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Know Your User: Building a Predictiv...

Milner, Mattie Nicole.

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Know Your User: Building a Predictive Model of Consumer Preference for Driverless Cars.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Know Your User: Building a Predictive Model of Consumer Preference for Driverless Cars./
Author:	Milner, Mattie Nicole.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	200 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-11, Section: B.
Contained By:	Dissertations Abstracts International81-11B.
Subject:	Transportation. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27838004
ISBN:	9798643171157

Know Your User: Building a Predictive Model of Consumer Preference for Driverless Cars.
Milner, Mattie Nicole.

Know Your User: Building a Predictive Model of Consumer Preference for Driverless Cars. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 200 p.

Source: Dissertations Abstracts International, Volume: 81-11, Section: B.

Thesis (Ph.D.)--Embry-Riddle Aeronautical University, 2020.

This item must not be sold to any third party vendors.

INTRODUCTION: This dissertation identifies factors significantly predicting participants' preference for riding in an autonomous vehicle rather than flying on a commercial aircraft. A plethora of research has investigated these two transportation industries independently; however, scarcely any research has considered the impact these two industries will have on each other. Travelers' preference for riding in an autonomous vehicle rather than a commercial aircraft was investigated through four different scenarios.METHOD: A regression equation was created to predict participants' preferred travel method and validated through a two-stage process. Stage 1 involved the creation of the regression equation, and a total of 1,008 participants responded to an online survey, providing information on demographics, travel-related behavior, and their preference for riding in an autonomous vehicle rather than flying on a commercial aircraft. Stage 2 involved validation of the regression equation, and 1,008 participants responded to the same online survey. Stage 2 participants' scores were predicted using the regression equation created in Stage 1. Then, their predicted scores and actual scores were compared to validate the equation throughout four different travel scenarios.RESULTS: In Stage 1, a backward stepwise regression assessed the twenty predictive factors (age, gender, ethnicity, social class, price, perceived value, familiarity, fun factor, wariness of new technology, personality (openness, conscientiousness, extraversion, agreeableness, and neuroticism), general vehicle affect, general airplane affect, vehicle comfort, vehicle external factors, airplane comfort, and airplane external factors). These factors were tested in four different scenarios, which varied only in the length of time participants would spend traveling.CONCLUSION: A predictive model was created for each scenario, and then all four models were validated in Stage 2 using participants' predicted scores and actual scores. Models were validated using a t-test, correlation, and comparison of cross-validated R2. The most robust model was for the four-hour trip, with six variables significantly predicting participants' preferred travel method, which accounted for 50.7% of the variance in the model (50.1% adjusted). Upper Social Class, Vehicle Affect, Airplane Affect, and Vehicle Comfort were the only significant predictors throughout all four scenarios. These four predictors will help other researchers and experts in the vehicle industry identify the first adopters of this new technology. The implications of the results and suggestions for future research are discussed.

ISBN: 9798643171157Subjects--Topical Terms:

555912
Transportation.
Subjects--Index Terms:

Autonomous vehicle preference

Know Your User: Building a Predictive Model of Consumer Preference for Driverless Cars.
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245 1 0 $a Know Your User: Building a Predictive Model of Consumer Preference for Driverless Cars.
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300 $a 200 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-11, Section: B.
500 $a Advisor: Rice, Stephen.
502 $a Thesis (Ph.D.)--Embry-Riddle Aeronautical University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a INTRODUCTION: This dissertation identifies factors significantly predicting participants' preference for riding in an autonomous vehicle rather than flying on a commercial aircraft. A plethora of research has investigated these two transportation industries independently; however, scarcely any research has considered the impact these two industries will have on each other. Travelers' preference for riding in an autonomous vehicle rather than a commercial aircraft was investigated through four different scenarios.METHOD: A regression equation was created to predict participants' preferred travel method and validated through a two-stage process. Stage 1 involved the creation of the regression equation, and a total of 1,008 participants responded to an online survey, providing information on demographics, travel-related behavior, and their preference for riding in an autonomous vehicle rather than flying on a commercial aircraft. Stage 2 involved validation of the regression equation, and 1,008 participants responded to the same online survey. Stage 2 participants' scores were predicted using the regression equation created in Stage 1. Then, their predicted scores and actual scores were compared to validate the equation throughout four different travel scenarios.RESULTS: In Stage 1, a backward stepwise regression assessed the twenty predictive factors (age, gender, ethnicity, social class, price, perceived value, familiarity, fun factor, wariness of new technology, personality (openness, conscientiousness, extraversion, agreeableness, and neuroticism), general vehicle affect, general airplane affect, vehicle comfort, vehicle external factors, airplane comfort, and airplane external factors). These factors were tested in four different scenarios, which varied only in the length of time participants would spend traveling.CONCLUSION: A predictive model was created for each scenario, and then all four models were validated in Stage 2 using participants' predicted scores and actual scores. Models were validated using a t-test, correlation, and comparison of cross-validated R2. The most robust model was for the four-hour trip, with six variables significantly predicting participants' preferred travel method, which accounted for 50.7% of the variance in the model (50.1% adjusted). Upper Social Class, Vehicle Affect, Airplane Affect, and Vehicle Comfort were the only significant predictors throughout all four scenarios. These four predictors will help other researchers and experts in the vehicle industry identify the first adopters of this new technology. The implications of the results and suggestions for future research are discussed.
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