Factors affecting intention to use ai chatbots for learning english purposes of efl learners

FACULTY OF BUSINESS ADMINISTRATION ---VU THI KIEU LINH FACTORS AFFECTING INTENTION TO USE AI CHATBOTS FOR LEARNING ENGLISH PURPOSES OF EFL LEARNERS Major: Business Administration Maj

INTRODUCTION

Reason for choosing the topic

Technological advancements are transforming our world, particularly in education, through the emergence of Artificial Intelligence in Education (AIEd) AIEd significantly enhances learning by automating tasks such as assessment generation, grading, and feedback, while also monitoring students' progress and identifying areas needing teacher intervention (Chaudhry & Kazim, 2022) By tailoring teaching methods to individual student contexts, AIEd helps educators improve their instructional strategies Furthermore, AI addresses learning gaps, evaluates effective pedagogies, and boosts students' attention retention, ultimately modernizing and streamlining the educational experience.

A recent trend in education is the use of AI chatbots, with 60% of Millennials reporting experience with them, and 70% of users responding positively, according to GilPress (2024) Many users favor chatbot interactions over human assistance due to the bots' quick response times and availability around the clock.

5 years, AI Chatbots are expected to become the most preferred technology solution in most sectors including education

Learning a foreign language in non-English speaking countries not only exposes students to new knowledge and cultures but also creates future career opportunities Many learners are now utilizing AI Chatbots as convenient tools for language learning, accessible anytime and anywhere These interactive programs significantly enhance motivation and engagement, particularly in technology-supported language learning environments AI Chatbots allow students to practice and improve their language skills in real-life scenarios, while also offering personalized learning experiences tailored to individual proficiency levels.

Understanding the factors that influence the choice of AI chatbots for learning English is essential for developers aiming to enhance these applications In Vietnam, while AI chatbots have been utilized in sectors like business and healthcare, there is limited research on their application in education This paper focuses on the topic “Factors Affecting Intention to Use AI Chatbots for Learning English among EFL Learners” to explore these influences further The research will also provide recommendations for educational organizations to optimize the learning experience for EFL learners, thereby supporting the advancement of English language education in today's context.

Literature review

The integration of technological innovations in education is increasingly prevalent, as traditional classrooms adopt these advancements to enhance teaching efficiency Understanding the factors that influence the acceptance of AI in learning is vital, both theoretically and practically, drawing the interest of numerous experts and scholars This understanding is essential for developing targeted solutions that advance the fields of technology and education Globally, various research studies have explored the psychological factors that shape learners' attitudes and behaviors towards using AI chatbots, utilizing established technology acceptance models.

A study by Chen et al (2020) in China, grounded in the Technology Acceptance Model (TAM), highlighted the significant relationship between perceived usefulness (PU) and perceived ease of use regarding users' attitudes and behavioral intentions toward Chatbots The findings suggest a future emphasis on enhancing these two critical factors in Chatbot development to improve user engagement and satisfaction.

A study conducted by Annamalai et al (2022) in Malaysia examined the intention of higher education students at three public schools to utilize chatbots for language learning The research revealed that factors such as performance expectancy, effort expectancy, social influence, and the impact of COVID-19 significantly influenced students' willingness to adopt chatbot technology for educational purposes.

19 fear have significant influences on students’ positive intention to use Chatbots to learn English

A study by Malik et al (2021) in India found that university students' perceptions of the usefulness and ease of use of Chatbots significantly enhance their attitudes toward these platforms Furthermore, the research confirmed that positive user attitudes play a crucial role in the decision to adopt Chatbots.

In Saudi Arabia, research by Mohammed (2023) explored the influence of attitude and perceived value on students' acceptance of chatbot technology The study specifically examined how perceived enjoyment and perceived ease of use contribute to students' perceived value Additionally, it identified perceived usefulness and perceived value as key factors affecting attitudes toward chatbot usage Ultimately, the research established a connection between these variables and students' willingness to adopt chatbots for learning purposes.

Currently, there is a lack of empirical studies in Vietnam regarding the application of AI chatbots in education Consequently, this research will concentrate on analyzing relevant foreign studies to explore this innovative educational technology.

Research subjects

This research examines the factors influencing EFL learners' intentions to use AI chatbots for learning English, focusing on the psychological elements that shape their expectations and trust in these applications.

Research scope

The research was conducted mainly within Ho Chi Minh City

Time scope: The research was carried out from December 2023 to March 2024.

Research objectives

The research aims to analyze and evaluate factors affecting the intention to use

This article explores the use of AI chatbots for English language learning among students in Ho Chi Minh City It analyzes the factors that influence learners' awareness, attitudes, and emotions towards AI chatbots, ultimately affecting their decision to utilize these tools for language acquisition The paper also offers recommendations for educational institutions to enhance the integration of AI chatbots into formal learning environments, aiming to optimize teaching and learning outcomes.

First, analyze the factors that influence EFL learners’ psychology, behavior, and decision to use AI Chatbots for learning English

Next, assess the impacts of learners’ trust and expectations on choosing different

Finally, conclude implications and propose recommendations for universities, organizations, and individuals to develop and apply properly into official learning programs to increase teaching and learning performance.

Research questions

From the stated research objectives, this research will address three following questions:

(1) What are the factors affecting the intention to use AI Chatbots for learning English purposes of EFL learners?

(2) How do learners’ trust and expectations impact on choosing different AI Chatbot tools?

(3) Will enhancing and applying AI Chatbot tools into formal learning programs help improve teaching and learning performance?

Research methodology

This research employed a quantitative method, utilizing a questionnaire designed according to the proposed theory within the context of Ho Chi Minh City A total of 158 participants were surveyed through a Google Forms questionnaire The collected data were analyzed using SPSS statistical software, applying Cronbach’s Alpha coefficient for scale testing and exploratory factor analysis (EFA) Subsequently, regression analysis was conducted to evaluate the impact of various factors on English as a Foreign Language (EFL) learners' intention to utilize AI chatbots for English learning purposes.

Structure of the study

The research topic includes 5 chapters:

Chapter 1: Introduction will present the reason for choosing the topic, introduce an overview of the research problem, and then clearly state the research subjects, objectives, scope of research, and research meanings

Chapter 2: Literature Review will summarize and comment on previous studies related to the topic: main research directions, theoretical basis, main research results, findings, and limitations of previous studies on issues that need further research Based on this information, this research will present related concepts, and propose hypotheses and a suggested research model

Chapter 3: Research Methodology will present the research design and measurement scales used to test the hypotheses of the proposed research model

Chapter 4: Findings and Discussion will analyze data obtained from an actual survey, present results, comment on proposed hypotheses, and then give discussions on research results

Chapter 5: Conclusion and Recommendations summarizes the content, concludes managerial implications, and highlights theoretical and practical contributions At the same time, this chapter also points out the limitations of the research paper to recommend and propose for further research

This paper aims to explore the factors that affect EFL learners' intentions to utilize AI chatbots for English language learning It will also offer recommendations for future research on effectively integrating AI chatbots into formal educational programs The structure of the paper encompasses an introduction, a literature review, research methodology, findings and discussion, and concludes with recommendations.

LITERATURE REVIEW

Definitions

The concept of intention to use is rooted in the behavioral intention theory developed by Fishbein and Ajzen in 1975 Intention refers to an individual's subjective willingness to engage in a specific behavior In their Theory of Reasoned Action (TRA), the authors assert that the intention to perform a behavior can effectively predict actual behavior TRA posits that when individuals view a behavior positively and believe that others expect them to engage in it, their intention to act increases, thereby increasing the likelihood of their participation in that behavior.

Behavioral intention, as described by Webster (1972), refers to the purposeful actions that individuals plan to undertake, which involves formulating conscious decisions about future behaviors Warshaw and Davis (1985) emphasized that behavioral intention is the arrangement of these conscious plans in a specific sequence to either perform or refrain from certain actions Research by Davis et al (1989) highlighted that the intention to use a system significantly influences actual usage, indicating that behavioral intention is a key factor determining user behavior, with other factors impacting behavior indirectly Additionally, Hill et al (1987) demonstrated that behavioral intentions are strong predictors of actions Overall, behavioral intention serves as a foundational measure of an individual's awareness and readiness to make decisions regarding specific behaviors.

In the realm of technology, the intention to use refers to a user's readiness to adopt a new system Studies in technology acceptance highlight that this intention serves as a precursor to the actual utilization of the system.

We are currently in the age of "big data," where the sheer volume of information exceeds our ability to collect and analyze it effectively The application of artificial intelligence (AI) has shown significant advantages across multiple sectors such as the economy, education, entertainment, and healthcare (Ali et al., 2023) Despite its widespread use, there is no universally accepted definition of AI, as many researchers believe that our understanding of conventional intelligence is still incomplete.

