, the digital landscape in the financial sector requires significant 

collaboration among stakeholders, where the most affected entities are the banks and related businesses - the main 
actors in applying NLP to daily processes; and the regulatory authorities - entities that provide policies, support 
with the necessary conditions, and build an environment for implementation. Additionally, the support of scientists 
and researchers is crucial in applying NLP artificial intelligence technology to banking activities during the digital 
transformation period. 
 

6.1. Recommendations for Regulatory Authorities 
 Regulatory bodies should issue specific policies and guidelines on the adoption of NLP in banking, while 

ensuring coherence and uniformity within the industry. Additionally, it is essential to organize training on NLP 
and its applications for bank officials and staff, and then encourage investment in the research and development of 
this technology. Regulatory authorities also need to support and encourage banks to implement NLP by creating a 
favorable environment with tax incentives and financial support, while regularly monitoring and evaluating the use 
of NLP in banks to ensure compliance with regulations and make adjustments when necessary. Furthermore, it is 
crucial to ensure transparency and fairness in the use of NLP to avoid creating inequalities among banks and to 
protect the rights of customers using related services. 
 

6.2. Recommendations for Banks and Related Enterprises 
 Banks need to enhance internal communication to staff about integrating new technologies, not only NLP but 

also other technology platforms. Organizing workshops and training on NLP can help improve staff awareness and 
understanding of this technology, thereby recognizing its potential and opportunities, and minimizing concerns 
related to adopting new technologies. 

 Banks can drive digital transformation by integrating NLP and similar technologies, creating better customer 
experiences and offering automated services. This approach helps attract and retain customers, increase sales, and 
create a competitive edge. Additionally, researching NLP in conjunction with other technologies such as artificial 
intelligence and Blockchain allows banks to develop advanced, flexible solutions, and create differentiation in an 
increasingly competitive banking market. 
 

6.3. Recommendations for Scientists and Researchers 
 Building a database and resources for NLP research must ensure accuracy, credibility, and diversity. 

Therefore, scientists and researchers need to continually update information and monitor new trends in the 
banking and technology sectors to maintain effectiveness. 

 Intensifying research and development of NLP solutions is essential, seeking practical applications with high 
potential in the banking sector such as automated translation, customer request processing, and data analysis to 
enhance service quality. Collaboration between entities and businesses also plays a critical role in this process. 
Moreover, information security, privacy, and legal compliance are vital considerations. Enhancing awareness of 
cybersecurity risks, establishing secure processing protocols, and implementing appropriate data security measures 
are necessary to ensure the sustainable development of NLP technology. Adhering to professional ethics and 
avoiding copyright infringement are also crucial in the research and adoption of NLP. 
 
 



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7. Conclusion 
NLP is a technology with wide-ranging potential adoptions in commercial banks, bringing many benefits to 

both the banks and customers. The research study utilized quantitative and qualitative research methods, along 
with theories such as TRA, TPB, UTAUT, and TAM models, to propose solutions for banks to effectively 
implement this artificial intelligence tool. Additionally, the research study is an important first step in the research 
process, ensuring the accuracy and applicability of the research in practice. 

 

References 
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. 

https://doi.org/10.1016/0749-5978(91)90020-T 
Attewell, P. (1992). Technology diffusion and organizational learning: The case of business computing. Organization Science, 3(1), 1–19. 

https://doi.org/10.1287/orsc.3.1.1 
Barnes, M. (2024, January 4). Survey finds AI, tech popular in financial services in Vietnam. Vietnam Briefing News. https://www.vietnam-

briefing.com/news/survey-finds-ai-tech-popular-in-financial-services-in-vietnam.html 

Binh. (2023). Đầy đủ về phương pháp nghiên cứu định lượng trong luận văn. Luanvanmaster. https://luanvanmaster.com/phuong-phap-
nghien-cuu-dinh-luong/ 

Capponi, A., & Lehalle, C. (2023). Machine learning and data sciences for financial markets: A guide to contemporary practices. Cambridge University 
Press. 

