Factor Analysis Model Research Articles

스크린골프 서비스품질과 인지적, 감정적 반응에 관한 태도 관계

The purpose of this study was to investigate relationship among screen golf service quality, cognitive and emotional responses on attitudes. The study recruited participants who had experience participating in screen golf facilities as the target population. Surveys were distributed primarily at screen golf facilities located in the capital area of Seoul and Gyeonggi province. A total surveys of 294 were used for analysis. The data processing methods included exploratory factor analysis, confirmatory factor analysis, reliability analysis, frequency analysis, correlation analysis, and structural equation modeling. Based on the above results, the following conclusions are suggested. First, it was found that the physical environment aspect of screen golf facilities' service quality influences cognitive responses. Second, the physical environment aspect of screen golf facilities' service quality influences emotional responses. Third, cognitive responses were found to influence attitudes. Screen golf facilities should devise measures to improve the physical environment aspect of service quality.

Sustainable Transport in the European Union: Exploring the Net-Zero Transition through Confirmatory Factor Analysis and Gaussian Graphical Modeling

The sustainability of the transport sector is targeted by various policies adopted by the European Union, and their impact must be constantly monitored in order to maximize the desired objective. This paper, through a two-stage investigation, aims to present a systemic approach of the sustainability dimensions in transport and to introduce an innovative technique to analyze the interdependencies between them. In the first stage, relevant indicators were selected from the Eurostat database for the content of four dimensions: economic, environmental, social and technological. The robustness of the developed dimensions was assessed and validated through a confirmatory factor analysis. In the second stage, a Gaussian graphical model was estimated as a technique integrating graphical and statistical modeling to identify complex structures of linkages between variables (as components of each dimension of sustainability). The structure of the network clearly highlights the dependence of transport on fossil fuel consumption as the main determinant of pollution in the sector (CO2 emissions). In addition, the central role of railways in decarbonizing transport is highlighted, in contrast to the limited, and isolated at one end of the network, role of electric vehicles. The findings support that affordability of this new technology plays an important role in its impact on zero-emission transition. Concentrating on the period 2013–2022, at EU27 level, the results are relevant in the context of decarbonization policies, offering useful insights both for future research and policy makers.

프로축구의 CSR활동, 구단동일시, 관람만족도와 충성도 간의 구조적 관계 분석

This study investigated the relationships among corporate social responsibility (CSR) activities within K-League(South Korea’s professional football league), team identification, spectator satisfaction, and team loyalty. To achieve this objective, a survey was conducted targeting individuals aged 20 and above, employing the primary data collection method of gathering a total of 251 questionnaires through non-probability convenience sampling. Data analysis was conducted using SPSS 27.0 and AMOS 20.0, involving techniques such as frequency analysis, reliability analysis, confirmatory factor analysis, correlation analysis, and structural equation modeling. The key findings were summarized as follows: Firstly, CSR activities within K-League significantly influence team identification. Secondly, team identification significantly impacts spectator satisfaction. Thirdly, team identification influences team loyalty. Lastly, spectator satisfaction does not affect team loyalty. This research not only presented the directionality of CSR activities within the K-League but also provided valuable theoretical support and data for the K-League aiming to implement CSR activities.

Factors affecting brand attachment in the social media channels: Case of university in Vietnam

Originated from the reason that Hanoi University of Industry (HaUI)’s current social media network is still limited, specifically HaUI's Fanpage has not yet been green-checked, thereby leading to weak brand attachment have little impact on university admissions. From collecting data from information sources such as newspapers, social networking sites, the author has inherited previous studies to propose a research model and conduct quantitative research analysis. Through designing a survey questionnaire and randomly sending it to individuals who are studying and working at HaUI, specifically lecturers, staff and especially the university students. The study obtained 338 valid responses that were coded and cleaned using SPSS and AMOS software. After the cleaning and coding step, reliability testing, exploratory factor analysis, confirmatory factor analysis and structural equation modelling were conducted. The research has made a significant theoretical contribution in terms of the important mediation role of brand awareness, perceived quality and university reputation in the relationship between social media communication and brand attachment. From there, it also provided the practical contribution to improve the association of the brand with students, to perfectionate the social networking sites,to share positive things, interesting, attractive and relevant lessons.

