UNIVERSITY OF ECONOMICS AND LAW 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 Major Code: 7340101 GRADUATION THESIS INSTRUCTOR PhD. Pham Trung Tuan Ho Chi Minh City, 01/2024 UNIVERSITY OF ECONOMICS AND LAW FACULTY OF BUSINESS ADMINISTRATION --- GRADUATION THESIS FACTORS AFFECTING INTENTION TO USE AI CHATBOTS FOR LEARNING ENGLISH PURPOSES OF EFL LEARNERS Major: Business Administration Major Code: 7340101 STUDENT: Vu Thi Kieu Linh ID CODE: K204071534 INSTRUCTOR: PhD. Pham Trung Tuan Ho Chi Minh City, 01/2024 i ABSTRACT AI Chatbot is an increasingly popular supporting tool due to the rapid development of technology. The new technology system helps improve performance in many different economic and social sectors, especially education.
This paper aims to examine the factors affecting the intention to use AI Chatbots for learning English purposes of EFL learners. By applying UTAUT2 model, the research analyzes and evaluates factors affecting the intention to use AI Chatbots for learning English purposes including Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, Price Value, and Habit. Through a survey of English learners in Ho Chi Minh City, 158 valid responses were processed using SPSS software. After the process of testing Cronbach’s Alpha reliability of the scale, EFA analysis, evaluating the extent of correlation, and regression analysis, six representative factors indicate that the initial hypotheses positively affect the dependent variable in the same direction, aligning with the proposed research model.
Then, based on the analyzed research findings, the paper points out some managerial implications and recommendations for educational institutions and organizations to enhance and integrate AI Chatbot applications into the formal learning system to increase teaching and learning performance. ii TABLE OF CONTENT ABSTRACT. i TABLE OF CONTENT. ii LIST OF TABLES.
vi LIST OF FIGURES. ix CHAPTER 1: INTRODUCTION. Reason for choosing the topic. General research objectives.
Specific research objectives. Structure of the study. 5 CHAPTER 2: LITERATURE REVIEW. Intention to use.
AI and Chatbot. Technology Acceptance Model (TAM). Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Previous studies related to the topic.
Research on the intention to use Chatbots for learning language. Research on the intention to use Chatbots for other learning purposes. General summary of prior experimental studies. Research hypotheses and suggested research model.
Suggested research model. 21 CHAPTER 3: RESEARCH METHODOLOGY. Intention to use AI Chatbots for learning language. Data analysis methods.
Cronbach’s Alpha reliability coefficient. Exploratory Factor Analysis (EFA). Pearson correlation analysis. 34 CHAPTER 4: RESEARCH FINDINGS AND DISCUSSION.
Data collection results. Descriptive statistics of the official research sample. Testing the reliability coefficient of the scale (Cronbach's Alpha index). Testing Cronbach's Alpha for Performance Expectancy (PE).
Testing Cronbach's Alpha for Effort Expectancy (EE). Testing Cronbach's Alpha for Social Influence (SI). Testing Cronbach's Alpha for Hedonic Motivation (HM). Testing Cronbach's Alpha for Price Value (PV).
Testing Cronbach's Alpha for Habit (HT). Testing Cronbach's Alpha for Intention to use (ITU). Exploratory Factor Analysis (EFA). Exploratory Factor Analysis of independent variables.
Exploratory Factor Analysis of dependent variables. Pearson correlation analysis. Testing the research model and the hypotheses. 57 CHAPTER 5: CONCLUSION AND MANAGERIAL IMPLICATIONS.
Limitations and recommendations for further research. Limitations on the research. Recommendations on the direction for further research. 74 APPENDIX 1: OFFICIAL SURVEY.
74 APPENDIX 2: SPSS ANALYSIS RESULTS. 79 vi LIST OF TABLES Table 2. Table of previous experimental studies. Performance Expectancy scale.
Effort Expectancy scale. Social Influence scale. Hedonic Motivation scale. Price Value scale.
Intention to use AI Chatbots for learning language scale. Cronbach's Alpha test results of Performance Expectancy. Cronbach's Alpha test results of Effort Expectancy. Cronbach's Alpha test results of Social Influence.
Cronbach's Alpha test results of Hedonic Motivation. Cronbach's Alpha test results of Price Value. Cronbach's Alpha test results of Habit. Cronbach's Alpha test results of Intention to use.
KMO and Bartlett's Test results of independent variables. Total Variance Explained result of independent variables. Rotated Component Matrix result of independent variables. KMO and Bartlett's Test results of independent variables (2).
Total Variance Explained result of independent variables (2). Rotated Component Matrix result of independent variables (2). KMO and Bartlett's Test results of dependent variables. Total Variance Explained result of dependent variables.
Rotated Component Matrix result of dependent variables. Pearson correlation analysis of the factors affecting Intention to use. Model Summary with 6 independent variables. ANOVA testing with 6 independent variables.
Regression coefficients with 6 independent variables. Summary of testing the research model and the hypotheses. 56 viii LIST OF FIGURES Figure 2. Technology Acceptance Model (TAM).
Unified Theory of Acceptance and Use of Technology 2 Model. Suggested research model. Gender characteristics of the sample. Age characteristics of the sample.
