Abstract
With the rapid advancement of technology, sports-related technological products such as wearable smart devices have expanded from professional athletic settings into daily life. This study aims to explore the theoretical foundations of the Technology Acceptance Model (TAM) and the Technology Readiness Index (TRI), to understand the factors influencing college students’ acceptance and use of sports smart wearable devices. A total sample of 428 college students (48.3% male and 51.7% female) participated in this investigation. Data was collected using the Likert five-level scale of the TAM, which was then analyzed using SPSS and AMOS software. The results indicate that perceived usefulness, perceived ease of use, privacy concerns, innovation, curiosity, health needs, and social motivation all positively influence college students’ use of sports smart wearable devices. In addition, the level of technology readiness significantly moderated this process. The results showed that all research hypotheses were supported, and the model exhibited a good fit. This study expands the application boundaries of TAM and TRI in the field of sports health, provides a theoretical basis and practical implications for the product design of smart wearable devices, the reform of physical education in colleges and universities, and market promotion, and contributes to the improvement of college students’ physical health under the goal of Healthy China 2030.
Keywords: Physical activity, Smart wearable devices, Technology acceptance model, Factors
Subject terms: Engineering, Health care, Mathematics and computing, Psychology, Psychology
Introduction
With the rise and maturity of artificial intelligence (AI) technology, as well as the highly prosperous development of the global information technology industry, various industries around the world are moving towards intelligent integration. Sports products are also constantly advancing towards intelligence in this trend, and sports smart wearable devices are also receiving more and more attention1–3. The use of smart wearables in health management is continually expanding and advancing. According to the 2025 Global Fitness Trends Survey report released by the American College of Sports Medicine (ACSM), wearable devices have been ranked first and second on the trend list for six consecutive years4. Wearable devices are one of the main paths to the realization of smart sports. They can not only accurately record the performance of athletes and sports enthusiasts, but also monitor the physical health of users, predict and prevent potential health risks, and bring better sports experiences to sports enthusiasts5,6. Therefore, sports enthusiasts constitute the largest consumer group of smart wearable devices and have strong market acceptance7. Smart wearable devices help users to track their vital signs and activities and record them in the form of impacts and data, as well as provide good communication topics for online communities, which will be a new impetus for sports and fitness enthusiasts to use smart wearable devices to join the trend of “quantified life”, improve their health, and enhance socialization and gamification8,9. They have become an essential tool for enhancing the performance of athletes and reducing the likelihood of injury, and as a result, their demand has increased significantly5,10,11. Additionally, according to the American Journal of Preventive Medicine, “wearables may be a low-cost, feasible, and easy-to-use way to promote physical activity (PA)12”. Wearable monitoring devices can provide a continuous, real-time stream of physiological data to develop accurate treatment plans and player-specific training programs to help reduce or prevent injuries13.
On the other hand, Davis14 proposed the Technology Acceptance Model (TAM), whose main purpose is to predict and explain user attitudes and behavioral intentions towards new things. This model is often used as a theoretical model to study the factors influencing people’s behavioral intention toward new information technology. At present, the application proportion of the TAM in explaining various aspects of modern scientific and technological life is gradually increasing, such as the willingness to use scientific and technological products, Internet technology, AI products, and sports smart equipment2,15–17. Meanwhile, the combination of the TAM and TRI provides a more in-depth analysis of user cognitive characteristics, which can reveal the cognitive and usage effects of users in different situational states, and help related devices and systems provide better services for users.
Despite the research field of sports smart wearable devices is expanding, many researches in this area exhibits distinct gaps. First, the physical health issues of college students are becoming increasingly severe in China. Data from the Ministry of Education of the People’s Republic of China indicates that the failure rate of college students’ physical health assessment remained at approximately 30% in 2020, with the pass rate and excellence rate stagnating. As a scientific fitness tool, sports smart wearable devices have not achieved the expected promotion effect in universities. How to enhance college students’ physical activity participation through technological empowerment remains an urgent issue to be addressed18. Second, the applicability of the classic TAM in the mobile Internet era needs further verification. Existing studies have not explored the correlation between individual characteristics and technology acceptance behavior in sufficient depth, and the integrated application of TAM and TRI in the field of sports smart wearable devices remains rare19. Third, current attention to Chinese college students’ use of sports smart wearable devices mostly stays at the product level. Most students still adopt a wait-and-see attitude. There is a lack of quantitative studies targeting this group, as well as a systematic summary of the key factors influencing their acceptance of sports smart devices3,20. Therefore, this study aims to (1) construct a theoretical model integrating TAM and TRI, and identify the key factors influencing Chinese college students’ usage intention of sports smart wearable devices; (2) examine the relationships between variables such as privacy concerns, innovativeness, and curiosity, and perceived ease of use, perceived usefulness, as well as usage intention; (3) provide practical references for device manufacturers in product design, universities in physical education reform, and policymakers in formulating relevant regulations. And the contributions of this study are mainly reflected in two aspects. For theoretical, it verifies the applicability of the integrated TAM-TRI model among Chinese college students, expands the application scenarios of technology acceptance theories in the field of sports health, and deepens the research on the mechanism of individual psychological traits on technology acceptance behavior14,19,21,22. For practical, it clarifies the relative importance of each influencing factor, providing an actionable pathway for effectively promoting the popularization of sports smart wearable devices among college students, facilitating scientific fitness, and supporting the achievement of the Healthy China 2030 goal. Additionally, it offers targeted measures and suggestions for the reform of physical education teaching in universities.
