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1、Adjunct Proceedings of the 9th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 17), September 2427, 2017, Oldenburg, Germany.Driving Acceptance:Applying Structural Equation Modelingto In-Vehicle Automation AcceptanceAbstractThe way a pe

2、rson chooses to utilize automated vehicle systems is not yet well understood. A structural equation model of acceptance of such systems of was constructed, applying theoretical models of automation acceptance to a real survey data set. The model indicated that perceptions of system utility may be mo

3、re important than user characteristics, suggesting that automated systems need to prove their utility to drivers. Additionally, research methods are suggested to fill model gaps.Author KeywordsMethods; Structural Equation Modeling (SEM); Technology Acceptance; Automated Driving.Keenan R. MayGeorgia

4、Institute of Technology Atlanta, GA, USA CCS ConceptsSocial and professional topicsUser characteristicsBrittany E. NoahGeorgia Institute of Technology Atlanta, GA, USA IntroductionAt the time of this writing, SAE Level 1 automated vehicles, or vehicles with vario

5、us intelligent alerting systems, are becoming common. Level 2 consumer vehicles exist (1; 15) and higher levels are expected by the 2020s 4. As automation technologies are added piecemeal to the market, drivers must make decisions about whether to utilize them.Understanding these decisions can infor

6、m system designers and manufacturers as to how they might to better reach potential users who could benefit from these systems.Theoretical Models of Automation AcceptanceDeciding whether or not to use an automated technology is a complex decision that may depend on a large network of factors. Concep

7、tual models have been developed to address technology acceptance, starting with the Technology Acceptance Model (TAM) by Davis (6; 7). TAM drew from Fishbein and Azjens model of how attitudes, beliefs, and intentions impact behavior 9, to predict system use. The factor attitude toward using is influ

8、enced by the perceived usefulness and perceived ease of use of the system, which is in turn influenced by external variables (see Figure 1). TAM is a broad framework, applicable to various technologies.Bruce N. WalkerGeorgia Institute of Technology Atlanta, GA, USA Permis

9、sion to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components

10、 of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from P.AutomotiveUI 17 Adjunct, Septemb

11、er 2427, 2017, Oldenburg, Germany 2017 Association for Computing Machinery. ACM ISBN 978-1-4503-5151-5/17/09$15.00/10.1145/3131726.3131755Work-in-Progress TuesdaySessionAutomotiveUI Adjunct Proceedings 17, Oldenburg, GermanyWhile TAM focuses on features of the technological system itse

12、lf and resultant attitudes and behaviors, subsequent models have included other factors.Goodhue and Thompson 11 proposed the Task-Technology Fit model, which focuses on how well the system fits the task. Dishaw and Strong 5 combined TAM and TTF concepts to produce a model that incorporates task-fit

13、and as system-related attitudes (Fig. 2). To better reflect automated system acceptance, Ghazizadeh, Lee and Boyle10 developed an Automation Acceptance Model (AAM) (Figure 3). As with prior models, the models causal flow starts with the external variables factor, which can include measurements of su

14、bjective norms and past experiences, and exerts influence on new factors compatibility and trust, as well as the TAM factors. Compatibility encompasses variables relating to both TTF-style task/need fit, as well as the extent to which a technology meshes with the possible users past experiences and

15、values 14. This trust factor reflects the extent to which a person believes that the system is capable of completing its primary functions. Importantly, compatibility and trust are sensitive to the following feedback mechanism: each time a persons decision process flows through the TAM causal struct

16、ure andStructural Equation Modeling: Applying Theory to DataThe aforementioned models are all conceptual. In contrast, Structural Equation Modeling (SEM) is a statistical modeling method that allows one to test conceptual models with real data sets. SEM is used to model relationships between unobser

17、vable constructs (also called latent variables or factors) such as personality, beliefs, and attitudes 4. It is concerned with not only (a) how to best define constructs through observable variables (similar to factor analytic methods); but also (b) what relationships are present and their strength;

18、 and (c) how this network of causality may influence behavior.Current StudyIn the current study, SEM is applied to a data set of survey responses on attitudes towards various in-vehicle automation technologies. The model was developed to instantiate the AAM model, while still achieving sufficient fi

19、t given the limitations of the data set. The modeling process was used to provide information about the relative significance of external variables compared to compatibility, as well as to assess how attitudes might differ for two types of vehicle automation systems: Level 1 and Level 2.MethodPartic

20、ipantsParticipants were a combination of paid Mechanical Turk contractors and volunteer snowball sample participants. There were 202 participants, aged 33 on average (SD = 10.5).ProcedureParticipants completed the survey using the web-based platform, Qualtrics. Upon providing consent, participants c

