Study on Individual Investors' Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach

Copyright © Author(s) 2025. All Rights Reserved. Published by GLOBAL PUBLICATION HOUSE. | Int. Journal of Business Management Page 108 of 129 Study on Individual Investors’ Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach By Author(s): Assoc., Ph.D. Le Thi Kim Nhung Thuongmai University Vuong Gia Khang Sunny Hills High school Abstract This study aims to evaluate the factors influencing individual investors’ intention to use artificial intelligence (AI) in stock price forecasting on the Vietnamese stock market, based on the Technology Acceptance Model (TAM). The research model includes the following factors: Perceived Usefulness; Perceived Ease of Use affecting Attitude; Attitude; Subjective Norms; and Trust affecting investors’ intention to use AI in predicting stock prices. The study surveyed 240 individual investors, and the collected data were cleaned and processed using the PLS-SEM regression method. The results highlight the prominent role of social influence, trust in algorithms, and positive perceptions in fostering individual investors’ acceptance of AI. These findings provide important practical implications for fintech companies and AI platform developers in formulating user engagement strategies, emphasizing the need to enhance ease of use, improve technological transparency, and strengthen communication through communities and social influence channels. Keywords Intention, use AI, stock price forecasting, individual investors, Vietnamese stock market, Technology Acceptance Model (TAM). How to cite: Le, T. K. N., & Vuong, G. K. (2025). Study on Individual Investors’ Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach. GPH-International Journal of Business Management, 8(10), 108-129. https://doi.org/10.5281/zenodo.17767584 ARTICLE ID: #02171 10.5281/ZENODO.17767584 VOLUME 08 ISSUE 10 OCT - 2025 e-ISSN 3027-0537 p-ISSN 3027-0375 Le, T. K. N., & Vuong, G. K. (2025). Study on Individual Investors’ Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach. GPH-International Journal of Business Management, 8(10), 108-129. https://doi.org/10.5281/zenodo.17767584 © 2025 GLOBAL PUBLICATION HOUSE | International Journal of Business Management 1. Issue Forecasting trends in financial markets has always been a crucial topic for both academia and professional investors. Such forecasts are typically constructed based on analyses of historical data and economic–financial indicators to support decision-making related to buying or selling financial assets (Lin & Marques, 2024). Traditionally, two common approaches are employed: fundamental analysis and technical analysis. While fundamental analysis is widely used in academic research to determine the intrinsic value of stocks, technical analysis relies on price trend patterns and is more commonly applied by professional investors (Lee, J., et al., 2021). Both methods, however, have certain limitations regarding accuracy and predictive capability, especially under highly volatile market conditions (D’Angelo & Grimaldi, 2017). In recent years, Artificial Intelligence (AI) has emerged as a tool with the potential to revolutionize financial forecasting. Techniques such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) enable the modeling of nonlinear relationships and the extraction of complex data patterns, thereby enhancing the accuracy of stock price predictions (Lin & Marques, 2024). AI is no longer merely a supporting tool; it has become a ―strategic analysis hub‖ that helps investors make rapid, precise, and riskaligned decisions (Belanche et al., 2019). Notably, robo-advisor systems in the Fintech sector have demonstrated their ability to replace traditional advisory services through AI-driven algorithms, offering low-cost investment solutions and 24/7 accessibility (Park & Yoon, 2025). According to Statista (2023), the global financial services industry invested approximately USD 35 billion in AI, with the banking sector accounting for 60% of this amount. Alongside this trend is a shift from internal software systems to cloud-based platforms (SaaS), which enhances scalability and reduces operational costs. Within this context, the application of AI in financial investment has become a strategic direction in many countries, particularly in emerging markets. In Vietnam, the stock market has been growing rapidly in both scale and the number of individual investors. However, most retail investors still lack decision-support tools based on big data analytics. Several domestic trading platforms and Fintech companies have begun integrating AI into their investment recommendation systems, yet the acceptance and use of AI among individual investors remain limited due to factors related to technological perception, trust, and social influence (Khuong, N.V., et al., 2022). Against this backdrop, this study aims to analyze the factors influencing individual investors’ intention to use AI in stock price forecasting in Vietnam, based on the Extended Technology Acceptance Model (TAM). Specifically, the study examines the roles of Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Trust (TR), and Subjective Norm (SN) in shaping Attitude (ATT) and Behavioral Intention (BI) to use AI in stock investment. Page No. 109 Study on Individual Investors’ Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach Volume 8 Issue No 10 (2025) Access: https://gphjournal.org/index.php/bm The research objectives include: (1) Identifying the factors that influence individual investors’ intention to use AI in forecasting stock prices; (2) Measuring the degree of influence of each factor within the extended TAM model on behavioral intention to use AI in the context of the Vietnamese stock market. 