Artificial Intelligence (AI) was first defined by Professor John McCarthy in 1955 as the science and engineering of creating intelligent machines Since then, AI has been applied across various fields, leading to numerous distinct definitions According to the National Artificial Intelligence Act (2020), AI is described as a machine-driven system that can make predictions, provide recommendations, or make decisions that affect real or virtual environments based on specific human objectives.

In the other way, artificial intelligence involves the simulation of human intelligence processes by machines, particularly computer systems (Laskowski & Tucci,

In 2023, AI applications include expert systems, natural language processing, speech recognition, and machine vision The focus of AI programming in learning is on data acquisition and the creation of algorithms, which are rules that convert data into actionable insights These algorithms provide computing devices with clear, step-by-step instructions for performing specific tasks.

AI chatbots, applications rooted in artificial intelligence, have gained significant popularity in recent years As defined by Lexico Dictionaries (2019), a chatbot is "a computer program designed to simulate conversation with human users, especially over the Internet." Additionally, Khanna et al (2015) describe chatbots as AI programs specifically created for enhancing Human-Computer Interaction (HCI).

Natural Language Processing (NLP) and sentiment analysis enable AI chatbots to engage in human-like communication through text or speech, facilitating natural conversations and interactions These virtual assistants effectively respond to user inquiries, showcasing their utility across various sectors, including education, business, e-commerce, health, and entertainment, as highlighted by Shawar and Atwell (2007) In addition to mimicking human interactions and providing entertainment, AI chatbots demonstrate their significant value in enhancing user experiences across diverse applications.

The evolution of chatbots began with ELIZA, a text-based program created in the 1960s to mimic human conversation By the 1980s, advancements led to the development of speech-based dialog systems and social robots Today, extensive research across various fields has resulted in sophisticated AI-powered chatbots capable of engaging in complex conversations A prime example is OpenAI’s ChatGPT, launched in November 2022, which gained 100 million users within two months due to its ability to quickly answer questions in multiple languages This trend highlights the potential for AI chatbots to enhance daily tasks across various sectors, particularly in education.

In today's globalized world, mastering foreign languages, particularly English, is essential English is the most widely spoken language globally, utilized in both native and non-native English-speaking countries (Rao, 2019) In Vietnam, English has been established as a mandatory foreign language subject across various educational levels, highlighting its significant importance in contemporary society.

English as a Foreign Language (EFL) pertains to the study of English by individuals in non-English-speaking countries, as highlighted by Gebhard (2006) In these environments, learners often face restricted chances to engage with English outside of their classroom settings, limiting their practical communication opportunities.

EFL, or English as a Foreign Language, refers to non-native English speakers learning the language in regions where it is not widely spoken or used as a medium of instruction These learners often pursue English for academic, travel, or career purposes but typically have limited exposure to the language outside the classroom, dedicating only a few hours per week to study To enhance their language skills and achieve fluency, it is crucial for EFL learners to adopt effective learning strategies.

Language learning applications leverage the combined strengths of Natural Language Processing (NLP) and Human-Computer Interaction (HCI) by integrating AI chatbots, which significantly enhance the user experience and facilitate language skill improvement Research by Fryer et al (2019) indicates that AI chatbots effectively accelerate language learning by providing essential conversation partners Additionally, Vázquez-Cano et al (2021) highlight that these interactive tools offer personalized feedback and the flexibility to learn anytime By simulating real conversations and delivering instant feedback on grammar, vocabulary, and pronunciation, chatbots create a safe and supportive environment for learners, as noted by Annamalai et al (2022) This approach not only boosts learners' confidence but also fosters a sense of achievement, motivating them to continue their studies.

In fact, AI chatbots can provide students with a flexible learning experience by tailoring lessons to the individual language proficiency of participants (Subbarao, 2023)

AI Chatbots offer tailored learning experiences by assessing individual user abilities and proposing suitable courses, ensuring that all students progress at an appropriate pace By acting as personalized tutors, these AI systems guide learners step-by-step, helping them achieve their educational development goals effectively.

Theoretical models

The Technology Acceptance Model (TAM), developed by Davis et al in 1989, is a prominent theoretical framework that explains how users accept and utilize technology This model builds upon Ajzen and Fishbein’s Theory of Reasoned Action, providing a comprehensive understanding of user behavior towards technology adoption.

(TRA) (Fishbein and Ajzen, 1975) By replacing TRA’s attitudinal measures, factors were determined to identify technology acceptance including Perceived Usefulness (PU) and Perceived Ease of Use (PEU)

Figure 2.1 Technology Acceptance Model (TAM) (Source: Davis et al (1989))

The TAM is also one of the most used models of what the information system uses to determine perceived usefulness and perceived ease of use

Perceived usefulness (PU) refers to the belief that using a specific system can improve job performance, while perceived ease of use relates to the expected effectiveness of the system in enhancing that performance.

Perceived ease of use (PEU) is defined as the belief that using a specific system will require minimal effort This concept is influenced by the anticipated ease with which potential users can operate the system.

Perception of technology can vary based on user attributes like age, gender, culture, and social status The Technology Acceptance Model (TAM) helps analyze when and why users adopt new technologies by identifying key factors that influence their decisions These factors encompass users' assessments of the technology's usefulness and ease of use, their attitudes towards its application, and their intentions to engage with it.

2.2.2 Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)

The Unified Theory of Acceptance and Use of Technology (UTAUT), introduced by Venkatesh et al in 2003, synthesizes eight prior research models to understand user acceptance of new information systems An extension of this model, UTAUT2, was developed by Venkatesh et al in 2012 to analyze technology user behavior in relation to evolving psychological factors UTAUT2 offers a detailed exploration of the various elements that shape individuals' intentions to adopt new technologies, focusing on seven key factors: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), and Habit (HT).

Figure 2.2 Unified Theory of Acceptance and Use of Technology 2 Model

• Performance expectancy is the extent to which an individual believes that using the system will be beneficial and enable him or her to enhance job performance

Effort expectancy refers to the perceived ease of using information systems, highlighting how users believe that technology can enhance their efficiency with minimal effort.

Social influence plays a crucial role in shaping an individual's decision to adopt new technology, as it reflects the perceptions of how others in their social circle view the use of that technology Essentially, the degree to which individuals feel that their peers endorse the use of a new system significantly impacts their willingness to embrace it.

Facilitation conditions refer to an individual's perception of how technical and organizational infrastructure enhances system usage These conditions include objective factors that simplify the execution of specific behaviors, ultimately promoting effective engagement with the system.

Hedonic motivation, reflecting the joy and satisfaction from technology use, is vital for technology acceptance and utilization Users are more inclined to embrace technology when it offers personal benefits; conversely, they are likely to avoid it if they perceive it as a threat.

Price value refers to the cost and pricing structure that greatly influences consumers' technology usage It involves weighing the trade-off between the price value and the perceived benefits when investing in technology applications.

Habits play a crucial role in shaping individual behavior, as they determine how likely users are to engage in actions automatically These habits often stem from past experiences, which in turn influence beliefs and future behaviors.

Previous studies related to the topic

Numerous studies have explored the use of AI chatbots in language learning, yet there is a notable scarcity of similar research in Vietnam This paper primarily aims to analyze existing foreign research on this topic to provide a comprehensive understanding of the application of AI chatbots in enhancing language acquisition.

2.3.1 Research on the intention to use Chatbots for learning language

• Using chatbots for English language learning in higher education

Annamalai et al (2022) investigated the factors influencing university students' intentions to use chatbots for language learning during the COVID-19 pandemic, surveying 360 higher education students from three Malaysian public universities Their research integrated the UTAUT model with Bansal's Push-Pull Mooring Habit Theory (PPMH) to analyze the adoption of new technological tools in this unique context The PPMH model identifies push factors as negative influences that prompt individuals to leave existing options, while pull factors are positive incentives that encourage the adoption of new alternatives.

This study examines the influence of four key factors—Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and COVID-19 Fear (CF)—on students' intentions to use Chatbots for learning English Descriptive data analysis reveals that PE, EE, and CF positively impact students' intentions, while SI exerts a push effect on their behavioral intentions Overall, these findings indicate a positive mooring effect, encouraging students to embrace Chatbots as a learning tool for English.