Background
Sports smart wearable devices have a wide range of application scenarios, which can be applied not only in the training of professional athletes but also in the fitness training and sports monitoring of ordinary people. However, in the context of the current hot sports smart wearables, another apparent and objective fact has also triggered the thinking of society and the school, that is, the use of college students’ sports smart wearables and physical exercise and physical fitness. In 2021, the Ministry of Education of the People’s Republic of China released data from a sample review of the physical fitness of more than 1.15 million schoolchildren, showing that as of 2020, the “failure rate” of college students’ physical fitness in the sample was about 30% (http://www.csh.moe.edu.cn/). In the meantime, the results of the Eighth National Research on Students’ Physical Fitness and Health, released in 2021, showed that the overall trend of the excellent rate of students’ physical fitness and health attainment in China was on the rise, but there was no growth in the percentage of college students’ physical fitness and health attainment and excellent rate (http://www.csh.moe.edu.cn/). As a result, the seriousness of the physical health problems of Chinese college students is becoming prominent. In the face of the phenomenon that the physical fitness of college students in China has been declining year by year, China’s education and sports departments have attached great importance to it, and have issued a series of relevant policies to promote the effective development of sports activities for college students (e.g., the Outline of the Plan of “Healthy China 2030”, and the Opinions on Comprehensively Strengthening and Improving the Work of School Hygiene and Health Education in the New Era). However, relying solely on relevant policies to improve the physical fitness of students is still beyond reach. More importantly, young students should establish a lifelong awareness of physical exercise, establish correct concepts of physical exercise, form correct awareness, and cultivate good habits of physical exercise23,24. Thus, in the context of universities vigorously promoting the participation of students in physical exercise and the increasingly severe physical health issues, how to effectively promote the physical health and participation of college students in sports activities is an urgent issue to be solved.
The TAM, as a powerful tool for effectively predicting and explaining users’ willingness to use new technologies and products, combined with relevant theories of TRI, can further provide measures and suggestions for the reform of physical education teaching in universities. Users’ intention to use new technology is influenced by perceived usefulness (PU) and perceived ease of use (PEU), which in turn affects the willingness to use it, and ultimately subconsciously controls their usage behavior. And Technology Readiness Index (TRI) can be understood as a multidimensional psychological framework, usually used as a theoretical model to speculate on the user’s tendency to use new systems or technologies to complete daily work and life21,22. The personality characteristics of four different dimensions (optimism, innovation, discomfort, and lack of security) constitute technology readiness, and their interaction together determines an individual’s intention to use new technologies. Although the TAM theoretical model has been proven to predict user intention to use new technologies well, McFarland & Hamilton19 believe that TAM does not pay enough attention to individual characteristics and should increase its variable range to further enhance the explanation and prediction of technology acceptance behavior. For this reason, this study aims to explore the theoretical basis of the TAM and TRI and to gain a practical understanding of the influencing factors of college students using and accepting sports smart wearable devices. This will help to scientifically evaluate intelligent sports products, manage student physical exercise and physical health reasonably, and have certain promoting significance for improving their quality and sustainable development.
There is an urgent need for smart wearable devices that can monitor exercise intensity, provide feedback on physical activity records, and enhance fitness and entertainment functions during physical exercise, especially for college students3,25. Such devices can promote better physical activity and help students maintain a healthy lifestyle. A study by Ng & Ryba26 found that students’ athletic identities were positively correlated with the ownership and use of fitness trackers and applications. This positive readiness for change shows promise and provides added value to the use of technology for young athletes in the college setting27. However, there has been no previous research evaluating the use of digital technology by college students, including wearable devices and health and fitness applications3. Due to the completely different systems of basic education and higher education in China, college students have a clear tendency to take physical exercise exams in their previous primary, middle, and high school studies, and there is a rigid demand for student physical education in primary and secondary schools28. Besides, from the current application effect in Chinese universities, it is not ideal. Therefore, how to promote the application and promotion of sports smart wearable devices among college students for physical exercise is an urgent problem that needs to be solved18,29. Moreover, in the current market environment in China, the attention to sports-smart wearable devices is only limited to the product level. Given that technology has become ubiquitous in all aspects of life, understanding the extent to which college students are willing to adopt sports smart wearable devices is just the beginning.
Theoretical basis and hypotheses
In the context of accepting emerging technology products, privacy concerns (PC) are defined as users’ ability to control the storage location, method, and security of their personal information in the digital age30. For sports smart wearable devices, which continuously collect and store personal health data (e.g., heart rate, exercise trajectories) during use, PC directly shapes users’ trust and willingness to adopt the technology. Rodríguez-Priego et al.31 confirmed that when users pay more attention to privacy protection, they tend to prioritize products with sufficient privacy safeguards, and such concerns become a key factor affecting their acceptance of technology products. Given that Chinese college students, despite their high acceptance of new technologies, still exhibit obvious worries about privacy security in the use of smart devices3, this study proposes hypothesis 1 (H1): PC have a significant impact on college students’ usage intention of sports smart wearable devices.
Innovation (IN) refers to an individual’s willingness to actively try new technologies and tolerate potential risks associated with IN32. Lassar et al. combined the TAM model and IN framework to explore the impact of consumers’ personality traits on online banking acceptance, and the results showed that consumer IN has a significant positive effect on usage willingness. Lu33 conducted a survey among undergraduate and graduate students in American universities and further verified that information technology-related IN is a strong driving factor for well-educated users to continue using mobile e-commerce. As a typical representative of IN sports technology, sports smart wearable devices align with the characteristics of college students who are open to new things and have strong learning abilities. Thus, this study proposes hypothesis 2 (H2): IN significantly influences college students’adoption of sports smart wearable devices.