2. Theoretical basis 2.1. Overview of Artificial Intelligence (AI) and Its Applications in Finance Artificial Intelligence (AI) is understood as the capability of computer systems to process, learns, and make decisions that simulate human intelligence (Kaplan & Haenlein, 2019). In the financial sector, AI is driving a profound transformation, particularly through technologies such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP). These technologies enable systems to identify patterns, forecast trends, and make investment decisions with far greater accuracy than traditional statistical methods (Lin & Marques, 2024). Recent studies have demonstrated the superior capability of AI in predicting stock price movements and supporting automated trading decisions. Bradley and Droisis (2024) employed a Multilayer Perceptron (MLP) neural network model to forecast stock prices and showed that the system could reduce prediction error to as low as 1.7% on real-world data. Similarly, Lin and Marques (2024) reviewed more than 379 studies on AI and stock markets, finding that models such as Long Short-Term Memory (LSTM), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) are among the most effective approaches for market prediction. In the Fintech sector, AI has been widely integrated into products such as roboadvisors, credit risk assessment systems, and automated trading platforms. According to Belanche, Casaló, and Flavián (2019), user attitude and social influence are key determinants of AI adoption in Fintech. Additionally, Park and Yoon (2025) highlight that the combination of AI and sustainable Fintech enhances transparency, efficiency, and reduces ethical risks in the financial system. In Vietnam, AI applications in financial investment are still in their early stages. Although several Fintech companies and trading platforms have implemented AI-based analytical tools, the level of acceptance and trust among individual investors remains limited (Khuong et al., 2022). This limitation stems from psychological factors, lack of technological understanding, and concerns regarding the accuracy of AI systems. These issues highlight the necessity of studying individual investors’ intention to use AI in order to promote digital transformation in Vietnam’s financial sector. Le, T. K. N., & Vuong, G. K. (2025). Study on Individual Investors’ Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach. GPH-International Journal of Business Management, 8(10), 108-129. https://doi.org/10.5281/zenodo.17767584 © 2025 GLOBAL PUBLICATION HOUSE | International Journal of Business Management 2.2. Theory of Planned Behavior and Technology Acceptance Model 2.2.1. Theory of Planned Behavior (TPB) TPB is one of the foundational behavioral theories that explains why individuals perform or do not perform a particular behavior. According to Ajzen (1991), an individual’s behavior is influenced by three main factors: (1) Attitude Toward Behavior (ATT) Attitude reflects the degree to which an individual perceives a behavior as favorable or unfavorable. In other words, if a person believes that performing a behavior will lead to positive outcomes, they are more likely to develop a positive attitude and be willing to act. In the context of this study, attitude toward using AI in stock price forecasting represents the extent to which individual investors feel interested, confident, or willing to incorporate AI tools into their investment activities. When investors perceive that AI helps them analyze faster, reduce errors, and enhance decision-making efficiency, a positive attitude is reinforced, thereby promoting their intention to use it (Belanche et al., 2019; Khuong et al., 2022). (2) Subjective Norm (SN) Subjective norm refers to the social pressure or influence from people around an individual (family, friends, colleagues, experts) that the individual perceives when considering whether to perform a behavior (Fishbein & Ajzen, 2009). This factor reflects the belief that ―others expect me to do something.