• A structural equation model analysis of English for specific purposes students’ attitudes regarding computer-assisted language learning: UTAUT2 model (Bessadok & Hersi, 2023)

This study investigated the factors affecting Saudi university students' acceptance and use of Blackboard as a Computer-Assisted Language Learning (CALL) platform in English as a Foreign Language (EFL) courses Utilizing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the research identified key predictors of students' behavioral intentions and usage of Blackboard’s LMS The results indicated that performance expectancy, social influence, effort expectancy, and price value significantly shape EFL students' attitudes towards CALL Additionally, the habit factor was found to be a strong predictor of both behavioral intention and technology use, suggesting that regular use of CALL can lead to automatic engagement in EFL learning The study highlighted the importance of enhancing technical and organizational support to improve the effectiveness of CALL among EFL students.

• A ChatBot for Learning Chinese: Learning Achievement and

Technology Acceptance (Chen et al., 2020)

Chen et al (2020) explored the application of Chatbots in learning Chinese through the lens of the Technology Acceptance Model (TAM) The study identified key variables, including perceived usefulness (PU) and perceived ease of use (PEU), which significantly influenced users' attitudes and intentions to utilize Chatbots Survey participants acknowledged the effectiveness and user-friendliness of Chatbots, expressing a willingness to continue using them in the future The findings highlighted that well-designed Chatbots can enhance the learning experience and lead to improved educational outcomes, suggesting that future developments should prioritize enhancing both PU and PEU.

2.3.2 Research on the intention to use Chatbots for other learning purposes

• Modeling Students’ Perceptions of Chatbots in Learning: Integrating

Technology Acceptance with the Value-Based Adoption Model (Mohammed, 2023)

In a study by Mohammed (2023), the perception and acceptance of Chatbots in student learning were examined using a combination of the Technology Acceptance Model (TAM) and the Value-based Model (VAM) The research identified key factors influencing Chatbot adoption among higher education students, including perceived ease of use, perceived usefulness, perceived enjoyment, and perceived risk Analyzing data from 432 respondents, the study revealed that perceived enjoyment and ease of use significantly enhance students' perceived value, while attitudes towards Chatbots are notably influenced by perceived usefulness and value The findings suggest that students’ acceptance of Chatbot technology is closely linked to their attitudes and perceived value Mohammed recommended that university lecturers in Saudi Arabia encourage the adoption of Chatbots in learning environments by effectively designing and implementing these tools in the future.

• Adoption of Chatbots for Learning among University Students: Role of

Perceived Convenience and Enhanced Performance (Malik et al., 2021)

A study by Malik et al (2021) explored university students' acceptance of Chatbots for learning, utilizing the Technology Acceptance Model (TAM) to examine factors influencing attitudes and behavioral intentions Key elements identified included perceived usefulness, perceived ease of use, and perceived convenience The findings confirmed that perceived usefulness significantly shapes user attitudes, while perceived ease of use enhances perceived usefulness, ultimately influencing user attitudes towards Chatbots in educational settings.

2.3.3 General summary of prior experimental studies

Table 2.1 Table of previous experimental studies

No Authors Research Topic Research Model Research Findings

Using chatbots for English language learning in higher education

Unified Theory of Acceptance and Use of Technology (UTAUT) and Push- Pull Mooring Habit Theory (PPMH)

Three factors PE, EE, and

CF have a positive impact on the intention to use Chatbots in learning English, while the factor SI is the opposite

A structural equation model analysis of English for specific purposes students’ attitudes regarding computer-assisted language learning:

Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)

PE, EE, SI, and PV have significant effects on EFL learners' CALL usage Besides, HT factor predicts the tendency of popular usage in the future

Both PU (Perceived Usefulness) and PEU (Perceived Ease of Use) significantly influence the intention to utilize chatbots for learning Chinese This suggests that improving chatbot applications can enhance their effectiveness and encourage broader adoption in language education.

Perceptions of Chatbots in Learning:

Perceived enjoyment and PEU strongly influence perceived value, and at the

Integrating Technology Acceptance with the Value-Based Adoption Model

(TAM) and Value- based Model (VAM) same time, perceived usefulness and perceived value significantly impact attitudes, resulting in students’ technology acceptance of Chatbot technology

Adoption of Chatbots for Learning among University Students:

Role of Perceived Convenience and Enhanced

PU mainly impacts technology user attitudes Additionally, PEU positively affects PU, leading to a further influence on user attitudes

The application of AI chatbots in education, particularly for learning English, primarily utilizes the TAM and UTAUT2 models in research Findings indicate that performance expectancy (perceived usefulness) and effort expectancy (perceived ease of use) are the key factors influencing users' intention and behavior toward adopting new technologies Additionally, other factors from the UTAUT2 model, such as social influence, hedonic motivation, price value, and habit, also significantly affect the intention to use innovative learning systems.

Research hypotheses and suggested research model

This study utilizes the UTAUT2 model by Venkatesh et al (2012) to explore the factors influencing the intention to use AI chatbots for learning English Key variables examined include performance expectancy, effort expectancy, social influence, hedonic motivation, price value, and habit Consequently, a research model will be proposed to encapsulate these relationships.

Performance expectancy, or perceived usefulness, significantly influences technology users' attitudes and intentions According to the Technology Acceptance Model (TAM) by David et al (1989), perceived usefulness is a crucial motivational factor that affects user satisfaction and behavioral intention When individuals recognize the potential benefits of a technological system, they are more likely to incorporate it into their learning processes (Lwoga & Komba, 2015) Research by Chen et al (2020) further supports this notion, demonstrating that the perceived usefulness of Chatbots positively affects students' attitudes toward adopting and using Chatbots for learning Additionally, a study conducted in Germany by Gansser and Reich reinforces these findings among Chatbot users.

(2021), results showed that performance expectancy plays a crucial role in clarifying behavioral intention and usage behavior of artificial intelligence products Therefore, the first hypothesis is presented as follows:

H1: Performance Expectancy (PE) has a positive effect on the intention of EFL learners to use AI Chatbots for learning English

Effort expectancy plays a crucial role in an individual's willingness to use a specific technological tool, as highlighted in the UTAUT model (Venkatesh et al., 2003) Gupta et al (2008) emphasized its direct impact on early technology adoption Research by Kumar and Silva (2020) demonstrated that perceived ease of use positively affects learners' attitudes toward adopting Chatbots in educational settings Conversely, if users perceive the Chatbot system as overly complicated and mentally taxing, the effort required to learn it may overshadow its benefits (Joshi, 2021).

Research indicates that user satisfaction is significantly influenced by the confirmation of effort expectancy in technological systems When utilizing AI chatbots, learners can anticipate numerous advantages, including immediate assistance with minimal effort required.

An intuitive interface and functional system in English-learning Chatbot applications enhance user experience, encouraging continued engagement after the initial use Therefore, this study proposes the following hypothesis:

H2: Effort Expectancy (EE) has a positive effect on the intention of EFL learners to use AI Chatbots for learning English

Research indicates that social influence significantly affects users' intentions to adopt AI Chatbots, with both positive and negative impacts Positive social effects can improve users' perceptions of the chatbots' usefulness and ease of use, while reducing perceived risks and barriers to adoption (Mogaji et al., 2021; Terblanche & Kidd, 2022) Conversely, negative social influence may lead to mistrust and concerns regarding the effectiveness and reliability of these technologies, ultimately fostering resistance to change (Magsamen-Conrad et al., 2015) Social influence can stem from various sources, including family, friends, colleagues, influencers, experts, and prior reviews (Fu et al., 2020) The growing acceptance and utilization of AI Chatbots across socio-economic sectors suggest that social influence predominantly fosters their adoption Therefore, the hypothesis regarding the impact of social influence is proposed.

H3: Social Influence (SI) has a positive effect on the intention of EFL learners to use AI Chatbots for learning English

Hedonic motivation plays a crucial role in technology adoption, significantly influencing individuals' attitudes toward accepting new technologies This motivation highlights the emotional benefits users derive from engaging with specific technologies, ultimately impacting their willingness to embrace innovation.

In 2012, research highlighted that hedonic motivation significantly influences learners' enjoyment when interacting with AI Chatbots for English learning When students perceive these chatbots as engaging and enjoyable, they are more likely to develop a positive attitude towards them and embrace their use in the learning process.

Research by Rapp et al (2021) highlights that chatbot technology fosters an engaging learning environment, addressing students' sociability and curiosity about new technologies Additionally, Keong et al (2022) and Marjerison et al (2022) found a positive correlation between perceived enjoyment and users' attitudes toward technology acceptance Brown and Venkatesh (2005) further emphasized that hedonic motivation plays a vital role in the acceptance and use of technology Consequently, increased hedonic motivation leads to stronger behavioral intentions toward adopting new technologies Thus, the research proposes the following hypothesis.

