# Perceptions of Autonomous Vehicles: A Case Study of Jordan

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## Abstract

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## Highlights

- The ordinal logit model was deployed to determine the factors attributed to individual acceptance of AVs, such as the cost, security, privacy, along with the environmental impact, among others.
- The results indicated that the cost of AVs greatly influences purchasing decisions, though if the cost is affordable, respondents were more interested in using AVs.

**This study helps to enhance the current understanding by highlighting road user perceptions, with practical implications for practitioners.**

## Abstract

## 1. Introduction

## 2. Literature Review

“the evaluation of that technology before having any interaction with it” (p. 253).

- Demographics: age, gender, or income;
- AVs related characteristics: privacy and security, environmental impact, or cost.

- Level five AVs have not been yet introduced in Jordan, unlike other automation levels. Therefore, investigating their potential impact on society can provide valuable insights into the acceptance and adoption of autonomous vehicle technology in the future.
- While level five AVs are not currently available in Jordan, this level of automation may become more prevalent in the future. Thus, there is a need to understand how potential road users perceive and accept this technology.
- Focusing on level five AVs allows the research objectives to be more narrowly defined, with a specific emphasis on the potential implications of fully autonomous driving.

## 3. Ordinal Logistic Regression Model

## 4. Method and Implementation

#### 4.1. Design Overview

#### 4.2. Population and Sample Profile

#### 4.3. Data Collection Instrument

#### 4.4. Descriptive Statistics

## 5. Results and Discussion

#### 5.1. Ordinal Logit Model and Hypothesis Development

- The dependent variable and at least one of the independent variables are measured on an ordinal scale;
- There must be no multicollinearity among the independent variables as the presence of multicollinearity results in difficulty to determine the relationship between the dependent and independent variables;
- The dependent variable categories are affected equally by each independent variable.

- The Variance Inflation Factor (VIF) was used to check multicollinearity.
- The goodness of fit was implemented to compare the observed data to the expected data generated by the model.
- The proportional odds assumption was applied to model ordinal categorical dependent variables.
- The parallel lines test was utilized to check whether the odds of belonging to one category compared to another category are the same for all levels of the independent variables.

#### 5.2. Parallel Lines Test

#### 5.3. Multicollinearity Check

#### 5.4. Goodness-of-Fit Check

#### 5.5. Ordinal Logistic Model (OLM) Testing

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

AVs | Autonomous Vehicles |

SAE | International Society of Automation Engineers |

ANOVA | Analysis of Variance |

CDF | Cumulative Distribution Function |

POM | Proportional Odds Model |

PPOM | Partial Proportional Odds Model |

DoS | Jordanian Department of Statistics |

VIF | Variance Inflation Factor |

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Variable | Frequency | Percentage | |
---|---|---|---|

Driving License | Yes | 324 | 55.7 |

No | 258 | 44.3 | |

Car/Household | 0 | 73 | 12.5 |

1 | 269 | 46.2 | |

2 | 165 | 28.4 | |

≤3 | 75 | 12.9 | |

Age (years) | 18–28 | 443 | 76.1 |

29–39 | 65 | 11.2 | |

40–50 | 63 | 10.8 | |

50+ | 11 | 1.9 | |

Gender | Male | 187 | 32.1 |

Female | 395 | 67.9 | |

Education | Undergraduate | 58 | 10 |

Bachelor/diploma | 498 | 85.6 | |

Postgraduate | 26 | 4.5 | |

Employment | University student | 312 | 53.6 |

Employee | 169 | 29 | |

Unemployed | 78 | 13.4 | |

Other | 23 | 4 | |

Family income (JOD) ^{1} | <1043 | 305 | 52.4 |

1043–1460 | 67 | 11.5 | |

1460–1670 | 18 | 3.1 | |

1670–1880 | 10 | 1.7 | |

1880–2090 | 17 | 2.9 | |

>2090 | 20 | 3.4 | |

Prefer not to answer | 145 | 24.9 | |

Mobility disability | Yes | 60 | 10.3 |

No | 522 | 89.7 | |

Governorate | North (Irbid, Ajloun, & Jerash) | 41 | 7 |

Mafraq | 7 | 1.2 | |

Salt | 17 | 2.9 | |

Zarqa | 86 | 14.8 | |

Amman | 414 | 71.1 | |

Madaba | 8 | 1.4 | |

South (Ma’an, Tafilah, Karak, & Aqaba) | 9 | 1.5 |

^{1}JOD 1 = USD 0.7.

Model | −2 Log-Likelihood | Chi-Square | $\mathrm{df}$ | p |
---|---|---|---|---|

Null Hypothesis ^{1} | 995.568 | |||

General | 937.191 | 58.376 | 48 | 0.145 ^{1} |

^{1}The null hypothesis states that the location parameters (slope coefficients) are the same across response categories.