Educational level characteristics of the sample. Occupation characteristics of the sample. Synthesis of research result. 57 ix GLOSSARY Term Explanation AI Artificial Intelligence AIEd Artificial Intelligence in Education CALL Computer-Assisted Language Learning EE Effort Expectancy EFA Exploratory Factor Analysis EFL English as a Foreign Language FC Facilitating Conditions HCI Human-Computer Interaction HM Hedonic Motivation HT Habit KMO Kaiser-Meyer-Olkin coefficient NLP Natural Language Processing PE Performance Expectancy PEU Perceived Ease of Use PPMH Push-Pull Mooring Habit Theory PU Perceived Usefulness PV Price Value SI Social Influence TAM Technology Acceptance Model TRA Theory of Reasoned Action UTAUT2 Unified Theory of Acceptance and Use of Technology 2 1 CHAPTER 1: INTRODUCTION 1.
Reason for choosing the topic Technological development is increasingly changing the world around us. It creates more opportunities for human life to become more modern and convenient in every field. In this context, a specific term that appears to describe a new trend of application of technology in education is Artificial Intelligence in Education (AIEd). AI plays a crucial role in reshaping education by automating and monitoring learners’ progress in various skills, and pinpointing areas where human-teacher intervention is necessary (Chaudhry & Kazim, 2022).
AIEd aids teachers in identifying optimal teaching methods based on individual student contexts and backgrounds. It automates tedious tasks such as generating assessments, grading, and providing feedback. Additionally, AI impacts students’ learning, addressing learning gaps, evaluating effective pedagogies, and enhancing attention retention. Recently, a new trend of AI commonly used in education is Chatbots.
According to GilPress (2024), 60% of Millennials have experienced AI Chatbots and 70% of total users have had a positive response. Most users tend to prefer interacting with bot services rather than humans due to their quick responses and 24/7 operations. In the next 5 years, AI Chatbots are expected to become the most preferred technology solution in most sectors including education. For students in non-English speaking countries, learning a foreign language is not only to discover new knowledge and culture but also to open up opportunities for future careers.
To optimize the language learning process, many learners have chosen to use AI Chatbots as a learning support tool because of the convenience of the ability to use them anytime and anywhere. AI Chatbots provide students with a wide range of opportunities to generate motivation and extend the learning boundaries in the digital era. Chatbots play a significant role in enhancing motivation and engagement levels among users, particularly in the context of technology-supported language learning (Petrović and Jovanović, 2021). These interactive programs enable language learners to apply and improve their language skills in practice for real-life scenarios.
Besides, 2 depending on learners’ levels, AI Chatbots can design and adjust a suitable learning program for each individual (Nghi et al. Understanding the factors that influence the decision to choose AI Chatbots in learning English is crucial for developers in improving the functionality and performance of these applications. However, in Vietnam, although AI Chatbots have been applied in some fields including business, customer care, and healthcare, there is too little research on applying AI Chatbots to education. Therefore, from the mentioned information along with the desire to further research the factors that influence the decision to choose AI Chatbot applications for language learning, this paper has chosen the topic “Factors affecting intention to use AI Chatbots for learning English purposes of EFL learners.” Then, the research will propose some recommendations for educational organizations to improve efficiency and optimize the learning experience for EFL learners, which positively contributes to the development of English learning in the modern context.
Literature review Technological transformation is becoming increasingly common in the field of education. Traditional classrooms are gradually integrating technological innovations into teaching to improve efficiency. Therefore, studying the factors affecting the acceptance of AI in learning is essential and meaningful both theoretically and practically, attracting the attention of many experts and scholars. This is crucial in finding directional solutions for the development of technology and education fields.
In the world, many research articles have examined and evaluated psychological factors affecting learners’ attitudes and behavior in using AI Chatbots based on theoretical models of technology acceptance. In China, based on Technology Acceptance Model (TAM), Chen et al. (2020) demonstrated the relationship between perceived usefulness (PU) and perceived ease of use to attitude and behavioral intention to use Chatbots. This study also implied the future of focusing on developing these two factors in Chatbots.
In Malaysia, Annamalai et al. (2022) studied at three public schools about the intention to use Chatbots for learning language purposes of higher students. The findings 3 indicate that performance expectancy, effort expectancy, social influence, and COVID- 19 fear have significant influences on students’ positive intention to use Chatbots to learn English. In India, Malik et al.
(2021) showed that the effects of perceived usefulness and perceived ease of use of university students about Chatbots positively influence their attitudes towards those platforms. Also, they confirmed the positive relationship between users’ attitudes in deciding to use Chatbots. In Saudi Arabia, Mohammed (2023) conducted research on how attitude and perceived value affect students’ acceptance of Chatbot technology. Specifically, he researched the effects of perceived enjoyment and perceived ease of use on students’ perceived value.
At the same time, two variables (perceived usefulness and perceived value) are identified as crucial variables that impact attitudes using chatbots. Then, his study concluded the relationship between the mentioned variables and students’ acceptance of using Chatbots for learning. In Vietnam, the author currently has not been able to find any empirical studies on applying AI Chatbots to the educational aspect. Therefore, this research will focus on analyzing related foreign research.
Research subjects The research subjects of the topic are factors affecting intention to use AI Chatbots for learning English of EFL learners. The research will deeply explore the psychological elements that lead to learners’ expectations and trust when using AI Chatbots 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.
General research objectives The research aims to analyze and evaluate factors affecting the intention to use AI Chatbots for learning English purposes of learners in Ho Chi Minh City. From analyzed and researched results, this paper will assess factors that negatively and positively impact consciousness, attitudes, and emotions when experiencing AI Chatbots, thereby leading to the decision to use them for learning English. Then, proposing some recommendations for schools and academic organizations to improve and apply AI Chatbots to official learning sites to optimize teaching and learning performance. Specific research objectives 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 AI Chatbots tools.