H4: Hedonic Motivation (HM) has a positive effect on the intention of EFL learners to use AI Chatbots for learning English

Price value plays a crucial role in technology acceptance, as it complements the effort expectancy factor by relating to the time and effort invested in adopting new technology (Venkatesh et al., 2012) Users are more inclined to embrace technology when they perceive that the benefits outweigh the costs, indicating that individuals are willing to bear technological usage expenses if they foresee positive outcomes Previous research, including studies by Moorthy et al (2019) and Palau-Saumell et al., has demonstrated the significant influence of price and value on technology acceptance.

A 2019 study revealed that improved information systems positively influence user experiences and emotions Furthermore, the research established a significant connection between price value and behavioral intention, driven by new perceptions and increased satisfaction Consequently, a hypothesis regarding the price value factor is proposed.

H5: Price Value (PV) has a positive effect on the intention of EFL learners to use AI Chatbots for learning English

Habit refers to the tendency of individuals to use technology automatically, shaped by prior learning and experiences (Venkatesh et al., 2012) Limayem et al (2007) characterized habit as a cognitive structure, highlighting its role as a significant predictor of behavioral intention in technology use In the realm of AI applications for learning, habit fosters a symbiotic relationship between users and technology (Jacucci et al., 2014), making it a crucial determinant of user engagement (Perez-Vega et al., 2021) This study illustrates how learners have integrated AI Chatbots into their English learning routines, suggesting that those with a habit of frequent technology use are more inclined to incorporate AI Chatbots into their educational processes Consequently, the research proposes the following hypothesis:

H6: Habit (HT) has a positive effect on the intention of EFL learners to use AI Chatbots for learning English

Figure 2.3 Suggested research model (Source: Author)

Chapter 2 presented relevant concepts about AI Chatbots in the context of EFL learners Based on two main theoretical bases including the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the study conducted a deeper understanding of the factors affecting EFL learners’ decision to use AI Chatbots for learning English In addition, this chapter also gave an overview of previous related empirical research to create a foundation for building a research model for the topic

The research developed a model featuring key hypotheses that incorporate various factors, including Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), and Habit (HT).

RESEARCH METHODOLOGY

Research design

The study commenced by identifying the key issues and objectives, focusing on the factors influencing the intention to use AI chatbots for English learning Subsequently, relevant theoretical frameworks and prior research were examined to develop a proposed research model The research was conducted in two phases: initial exploratory research to create a questionnaire, followed by formal quantitative analysis.

The author conducted preliminary research and questionnaire design by referencing previous studies to develop suitable measurement scales Given constraints related to cost, demographics, and time, the optimal sample size was determined to be the maximum feasible Feedback and revisions were gathered from five selected respondents to create a draft questionnaire This questionnaire featured survey questions formulated by the author and employed a 5-point Likert Scale to assess research variables, ranging from Strongly Disagree to Strongly Agree.

The formal research phase involved refining the questionnaire and research scales based on preliminary findings Utilizing quantitative methods, the study surveyed EFL learners in Ho Chi Minh City through an online Google Forms questionnaire The collected data were analyzed using SPSS software to evaluate the proposed model, aiming to confirm or refute the hypotheses and identify any new relationships or elements not previously considered in the research model.

The research process is carried out through the steps in the following figure

Figure 3.1 Research process (Source: Author)

The research begins by identifying the problem and purpose based on current practical information A research model is then developed using theoretical frameworks and prior studies, which aids in refining measurement scales and questionnaires for the formal study After finalizing the questionnaire, the research proceeds with surveying selected samples Ultimately, the study evaluates the data to assess the influence of various factors on the intention to use AI chatbots, culminating in conclusions and recommendations.

Measurement scales

This research investigates the factors affecting the intention to utilize AI chatbots for learning English The study employs a 5-point Likert scale to measure responses, ranging from "Strongly Disagree" to "Strongly Agree."

1 PE1 AI Chatbots design a specific learning pathway to save time and effort when learning English Expert opinions

2 PE2 AI Chatbots help identify the weaknesses that need improvement when learning English

3 PE3 Learning English with AI Chatbots helps achieve learning goals faster

4 PE4 Using AI Chatbots to learn English helps improve English skills

1 EE1 The interface of AI Chatbots easy to use for learning English

2 EE2 Using AI Chatbots to learn English requires less effort Malik et al (2021)

3 EE3 Interacting with AI Chatbots is simple and clear when learning English Mohammed et al

4 EE4 It is very easy to find documents and lessons on AI Chatbot applications Annamalai et al

1 SI1 I use AI Chatbots to learn English because of recommendations from my friends

2 SI2 I will use AI Chatbots to learn English if everyone around me uses them

3 SI3 People whose ìnluences on me think that I should use AI Chatbots to learn English

4 SI4 I will use AI Chatbots to learn English if they receive good reviews from previous users Expert opinions

1 HM1 I feel happy using AI Chatbots to learn

2 HM2 I enjoy interacting with AI Chatbots when learning English

Using AI Chatbots to learn English provides me with a lot of enjoyment in my studying process

4 HM4 Features of English-learning AI Chatbot applications entertain me

1 PV1 I can save costs (course costs, travel costs, etc.) when learning English through AI Chatbots

2 PV2 AI Chatbots are reasonably priced for learning English

3 PV3 AI Chatbots offers better value of money when learning English

4 PV4 I am willing to pay for an English course through AI Chatbots if the results are as my expectation

1 HT1 I usually use AI Chatbots to learn English

2 HT2 I use AI Chatbots for learning without thinking

3 HT3 Using AI Chatbots to learn English has become my daily habit

4 HT4 Using AI Chatbots for English-learning is a part of my daily routine

3.2.7 Intention to use AI Chatbots for learning language

Table 3.7 Intention to use AI Chatbots for learning language scale

1 ITU1 I will learn English through AI Chatbots anytime depending on my needs Expert opinions

2 ITU2 I will usually use AI Chatbots to learn

English in the future Mohammed et al

3 ITU3 I think using AI Chatbots will enhance my

4 ITU4 I will continue using AI Chatbots for learning

Formal research

Following the initial research phase, the study proceeded with formal research utilizing a quantitative approach Primary data was gathered through Google Forms, targeting online users in Ho Chi Minh City who have engaged with English-learning chatbot applications The participants in the research were categorized into specific groups for analysis.

5 age groups: under 15 years old (1), from 15 - 18 years old (2), from 19 - 22 years old

(3), from 23 - 26 years old (4), over 26 years old (5)

The author analyzed the collected data to identify factors influencing English learners' intention to use AI chatbots This research highlights the significance of gathering and defining sample descriptions as a crucial part of the research process.

The formal quantitative research sample in this topic is EFL learners who have experienced or are planning to use AI Chatbots to learn English

The research employed a non-probability sampling method, determined by the data analysis techniques utilized According to Harris (1985), for effective multivariate regression analysis, the sample size should equal the number of independent variables plus at least 50 Hair et al (2014) further indicated that for exploratory factor analysis (EFA), a minimum sample size of 50 is recommended, with a preference for 100 or more They proposed a ratio of analyzed to observed variables of 1:5 Given that this study involves 7 measurement scales corresponding to 28 observed variables, a minimum sample size of 140 is required (5 x 28 = 140).

The study selected a sample size of 150 to 200 participants to enhance research reliability and accommodate the potential removal of non-compliant samples, such as those with incomplete or missing responses.

The research gathered data through an online survey conducted via Google Forms, which was distributed to over 150 university students in Ho Chi Minh City through email Additionally, the survey link was shared on social media platforms to reach a broader demographic and include diverse age groups for comprehensive data collection.

The survey results will be collected and organized as respondents complete the research form, while the author will filter out any data samples that do not meet the necessary criteria to ensure valid results for data analysis.

The Likert scale, developed by American social psychologist Rensis Likert in 1932, is a tool designed to evaluate and measure individuals' attitudes, opinions, and perceptions on specific issues Recognizing the challenge of quantifying intangible emotions and thoughts, Likert created this scale to facilitate numerical coding of opinions, making research more accessible Today, the Likert scale is widely utilized across various fields, including social science research, education, business, and market research.

In this study, questions about independent variables and dependent variables are evaluated using a 5-level Likert scale from 1 to 5:

The 5-point Likert scale is chosen for its efficiency in collecting data quickly from a large number of respondents, allowing for reliable estimates of individuals' abilities However, it is important to acknowledge its limitations, including potential data bias when respondents overly concentrate on a particular scale point and the lack of detailed insights into their attitudes or opinions.

The questionnaire includes 3 main parts:

Part 1: Survey of general information about the research problem

This section discusses the frequency and purpose of AI Chatbot usage, allowing for the filtration of data to identify appropriate samples for research on utilizing AI Chatbots in English language learning.

