A Study on the Factors Influencing the Usage Intention of Consumer Groups in Smart Chinese Medicine Pharmacies Based on Innovation Diffusion Theory and Structural Equation Modeling

A Study on the Factors Influencing the Usage Intention of Consumer Groups in Smart Chinese Medicine Pharmacies Based on Innovation Diffusion Theory and Structural Equation Modeling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Study on the Factors Influencing the Usage Intention of Consumer Groups in Smart Chinese Medicine Pharmacies Based on Innovation Diffusion Theory and Structural Equation Modeling Ming-chen CAO, Fan-bo JING, Ze-nan ZHANG, Wen-xiao WANG, Zhong-wei XIAO, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7530904/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The development of smart traditional Chinese medicine (TCM) pharmacies in China is still in the exploratory stage, with limited research on consumer group analysis and factors influencing usage intention. This study aims to explore the factors affecting consumers' willingness to use smart TCM pharmacy services from the consumer perspective. Methods Based on the diffusion of innovation theory, this study analyzes the factors influencing the use of smart TCM pharmacy services. Relative advantage, trialability, observability, social influence, performance expectancy, perceived risk, and consumer innovativeness were treated as latent variables. A survey questionnaire was designed to assess consumers' willingness to use smart pharmacy services, and a model of influencing factors was constructed. Statistical analyses included reliability and validity tests of the questionnaire, discrimination tests, t-tests and one-way ANOVA for differences in usage intention among different individuals, correlation analysis between latent variables and usage intention, and the construction of a structural equation model among influencing factors. Results A total of 175 valid questionnaires were collected; overall reliability was 0.962, with Cronbach's alpha values for each latent variable exceeding 0.7, KMO value = 0.952, and standardized factor loadings for each measurement item under each latent variable greater than 0.4. The t-test and one-way ANOVA results indicated that gender, age, education level, occupation, and income level did not have statistically significant differences in willingness to use smart TCM pharmacy services. Correlation analysis shows that the correlation coefficient between individual innovativeness and willingness to use is 0.893. The correlation coefficients for performance expectancy, social influence, observability, trialability, and perceived usefulness are 0.873, 0.871, 0.830, 0.826, and 0.619 respectively, while perceived risk has a relatively low correlation coefficient of only 0.39. Path analysis indicates that if consumers are willing to try new technologies, products, or services, if medical staff recommend them, and if users are willing to abandon the smart Chinese medicine pharmacy service after unsatisfactory trials, these factors have a significant positive impact on willingness to use. Conversely, the ability to evaluate the smart Chinese medicine pharmacy service after use has a significant negative impact on willingness to use. The structural model shows that individual innovativeness has a significant effect on willingness to use at the 0.05 level, with a standardized path coefficient of 0.531, indicating a positive influence. At the same time, performance expectancy and social influence also have significant positive effects on willingness to use, with standardized path coefficients of 0.365 and 0.278, respectively. Conclusion With the development and application of smart Chinese medicine pharmacies, consumer-related research on influencing factors will become a key focus for future promotion and application. Future efforts should continuously explore mature and stable operational models, enhance the social influence of smart Chinese medicine pharmacies, strengthen recommendation channels through medical staff, provide trial opportunities for individuals with strong innovativeness, and continuously promote the improvement of relevant legal and regulatory systems. Smart Chinese pharmacy Diffusion of innovations theory Intention to use Influencing factors Structural equation modeling Introduction In recent years, the "Internet + Healthcare" industry has developed rapidly, playing a significant role in enhancing the quality and efficiency of medical services while improving patient treatment conditions [1]. Many traditional Chinese medicine (TCM) hospitals, particularly Grade A tertiary TCM hospitals, have long faced challenges such as inadequate supply of medical resources, insufficient pharmacy resource allocation, labor-intensive herbal decoction preparation, and prolonged waiting times for medication dispensing. These issues have resulted in reduced patient satisfaction and strained physician-patient relationships[2]. Consequently, an "Internet + Pharmaceutical Care" model has emerged to achieve remote, convenient, and equitable pharmaceutical services [3-6]. Against the backdrop of advancements in "Internet Plus", big data, and artificial intelligence, hospitals have leveraged internet and IoT platforms to address patients' diversified medication needs and mitigate supply-demand imbalances in medical resources. By integrating online and offline resources using modern information technology and automated control systems, they innovatively combine pharmacy management systems with conventional treatment-medication models, establishing modern intelligent TCM pharmacies that provide one-stop pharmacy services [7].Through informatized workflows, optimized storage environments, and efficient dispensing methods, intelligent TCM pharmacies enhance pharmaceutical service efficiency and quality while advancing medication counseling and health management services. These platforms enable fully digitalized management—covering payment, herbal decoction preparation, delivery, and consultation—delivering comprehensive pharmacy services [8-10]. This evolution epitomizes the informatization of TCM services and the enhancement of TCM service capabilities, marking the transition of hospital pharmacies toward intelligent and networked development. Its purpose is to bridge the "last-mile" gap in TCM services, empowering the innovation and heritage of the TCM industry [11]. At present, the construction and development of intelligent TCM pharmacies in China remain in the exploratory stage. Existing literature primarily focuses on operational models and service scale of such pharmacies, with limited research on consumer group analysis and factors influencing usage intention. Numerous refinements are still required during their implementation. From the consumer perspective, this study employs questionnaire surveys, data statistics, and structural equation modeling (SEM) analysis to investigate factors affecting consumers' adoption of intelligent TCM pharmacy services. Strategic recommendations for development are proposed to facilitate the advancement of intelligent TCM pharmacies. Research Methods Questionnaire Survey Method: Based on the diffusion of innovation theory, this study investigates the relationship between seven variables—relative advantage, trialability, observability, social influence, performance expectancy, perceived risk, and consumer innovativeness—and the willingness to use. The questionnaire uses a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Statistical Analysis Method: SPSS 26.0 statistical software was used to analyze the factors influencing consumers’ willingness to use smart Chinese medicine pharmacy services. This includes reliability and validity analysis of the questionnaire, descriptive statistical analysis, and correlation analysis, as well as constructing a regression model for the willingness to use smart Chinese medicine pharmacy services among consumer groups. 2. Questionnaire Design and Collection The data for this study were collected through a questionnaire survey, which included 7 latent variables and a total of 22 measurement items. The questionnaire was distributed both online and offline. After excluding invalid responses, a total of 175 valid questionnaires were collected, with an overall effective response rate of 93.08%. 3 Research Content and Results 3.1 Reliability and Validity Analysis 3.1.1 Reliability Analysis As shown in Table 1, the reliability analysis shows that the Cronbach’s Alpha values for the seven latent variables—relative advantage (perceived usefulness), trialability, observability, social influence, performance expectancy, perceived risk, and consumer innovativeness—are 0.972, 0.950, 0.964, 0.981, 0.964, 0.974, and 0.978, respectively, indicating that the questionnaire has good reliability. Table 1 Reliability Analysis Results of the Questionnaire Latent variable Measurement item Cronbach’s Alpha Cronbach’s Alpha Relative Advantage (Perceived Usefulness) A1: Compared to traditional pharmacies, smart Chinese medicine pharmacies are more convenient. 0.972 0.972 A2: The smart Chinese medicine pharmacy service can shorten my medical treatment process. 0.959 A3: The smart Chinese medicine pharmacy service allows me to access higher-quality medical resources (such as renowned medical experts, personalized rehabilitation plans, etc.). 0.955 A4: The smart Chinese medicine pharmacy can provide me with more comprehensive services. 0.963 Trialability B1: I am willing to try it, and if I am not satisfied, I will stop using the smart Chinese medicine pharmacy service. 0.916 0.950 B2: I hope there are free or low-cost trial opportunities for services like online consultations and Chinese medicine decoction. 0.925 B3: I hope to refer to data and feedback from others’ experiences with the smart Chinese medicine pharmacy service. 0.938 Observability C1: I believe the advantages and convenience of the smart Chinese medicine pharmacy service are obvious. 0.947 0.962 C2: I hope to be able to provide feedback on the smart Chinese medicine pharmacy service after using it. 0.951 Social Influence D1: National policy support and encouraging measures will motivate me to use this service. 0.945 0.964 D2: If medical staff recommend it, I will use this service. 0.941 D3: Promotion of internet hospitals by physical hospitals will guide me to use this service. 0.944 D4: Others’ overall evaluations will influence my decision to use the smart Chinese medicine pharmacy service. 0.971 Performance Expectation E1: Using this service makes it easier for me to consult with doctors. 0.977 0.981 E2: Using this service reduces the hassle of visiting physical hospitals. 0.972 E3: Using this service helps improve my health condition. 0.974 E4: I can effectively use this service to meet my medical needs, such as online follow-ups, prescription renewals, and medicine delivery. 0.977 Perceived Risk F1: I’m worried that smart Chinese medicine pharmacies might leak my personal information. 0.958 0.964 F2: I’m concerned that there are currently no laws or regulations to govern the standardized services of smart Chinese medicine pharmacies. 0.948 F3: I’m worried about the quality of the medicines provided by smart Chinese medicine pharmacies. 0.956 F4: I’m concerned that the services offered by smart Chinese medicine pharmacies may not be professional enough. 0.945 Individual Innovativeness G1: I usually pay close attention to the development of new things or new technologies. 0.955 0.974 G2: I’m generally more open to accepting new things or new ideas. 0.951 G3: I’m very willing to try new technologies, products, or services. 