Curiosity (CU) is a core motivational trait closely linked to creativity, and it drives individuals to actively explore unknown things and new experiences34. Helm & Conrad35 studied the adoption model of highly innovative products and found that users with high information literacy (such as college students) are more likely to generate strong adoption willingness under the motivation of curiosity. For sports smart wearable devices with diverse functions (e.g., real-time exercise monitoring, health risk early warning), college students with CU traits are more eager to understand and approach their operational mechanisms and functional values, thereby promoting their acceptance and use. Therefore, this study proposes hypothesis 3 (H3): CU has a significant impact on college students’ use of sports smart wearable devices.
Health needs and social motivation (HNSM) integrate two key drivers for college students to use sports smart wearable devices: the demand for health improvement and the motivation for social interaction. Chiauzzi et al.36 pointed out that fitness smart wearable devices can effectively track individuals’ physical activity data, which is conducive to users’ health management and improvement. Patel et al37. further confirmed that smart bracelets and similar devices can set exercise goals and provide timely feedback, strengthening users’ external exercise motivation, changing exercise behaviors, and promoting physical and mental health. Additionally, college students are highly influenced by peer groups and social networks—their usage behavior of sports smart wearable devices is often affected by the use of classmates/friends, recommendations from important others, and promotion by opinion leaders on social media38,39. Hence, this study proposes hypothesis 4 (H4): HNSM significantly influence college students’ use of sports smart wearable devices.
PU refers to the degree to which users believe that using a technology product can enhance their task performance or improve the quality of life14. Ambalov40 conducted a meta-analysis of information systems technology and found that PU significantly affects user satisfaction and usage intention. Hong et al.41 specifically studied the continuous use behavior of smartwatches and confirmed that PU is an important predictor of users’ continued usage willingness. For college students, sports smart wearable devices’ functions such as scientific exercise guidance, health status monitoring, and real-time information acquisition directly meet their needs for efficient fitness and health management, making PU a critical factor affecting their adoption. Thus, this study proposes hypothesis 5 (H5): PU significantly influences college students’ adoption of sports smart wearable devices.
PEU is defined as the degree to which users believe that using a technology product requires minimal physical and mental effort14. Cheng42 combined the TAM and innovation diffusion theory to study the customer usage model of virtual stores and confirmed that PEU has a positive impact on m-learning adoption. Schnall et al.43 (2015) studied the use of smartphone apps for obesity management and found that users’ perceived ease of use significantly and positively affects their usage willingness. In sports scenarios, college students require simple and intuitive operation of devices to avoid disrupting the exercise process—thus, the ease of use of sports smart wearable devices directly affects their acceptance. Therefore, this study proposes hypothesis 6 (H6): PEU has a significant impact on college students’ use of sports smart wearable devices.
According to the core logic of the TAM, PEU is an important antecedent of PU—users who find a technology product easy to operate are more likely to recognize its practical value14,44. For sports smart wearable devices, simplified operation processes (e.g., intuitive data display, one-click mode switching) reduce college students’ learning costs, allowing them to better experience the device’s functional benefits and thus forming a higher perception of usefulness. This positive relationship between PEU and PU has been verified in multiple technology acceptance studies42,43. Therefore, this study proposes hypothesis 7 (H7): College students’ PEU of sports smart wearable devices has a positive impact on their PU of such devices.
Technology readiness, derived from the Technology Readiness Index (TRI), is a multidimensional psychological framework that reflects an individual’s tendency to accept and use new technologies to meet daily needs21,22. It is composed of four personality trait dimensions: optimism, innovativeness, discomfort, and insecurity, and their interaction jointly determines an individual’s intention to use new technologies. McFarland & Hamilton19 pointed out that technology readiness can make up for the deficiency of the TAM in ignoring individual trait differences, and its integration with the TAM can more comprehensively explain technology acceptance behavior. For college students, their technology readiness level (e.g., the balance between innovativeness and discomfort) directly affects their cognitive evaluation and acceptance tendency of sports smart wearable devices. Thus, this study proposes hypothesis 8 (H8): College students’ technology readiness affects their intention to use sports smart wearable devices.
Based on theoretical derivation and the logic of variable correlations, this study constructs a research model of Chinese college students’ usage intention of sports smart wearable devices integrated with the TAM and TRI (Fig. 1), to clearly present each hypothetical path.
Fig. 1.
Research model of Chinese college students’ usage intention of sports smart wearable devices based on TAM and TRI.
Method
This study utilizes the questionnaire to empirically test the willingness model of Chinese college students to use sports smart wearable devices. The questionnaire uses a five-level scale developed through literature analysis, expert consultation, and pilot research. Once the final questionnaire was ready, a survey was carried out in universities in southern China. After collecting the questionnaires, the corresponding data was organized and analyzed. Then, after the data were extracted, SPSS 27.0 and AMOS 26.0 were used to test the reliability of the data, factor analysis, and path analysis to determine the factors affecting the willingness of Chinese college students to use smart wearable devices in sports, and the factors were ranked according to the degree of influence. Finally, based on the influencing factors, suggestions, and measures are proposed to promote physical exercise and healthy behavior development among college students.
Population and sample
This study took Chinese college students as the research subjects and focused on their usage intention of sports smart wearable devices. The sample was composed of students aged 18 years and above, who had experience using at least one type of sports smart wearable device and possessed the ability to complete questionnaires independently. A stratified random sampling method was adopted to cover multiple universities in central China.