Make a Charitable Birthday Wish on Facebook: An Extended Theory of Planned Behavior

ABSTRACT Social media fundraising has become an important tool for nonprofits to raise money. Focusing on one specific fundraising tool, Facebook birthday fundraiser, the current study developed an extended Theory of Planned Behavior (TPB) model and included three external variables, relationship strength with the fundraiser creator, perceived risk regarding personal information safety, and personal relevance of the fundraising cause. An online survey was conducted to investigate their influences on Facebook users’ donation intention and behaviors. Confirmatory factor analysis and structural equation modeling were conducted, and hypothetical correlations were tested. Results showed that relationship strength significantly influenced subjective norms, attitude toward the fundraiser, self-efficacy, and perceived behavior control. Perceived risk positively influenced subjective norms. Personal relevance positively affected donation intention. Relationships among factors from previous TBP studies were also tested and verified. Implications and limitations of the study were discussed.

COVID-19 experiences and cruise booking behavior: Insights from YouTube viewers applying revised protection motivation theory

The purpose of this study is to examine the relationship between risk perceptions (crowding risk perception, health risk perception) and self-protective behavioral intentions and booking intention for COVID-19 using the theory of protection motivation previously verified in the study and to analyze the structural relationship between all variables. In particular, it was intended to investigate the specific impact relationship between COVID-19 risk perception, including crowding risk perception and health risk perception, and self-protective behavior and cruise booking intention. The survey was conducted on subscribers of travel YouTube channels with cruise video content, and the respondents actively responded by providing convenience store beverage coupons. In an online survey conducted for a month from January 15, 2023, 312 samples were collected from travelers who have experienced cruises in the past five years. SPSS and AMOS statistical programs were used, and first, frequency analysis and discriminant validity analysis were verified. In addition, the verification of structural relevance between variables was analyzed by the covariance-based structural equation modeling (CB-SEM). Demographic characteristics, confirmatory factor analysis, correlation analysis, and structural model analysis verified the hypothesis. Based on the results of the analysis, this study presents basic evidence useful for establishing management strategies for cruise companies.

Receptivity to Instructional Computer-Based Feedback (RIF)

Abstract: Feedback can play a pivotal role in learning but is only effective when students use it. However, the use of feedback varies greatly from student to student and these differences must be explained. Hence, the construct receptivity to instructional feedback (RIF) was introduced to assess students’ attitudes towards and engagement with human-provided feedback in multiple English-speaking countries. However, RIF scales have not yet been used in computer-based assessment contexts nor in German-speaking countries; their application is thus limited. This study examined an adapted and translated RIF scale using confirmatory factor analysis (CFA) and exploratory structural equation modeling (ESEM). We capitalized on data sets comprising German-speaking university ( N = 464) and secondary school ( N = 1,207) students. The bifactor-CFA model showed a good model fit and revealed a general receptivity factor alongside the four specific factors: experiential attitudes, instrumental attitudes, cognitive engagement, and behavioral engagement with feedback. Scalar measurement invariance allowed for cross-gender and educational level comparisons. The relationships with broader personality characteristics were consistent with other contexts, thus indicating discriminant validity. General receptivity, cognitive engagement, and experiential attitudes positively correlated with GPA, suggesting convergent validity. Results support the stability of the RIF scale’s adaptation and translation. Assessment implications are discussed.

Rural tourism in India and pro-tourism behavior

Purpose This paper aims to understand tourist behavior in Indian rural tourism by integrating the stimuli-organism-response (SOR) framework, service quality model (SERVQUAL) model and theory of planned behavior. It investigates the role of positive tourist-host interactions, destination image, trust and attitude in shaping pro-tourism intentions. Additionally, it explores the moderating effect of Indian traditionality on these relationships. Design/methodology/approach This study uses a mixed-methods approach, combining qualitative data from 15 respondents with quantitative analysis involving 580 tourists engaged in Indian rural tourism. Confirmatory factor analysis and structural equation modeling are used to examine relationships among tourist-host interactions, destination image, trust, attitude and pro-tourism intentions. Findings The study reveals that positive interactions and a positive destination image play crucial roles in building trust and shaping favorable attitudes toward rural tourism. Trust and attitude, in turn, significantly drive pro-tourism intentions. Furthermore, tourist attitude partially mediates the relationship between trust and intentions. The study introduces the novel concept of Indian traditionality as a moderator, highlighting its impact on trust, attitude and pro-tourism intentions. Research limitations/implications Limitations include the focus on Indian rural tourism, which may limit generalizability, and potential response bias due to self-reported data. Future research could explore other cultural contexts and use diverse data collection methods. Practical implications This research offers practical insights for policymakers and stakeholders, emphasizing the importance of fostering positive tourist–host interactions and cultivating a positive destination image to enhance the overall tourist experience and promote sustainable rural tourism practices. Originality/value This study’s innovation lies in harmonizing theoretical frameworks to shed light on Indian rural tourism’s multifaceted aspects. It introduces Indian traditionality as a cultural moderator, enriching understanding of tourist behavior. The research contributes to academia and industry by guiding the development of sustainable tourism practices and improving the tourist experience.