0.978 3.1.2 Validity Analysis The collected data were used to conduct the KMO value and Bartlett's test of sphericity. The overall test results for the seven latent variables and the questionnaire are shown in Table 2. The overall KMO value of the questionnaire was greater than 0.9, and Bartlett's test of sphericity reached a significant level, indicating that the questionnaire has good structural validity. Table 2 Results of Questionnaire Validity Analysis Latent variable KMO Measure of Sampling Adequacy Bartlett's Test of Sphericity χ² df p Relative Advantage 0.846 1009.282 6 0.000 Trialability 0.771 518.673 3 0.000 Observability 0.500 286.891 1 0.000 Social Influence 0.835 929.906 6 0.000 Performance Expectation 0.813 1188.575 6 0.000 Perceived Risk 0.866 855.031 6 0.000 Individual Innovativeness 0.768 750.572 3 0.000 Overall 0.929 11411.727 630 0.000 The standardized factor loadings of the latent variables are shown in Table 3. The standardized factor loadings for each measurement item of the latent variables are all greater than 0.7, indicating that all 22 measurement items can be used to explain the respective latent variables. Table 3 Factor Analysis of Measurement Items Latent variable Measurement item Standardized Factor Loading Latent variable Measurement item Standardized Factor Loading Relative Advantage A1 0.887 Performance Expectation E1 0.870 A2 0.936 E2 0.919 A3 0.950 E3 0.858 A4 0.918 E4 0.908 Trialability B1 0.794 Perceived Risk F1 0.874 B2 0.761 F2 0.917 B3 0.851 F3 0.891 Observability C1 0.857 F4 0.925 C2 0.883 Individual Innovativeness G1 0.766 Social Influence D1 0.921 G2 0.763 D2 0.893 G3 0.817 D3 0.856 D4 0.743 3.2 Discriminant Validity Analysis The purpose of discriminant validity analysis is to determine whether the questionnaire scale items are effective and appropriate. The principle involves first summing the scores of the items to be analyzed, then dividing them into high-score and low-score groups (using the 27th and 73rd percentiles as boundaries). Then, a t-test was used to compare the differences between the high-score and low-score groups. If there were differences, it indicated that the scale items were appropriately designed; otherwise, it indicated that the scale items could not distinguish the information and were poorly designed and should be deleted. As shown in Table 4, the questionnaire items in this study were reasonably designed. As shown in Table 5, the square roots of the AVE values for each latent variable are all greater than the maximum absolute value of the correlations between factors, indicating good discriminant validity.. Table 4 Project Analysis (Discrimination) Results Measurement item Group (Mean ± Standard Deviation) t (decision value) p low-score ( n =64) high-score ( n =48) A1 3.67±0.87 4.96±0.29 11.004 0.000** A2 3.67±0.93 5.00±0.00 11.465 0.000** A3 3.59±0.85 5.00±0.00 13.247 0.000** A4 3.58±0.87 5.00±0.00 13.085 0.000** B1 3.72±0.81 5.00±0.00 12.716 0.000** B2 3.81±0.81 5.00±0.00 11.670 0.000** B3 3.69±0.75 5.00±0.00 13.939 0.000** C1 3.70±0.79 5.00±0.00 13.126 0.000** C2 3.78±0.74 5.00±0.00 13.093 0.000** D1 3.72±0.74 5.00±0.00 13.764 0.000** D2 3.67±0.76 5.00±0.00 14.034 0.000** D3 3.70±0.77 5.00±0.00 13.473 0.000** D4 3.59±0.83 5.00±0.00 13.548 0.000** E1 3.75±0.73 5.00±0.00 13.612 0.000** E2 3.75±0.71 5.00±0.00 14.031 0.000** E3 3.66±0.74 5.00±0.00 14.540 0.000** E4 3.70±0.75 5.00±0.00 13.849 0.000** F1 3.38±0.98 4.96±0.29 12.192 0.000** F2 3.48±0.93 5.00±0.00 13.098 0.000** F3 3.31±0.94 5.00±0.00 14.351 0.000** F4 3.33±0.94 5.00±0.00 14.172 0.000** G1 3.70±0.75 5.00±0.00 13.849 0.000** G2 3.67±0.76 5.00±0.00 14.034 0.000** G3 3.72±0.72 5.00±0.00 14.176 0.000** * p <0.05 ** p <0.01 Table 5 Discriminant Validity: Pearson Correlations and Square Roots of AVE Values Relative Advantage Trialability Observability Social Influence Performance Expectation Perceived Risk Individual Innovativeness Willingness to use Relative Advantage 0.945 Trialability 0.63 0.929 Observability 0.646 0.918 0.949 Social Influence 0.647 0.896 0.922 0.933 Performance Expectation 0.68 0.882 0.922 0.92 0.964 Perceived Risk 0.318 0.447 0.375 0.416 0.39 0.933 Individual Innovativeness 0.613 0.785 0.798 0.829 0.832 0.448 0.964 Willingness to use 0.631 0.832 0.843 0.878 0.883 0.407 0.914 0.977 The numbers on the diagonal are the square root values of the AVE. 3.3 Analysis of Factors Influencing Consumers' Willingness to Use Smart Traditional Chinese Medicine Pharmacies 3.3.1 Descriptive Statistics of Latent Variables This study conducted a statistical analysis of the basic characteristics of each latent variable. The results are shown in Table 6. Among the valid samples, the maximum value for all latent variables was 5, and the minimum value was 1, indicating a wide range of perceptions among respondents for each question. The standard deviations of the latent variables were relatively large, generally above 0.65, suggesting that respondents' answers varied for each question. This variation reflects differences among the respondents and aligns with the objectives of this study. Table 6 Descriptive Statistical Analysis Results Latent variable Measurement item Minimum value Maximum value Average Standard deviation Variance value standard error Relative Advantage A1 1 5 4.38 0.069 0.914 0.835 A2 1 5 4.38 0.07 0.926 0.857 A3 1 5 4.33 0.07 0.925 0.855 A4 1 5 4.29 0.072 0.947 0.897 Trialability B1 1 5 4.39 0.059 0.787 0.62 B2 1 5 4.47 0.057 0.756 0.572 B3 1 5 4.41 0.06 0.789 0.622 Observability C1 1 5 4.4 0.06 0.788 0.621 C2 1 5 4.44 0.056 0.747 0.558 Social Influence D1 1 5 4.42 0.057 0.76 0.578 D2 1 5 4.37 0.06 0.791 0.626 D3 1 5 4.37 0.06 0.798 0.637 D4 1 5 4.32 0.066 0.878 0.771 Performance Expectation E1 1 5 4.42 0.058 0.761 0.579 E2 1 5 4.43 0.056 0.739 0.546 E3 1 5 4.38 0.06 0.799 0.639 E4 1 5 4.42 0.058 0.768 0.589 Perceived Risk Perceived Risk F1 1 5 3.88 0.087 1.156 1.336 F2 1 5 4 0.082 1.083 1.172 F3 1 5 3.82 0.092 1.218 1.484 F4 1 5 3.87 0.086 1.133 1.283 Innovativeness G1 1 5 4.3 0.058 0.769 0.592 G2 1 5 4.29 0.059 0.779 0.608 G3 1 5 4.32 0.057 0.758 0.575 3.3.1 Univariate and Correlation Analysis 3.3.1.1 Analysis of Differences in Willingness to Use Based on Different Demographic Characteristics The difference in willingness to use by gender was analyzed using an independent two-sample t-test（Table 7 and 8）; differences by age, occupation, education level, and income were analyzed using one-way ANOVA（Table 9）. As shown in Table 7, 8 and 9, there were no statistically significant differences in willingness to use smart traditional Chinese medicine pharmacy services based on gender, age, occupation, education level, or income (p > 0.05). Table 7 Group Statistics Gender Mean Standard Deviation Standard Error of the Mean Willingness to use Male 4.35 .837 .101 Female 4.42 .689 .067 Table 8 Independent samples test Table 9 The result of ANOVA Sum of Squares Degrees of Freedom Mean Square F Significance Age Between groups 1.944 3 .648 .376 .771 Within groups 294.913 171 1.725 Total 296.857 174 Education Between groups 2.515 3 .838 .831 .479 Within groups 172.593 171 1.009 Total 175.109 174 Occupation Between groups 131.968 3 43.989 1.805 .148 Within groups 4168.032 171 24.374 Total 4300.000 174 Monthly income level Between groups 4.653 3 1.551 .853 .467 Within groups 310.982 171 1.819 Total 315.634 174 3.3.2.1 Correlation Analysis Between Latent Variables and Usage Intention As shown in Table 10, at a significance level of 0.01, all seven latent variables in the study are correlated with usage intention. Among them, individual innovativeness has the highest correlation coefficient with usage intention, at 0.893, followed by performance expectancy, social influence, observability, trialability, and perceived usefulness, with correlation coefficients of 0.873, 0.871, 0.830, 0.826, and 0.619, respectively. The correlation coefficient between perceived risk and usage intention is 0.39. Table 10 Independent samples test Latent variable Relative Advantage Trialability Trialability Social Influence Performance Expectation Perceived Risk Individual Innovativeness Willingness to use Relative Advantage 1 .630 ** .646 ** .647 ** .680 ** .318 ** .614 ** .619 ** Trialability .630 ** 1 .918 ** .896 ** .882 ** .447 ** .789 ** .826 ** Observability .646 ** .918 ** 1 .922 ** .922 ** .375 ** .799 ** .830 ** Social Influence .647 ** .896 ** .922 ** 1 .920 ** .416 ** .835 ** .871 ** Performance Expectation .680 ** .882 ** .922 ** .920 ** 1 .390 ** .831 ** .873 ** Perceived Risk .318 ** .447 ** .375 ** .416 ** .390 ** 1 .448 ** .391 ** Individual Innovativeness .614 ** .789 ** .799 ** .835 ** .831 ** .448 ** 1 .893 ** Willingness to use .619 ** .826 ** .830 ** .871 ** .873 ** .391 ** .893 ** 1 **. At the 0.01 level (two-tailed), the correlation is significant. 3.3.1.2 Correlation Analysis Between Latent Variables and Usage Intention The results of the correlation analysis are shown in Table 11. The highest correlation coefficient with usage intention is individual innovativeness, at 0.893, followed by performance expectancy, social influence, observability, trialability, and perceived usefulness, with correlation coefficients of 0.873, 0.871, 0.830, 0.826, and 0.619, respectively. The correlation coefficient between perceived risk and usage intention is only 0.39. Table 11 Correlation Analysis Results Between Latent Variables and Usage Intention Latent variable Relative Advantage Trialability Observability Social Influence Performance Expectation Perceived Risk Individual Innovativeness Willingness to use Relative Advantage 1 Trialability .630 ** 1 Observability .646 ** .918 ** 1 Social Influence .647 ** .896 ** .922 ** 1 Performance Expectation .680 ** .882 ** .922 ** .920 ** 1 Perceived Risk .318 ** .447 ** .375 ** .416 ** .390 ** 1 Individual Innovativeness .614 ** .789 ** .799 ** .835 ** .831 ** .448 ** 1 Willingness to use .619 ** .826 ** .830 ** .871 ** .873 ** .391 ** .893 ** 1 **. The correlation is significant at the 0.01 level (two-tailed). 3.3.2 Path Analysis Path analysis is a model based on linear regression methods used to analyze the complex path relationships among variables. As shown in Table 12, the standardized path coefficient of G3 on usage intention is 0.514 > 0 (z = 5.876, p = 0.000 < 0.01), indicating that if consumers have a willingness to try new technologies, products, or services, it will have a significant positive impact on their usage intention; The standardized path coefficient of D2 on usage intention is 0.206 > 0 (z = 2.028, p = 0.043 < 0.05), indicating that recommendations from medical staff have a significant positive impact on usage intention; The standardized path coefficient of C2 on usage intention is -0.354 < 0 (z = -3.338, p = 0.001 < 0.01), indicating that being able to evaluate the smart Chinese medicine pharmacy service after use has a significant negative impact on usage intention; The standardized path coefficient of B1 on usage intention is 0.139 > 0 (z = 2.068, p = 0.039 < 0.05), indicating that the attitude of “I will try using it, but if unsatisfied, I will abandon the smart Chinese medicine pharmacy service” has a significant positive impact on usage intention. Table 12 Summary of Model Regression Coefficients X → Y Unstandardized Path Coefficient SE z (CR value) p Standardized Path Coefficient G3 → Willingness to use 0.509 0.087 5.876 0.000 0.514 D2 → Willingness to use 0.195 0.096 2.028 0.043 0.206 C2 → Willingness to use -0.355 0.106 -3.338 0.001 -0.354 B1 → Willingness to use 0.132 0.064 2.068 0.039 0.139 3.3.3 Structural Equation Modeling Structural equation modeling (SEM) can simultaneously analyze the relationships between multiple independent and dependent variables. For research data obtained through surveys, SEM uses raw data, making its results more convincing compared to regression analysis. The model fit of SEM is the foundation of statistical analysis; only when the model fit reaches an acceptable range can the research results be considered to have a reliable theoretical basis, giving the findings practical significance and guidance in real-world applications. After importing the survey data into AMOS 25.0 software, as shown in Table 13, all model indicators