# Willingness to Use Algorithms Varies with Social Information on Weak vs. Strong Adoption: An Experimental Study on Algorithm Aversion

## Abstract

**:**

## 1. Introduction

## 2. Literature Review and Hypothesis Development

**H**.

## 3. Research Methods

#### 3.1. Participants

_{age}= 10.97). The experiment was programmed as a survey in Qualtrics. The survey was conducted on 28 November 2022. The average completion time was 7.02 min. Subjects received a fixed show-up fee of USD 0.30 and a performance-based bonus that could be as high as USD 1.67.

#### 3.2. Design

#### 3.3. Procedure

## 4. Results

#### 4.1. Forecast Accuracy

#### 4.2. Willingness to Use the Algorithm

^{2}(n = 254) = 4.69; p = 0.030). Thus, H cannot be rejected. Social information about frequent use of the algorithm leads subjects to use the algorithm significantly more often. Information about prior willingness to use an algorithm from other economic agents has an impact on the decision to use an algorithm.

^{2}(n = 127) = 3.69; p = 0.055) and Treatment 2 (χ

^{2}(n = 127) = 8.14; p = 0.004), women use the algorithm more frequently than men. The comparison of treatments by gender shows that the effect is mainly driven by women (χ

^{2}(n = 128) = 3.20; p = 0.073) and less by men (χ

^{2}(n = 126) = 0.70; p = 0.402). At low adoption (T1), 61.40% of women used the algorithm, whereas at high adoption (T2) 76.06% of women already used the algorithm. For men, on the other hand, at T1 (and T2, respectively), 44.29% (51.79%) used the algorithm (Table 4). In contrast, the age of the subjects shows no statistically significant effect on decisions (t(252) = 1.97; p = 0.278).

^{2}(n = 75) = 3.71; p = 0.054). Subjects who have a high ATI score use the algorithm in 46.67% of cases when they receive information about low acceptance, and in 57.30% of cases when they receive information about high acceptance (χ

^{2}(n = 179) = 2.03; p = 0.154). Thus, in particular, subjects (84.21%) who have low ATI are more likely to be influenced and persuaded to use the algorithm by social information about high acceptance than subjects (57.30%) who have high ATI (χ

^{2}(n = 127) = 8.52; p = 0.004).

## 5. Discussion

## 6. Conclusions

## Funding

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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Maximum Deviation in % | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | >15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Bonus in Coins | 50 | 47 | 43 | 40 | 37 | 33 | 30 | 27 | 23 | 20 | 17 | 13 | 10 | 7 | 3 | 0 |

Basis of Performance-Related Bonus | |||
---|---|---|---|

Own Forecasts | Forecasting Calculator (Algorithm) | t-Test | |

Ø absolute forecast error [in USD] | 20.51 | 10.90 | t(252) = 16.21; p < 0.001; d = 2.06 |

Ø relative forecast error [in %] | 18.51 | 8.56 | t(252) = 14.19; p < 0.001; d = 1.80 |

Ø performance-related bonus [in USD] | 0.51 | 0.83 | t(252) = 17.47; p < 0.001; d = 2.22 |

Total | Forecasting Calculator (Algorithm) | Own Forecasts | |||
---|---|---|---|---|---|

n | n | % | n | % | |

Social low acceptance (T1) | 127 | 66 | 51.97% | 61 | 48.03% |

Social high acceptance (T2) | 127 | 83 | 65.35% | 44 | 34.65% |

Gender | Forecasting Calculator (Algorithm) | Own Forecast | |||
---|---|---|---|---|---|

n | % | n | % | ||

Social low acceptance (T1) | male | 31 | 44.29% | 39 | 55.71% |

female | 35 | 61.40% | 22 | 38.60% | |

Social high acceptance (T2) | male | 29 | 51.79% | 27 | 48.21% |

female | 54 | 76.06% | 17 | 23.94% |

ATI Score * | Total | Thereof Use Algorithm | Thereof Use Own Forecasts | ||
---|---|---|---|---|---|

n | % | % | % | ||

Social low acceptance (T1) | ≤3.5 | 37 | 29.13% | 64.86% | 35.14% |

>3.5 | 90 | 70.87% | 46.67% | 53.33% | |

Social high acceptance (T2) | ≤3.5 | 38 | 29.92% | 84.21% | 15.79% |

>3.5 | 89 | 70.08% | 57.30% | 42.70% |

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**MDPI and ACS Style**

Judek, J.R.
Willingness to Use Algorithms Varies with Social Information on Weak vs. Strong Adoption: An Experimental Study on Algorithm Aversion. *FinTech* **2024**, *3*, 55-65.
https://doi.org/10.3390/fintech3010004

**AMA Style**

Judek JR.
Willingness to Use Algorithms Varies with Social Information on Weak vs. Strong Adoption: An Experimental Study on Algorithm Aversion. *FinTech*. 2024; 3(1):55-65.
https://doi.org/10.3390/fintech3010004

**Chicago/Turabian Style**

Judek, Jan René.
2024. "Willingness to Use Algorithms Varies with Social Information on Weak vs. Strong Adoption: An Experimental Study on Algorithm Aversion" *FinTech* 3, no. 1: 55-65.
https://doi.org/10.3390/fintech3010004