Crafting a Job among Chinese Employees: The Role of Empowering Leadership and the Links to Work-Related Outcomes.

The present study aims to examine the process through which empowering leadership shapes employees' work engagement and in-role performance by facilitating job-crafting behaviors, specifically seeking resources, seeking challenges, and reducing demands. Based on the extensive data from 733 Chinese employees across various organizations located predominantly in Chongqing and Xi'an, China, we carried out different types of statistical analysis such as confirmatory factor analysis (CFA) and structural equation modeling (SEM) to examine the relationships among empowering leadership, specific job-crafting behaviors, work engagement and in-role performance, test our hypothesis and our conceptual model. The results from structural equation modeling (SEM) suggested that empowering leadership was positively related to employees' work engagement and in-role performance; empowering leadership was positively related to employees' job crafting (seeking resources, seeking challenges and reducing demands); seeking resources, seeking challenges and reducing demands were positively related to in-role performance, and seeking challenges and reducing demands were positively related to work engagement. In the relationship between empowering leadership and in-role performance, seeking resources serves as a mediating factor. Similarly, seeking challenges mediates the association between empowering leadership and both work engagement and in-role performance. Furthermore, reducing demands mediates the links between empowering leadership and both work engagement and in-role performance. The implications of these findings are subsequently discussed.

GA-BP neural network-based nonlinear regression model for machining errors of compressor blades

Given that blade machining errors can substantially degrade aeroengine performance and reliability, there is a critical imperative to implement stringent control over blade machining errors during the manufacturing process. Nevertheless, the prevailing complexity surrounding the numerous machining error types and their obfuscated interrelationships impedes the comprehensive control of blade profile errors. Consequently, unveiling the nonlinear relationships between the distinct machining errors represents a task of paramount importance. To uncover these nonlinear relationships, this work pioneers the application of factor analysis and Genetic Algorithm-optimized BP neural networks to enable nonlinear regression for diverse blade machining errors. Specifically, this paper first constructs an orthogonal factor analysis model to reduce the dimensionality of complex, high-dimensional blade machining error data and extract underlying correlations. This analysis reveals three distinct error groups, each dominated by a latent common factor. Building on these factor analysis outputs, GA-BP neural networks are then leveraged to perform nonlinear regression for intra-group errors. Guided by the Maximal Information Coefficients (MIC) between different errors, we identify control error types within each group to serve as network inputs, with other errors designated as outputs. To prevent overfitting and accelerate convergence of the neural network, Monte Carlo method is used to augment the limited raw error dataset. The augmented data trains the GA-BP network, establishing multivariate nonlinear models with control errors as independent variables and other errors as dependent variables. The model accuracy is validated by a separate validation set. The comparisons between the predicted and true values in the validation set reveal that the relative errors are around 5 %, indicating that the models achieve an accuracy of approximately 95 %. These results demonstrate the satisfactory performance of the established models. Overall, this work elucidates previously unclear relationships between distinct blade machining errors. These new insights establish the foundation for comprehensive error control and will directly benefit blade design, manufacturing, and performance.

An integrated model combining BERT and tree-augmented naive Bayes for analyzing risk factors of construction accident

PurposeLearning from past construction accident reports is critical to reducing their occurrence. Digital technology provides feasibility for extracting risk factors from unstructured reports, but there are few related studies, and there is a limitation that textual contextual information cannot be considered during extraction, which tends to miss some important factors. Meanwhile, further analysis, assessment and control for the extracted factors are lacking. This paper aims to explore an integrated model that combines the advantages of multiple digital technologies to effectively solve the above problems.Design/methodology/approachA total of 1000 construction accident reports from Chinese government websites were used as the dataset of this paper. After text pre-processing, the risk factors related to accident causes were extracted using KeyBERT, and the accident texts were encoded into structured data. Tree-augmented naive (TAN) Bayes was used to learn the data and construct a visualized risk analysis network for construction accidents.Findings The use of KeyBERT successfully considered the textual contextual information, prompting the extracted risk factors to be more complete. The integrated TAN successfully further explored construction risk factors from multiple perspectives, including the identification of key risk factors, the coupling analysis of risk factors and the troubleshooting method of accident risk source. The area under curve (AUC) value of the model reaches up to 0.938 after 10-fold cross-validation, indicating good performance.Originality/value This paper presents a new machine-assisted integrated model for accident report mining and risk factor analysis, and the research findings can provide theoretical and practical support for accident safety management.