fall within acceptable ranges, indicating a good model fit. This suggests that the model is accurate and stable, and it can be used to explain consumers’ willingness to use smart traditional Chinese medicine pharmacy services. Table 13 Structural Equation Model Fit Indices Common Indicators Value Criteria Result χ 2 813.815 - Meets the requirements df 247 - Meets the requirements χ 2 / df 3.295 0.9 Meets the requirements RMSEA 0.115 <0.10 Meets the requirements RMR 0.023 0.9 Meets the requirements NFI 0.9 >0.9 Meets the requirements NNFI 0.913 >0.9 Meets the requirements TLI 0.913 >0.9 Meets the requirements AGFI 0.663 >0.9 Meets the requirements IFI 0.929 >0.9 Meets the requirements PGFI 0.565 >0.5 Meets the requirements PNFI 0.741 >0.5 Meets the requirements PCFI 0.764 >0.5 Meets the requirements SRMR 0.028 <0.1 Meets the requirements The relationships of each path in the model output are shown in Table 14, where → indicates regression or measurement relationships. The unstandardized regression coefficient refers to the effect value of each unit change in the predictor variable on the dependent variable, reflecting the magnitude of the effect of the independent variable on the dependent variable when other factors remain constant. SE is the standard error, representing the precision of the regression coefficient. The z-value (CR value) is the regression coefficient divided by the standard error, used to test whether the regression coefficient is significant; it is usually represented by the t-value. This indicator is used to analyze the consistency among the observed variables of a latent variable, with a CR value above 0.7 indicating good composite reliability. The p-value is the significance level of the hypothesis test, used to determine whether the regression coefficient is significant, with 0.05 commonly used as the significance threshold. The standardized regression coefficient is obtained after standardizing both the independent and dependent variables, representing the effect of each standard deviation change in the predictor variable on the dependent variable. It can be used to compare the impact sizes of predictor variables measured in different units on the dependent variable. Table 14 Structural Equation Model Regression Coefficients Table X → Y Unstandardized Path Coefficient SE Z (CRValue) p Standardized Path Coefficient Relative Advantage → Willingness to use -0.006 0.038 -0.151 0.880 -0.006 Trialability → Willingness to use 0.203 0.241 0.843 0.399 0.194 Observability → Willingness to use -0.347 0.386 -0.898 0.369 -0.335 Social Influence → Willingness to use 0.278 0.181 1.532 0.026 0.268 Performance Expectation → Willingness to use 0.365 0.151 2.418 0.016 0.347 Perceived Risk → Willingness to use -0.022 0.028 -0.778 0.437 -0.030 Individual Innovativeness → Willingness to use 0.540 0.067 8.089 0.000 0.531 Table 14 shows the relationships of latent variables in terms of their influence and measurement. Individual innovativeness has a significant impact on the intention to use at the 0.05 level, with a standardized path coefficient of 0.531, indicating a positive influence of individual innovativeness on the intention to use. Similarly, performance expectancy and social influence also have significant positive effects on the intention to use, with standardized path coefficients of 0.365 and 0.278, respectively. Discussion Consumers' willingness to use smart traditional Chinese medicine pharmacy services is mainly influenced by individual innovativeness, performance expectancy, social influence, observability, trialability, and perceived usefulness, among which individual innovativeness, performance expectancy, and social influence are the most important. Numerous studies have shown that individual innovativeness affects users' willingness to use new services. Users with high innovativeness are more willing to challenge, try, and explore new things. Regarding smart traditional Chinese medicine pharmacies, as a new concept, consumers with higher innovativeness pay more attention to new things and thus have a stronger interest in this new business model, making them more likely to decide to use smart pharmacy services. Conversely, consumers with lower innovativeness tend to hesitate or adopt a wait-and-see attitude toward new things due to their personal characteristics, which reduces their willingness to use smart traditional Chinese medicine pharmacy services. The emergence of new services must be able to improve people's quality of life and save time costs, and have significant advantages compared to existing services in order to increase people's willingness to use them. Consumers generally recognize the convenience and other benefits of smart traditional Chinese medicine (TCM) pharmacies. The more consumers can feel the convenience brought by smart pharmacies, the stronger their willingness to use smart TCM pharmacy services will be. Smart TCM pharmacies have their own advantages in areas such as drug delivery, health check-ups, and remote diagnosis and treatment. When consumers hold a positive attitude toward the advantages of smart TCM pharmacy services, their willingness to use these services becomes stronger. Social influence factors have a positive impact on consumers' decisions to use these services. National policy support and encouragement measures play a leading role, followed by promotion and guidance from physical hospitals regarding internet hospitals, as well as recommendations from medical staff. Lastly, consumers also refer to the comprehensive evaluations of others. Doctors, pharmacists, and other medical professionals in offline physical hospitals serve as important communication channels in the medical and health field and play a crucial role in influencing consumers' behavior toward using smart TCM pharmacy services. Therefore, when providing services to patients, doctors and pharmacists should appropriately introduce smart TCM pharmacy services and remind them of advantages such as saving their own consultation time through smart TCM pharmacies, allowing people to be subtly exposed to these services. This study analyzed the factors influencing patients' willingness to use smart Chinese medicine pharmacy services. Based on the scores of various latent variables, it was found that consumers' willingness to use these services all scored above 4.5, indicating a strong willingness among patients to use smart Chinese medicine pharmacy services. However, since smart Chinese medicine pharmacies are still in the early stages of development, their operational models and regulatory mechanisms are still being explored. Patients have a relatively low awareness of smart Chinese medicine pharmacies, and there are many issues during the development process that hinder their growth. Therefore, to promote better development of smart Chinese medicine pharmacies, enable more patients to benefit, and further increase patients' willingness to use these services, the following recommendations are proposed based on the research conclusions of this study for the future practice of smart Chinese medicine pharmacies:(1) Explore mature and stable operational models, strengthen the integration of various medical service resources, and provide convenient and efficient medical consultation channels so that consumers can improve their health status or meet medical needs through these services, thereby satisfying the diverse needs of different segmented user groups to increase performance expectations; (2) Enhance the social influence of smart Chinese medicine pharmacies, especially through recommendations by medical staff (physicians, pharmacists, etc.), as well as promotion and guidance from physical hospitals and internet hospitals. At the same time, patients and consumers should be given sufficient autonomy to choose freely, ensuring their rights to accept, refuse, or withdraw from using the service at any time, thus avoiding resistance; (3) Provide opportunities for individuals with strong innovative capabilities to try smart Chinese medicine pharmacy services for free or at low cost, promote the sharing and dissemination of experiential data, and build a dedicated information-sharing platform. This platform should facilitate communication between consumers and service providers, strengthen interactions between smart Chinese medicine pharmacies, related medical institutions, and consumers, as well as among consumers themselves, to promote information sharing. Consumers who have used the service can choose to post service reviews on the platform, but guidance should be provided on how and where to comment to avoid negatively impacting willingness to use the service. Additionally, leverage big data technology to share information with potential consumers and continuously expand communication channels; (4) Continuously deepen and improve the relevant legal and regulatory framework, and steadily advance national policy support and incentive measures. However, this study still has certain limitations. First, the sample lacks representativeness. The research sample is mainly concentrated among middle-aged and young adults aged 31 to 50, primarily with a college or vocational education background, and an income level mostly between 4,001 and 8,000 yuan. This results in insufficient coverage of special groups such as the elderly, low-income, and impoverished populations, which may affect the generalizability and applicability of the findings. Second, there are limitations in the data collection method. The questionnaire was primarily distributed via hospital QR code scanning, which may have restricted sample diversity and led to underrepresentation of certain occupations and income levels. Additionally, regarding latent variables, although the latent variables and measurement items used in the study passed reliability and validity tests, they may not comprehensively cover all factors influencing the willingness to use smart Chinese medicine pharmacies, leaving the possibility of omitted variables. Finally, there are limitations in the research methodology. Although structural equation modeling was employed, the study did not consider other complex factors that might affect usage intention, such as cultural background and regional differences, which could limit the model’s explanatory power and predictive capability. Declarations Duplicate publication Material submitted is original and not published or submitted for publication elsewhere in any language. Ethics approval and consent to participate Ethics approval for this study was obtained from the Institutional Review Board (IRB) of the Qingdao West Coast New Area Second Traditional Chinese Medicine Hospital. All participants provided informed consent prior to the study. Clinical trial number Not applicable. Consent for publication All authors consent to publication. Research Data Policy and Data Availability Statements The materials described in the manuscript, including all relevant raw data, will be freely available to any researcher wishing to use them for non-commercial purposes, without breaching participant confidentiality. Data availability The datasets used or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no competing non-financial/financial interests. Funding Declaration This study was supported in part by grants from 2024 Qingdao Municipal Medical and Health Science and Technology Plan (No. 2024-WJKY176), 2024 National Administration of Traditional Chinese Medicine Monitoring and Statistics Center Self-selected Research Project on Deepening Medical Reform and Traditional Chinese Medicine Policy (No.:YGZXKT2024232), mainly funded the writing and grammatical improvement of this article. Contributions Ming-Chen CAO, Meng-xiang FANG，Linwei Li and Fan-bo JING contributed to the conception of the study; Wen-xiao WANG，Linwei Li and Xiao-min XING contributed significantly to manuscript preparation; Zhong-wei XIAO, Wen-jing LI and Ze-nan ZHANG helped perform the analysis with constructive discussions; Zhong-wei Xiao, Long XU and Xiao-min XING organized the tables and pictures of the full text. All authors contributed significantly, read and approved the final manuscript. Acknowledgements I am grateful to Mr. JING for his valuable guidance throughout the writing of this thesis. I also appreciate the teachers from the affiliated hospital of Qingdao University for their cooperation in this study. References Shad Z, Nisar M, Maqbool T, et al. The Role of Smart Pharmacy Automation in Improving Medication Safety: A Systematic Review[J]. 