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Field
Randomization Method
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Before
An asymmetric randomization is conducted and implemented via the Qualtrics platform, using stratified randomization on key covariates to ensure balanced groups. The allocation procedure follows a 1:1.2:1.8 ratio to ensure enough statistical power even in case of partial non-compliance to the treatments.
Intervention assignment:
After the baseline survey, participants are allocated to interventions in the order of enrollment. The first ∼120 qualified respondents are assigned to Intervention 1 (TBV training vs. placebo), and the next ∼120 respondents are assigned to Intervention 2(AI assistance). Within the first group (Intervention 1), participants are randomized 1:1 to TBV training vs. placebo. We use stratification on important covariates – gender, field of education, years of experience, and baseline AI aversion – to achieve balance between the TBV and placebo groups and explore potentially meaningful heterogeneous treatment effects. Similarly, within the second group (Intervention 2), participants are randomized 1:1 into the two AI conditions (General AI vs. Agentic AI), again using stratified randomization on the same covariates to ensure balanced characteristics across these AI groups and enable heterogeneous treatment effects analysis. (No new participants are directly assigned to a “No AI” condition in Intervention 2, since the No AI comparison group consists of the TBV-trained participants from Intervention 1.) We will record the randomization procedure with software logs, including random seeds and assignment timestamps, to ensure transparency.
This stratified approach prevents detectable imbalances in meaningful observable characteristics and upholds group equivalence. All participants provide informed consent before randomization. We will verify ex-post that the groups are balanced on baseline covariates (e.g., demographics, experience, etc.), and if any notable imbalance arises by chance, we will control for those covariates in the analysis as a precaution.
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After
An asymmetric randomization is conducted and implemented via the Qualtrics platform, using stratified randomization on key covariates to ensure balanced groups. The allocation procedure follows a 1:1.2:1.8 ratio to ensure enough statistical power even in case of partial non-compliance to the treatments.
Intervention assignment:
Randomization is implemented via the Qualtrics platform, using purposely defined minimized
randomization algorithms based on three covariates: a) field of education (STEM or Non-
STEM); b) years of M&A experience (dichotomized); c) baseline AI aversion (di-
chotomized).
This provides the baseline allocation of participants to the experimental conditions by putting
them into eight strata. Within each stratum, an imbalance score is computed across the baseline
covariates, and each participant is assigned to the most-needed arm using a biased-coin probability
of p = 0.80 (rather than deterministically). This probabilistic covariate-adaptive randomization
preserves balance while avoiding the inferential problems of deterministic minimization in multi-
arm trials with unequal allocation. The minimization variables are included in the adjusted primary
analysis.
This minimized randomization approach within blocks prevents detectable imbalances in meaningful
observable characteristics while accommodating the practical constraints of field implementation,
ensuring enough statistical power to detect meaningful statistical effects across conditions.
All participants provide informed consent before randomization.
We will verify ex-post that the groups are balanced on baseline covariates within each block and
overall. If any notable imbalance arises despite minimization, we will control for those covariates
in the relevant econometric analyses.
We will record the randomization procedure with software logs, including assignment timestamps,
to ensure transparency.
The assignment method was already updated in the pre-analysis plan on the 10th of April, thus we register this as a minor change.
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