Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Literature Review
Methods of Model Group 3: Av Mode Choice Model
Survey Design and Data Collection
Results
Limitations
Discussion
References
All Topics
Economics

Risk preference and adoption of autonomous vehicles

Profile image of Shenhao WangShenhao Wang

2019, Transportation Research Part A-policy and Practice

https://doi.org/10.1016/J.TRA.2019.06.007
visibility

…

description

28 pages

link

1 file

Abstract

Despite an increasingly large body of research that focuses on the potential demand for autonomous vehicles (AVs), risk preference is an understudied factor. Given that AV technology and how it will interact with the evolving mobility system are highly risky, this lack of research on risk preference is a critical gap in current understanding. By using a stated preference survey of 1,142 individuals from Singapore, this study achieves three objectives. First, it develops one measure of psychometric risk preference and operationalizes prospect theory to create two economic risk preference parameters. Second, it examines how these psychometric and economic risk preferences are associated with socioeconomic variables. Third, it analyzes how risk preference influences the mode choice of AVs. The study finds that risk preference parameters are significantly associated with socioeconomic variables: the elderly, poor, females, and unemployed Singaporeans appear more risk-averse and tend to overestimate small probabilities of losses. Furthermore, all three risk preference parameters contribute to the prediction of AV adoption. These modeling results have policy implications at both the aggregate and disaggregate levels. At the aggregate level, people misperceive probabilities, are overall risk-averse, and hence under-consume AVs relative to the social optimum. At the disaggregate level, the elderly, poor, female, and unemployed are more risk-averse and thus are less likely to adopt AVs. These results suggest that it might be valuable for governments to implement policies to encourage technology adoption, particularly for disadvantaged social groups, although caution remains due to uncertainty in the long-term effects of AVs. Individualized risk preference parameters could also inform how to design regulations, safety standards, and liability allocations of AVs since many regulations are essentially mechanisms for risk allocation. One limitation of the paper is that risk preference is measured and modeled only as individual-specific but not alternative-specific variables. Future studies should examine the relationship between the multiple components of risk preference and the multiple risky aspects of AVs.

Related papers

Estimating the Value of Risk Reduction for Car Occupants
SHahram Fattahi

EUROPEAN RESEARCH STUDIES JOURNAL, 2019

The paper tries to estimate the willingness to pay the value of statistical life of drivers in Tabriz highways. Design/Methodology/Approach: Data were obtained using a questionnaire whish was distributed among people who travel daily on Tabriz highways to study or work in 2017. Stated choice technique and the mixed logit model are used to estimate the individual's preferences to reduce the risk of fatality and injury. Findings: The results indicated the effects of cost, trip time, number of fatalities and injuries, number of speed cameras and speed limits on choosing a driving route. In addition, investigation of interactive effects of demographic variables and indices shows that only the interactive effects of age, income with indices are statistically significant. Practical Implications: Some policy implications to increase investment on highways and financimg road projects by road users including drivers. Originality/Value: Goverment strategy on highways development is to eliminate or reduce the number of fatalities and injuries.

View PDFchevron_right
A comparison of stated preference methods for the valuation of improvement in road safety
Glenn Jenkins

Economic Analysis and Policy, 2018

This paper presents an empirical comparison of the contingent valuation method (CVM) and choice experiment (CE) method in the estimation of the non-market value of road safety improvements. In this study we used both the CVM and the CE method to identify the preferences and tradeoffs of automobile drivers in North Cyprus for road safety improvements. Mixed logit and payment ladder approaches were used to assess the drivers' willingness to pay (WTP) for road safety improvements. Although the CVM yielded higher values than the CE, the differences between the estimates of WTP derived from these two methods were found to be statistically insignificant. The value of a statistical life (VSL) and the value of an injury (VI) were estimated for car accidents, and the value was found for the annual economic welfare loss from such deaths and injuries in North Cyprus. The point estimate of the value of a statistical life expressed in euros is €717,000, and the value of an injury is €16,885. The point estimate of the VSL for North Cyprus obtained from this study was below €1 million, which places it among the bottom 30% of the estimates made internationally for these parameters. When aggregated over the whole country for 2014, the total annual economic welfare burden was €46.7 million, which is equivalent to an economic welfare loss of 1.5% of the gross national product (GNP) in that year.