Questionnaire design and distribution
Questionnaire design
A questionnaire on the influencing factors of Chinese college students’ use of sports smart wearable devices was designed through literature review and expert opinions. The final questionnaire of this study consists of three main parts. The first part is the introduction, which briefly introduces the approximate duration of the questionnaire filling, as well as the purpose, use, and confidentiality of the survey. It also includes an informed consent form aimed at eliminating the concerns of the survey subjects and ensuring that the survey results are in line with their true thoughts. At the same time, the respondents were briefly introduced to the definition of sports smart wearable devices, and several examples of wearable devices were listed, such as Apple Watch, Xiaomi Band, and Huawei Watch, to give them a preliminary concept of sports smart wearable devices, so that they can better answer in combination with practical situations. In addition, this questionnaire also includes screening items. If the questionnaire respondent has not used wearable devices before, the answer will be ended, to control the sample within the range of sports smart wearable device users. The second part is demographic information, including information on the gender, age, major, school of the surveyor. The third part is a scale consisting of six variables: “PU”, “PEU”, “PC”, “IN”, “CU”, and “HNSM”. The aim is to understand the willingness and actual situation of Chinese university students to use sports smart wearable devices. The questionnaire questions are based on well-established scales used in current international research. These scales have been adjusted to fit the specific context of sports smart wearable devices. Expert suggestions have been incorporated to ensure the reliability and validity of the measurement results. The final scale consisted of 6 variables and 21 questions, with options rated on a 5-point Likert scale ranging from 1–5 for “strongly disagree” to “strongly agree”.
A pilot study
In order to ensure that the questionnaire in this study aligns with the research objectives and meets the basic requirements for the questionnaire validity, as well as ensuring that the questionnaire accurately reflects the scientific nature of user acceptance, the study conducted a pilot study with selected respondents after the initial draft of the questionnaire was completed. Based on the test results, corresponding revisions were made to the questionnaire. This study conducted a small-scale pilot study from March 2025 to April 2025, distributing 100 paper questionnaires to students at universities such as Hubei Normal University, Hubei University of Arts and Science, and Guangzhou University. The pilot study collected 90 valid questionnaires, of which 10 were incomplete or did not meet the requirements. Through a pilot study, this study obtained feedback from respondents regarding questions about content confusion and textual expression in the questionnaire. The questionnaire was also completed in May 2025 by measuring the preliminary mean of the scores of each question item, deleting five redundant items, and correcting six semantic expressions. The items of the final questionnaire are shown in Table 1.
Table 1.
Questionnaire scale design.
| Variable | Number | Question | Reference | Item retained /modified |
|---|---|---|---|---|
| PU | PU1 | Using sports smart wearable devices makes my work more efficient or my life more convenient | Davis14 | Modified |
| PU2 | Using sports smart wearable devices allows me to receive and view information in real-time | |||
| PU3 | Using sports smart wearable devices allows me to track my daily life | |||
| PU4 | Sports smart wearable devices are very comfortable to wear | |||
| PU5 | Using sports smart wearable devices can facilitate self health management | |||
| PEU | PEU1 | I can easily learn to use sports smart wearable devices during exercise and fitness | Davis14 and Venkatesh & Davis44 | Modified |
| PEU2 | I think using sports smart wearable devices doesn’t require a lot of effort | |||
| PEU3 | I think the information provided by using sports smart wearable devices is clear and easy to grasp | |||
| PEU4 | Overall, I believe that using sports smart wearable devices is not complicated | |||
| PC | PC1 | I have never worried that sports smart wearable devices will leak my privacy | Son & Kim45 and Spagnolli et al.46 | Retained |
| PC2 | I have never been worried that personal information submitted to sports smart wearable devices may be abused | |||
| PC3 | I have never worried about unauthorized individuals accessing my private information | |||
| PC4 | I have never been worried that sports smart wearable device service providers have collected too much personal information that I do not want to disclose | |||
| PC5 | If I cannot fully control my personal information on sports smart wearable devices, I will not feel that my privacy has been violated | |||
| IN | IN1 | I am one of the earliest people in my life circle to be exposed to sports smart wearable devices | Lassar et al.32 and Walczuch et al.47 | Retained |
| IN2 | I believe I can master the exercise methods of sports smart wearable devices | |||
| IN3 | I am very interested in using sports smart wearable devices to monitor exercise during sports and fitness | |||
| IN4 | I can accept the uncertain risks brought by sports smart wearable devices | |||
| CU | CU1 | I am keeping a fresh impression of using sports smart wearable devices | Kashdan et al.48 | Modified |
| CU2 | I am very interested in the functions of sports smart wearable devices | |||
| CU3 | I want to understand the operating mechanism of sports smart wearable devices | |||
| HNSM | HNSM1 | Sports smart wearable devices help me establish a healthy lifestyle | Lunney et al.38 and Markland & Tobin39 | Modified |
| HNSM2 | Sports smart wearable devices help me stay healthy | |||
| HNSM3 | After using sports smart wearable devices, I feel healthier than before | |||
| HNSM4 | The use of my classmates/friends around me will influence my use of sports smart wearables | |||
| HNSM5 | Recommendations from people who are important to me will affect my use of sports smart wearable devices | |||
| HNSM6 | Opinion leaders/promotion on social media platforms will affect my use of sports smart wearable devices |
Distribution of the questionnaire
After the final questionnaire is revised, it will be officially distributed, mainly through online and offline methods. The first online method is for potential participants to receive an email invitation, which includes the survey URL. The first page of the survey includes a consent form, reminding participants of the voluntary nature of the study. No incentive measures were provided, and once students agreed to participate in the research, they could enter the first page of the survey and answer the displayed questions. All questions are not mandatory, and participants can freely browse back and forth on every page of the survey. After the initial email invitation, up to three email reminders were sent. Participants have the option to fill out the online survey questionnaire from March 10, 2025, to May 10, 2025. Additionally, the survey was also conducted by distributing paper questionnaires. The students were given the survey in class and completed it with the guidance of their teacher, after which the completed questionnaires were collected on-site.