Chen, C., Huang, H., & Chen, H. (2020, May 4). NLP in fintech applications: Past, present and future. arXiv. 
https://arxiv.org/abs/2005.01320 

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. 
Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982 

Deepalakshmi, M., V., Y., & S., R. (2019, September 13). Adoption of banking technology: Benefits attained and challenges faced by bank 
employees. IRE Journals. https://www.irejournals.com/paper-details/1701616 

DeepLearning.AI. (2023, January 11). Natural language processing (NLP) [A complete guide]. https://www.deeplearning.ai/resources/natural-
language-processing/ 

Elets BFSI. (2023, September 13). Empowering banking with natural language processing (NLP). BFSI. 
https://bfsi.eletsonline.com/empowering-banking-with-natural-language-processing-nlp/ 

Faccia, A., Manni, F., & Capitanio, F. (2021). Mandatory ESG reporting and XBRL taxonomies combination: ESG ratings and income 
statement, a sustainable value-added disclosure. Sustainability, 13(16), 8876. https://doi.org/10.3390/su13168876 

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley. 
https://www.researchgate.net/publication/233897090 

Fridgen, G., Hartwich, E., Rägo, V., Rieger, A., & Stohr, A. (2022, May 11). Artificial intelligence as a call for retail banking: Applying digital 
options thinking to artificial intelligence adoption. AIS Electronic Library (AISeL). https://aisel.aisnet.org/ecis2022_rp/103 

Horani, O. M., Al-Adwan, A. S., Yaseen, H., Hmoud, H. Y., Al-Rahmi, W. M., & Alkhalifah, A. (2023). The critical determinants impacting 
artificial intelligence adoption at the organizational level. Information Development. Advance online publication. 
https://doi.org/10.1177/02666669231166889 

Huong, L. N. Q., & Binh, N. H. (2022, January 25). Chatbot trong lĩnh vực ngân hàng: Thực trạng và xu hướng ứng dụng tại Việt Nam. 

Tạp chí Khoa học Đào tạo Ngân hàng. https://vjol.info.vn/index.php/HVNH-KHDAOTAONH/article/view/66084 
International Banker. (2021, April 12). How the financial industry is using natural language processing. International Banker. 

https://internationalbanker.com/technology/how-the-financial-industry-is-using-natural-language-processing/ 

İrsoy, O., Gosangi, R., Zhang, H., Wei, M., Lund, P., Pappadopulo, D., Fahy, B., Nephytou, N., & Ortiz, C. (2019, August 2). Dialogue act 
classification in group chats with DAG-LSTMs. arXiv. https://arxiv.org/abs/1908.01821 

Klein, A. Z., Cai, H., Weissenbacher, D., Levine, L. D., & Gonzalez-Hernandez, G. (2020). A natural language processing pipeline to advance 
the use of Twitter data for digital epidemiology of adverse pregnancy outcomes. Journal of Biomedical Informatics, 112, 100076. 
https://doi.org/10.1016/j.yjbinx.2020.100076 

Lázaro, E., Yepez, J., Moscardó, V., Marín, P., López-Masés, P., Gimeno, T., & Sabatier, P. (2024). Efficiency of natural language processing 
as a tool for analysing quality of life in patients with chronic diseases: A systematic review. Computers in Human Behavior Reports, 
100, 407. https://doi.org/10.1016/j.chbr.2024.100407 

Lindvall, C., Lilley, E. J., Zupanc, S. N., Chien, I., Udelsman, B. V., Walling, A. M., Cooper, Z., & Tulsky, J. A. (2019). Natural language 
processing to assess end-of-life quality indicators in cancer patients receiving palliative surgery. Journal of Palliative Medicine, 22(2), 
183–187. https://doi.org/10.1089/jpm.2018.0326 