Part 2: Focus on the main research questions

The study examines the independent variables influencing the intention to use AI chatbots for learning English, including performance expectancy, effort expectancy, social influence, hedonic motivation, price value, and habit In contrast, the dependent variable centers on users' intention to utilize AI chatbots for English learning, encompassing their needs, beliefs, and overall satisfaction with the learning experience.

Part 3: Survey of general information about survey participants Asked information including age, gender, education level, and occupation of people who have experienced AI Chatbots This information would help evaluate and analyze data for each survey subject, thereby finding general characteristics and trends of users in using

Part 2 of the survey employs a 5-level Likert scale ranging from Strongly Disagree to Strongly Agree to assess participants' responses The collected data will be analyzed to explore the relationship between various influencing factors and the intention to use AI chatbots for English learning.

Data analysis methods

Descriptive statistics play a crucial role in summarizing and interpreting the characteristics of a specific data set, offering clear insights into its samples and measures (Hayes, 2023) This foundational analysis is essential for subsequent quantitative research steps Key types of descriptive statistics include central measures such as the mean, median, and mode, which provide valuable information about the data's distribution and central tendency.

This research employs descriptive statistics to summarize the characteristics of data gathered from official studies It presents a comprehensive overview of the sample and demographic factors through tables and visual charts, utilizing numerical tools such as averages and standard deviations The analysis highlights key characteristics of the survey sample, including gender, age group, educational status, and frequency of use.

Descriptive statistics play a crucial role in analyzing data by exploring potential relationships between variables They serve as an initial step in data analysis, helping to interpret the data and uncover any trends or patterns that may exist.

To ensure the reliability of the scales, the research utilized Cronbach’s Alpha Coefficient through SPSS 20 software, which evaluates reliability by comparing the common variance among items in an instrument with the overall variance This coefficient measures the internal consistency of a scale or questionnaire, indicating how well the items reflect the same underlying construct According to Nunnally (1978), a variable-total correlation coefficient of 0.3 or higher signifies that the variable meets the reliability requirements, while Hair et al (2006) suggest that a Cronbach’s Alpha of 0.7 or above indicates adequate internal consistency Results meeting or exceeding these thresholds will proceed to the next phase of exploratory factor analysis (EFA).

Exploratory Factor Analysis (EFA) is a statistical method technique used in the case of the survey that collects a large number of variables including observed variables

By reducing a set of variables to a set of fewer and more meaningful variables, the approach allows researchers to examine relationships between variables, generate questions related to research subjects, and determine latent variables

The EFA method assesses two key scale values: convergent and discriminant validity (Izquierdo et al., 2014) This approach is essential for identifying variables influenced by multiple factors and for correcting misassignments of variables to factors.

Criteria utilized to evaluate variables according to the EFA method:

➢ Kaiser-Meyer-Olkin coefficient (KMO)

Kaiser-Meyer-Olkin coefficient (KMO) is used to evaluate the magnitude of the correlation among variables and their suitability Kaiser (1974) supposed that the KMO index must be higher 0.5 (0.5 ≤ KMO ≤ 1) for appropriate factor analysis In contrast, if KMO is below 0.5, researchers need to consider collecting more data or eliminating less meaningful observed variables

Bartlett’s test of sphericity examines whether there is a correlation between the variables participating in the EFA or not When the sig value of the Bartlett test is less than 0.05, the observed variables in the EFA method are correlated with each other On the contrary, if sig is greater than 0.05, it implies that the observed variables are not correlated and EFA analysis is not appropriate (Hair et al., 2009)

Total variance explained explains a certain proportion of the variance of the observed variables According to Merenda (1997), the total variance explained must achieve a cumulative variance percentage of at least 50%, indicating that the factor analysis is appropriate and accepted Meanwhile, Hair et al (2009) said the total variance explained explaining 60% of the total variance is good

Eigenvalue is a commonly used criterion to determine the number of factors in EFA analysis In this criterion, only factors with Eigenvalue ≥ 1 will be retained in the analytical model (Gerbing & Anderson, 1988)

The Factor Loading coefficient represents the correlation between observed variables and underlying factors; a higher absolute value indicates a stronger correlation, while a lower value signifies a weaker relationship.

For a minimum sample size of 100, Hair et al (2009) stated that:

▪ Absolute value of factor loading ranges from 0.3 to 0.4 is considered the minimum condition for the observed variable to be retained

▪ Absolute value of factor loading is higher 0.5 is the optimal level, and observed variables have good statistical significance

However, this reference coefficient will change based on different sample sizes Therefore, it is necessary to consider the corresponding loading factor for each different research

Pearson correlation analysis is a statistical method employed to assess the linear relationship between independent and dependent variables (Schober, 2018) It is commonly conducted prior to regression analysis to identify potential multicollinearity issues, particularly when independent variables exhibit strong correlations among themselves.

In correlated data, changes in the magnitude of one variable are associated with changes in another variable, which can occur in the same direction (positive correlation) or in opposite directions (negative correlation) This means that higher values of one variable are related to either higher or lower values of the other variable, and the reverse is also true.

Pearson correlation coefficient (r) has values ranging from -1 to +1 (the r coefficient is only meaningful when sig < 0.05) The absolute magnitude of the observed correlation coefficient can be specifically determined as follows:

Regression analysis, as defined by Draper and Smith (1998), encompasses statistical methods aimed at estimating the relationship between a dependent variable and independent variables This technique is commonly utilized to predict the value of the dependent variable based on available data from explanatory variables, as well as to assess the influence of specific explanatory variables on the dependent variable.

In multivariate regression, the relationship is unidirectional, with independent variables influencing the dependent variable Unlike Pearson correlation, where coefficients remain consistent, regression analysis reveals distinct effects between variables For instance, the influence of variable A on variable B differs significantly from the impact of B on A, indicating that changes in A do not correspond directly to changes in B.

Based on the theory discussed in Chapter 2, Chapter 3 presents Research Methodology including research methods, research process, formal research, and data analysis methods used for the topic

RESEARCH FINDINGS AND DISCUSSION

Data collection results

An online survey created using Google Forms collected a total of 173 responses After screening for invalid entries that were either incomplete or did not meet established criteria, 158 valid survey forms were retained for data analysis, resulting in a valid response rate of 91.33%.

Descriptive statistics of the official research sample

Figure 4.1 Gender characteristics of the sample

(Source: Results of data analysis)

In a survey of 158 responses, females accounted for 82, representing 51.9%, while males contributed 76 responses This indicates a minimal gender difference of just 3.8%, suggesting that both genders engage equally in language learning, highlighting its importance for everyone.

Figure 4.2 Age characteristics of the sample

The survey reveals that the largest age group participating in English learning is the young demographic of 19 to 22 years old, comprising 44.3% of respondents, or 70 individuals This trend is attributed to their quick adaptability to technology and a strong desire to learn English for future career opportunities In contrast, younger age groups, such as those aged 15 to 18 and under 15, represent 15.8% and 13.9% respectively, showing interest in English but perceiving it as less essential The 23 to 26 age group accounts for 14.6%, while those over 26 make up 11.4% These older demographics, primarily working professionals, often lack the time to engage with new technologies or feel the need to learn English, as many have established careers that do not require further language skills.

Figure 4.3 Educational level characteristics of the sample

In a recent survey, university and college students represent the largest group at 53.2%, highlighting their significant need for English proficiency due to academic and career requirements High school students, making up 28.5%, also recognize the importance of English as they prepare for higher education and future employment Graduates with bachelor's degrees account for 13.9% of respondents, actively seeking to enhance their language skills for the job market Conversely, individuals with higher education qualifications comprise only 4.4% of the total, as they typically possess sufficient English knowledge and skills for their professional roles, reducing their need for further English learning.

Figure 4.4 Occupation characteristics of the sample

The undergraduate group, comprising university students, represents a significant 59.5% of survey responses, which is double the 28.5% from students and notably higher than the 12.0% from working surveyors This high percentage of undergraduates may indicate a strong emphasis on future preparation, particularly for international careers and advanced research demands Proficiency in English is increasingly seen as a crucial factor for career advancement and personal growth In contrast, working individuals often lack the time to explore new technological tools, such as AI chatbots, to enhance their English learning.

