Table 3

Smart PLS moderation structural model

PathPatch coefficientt valuep valueLower bound 95% CIUpper bound 95% CITotal effectt valuep valueLower bound 95% CIUpper bound 95% CI
Ad relevance → Attitude towards PA0.413.640.000.160.610.413.670.000.160.60
Risk beliefs → Attitude towards PA0.294.130.000.420.150.294.090.000.420.15
AI beneficial → Attitude towards PA0.363.620.000.160.550.363.630.000.160.55
Ad relevance × Risk beliefs → Attitude towards PA0.283.850.000.150.430.283.930.000.160.43
Ad relevance × AI beneficial → Attitude towards PA0.153.880.000.070.230.153.780.000.070.23
Ad relevance → Attitude towards retailer0.525.120.000.300.710.729.000.000.550.87
Risk beliefs → Attitude towards retailer−0.030.540.59−0.120.070.173.260.000.270.07
AI beneficial → Attitude towards retailer−0.131.320.19−0.320.060.050.600.55−0.110.20
Attitude towards PA → Attitude towards retailer0.494.160.000.270.730.494.260.000.270.71
Ad relevance × AI → Attitude towards retailerNANANANANA0.082.720.010.030.14
Ad relevance × Risk → Attitude towards retailerNANANANANA0.142.960.000.060.24
Age → Attitude towards PA0.071.440.15−0.030.170.071.440.15−0.020.16
Age → Attitude towards retailer0.102.110.040.180.01−0.061.420.16−0.150.02
Gender → Attitude towards PA0.051.060.29−0.040.160.051.060.29−0.050.15
Gender → Attitude towards retailer−0.051.060.29−0.130.04−0.020.460.65−0.110.07
Education → Attitude towards PA0.091.620.11−0.020.200.091.640.10−0.010.20
Education → Attitude towards retailer0.050.880.38−0.060.160.091.580.11−0.020.21
Online shopping frequency → Attitude towards PA0.122.470.010.030.210.122.450.010.020.21
Online shopping frequency → Attitude towards retailer−0.040.910.36−0.120.040.020.410.68−0.070.10
Manipulation → Attitude towards PA0.030.570.57−0.070.120.030.580.57−0.070.12
Manipulation → Attitude towards retailer−0.051.100.27−0.130.04−0.030.760.45−0.130.05

Note(s): n = 189; PA = Programmatic Advertising; CI = Confidence Interval; NA = Not Applicable; Italicized paths are statistically significant at 0.05 significance level

CIs obtained through a complete, bias-corrected and accelerated bootstrap procedure with 5,000 samples

Dummy variables: Age (0 = Younger than 35; 1 = 35 and older), gender (0 = Female; 1 = Male), education (0 = Less than a Bachelor's degree; 1 = At least a Bachelor's degree), shopping frequency (0 = Shops online weekly; 1 = Does not shop online weekly) and manipulation (0 = PA without AI condition; 1 = PA with AI condition)

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