Machiraju, S., & Modi, R. (2017). Natural language processing. In Developing bots with Microsoft Bots Framework (pp. 203–232). Apress. 
https://doi.org/10.1007/978-1-4842-3312-2_9 

Neto, A. J. M., & Fernandes, M. A. (2019). Chatbot and conversational analysis to promote collaborative learning in distance education. In 
2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT) (pp. 138–142). IEEE. 
https://doi.org/10.1109/ICALT.2019.00102 

Pereira, K., & Shroff, R. (2022). Natural language processing in banking: Current uses. CFA Institute. 
https://www.arx.cfa/research/2022/12/soc191222-natural-language-processing-in-banking 

Poria, S., Chaturvedi, I., Cambria, E., & Hussain, A. (2016, December 1). Convolutional MKL-based multimodal emotion recognition and 
sentiment analysis. In 2016 IEEE 16th International Conference on Data Mining (ICDM) (pp. 439–448). IEEE. 
https://doi.org/10.1109/ICDM.2016.0054 

Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., & Huang, X. (2020). Pre-trained models for natural language processing: A survey. Science China 
Technological Sciences, 63(10), 1872–1897. https://doi.org/10.1007/s11431-020-1647-3 

Reshma, M. (2018, September). Top 5 use cases of NLP in finance. Apiway. https://apiway.ai/community/articles/821-top-5-use-cases-of-
nlp-in-finance 

Robert, G., & Cornwell, J. (2013). Rethinking policy approaches to measuring and improving patient experience. Journal of Health Services 
Research & Policy, 18(2), 67–69. https://doi.org/10.1177/1355819612473583 

Shevlin, R. (2021). What’s going on in banking 2021: Fintech trends [Report]. Cornerstone Advisors. https://www.crnrstone.com/banking-
2021 

Staff GBAF Publications Ltd. (2023, March 6). 5 use cases of NLP in banking and finance. Global Banking & Finance Review. 
https://www.globalbankingandfinance.com/5-use-cases-of-nlp-in-banking-and-finance/ 

Thu, P. (2021, February 2). Ứng dụng trí tuệ nhân tạo trong hoạt động ngân hàng. Báo Lao Động. https://laodong.vn/tien-te-dau-tu/ung-
dung-tri-tue-nhan-tao-trong-hoat-dong-ngan-hang-876770.ldo 

Van Phuoc, N. (2022). The critical factors impacting artificial intelligence applications adoption in Vietnam: A structural equation modeling 
analysis. Economies, 10(6), 129. https://doi.org/10.3390/economies10060129 

Velupillai, S., Suominen, H., Liakata, M., Roberts, A., Shah, A. D., Morley, K. I., Osborn, D., Hayes, J., Stewart, R., Downs, J., Chapman, W. 
W., & Dutta, R. (2018). Using clinical natural language processing for health outcomes research: Overview and actionable 
suggestions for future advances. Journal of Biomedical Informatics, 88, 11–19. https://doi.org/10.1016/j.jbi.2018.10.005 

Venkatesh, V., Morris, M., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS 
Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 

Vu, T. S., Vu, T. H. N., Nguyen, N. C., Nguyen, L. H., Tran, T. M., & Trinh, T. N. (2024, March 25). Nâng cao chất lượng chatbot chăm sóc 

khách hàng tại các ngân hàng thương mại Việt Nam. Tạp chí Ngân Hàng. https://tapchinganhang.gov.vn/nang-cao-chat-luong-
chatbot-cham-soc-khach-hang-tai-cac-ngan-hang-thuong-mai-viet-nam.htm 