View PDFchevron_right
Estimating the Value of Life, Injury, and Travel Time Saved Using a Stated Preference Framework
Glenn Jenkins

Accident Analysis & Prevention, 2016

The incidence of death and injury from automobile accidents in North Cyprus is 3.6 times greater than the average for the EU. With the prospect of North Cyprus entering the EU, many investments will need to be undertaken in order to reduce these figures and reach EU norms. A key task will be to select the investments that are justified on the basis of a cost-benefit or cost-effectiveness analysis and not to waste resources through poor project selection. The objective of this study is to provide local estimates of the value of a statistical life and injury along with the value of time savings. These are among the key parameters needed for the evaluation of the benefits of such projects. In this study we conducted a stated choice experiment to identify the preferences and trade-offs of automobile drivers in North Cyprus for improved travel times, travel costs and safety. The choice of route was examined using mixed logit models to obtain the marginal utilities associated with each attribute of the routes that consumers choose. These estimates were used to assess the individuals" willingness to pay (WTP) to avoid fatalities and injuries and to save travel time. We then used the results to obtain community-wide estimates of the value of a statistical life (VSL) saved, the value of injury (VI) prevented, and the value per hour of travel time saved.

View PDFchevron_right
THE EFFECTS OF PERCEIVED RISK ON THE INTENTION TO USE AUTONOMOUS VEHICLES
Cristina A L C Â N T A R A de Brito Vieite, Las Casas

Brazilian Journal of Marketing, 2023

This study sought to verify the effects of perceived risk on the intention to use autonomous vehicles (AV) and the possible influence of 'novelty' and 'anxiety'.

View PDFchevron_right
Individual Risk Attitudes: New Evidence from a Large
Thomas Dohmen

This paper presents new evidence on the distribution of risk attitudes in the population, using a novel set of survey questions and a representative sample of roughly 22,000 individuals living in Germany. Using a question that asks about willingness to take risks on an 11-point scale, we find evidence of heterogeneity across individuals, and show that willingness to take risks is negatively related to age and being female, and positively related to parental education and height. We test the behavioral relevance of this survey measure by conducting a complementary field experiment, based on a representative sample of 450 subjects, and find that the measure is a good predictor of actual risk-taking behavior. We use a more standard lottery question to measure risk preference, and find similar results regarding heterogeneity and determinants of risk preferences. We also estimate the coefficient of relative risk aversion for each individual in the sample based on lottery responses. Using five questions about willingness to take risks in specific domains -car driving, financial matters, sports and leisure, career, and health -the paper studies the impact of context on risk attitudes, finding a strong but imperfect correlation across contexts. Using data on a collection of risky behaviors from different contexts, including traffic offenses, portfolio choice, smoking, occupational choice, participation in sports, and migration, the paper compares the predictive power of all of the risk measures. Strikingly, the general risk question predicts all behaviors whereas the standard lottery measure does not. The best overall predictor for any specific behavior is typically the corresponding context-specific measure. These findings call into the question the current preoccupation with lottery measures of risk preference, and point to variation in risk perceptions as an understudied determinant of risky behavior.

View PDFchevron_right
Risk perception and the value of safety for low probability, high consequence risks: theoretical and empirical investigation
Ozlem Ozdemir

2000

Hit Next 10 Years 3.13 Regression Analysis of [10(this year)+2(next5years)+l(nextl0years)]/13.. 3.14 Regression Analysis of [3(this year)+2(next5years)+(nextl0years)]/6... 3.15 Regression Analysis of Multiplicative Effect 73 A. 1 Regression Analysis with Measuring Perceived Risk (Response to Risk Ladder Question) A.2 Regression Analysis for Three Periods Simultaneously A.3 Regression Analysis of Three Periods Individually 100 A.4 Regression Analysis of (this year)+next5years+nextl0years)/3 109 A. 5 Regression Analysis of [ 10(this year)+2(next5years)+1 (next 1 Oyears)]/13.. A.6 Regression Analysis of [3(this year)+2(next5years)+l(nextl0years)]/6... 115 A.7 Regression Analysis of (this yearHnext5years+nextl Oyears)/3 *Perceived Severity (Rank) 118 B.