21、ompleted a set of items to ascertain their attitudes towards different Level 1 and 2 systems.Survey ItemsEXTERNAL VARIABLESA variety of potentially informative external variables items were responded to by participants. Some of these were general demographics, while others captured technology and dr

22、iving-related factors. Participants reported their age, education level, and gender. They also reported the extent to which they enjoy driving in four different driving environments: rural, urban, highway, and suburban.Participants rated their level of confidence driving in each environment, and how

23、 many hours per week they tended to drive in each environment. Finally, participants completed the technology experience subscale from 12.The SEM method allows items to be combined into item parcels, which are combinations of item responses that allow for more easily interpreted data. Three such ite

24、m parcelscauses system interactions or experiencewith the non-automated task those experiences influence the compatibility factor. This reflects the “voluntary” nature of consumer automation adoption, to be contrasted withtraditional tool usage in job-related contexts. This cycle of experiences modi

25、fies trust and compatibility, which affect the TAM factors. The authors recommended that the AAM framework be used to explain adoption of automated vehicles. There have also been efforts to improve acceptance models by incorporating additional, general variables, which can include demographics, soci

26、o-economic items, and personal items, into TAM models 3. While a variety of variables might be considered, 2 found that the external variables of age, gender, and general technological confidence/ expertise impacted attitudes toward technology use.191Figure 1. TAM Model. Reproduced from 9.Figure 2.

27、TAM-TTF model. Reproduced from 5.Work-in-Progress TuesdaySessionAutomotiveUI Adjunct Proceedings 17, Oldenburg, Germanywere made with sums of the aforementioned driving experience item sets. Lastly, responses to the three-item subscale on technology experience were combined into an item parcel. Thes

28、e were summed to create a “technology experience” parcel. These item parcels formed the external variables factor.COMPATIBILITYParticipants responded to Likert-type questions asking about the ability of six in-vehicle automation systems to affect their safety when driving. The Level 1 systems were l

29、ane departure warning, cruise control, and frontal collision alert. Accordingly, the Level 2 systems were automated lane keeping, automated cruise control, and frontal collision avoidance. Item responses were summed to produce two item parcels one for each automation level.PERCEIVED USEFULNESSPartic

30、ipants answered a set of questions about the extent to which they estimated the six Level 1 and Level 2 systems to be useful. These were combined into two item parcels, reflecting perceived usefulness of Level 1 and Level 2 systems; these defined the perceived usefulness factor.ATTITUDE TOWARD USING

31、Participants were also asked about the benefits and risks they perceived Level 1 and 2 automated systems. Item parcels were computed by sums for the benefits and risks questions, then combined by subtracting the risks from the benefits to create an adjusted benefits item parcel. Additionally, partic

32、ipants answered a set of questions about their positive and negative emotions related to the automated systems, using the PANAS 16. These questions were combined into “positive affect” and “negative affect” item parcels for Level 1 and Level 2 automation systems.BEHAVIORAL INTENTION TO USEParticipan

33、ts responded to Likert-type items with the extent to which they believed they would use the six paired Level 1 and Level 2 systems. These questions were again asked in the context of the four different driving environments (highway,Modeling ProcessThe model in Figure 4 represents the SEM developed w

34、ith the aforementioned survey items and item parcels. The structure of this model was derived from both exploratory factor analysis and theoretical reasoning considering prior models and the limitations of the data set, and includes 5 out of the 8 factors from the AAM model.After initial model const

35、ruction, Wald and Lagrange Multiplier tests were used to determine how to optimize the model.Specifically, Lagrange Multiplier tests suggested that correlating several error terms would increase model fit, and these modifications were made. Gender, education, and average hours driven per week were r

36、emoved from the external variables factor as recommended by the Wald test (noted in the figure with dashed lines); model fit was unchanged as a result of removing these variables. Notably, age was not a significant contributor.Fit for the final model was fair: a CFI of 0.836 was achieved (Comparativ

37、e Fit Index; closer to 1 is better), along with a RMSEA of 0.117 (smaller is better), and a c2(167) = 603.760 (smaller is better). The statistics reported here are the robust model statistics, since Mardias Coefficent was 25.70, which is above the limit for assuming multivariate normality 4.MODEL IN

38、TERPRETATIONFor interpretation, the standardized weights (Tables 1 and 2, Figure 4) can be directly compared to assess the strength of each factors impact on the subsequent factor. Factor loadings can be interpreted to ascertain relative contributions of different observed variables to each factor.