2025. Šipetić T, Rajković D, Bogavac Stanojević N, et al. SMART pharmacists serving the new needs of the post-COVID patients, leaving no-one behind[J]. Pharmacy, 2023, 11(2): 61. Efendi A, Huang C Y. Robot arm technology in Detection and Manipulation in Smart Pharmacies: A Review[J]. International Journal of Humanoid Robotics, 2025. ZHONG Y, LI H, OU B, et al. Construction and Practice of Smart Pharmacy Management Model in Our Hospital Based on “Internet+ TCM”[J]. China Pharmacy, 2019: 2460-2468. Qianqian S U N, Chunyu L I U, Siyu L I, et al. Development of shared traditional Chinese medicine pharmacy from the perspective of primary medical care[J]. China Pharmacy, 2023, 34(3): 269-274. Hua L, Ma Y, Meng X, et al. A smart health-oriented traditional chinese medicine pharmacy intelligent service platform[C]//International Conference on Health Information Science. Cham: Springer International Publishing, 2019: 23-34. Zhang Q, Bai C, Yang L T, et al. A unified smart Chinese medicine framework for healthcare and medical services[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019, 18(3): 882-890.. Badr N G, Khiami M. Improving access to prescription-based care through patient-centered smart pharmacy ecosystems[C]//ITM Web of Conferences. EDP Sciences, 2024, 62: 02003. Lin A C, Lee J, Gabriel M K, et al. The Pharmacy 5.0 framework: A new paradigm to accelerate innovation for large-scale personalized pharmacy care[J]. American Journal of Health-System Pharmacy, 2024, 81(5): e141-e147. Pires C, Sousa M J. The Role of Community Pharmacies in Smart Cities: A Brief Systematic Review and a Conceptual Framework[C]//Proceedings of International Conference on Information Technology and Applications. Springer, Singapore, 2023: 629-641.. Bayrami A, Shahriari M R, Lotfi F H. Identifying the Factors Affecting Smart Pharmaceutical Distribution[J]. Journal of Resource Management and Decision Engineering, 2025, 4(1): 1-12. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7530904","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515709078,"identity":"cb60045a-02cb-4dba-aeb6-79a15b422165","order_by":0,"name":"Ming-chen CAO","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Ming-chen","middleName":"","lastName":"CAO","suffix":""},{"id":515709079,"identity":"d30609cf-33ca-4cc6-8baa-8e6bcd14b924","order_by":1,"name":"Fan-bo JING","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Fan-bo","middleName":"","lastName":"JING","suffix":""},{"id":515709080,"identity":"51dbe849-ea53-4b78-afe0-b056cda5a96c","order_by":2,"name":"Ze-nan ZHANG","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Ze-nan","middleName":"","lastName":"ZHANG","suffix":""},{"id":515709081,"identity":"9869e54f-a20e-4dbd-b2dd-869e280091a3","order_by":3,"name":"Wen-xiao WANG","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Wen-xiao","middleName":"","lastName":"WANG","suffix":""},{"id":515709082,"identity":"7b8bceae-32ca-4e15-9504-8f5dbce0ae1a","order_by":4,"name":"Zhong-wei XIAO","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Zhong-wei","middleName":"","lastName":"XIAO","suffix":""},{"id":515709085,"identity":"d49d0756-c2a4-4638-a8c0-aa1605fae411","order_by":5,"name":"Long XU","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"XU","suffix":""},{"id":515709088,"identity":"1f4f8ac8-7893-4977-b13d-2086c8727fe0","order_by":6,"name":"Wen-jing LI","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Wen-jing","middleName":"","lastName":"LI","suffix":""},{"id":515709090,"identity":"5a9877c3-3134-48ac-b25a-2e45d2fdae41","order_by":7,"name":"Xiao-min XING","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao 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15:50:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":944482,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7530904/v1/e0f742cc-db74-45f7-a2eb-0e11805cf622.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Study on the Factors Influencing the Usage Intention of Consumer Groups in Smart Chinese Medicine Pharmacies Based on Innovation Diffusion Theory and Structural Equation Modeling","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, the \u0026quot;Internet + Healthcare\u0026quot; industry has developed rapidly, playing a significant role in enhancing the quality and efficiency of medical services while improving patient treatment conditions [1]. Many traditional Chinese medicine (TCM) hospitals, particularly Grade A tertiary TCM hospitals, have long faced challenges such as inadequate supply of medical resources, insufficient pharmacy resource allocation, labor-intensive herbal decoction preparation, and prolonged waiting times for medication dispensing. These issues have resulted in reduced patient satisfaction and strained physician-patient relationships[2]. Consequently, an \u0026quot;Internet + Pharmaceutical Care\u0026quot; model has emerged to achieve remote, convenient, and equitable pharmaceutical services [3-6]. Against the backdrop of advancements in \u0026quot;Internet Plus\u0026quot;, big data, and artificial intelligence, hospitals have leveraged internet and IoT platforms to address patients\u0026apos; diversified medication needs and mitigate supply-demand imbalances in medical resources. By integrating online and offline resources using modern information technology and automated control systems, they innovatively combine pharmacy management systems \u0026nbsp;with conventional treatment-medication models, establishing modern intelligent TCM pharmacies that provide one-stop pharmacy services [7].Through informatized workflows, optimized storage environments, and efficient dispensing methods, intelligent TCM pharmacies enhance pharmaceutical service efficiency and quality while advancing medication counseling and health management services. These platforms enable fully digitalized management\u0026mdash;covering payment, herbal decoction preparation, delivery, and consultation\u0026mdash;delivering comprehensive pharmacy services [8-10]. This evolution epitomizes the informatization of TCM services and the enhancement of TCM service capabilities, marking the transition of hospital pharmacies toward intelligent and networked development. Its purpose is to bridge the \u0026quot;last-mile\u0026quot; gap in TCM services, empowering the innovation and heritage of the TCM industry [11].\u003c/p\u003e\n\u003cp\u003eAt present, the construction and development of intelligent TCM pharmacies in China remain in the exploratory stage. Existing literature primarily focuses on operational models and service scale of such pharmacies, with limited research on consumer group analysis and factors influencing usage intention. Numerous refinements are still required during their implementation. From the consumer perspective, this study employs questionnaire surveys, data statistics, and structural equation modeling (SEM) analysis to investigate factors affecting consumers\u0026apos; adoption of intelligent TCM pharmacy services. Strategic recommendations for development are proposed to facilitate the advancement of intelligent TCM pharmacies.\u003c/p\u003e\n"},{"header":"Research Methods ","content":"\u003cp\u003eQuestionnaire Survey Method: Based on the diffusion of innovation theory, this study investigates the relationship between seven variables—relative advantage, trialability, observability, social influence, performance expectancy, perceived risk, and consumer innovativeness—and the willingness to use. The questionnaire uses a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree).\u003c/p\u003e\n\u003cp\u003eStatistical Analysis Method: SPSS 26.0 statistical software was used to analyze the factors influencing consumers’ willingness to use smart Chinese medicine pharmacy services. This includes reliability and validity analysis of the questionnaire, descriptive statistical analysis, and correlation analysis, as well as constructing a regression model for the willingness to use smart Chinese medicine pharmacy services among consumer groups. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. Questionnaire Design and Collection\u003c/p\u003e\n\u003cp\u003eThe data for this study were collected through a questionnaire survey, which included 7 latent variables and a total of 22 measurement items. The questionnaire was distributed both online and offline. After excluding invalid responses, a total of 175 valid questionnaires were collected, with an overall effective response rate of 93.08%.\u003c/p\u003e\n\u003cp\u003e3 Research Content and Results\u003c/p\u003e\n\u003cp\u003e3.1 Reliability and Validity Analysis\u003c/p\u003e\n\u003cp\u003e3.1.1 Reliability Analysis\u003c/p\u003e\n\u003cp\u003eAs shown in Table 1, the reliability analysis shows that the Cronbach’s Alpha values for the seven latent variables—relative advantage (perceived usefulness), trialability, observability, social influence, performance expectancy, perceived risk, and consumer innovativeness—are 0.972, 0.950, 0.964, 0.981, 0.964, 0.974, and 0.978, respectively, indicating that the questionnaire has good reliability.\u003c/p\u003e\n\u003cp\u003eTable 1 Reliability Analysis Results of the Questionnaire\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"114%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLatent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeasurement item\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCronbach’s Alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCronbach’s Alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eRelative Advantage (Perceived Usefulness) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA1: Compared to traditional pharmacies, smart Chinese medicine pharmacies are more convenient.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA2: The smart Chinese medicine pharmacy service can shorten my medical treatment process. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA3: The smart Chinese medicine pharmacy service allows me to access higher-quality medical resources (such as renowned medical experts, personalized rehabilitation plans, etc.). \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA4: The smart Chinese medicine pharmacy can provide me with more comprehensive services.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eTrialability \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB1: I am willing to try it, and if I am not satisfied, I will stop using the smart Chinese medicine pharmacy service. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB2: I hope there are free or low-cost trial opportunities for services like online consultations and Chinese medicine decoction. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB3: I hope to refer to data and feedback from others’ experiences with the smart Chinese medicine pharmacy service.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObservability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC1: I believe the advantages and convenience of the smart Chinese medicine pharmacy service are obvious. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC2: I hope to be able to provide feedback on the smart Chinese medicine pharmacy service after using it.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eSocial Influence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD1: National policy support and encouraging measures will motivate me to use this service. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD2: If medical staff recommend it, I will use this service. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD3: Promotion of internet hospitals by physical hospitals will guide me to use this service. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD4: Others’ overall evaluations will influence my decision to use the smart Chinese medicine pharmacy service.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003ePerformance Expectation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE1: Using this service makes it easier for me to consult with doctors. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE2: Using this service reduces the hassle of visiting physical hospitals. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE3: Using this service helps improve my health condition. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE4: I can effectively use this service to meet my medical needs, such as online follow-ups, prescription renewals, and medicine delivery.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003ePerceived Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF1: I’m worried that smart Chinese medicine pharmacies might leak my personal information. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF2: I’m concerned that there are currently no laws or regulations to govern the standardized services of smart Chinese medicine pharmacies. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF3: I’m worried about the quality of the medicines provided by smart Chinese medicine pharmacies. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF4: I’m concerned that the services offered by smart Chinese medicine pharmacies may not be professional enough. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eIndividual Innovativeness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG1: I usually pay close attention to the development of new things or new technologies. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG2: I’m generally more open to accepting new things or new ideas. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG3: I’m very willing to try new technologies, products, or services.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.1.2 Validity Analysis\u003c/p\u003e\n\u003cp\u003eThe collected data were used to conduct the KMO value and Bartlett's test of sphericity. The overall test results for the seven latent variables and the questionnaire are shown in Table 2. The overall KMO value of the questionnaire was greater than 0.9, and Bartlett's test of sphericity reached a significant level, indicating that the questionnaire has good structural validity.\u003c/p\u003e\n\u003cp\u003eTable 2 Results of Questionnaire Validity Analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eLatent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eKMO Measure of Sampling Adequacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003eBartlett's Test of Sphericity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eχ²\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRelative Advantage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1009.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTrialability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e518.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObservability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e286.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSocial Influence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e929.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerformance Expectation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1188.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerceived Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e855.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIndividual Innovativeness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e750.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11411.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe standardized factor loadings of the latent variables are shown in Table 3. The standardized factor loadings for each measurement item of the latent variables are all greater than 0.7, indicating that all 22 measurement items can be used to explain the respective latent variables.\u003c/p\u003e\n\u003cp\u003eTable 3 Factor Analysis of Measurement Items\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLatent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeasurement item\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandardized Factor Loading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLatent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeasurement item\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandardized Factor Loading\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eRelative Advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003ePerformance Expectation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eTrialability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003ePerceived Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eObservability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eIndividual Innovativeness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eSocial Influence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.2 Discriminant Validity Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe purpose of discriminant validity analysis is to determine whether the questionnaire scale items are effective and appropriate. The principle involves first summing the scores of the items to be analyzed, then dividing them into high-score and low-score groups (using the 27th and 73rd percentiles as boundaries). Then, a t-test was used to compare the differences between the high-score and low-score groups. If there were differences, it indicated that the scale items were appropriately designed; otherwise, it indicated that the scale items could not distinguish the information and were poorly designed and should be deleted. As shown in Table 4, the questionnaire items in this study were reasonably designed. As shown in Table 5, the square roots of the AVE values for each latent variable are all greater than the maximum absolute value of the correlations between factors, indicating good discriminant validity..\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 4 Project Analysis (Discrimination) Results\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eMeasurement\u003c/p\u003e\n \u003cp\u003eitem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eGroup (Mean ± Standard Deviation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003et\u0026nbsp;\u003c/em\u003e(decision value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elow-score (\u003cem\u003en\u003c/em\u003e=64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ehigh-score (\u003cem\u003en\u003c/em\u003e=48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.67±0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.96±0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.67±0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.59±0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.58±0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.72±0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.81±0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.69±0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.70±0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.78±0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.72±0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.67±0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.70±0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.59±0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.75±0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.75±0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eE3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.66±0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eE4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.70±0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.38±0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.96±0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.48±0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.31±0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.33±0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.70±0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.67±0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.72±0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00±0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e* \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 ** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5 Discriminant Validity: Pearson Correlations and Square Roots of AVE Values\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"582\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRelative Advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTrialability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eObservability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSocial Influence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePerformance Expectation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePerceived Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIndividual Innovativeness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRelative Advantage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTrialability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObservability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSocial Influence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerformance Expectation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerceived Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIndividual Innovativeness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe numbers on the diagonal are the square root values of the AVE.\u003c/p\u003e\n\u003cp\u003e3.3 Analysis of Factors Influencing Consumers' Willingness to Use Smart Traditional Chinese Medicine Pharmacies 3.3.1 Descriptive Statistics of Latent Variables\u003c/p\u003e\n\u003cp\u003eThis study conducted a statistical analysis of the basic characteristics of each latent variable. The results are shown in Table 6. Among the valid samples, the maximum value for all latent variables was 5, and the minimum value was 1, indicating a wide range of perceptions among respondents for each question. The standard deviations of the latent variables were relatively large, generally above 0.65, suggesting that respondents' answers varied for each question. This variation reflects differences among the respondents and aligns with the objectives of this study.