View PDFchevron_right
Willingness-to-pay for reducing fatal accident risk in urban areas: an Internet-based Web page stated preference survey
Juan de Dios Ortuzar

Accident Analysis & Prevention, 2004

Contemporary transport project evaluation requires the ability to value reductions in the number of estimated fatal and non-fatal accidents after project implementation. In this quest, we designed a stated preference (SP) experiment to estimate willingness-to-pay (WTP) for reducing fatal accident risk in urban areas. The survey was implemented in a Web page allowing rapid turnover and a complete customisation of the interview. The sample was presented with a series of route choice situations based on travel time, cost and number of car fatal accidents per year. With this data we estimated Multinomial Logit (MNL) and Mixed Logit (ML) models based on a consistent microeconomic framework; the former with linear and non-linear utility specifications and allowing for various stratifications of the data. The more flexible ML models also allow to treat the repeated observations problem common to SP data and, as expected, gave a better fit to the data in all cases. Based on these models, we estimated subjective values of time, that were consistent with previous values obtained in the country, and also sensible values for the WTP for reductions in fatal accident risk. Thus, the Internet appears as a potentially very interesting medium to carry out complex stated choice surveys.

View PDFchevron_right
A Comparison of Stated and Revealed Risk Preferences using Safety-First
Sankalp Sharma

2016

In this paper we determine the risk preferences of crop producers using the safety-first method. Our methodology is unique, since it utilizes both price and yield risk in the producer's optimization problem. We then compare the derived preferences with the stated preferences of the producers. The stated preferences are obtained from a lottery-style game designed to elicit producers' risk preferences. The study is conducted with data from producers in Nebraska, Iowa and South Dakota and the results indicate that there is in fact a relationship between the preferences derived from our structural model and the ones stated by the producers in the game. This is an important result from a policy-making perspective, as it validates the use of behavior elicitation surveys about risk preferences. Such surveys are often easier to administer and less expensive than collecting information on revealed preferences.

View PDFchevron_right
Individual risk attitudes: New evidence from a large, representative, experimentally-validated survey
Thomas Dohmen

2005

This paper presents new evidence on the distribution of risk attitudes in the population, using a novel set of survey questions and a representative sample of roughly 22,000 individuals living in Germany. Using a question that asks about willingness to take risks on an 11-point scale, we find evidence of heterogeneity across individuals, and show that willingness to take risks is negatively related to age and being female, and positively related to parental education and height. We test the behavioral relevance of this survey measure by conducting a complementary field experiment, based on a representative sample of 450 subjects, and find that the measure is a good predictor of actual risk-taking behavior. We use a more standard lottery question to measure risk preference, and find similar results regarding heterogeneity and determinants of risk preferences. We also estimate the coefficient of relative risk aversion for each individual in the sample based on lottery responses. Using five questions about willingness to take risks in specific domains -car driving, financial matters, sports and leisure, career, and health -the paper studies the impact of context on risk attitudes, finding a strong but imperfect correlation across contexts. Using data on a collection of risky behaviors from different contexts, including traffic offenses, portfolio choice, smoking, occupational choice, participation in sports, and migration, the paper compares the predictive power of all of the risk measures. Strikingly, the general risk question predicts all behaviors whereas the standard lottery measure does not. The best overall predictor for any specific behavior is typically the corresponding context-specific measure. These findings call into the question the current preoccupation with lottery measures of risk preference, and point to variation in risk perceptions as an understudied determinant of risky behavior.

View PDFchevron_right
Internet-based surveys to elicit the value of risk reductions
Juan de Dios Ortuzar

2004

The widespread use of the Internet in recent years has allowed for a new way of conducting surveys. A survey located in a web page can be a very cost-effective tool for studies in marketing research, environmental economics and transport demand analysis. This type of surveys are inexpensive as respondents are contacted by e-mail and are invited to answer a questionnaire which is reached by means of a hyperlink. Once the survey is answered, responses are automatically saved in a data-base file from where information is available for processing and/or to be used for modelling purposes.