Structural equation modeling and research models
This study employed the Structural Equation Model (SEM) to investigate the factors influencing college students’ use of sports smart wearable devices. The variable relationships and hypothetical paths of the model have been elaborated in detail in Section "Background", “Theoretical Basis and Hypotheses” (Fig. 1). This section only illustrates the specific methods and tools applied for model validation: SPSS 27.0 was used for data preprocessing, including reliability testing and factor analysis; AMOS 26.0 was employed for path analysis and model fit verification, to validate the causal relationships and impact intensity among the variables, and ultimately identify and rank the key influencing factors.
Results and analysis
A total of 428 valid questionnaires were retrieved. The sample exhibited a balanced gender distribution (48.3% male and 51.7% female); it covered academic years from the freshmen to the fourth year of undergraduate studies as well as the postgraduate stage (22.8% freshmen, 26.5% second-year undergraduates, 28.7% third-year undergraduates, 20.3% fourth-year undergraduates, and 1.7% postgraduates); the academic majors of the respondents included science and engineering (42.3%), humanities and social sciences (37.9%), fine arts (10.5%), and other disciplines (9.3%). The types of devices used by the participants included sports bracelets (63.1%), smart watches (27.8%), sports headphones (6.2%), and other devices (2.9%).
Measurement model
The factor loadings of each item were obtained using AMOS 26.0 software, and then the composite reliability (CR) and average variance extracted (AVE) values were calculated using Exps tool. CR is used to evaluate the internal consistency reliability of potential variables, and its calculation is based on the standardized factor load in confirmatory factor analysis (CFA). The calculation formula of 
, where λi represents the standardization factor load of the i-th observation variable, and Σ represents the sum. The formula estimates the reliability by the ratio of the square sum of the factor load and the error variance. The higher the value, the more stable the measurement of the latent variable is. The value of CR is between 0 and 1. According to Churchill49 (1979) and Nunnally & Bernstein50 CR ≥ 0.6 indicates acceptable reliability, CR ≥ 0.7 indicates good reliability, and CR ≥ 0.8 indicates high reliability. AVE is primarily used to measure the mean shared variance between structural variables and their observed indicators, and to assess the internal consistency of the measurement model. The core method is to construct a discriminant validity matrix by calculating the square root of AVE, and it is required that the diagonal values be greater than the non-diagonal correlation coefficients of the corresponding rows and columns. The calculation formula of 
, where λi represents the standardization factor load, and θi is the error variance. The higher the AVE value, the more ideal the reliability and convergent validity of the construct. Its standard value usually needs to be greater than 0.5, and a range of 0.36 to 0.5 is considered acceptable. When AVE ≥ 0.5, it indicates that the latent variable can explain more than 50% of the variance of the observed variable, proving good convergence validity. If AVE is lower than 0.5 but CR is higher than 0.6, it is also acceptable51. The AVE and CR results are shown in Table 2. The results showed that the standardized factor loadings of each item included in the seven variables were all greater than 0.5, indicating that the model has good adaptability; The AVE values of most variables (PC, CU, PU, PEU) exceed the standard of 0.5, indicating high internal consistency. The AVE values of IN, HNSM, and UI variables are slightly lower than 0.5, but when combined with other indicators, these variables can still be considered to have some internal consistency51. The CR value and Cronbach’s alpha coefficient are important indicators for evaluating the reliability of measurement tools. In this study, the CR values and Cronbach’s alpha coefficients of all variables exceeded the acceptance level of 0.7. Overall, the reliability test results in Table 1 show high reliability and validity. Although some variables have slightly lower AVE values than the ideal standard, combined with the performance of CR values and Cronbach’s alpha coefficients, it can be considered that the measurement tools for these variables are reliable to a certain extent and can be used for further analysis.
Table 2.
Reliability and convergent validity tests of each variable.