\u003c/p\u003e\n\u003cp\u003eTable 6 Descriptive Statistical Analysis Results\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"608\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eLatent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eMeasurement item\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eMinimum value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eMaximum value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eVariance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003estandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eRelative Advantage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eTrialability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eObservability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eSocial Influence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003ePerformance Expectation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eE3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eE4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003ePerceived Risk Perceived Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eInnovativeness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;3.3.1 Univariate and Correlation Analysis\u003c/p\u003e\n\u003cp\u003e3.3.1.1 Analysis of Differences in Willingness to Use Based on Different Demographic Characteristics \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe difference in willingness to use by gender was analyzed using an independent two-sample t-test（Table 7 and 8）; differences by age, occupation, education level, and income were analyzed using one-way ANOVA（Table 9）. As shown in Table 7, 8 and 9, there were no statistically significant differences in willingness to use smart traditional Chinese medicine pharmacy services based on gender, age, occupation, education level, or income (p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eTable 7 Group Statistics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eStandard Error of the Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Table 8 Independent samples test\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eTable 9 The result of ANOVA\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSum of Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDegrees of Freedom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSignificance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBetween groups \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.771\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWithin groups \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e294.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e296.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBetween groups \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWithin groups \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e172.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e175.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBetween groups \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e131.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWithin groups \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4168.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4300.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMonthly income level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBetween groups \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWithin groups \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e310.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e315.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.3.2.1 Correlation Analysis Between Latent Variables and Usage Intention \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in Table 10, at a significance level of 0.01, all seven latent variables in the study are correlated with usage intention. Among them, individual innovativeness has the highest correlation coefficient with usage intention, at 0.893, followed by performance expectancy, social influence, observability, trialability, and perceived usefulness, with correlation coefficients of 0.873, 0.871, 0.830, 0.826, and 0.619, respectively. The correlation coefficient between perceived risk and usage intention is 0.39.\u003c/p\u003e\n\u003cp\u003eTable 10 Independent samples test\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"108%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLatent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRelative Advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTrialability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTrialability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSocial Influence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePerformance Expectation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePerceived Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eIndividual Innovativeness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRelative Advantage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.630\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.646\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.647\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.680\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.318\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.614\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.619\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTrialability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.630\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.918\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.896\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.882\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.447\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.789\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.826\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObservability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.646\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.918\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.922\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.922\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.375\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.799\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.830\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSocial Influence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.647\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.896\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.922\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.920\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.416\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.835\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.871\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerformance Expectation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.680\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.882\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.922\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.920\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.390\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.831\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.873\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerceived Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.318\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.447\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.375\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.416\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.390\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.448\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.391\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndividual Innovativeness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.614\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.789\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.799\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.835\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.831\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.448\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.893\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.619\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.826\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.830\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.871\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.873\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.391\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.893\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e**. At the 0.01 level (two-tailed), the correlation is significant.\u003c/p\u003e\n\u003cp\u003e3.3.1.2 Correlation Analysis Between Latent Variables and Usage Intention \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of the correlation analysis are shown in Table 11. The highest correlation coefficient with usage intention is individual innovativeness, at 0.893, followed by performance expectancy, social influence, observability, trialability, and perceived usefulness, with correlation coefficients of 0.873, 0.871, 0.830, 0.826, and 0.619, respectively. The correlation coefficient between perceived risk and usage intention is only 0.39.\u003c/p\u003e\n\u003cp\u003eTable 11 Correlation Analysis Results Between Latent Variables and Usage Intention\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"121%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLatent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRelative Advantage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTrialability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eObservability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSocial Influence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePerformance Expectation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePerceived Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIndividual Innovativeness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRelative Advantage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTrialability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.630\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObservability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.646\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.918\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSocial Influence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.647\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.896\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.922\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerformance Expectation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.680\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.882\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.922\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.920\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerceived Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.318\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.447\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.375\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.416\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.390\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIndividual Innovativeness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.614\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.789\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.799\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.835\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.831\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.448\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.619\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.826\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.830\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.871\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.873\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.391\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.893\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e**. The correlation is significant at the 0.01 level (two-tailed).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.3.2 Path Analysis \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePath analysis is a model based on linear regression methods used to analyze the complex path relationships among variables. As shown in Table 12, the standardized path coefficient of G3 on usage intention is 0.514 \u0026gt; 0 (z = 5.876, p = 0.000 \u0026lt; 0.01), indicating that if consumers have a willingness to try new technologies, products, or services, it will have a significant positive impact on their usage intention; The standardized path coefficient of D2 on usage intention is 0.206 \u0026gt; 0 (z = 2.028, p = 0.043 \u0026lt; 0.05), indicating that recommendations from medical staff have a significant positive impact on usage intention; The standardized path coefficient of C2 on usage intention is -0.354 \u0026lt; 0 (z = -3.338, p = 0.001 \u0026lt; 0.01), indicating that being able to evaluate the smart Chinese medicine pharmacy service after use has a significant negative impact on usage intention; The standardized path coefficient of B1 on usage intention is 0.139 \u0026gt; 0 (z = 2.068, p = 0.039 \u0026lt; 0.05), indicating that the attitude of “I will try using it, but if unsatisfied, I will abandon the smart Chinese medicine pharmacy service” has a significant positive impact on usage intention.