## 3 Results
### Regression Analysis of the Predictors of the Intention to Use Bing Chat (or GPT) in the Future  
The rather high percentage of _positive_ statements (\"4--I agree\" and \"5--I totally agree\") of participants in our study regarding the individual items of the _Intention to Use_ assessment scale (see the Appendix A), which was in the range from 75% to 79%, slightly exceeds the findings of a large-scale survey that was performed among college students in Germany [15] in May and June 2023, in which 63.2% of the respondents reported using ChatGPT or other AI tools. The data analysis regarding the _correlation_ between _usability_ and _user experience_ variables, on the one side, and _Intention to Use_ Bing Chat, on the other, revealed that the highest correlations (Pearson; statistically significant at \(p\) < 0.001) were obtained for _Trust_ (r = 0.55), _Perceived Usefulness_ (r = 0.42), _Personification_ (r = 0.40), _Cognitive Involvement_ (r = 0.37), _Design Appeal_ (r = 0.37), _Information Quality_ (r = 0.35), _Risk Perception_ (r = 0.34), and _Information Display_ (r = 0.33), respectively, on the one side, and _Intention to Use_ Bing Chat on the other. Lower correlation coefficients (statistically significant at \(p\) < 0.01) were obtained for _General Usability_ (r = 0.29) and _Learnability_ (r = 0.25), respectively, on the one side, and _Intention to Use_ Bing Chat on the other. Interestingly, no statistically significant correlation (r < 0.1) was found for _System Reliability_ and _Visual Design and Navigation_ variables, on the one side, and _Intention to Use_ Bing Chat on the other. To provide a more detailed investigation and better meet the requirement of a minimal number of subjects to perform a regression analysis (see: [64]), two separate regression analyses were performed (see Tables 3 and 4)--one for the variables in (a) the _usability_ category and one for those in (b) the _user experience_ category. The data presented in Table 3 reveal that, according to the results of stepwise regression analysis, the only predictor of the dependent variable _Intention to Use_ Bing Chat among the _usability_ variables was _Perceived Usefulness_. For comparison, it must be noted that the same result was obtained using regression analysis with the \"Enter\" method. However, in the presented stepwise regression analysis, the explanatory power (common variance with the dependent variable) of _Perceived Usefulness_ as a predictor is considered _weak_ with R\({}^{2}\) of only 0.174. When only the _user experience_ variables are used as predictors of _Intention to Use_ Bing Chat, results of stepwise regression analysis that are presented in Table 4 reveal two predictor variables: _Trust_ and _Design Appeal_ (these two variables also appear as the only predictors when stepwise regression is used on joint sets of _usability_ and _user experience_ variables). Again, for comparison, it must be noted that, among the _user experience_ variables, _Trust_ was the only predictor obtained with the use of the \"Enter\" method. In the results of the stepwise regression analysis that are presented in Table 4, the explanatory power (common variance with the dependent variable) of _Trust_ and _Design Appeal_ as predictors is considered _moderate_ with R\({}^{2}\) of 0.335. The results of the regression analyses interpreted above (also, see Tables 3 and 4) respond to the _third research question_ (RQ3) of our study: \"Which usability and user experience variables are best predictors of _Intention to Use_ Bing Chat (or ChatGPT/GPT-4) in the future?\" Regarding the _usability_ variables, this was _Perceived Usefulness_, while in