‖ In the context of stock price forecasting, SN plays a particularly important role because users often consult communities, forums, or investment experts before adopting new tools such as AI (Belanche et al., 2019). For example, if most investors in one’s network trust and use AI platforms for stock prediction, that individual is likely to be positively influenced and develop a stronger intention to adopt similar technology. (3) Perceived Behavioral Control (PBC) PBC reflects an individual’s perception of their ability to control the performance of a behavior—this includes personal capability, resource conditions (time, skills, finances), and external support (Ajzen, 1991). If investors feel they lack technological knowledge, do not understand the algorithms, or lack the necessary tools, low PBC will lead to a weaker intention to use AI (Chang et al., 2015). Conversely, when users feel confident in their data analysis skills and understand how AI operates, they are more likely to take the initiative in adopting AI. 2.2.2. Technology Acceptance Model – TAM This study examines the intention to use AI through the Technology Acceptance Model (TAM). TAM was developed by Fred Davis (1989), based on the theoretical foundation of the Theory of Reasoned Action (TRA) by Ajzen and Fishbein (1975). The Page No. 111 Study on Individual Investors’ Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach Volume 8 Issue No 10 (2025) Access: https://gphjournal.org/index.php/bm model aims to explain the factors that influence the decision to adopt and use a new technology. According to Davis (1989), TAM focuses on two core constructs: (1) Perceived Usefulness (PU): This refers to the degree to which users believe that using a technological system will improve their job performance, solve problems, or help them achieve desired outcomes. In this study, PU is measured through AI’s ability to generate higher returns and enhance portfolion management. The usefulness of AI is reinforced by its demonstrated superior performance. Algorithmic alpha-seeking models have been shown to outperform traditional methods in improving average investment returns. PU of AI in investment is formed through three key dimensions: - Big Data Analytical Capability: The ability to process large volumes of raw and unstructured data far beyond the capacity of an individual investor. - Accurate Prediction: The use of deep learning and hybrid AI models enables analysis of historical data, market trends, and macroeconomic factors to forecast future stock prices. - Economic and Convenience Benefits: Perceived benefits (PB), including economic benefits (EB) and convenience (CV), act as strong motivational drivers (Khuong, N.V., et al., 2022). (2) Perceived Ease of Use (PEOU): This factor measures the extent to which users feel that using a technological system is easy and does not require excessive effort. When a technology is designed to be intuitive and user-friendly, the likelihood of user acceptance increases significantly. Figure 1. Technology Acceptance Model - TAM Source: Fred Davis (1989) Le, T. K. N., & Vuong, G. K. (2025). Study on Individual Investors’ Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach. GPH-International Journal of Business Management, 8(10), 108-129. https://doi.org/10.5281/zenodo.17767584 © 2025 GLOBAL PUBLICATION HOUSE | International Journal of Business Management According to TAM, if users believe that a technology provides value and is easy to use, they will be more inclined to adopt it. In FinTech, PEOU depends on the quality of the user experience (UX) and user interface (UI), requiring firms to prioritize investment in design and usability testing. However, for predictive AI, PEOU is not limited merely to interface simplicity (Park & Yoon, 2025). 2.3. Proposed Research Model Investment in the stock market can only be conducted when individuals meet the necessary legal requirements and possess basic financial capacity. Therefore, the factor PBC (Perceived Behavioral Control) no longer plays a decisive role in the intention to use AI, because: (i) Behavioral conditions are already inherently ensured for individual investors; (ii) Technological capability factors are indirectly captured through the variable Perceived Ease of Use (PEOU) in the TAM model (Davis, 1989; Venkatesh & Davis, 2000). Thus, this study excludes PBC from the extended TPB model in order to focus on cognitive, psychological, and social factors that align more closely with the behavioral characteristics of qualified individual investors. On the other hand, given ongoing debates surrounding AI regarding accuracy, transparency, and risk, the construct of Trust is a critical determinant of user decision-making (Gefen et al., 2003). AI models employ complex algorithms that investors may find difficult to understand, making it challenging to interpret how AI generates stock price forecasts (Park & Yoon, 2025). Therefore, this study incorporates the factor “Trust.” Trust is a crucial mediating element in the acceptance of AI for stock price prediction. Trust in AI is often measured through dimensions such as competence, reliability, and transparency. Specialized measurement tools, such as the Trust in Automation Scale, are used to quantify users’ trust in specific systems (Maier, T., et al, 2022). In investment contexts, Trust is strengthened by Performance. The improvement in investment outcomes achieved through AI-based price forecasting compared with traditional