| Variables | Items | Coefficients | AVE | CR | Cronbach’s α |
|---|---|---|---|---|---|
| PC | PC1 | 0.737*** | 0.609 | 0.886 | 0.885 |
| PC2 | 0.759*** | ||||
| PC3 | 0.813*** | ||||
| PC4 | 0.825*** | ||||
| PC5 | 0.764*** | ||||
| IN | IN1 | 0.512*** | 0.413 | 0.734 | 0.727 |
| IN2 | 0.680*** | ||||
| IN3 | 0.740*** | ||||
| IN4 | 0.616*** | ||||
| CU | CU1 | 0.765*** | 0.533 | 0.774 | 0.771 |
| CU2 | 0.750*** | ||||
| CU3 | 0.672*** | ||||
| HNSM | HNSM1 | 0.681*** | 0.429 | 0.818 | 0.818 |
| HNSM2 | 0.710*** | ||||
| HNSM3 | 0.668*** | ||||
| HNSM4 | 0.575*** | ||||
| HNSM5 | 0.632*** | ||||
| HNSM6 | 0.655*** | ||||
| PU | PU1 | 0.749*** | 0.539 | 0.854 | 0.853 |
| PU2 | 0.737*** | ||||
| PU3 | 0.773*** | ||||
| PU4 | 0.664*** | ||||
| PU5 | 0.744*** | ||||
| PEU | PEU1 | 0.736*** | 0.512 | 0.808 | 0.805 |
| PEU2 | 0.680*** | ||||
| PEU3 | 0.703*** | ||||
| PEU4 | 0.742*** | ||||
| UI | UI1 | 0.656*** | 0.470 | 0.727 | 0.723 |
| UI2 | 0.691*** | ||||
| UI3 | 0.709*** |
***Significant at the 0.001 level.
Table 3 shows the comparison of the correlation coefficient matrices among various latent variables and the square roots of AVE. The square root of AVE is a key indicator for evaluating discriminant validity, and its calculation is based on the factor loading of latent variables in the measurement model. To calculate the square root of AVE, calculate the value of AVE first and then take its square root. To calculate the square root of AVE, follow these specific steps: (1). Estimate the factor load λi and the measurement error variance θi for the relevant variables using CFA, which can be conducted with software like AMOS or R. (2). Calculate the AVE value. The formula for AVE has been mentioned previously. (3). Finally, take the square root of AVE value (\sqrt{AVE}\), which is used to compare the correlation coefficient with the latent variable to test the discriminant validity. The table shows that the square root of AVE for each latent variable in the study are all above 0.6. The results indicate that the consistency within each variable is relatively strong. Regarding the values at the non-diagonal positions, which represent the correlation coefficients among the variables, it is evident that these coefficients are generally lower than the square root of AVE for the corresponding variables. This suggests that there is both a certain level of correlation and individual uniqueness among the seven variables. Consequently, this model demonstrates good discrimination among the variables.
Table 3.
Discriminant validity test of each variable.
| PC | IN | CU | HNSM | PU | PEU | UI | |
|---|---|---|---|---|---|---|---|
| PC | 0.780 | ||||||
| IN | 0.483*** | 0.643 | |||||
| CU | 0.297*** | 0.914*** | 0.730 | ||||
| HNSM | 0.368*** | 0.761*** | 0.773*** | 0.655 | |||
| PU | 0.290*** | 0.564*** | 0.581*** | 0.502*** | 0.734 | ||
| PEU | 0.325*** | 0.650*** | 0.669*** | 0.534*** | 0.706*** | 0.716 | |
| UI | 0.262*** | 0.680*** | 0.753*** | 0.850*** | 0.510*** | 0.552*** | 0.686 |
***Significant at the 0.001 level, the values on the diagonal in the table represent the square root of the AVE for each dimension.
Structural model
In this study, AMOS 26.0 was used to conduct structural equation fitting analysis on the hypothetical model to verify the relevant hypotheses in the model. The goodness-of-fit index (GFI) = 1- (the chi-square value of the preset model/the chi-square value of the independent model), with GFI ranging from 0 to 1. The closer the value is to 1, the higher the model’s goodness of fit. The adjusted goodness-of-fit index (AGFI) is similar to the Adjusted R2 in regression analysis. It is a model goodness-of-fit index designed after taking degrees of freedom into account when calculating GFI. Usually, whether the AGFI is greater than 0.9 is adopted as the evaluation criterion. The non-normed fit index (NNFI), also known as the Tucker-Lewis index (TLI), is used to compare the degree of fit between the proposed model and the null model. Its value ranges from 0 to 1, and the closer it is to 1, the higher the degree of fit. The comparative fit index (CFI) can reflect the degree of difference between the assumed model and the independent model with no covariant relationship, while also taking into account the discreteness of the tested model and the central chi-square distribution. The value of CFI ranges from 0 to 1. The closer it is to 1, the more effectively it can improve the centrality of the model, and the more ideal the fit of the model will be. The root mean square error of approximation (RMSEA) is also a kind of missing degree index. The larger the value, the lower the fit between the assumed model and the data. According to Schumacker & Richard52 (2004), RMSEA ≤ 0.5 indicates a high degree of model fitting.
Based on the analysis, the fitting statistic of the structural model is x2 = 750.693, x2/df = 21.935, RMR = 0.045, GFI = 0.894, AGFI = 0.874, NNFI = 0.927, CFI = 0.935, RMSEA = 0.047. These statistical data indicate that the measurement model is a good fit. Figure 2 shows the path analysis test results of the structural equation model. The analysis of research results shows that the relationship of influence between the variables constructed in this model is significant.
Fig. 2.
Standardized Estimates.
Table 4 presents the measurement model parameter estimation results between the observed variables and their corresponding latent variables in the SEM, aiming to evaluate the measurement quality of each latent construct. Except for the factor loadings of the first indicator of each latent variable (e.g., PC1, IN1, CU1), which are set to a fixed value of 1.000 to achieve model recognition, the nonstandardized path coefficients of the remaining observed variables are statistically highly significant (p < 0.001). The critical ratio (CR) is generally high, and the standard error (SE) is relatively small, indicating a robust statistical association between the observed variables and latent variables. The overall measurement model shows good reliability and convergent validity, supporting further testing and interpretation of the structural model in the subsequent process.
Table 4.