Testing the reliability coefficient of the scale (Cronbach's Alpha index)

The surveyed scales from collected data were checked the reliability via SPSS 20 statistical data processing software The observed and tested variables yield results presented in the following section

4.3.1 Testing Cronbach's Alpha for Performance Expectancy (PE)

Table 4.1 Cronbach's Alpha test results of Performance Expectancy

Performance Expectancy (PE): Cronbach’sAlpha = 816

Scale Variance if Item Deleted

Cronbach's Alpha if Item Deleted

The Performance Expectancy scale achieved a Cronbach's Alpha score of 0.816, exceeding the acceptable threshold of 0.6 Additionally, the variable-total correlation coefficients for all observed variables in the scale were above 0.3, confirming their validity for inclusion in the Exploratory Factor Analysis (EFA).

4.3.2 Testing Cronbach's Alpha for Effort Expectancy (EE)

Table 4.2 Cronbach's Alpha test results of Effort Expectancy

Effort Expectancy (EE): Cronbach’s Alpha = 793

The Effort Expectancy scale achieved a Cronbach's Alpha of 0.793, surpassing the acceptable threshold of 0.6 Additionally, the variable-total correlation coefficients for all observed variables exceeded 0.3, confirming their validity for inclusion in the Exploratory Factor Analysis (EFA).

4.3.3 Testing Cronbach's Alpha for Social Influence (SI)

Table 4.3 Cronbach's Alpha test results of Social Influence

Social Influence (SI): Cronbach’sAlpha = 816

The Social Influence scale achieved a Cronbach's Alpha of 0.816, surpassing the acceptable threshold of 0.6 Additionally, the variable-total correlation coefficients for all observed variables exceeded 0.3, confirming their validity for inclusion in the Exploratory Factor Analysis (EFA).

4.3.4 Testing Cronbach's Alpha for Hedonic Motivation (HM)

Table 4.4 Cronbach's Alpha test results of Hedonic Motivation

Hedonic Motivation (HM): Cronbach’sAlpha = 732

The Hedonic Motivation scale demonstrates a Cronbach's Alpha of 0.732, surpassing the acceptable threshold of 0.6 Additionally, the variable-total correlation coefficients for all observed variables exceed 0.3, confirming their validity Consequently, all observed variables are deemed acceptable and will be utilized in the Exploratory Factor Analysis (EFA).

4.3.5 Testing Cronbach's Alpha for Price Value (PV)

Table 4.5 Cronbach's Alpha test results of Price Value

Price Value (PV): Cronbach’sAlpha = 826

The Cronbach's Alpha result for the Price Value scale is 0.826, exceeding the acceptable threshold of 0.6 Additionally, the variable-total correlation coefficients for all observed variables in the scale are above 0.3, confirming their suitability for inclusion in the Exploratory Factor Analysis (EFA).

4.3.6 Testing Cronbach's Alpha for Habit (HT)

Table 4.6 Cronbach's Alpha test results of Habit

The Habit scale achieved a Cronbach's Alpha score of 0.884, exceeding the acceptable threshold of 0.6 Additionally, the variable-total correlation coefficients for all observed variables in the scale are above 0.3, confirming that these variables are reliable and suitable for inclusion in the Exploratory Factor Analysis (EFA).

4.3.7 Testing Cronbach's Alpha for Intention to use (ITU)

Table 4.7 Cronbach's Alpha test results of Intention to use

Intention to use (ITU): Cronbach’sAlpha = 832

The Cronbach's Alpha for the Intention to Use scale is 0.832, exceeding the acceptable threshold of 0.6 Additionally, the variable-total correlation coefficients for all observed variables in the scale are above 0.3, indicating that the scales are suitable for the research Consequently, all observed variables are validated and will be included in the subsequent Exploratory Factor Analysis (EFA).

Exploratory Factor Analysis (EFA)

4.4.1 Exploratory Factor Analysis of independent variables

Based on the results of testing the reliability of the scales through Cronbach's Alpha, the research performed Exploratory Factor Analysis (EFA) with 24 observed variables

Table 4.8 KMO and Bartlett's Test results of independent variables

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .865

The exploratory factor analysis results indicate a KMO coefficient of 0.865, which confirms the suitability of the data for analysis, as it falls within the acceptable range of 0.5 to 1 Furthermore, the Bartlett test yields a significance index of 0.000, which is less than 0.05, demonstrating a statistically significant correlation among the observed variables.

Table 4.9 Total Variance Explained result of independent variables

Initial Eigenvalues Extraction Sums of Squared

Utilizing the factor extraction method, 24 independent variables were grouped into 6 distinct factors, achieving a Total Variance Explained of 67.275%, which exceeds the 50% threshold The Eigenvalue coefficient of 1.197, being greater than 1, confirms the appropriateness and acceptance of the factor analysis conducted.

Table 4.10 Rotated Component Matrix result of independent variables

The analysis of the rotated component matrix includes 24 independent variables, revealing that variable HM4 has a Factor Loading coefficient below 0.5, indicating it is unsatisfactory and will be excluded Consequently, a second exploratory factor analysis (EFA) will be performed.

Table 4.11 KMO and Bartlett's Test results of independent variables (2)

After removing the observed variable HM4, the KMO coefficient increased to 0.866, indicating a strong correlation among the variables, while the Bartlett test yielded a significance level below 0.05 These results confirm that the application of Exploratory Factor Analysis (EFA) is suitable for this dataset.

Table 4.12 Total Variance Explained result of independent variables (2)

The Total Variance Explained value at this time is 67,433% > 50% at the Eigenvalue coefficient of 1.188 (> 1), with 6 factors explained from 23 observed variables

Table 4.13 Rotated Component Matrix result of independent variables (2)

The analysis of the rotated component matrix reveals that the Factor Loading coefficients for all variables exceed 0.5, confirming that the dataset meets the necessary criteria Furthermore, the findings from the second Exploratory Factor Analysis (EFA) highlight a significant correlation among the variables.

23 variables and their suitability to the research

4.4.2 Exploratory Factor Analysis of dependent variables

Based on the Cronbach's Alpha reliability test results of the above scale for the dependent variable, the observed variables were conducted EFA analysis

Table 4.14 KMO and Bartlett's Test results of dependent variables

The KMO coefficient = 0.808 (> 0.5) and the result of the Bartlett test with Sig coefficient = 0.00 (< 0.05) show that using the EFA analysis is appropriate for this data set

Table 4.15 Total Variance Explained result of dependent variables

The Total Variance Explained is 66.445% (> 50%) and the Eigenvalue coefficient is 2.658 (> 1) In addition, there is 1 factor explained from 4 observed variables

Table 4.16 Rotated Component Matrix result of dependent variables

The Factor Loading coefficients of the variables are greater than 0.5 Thus, there is a correlation between variables, used for analysis in the next step.

Pearson correlation analysis

Linear correlation refers to the relationship between two variables that can be visually represented as a straight line on a graph Researchers often utilize the Pearson correlation coefficient (r) to measure the strength of this linear relationship between two quantitative variables However, it's important to note that Pearson correlation analysis is not applicable when one or both of the variables are qualitative or binary.

Table 4.17 Pearson correlation analysis of the factors affecting Intention to use

PE EE SI HM PV HT ITU

Pearson correlation analysis reveals a strong linear relationship among the factors influencing the intention to use AI chatbots for learning English The independent factors, which include Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Hedonic Motivation (HM), and Perceived Value (PV), significantly contribute to this intention.

HT have Sig coefficients less than 0.05, and therefore it can be said that all independent variables (PE, EE, SI, HM, PV, and HT) correlate with the dependent variable ITU

The correlation analysis indicates that Performance Expectancy (PE) has the strongest positive correlation with the Intention to Use (ITU), evidenced by a Pearson correlation coefficient of 0.700 Other factors such as Effort Expectancy (EE), Perceived Value (PV), and Hedonic Tone (HT) also demonstrate significant positive correlations, ranging from 0.630 to 0.659 Meanwhile, Social Influence (SI) and Habitual Use (HM) show lower correlations at 0.540 and 0.495, respectively Overall, all these factors positively influence the intention to use the technology.

Regression analysis

A regression analysis was conducted using six independent variables: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Hedonic Motivation (HM), Price Value (PV), and Habit (HT), to predict the dependent variable, Intention to Use (ITU) The multiple regression function was reformulated to reflect these relationships.

ITU = β0 + β1*PE + β2*EE + β3*SI + β4*HM + β5*PV + β6*HT + ε

The analysis results using SPSS software are detailed in the following tables

Table 4.18 Model Summary with 6 independent variables

Std Error of the Estimate Durbin-Watson

The adjusted R square value, or Coefficient of Determination, is 0.811, indicating that the independent variables can explain 81.1% of the variance in the dependent variable, Intention to Use (ITU) This strong value suggests that the regression model effectively measures both the level and direction of the impact of the independent variables on ITU.

The Durbin-Watson coefficient is 1.878, indicating that the independent variables exhibit a degree of multicollinearity, while also confirming that there is no first-order serial correlation between the error terms.