valuation methods is considered a core driver of Trust. Through demonstrated superior performance, AI systems can validate the added value they provide, thereby establishing confidence in their predictive capability. This trust becomes a critical bridge that directly influences the intention to use AI in stock price forecasting (Maier et al., 2022). Page No. 113 Study on Individual Investors’ Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach Volume 8 Issue No 10 (2025) Access: https://gphjournal.org/index.php/bm The proposed research model is as follows: Figure 2. Proposed Research Model Source: Proposed by the research team The research model illustrated above is interpreted with the following research hypotheses: Hypothesis H1: Attitude toward using AI (ATT) has a positive correlation with the intention to use AI in stock price forecasting (BI). Hypothesis H2: Subjective Norm (SN) has a positive correlation with the intention to use AI in stock price forecasting (BI). Hypothesis H3: Trust (TR) has a positive correlation with the intention to use AI in stock price forecasting (BI). Hypothesis H4: Perceived Usefulness (PU) has a positive correlation with Attitude (ATT). Hypothesis H5: Perceived Ease of Use (PEOU) has a positive correlation with Attitude (ATT). Perceived Usefulness (PU) Perceived Ease of Use (PEOU) Trust (TR) Subjective Norm (SN) Attitude (ATT) Behavioral Intention (BI) to use AI in stock price forecasting Le, T. K. N., & Vuong, G. K. (2025). Study on Individual Investors’ Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach. GPH-International Journal of Business Management, 8(10), 108-129. https://doi.org/10.5281/zenodo.17767584 © 2025 GLOBAL PUBLICATION HOUSE | International Journal of Business Management Table 1. Proposed Measurement Scales Variable Measurement Scales Source 1. Perceived Usefulness (PU): The degree to which an individual believes that using AI in stock price forecasting will provide benefits and achieve personal goals. PU1 AI-based stock price prediction helps me save time in market analysis Venkatesh & Davis (2000) PU2 Using AI in stock price forecasting enhances my decision-making in stock investment PU3 Using AI in stock price forecasting helps me invest more effectively 2. Perceived Ease of Use (PEOU): The degree to which an individual believes that using a technology will not require excessive effort. PEOU 1 I find it easy to learn how to use AI in stock investment Davis (1989) PEOU 2 The AI interface and usage are easy to understand and convenient PEOU 3 I have sufficient skills to use AI applications for stock investment 3. Attitude (ATT) ATT1 I find using AI in stock price forecasting interesting Ajzen (1991) ATT2 I think using AI in stock price forecasting is better than traditional forecasting methods ATT3 Using AI in stock price forecasting is easier than traditional forecasting methods 4. Trust (TR): Confidence in AI’s stock price prediction capabilities TR1 I trust the accuracy of AI tools in stock price forecasting Gefen et al., (2003). TR2 I trust the AI algorithms used in stock market analysis TR3 I believe that using AI in stock price forecasting will yield high investment performance TR4 I feel secure when using AI for stock investment 5. Subjective Norms (SN) SN1 My family supports my use of AI in stock price forecasting Ajzen (1991) SN2 My friends support my use of AI in stock price forecasting SN3 Influential people in the stock market recommend that I use AI in stock price forecasting 6. Behavioral Intention (BI): Intention to use AI in stock price forecasting BI1 I intend to use AI in stock investment in the near future Ajzen (1991), Pavlou (2003) BI2 I plan to use AI more frequently in stock price forecasting BI3 I am willing to recommend friends and family to use AI in stock price forecasting Source: Developed and adapted by the research team Page No. 115 Study on Individual Investors’ Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach Volume 8 Issue No 10 (2025) Access: https://gphjournal.org/index.php/bm 3. Research Methodology - Data Collection Method: The survey was designed using a 5-point Likert scale: 1. Strongly disagree 2. Disagree 3. Neutral 4. Agree 5. Strongly agree After designing the questionnaire, the research team conducted a pilot survey with 10 investors, then refined the measurement scales before implementing the full-scale survey. Data collection was carried out using convenience sampling and the snowball sampling method (i.e., identifying the next respondents based on recommendations from surveyed participants) to ensure an adequate sample size. - Data Analysis Method: A quantitative research approach was employed to process the data collected from individual investors in the Vietnamese stock market. The general structural regression equations are: BI = a*ATT + b*TR+c*SN ATT= d*PU+ e*PEOU SMARTPLS software was used to test hypotheses and assess the impact of the factors. Step 1: Measurement Model Assessment The measurement model was evaluated by examining the quality of observed variables (outer loadings), reliability of the scales (Cronbach’s Alpha), Convergent validity and Discriminant validity. Step 2: Structural Model Assessment Once the measurement model met the required criteria, the structural model was assessed through, path relationships and coefficients, overall determination coefficient (R²), effect size (f²). 