Mikulas

Introduction#

Structure: maybe it’s better to omit the headers from the introduction: I think the structure is clear enough by itself not to lose any readability from doing that.

Main RQ: Algorithm aversion $\rightarrow$ adoption of fintech solutions.

• Maybe you should briefly pay attention to the challenges to identifying the impact (instead of correlation) of algorithm aversion on likelihood of adoption of fintech solutions.
  • Omitted variables
  • Reverse causality
  • Measurement of algorithm aversion
  • (If dichotomous): non-random selection into the treatment group
  • Possible solution: Matching estimators

Theoretical framework#

Debate: algorithm aversion vs. no algorithm aversion

• Fits within the debate of behavioral economics

Methodology#

• What is the structure of the surveys? Are there individuals from the same households? The same individuals twice? Survey taken at different points in time?

• Can you explain how you match the respondents from the first dataset to the respondents in the second dataset?

• The summation of code numbers should probably be relegated to the appendix

• You can also employ PCA or factor analysis (or just linear regression) to reduce the dimensionality of all of those variables

• You can use subsets of those variables in robustness checks of your analyses

• Moderating variables

  • Are these moderating variables or control variables?

• What is the structure of the survey? Is it a one-off (cross-sectional) survey?

• You can test whether the effect is moderated by other variables in the survey

  • Do you have any idea about what it could be theoretically?

• Cryptocurrencies vs. banking services: very different things, so maybe they deserve a different analysis and different theoretical framework?

  • Risk preferences play a large role in cryptocurrencies.