


































Communication, Society and Media 
ISSN 2576-5388 (Print) ISSN 2576-5396 (Online) 

Vol. 8, No. 2, 2025 

www.scholink.org/ojs/index.php/csm 

10 

 

Original Paper 

Exploration of the Relationship between College Youth’s 

Willingness to Use Large Models and their Behavior: Based on 

the Mediating Effect of Human-Computer Trust 

Fangxin Huang
1* 

1 
Media College of Guizhou University, postgraduate student of the School of Media, Guizhou, Guiyang 

550025, China 

* 
Fangxin Huang, Media College of Guizhou University, postgraduate student of the School of Media, 

Guizhou, Guiyang 550025, China 

 

Received: June 3, 2025         Accepted: June 20, 2025         Online Published: July 10, 2025 

doi:10.22158/csm.v8n2p10                       URL: http://dx.doi.org/10.22158/csm.v8n2p10 

 

Abstract 

As technology advances quickly, large language models are growing fast. College students, who are 

used to digital tools, use these models more and more, but there are still trust problems. 

This study used both surveys and interviews. It built a research model based on human-computer trust 

theory and the SIRAM model to find out what affects college students’ willingness to use these models, 

and how trust plays a middle role. 

Using SPSS 26.0 and AMOS 26.0 to analyze survey data, we found that cognitive and emotional trust 

greatly influence usage intention. Factors like usefulness, enjoyment, sociability, and adaptability also 

affect intention through trust. Interviews looked at how students use the models, trust issues, and ways 

to build trust, helping improve human-computer relationships. 

Keywords 

large models, human-computer trust, SIRAM model, trust crisis 

 

1. Introduction: Human-Computer Trust in the Era of Large Models 

Amid rapid technological advancement, large language models (LLMs) like ChatGPT and Wenxin 

Yiyan have surged, with open-source models from DeepSeek driving innovation. These text-pre-trained 

deep learning models propel AI from perception to creation, profoundly impacting society and 

economy. 

 



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As digital-era pioneers, college youth widely use LLMs, altering their learning and life while facing 

higher digital literacy demands. China regulates such applications via policies like the Interim 

Measures for the Administration of Generative Artificial Intelligence Services, focusing on ideology 

and academic fairness in universities. 

Yet LLMs bring both opportunities and challenges, with prominent human-machine trust crises: 

questionable information accuracy, value coordination complexities, and technical stability concerns, 

threatening youth’s cognition and development. Studying trust mechanisms from a journalism and 

communication perspective holds significant theoretical and practical value, forming the research 

background. Against this backdrop, this study proposes the following research questions to guide 

in-depth exploration: 

Research Question 1 

Does the dimension of human-machine trust exhibit a mediating or moderating effect in the 

decision-making process of youth groups regarding LLM technology use? 

Research Question 2 

What is the current status of university youth’s LLM usage? What trends are presented by the current 

human-machine trust crisis? 

Research Question 3 

What implications can the factor of trust bring to the design and development of future AI products? In 

what direction will future human-machine trust relationships evolve in the LLM era? 

 

2. Literature Review and Theoretical Origin 

2.1 The Intertwined Context of Large Models and Human-Computer Trust 

Large models, as key AI technologies, have drawn wide attention. With growing autonomy, they’ve 

shifted from ―imitating human thinking‖ to ―human-like thinking‖ (Zhang & Ren, 2023), but this has 

also brought a trust crisis. 

Scholars note AI trust is often analyzed through cognitive (perceptions of reliability, ability) and 

emotional (from interaction resonance) trust (Johnson & Grayson, 2005; Cai & Law, 2022). Existing 

studies on LLM trustworthiness mainly focus on the models themselves (Sun, Huang, Wang et al., 2024; 

Alghamdi, Masoud, Alnahit et al., 2024; Hong, Duan, Zhang et al., 2024; Wu & Sun, 2023), while 

fewer explore college youth’s acceptance of large models from human-machine communication and 

trust perspectives, making such exploration valuable. 

2.2 SIRAM Model 

Traditional models like TAM and UTAUT, though widely used to study user behavioral intention, are 

less suitable for new AI acceptance research due to AI’s human-like thinking (Lu, Cai, & Gursoy, 

2019). 



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The Socially Interactive Robots Acceptance Model (SIRAM), built by integrating UTAUT and TAM 

(Shin & Choo, 2011), focuses on factors influencing the acceptance of socially interactive robots, 

including perceived usefulness, enjoyment, sociability, adaptivity, and social presence. It’s more fitting 

for AI acceptance research as it incorporates traits of socially interactive robots. 

Given modern large language models have human-like attributes and rich ―human-oriented‖ value 

(Chen, 2024), this study adopts the SIRAM model, taking perceived usefulness, enjoyment, sociability, 

and adaptivity as independent variables, cognitive and emotional trust as mediators, and usage intention 

as the dependent variable. 

