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Robo-advisor acceptance: Do gender and generation matter?

Gianna Figà-Talamanca1, Paola Musile Tanzi2, Eleonora D'Urzo1

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Robo-advice adoption is driven by ease of use for younger generations (Gen Z, Y) and perceived usefulness for older generations (Gen X+). While gender shows no significant adoption difference in highly educated groups, generational factors remain crucial for robo-advice technology uptake.

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Area of Science:

  • Financial Technology
  • Human-Computer Interaction
  • Behavioral Economics

Background:

  • Robo-advice technology, powered by artificial intelligence, offers virtual financial guidance.
  • Existing research indicates robo-advice's potential to enhance financial inclusion.
  • Understanding adoption drivers is key for broader technology integration.

Purpose of the Study:

  • To analyze robo-advice adoption using the Technology Acceptance Model (TAM).
  • To explore generational and gender differences in adoption among a highly educated sample.
  • To identify specific factors influencing adoption across different age cohorts.

Main Methods:

  • Application of the Technology Acceptance Model (TAM).
  • Analysis of a highly educated demographic sample.
  • Comparative study of generational (Gen Z, Y, X+) and gender influences on adoption.

Main Results:

  • No significant gender differences were found in the causal links to adoption, though structural variations exist.
  • Generational cohorts significantly impact the adoption pathway for robo-advice.
  • Ease of use drives adoption for Gen Z and Y; perceived usefulness is key for Gen X+.

Conclusions:

  • Gender differences in robo-advice adoption diminish within highly educated populations.
  • Generational effects remain a significant factor influencing the uptake of robo-advice technology.
  • Tailoring adoption strategies based on generational preferences is essential for effective implementation.