A Factor Analysis of Consumer Expectations for Autonomous Cars

Ro, Yuna; Ha, Young-Wook

doi:10.1080/08874417.2017.1295791

Cited by 38 publications

(26 citation statements)

References 16 publications

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“…Such strategies may be most needed for females, who were found in the present study and previous research to be more likely to express negative reactions to the prospect of using AVs (Hohenberger et al, 2016;König & Neumayr, 2017;Kyriakidis et al, 2015;Regan et al, 2017;Schoettle & Sivak, 2014). Consistent with previous research, a major impediment to adoption appears to be the safety concerns that featured in both Quadrants 2 and 4 and the trust issues noted in Quadrant 4 (Bansal et al, 2016;Choi & Ji, 2015;Kohl et al, 2017;König & Neumayr, 2017;Kyriakidis et al, 2015;Menon et al, 2016;Ro & Ha, 2017;Schoettle & Sivak, 2014). Communications specifically addressing this topic are therefore likely to be important elements of any public education strategies that aim to increase receptivity.…”

Section: General Approach To Communicationssupporting

confidence: 70%

“…Of note are the issues identified in previous studies that did not feature to any meaningful extent in the respondents' spontaneous mentions of the factors they associate with AVs. In particular, despite a substantial amount of attention given in the literature to ethical, liability, and hacking concerns (Collingwood, 2017;Goodall, 2017;König & Neumayr, 2017;Kyriakidis et al, 2015;Ro & Ha, 2017;Schellekens, 2015;Schoonmaker, 2016;Sparrow & Howard, 2017), these aspects received minimal attention in the data. This suggests that previous quantitative surveys that have listed these issues and asked respondents to report whether they find them to be of concern may only reflect prompted concerns and not salient issues.…”

Section: General Approach To Communicationsmentioning

confidence: 99%

“…sensation seeking and risk taking), and the outcome variables of intentions to use AVs and willingness to pay additional amounts for AV technology. This work has primarily involved quantitative surveys, although notable methodological exceptions include studies using online content from Twitter feeds and blog posts on the topic of vehicle automation (Kohl, Mostafa, Böhm, & Krcmar, 2017;Ro & Ha, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Dimensions of attitudes to autonomous vehicles

Pettigrew

Worrall

Talati

et al. 2019

Urban, Planning and Transport Research

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For the benefits of autonomous vehicles (AVs) to be optimized, the fleet conversion process needs to be efficient and timely. This study explored public attitudes to AVs to inform strategies to increase receptivity to the wide-scale use of AVs. A national online survey was administered to a sample of 1,624 Australians aged 16+ years. The survey featured open-ended questions that scoped respondents' perceptions of AVs. A grounded, thematic analysis identified two primary dimensions in the data: response valence (how positive or negative the comments were about the advent of AVs) and response type (the extent to which the comments reflected a cognitive or emotional response). This resulted in a dimensional analysis featuring four quadrants that captured the topics that were most frequently raised spontaneously by respondents. The quadrant characterized by comments that were positive/neutral and cognitive in nature was the most substantial, indicating general acceptance. Where concerns were expressed, they typically related to perceived safety, trust, and control issues, and tended to be more emotional in nature. The results highlight the importance of providing the public with concrete information about AVs to address fear levels and to resolve trust and control issues.

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Section: General Approach To Communicationssupporting

confidence: 70%

Section: General Approach To Communicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dimensions of attitudes to autonomous vehicles

Pettigrew

Worrall

Talati

et al. 2019

Urban, Planning and Transport Research

View full text Add to dashboard Cite

show abstract

“…Only a few address how respondents feel about the prospect of AVs in broader terms (e.g. their presence on the road (Schoettle & Sivak, 2014b); their impact on driver behaviour (uSwitch, 2016), or AVs as moral agents (Ro & Ha, 2017)).…”

Section: Sharing the Road With Avsmentioning

confidence: 99%

Public discomfort at the prospect of autonomous vehicles: Building on previous surveys to measure attitudes in 11 countries

Tennant

Stares

Howard

2019

Transportation Research Part F: Traffic Psychology and Behaviou

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“…Ro and Ha () present a study intended to explore the consumers' expectations for novel autonomous cars in Korea. The experimental data was collected from 1.506 potential autonomous car users.…”

Section: Overview Of Efa Applied In Data Miningmentioning

confidence: 99%

Survey on establishing the optimal number of factors in exploratory factor analysis applied to data mining

Iantovics

Rotar²,

Morar

2018

WIREs Data Min & Knowl

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In many types of researches and studies including those performed by the sciences of agriculture and plant sciences, large quantities of data are frequently obtained that must be analyzed using different data mining techniques. Sometimes data mining involves the application of different methods of statistical data analysis. Exploratory Factor Analysis (EFA) is frequently used as a technique for data reduction and structure detection in data mining. In our survey, we study the EFA applied to data mining, focusing on the problem of establishing of the optimal number of factors to be retained. The number of factors to retain is the most important decision to take after the factor extraction in EFA. Many researchers discussed the criteria for choosing the optimal number of factors. Mistakes in factor extraction may consist in extracting too few or too many factors. An inappropriate number of factors may lead to erroneous conclusions. A comprehensive review of the state-of-the-art related to this subject was made. The main focus was on the most frequently applied factor selection methods, namely Kaiser Criterion, Cattell's Scree test, and Monte Carlo Parallel Analysis. We have highligthed the importance of the analysis in some research, based on the research specificity, of the total cumulative variance explained by the selected optimal number of extracted factors. It is necessary that the extracted factors explain at least a minimum threshold of cumulative variance. ExtrOptFact algorithm presents the steps that must be performed in EFA for the selection of the optimal number of factors. For validation purposes, a case study was presented, performed on data obtained in an experimental study that we made on Brassica napus plant. Applying the ExtrOptFact algorithm for Principal Component Analysis can be decided on the selection of three components that were called Qualitative, Generative, and Vegetative, which explained 92% of the total cumulative variance.

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A Factor Analysis of Consumer Expectations for Autonomous Cars

Cited by 38 publications

References 16 publications

Dimensions of attitudes to autonomous vehicles

Dimensions of attitudes to autonomous vehicles

Public discomfort at the prospect of autonomous vehicles: Building on previous surveys to measure attitudes in 11 countries

Survey on establishing the optimal number of factors in exploratory factor analysis applied to data mining

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