 

 

Figure 1. The SIRAM Model 

 

3. Research Design 

3.1 Research Hypotheses 

Drawing on literature and theories, this study develops a model with the following hypotheses: 

H1: College youth’s cognitive trust in large models positively impacts usage intention. 

H2: Their emotional trust in large models positively impacts usage intention. 

(Shi et al. (Shi, Gong, & Gursoy, 2021) found cognitive and emotional trust significantly influence AI 

adoption willingness) 

H3: Perceived usefulness positively affects usage intention. 

H4: Perceived usefulness positively affects cognitive trust. 

H5: Perceived usefulness positively affects emotional trust. 

(Yoon et al. (Yoon & Rolland, 2015) confirmed perceived usefulness impacts usage intention; Zhou 

(Zhou, Tang, & Xiao, 2021) and Geng (2021) linked it to cognitive/emotional trust) 

H6: Perceived enjoyment positively affects usage intention. 



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H7: Perceived enjoyment positively affects cognitive trust. 

H8: Perceived enjoyment positively affects emotional trust. 

(Cheng and Le (2018) and Lawson (Lawson, Mayer, Adamo-Villani et al., 2021) showed perceived 

enjoyment influences trust) 

H9: Perceived sociability positively affects usage intention. 

H10: Perceived sociability positively affects cognitive trust. 

H11: Perceived sociability positively affects emotional trust. 

(Junglas et al. (Junglas, Goel, Abraham et al., 2013) noted perceived sociability boosts usage intention; 

Chen et al. (Chen & Zhang, 2023) linked human-like interaction to trust) 

H12: Perceived adaptability positively affects usage intention. 

H13: Perceived adaptability positively affects cognitive trust. 

H14: Perceived adaptability positively affects emotional trust. 

(Young et al. (Young, Hawkins, Sharlin, & Igarashi, 2009) highlighted user expectations for 

adaptability; Schneider (Schneider & Kummert, 2021) found adaptability enhances trust) 

H15a-d: Cognitive trust mediates the relationships between (a) perceived usefulness, (b) enjoyment, (c) 

sociability, (d) adaptability and usage intention. 

H16a-d: Emotional trust mediates the relationships between (a) perceived usefulness, (b) enjoyment, (c) 

sociability, (d) adaptability and usage intention. 

(Xu and Liu (2021) identified trust as a key mediator in new technology acceptance) 

These hypotheses form the theoretical model of college youth’s willingness to use large models. 

 

 

Figure 2. Theoretical Model of University Youth Groups’ Intention to Use Large Language Models 

 



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3.2 Questionnaire Design and Data Collection 

The scales used in this study are all derived from mature scales. Combined with the research scenario 

of large models, each variable is operationally defined, and a 5-point Likert scale is adopted for 

scoring. 

There are 447 valid questionnaires in the end, with an effective rate of 86.3%.SPSS 26.0 is used to 

process and analyze the data to verify the model and hypotheses of this study. 

After analyzing the questionnaire data, some respondents are selected for in-depth interviews to 

supplement and explain the questionnaire results. 

 

4. Research Findings and Conclusions 

4.1 Reliability and Validity Test of the Scale 

This study first used SPSS 26.0 for reliability and validity tests. Reliability was assessed via latent 

variables’ Cronbach’s α and composite reliability. All Cronbach’s α values (0.873-0.928) exceeded 

0.7,indicating high questionnaire consistency and good internal structure, suitable for analysis and 

empirical tests. 

 

Table 3. Results of Cronbach’s α Reliability Analysis for the Overall Sample 

Cronbach’s Alpha  Number of Items 

0.878  23 

 

Table 4. Reliability Statistics of the Scale 

Variable Item 

Corrected 

Item-Total 

Correlation 

Cronbach’s Alpha if 

Item Deleted 
Cronbach’s α 

Perceived 

Usefulness 

PU1 0.766 0.852 

0.886 

PU2 0.687 0.871 

PU3 0.69 0.87 

PU4 0.774 0.852 

PU5 0.727 0.864 

Perceived 

Enjoyment 

PE1 0.757 0.793 

0.863 PE2 0.734 0.814 

PE3 0.731 0.818 

Perceived 

Sociality 

PS1 0.716 0.738 

0.83 PS2 0.751 0.702 

PS3 0.605 0.844 



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Perceived 

Adaptability 

PA1 0.78 0.839 

0.887 PA2 0.762 0.854 

PA3 0.796 0.824 

Intention to 

Use 

IT1 0.694 0.791 

0.841 IT2 0.706 0.778 

IT3 0.717 0.769 

Cognitive 

Trust 

CT1 0.755 0.773 

0.856 CT2 0.717 0.81 

CT3 0.716 0.81 

Emotional 

Trust 

ET1 0.798 0.835 

0.891 ET2 0.759 0.869 

ET3 0.804 0.83 

 

Additionally, KMO and Bartlett’s sphericity tests were performed on scale items to determine 

suitability for factor analysis (KMO>0.7 and p<0.05). This study yielded a KMO value of 0.914 and 

Bartlett’s test p-value of 0.001,confirming good validity and suitability for factor analysis. 