Identification of risk key factors and prevention strategies for collision accidents between merchant and fishing vessels in China waters based on complex network

In recent years, Collision Accidents between Merchant and Fishing Vessels (CAMF) in China waters have been increasing, resulting in severe casualties and property losses. To effectively prevent such frequent collision accidents and ensure the safety of waterways and navigation routes, this study aims to identify the key risk factors associated with CAMF. Firstly, the Human Factors Analysis and Classification System (HFACS) model is employed as a comprehensive framework to extract the risk factors contributing to CAMF. By introducing edge length as the weight of network edges, a directed weighted network of CAMF is constructed. Secondly, the overall topological characteristics of the network are analyzed, and the network's robustness is studied based on the degree centrality, weighted closeness centrality, and weighted betweenness centrality of the risk factors in the network, and the key factors contributing to the accidents are identified. The research findings indicate that the network exhibits evident scale-free characteristics and small-world network properties. Deliberate attacks on approximately top 15% of the risk factors lead to a 55% degradation in the global efficiency of the accident network. Ultimately, accident prevention and control strategies and recommendations are proposed from the perspectives of both merchant and fishing vessels.

Relationship between morning blood pressure variability and cerebral microbleed burden in patients with hypertension.

Cerebral microbleeds (CMBs) can be understood as a type of target organ damage caused by hypertension. We aimed to explore the association of the CMB burden with morning blood pressure (BP) variability in patients with hypertension. We divided patients with hypertension into two groups: a group with 1-10 CMBs and a group with more than 10 CMBs. The duration, grade, medication, and control of hypertension were recorded in all patients. Morning home BP measurements were performed every 3 days for a month. A total of 791 patients were recruited. Full factor model analysis showed that higher morning home diastolic BP variability (standard deviation [SD], OR=1.080, 95% CI: 1.024-1.140, P=.005; coefficient of variation [CV], OR=1.076, 95% CI: 1.028-1.128, P=.002) was associated with more than 10 CMBs. Morning home systolic and diastolic blood pressure variability (SD, CV, average real variability) in more than 10 non-lobar CMBs group was significantly higher than that in 1-10 non-lobar CMBs group (P<.05).The multivariate analysis showed higher morning home diastolic blood pressure variability (SD, OR=1.124, 95% CI: 1.031-1.224, P=.008; CV, OR=1.099, 95% CI: 1.019-1.186, P=.015; average real variability, OR=1.055, 95% CI: 0.995-1.120, P=.075) was associated with more than 10 non-lobar CMBs. There was no significant relationship between morning home systolic blood pressure variability and more than 10 non-lobar CMBs (P>.05). Higher morning home diastolic blood pressure variability was associated with more than 10 CMBs and more than 10 non-lobar CMBs.

Agree to agree: correcting acquiescence bias in the case of fully unbalanced scales with application to UK measurements of political beliefs

AbstractA methodologically important area in political science is measuring the ideology of voters. This task can be difficult, and researchers often rely on ‘off the shelf’ datasets. Many of these datasets contain unbalanced Likert scales, which risk acquiescence bias. This paper proposes a strategy for dealing with this issue. I first demonstrate using two comparable datasets from the UK how unbalanced scales produce distorted distributions and can affect regression results. Then, building on past research that utilises factor analysis to eliminate the influence of acquiescence bias, I demonstrate how researchers can utilise a person intercept confirmatory factor analysis model to obtain factor scores corrected for acquiescence in the case of fully unbalanced scales. I conclude with practical recommendations for researchers and survey designers moving forward.