4. Research Results 4.1. Descriptive Information of the Sample The survey was conducted with 240 participants, all individual investors active in the Vietnamese stock market. The details of participants’ age, gender, and income are as follows: Le, T. K. N., & Vuong, G. K. (2025). Study on Individual Investors’ Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach. GPH-International Journal of Business Management, 8(10), 108-129. https://doi.org/10.5281/zenodo.17767584 © 2025 GLOBAL PUBLICATION HOUSE | International Journal of Business Management Table 8. Coefficients of Determination (R Square) Explaining the Dependent Variables R Square R Square Adjusted ATT 0,721 0,718 BI 0,810 0,807 Source: Results of the research team’s evaluation The results from Table 7 show that the R² value for the factor Attitude (ATT) is 0.721, with an adjusted R² of 0.718. This indicates that the independent variables explain 72.1% of the variance in ―Attitude‖ (ATT). The R² value for the factor ―Behavioral Intention to Use AI in Stock Price Forecasting‖ (BI) is 0.810, and the adjusted R² is 0.807, which is considered appropriate for this study. Accordingly, the independent variables explain 81% of the variance in ―Behavioral Intention to Use AI in Stock Price Forecasting‖ (BI). 5. Discussion of Findings (1) The Impact of Perceived Ease of Use (PEOU) on Attitude (ATT) The PLS-SEM results show that Perceived Ease of Use (PEOU) has a strong and statistically significant effect on Attitude (ATT) toward using AI in stock price forecasting, with a path coefficient of 0.882 (p < 0.05). This confirms that Hypothesis H5 is accepted and aligns with previous studies by Davis (1989) and Venkatesh & Davis (2000) in the Technology Acceptance Model (TAM). The mean values of the three observed PEOU indicators range from 3.8 to 3.918, all above the midpoint of 3 on the 5-point Likert scale, indicating that the majority of respondents have a positive perception of the convenience and accessibility of AI tools in stock investment. Table 9. Mean Values of the PEOU Scale Code Mean Interpretation PEOU1 3,800 Users find it easy to learn how to use AI in stock investment PEOU2 3,918 The interface and operations if AI applications are perceived as userfriendly and easy to understand. PEOU3 3,800 Users feel confident that they possess sufficent skills to use AI for investment purposes. Source: Research findings The statements regarding learnability (PEOU1), interface convenience and userfriendliness (PEOU2), and users’ confidence in operating the system (PEOU3) all received positive evaluations. This result is consistent with the TAM model (Davis, 1989), which Page No. 123 Study on Individual Investors’ Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach Volume 8 Issue No 10 (2025) Access: https://gphjournal.org/index.php/bm posits that perceived ease of use is a key factor shaping the attitude toward technology acceptance. In the context of an increasingly digitalized financial market, younger investors—who are familiar with technological applications—are more likely to access AI tools and tend to expect AI to help them reduce analysis time and minimize subjective errors. Therefore, the high PEOU scores indicate that AI currently meets investor expectations for convenience, which also explains why PEOU exhibits a strong impact (0.882) on the attitude toward usage (ATT) in the research model. (2) The Influence of Factors on Behavioral Intention (BI) The regression results show that Attitude (ATT), Subjective Norm (SN), and Trust (TR) all exert positive and statistically significant influences on the behavioral intention to use AI in stock price forecasting (BI), as expressed by the following equation: BI = 0.256*ATT + 0.472*SN + 0.242*TR - The Influence of SN on BI The variable Subjective Norm (SN) has the largest coefficient, 0.472, among the three factors, indicating that it has the strongest impact on the intention to use AI (BI). In other words, when the level of support from family, friends, and influential individuals increases, investors’ intention to adopt AI in stock price forecasting increases significantly. This finding aligns with the subjective norm component in TPB/TAM2, which states that the adoption of new technology is strongly influenced by the surrounding social environment. Table 10. Mean Values of the Subjective Norm Variables Code Mean Interpretation SN1 3,747 Family members generally support the respondent’s use of AI in stock price forecasting. SN2 3,865 Friends express supportive attitudes and encourage the use of AI in stock price forecasting. SN3 3,841 Influential market participants (expert, KOLs, well-known investors) recommend the use of AI. Source: Research findings The mean values of all SN observed variables exceed 3.7 (on a 5-point scale), reflecting a