 

Table 5. KMO and Bartlett’s Test Table 

KMO 0.914 

Bartlett’s Test of Sphericity 

Approximate Chi-Square 4679.589 

df 247 

P-value 0.000 

 

4.2 Correlation Analysis of Research Variables 

This study first used SPSS 26.0 for reliability and validity tests. Reliability was assessed via latent 

variables’ Cronbach’s α and composite reliability. All Cronbach’s α values (0.873-0.928) exceeded 

0.7,indicating high questionnaire consistency and good internal structure, suitable for analysis and 

empirical tests. 

 

Table 6. Pearson Correlation Analysis Results among Various Dimensions 

Dimension PU PE PS PA ET CT IT 

PU 1       

PE .465** 1      

PS .245** .439** 1     

PA .486** .441** .368** 1    



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ET .319** .456** .436** .413** 1   

CT .487** .367** .315** .465** .427** 1  

IT .473** .478** .416** .519** .574** .526** 1 

**Significant correlation at the 0.01 level(two-tailed). 

 

Data in Table 6 shows all seven variables had significance levels below 0.01, indicating significant 

positive correlations, preliminarily validating the hypotheses. 

4.3 Structural Equation Model Test 

Aiming at the research hypotheses put forward above, this study uses AMOS 26.0 software for 

hypothesis testing. The structural equation model diagram is shown in the figure: 

 

 
 

Most of the fit indices of the structural equation of this study are ideally fitted, and the model 

hypothesis test can be further carried out. 

 



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Table 7. Parameter Estimation Table of the Confirmatory Factor Analysis Model for the Research 

Model 

Statistical Test Index Evaluation Indicators 
Adapted Standards or 

Critical Values 
Test Result Data 

Absolute Fit Index CMIN/DF Value Between 1-3 1.82 

square(X²)Value 
P>0.05(Not reaching a 

significant level) 
0 

RMR Value <0.08 0.05 

RMSEA Value <0.08 0.05 

Incremental Fit Index IFI >0.90 0.95 

TLI >0.90 0.94 

NFI >0.90 0.9 

Parsimonious Fit Index PGFI >0.50 0.71 

PNFI >0.50 0.77 

 

4.4 Model Analysis and Hypothesis Testing 

Hypothesis verification results: 

 

Table 8. Summary Table of Model Path Hypothesis Test Results 

Path Relationship 
Standardized 

Estimate 
S.E. C.R. P Hypothesis 

Supported 

or Not 

Cognitive Trust→Usage Intention 0.224 0.061 3.647 *** H1 Supported 

Affective Trust→Usage Intention 0.2 0.059 3.392 *** H2 Supported 

Perceived Usefulness→Usage 

Intention 
0.145 0.057 2.55 0.011 H3 Supported 

Perceived Usefulness→Cognitive 

Trust 
0.339 0.064 5.009 *** H4 Supported 

Perceived Usefulness→Affective Trust 0.078 0.061 1.284 0.199 H5 
Not 

Supported 

Perceived Enjoyment→Usage 

Intention 
0.124 0.06 2.061 0.039 H6 Supported 

Perceived Enjoyment→Cognitive 

Trust 
0.114 0.069 1.652 0.099 H7 

Not 

Supported 

Perceived Enjoyment→Affective Trust 0.246 0.068 3.635 *** H8 Supported 



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Perceived Sociality→Usage Intention 0.163 0.081 2.028 0.043 H9 Supported 

Perceived Sociality→Cognitive Trust 0.175 0.091 1.918 0.055 H10 
Not 

Supported 

Perceived Sociality→Affective Trust 0.368 0.091 4.031 *** H11 Supported 

Perceived Adaptability→Usage 

Intention 
0.119 0.057 2.105 0.035 H12 Supported 

Perceived Adaptability→Cognitive 

Trust 
0.231 0.065 3.535 *** H13 Supported 

Perceived Adaptability→Affective 

Trust 
0.165 0.063 2.63 0.009 H14 Supported 

 

4.5 Mediating Effect Analysis 

This study used AMOS 26.0’s Bootstrap method to test cognitive and emotional trust as mediators 

between perceived factors (usefulness, enjoyment, sociability, adaptability) and behavioral intention: 

cognitive trust mediated perceived usefulness (95%CI [0.022,0.134], p<0.05) and adaptability (95%CI 

[0.010,0.109], p<0.05)→H15a, H15d supported; it did not mediate enjoyment (CI[-0.014,0.083]) or 

sociability (CI[-0.008,0.105])→H15b, H15c not supported; emotional trust mediated enjoyment 

(CI[0.010,0.106]), sociability (CI[0.018,0.152]), and adaptability (CI[0.001,0.081], all p<0.05)→H16b, 

H16c, H16d supported; it did not mediate usefulness (CI[-0.013,0.050])→H16a not supported. 

