# Chatots in Airport Customer Service--Exploring Use Cases and Technology Acceptance
## 6 Results
### Hypothesis Analyses
#### 6.2.2 The Impact of PU, PEU, Trust, and PE on BI  
A linear regression analysis conducted with the entire sample points to a highly significant influence of PU on BI (\(\beta=0.422\); \(p<0.001\)), and PEU (\(\beta=0.207\); \(p=0.006\)), Trust (\(\beta=0.198\); \(p<0.001\)), and PE (\(\beta=0.116\); \(p=0.005\)) also show a significant positive impact  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Question** & **\#** & **\%** \\ \hline In what context have you used chatbots before? & & \\ Online Shopping & 72 & 37.7 \\ Banking & 73 & 38.2 \\ Education & 26 & 13.6 \\ Health Care & 33 & 17.3 \\ Airports & 25 & 13.1 \\ Travel-related & 76 & 39.8 \\ None & 28 & 14.7 \\ Other & 38 & 19.9 \\ \hline Where would you like to access a chatbot? & & \\ On the website of the airport & 106 & 55.5 \\ On the social media channel of the airport & 30 & 15.7 \\ On a touchscreen at the airport & 63 & 33.0 \\ In a mobile application & 107 & 56.0 \\ Other & 44 & 23.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chatbot experiences and access point preferences for an airport chatbot found in the collected sample (\(n=191\)).  
on respondents' _Behavioral Intention_ to use a chatbot in an airport customer service scenario. The overall model fit lies at \(R^{2}=0.666\), explaining 66% of the variance and consequently showing that Hypotheses H1-H4 are all supported by the collected data (cf. Figure 4).  
Looking at the two language groups separately, we found less support for our conceptual model. Specifically, in the English sample, it is again PU that has the highest positive impact on BI (\(\beta=0.459\); \(p<0.001\)), followed by PEU (\(\beta=0.216\); \(p=0.02\)) and Trust (\(\beta=0.143\); \(p=0.039\)). The influence of PE on BI, however, is not significant (\(\beta=0.121\); \(p=0.088\)). Thus, when only considering the English sample, H4 needs to be rejected. Yet, the other three constructs, i.e., PU, PEU, and Trust, are still able to explain 65% of the variance in people's _Behavioral Intention_ to use chatbots for airport customer service scenarios (corrected English model: \(R^{2}=0.653\); cf. Figure 5).  
Finally, when only considering the German sample, we also see some deviations from the initial results. Again, PU has the highest impact on BI (\(\beta=0.365\); \(p=0.003\)). This time, however, it is followed by PE (\(\beta=0.325\); \(p=0.003\)) and Trust (\(\beta=0.298\); \(p=0.002\)), whereas PEU does not have a significant positive effect on BI in the German sample (\(\beta=0.130\); \(p=0.310\)). Consequently, if we only consider the German sample, H2 needs to be rejected. Interestingly, we see that when only considering PU, PE, and Trust for these data, the model fit increases and is then able to explain almost 77% of the variance of BI (corrected German model: \(R^{2}=0.765\); cf. Figure 6).  
Figure 4: _Perceived Usefulness_, _Perceived Ease of Use_, _Trust_, and _Perceived Enjoyment_ explain 66% of the variance in all respondents’ _Behavioral Intention_ to use chatbots for airport customer service scenarios.  
Figure 5: _Perceived Usefulness_, _Perceived Ease of Use_, and _Trust_ explain 65% of the variance in English-speaking respondents’ _Behavioral Intention_ to use chatbots for airport customer service scenarios.