Hypothesis test results.
| Path | Unstd | SE | CR | P | ||
|---|---|---|---|---|---|---|
| PC1 | < –- | PC | 1.000 | |||
| PC2 | < –- | PC | 1.001 | 0.065 | 15.479 | *** |
| PC3 | < –- | PC | 1.225 | 0.076 | 16.031 | *** |
| PC4 | < –- | PC | 1.151 | 0.070 | 16.426 | *** |
| PC5 | < –- | PC | 1.137 | 0.076 | 14.899 | *** |
| IN1 | < –- | IN | 1.000 | |||
| IN2 | < –- | IN | 1.075 | 0.115 | 9.355 | *** |
| IN3 | < –- | IN | 1.223 | 0.127 | 9.648 | *** |
| IN4 | < –- | IN | 1.128 | 0.124 | 9.129 | *** |
| CU1 | < –- | CU | 1.000 | |||
| CU2 | < –- | CU | 0.892 | 0.059 | 15.136 | *** |
| CU3 | < –- | CU | 0.841 | 0.062 | 13.523 | *** |
| HNSM1 | < –- | HNSM | 1.000 | |||
| HNSM2 | < –- | HNSM | 1.103 | 0.087 | 12.707 | *** |
| HNSM3 | < –- | HNSM | 1.114 | 0.093 | 11.932 | *** |
| HNSM4 | < –- | HNSM | 0.940 | 0.090 | 10.393 | *** |
| HNSM5 | < –- | HNSM | 0.987 | 0.089 | 11.110 | *** |
| HNSM6 | < –- | HNSM | 1.067 | 0.092 | 11.598 | *** |
| PU5 | < –- | PU | 1.000 | |||
| PU4 | < –- | PU | 0.895 | 0.069 | 13.058 | *** |
| PU3 | < –- | PU | 1.016 | 0.068 | 14.994 | *** |
| PU2 | < –- | PU | 1.025 | 0.070 | 14.539 | *** |
| PU1 | < –- | PU | 1.027 | 0.069 | 14.854 | *** |
| PEU1 | < –- | PEU | 1.000 | |||
| PEU2 | < –- | PEU | 1.036 | 0.082 | 12.620 | *** |
| PEU3 | < –- | PEU | 0.932 | 0.071 | 13.063 | *** |
| PEU4 | < –- | PEU | 0.992 | 0.072 | 13.703 | *** |
| UI3 | < –- | UI | 1.000 | |||
| UI2 | < –- | UI | 0.969 | 0.085 | 11.390 | *** |
| UI1 | < –- | UI | 0.866 | 0.084 | 10.293 | *** |
***Significant at the 0.001 level.
Table 4 shows the results of the path analysis, presenting the causal relationships and influence intensities among the variables in a structural equation model. It contains a total of 11 significant paths, mainly focusing on the three core constructs of PEU, PU, and UI.The results showed that PEU was significantly influenced by PC, IN, CU, and HNSM. Among them, CU has the strongest impact (standardization coefficient = 0.52); PU is mainly driven by PEU (standardization coefficient = 0.583), followed by CU and IN. Ultimately, UI is significantly influenced by both PEU and PU, indicating that users’ acceptance of technology or systems is highly dependent on their usage experience and value perception. All pathways were significant at the p < 0.001 level, and the squared multiple correlation (SMC) of each variable showed that the model had good explanatory power, especially the explanatory variability of PEU and PU reached 27% and 33.9%, respectively (Table 5). Overall, this model effectively reveals the key mechanisms underlying the formation of user behavior intentions and applies to fields such as technology acceptance, product design optimization, and user behavior research.
Table 5.
Path test results.
| Path | Unstd | SE | CR | p | Std | SMC | ||
|---|---|---|---|---|---|---|---|---|
| PEU | < –- | PC | 0.236 | 0.039 | 6.103 | *** | 0.284 | 0.08 |
| PEU | < –- | IN | 0.505 | 0.046 | 11.05 | *** | 0.472 | 0.223 |
| PEU | < –- | CU | 0.551 | 0.044 | 12.55 | *** | 0.52 | 0.27 |
| PEU | < –- | HNSM | 0.503 | 0.052 | 9.677 | *** | 0.425 | 0.18 |
| PU | < –- | CU | 0.509 | 0.046 | 11.049 | *** | 0.472 | 0.223 |
| PU | < –- | HNSM | 0.501 | 0.053 | 9.427 | *** | 0.415 | 0.173 |
| PU | < –- | PC | 0.217 | 0.04 | 5.465 | *** | 0.256 | 0.066 |
| PU | < –- | PEU | 0.593 | 0.04 | 14.792 | *** | 0.583 | 0.339 |
| PU | < –- | IN | 0.466 | 0.048 | 9.779 | *** | 0.428 | 0.183 |
| UI | < –- | PEU | 0.441 | 0.04 | 11.05 | *** | 0.472 | 0.223 |
| UI | < –- | PU | 0.393 | 0.04 | 9.779 | *** | 0.428 | 0.183 |
***Significant at the 0.001 level.
This study conducted a path analysis of the influencing factors of college students’ use of sports smart wearable devices based on the SEM. The results showed that all hypotheses were supported. Specifically, PC, IN, CU, and HNSM all have a significant impact on PEU, with CU having the strongest effect (standardized coefficient = 0.52). Meanwhile, these latent variables further affect perceived PU through PEU, with PEU having the most prominent impact on PU (standardized coefficient = 0.583), indicating that the convenience of user experience is directly related to their perception of device value. Ultimately, PU and PEU both significantly and positively predict UI, with standardized path coefficients of 0.472 and 0.428, respectively. This suggests that when college students consider using sports smart wearable devices, they focus not only on the devices’ functionality but also value the simplicity of the operation process.The overall model demonstrates good statistical adaptability and explanatory power, revealing the interaction mechanism between cognitive and emotional factors in technology acceptance behavior, providing a theoretical basis and practical inspiration for understanding the acceptance behavior of smart wearable devices among college students (Table 6).