 

Table 9. Results of Standardized Bootstrapping Mediation Effect Tests 

Hypothesis Hypothesized Path Estimate SE Lower Upper P Test Result 

H15a 

Perceived 

Usefulness→Cognitive 

Trust→Usage Intention 

0.072 0.028 0.022 0.134 0.003 Supported 

H15b 

Perceived 

Enjoyment→Cognitive 

Trust→Usage Intention 

0.026 0.024 -0.014 0.083 0.216 
Not 

Supported 

H15c 
Perceived Sociality→Cognitive 

Trust→Usage Intention 
0.039 0.029 -0.008 0.105 0.096 

Not 

Supported 

H15d 

Perceived 

Adaptability→Cognitive 

Trust→Usage Intention 

0.052 0.025 0.01 0.109 0.006 Supported 

H16a Perceived 0.016 0.016 -0.013 0.05 0.272 Not 



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Usefulness→Affective 

Trust→Usage Intention 

Supported 

H16b 

Perceived 

Enjoyment→Affective 

Trust→Usage Intention 

0.049 0.025 0.01 0.106 0.009 Supported 

H16c 
Perceived Sociality→Affective 

Trust→Usage Intention 
0.074 0.035 0.018 0.152 0.006 Supported 

H16d 

Perceived 

Adaptability→Affective 

Trust→Usage Intention 

0.033 0.021 0.001 0.081 0.039 Supported 

 

Empirical results: Cognitive and emotional trust directly impact college youth’s usage intention. 

Perceived usefulness affects usage intention and cognitive trust (not emotional trust); enjoyment and 

sociability affect usage intention and emotional trust (not cognitive trust); adaptability impacts all three. 

Cognitive trust mediates usefulness, adaptability and usage intention (not enjoyment, sociability); 

emotional trust mediates enjoyment, sociability, adaptability and usage intention (not usefulness). 

Interviews supplemented quantitative findings. 

4.6 Analysis of the Current Situation of College Youth Using Large Models 

Trained on massive text, large models aid college students across fields. Most use them to boost 

learning efficiency, with trust in capabilities correlating to usage willingness.F2 cited their 

comprehensive functions; F4 noted faster material organization for economics assignments; M8 called 

them a ―knowledge treasure trove‖ for interdisciplinary insights. F1 found combined use of models like 

DeepSeek and Wenxin Yiyan enhances reliability and trust. 

Human-like interaction fosters emotional trust, boosting usage intent.M9 tried ChatGPT out of curiosity, 

impressed by its speed and accuracy. F3 felt its warmth: ―It comforted me when I was down‖. F5 said, 

―It feels like chatting with a real person—I get emotionally invested‖. 

Yet trust crises exist: M9 encountered historical timeline errors (hallucinations); M10 criticized 

utilitarian ethical views (value bias); F3 faced crashes (technical instability). Privacy concerns also 

erode trust. 

To build long-term trust, students need better digital literacy; developers must reduce errors, calibrate 

values, and ensure security. Large models are tools—harmonious human-machine coexistence requires 

collaboration. 

 

 

 



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5. Conclusion 

This study adopts a combined qualitative and quantitative research method, constructing an empirical 

model based on the human-computer trust theory and the SIRAM model to explore the driving factors 

influencing college youth’s willingness and behavior to use large models, as well as the mediating 

effect of trust dimensions. Data were collected through questionnaires and analyzed using SPSS 26.0 

and AMOS 26.0.It was found that cognitive trust and emotional trust significantly affect college 

youth’s intention to use large models, while factors such as perceived usefulness, enjoyment, sociability, 

and adaptability indirectly influence usage intention by acting on trust. Additionally, through in-depth 

interviews, this study analyzed the current situation of college youth’s use of large models, providing 

theoretical and practical references for optimizing the application of large models and building 

harmonious human-computer trust relationships. 

Large models, as tools to assist humans in exploring the world and creating value, cannot replace 

human thinking and creativity. As the backbone of future social development, college youth should 

maintain a rational attitude when using large models, give full play to their subjective initiative, 

carefully identify and reasonably use the content generated by the models. Only by achieving 

collaborative cooperation with large models—utilizing their powerful computing and generation 

capabilities to expand cognitive boundaries while relying on one’s own wisdom to ensure the accuracy 

of information and the correctness of value orientation—can a good human-computer relationship be 

gradually established, moving towards a harmonious future of human-machine coexistence. 

 

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