generally favorable social environment: investors not only receive support from family and friends but are also ―pulled‖ by recommendations from KOLs and stock market experts. In the context where AI in investment remains relatively new and complex, this result indicates that many individual investors tend to rely on social cues when making decisions, rather than relying solely on their own technical assessments. Le, T. K. N., & Vuong, G. K. (2025). Study on Individual Investors’ Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach. GPH-International Journal of Business Management, 8(10), 108-129. https://doi.org/10.5281/zenodo.17767584 © 2025 GLOBAL PUBLICATION HOUSE | International Journal of Business Management - The Influence of ATT on BI In the regression model, Attitude (ATT) has a coefficient of 0.256 in predicting Behavioral Intention (BI), indicating that this is one of the key factors influencing investors’ decisions to adopt AI. Prior to examining the effect, the research team analyzed respondents’ level of agreement with the three ATT observed variables. Table 11. Mean Values of ATT Variables Code Mean Interpretation ATT1 3,971 Users find the use of AI in stock price forecasting enjoyable. ATT2 3,894 Users believe that using AI delivers better effectiveness compared to traditional forecasting methods. ATT3 3,918 Users perceive AI as easier to use than traditional forecasting approaches. Source: Research findings The results indicate that users’ attitudes toward applying AI in stock price forecasting are generally positive, with an average score of 3.928/5. ATT1 has the highest mean (3.971), reflecting that the sense of enjoyment and novelty is a prominent factor when investors first engage with AI. ATT3 (3.918) and ATT2 (3.894) indicate that most survey participants perceive AI as easy to use and more effective than traditional analytical methods. Overall, these results are consistent with the TAM model (Davis, 1989), where a positive attitude is a necessary condition for forming the intention to use technology. When users perceive AI as both enjoyable, effective, and user-friendly, they are more likely to trust the tool, which in turn increases their willingness to apply it in actual investment activities. - Influence of TR on BI The Trust (TR) factor has a path coefficient of 0.242, indicating a positive and significant influence on investors’ intention to use AI. Although its effect is lower than Subjective Norm (SN) and Attitude (ATT), TR still plays an important role as trust is a core factor in decisions related to new technology. Table 12. Mean Values of TR Variables Code Mean Interpretation TR1 3,547 Users trust the accuracy of AI tools in stock price forecasting. TR2 3,735 Users trust the AI algothrihms used in stock analysis. TR3 3,700 Users expect AI to deliver high investment performance. TR4 3,312 Users’ sense of security when using AI in stock investment is moderate Source: Research findings Page No. 125 Study on Individual Investors’ Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach Volume 8 Issue No 10 (2025) Access: https://gphjournal.org/index.php/bm The results show that TR2 (3.735) and TR3 (3.700) have the highest values, indicating that users evaluate AI positively in terms of analytical capability and performance potential. TR4 (3.312) reflects a noticeably lower level of ―security when using AI‖ compared to the other statements. This suggests that users place more trust in the technical aspects (algorithms, accuracy) than in safety and psychological comfort when applying AI. The lower sense of security reflects concerns about market risk, the transparency of AI models, or limited understanding of how the algorithms operate. (3) Behavioral Intention to Use AI in Stock Price Forecasting (BI) Based on the regression equation: BI = 0.256*ATT + 0.427*SN + 0.242*TR The intention to use AI in stock price forecasting is not only driven by individual needs but is also reinforced by the social environment and the level of trust in the technology, in which: - SN (0.427) is the strongest influencing factor, indicating that investors’ behavior still depends significantly on recommendations from family, friends, the financial community, and KOLs. - ATT (0.256) shows that a positive attitude contributes steadily to the intention to use AI. - TR (0.242) confirms that trust in the accuracy and safety of AI also promotes the decision to adopt this technology. The study results show that the mean values of the three BI observed variables range from 3.859 to 3.929, indicating that the behavioral intention of individual investors to use AI is relatively high. These figures reflect that most survey participants are not only interested in applying AI in the near future but also tend to continue using it and share this experience with others. Table 13. Mean Values of BI Variables Code Mean Interpretation BI1 3,900 Investors intend to use AI in stock price forecasting in the near future BI2 3,859 Investors plan to use AI more frequently in market analysis. BI3 3,929 Investors are willing to recommend AI to family