Factors associated with antiretroviral treatment adherence among people living with HIV in Guangdong Province, China: a cross sectional analysis

BackgroundUnderstanding factors associated with antiretroviral treatment (ART) adherence is crucial for ART success among people living with HIV (PLHIV) in the “test and treat” era. Multiple psychosocial factors tend to coexist and have a syndemic effect on ART adherence. We aimed to explore factors associated with ART adherence and the syndemic effect of multiple psychosocial factors on ART adherence among PLHIV newly starting ART in Guangdong Province, China.MethodsNewly diagnosed PLHIV from six cities in Guangdong Province were recruited between May 2018 and June 2019, and then followed up from May 2019 to August 2020. Baseline and follow-up data were collected from a questionnaire and the national HIV surveillance system, the follow-up data of which were analyzed in this study. A Center for Adherence Support Evaluation (CASE) index > 10 points was defined as optimal ART adherence, which was measured via participants’ self-reported adherence during follow-up survey. Multivariable logistic regression was used to identify factors associated with ART adherence. Exploratory factor analysis (EFA) and multi-order latent variable structural equation modeling (SEM) were performed to explore the syndemic effect of multiple psychosocial factors on ART adherence.ResultsA total of 734 (68.53%) follow-up participants were finally included in this study among the 1071 baseline participants, of whom 91.28% (670/734) had self-reported optimal ART adherence. Unemployment (aOR = 1.75, 95%CI: 1.01–3.02), no medication reminder (aOR = 2.28, 95%CI: 1.09–4.74), low medication self-efficacy (aOR = 2.28, 95%CI: 1.27–4.10), low social cohesion (aOR = 1.82, 95%CI: 1.03–3.19), no social participation (aOR = 5.65, 95%CI: 1.71–18.63), and ART side effects (aOR = 0.46, 95%CI: 0.26–0.81) were barriers to optimal ART adherence. The EFA and second-order latent variable SEM showed a linear relationship (standardized coefficient = 0.43, P < 0.001) between ART adherence and the latent psychosocial (syndemic) factor, which consisted of the three latent factors of medication beliefs and self-efficacy (standardized coefficient = 0.65, P < 0.001), supportive environment (standardized coefficient = 0.50, P < 0.001), and negative emotions (standardized coefficient=-0.38, P < 0.01). The latent factors of medication beliefs and self-efficacy, supportive environment, and negative emotions explained 42.3%, 25.3%, and 14.1% of the variance in the latent psychosocial factor, respectively.ConclusionsAbout nine out of ten PLHIV on ART in Guangdong Province self-reported optimal ART adherence. However, more efforts should be made to address barriers to optimal ART adherence.

Factors influencing green energy consumer behaviour in Australia

This research explores the factors influencing green energy consumer behaviour in Australia by examining Australian consumers' attitudes towards green energy from the Green Perceived Value (GPV) perspective, which integrates functional, emotional, conditional, and social dimensions. The data was collected through a survey of 380 participants on Amazon Mechanical Turk. This study employs factor analysis and structural equation modelling to report positive attitudes towards green energy. The positive perception is notably centred on its practical benefits, superior quality, and environmental advantages. These findings suggest that marketing strategies should highlight green energy's functional and environmental benefits to encourage consumer adoption. The study underscores the necessity of targeted educational campaigns aimed at younger demographics through digital media and the importance of emphasising green energy's economic and environmental benefits. Also, it recommends leveraging social and emotional value messaging and fostering community engagement to enhance the attractiveness of green energy solutions. Contributing to the discourse on green energy adoption in Australia, this research advocates for inclusive policies that ensure the accessibility and affordability of green energy for a diverse demographic spectrum. It calls for expanded research to further explore the GPV dimensions and include potential consumers in different cultural contexts. It offers a comprehensive framework to support strategies for increasing green energy adoption that are aligned with sustainability objectives.

Psychological factors influencing consumer intentions to consume cultured meat, fish and dairy

This study investigates the structure of factors that influence consumer intentions to both try and to consume cultured proteins, and their intentions to substitute vegan, vegetarian and omnivore diets with these alternative protein sources. Comprehensive survey data (N = 3862) was collected from three Nordic countries (Denmark, Finland, and Norway) and analysed using confirmatory factor analysis and structural equation modelling. Theoretically, this article draws from behavioural models of environmental psychology, identity theory, and attitude theory. Results indicate that beliefs about the necessity of an industry producing cultured proteins and impacts of cultured proteins on the global economy are significant predictors of consumer intentions. Moreover, participants who exhibited high levels of general and food innovativeness were more likely to express positive intentions to consume cultured proteins. Social norms influenced consumer intentions: Individuals surrounded by positive attitudes and intentions toward cultured proteins within their social networks were more inclined to want to consume these products. The predictor variables in the final model accounted for between 39% and 66% of the variance in the different cultured proteins related intentions. Understanding consumer intentions better can inform targeted communication strategies aimed at promoting the advantages of cultured proteins and facilitating its adoption.

