rmANOVA and paired sample t-test
| Construct | Scenario | Mean | Std. Dev. | df/error | F*/t** | Sig |
|---|---|---|---|---|---|---|
| Usefulness* | RSS | 2.7977 | 0.85479 | 1.462/367.057 | 31.252 | <0.001 |
| ARSS | 3.2023 | 0.96053 | ||||
| XARSS | 3.3292 | 1.04976 | ||||
| Entertainment* | RSS | 2.8172 | 0.81981 | 1.750/439.214 | 18.635 | <0.001 |
| ARSS | 3.0964 | 0.91896 | ||||
| XARSS | 3.1678 | 0.94932 | ||||
| Informativeness* | RSS | 2.9206 | 0.91520 | 1.740/436.649 | 61.891 | <0.001 |
| ARSS | 3.3506 | 0.91306 | ||||
| XARSS | 3.6655 | 0.84507 | ||||
| Irritation* | RSS | 2.2260 | 0.84444 | 1.671/419.324 | 8.824 | <0.001 |
| ARSS | 2.4826 | 1.00393 | ||||
| XARSS | 2.4588 | 1.04002 | ||||
| Purchase Intention* | RSS | 3.1637 | 0.86789 | 1.448/363.442 | 1.431 | 0.240 |
| ARSS | 3.0595 | 0.92332 | ||||
| XARSS | 3.1200 | 0.98278 | ||||
| Trust** | ARSS | 3.1164 | 0.82783 | 251 | −1.210 | 0.228 |
| XARSS | 3.1627 | 0.88032 |
| Construct | Scenario | Mean | Std. Dev. | df/error | Sig | |
|---|---|---|---|---|---|---|
| Usefulness* | RSS | 2.7977 | 0.85479 | 1.462/367.057 | 31.252 | |
| ARSS | 3.2023 | 0.96053 | ||||
| XARSS | 3.3292 | 1.04976 | ||||
| Entertainment* | RSS | 2.8172 | 0.81981 | 1.750/439.214 | 18.635 | |
| ARSS | 3.0964 | 0.91896 | ||||
| XARSS | 3.1678 | 0.94932 | ||||
| Informativeness* | RSS | 2.9206 | 0.91520 | 1.740/436.649 | 61.891 | |
| ARSS | 3.3506 | 0.91306 | ||||
| XARSS | 3.6655 | 0.84507 | ||||
| Irritation* | RSS | 2.2260 | 0.84444 | 1.671/419.324 | 8.824 | |
| ARSS | 2.4826 | 1.00393 | ||||
| XARSS | 2.4588 | 1.04002 | ||||
| Purchase Intention* | RSS | 3.1637 | 0.86789 | 1.448/363.442 | 1.431 | 0.240 |
| ARSS | 3.0595 | 0.92332 | ||||
| XARSS | 3.1200 | 0.98278 | ||||
| Trust** | ARSS | 3.1164 | 0.82783 | 251 | −1.210 | 0.228 |
| XARSS | 3.1627 | 0.88032 |
Note(s): rmAnova with Greenhouse-Geisser correction, significance level is 0.05; *(rmANOVA), **(paired sample t-test), RSS (Regular Shopping Scenario), ARSS (Augmented Reality Shopping Scenario), XARSS (Augmented Reality Shopping Scenario with Explainable Artificial Intelligence)
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