39、The model is visualized in Figure 4, with annotated standardized weights and significances (noted with asterisks). Components of the AAM model that were not instantiated are included in this figure with dashed lines.ResultsStructure Level ResultsAs shown in Table 1, the external variables factor did

40、 not load heavily on compatibility, indicating that the external variables factor could be improved. The causal chain of subsequent factors was strong: compatibility was strongly influential on perceived usefulness, which weighed heavily on attitude toward using; this in turn had a large impact on b

41、ehavioral intention to use.Item/ Item Parcel Level ResultsThe external variables factor was defined most by driving enjoyment, followed by driving confidence and then technology experiences (Table 2). Both compatibility and perceived usefulness were more heavily loaded on by Level 2 automation attit

42、udes. Attitude toward using was not significantly loaded on by any of the PANAS scores; theStructural RelationshipStandardized WeightExternal Variables CompatibilityR = .362*Compatibility Perceived UsefulnessR = .928*Perceived Usefulness Attitude toward UsingR = .964*Attitude toward Using Behavioral

43、 Intent to UseR = .826*Table 1. Structural weights.Item or item parcelAgeTech Experience Driving ConfidenceDriving Enjoyment Perc. Safety L1 Perc. Safety L2Perc. Useful L1 Perc. Useful L2 Positive Affect L1 Negative Affect L1 Positive Affect L2 Negative Affect L2Concerns/ BenefitsL1 IntentHighway L1

44、 Intent Rural L1 Intent SuburbanL1 IntentUrban L2 IntentHighwayL2 IntentRuralL2 Intent SuburbanL2 IntentUrbanStandardized WeightR = .081R = .356* R = .593*R = .765* R = .640* R = .848*R = .582* R = .889* R = -.013R = -.042R = -.073R = .020 R = .437*R = .582* R = .624*R = .746*R = .773* R = .828*R =

45、.802*R = .896* R = 908*FactorExternal VariablesCompatibilityPerceived UsefulnessAttitude toward UsingBehavioral Intention to UseTable 2. Factor loadings.rural, urban, and suburban). Eight parcels were calculated as the sum of Level 1 or Level 2 responses within each environment.192Work-in-Progress T

46、uesdaySessionAutomotiveUI Adjunct Proceedings 17, Oldenburg, Germanyconcerns/benefits factor did load to a significant, but moderate, amount. Finally, for behavioral intent to use, Level 2 intentions loaded more strongly. Loadings varied only slightly by driving environment. However, loadings were h

47、ighest for intent to use in urban environments.DiscussionOverall, the AAM model was a good fit for this data set, indicating that this theoretical framework is a good way of understanding vehicle automation acceptance.However, instantiating the entire framework will require several additional steps.

48、 First, there is not yet a widely accepted method for measuring use-based trust; adding this to the model would be helpful for model fit. Second, perceived ease of use was missing. This, too, is difficult to measure, without using a driving simulator. Third, an alternative to the PANAS may need to b

49、e established for the attitude toward using factor as none of the PANAS parcels contributed significantly to the factor. Procedures need to be adjusted to reduce speculation about emerging technologies that may not have clear emotional associations. Fourth, researchers need to ascertain what items a

50、re germane to the external variables factor, such as past experiences with automation, driving, and technology, instead of relying on demographic items. The lack of impact of demographic variables supports the task/technology focus of TAM. Finally, since the compatibility factor had a large impact o

51、n subsequent factors future research should focus here to capture the use-attitudes feedback loop laid out by 10.Incorporating “experiences” with automated systems, through online surveys coupled with simulations or videos, could aid in these efforts to improve measurement validity.Model limitations

52、 aside, these findings indicate that designers of consumer-facing automated vehicle systems may benefit from enabling the feedback loop of positive interactions central to the AAM. Users can be expected to experiment with these technologies and modify their acceptance based on each experience. Autom

53、ation and user interface designers could benefit from facilitating experiences in which the utility of automated systems can be demonstrated.ConclusionUnderstanding acceptance of emergent in-vehicle technologies is key to getting these systems adopted by those who would benefit from them. This study

54、 indicates how SEM can be used to better understand how people decide to accept in-vehicle automation.193Figure 3. AAM Model. Reproduced from 10.Figure 4. Structural Equation Model produced in this study.Work-in-Progress TuesdaySessionAutomotiveUI Adjunct Proceedings 17, Oldenburg, Germany12.Mike A.

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57、f Innovations. Retrieved (/?itemid=%7Clibrary/m/aleph%7C006256656).SAE International. 2014. Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems. SAE Int. J3016 (2014), 112.DOI:/10.4271/J3016_201609David Watson, Lee A. Clark, Auke Tellegen. 1988. Development and validation of brief measures of positive and negative affect: the PANAS scales. Journal of personality and social psychology 54.6 (1988): 1063.AcknowledgementsThe authors would like to thank Dr. Susan Embretson for her mentorshi