\u003c/p\u003e\n\u003cp\u003eTable 12 Summary of Model Regression Coefficients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"107%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e→\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUnstandardized Path Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ez\u003c/em\u003e (CR value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandardized Path Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e→\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e→\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e→\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e→\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.3.3 Structural Equation Modeling\u003c/p\u003e\n\u003cp\u003eStructural equation modeling (SEM) can simultaneously analyze the relationships between multiple independent and dependent variables. For research data obtained through surveys, SEM uses raw data, making its results more convincing compared to regression analysis. The model fit of SEM is the foundation of statistical analysis; only when the model fit reaches an acceptable range can the research results be considered to have a reliable theoretical basis, giving the findings practical significance and guidance in real-world applications. After importing the survey data into AMOS 25.0 software, as shown in Table 13, all model indicators fall within acceptable ranges, indicating a good model fit. This suggests that the model is accurate and stable, and it can be used to explain consumers’ willingness to use smart traditional Chinese medicine pharmacy services.\u003c/p\u003e\n\u003cp\u003eTable 13 Structural Equation Model Fit Indices\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"93%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCommon Indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCriteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eResult\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e813.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeets the requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeets the requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeets the requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeets the requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeets the requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeets the requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeets the requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeets the requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeets the requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeets the requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeets the requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeets the requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeets the requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeets the requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeets the requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeets the requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe relationships of each path in the model output are shown in Table 14, where → indicates regression or measurement relationships. The unstandardized regression coefficient refers to the effect value of each unit change in the predictor variable on the dependent variable, reflecting the magnitude of the effect of the independent variable on the dependent variable when other factors remain constant. SE is the standard error, representing the precision of the regression coefficient. The z-value (CR value) is the regression coefficient divided by the standard error, used to test whether the regression coefficient is significant; it is usually represented by the t-value. This indicator is used to analyze the consistency among the observed variables of a latent variable, with a CR value above 0.7 indicating good composite reliability. The p-value is the significance level of the hypothesis test, used to determine whether the regression coefficient is significant, with 0.05 commonly used as the significance threshold. The standardized regression coefficient is obtained after standardizing both the independent and dependent variables, representing the effect of each standard deviation change in the predictor variable on the dependent variable. It can be used to compare the impact sizes of predictor variables measured in different units on the dependent variable.\u003c/p\u003e\n\u003cp\u003eTable 14 Structural Equation Model Regression Coefficients Table\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"91%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e→\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUnstandardized Path Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eZ\u0026nbsp;\u003c/em\u003e(CRValue)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandardized Path Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRelative Advantage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e→\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTrialability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e→\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObservability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e→\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSocial Influence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e→\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerformance Expectation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e→\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerceived Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e→\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndividual Innovativeness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e→\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWillingness to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 14 shows the relationships of latent variables in terms of their influence and measurement. Individual innovativeness has a significant impact on the intention to use at the 0.05 level, with a standardized path coefficient of 0.531, indicating a positive influence of individual innovativeness on the intention to use. Similarly, performance expectancy and social influence also have significant positive effects on the intention to use, with standardized path coefficients of 0.365 and 0.278, respectively.\u003c/p\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eConsumers' willingness to use smart traditional Chinese medicine pharmacy services is mainly influenced by individual innovativeness, performance expectancy, social influence, observability, trialability, and perceived usefulness, among which individual innovativeness, performance expectancy, and social influence are the most important. Numerous studies have shown that individual innovativeness affects users' willingness to use new services. Users with high innovativeness are more willing to challenge, try, and explore new things. Regarding smart traditional Chinese medicine pharmacies, as a new concept, consumers with higher innovativeness pay more attention to new things and thus have a stronger interest in this new business model, making them more likely to decide to use smart pharmacy services. Conversely, consumers with lower innovativeness tend to hesitate or adopt a wait-and-see attitude toward new things due to their personal characteristics, which reduces their willingness to use smart traditional Chinese medicine pharmacy services.\u003c/p\u003e\u003cp\u003eThe emergence of new services must be able to improve people's quality of life and save time costs, and have significant advantages compared to existing services in order to increase people's willingness to use them. Consumers generally recognize the convenience and other benefits of smart traditional Chinese medicine (TCM) pharmacies. The more consumers can feel the convenience brought by smart pharmacies, the stronger their willingness to use smart TCM pharmacy services will be. Smart TCM pharmacies have their own advantages in areas such as drug delivery, health check-ups, and remote diagnosis and treatment. When consumers hold a positive attitude toward the advantages of smart TCM pharmacy services, their willingness to use these services becomes stronger. Social influence factors have a positive impact on consumers' decisions to use these services. National policy support and encouragement measures play a leading role, followed by promotion and guidance from physical hospitals regarding internet hospitals, as well as recommendations from medical staff. Lastly, consumers also refer to the comprehensive evaluations of others. Doctors, pharmacists, and other medical professionals in offline physical hospitals serve as important communication channels in the medical and health field and play a crucial role in influencing consumers' behavior toward using smart TCM pharmacy services. Therefore, when providing services to patients, doctors and pharmacists should appropriately introduce smart TCM pharmacy services and remind them of advantages such as saving their own consultation time through smart TCM pharmacies, allowing people to be subtly exposed to these services.\u003c/p\u003e\u003cp\u003eThis study analyzed the factors influencing patients' willingness to use smart Chinese medicine pharmacy services. Based on the scores of various latent variables, it was found that consumers' willingness to use these services all scored above 4.5, indicating a strong willingness among patients to use smart Chinese medicine pharmacy services. However, since smart Chinese medicine pharmacies are still in the early stages of development, their operational models and regulatory mechanisms are still being explored. Patients have a relatively low awareness of smart Chinese medicine pharmacies, and there are many issues during the development process that hinder their growth. Therefore, to promote better development of smart Chinese medicine pharmacies, enable more patients to benefit, and further increase patients' willingness to use these services, the following recommendations are proposed based on the research conclusions of this study for the future practice of smart Chinese medicine pharmacies:(1) Explore mature and stable operational models, strengthen the integration of various medical service resources, and provide convenient and efficient medical consultation channels so that consumers can improve their health status or meet medical needs through these services, thereby satisfying the diverse needs of different segmented user groups to increase performance expectations; (2) Enhance the social influence of smart Chinese medicine pharmacies, especially through recommendations by medical staff (physicians, pharmacists, etc.), as well as promotion and guidance from physical hospitals and internet hospitals. At the same time, patients and consumers should be given sufficient autonomy to choose freely, ensuring their rights to accept, refuse, or withdraw from using the service at any time, thus avoiding resistance; (3) Provide opportunities for individuals with strong innovative capabilities to try smart Chinese medicine pharmacy services for free or at low cost, promote the sharing and dissemination of experiential data, and build a dedicated information-sharing platform. This platform should facilitate communication between consumers and service providers, strengthen interactions between smart Chinese medicine pharmacies, related medical institutions, and consumers, as well as among consumers themselves, to promote information sharing. Consumers who have used the service can choose to post service reviews on the platform, but guidance should be provided on how and where to comment to avoid negatively impacting willingness to use the service. Additionally, leverage big data technology to share information with potential consumers and continuously expand communication channels; (4) Continuously deepen and improve the relevant legal and regulatory framework, and steadily advance national policy support and incentive measures.