Wearable Computing: Canonical Correlation Analysis (CFA) Statistical Method to Validate the Measurement Models Smart Ergonomic Shoes - Nagpal, Rakhi; Singh, Pritpal; Angra, Prikshat Kumar; Cheema, Gagandeep Singh; Rehman, Muzzamil (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Wearable sensors have become increasingly popular over the past few years, and there are now a number of different products on the market that may be used for monitoring one’s own health and activities. A more modern health care system ought to be able to provide better medical services to people at any time and any location, in a manner that is both inexpensive and patient-friendly. This study intends to evaluate the measuring model and offer evidence for the links between influential factors and purchase intention. To do so, it will make use of CFA. The results of the CFA will contribute to a better knowledge of the elements that have a substantial impact on purchase intention in the context of smart ergonomic footwear, since this understanding will be furthered by the findings of the CFA. In general, canonical correlation analysis (CFA) is an effective statistical method that contributes to the validation of measurement models, the evaluation of correlations between variables, and the improvement of the robustness of the study’s conclusions. The coefficient for Customer Perceived Values is statistically significant at the 0.05 level (since the p-value is less than 0.05). The positive coefficient suggests that there is a positive relationship between Customer Perceived Values and the outcome variable (purchase intention) in the analysis. The coefficient for Personal Values is statistically significant at the 0.05 level. The positive coefficient suggests that there is a positive relationship between Personal Values and purchase intention in the analysis. The coefficient for Social Factors is highly statistically significant (p-value is close to 0) at the 0.05 level.