Table 4.19 ANOVA testing with 6 independent variables

Model Sum of Squares Df Mean Square F Sig

The F test result in the ANOVA table assesses the generalizability of the linear regression model to the population With a sample size of 158, the ANOVA analysis confirms the model's applicability to a larger population The significance value of the F test is 0.000, which is less than 0.05, indicating that the proposed linear regression model is appropriate for the entire population.

Table 4.20 Regression coefficients with 6 independent variables

The Standardized Beta Coefficients indicate the positive influence of independent factors on the dependent variable, with all analyzed coefficients exceeding 0 Significance values below 0.05 confirm that all six independent variables are relevant in the model Among these, the Performance Expectancy (PE) variable exhibits the highest Beta coefficient at β = 0.339, signifying its substantial impact on the intention to use AI chatbots for learning English Conversely, the Habit factor (HT) shows the least effect, with a Beta coefficient of 0.094.

The Variance Inflation Factor (VIF) is utilized to assess multicollinearity among independent variables As shown in Table 4.20, all VIF values are below 2, indicating the absence of multicollinearity among the independent factors.

The standardized regression equation after analysis is presented:

ITU = -1.005 + 0.339*PE + 0.263*EE + 0.167*SI + 0.173*HM + 0.271*PV + 0.094*HT + ε

Through the above formula, some features can be inferred as follows:

• Beta PE coefficient = 0.339 shows the interaction, if the “Performance Expectancy” factor increases by 1 point, the “Intention to use” will increase by 0.339 points (assumed that other factors are unchanged)

• Beta EE coefficient = 0.263 shows the interaction, if the “Effort Expectancy” factor increases by 1 point, the “Intention to use” will increase by 0.263 points (assumed that other factors are unchanged)

• Beta SI coefficient = 0.167 shows the interaction, if the “Social Influence” factor increases by 1 point, the “Intention to use” will increase by 0.167 points (assumed that other factors are unchanged)

• Beta HM coefficient = 0.173 shows the interaction, if the “Hedonic Motivation” factor increases by 1 point, the “Intention to use” will increase by 0.173 points (assumed that other factors are unchanged)

• Beta PV coefficient = 0.271 shows the interaction, if the “Price Value” factor increases by 1 point, the “Intention to use” will increase by 0.271 points (assumed that other factors are unchanged)

• Beta HT coefficient = 0.094 shows the interaction, if the “Habit” factor increases by 1 point, the “Intention to use” will increase by 0.094 points (assumed that other factors are unchanged).

Testing the research model and the hypotheses

Table 4.21 Summary of testing the research model and the hypotheses

H1: Performance Expectancy (PE) has a positive effect on the intention of EFL learners to use AI Chatbots for learning

H2: Effort Expectancy (EE) has a positive effect on the intention of EFL learners to use

AI Chatbots for learning English 263 000 Accepted

H3: Social Influence (SI) has a positive effect on the intention of EFL learners to use

AI Chatbots for learning English 167 000 Accepted

H4: Hedonic Motivation (HM) has a positive effect on the intention of EFL learners to use AI Chatbots for learning

H5: Price Value (PV) has a positive effect on the intention of EFL learners to use AI

H6: Habit (HT) has a positive effect on the intention of EFL learners to use AI Chatbots for learning English

The standardized regression coefficients for all six factors—Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, Price Value, and Habit—are positive and statistically significant, with Sig values below 0.05 This indicates that each factor positively influences EFL learners' intention to use AI chatbots for English learning, leading to the acceptance of all six hypotheses in the model.

Figure 4.5 Synthesis of research result (Source: Author)

Research discussions

This study investigates EFL learners' experiences and perceptions of using Chatbots for English language learning Utilizing the UTAUT2 model, the findings indicate that factors such as Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, Price Value, and Habit significantly enhance the intention to utilize AI Chatbots in the learning process.

Performance Expectancy significantly influences EFL learners' intention to use AI Chatbots for English learning, with a Beta coefficient of 0.339, aligning with Malik et al (2021), who found that perceived usefulness is crucial for adopting Chatbots in education Additional studies by Annamalai et al (2022), Bessadok et al (2023), and Chen et al (2020) further confirm that performance expectancy positively affects students' willingness to utilize AI Chatbots for learning These insights suggest that educational institutions should enhance the integration of AI Chatbots to improve teaching and learning outcomes, as highlighted by Okonkwo et al (2020) Overall, the research emphasizes that Performance Expectation is vital for fostering a more effective learning experience, ultimately boosting educational efficiency and effectiveness.

The Effort Expectancy factor plays a crucial role in predicting English learners' intention to utilize Chatbots, supporting findings from prior studies (Chen et al 2020; Malik et al 2021; Annamalai et al 2022; Bessadok et al 2023; Mohammed et al 2023) Learners are more likely to view Chatbot applications as effective learning tools when they are easy to use and manage Consequently, educational institutions and app developers should prioritize creating intuitive and user-friendly Chatbots that offer valuable support, fostering a convenient learning environment that minimizes time and effort for users.

Research indicates that social influence positively affects the intention to use AI Chatbots for English learning, contrasting with Annamalai et al (2022), who found that peer influence did not impact this intention Conversely, Bessadok et al (2023) highlighted that social factors significantly shape EFL students' attitudes towards Chatbot usage, with input from family, friends, and reviewers fostering positive perceptions The study emphasizes that reliable information and feedback from other users enhance the intention to adopt AI Chatbots, leading to the conclusion that social influence plays a crucial role in learners' decisions to engage with this technology.

The Hedonic Motivation factor significantly enhances learner engagement with AI chatbots, as highlighted by Mohammed (2023), who found that enjoyment during the learning process greatly influences learner acceptance This enjoyment fosters a belief in the value of chatbots as essential tools for creating an interesting and rewarding learning environment Additionally, chatbots can motivate learners by providing customized content tailored to individual educational styles and preferences, leading to improved study outcomes (Baskara, 2023) Consequently, the Hedonic Motivation factor positively impacts the intention to adopt new technology systems in education, such as AI chatbots.

Price value significantly influences learners' attitudes toward the adoption of Chatbots in English learning, aligning with findings from Bessadok et al (2023) Learners are more inclined to embrace Chatbots as effective learning tools when they perceive substantial benefits in learning outcomes Additionally, the research indicates that learners are likely to prefer Chatbot applications for English study if they believe these options offer greater value compared to traditional courses at educational institutions or centers.

Despite receiving the lowest impact result with a Beta coefficient of 0.094, Habit remains a significant predictor of the future acceptance of AI Chatbots in education, as supported by Bessadok et al (2023) Their research emphasizes that understanding users' technology usage habits can forecast emerging trends Consequently, educational institutions and enterprises can formulate effective strategies to adapt to the rapidly evolving landscape This factor holds potential benefits for future research in educational settings and practical applications.

In Chapter 4, the study utilized a quantitative survey involving 158 participants to analyze the impact of various factors on EFL learners' intention to use AI Chatbots for English learning The findings, derived from SPSS analysis, indicate that all six factors in the model positively influence this intention, with Performance Expectancy exhibiting the strongest effect, as indicated by the highest Beta coefficient Following this, Price Value, Effort Expectancy, Hedonic Motivation, and Social Influence also contribute positively Although Habit has the least influence on learners' intentions, it remains a crucial factor for predicting future usage trends.

CONCLUSION AND MANAGERIAL IMPLICATIONS

Conclusion

This study investigates the factors influencing learners' intentions to use AI chatbots for English learning in Ho Chi Minh City The research model identifies six independent variables impacting the dependent variable, which is the intention to use AI chatbots: (1) Performance Expectancy, (2) Effort Expectancy, (3) Social Influence, (4) Hedonic Motivation, (5) Price Value, and (6) Habit The framework is grounded in established theoretical foundations, specifically the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), drawing from previous research in the field.

The study utilized a quantitative research approach, employing a questionnaire created via Google Forms to gather data from 158 respondents The collected data was analyzed using SPSS software, with reliability assessed through Cronbach’s Alpha and exploratory factor analysis (EFA) This process revealed six significant factors, and the findings confirmed that the initial hypotheses aligned with the proposed research model.

Research indicates that the intention to use AI chatbots for learning English is influenced by six key factors, all moving in the same direction Among these, Performance Expectancy exerts the most significant impact on English learners' intentions Following this, Price Value, Effort Expectancy, Hedonic Motivation, and Social Influence play important roles in shaping these intentions Although Habit has the least effect on the intention to use AI chatbots, it remains a crucial element for forecasting future trends in learning preferences.