Model | Unstandardised | Standardised | 95%CI | Collinearity | |||||
---|---|---|---|---|---|---|---|---|---|

B | Std.Error | t | Sig. | LB | UB | Tolerance | VIF | ||

Predictors ^{1} | 2.129 | 0.186 | 11.433 | 0 | 1.763 | 2.495 | |||

Cost ^{2} | 0.013 | 0.046 | 0.012 | 0.281 | 0.779 | −0.078 | 0.103 | 0.97 | 1.03 |

Privacy ^{3} | 0.032 | 0.04 | 0.036 | 0.801 | 0.423 | −0.047 | 0.111 | 0.873 | 1.145 |

Enviro. ^{4} | −0.039 | 0.041 | −0.042 | −0.95 | 0.342 | −0.119 | 0.042 | 0.888 | 1.126 |

Security ^{5} | −0.007 | 0.048 | −0.007 | −0.156 | 0.876 | −0.102 | 0.087 | 0.993 | 1.007 |

^{1}Dependent variable: are you thinking of buying a self-driving car? Independent variable: Cost, Privacy, Environment, and Security;

^{2}Cost: what do you think about the cost of this type of vehicle?;

^{3}Privacy: these vehicles rely on large-scale data collection (user travel behaviours, travel time, work and home location, mobile phone number, …) to ensure a high level of security and improve traffic flow. Is this data collection a concern?;

^{4}Environment impact: how do you classify these vehicles from an environmental point of view?;

^{5}Security: these vehicles contain a system that enables the vehicle to identify the owners of the vehicle with voice, fingerprints and network detection, how should the level of safety and protection of the vehicle from thefts and harm to passengers be evaluated?

Model | Chi-Square | df | Sig. |
---|---|---|---|

Pearson | 816.538 | 776 | 0.152 |

Deviance | 684.650 | 776 | 0.992 |

Model ^{1} | −2 Log-Likelihood | Chi-Square | df | p |
---|---|---|---|---|

Intercept Only | 1040.564 | |||

Final | 995.568 | 44.996 | 16 | 0.000 |

^{1}Link function: Logit.

Category | Estimate | Std.Error | Wald | df | p | Odds Ratio | 95% C.I. | ||
---|---|---|---|---|---|---|---|---|---|

LB | UB | ||||||||

Threshold | [BuyAV=0] | −1.879 | 0.758 | 6.151 | 1 | 0.013 | −3.363 | −0.394 | |

[BuyAV=1] | −0.187 | 0.747 | 0.062 | 1 | 0.803 | −1.651 | 1.277 | ||

[BuyAV=2] | 1.637 | 0.75 | 4.762 | 1 | 0.029 | 0.167 | 3.108 | ||

[BuyAV=3] | 2.89 | 0.757 | 14.579 | 1 | 0 | 1.407 | 4.374 | ||

Location | [Privacy=0] | 1.552 | 0.464 | 11.171 | 1 | 0.001 | 4.721 | 0.642 | 2.462 |

[Privacy=1] | −0.437 | 0.322 | 1.836 | 1 | 0.175 | 0.646 | −1.068 | 0.195 | |

[Privacy=2] | −0.439 | 0.274 | 2.564 | 1 | 0.109 | 0.644 | −0.977 | 0.098 | |

[Privacy=3] | 0.074 | 0.275 | 0.072 | 1 | 1.077 | 0.788 | −0.465 | 0.613 | |

[Privacy=4] | 0 | . | . | 0 | . | . | . | ||

[Security=0] | 0.319 | 0.368 | 0.751 | 1 | 0.086 | 1.376 | −0.402 | 1.039 | |

[Security=1] | −0.084 | 0.272 | 0.094 | 1 | 0.059 | 0.920 | −0.617 | 0.45 | |

[Security=2] | −0.274 | 0.214 | 1.641 | 1 | 0 | 0.759 | −0.692 | 0.145 | |

[Security=3] | −0.082 | 0.216 | 0.145 | 1 | 0.004 | 0.921 | −0.505 | 0.341 | |

[Security=4] | 0 | . | . | 0 | . | . | . | ||

[Environ.=0] | 0.517 | 0.386 | 1.795 | 1 | 0.18 | 1.676 | −0.24 | 1.274 | |

[Environ.=1] | 0.059 | 0.278 | 0.044 | 1 | 0.833 | 1.061 | −0.486 | 0.604 | |

[Environ.=2] | 0.259 | 0.203 | 1.634 | 1 | 0.201 | 1.296 | −0.138 | 0.656 | |

[Environ.=3] | 0.327 | 0.223 | 2.14 | 1 | 0.143 | 1.386 | −0.111 | 0.765 | |

[Environ.=4] | 0a | . | . | 0 | . | . | . | ||

[Cost= 0] | 0.816 | 0.707 | 1.33 | 1 | 0.249 | 2.261 | −0.571 | 2.202 | |

[Cost=1] | 1.329 | 0.717 | 3.432 | 1 | 0.034 | 3.778 | −0.077 | 2.735 | |

[Cost=2] | 0.854 | 0.73 | 1.369 | 1 | 0.242 | 2.350 | −0.576 | 2.284 | |

[Cost=3] | 0.931 | 0.79 | 1.388 | 1 | 0.039 | 2.539 | −0.618 | 2.481 | |

[Cost=4] | 0 | . | . | 0 | . | . | . |

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## Share and Cite

**MDPI and ACS Style**

Abudayyeh, D.; Almomani, M.; Almomani, O.; Alsoud, H.; Alsalman, F.
Perceptions of Autonomous Vehicles: A Case Study of Jordan. *World Electr. Veh. J.* **2023**, *14*, 133.
https://doi.org/10.3390/wevj14050133

**AMA Style**

Abudayyeh D, Almomani M, Almomani O, Alsoud H, Alsalman F.
Perceptions of Autonomous Vehicles: A Case Study of Jordan. *World Electric Vehicle Journal*. 2023; 14(5):133.
https://doi.org/10.3390/wevj14050133

**Chicago/Turabian Style**

Abudayyeh, Dana, Malek Almomani, Omar Almomani, Hadeel Alsoud, and Farah Alsalman.
2023. "Perceptions of Autonomous Vehicles: A Case Study of Jordan" *World Electric Vehicle Journal* 14, no. 5: 133.
https://doi.org/10.3390/wevj14050133