Mathematical Modeling and Structural Equation Analysis of Acceptance Behavior Intention to AI Medical Diagnosis Systems
This study builds on Davis’ TAM by integrating environmental and psychological variables relevant to AI medical diagnostics. This study developed a mathematical theoretical model called the “AI medical diagnosis-acceptance evaluation model” (AMD-AEM) to better understand acceptance behavior intentio...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/15/2390 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849405848199102464 |
|---|---|
| author | Kai-Chao Yao Sumei Chiang |
| author_facet | Kai-Chao Yao Sumei Chiang |
| author_sort | Kai-Chao Yao |
| collection | DOAJ |
| description | This study builds on Davis’ TAM by integrating environmental and psychological variables relevant to AI medical diagnostics. This study developed a mathematical theoretical model called the “AI medical diagnosis-acceptance evaluation model” (AMD-AEM) to better understand acceptance behavior intention. Using mathematical modeling, we established reflective measurement model indicators and structural equation relationships, where linear structural equations illustrate the interactions among latent variables. In 2025, we collected empirical data from 2380 patients and medical staff who have experience with AI diagnostic systems in teaching hospitals in central Taiwan. Smart PLS 3 was employed to validate the AMD-AEM model. The results reveal that perceived usefulness (PU) and information quality (IQ) are the primary predictors of acceptance behavior intention (ABI). Additionally, perceived ease of use (PE) indirectly influences ABI through PU and attitude toward use (ATU). AI emotional perception (AEP) notably shows a significant positive relationship with ATU, highlighting that warm and positive human–AI interactions are crucial for user acceptance. IQ was identified as a mediating variable, with variance accounted for (VAF) coefficient analysis confirming its complete mediation effect on the path from ATU to ABI. This indicates that information quality enhances user attitudes and directly increases acceptance behavior intention. The AMD-AEM model demonstrates an excellent fit, providing valuable insights for academia and the healthcare industry. |
| format | Article |
| id | doaj-art-c21637fdaa3e48e8a5ff90c9413e28c4 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-c21637fdaa3e48e8a5ff90c9413e28c42025-08-20T03:36:34ZengMDPI AGMathematics2227-73902025-07-011315239010.3390/math13152390Mathematical Modeling and Structural Equation Analysis of Acceptance Behavior Intention to AI Medical Diagnosis SystemsKai-Chao Yao0Sumei Chiang1Department of Industrial Education and Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd., Changhua City 500208, TaiwanDepartment of Industrial Education and Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd., Changhua City 500208, TaiwanThis study builds on Davis’ TAM by integrating environmental and psychological variables relevant to AI medical diagnostics. This study developed a mathematical theoretical model called the “AI medical diagnosis-acceptance evaluation model” (AMD-AEM) to better understand acceptance behavior intention. Using mathematical modeling, we established reflective measurement model indicators and structural equation relationships, where linear structural equations illustrate the interactions among latent variables. In 2025, we collected empirical data from 2380 patients and medical staff who have experience with AI diagnostic systems in teaching hospitals in central Taiwan. Smart PLS 3 was employed to validate the AMD-AEM model. The results reveal that perceived usefulness (PU) and information quality (IQ) are the primary predictors of acceptance behavior intention (ABI). Additionally, perceived ease of use (PE) indirectly influences ABI through PU and attitude toward use (ATU). AI emotional perception (AEP) notably shows a significant positive relationship with ATU, highlighting that warm and positive human–AI interactions are crucial for user acceptance. IQ was identified as a mediating variable, with variance accounted for (VAF) coefficient analysis confirming its complete mediation effect on the path from ATU to ABI. This indicates that information quality enhances user attitudes and directly increases acceptance behavior intention. The AMD-AEM model demonstrates an excellent fit, providing valuable insights for academia and the healthcare industry.https://www.mdpi.com/2227-7390/13/15/2390mathematical modelingpartial least squares SEMAI acceptancemediation analysisTAM |
| spellingShingle | Kai-Chao Yao Sumei Chiang Mathematical Modeling and Structural Equation Analysis of Acceptance Behavior Intention to AI Medical Diagnosis Systems Mathematics mathematical modeling partial least squares SEM AI acceptance mediation analysis TAM |
| title | Mathematical Modeling and Structural Equation Analysis of Acceptance Behavior Intention to AI Medical Diagnosis Systems |
| title_full | Mathematical Modeling and Structural Equation Analysis of Acceptance Behavior Intention to AI Medical Diagnosis Systems |
| title_fullStr | Mathematical Modeling and Structural Equation Analysis of Acceptance Behavior Intention to AI Medical Diagnosis Systems |
| title_full_unstemmed | Mathematical Modeling and Structural Equation Analysis of Acceptance Behavior Intention to AI Medical Diagnosis Systems |
| title_short | Mathematical Modeling and Structural Equation Analysis of Acceptance Behavior Intention to AI Medical Diagnosis Systems |
| title_sort | mathematical modeling and structural equation analysis of acceptance behavior intention to ai medical diagnosis systems |
| topic | mathematical modeling partial least squares SEM AI acceptance mediation analysis TAM |
| url | https://www.mdpi.com/2227-7390/13/15/2390 |
| work_keys_str_mv | AT kaichaoyao mathematicalmodelingandstructuralequationanalysisofacceptancebehaviorintentiontoaimedicaldiagnosissystems AT sumeichiang mathematicalmodelingandstructuralequationanalysisofacceptancebehaviorintentiontoaimedicaldiagnosissystems |