The study identifies key factors that influence EFL learners' interest in the English learning process Additionally, it outlines limitations and offers recommendations for educational institutions and organizations to focus on essential elements that can enhance AI Chatbot applications, ultimately improving teaching and learning outcomes.

Managerial implications

In the context of rapid technological advancements, the integration of supportive technology tools in education is crucial for enhancing academic outcomes Based on the research findings, the author recommends that schools and educational institutions implement AI chatbots on official learning platforms to optimize teaching and learning effectiveness.

In today's information-rich environment, users are increasingly focused on results when selecting products or services, prompting enterprises to enhance learning applications to boost performance expectancy in AI Chatbot adoption The perceived usefulness of Chatbots significantly influences learners' attitudes towards their academic performance and achievement By offering personalized support, AI Chatbots can help learners monitor their progress, optimize time and effort, and enhance learning efficiency through tailored learning paths that align with individual needs, such as difficulty levels and topic focus Academic organizations can leverage Chatbot technology to provide personalized recommendations based on learner progress and preferences, along with real-time feedback addressing weaknesses and guiding improvement Furthermore, English learning applications should implement appropriate assessments to evaluate new knowledge and skills after each topic.

To enhance effort expectancy in English learning, it is essential to design a user-friendly Chatbot application interface A simple and intuitive interface fosters a positive learning experience, making the process more comfortable for learners Organizations should focus on creating clear buttons, menus, and icons, while offering various interactive options to improve user engagement Additionally, providing timely and personalized responses to learners' inquiries, ensuring compatibility across multiple devices (smartphones, tablets, laptops, and desktops) to track progress, and offering robust online support services (live chat, email, and phone) are crucial for addressing learners' needs efficiently.

To enhance hedonic motivation in English learning, application developers should prioritize creating visually appealing interfaces and engaging lesson content By incorporating suitable colors, images, icons, and animated graphics, English learning apps can make AI chatbots more attractive to users Furthermore, integrating rating and reward systems within learning activities fosters a motivating and challenging environment Additionally, designing simulation games following theoretical lessons can significantly improve the learning experience and aid in better retention of the material.

Habit plays a crucial role in predicting future trends, as it stems from daily repetitive actions that evolve into automatic behaviors English learners benefit from a study environment that allows them to monitor and control their learning without constraints of time and space, facilitating a shift from passive to active learning The analysis reveals that AI Chatbots enable language learning anytime and anywhere with an internet-enabled device, aligning with learners' schedules This flexibility empowers learners to take charge of their education and progress at their own pace, seamlessly integrating language learning into their daily routines Consequently, researchers can leverage these insights to anticipate learners' behaviors in the English learning process and develop targeted solutions and strategies As learners recognize the convenience and effectiveness of AI Chatbot applications for English learning, their usage is likely to increase in the future.

Limitations and recommendations for further research

Despite its practical and theoretical contributions, this research still has certain limitations in terms of time, sample size, and some other conditions

Due to time constraints, the research is subject to certain errors that adversely impact the analysis, thereby restricting its scope Consequently, the study may concentrate on specific aspects of the issue, lacking a comprehensive analysis and validation of the findings, which undermines the credibility and representativeness of the results.

This study specifically targets English learners, particularly university students, in Ho Chi Minh City The limited focus and narrow scope of the research, coupled with restricted primary data collection, diminish the representativeness of the sample and subsequently impact the reliability of the findings.

Besides, the study mainly analyzed and evaluated based on six factors including

The intention to use AI chatbots for English learning is influenced by several key factors, including Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, Price Value, and Habit However, due to time constraints and other limitations, additional factors affecting this intention were not explored.

5.3.2 Recommendations on the direction for further research

Despite the limitations of this research, future studies can leverage the discussions and findings to refine hypotheses and explore overlooked areas, ultimately enhancing knowledge and elevating the quality of subsequent research.

Due to time and resource constraints, the current research focused solely on English learners in Ho Chi Minh City, which may limit its comprehensiveness and representativeness To enhance the understanding of factors influencing the intention to use AI chatbots for English learning, future studies should consider expanding the research scope to include various provinces and cities throughout Vietnam.

To enhance the credibility and persuasiveness of the research, future studies should explore additional factors that affect the intention to use AI chatbots for English learning This approach will provide readers with a more thorough and well-rounded understanding of the topic.

Chapter 5 provided managerial implications based on the analyzed results to help academic institutions and organizations consider and predict learners’ psychology and behavior in learning English process On the other hand, the research also indicated the remaining limitations in the study process such as the lack of comprehensiveness and representativeness due to limitations in time and resources Then, some recommendations are proposed for future research to be developed more convincingly and reliably.

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OFFICIAL SURVEY

Currently, I need to survey the topic “FACTORS AFFECTING INTENTION TO USE AI

CHATBOTS FOR LEARNING ENGLISH PURPOSES OF EFL LEARNERS”, I would like to ask for your help to have more data to serve the project

I commit that all information collected from this survey will only be used for scientific research projects and not for any other purposes

Thank you very much I appreciate your help and wish you lots of joy and health!

Have you ever used AI Chatbot tools before?

Do you often use AI Chatbot tools?

This survey part is based on your previous experiences with factors affecting your intention to use an AI Chatbot for learning English

For each statement below, please indicate your level of agreement or disagreement with the use of AI Chatbots for learning English

1 Performance Expectancy when using AI Chatbot to learn English

1 PE1 AI Chatbots design a specific learning pathway to save time and effort when learning English

2 PE2 AI Chatbots help identify the weaknesses that need improvement when learning English

3 PE3 Learning English with AI Chatbots helps achieve learning goals faster

4 PE4 Using AI Chatbots to learn English helps improve English skills

2 Effort Expectancy when using AI Chatbot to learn English

1 EE1 The interface of AI Chatbots easy to use for learning English

2 EE2 Using AI Chatbots to learn English requires less effort

3 EE3 Interacting with AI Chatbots is simple and clear when learning

4 EE4 It is very easy to find documents and lessons on AI Chatbot applications

3 Social Influence when using AI Chatbot to learn English

1 SI1 I use AI Chatbots to learn English because of recommendations from my friends

2 SI2 I will use AI Chatbots to learn English if everyone around me uses them

3 SI3 People whose ìnluences on me think that I should use AI

4 SI4 I will use AI Chatbots to learn English if they receive good reviews from previous users

4 Hedonic Motivation when using AI Chatbot to learn English

1 HM1 I feel happy using AI Chatbots to learn English

2 HM2 I enjoy interacting with AI Chatbots when learning English

3 HM3 Using AI Chatbots to learn English provides me with a lot of enjoyment in my studying process

4 HM4 Features of English-learning AI Chatbot applications entertain me

5 Price Value when using AI Chatbot to learn English

1 PV1 I can save costs (course costs, travel costs, etc.) when learning

2 PV2 AI Chatbots are reasonably priced for learning English

3 PV3 AI Chatbots offers better value of money when learning English

4 PV4 I am willing to pay for an English course through AI Chatbots if the results are as my expectation

6 Habit when using AI Chatbot to learn English

1 HT1 I usually use AI Chatbots to learn English

2 HT2 I use AI Chatbots for learning without thinking

3 HT3 Using AI Chatbots to learn English has become my daily habit

4 HT4 Using AI Chatbots for English-learning is a part of my daily routine

7 Intention to use AI Chatbots for learning language

1 ITU1 I will learn English through AI Chatbots anytime depending on my needs

2 ITU2 I will usually use AI Chatbots to learn English in the future

3 ITU3 I think using AI Chatbots will enhance my English-learning performance

4 ITU4 I will continue using AI Chatbots for learning English

PART 3: PERSONAL INFORMATION OF RESPONDENTS

Thank you for dedicating your time to participate in the survey Your insights are crucial for the researcher to gain a deeper understanding of the community's needs and desires.

Thank you very much and I hope you have a nice day!

SPSS ANALYSIS RESULTS

Kaiser-Meyer-Olkin Measure of Sampling Adequacy ,865

Component Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

PE EE SI HM PV HT

Model Change Statistics Durbin-Watson df2 Sig F Change

Model Sum of Squares df Mean Square F Sig

B Std Error Beta Zero-order

Tiêu đề	Factors Affecting Intention To Use Ai Chatbots For Learning English Purposes Of Efl Learners
Tác giả	Vu Thi Kieu Linh
Người hướng dẫn	PhD. Pham Trung Tuan
Trường học	University of Economics and Law
Chuyên ngành	Business Administration
Thể loại	Graduation Thesis
Năm xuất bản	2024
Thành phố	Ho Chi Minh City

Định dạng
Số trang	105
Dung lượng	1,85 MB