Recommendations of algorithms are frequently superior to human judgment, but humans often fail to seize the full potential of algorithms. Research has made initial advances to increase the use of algorithmic advice, but achieved levels remain far from optimal. However, research has not yet considered user personality traits to develop interventions to increase the use of algorithms. We propose to communicate the algorithmic advice via a persuasive message that fits the user’s regulatory focus, i.e., the tendency towards promotion- versus prevention focus, to create regulatory fit. To test our hypotheses, we conduct an experiment with a forecasting task and 605 participants. Results support a positive effect of a persuasive message on the use of algorithmic advice. Further, results do not support a regulatory fit effect. Our article adds to theory and practice by developing and empirically testing novel interventions to increase the use of algorithms. Next research steps are discussed.

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Seizing the Potential of Algorithms: The Power of Personalized Persuasive Messages on the Use of Algorithmic Advice

  • Simon Asbach,
  • Lorenz Graf-Vlachy,
  • Andreas Fügener

摘要

Recommendations of algorithms are frequently superior to human judgment, but humans often fail to seize the full potential of algorithms. Research has made initial advances to increase the use of algorithmic advice, but achieved levels remain far from optimal. However, research has not yet considered user personality traits to develop interventions to increase the use of algorithms. We propose to communicate the algorithmic advice via a persuasive message that fits the user’s regulatory focus, i.e., the tendency towards promotion- versus prevention focus, to create regulatory fit. To test our hypotheses, we conduct an experiment with a forecasting task and 605 participants. Results support a positive effect of a persuasive message on the use of algorithmic advice. Further, results do not support a regulatory fit effect. Our article adds to theory and practice by developing and empirically testing novel interventions to increase the use of algorithms. Next research steps are discussed.