View PDFchevron_right

References (49)

  1. Allais, M. (1953). Le comportement de l'homme rationnel devant le risque: Critique des postulats et axiomes de l'école américaine. Econometrica: Journal of the Econometric Society, 503-546.
  2. Bansal, P., & Kockelman, K. M. (2017). Forecasting Americans' long-term adoption of connected and autonomous vehicle technologies. Transportation Research Part A: Policy and Practice, 95, 49-63.
  3. Camerer, C. F., & Kunreuther, H. (1989). Decision processes for low probability events: Policy implications. Journal of Policy Analysis and Management, 8(4), 565-592.
  4. Chen, D., Ahn, S., Chitturi, M., & Noyce, D. A. (2017). Towards vehicle automation: Roadway capacity formulation for traffic mixed with regular and automated vehicles. Transportation Research Part B: Methodological, 100, 196-221.
  5. Chen, Z., He, F., Yin, Y., & Du, Y. (2017). Optimal design of autonomous vehicle zones in transportation networks. Transportation Research Part B: Methodological, 99, 44-61.
  6. Chen, Z., He, F., Zhang, L., & Yin, Y. (2016). Optimal deployment of autonomous vehicle lanes with endogenous market penetration. Transportation research part C: emerging technologies, 72, 143-156.
  7. Daziano, R. A., Sarrias, M., & Leard, B. (2017). Are consumers willing to pay to let cars drive for them? Analyzing response to autonomous vehicles. Transportation research part C: emerging technologies, 78, 150-164.
  8. De Almeida Correia, G. H., & Van Arem, B. (2016). Solving the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP): A model to explore the impacts of self-driving vehicles on urban mobility. Transportation Research Part B: Methodological, 87, 64-88.
  9. De Oliveira, Í. R. (2017). Analyzing the performance of distributed conflict resolution among autonomous vehicles. Transportation Research Part B: Methodological, 96, 92-112.
  10. De Palma, A., Ben-Akiva, M., Brownstone, D., Holt, C., Magnac, T., McFadden, D., . . . Wakker, P. (2008). Risk, uncertainty and discrete choice models. Marketing Letters, 19(3-4), 269- 285.
  11. Dhami, S. (2016). The Foundations of Behavioral Economic Analysis: Oxford University Press.
  12. Dupas, P. (2014). Short -run subsidies and long -run adoption of new health products: Evidence from a field experiment. Econometrica, 82(1), 197-228.
  13. Elias, W., & Shiftan, Y. (2012). The influence of individual's risk perception and attitudes on travel behavior. Transportation Research Part A: Policy and Practice, 46(8), 1241-1251.
  14. Fagnant, D. J., & Kockelman, K. (2015). Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167-181.
  15. Fehr-Duda, H., & Epper, T. (2012). Probability and risk: Foundations and economic implications of probability-dependent risk preferences. Annu. Rev. Econ., 4(1), 567-593.
  16. Freemark, Y., Hudson, A., & Zhao, J. (2019). Are cities prepared for autonomous vehicles? Planning for technological change by U.S. local governments? Journal of the American Planning Association (In Press).
  17. Goldstein, W. M., & Einhorn, H. J. (1987). Expression theory and the preference reversal phenomena. Psychological review, 94(2), 236.
  18. Goncalo de Almeida Correia, G. H., Looff, E., van Cranenburgh, S., Snelder, M., & van Arem, B. (2019). On the impact of vehicle automation on the value of travel time while performing work and leisure activities in a car: Theoretical insights and results from a stated preference survey. Transportation Research Part A: Policy and Practice, 119, 359-382.
  19. Gonzalez, R., & Wu, G. (1999). On the shape of the probability weighting function. Cognitive psychology, 38(1), 129-166.
  20. Greene, D. L., German, J., & Delucchi, M. A. (2008). Fuel economy: the case for market failure Reducing climate impacts in the transportation sector (pp. 181-205): Springer.
  21. Guerra, E. (2016). Planning for cars that drive themselves: Metropolitan Planning Organizations, regional transportation plans, and autonomous vehicles. Journal of Planning Education and Research, 36(2), 210-224.
  22. Haboucha, C. J., Ishaq, R., & Shiftan, Y. (2017). User preferences regarding autonomous vehicles. Transportation research part C: emerging technologies, 78, 37-49.