https://doi.org/10.1016/0749-5978(91)90020-T?utm_source=chatgpt.com
https://doi.org/10.1287/orsc.3.1.1?utm_source=chatgpt.com
https://www.vietnam-briefing.com/news/survey-finds-ai-tech-popular-in-financial-services-in-vietnam.html?utm_source=chatgpt.com
https://www.vietnam-briefing.com/news/survey-finds-ai-tech-popular-in-financial-services-in-vietnam.html?utm_source=chatgpt.com
https://luanvanmaster.com/phuong-phap-nghien-cuu-dinh-luong/?utm_source=chatgpt.com
https://luanvanmaster.com/phuong-phap-nghien-cuu-dinh-luong/?utm_source=chatgpt.com
https://arxiv.org/abs/2005.01320?utm_source=chatgpt.com
https://doi.org/10.1287/mnsc.35.8.982?utm_source=chatgpt.com
https://www.irejournals.com/paper-details/1701616?utm_source=chatgpt.com
https://www.deeplearning.ai/resources/natural-language-processing/?utm_source=chatgpt.com
https://www.deeplearning.ai/resources/natural-language-processing/?utm_source=chatgpt.com
https://bfsi.eletsonline.com/empowering-banking-with-natural-language-processing-nlp/?utm_source=chatgpt.com
https://doi.org/10.3390/su13168876?utm_source=chatgpt.com
https://www.researchgate.net/publication/233897090?utm_source=chatgpt.com
https://aisel.aisnet.org/ecis2022_rp/103?utm_source=chatgpt.com
https://doi.org/10.1177/02666669231166889?utm_source=chatgpt.com
https://vjol.info.vn/index.php/HVNH-KHDAOTAONH/article/view/66084?utm_source=chatgpt.com
https://internationalbanker.com/technology/how-the-financial-industry-is-using-natural-language-processing/?utm_source=chatgpt.com
https://arxiv.org/abs/1908.01821?utm_source=chatgpt.com
https://doi.org/10.1016/j.yjbinx.2020.100076?utm_source=chatgpt.com
https://doi.org/10.1016/j.chbr.2024.100407?utm_source=chatgpt.com
https://doi.org/10.1089/jpm.2018.0326?utm_source=chatgpt.com
https://doi.org/10.1007/978-1-4842-3312-2_9?utm_source=chatgpt.com
https://doi.org/10.1109/ICALT.2019.00102?utm_source=chatgpt.com
https://www.arx.cfa/research/2022/12/soc191222-natural-language-processing-in-banking?utm_source=chatgpt.com
https://doi.org/10.1007/s11431-020-1647-3?utm_source=chatgpt.com
https://apiway.ai/community/articles/821-top-5-use-cases-of-nlp-in-finance?utm_source=chatgpt.com
https://apiway.ai/community/articles/821-top-5-use-cases-of-nlp-in-finance?utm_source=chatgpt.com
https://doi.org/10.1177/1355819612473583?utm_source=chatgpt.com
https://www.crnrstone.com/banking-2021?utm_source=chatgpt.com
https://www.crnrstone.com/banking-2021?utm_source=chatgpt.com
https://www.globalbankingandfinance.com/5-use-cases-of-nlp-in-banking-and-finance/?utm_source=chatgpt.com
https://laodong.vn/tien-te-dau-tu/ung-dung-tri-tue-nhan-tao-trong-hoat-dong-ngan-hang-876770.ldo?utm_source=chatgpt.com
https://laodong.vn/tien-te-dau-tu/ung-dung-tri-tue-nhan-tao-trong-hoat-dong-ngan-hang-876770.ldo?utm_source=chatgpt.com
https://doi.org/10.3390/economies10060129?utm_source=chatgpt.com
https://doi.org/10.1016/j.jbi.2018.10.005?utm_source=chatgpt.com
https://doi.org/10.2307/30036540?utm_source=chatgpt.com
https://tapchinganhang.gov.vn/nang-cao-chat-luong-chatbot-cham-soc-khach-hang-tai-cac-ngan-hang-thuong-mai-viet-nam.htm?utm_source=chatgpt.com
https://tapchinganhang.gov.vn/nang-cao-chat-luong-chatbot-cham-soc-khach-hang-tai-cac-ngan-hang-thuong-mai-viet-nam.htm?utm_source=chatgpt.com

