Table 2

Regression analyses used to test our hypotheses

Panel A: All casesPanel B: HumanPanel C: Algorithm
Coeff.p valueCoef.p valueCoef.p value
Advice rationale1.1980.002 aAdvice rationale1.1980.003 a1.8520.000 a
Accountability0.0630.790Accountability0.0630.7900.4670.243
Type of adviser0.0910.763     
Advice rationale × Accountability1.0250.039 aAdvice rationale × Accountability1.0250.039 a−0.2270.738
Advice rationale × Type of Adviser0.6530.291Constant5.5760.0005.6670.000
Accountability × Type of adviser0.4040.384     
   Simple Effects
   Advice rationale @ Accountability = 12.2230.000 a1.6250.001 a
Advice rationale × Accountability × Type of adviser−1.2510.081 a     
   Accountability @ Advice rationale = 11.0880.021 a0.2400.662
Constant5.5760.000     
N248 N129 119 
R_Sq0.236 R_Sq0.263 0.202 

Note(s): Results are obtained using regression analysis with robust standard errors. p-values are two tailed, except where used for hypothesis testing, marked with an a

The dependent variable, advice-taking is measured on an 11-point Likert scale; higher values on the variable represent more advice-taking from the human/algorithmic adviser (depending on the type of adviser condition)

Type of adviser is an indicator variable equal to 1 (0) when the adviser was an algorithm (when the adviser was a human)

Advice rationale is an indicator variable equal to 1 (0) when an advice rationale was present (absent)

Accountability is an indicator variable equal to 1 (0) when accountability is high (low)

Source(s): Authors’ own work

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