  23. Harbeck, E. L., Glendon, A. I., & Hine, T. J. (2017). Reward versus punishment: Reinforcement sensitivity theory, young novice drivers' perceived risk, and risky driving. Transportation research part F: traffic psychology and behaviour, 47, 13-22.
  24. Hensher, D. A., Ho, C., & Knowles, L. (2016). Efficient contracting and incentive agreements between regulators and bus operators: The influence of risk preferences of contracting agents on contract choice. Transportation Research Part A: Policy and Practice, 87, 22- 40.
  25. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society, 263-291.
  26. Kam, C. D. (2012). Risk attitudes and political participation. American Journal of Political Science, 56(4), 817-836.
  27. Kam, C. D., & Simas, E. N. (2012). Risk attitudes, candidate characteristics, and vote choice. Public Opinion Quarterly, nfs055.
  28. Knight, F. H. (1921). Risk, uncertainty and profit. New York: Hart, Schaffner and Marx.
  29. Krueger, R., Rashidi, T. H., & Rose, J. M. (2016). Preferences for shared autonomous vehicles. Transportation research part C: emerging technologies, 69, 343-355.
  30. Kyriakidis, M., Happee, R., & de Winter, J. C. (2015). Public opinion on automated driving: Results of an international questionnaire among 5000 respondents. Transportation research part F: traffic psychology and behaviour, 32, 127-140.
  31. Lamotte, R., de Palma, A., & Geroliminis, N. (2017). On the use of reservation-based autonomous vehicles for demand management. Transportation Research Part B: Methodological, 99, 205-227.
  32. Liu, E. M. (2013). Time to change what to sow: Risk preferences and technology adoption decisions of cotton farmers in China. Review of economics and statistics, 95(4), 1386- 1403.
  33. Louviere, J. J., Hensher, D. A., & Swait, J. D. (2000). Stated choice methods: analysis and applications: Cambridge University Press.
  34. Madrian, B. C. (2014). Applying insights from behavioral economics to policy design. Annu. Rev. Econ., 6(1), 663-688.
  35. Maynard, T., Beecroft, N., & Gonzalez, S. (2014). Autonomous vehicles: handing over control.
  36. Mersky, A. C., & Samaras, C. (2016). Fuel economy testing of autonomous vehicles. Transportation research part C: emerging technologies, 65, 31-48.
  37. Morikawa, T., Ben-Akiva, M., & McFadden, D. (2002). Discrete choice models incorporating revealed preferences and psychometric data. Advances in Econometrics, 16, 29-56.
  38. Mosley, P., & Verschoor, A. (2005). Risk attitudes and the 'vicious circle of poverty'. The European journal of development research, 17(1), 59-88.
  39. Neumann, J. v., & Morgenstern, O. (1944). Theory of games and economic behavior: Princeton university press Princeton.
  40. Nicholson, W., & Snyder, C. (2011). Microeconomic theory: Basic principles and extensions: Nelson Education.
  41. Prelec, D. (1998). The probability weighting function. Econometrica, 497-527.
  42. Starkey, N. J., & Isler, R. B. (2016). The role of executive function, personality and attitudes to risks in explaining self-reported driving behaviour in adolescent and adult male drivers. Transportation research part F: traffic psychology and behaviour, 38, 127-136.
  43. Sunstein, C. R. (2003). Terrorism and probability neglect. Journal of Risk and uncertainty, 26(2- 3), 121-136.
  44. Talebpour, A., & Mahmassani, H. S. (2016). Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transportation research part C: emerging technologies, 71, 143-163.
  45. Tanaka, T., Camerer, C. F., & Nguyen, Q. (2010). Risk and time preferences: linking experimental and household survey data from Vietnam. American economic review, 100(1), 557-571.
  46. Tversky, A., & Fox, C. R. (1995). Weighing risk and uncertainty. Psychological review, 102(2), 269. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4), 297-323.
  47. Wu, X., & Nie, Y. M. (2011). Modeling heterogeneous risk-taking behavior in route choice: A stochastic dominance approach. Transportation Research Part A, 45, 896-915.
  48. Xu, X., & Fan, C.-K. (2018). Autonomous vehicles, risk perceptions and insurance demand: An individual survey in China. Transportation Research Part A: Policy and Practice.
  49. Yap, M. D., Correia, G., & Van Arem, B. (2016). Preferences of travellers for using automated vehicles as last mile public transport of multimodal train trips. Transportation Research Part A: Policy and Practice, 94, 1-16.