\u003c/p\u003e\u003cp\u003eHowever, this study still has certain limitations. First, the sample lacks representativeness. The research sample is mainly concentrated among middle-aged and young adults aged 31 to 50, primarily with a college or vocational education background, and an income level mostly between 4,001 and 8,000 yuan. This results in insufficient coverage of special groups such as the elderly, low-income, and impoverished populations, which may affect the generalizability and applicability of the findings. Second, there are limitations in the data collection method. The questionnaire was primarily distributed via hospital QR code scanning, which may have restricted sample diversity and led to underrepresentation of certain occupations and income levels. Additionally, regarding latent variables, although the latent variables and measurement items used in the study passed reliability and validity tests, they may not comprehensively cover all factors influencing the willingness to use smart Chinese medicine pharmacies, leaving the possibility of omitted variables. Finally, there are limitations in the research methodology. Although structural equation modeling was employed, the study did not consider other complex factors that might affect usage intention, such as cultural background and regional differences, which could limit the model\u0026rsquo;s explanatory power and predictive capability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eDuplicate publication\u003c/p\u003e\n\u003cp\u003eMaterial submitted is original and not published or submitted for publication elsewhere in any language.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Ethics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eEthics approval for this study was obtained from the Institutional Review Board (IRB) of the Qingdao West Coast New Area Second Traditional Chinese Medicine Hospital. All participants provided informed consent prior to the study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Clinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Consent for publication\u003c/p\u003e\n\u003cp\u003eAll authors consent to publication.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Research Data Policy and Data Availability Statements\u003c/p\u003e\n\u003cp\u003eThe materials described in the manuscript, including all relevant raw data, will be freely available to any researcher wishing to use them for non-commercial purposes, without breaching participant confidentiality.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Data availability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets used or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Competing interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing non-financial/financial interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Funding Declaration\u003c/p\u003e\n\u003cp\u003eThis study was supported in part by grants from 2024 Qingdao Municipal Medical and Health Science and Technology Plan (No. 2024-WJKY176), 2024 National Administration of Traditional Chinese Medicine Monitoring and Statistics Center Self-selected Research Project on Deepening Medical Reform and Traditional Chinese Medicine Policy (No.:YGZXKT2024232), mainly funded the writing and grammatical improvement of this article.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Contributions\u003c/p\u003e\n\u003cp\u003eMing-Chen CAO, Meng-xiang FANG，Linwei Li and Fan-bo JING contributed to the conception of the study; Wen-xiao WANG，Linwei Li and Xiao-min XING contributed significantly to manuscript preparation; Zhong-wei XIAO, Wen-jing LI and Ze-nan ZHANG helped perform the analysis with constructive discussions; Zhong-wei Xiao, Long XU and Xiao-min XING organized the tables and pictures of the full text. All authors contributed significantly, read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Acknowledgements\u003c/p\u003e\n\u003cp\u003eI am grateful to Mr. JING for his valuable guidance throughout the writing of this thesis. I also appreciate the teachers from the affiliated hospital of Qingdao University for their cooperation in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShad Z, Nisar M, Maqbool T, et al. The Role of Smart Pharmacy Automation in Improving Medication Safety: A Systematic Review[J]. 2025.\u003c/li\u003e\n\u003cli\u003e\u0026Scaron;ipetić T, Rajković D, Bogavac Stanojević N, et al. SMART pharmacists serving the new needs of the post-COVID patients, leaving no-one behind[J]. Pharmacy, 2023, 11(2): 61.\u003c/li\u003e\n\u003cli\u003eEfendi A, Huang C Y. Robot arm technology in Detection and Manipulation in Smart Pharmacies: A Review[J]. International Journal of Humanoid Robotics, 2025.\u003c/li\u003e\n\u003cli\u003eZHONG Y, LI H, OU B, et al. Construction and Practice of Smart Pharmacy Management Model in Our Hospital Based on \u0026ldquo;Internet+ TCM\u0026rdquo;[J]. China Pharmacy, 2019: 2460-2468.\u003c/li\u003e\n\u003cli\u003eQianqian S U N, Chunyu L I U, Siyu L I, et al. Development of shared traditional Chinese medicine pharmacy from the perspective of primary medical care[J]. China Pharmacy, 2023, 34(3): 269-274.\u003c/li\u003e\n\u003cli\u003eHua L, Ma Y, Meng X, et al. A smart health-oriented traditional chinese medicine pharmacy intelligent service platform[C]//International Conference on Health Information Science. Cham: Springer International Publishing, 2019: 23-34.\u003c/li\u003e\n\u003cli\u003eZhang Q, Bai C, Yang L T, et al. A unified smart Chinese medicine framework for healthcare and medical services[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019, 18(3): 882-890..\u003c/li\u003e\n\u003cli\u003eBadr N G, Khiami M. Improving access to prescription-based care through patient-centered smart pharmacy ecosystems[C]//ITM Web of Conferences. EDP Sciences, 2024, 62: 02003.\u003c/li\u003e\n\u003cli\u003eLin A C, Lee J, Gabriel M K, et al. The Pharmacy 5.0 framework: A new paradigm to accelerate innovation for large-scale personalized pharmacy care[J]. American Journal of Health-System Pharmacy, 2024, 81(5): e141-e147.\u003c/li\u003e\n\u003cli\u003ePires C, Sousa M J. The Role of Community Pharmacies in Smart Cities: A Brief Systematic Review and a Conceptual Framework[C]//Proceedings of International Conference on Information Technology and Applications. Springer, Singapore, 2023: 629-641..\u003c/li\u003e\n\u003cli\u003eBayrami A, Shahriari M R, Lotfi F H. Identifying the Factors Affecting Smart Pharmaceutical Distribution[J]. Journal of Resource Management and Decision Engineering, 2025, 4(1): 1-12.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Smart Chinese pharmacy, Diffusion of innovations theory, Intention to use, Influencing factors, Structural equation modeling","lastPublishedDoi":"10.21203/rs.3.rs-7530904/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7530904/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe development of smart traditional Chinese medicine (TCM) pharmacies in China is still in the exploratory stage, with limited research on consumer group analysis and factors influencing usage intention. This study aims to explore the factors affecting consumers' willingness to use smart TCM pharmacy services from the consumer perspective.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eBased on the diffusion of innovation theory, this study analyzes the factors influencing the use of smart TCM pharmacy services. Relative advantage, trialability, observability, social influence, performance expectancy, perceived risk, and consumer innovativeness were treated as latent variables. A survey questionnaire was designed to assess consumers' willingness to use smart pharmacy services, and a model of influencing factors was constructed. Statistical analyses included reliability and validity tests of the questionnaire, discrimination tests, t-tests and one-way ANOVA for differences in usage intention among different individuals, correlation analysis between latent variables and usage intention, and the construction of a structural equation model among influencing factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 175 valid questionnaires were collected; overall reliability was 0.962, with Cronbach's alpha values for each latent variable exceeding 0.7, KMO value\u0026thinsp;=\u0026thinsp;0.952, and standardized factor loadings for each measurement item under each latent variable greater than 0.4. The t-test and one-way ANOVA results indicated that gender, age, education level, occupation, and income level did not have statistically significant differences in willingness to use smart TCM pharmacy services. Correlation analysis shows that the correlation coefficient between individual innovativeness and willingness to use is 0.893. The correlation coefficients for performance expectancy, social influence, observability, trialability, and perceived usefulness are 0.873, 0.871, 0.830, 0.826, and 0.619 respectively, while perceived risk has a relatively low correlation coefficient of only 0.39. Path analysis indicates that if consumers are willing to try new technologies, products, or services, if medical staff recommend them, and if users are willing to abandon the smart Chinese medicine pharmacy service after unsatisfactory trials, these factors have a significant positive impact on willingness to use. Conversely, the ability to evaluate the smart Chinese medicine pharmacy service after use has a significant negative impact on willingness to use. The structural model shows that individual innovativeness has a significant effect on willingness to use at the 0.05 level, with a standardized path coefficient of 0.531, indicating a positive influence. At the same time, performance expectancy and social influence also have significant positive effects on willingness to use, with standardized path coefficients of 0.365 and 0.278, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eWith the development and application of smart Chinese medicine pharmacies, consumer-related research on influencing factors will become a key focus for future promotion and application. Future efforts should continuously explore mature and stable operational models, enhance the social influence of smart Chinese medicine pharmacies, strengthen recommendation channels through medical staff, provide trial opportunities for individuals with strong innovativeness, and continuously promote the improvement of relevant legal and regulatory systems.\u003c/p\u003e","manuscriptTitle":"A Study on the Factors Influencing the Usage Intention of Consumer Groups in Smart Chinese Medicine Pharmacies Based on Innovation Diffusion Theory and Structural Equation Modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-19 13:47:07","doi":"10.21203/rs.3.rs-7530904/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"687322c8-9625-4572-a31a-905311498352","owner":[],"postedDate":"September 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-27T14:53:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-19 13:47:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7530904","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7530904","identity":"rs-7530904","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}