Understanding residents' engagement for the protection of urban green spaces by enriching the value‐belief‐norm theory with relational values—A case study of Munich (Germany)

Abstract Urban green spaces (UGS) provide mechanisms through which people connect and interact with each other, strengthening social relationships as well as human‐nature connections, both of high relevance for sustainable development. However, what determines urban residents' engagement in activities for the protection of UGS still lacks a systematic understanding. Our study aims to address this gap by enriching the value‐belief‐norm (VBN) theory with relational values based on a questionnaire (N = 221) among residents engaging in UGS maintenance in the city of Munich (Germany). Exploratory factor analysis and structural equation modelling showed that both individual responsibility and societal responsibility guide urban residents in their actions towards UGS protection. Furthermore, we found a direct impact of biospheric, altruistic and hedonic values on engagement for the protection of UGS. Therefore, this study signals the importance of investing in actively promoting pluralistic values among urban residents as cities today urgently need a reconnection of the human‐nature relationship and UGS stewardship actions. As we further confirm that relational values have impact on the constructs of the VBN theory, we conclude with a plea for recognizing the potential of relational values as enablers of change towards more responsible behaviours towards urban nature. Read the free Plain Language Summary for this article on the Journal blog.

Effects of operators’ strategic approach to local content and in-country spending requirements on financial performance of upstream petroleum sector firms, Ghana

Local content requirements and in-country spending mandates have emerged as crucial tools in the upstream petroleum sector, aiming to distribute socio-economic benefits derived from resource production to the general citizenry within the host economies. However, understanding how operators' strategic responses to these requirements affect firm financial performance remains limited due to data constraints. Consequently, drawing on resource dependence, social exchange, and linkage and spill-over effect theories, this paper develops a conceptual framework to elucidate the relationships among local content requirements, in-country spending, local linkage factors, operators' strategic approaches & business practices, and financial performance, including their implications. To address data limitations, a mixed-methods approach was adopted, combining content analysis of financial reports from 11 global upstream petroleum operators in Ghana and key informant interviews, with survey primary data collected via online questionnaires. This methodological triangulation enabled a comprehensive examination of how operators' strategic responses affect financial performance in the Ghanaian upstream petroleum sector. Analytical techniques used included factor analysis and Partial Least Squares Structural Equation Modeling (PLS-SEM) from SPSS and SmartPLS3, respectively. The study found that while local content requirements and in-country spending mandates do not significantly influence operator strategic approaches or financial performance, local linkage factors have a strong positive effect on strategic approaches, which in turn positively affect financial performance. Furthermore, local linkage factors partially mediate the relationship between local content requirements and strategic approaches, as well as financial performance. These findings underscored the importance of prioritizing investments in local linkage factors to stimulate industrialization and foster cross-sectorial outsourcing opportunities. The study contributes to resource dependence, social exchange, and linkage and spill-over effect theories by highlighting the role of strategic approaches and local linkages in promoting financial performance. It also offers insights for policymakers and industry practitioners, emphasizing the need to enhance competitiveness and capacity within the host economy. Additionally, the study confirms the predictive relevance of the model, indicating its robustness in explaining the dynamics of local content requirements and in-country spending mandates in the petroleum sector.

Exploring the Key Factors Influencing College Students’ Willingness to Use AI Coding Assistant Tools: An Expanded Technology Acceptance Model

The application of artificial intelligence (AI) in programming assistance has garnered researchers’ attention for its potential to reduce learning costs for users, increase work efficiency, and decrease repetitive coding tasks. However, given the novelty of AI Coding Assistant Tools (AICATs), user acceptance is currently limited, and the factors influencing this phenomenon are unclear. This study proposes an expanded model based on the Technology Acceptance Model (TAM) that incorporates the characteristics of AICAT users to explore the key factors affecting college students’ willingness to use AICATs. Utilizing a survey methodology, 303 Chinese participants completed the questionnaire. Factor analysis and Structural Equation Modeling (SEM) results indicate that users’ dependence worry (DW) about AICATs positively affects perceived risk (PR), which in turn negatively impacts perceived usefulness (PU) and perceived ease of use (PEOU), thus reducing user willingness to use. Dependence concerns also negatively impact perceived trust (PT), while PT positively affects PU and PEOU, thereby enhancing willingness to use. Additionally, a user’s self-efficacy (SE) negatively impacts DW and positively affects PEOU. This study discusses the potential significance of these findings and offers suggestions for AICAT developers to foster and promote widespread use.