Related papers

Estimating the willingness to pay and value of risk reduction for car occupants in the road environment
Juan de Dios Ortuzar

Transportation Research Part a Policy and Practice, 2009

In recent years there has been a re-focus on the valuation of a statistical life from the ex post or human capital method to an ex ante willingness to pay (WTP) approach. This is in part a recognition that we may have been undervaluing the cost of fatalities and injuries to society associated with crashes, but also a strong belief in the need to focus on establishing the amount, ex ante, that individuals are willing to pay to reduce the risk of exposure to circumstances that might lead to death or degree of injury on the road network. This study has developed a framework in which to identify the degree of preference heterogeneity in willingness to pay by individuals who are drivers or passengers in cars to avoid being killed or injured. A stated choice experiment approach is developed. The empirical setting is a choice of route for a particular trip that a sample of individuals periodically undertakes in Australia. The particular trip is described in enough detail to provide the respondent with a familiar market environment, providing all the relevant background information required for making a decision. Mixed logit models are estimated to obtain the marginal (dis)utilities associated with each influence on the choice amongst the attribute packages offered in the stated choice scenarios. These estimates are used to obtain the WTP distributions for fatality and injury avoidance, which are then aggregated to obtain estimates of the value of risk reduction (VRR), of which the fatality class is also known as the value of a statistical life (VSL).

View PDFchevron_right
Asymmetric preferences for road safety: Evidence from a stated choice experiment among car drivers
Juan de Dios Ortuzar

Transportation Research Part F: Traffic Psychology and Behaviour, 2015

Recent research has proposed fitting responses from discrete choice experiments to asymmetric value functions consistent with prospect theory, taking into account respondents' reference points in their valuation of choice attributes. Previous studies have mainly concentrated on travel time and cost attributes, while evidence regarding road safety attributes is very limited. This paper investigates the implicit utility of a road safety attribute, defined as the number of casualties per year in alternative car trip choices, when safety improves or deteriorates.

View PDFchevron_right
Road Safety Valuation under a Stated Choice Framework
Juan de Dios Ortuzar

2006

The value of fatal risk reductions is a vital input for road safety cost-benefit analysis and has been traditionally estimated by means of contingent valuation in spite of the growing criticism surrounding this approach. Furthermore, many scholars believe that risk-money trade-offs are not well understood because of the difficulty of internalising tiny risks; the authors have applied the Stated Choice

View PDFchevron_right
Associations between risk judgments and demand for transport risk mitigation
Ingunn Lund

Journal of Risk Research, 2012

The present study tested a hypothesized model regarding associations between risk sensitivity, risk perception, transport priorities, worry, and demand for risk mitigation in transport. An additional aim was to investigate differences in risk perception, worry, risk sensitivity, transport priorities, and demand for risk mitigation in age-groups, gender, and educational levels. A mail survey was conducted in a representative sample of the Norwegian public over 18 years (n = 1947) in 2008. The response rate was 31%. The results showed that transport priorities were the strongest predictor of demand for risk mitigation. Risk perception seems to be mediated by worry. Risk sensitivity was directly associated with transport risk perception and, not as predicted, also directly with demand for risk mitigation. The two youngest age-groups (18-30 and 31-50 years) perceived the probabilities of transport accidents in private transportation as significantly larger and also judged other nontransport risks to be larger than those over 50 years of age. The youngest age-group was more worried about injuries from private transportation, but reported lower demands for risk mitigation. The findings merit further research into the relationship between risk perception and demand for risk mitigation. The results indicated that younger individuals were more worried about injuries and assessed the probability of accidents to be larger, while they report a lower demand for risk mitigation compared to older age-groups.

View PDFchevron_right
Individual Risk Preferences and Better Car Replacement
Journal of Finance and Economics

We conduct a survey to examine the impact of individual risk preferences on better car replacement decision in the insurance market. The survey consists of a lottery choice task and an insurance questionnaire. Our results strongly support contentions that consumers are significantly influenced by their risk attitudes and the insurance premium. Further, we find that female consumers are more influenced by their degree of risk aversion than male consumers when making such insurance decision. Moreover, our results show that when the level of risk aversion is controlled, the individual risk preferences have different magnitudes of impact on insurance decision making. JEL Classifications: C91, G22

View PDFchevron_right

Related topics

  • Economics

    • Cited by

    Individual Predictors of Autonomous Vehicle Public Acceptance and Intention to Use: A Systematic Review of the Literature
    fahimeh golbabaei