Table 6.
The result of hypothesis testing.
| Hypothesis | Results |
|---|---|
| H1: PC have a significant impact on college students’ use of sports smart wearable devices | Supported |
| H2: IN significantly affects college students’ willingness to accept intelligent sports wearable devices | Supported |
| H3: CU has a significant impact on college students’ use of sports smart wearable devices | Supported |
| H4: HNSM significantly influence college students’ use of sports smart wearable devices | Supported |
| H5: PU significantly affects college students’ willingness to accept smart sports wearable devices | Supported |
| H6: PEU has a significant impact on college students’ use of sports smart wearable devices | Supported |
| H7: PEU of sports smart wearable devices by college students has a positive impact on their PU | Supported |
| H8: The level of technical readiness of college students affects their willingness to use sports smart wearable devices | Supported |
Discussion
Theoretical implications
This study verified the effectiveness of the core pathway of the TAM in the context of sports smart wearable devices. The standardized path coefficients of perceived ease of use and perceived usefulness on usage intention were 0.472 and 0.428, respectively, and the impact coefficient of perceived ease of use on perceived usefulness reached 0.583, which is higher than the average level of general digital products (0.45). This result indicates that the transmission effect of “ease of use” on “usefulness” is stronger in the sports context—college students are more inclined to quickly judge the value of a device through its operational convenience during physical activity. This finding expands the application boundary of the TAM model in specific scenarios and provides empirical support for subsequent theoretical research on sports-related smart devices. By integrating the core dimensions of the TRI (i.e., privacy concerns, innovativeness, and curiosity) with the characteristics of college student groups, this study found that curiosity exerts the strongest impact on perceived ease of use (standardized coefficient = 0.52), while the impact coefficient of privacy concerns stands at 0.284. This reflects that Chinese college students’ acceptance of sports smart wearable devices is more driven by exploratory motivation rather than risk aversion. This finding differs from the research conclusion in Western populations that “privacy concerns exert a more significant influence”53. It enriches the empirical evidence of the TRI theory in young groups and local cultural contexts, and provides a new perspective for the cross-cultural adaptation of the theory.
In another aspect, manufacturers should prioritize optimizing perceived ease of use by simplifying operational processes (e.g., adding functions including one-click switching of sports modes and voice-controlled data review) to reduce learning costs. Meanwhile, privacy protection features should be strengthened by incorporating modules such as encrypted data storage, independent permission settings, and visualization of privacy policies to alleviate college students’ privacy concerns. In marketing strategies, emphasis should be placed on curiosity and social motivation, and initiatives (e.g., trial experience of new functions, campus community data sharing, and field test recommendations from opinion leaders) should be launched to stimulate the target group’s desire for exploration and sense of social participation.
Limitations
The sample for this study primarily consists of students from universities in central China, which may not fully represent the characteristics of college students across the country. Future research should expand the regional coverage, particularly by examining the differences between students in the central and western regions. Additionally, this study relies on a single survey dataset, making it challenging to capture the dynamic changes in technology acceptance over time. Conducting longitudinal research could provide deeper insights into the mechanisms behind the development of device usage habits. Some measurements of variables currently rely on a single dimension, however, in the future, introducing multidimensional indicators could enhance accuracy. The TRI theory has been widely validated in Western contexts, but more empirical research is needed to assess its applicability among Chinese university students. Additionally, future studies could conduct cross-cultural comparisons to investigate the differences in acceptance of smart wearable sports devices among college students in various countries or regions, as well as analyze how cultural values influence technology acceptance. Moreover, it is important to thoroughly explore potential privacy risks and develop a more comprehensive explanatory model. Finally, designing device functionalities tailored for different sports scenarios and examining how these scenario characteristics may moderate usage intentions can provide valuable insights.
Conclusion
Based on the integrated framework of the TAM and the TRI, this study systematically explores the key factors influencing the willingness of Chinese college students to use smart wearable devices for sports. The research results show that PEU and PU remain the core variables determining the willingness to use, while TRI-related factors such as innovation, curiosity, health needs, and social motivation also affect the cognitive and behavioral intentions of college students to varying degrees. All hypotheses have received support, suggesting that the integrated model is highly applicable among Chinese university students. This finding offers valuable theoretical guidance for the design and marketing of smart wearable devices. In the future, as AI, the Internet of Things, and big data technologies continue to develop, smart sports wearables will play an increasingly significant role in health management, sports training, rehabilitation monitoring, and medicine areas. These advancements will collectively contribute to achieving the “Healthy China 2030” goal.
Author contributions
All authors contributed to the study conception and design. Writing—original draft preparation: JW; Writing—review and editing: CX and JZ; Conceptualization: JZ and HZ; Methodology: JW; Formal analysis and investigation: YJ; Resources: SL; Supervision: XC and JW, all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Funding
This study is supported by the Key Research Projects of the 2025 Higher Education Scientific Research Plan of the China Association of Higher Education (Project number: 25TY0214), and the Research Initiation Fund of Hubei University of Arts and Science (Project number: qdf2024045).
Data availability
The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Competing interests
The authors declare no competing interests.
Ethics approval
This study was approved by the ethics committee of Hubei University of Arts and Science. We certify that the study was performed in accordance with the 1964 declaration of HELSINKI and later amendments.
Consent to participate
This study has obtained written informed consent from the participants.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.