and friends for stock forecasting. Source: Research findings The high scores of the BI variables indicate growing user confidence in the practical benefits of AI in stock investment. Vietnamese investors are beginning to perceive AI as a reliable analytical tool that helps improve information processing speed and reduces decisionmaking pressure in a volatile market. Among the three observed variables, BI3 (3.929) is the highest, reflecting a tendency to share the AI experience with others. BI1 and BI2 are also Le, T. K. N., & Vuong, G. K. (2025). Study on Individual Investors’ Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach. GPH-International Journal of Business Management, 8(10), 108-129. https://doi.org/10.5281/zenodo.17767584 © 2025 GLOBAL PUBLICATION HOUSE | International Journal of Business Management above 3.8, showing that users view AI not just as a short-term interest but as a long-term tool in their investment strategy. (4) Implications from the Research Results From the above results, several practical implications can be drawn for AI tool providers, financial institutions, and technology platform developers to promote sustainable usage behavior among investors. (i) Enhancing trust in technology is crucial because trust (TR) is one of the most significant factors influencing the intention to use AI. Investors are likely to maintain longterm usage only if they feel confident about the accuracy and transparency of the tools. Therefore, developers should prioritize providing clear information on how AI models process data, the sources of input data, prediction accuracy across different market conditions, and the technical limitations of the models. Publishing performance reports and illustrating results through practical examples such as backtesting or case studies can help users understand that predictions are not generated by a ―black box‖ of unknown risk but are built on verified data and algorithms. When investors feel reassured, they are more likely to integrate AI into their investment strategy rather than treat it as a temporary experiment. (ii) Social influence (SN) is the strongest factor in the regression model. This indicates that individual investment behavior is deeply embedded in the community context. Businesses should therefore focus on fostering network effects among users. Building online communities where users can share experiences, discuss strategies, analyze results, and provide recommendations can create psychological reinforcement among investors. Such an environment increases the feeling of being ―accompanied,‖ thereby promoting positive herd behavior toward technology adoption. At the same time, influencer-driven communication campaigns in the financial sector continue to play a critical role in shaping users’ AI adoption behavior. (iii) Enhancing user experience on AI platforms. An intuitive interface, fast processing speed, dynamic charting features, and intelligent alert mechanisms can make investors perceive AI as not only useful but also significantly more convenient than traditional methods. The high score of BI3 indicates users’ willingness to recommend AI to others, showing that positive word-of-mouth is a strong motivator. This opens opportunities for businesses to implement referral programs, incentivizing users to introduce the platform to friends and colleagues. Such initiatives can not only expand the user network but also enhance trust through social confirmation from acquaintances, which is a key factor in personal finance. Page No. 127 Study on Individual Investors’ Intention to Use AI in Stock Price Forecasting on the Vietnamese Stock Market: A Technology Acceptance Model (TAM) Approach Volume 8 Issue No 10 (2025) Access: https://gphjournal.org/index.php/bm 6. Conclusion This study examined the factors influencing individual investors’ intention to use AI in stock price prediction in the Vietnamese stock market, based on the Technology Acceptance Model (TAM). The results indicate that Perceived Ease of Use (PEOU) has a strong effect on Attitude toward AI usage (ATT), with a regression coefficient of ATT = 0.882. The Perceived Usefulness (PU) factor was not statistically significant in the model. This suggests that investors value the convenience and ease of handling AI tools more than the expected benefits in investment performance. The regression model identifies three main factors driving the intention to use AI (BI) in stock price prediction: Attitude (ATT), Subjective Norms (SN), and Trust in technology (TR). Among them, Subjective Norms have the strongest effect (β = 0.472), followed by Attitude (β = 0.256) and Trust (β = 0.242). The study provides empirical evidence on technology acceptance behavior in the financial sector in Vietnam and offers practical implications for AI product development and technology communication targeting individual investors. However, according to the original TAM, PEOU can affect BI both directly and indirectly. In this study, the direct relationship between PEOU and BI was excluded. 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