    Journal of Open Innovation: Technology, Market, and Complexity

    Fully autonomous vehicles (AV) would potentially be one of the most disruptive technologies of our time. The extent of the prospective benefits of AVs is strongly linked to how widely they will be accepted and adopted. Monitoring and tracking of individuals’ reactions and intentions to use AVs are critical. The current study aims to explore and classify individual predictors (i.e., influential factors or determinants) of public acceptance of, and intention to use AVs, by conducting a systematic literature review and developing a conceptual framework to map out the individual influential factors that shape public attitudes towards AVs, which influence user acceptance and adoption preferences. This framework contains the key factors identified in the systematic review—i.e., demographic, psychological, and mobility behavior characteristics. The findings of the review disclose that public perceptions and adoption intentions vary significantly among different socio-demographic cohorts. C...

    View PDFchevron_right
    Costs and Benefits of Electrifying and Automating Bus Transit Fleets
    Moataz Mohamed

    Sustainability, 2020

    Diesel-powered, human-driven buses currently dominate public transit options in most U.S. cities, yet they produce health, environmental, and cost concerns. Emerging technologies may improve fleet operations by cost-effectively reducing emissions. This study analyzes both battery-electric buses and self-driving (autonomous) buses from both cost and qualitative perspectives, using the Capital Metropolitan Transportation Authority’s bus fleet in Austin, Texas. The study predicts battery-electric buses, including the required charging infrastructure, will become lifecycle cost-competitive in or before the year 2030 at existing U.S. fuel prices ($2.00/gallon), with the specific year depending on the actual rate of cost decline and the diesel bus purchase prices. Rising diesel prices would result in immediate cost savings before reaching $3.30 per gallon. Self-driving buses will reduce or eliminate the need for human drivers, one of the highest current operating costs of transit agencies...

    View PDFchevron_right
    A General Framework to Forecast the Adoption of Novel Products: A Case of Autonomous Vehicles
    Ishant Sharma

    Transportation Research Part B: Methodological

    Due to the unavailability of prototypes, the early adopters of novel products actively seek information from multiple sources (e.g., media and social networks) to minimize the potential risk. The existing behavior models not only fail to capture the information propagation within the individual's social network, but also they do not incorporate the impact of such word-ofmouth (WOM) dissemination on the consumer's risk preferences. Moreover, even cuttingedge forecasting models rely on crude/synthetic consumer behavior models. We propose a general framework to forecast the adoption of novel products by developing a new consumer behavior model and integrating it into a population-level agent-based model. Specifically, we extend the hybrid choice model to estimate consumer behavior, which incorporates social network effects and interplay between WOM and risk aversion. The calibrated consumer behavior model and synthetic population are passed through the agent-based model for forecasting the product market share. We apply the proposed framework to forecast the adoption of autonomous vehicles (AVs) in Nashville, USA. The consumer behavior model is calibrated with a stated preference survey data of 1,495 Nashville residents. The output of the agent-based model provides the effect of the purchase price, post-purchase satisfaction, and safety measures/regulations on the forecasted AV market share. With an annual AV price reduction of 5% at the initial purchase price of $40,000 and 90% of satisfied adopters, AVs are forecasted to attain around 85% market share in thirty years. These findings are crucial for policymakers to develop infrastructure plans and manufacturers to conduct an after-sales cost-benefit analysis.

    View PDFchevron_right
    Public Acceptance towards Emerging Autonomous Vehicle Technology: A Bibliometric Research
    Booi Chen Tan

    Sustainability

    In the current challenging and competitive dynamic business world today, automotive companies have been rapidly developing and promoting autonomous vehicles (AVs), which aim to reduce crashes, energy consumption, pollution, and congestion and increase transport accessibility. To ensure the successful adoption of AVs, an increasing number of studies have been conducted to understand public acceptance. This paper used the bibliometric technique to understand the distribution, emerging trend, and the research cluster in the context of AV technology acceptance through knowledge mapping. The Web of Science database was used to retrieve 401 scientific articles from 2000 to June 2022. The findings reported that the previous studies mainly focused on the research clusters related to the domains of attitude, trust, technology, impact, and models. Finally, this study added to the existing body of literature by providing the current knowledge landscape to guide the future research.

    View PDFchevron_right
    580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved