Naming is framing: The framing effect of technology name on public attitude toward automated vehicles

Abstract

Vehicles with automated driving systems are called by many names, which are used interchangeably in public discourse, with different and at times misleading meanings. In two studies (total N = 908), we examined the naming effects on people’s cognitive (perceived benefit and risk), affective (negative and positive affect), and behavioral responses (behavioral intention) to and trust in these vehicles in the Chinese context. Study 1 considered four names (intelligent, automated, autonomous, and driverless vehicles). Study 2 presented an identical description of vehicles with full automation and considered their five names (fully intelligent, fully automated, fully autonomous, fully driverless, and driverless vehicles). We corroborated the naming effects on affective responses and trust. The framing of “driverless vehicle” was less favorable in Study 1 but more favorable in Study 2. Technology names indirectly influenced behavioral intention through certain cognitive and affective responses. Theoretical and practical implications of our results are discussed.

Keywords

attitudinal responses automated vehicles driverless vehicles framing effect technology name

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References

Abraham

Seppelt

Mehler

Reimer

(2017) What’s in a name: Vehicle technology branding & consumer expectations for automation. In: Proceedings of the 9th international conference on automotive user interfaces and interactive vehicular applications, Oldenburg, 24–27 September.

Anania

Rice

Walters

Pierce

Winter

Milner

(2018) The effects of positive and negative information on consumers’ willingness to ride in a driverless vehicle. Transport Policy 72: 218–224.

Banks

Plant

Stanton

(2018) Driver error or designer error: Using the Perceptual Cycle Model to explore the circumstances surrounding the fatal Tesla crash on 7th May 2016. Safety Science 108: 278–285.

Boersma

Poortvliet

Gremmen

(2019a) The elephant in the room: How a technology’s name affects its interpretation. Public Understanding of Science 28: 218–233.

Boersma

Poorvliet

Gremmen

(2019b) Naming is framing: The effects of a technological name on the interpretation of a technology. Journal of Science Communication 18: A04.

Brell

Philipsen

Ziefle

(2019) sCARy! Risk perceptions in autonomous driving: The influence of experience on perceived benefits and barriers. Risk Analysis 39: 342–357.

Choi

(2015) Investigating the importance of trust on adopting an autonomous vehicle. International Journal of Human-Computer Interaction 31: 692–702.

Chong

Druckman

(2007) Framing theory. Annual Review of Political Science 10: 103–126.

Clarke

Hart

Schuldt

Evensen

DTN

Boudet

Jacquet

, et al. (2015) Public opinion on energy development: The interplay of issue framing, top-of-mind associations, and political ideology. Energy Policy 81: 131–140.

10.

Cunningham

Regan

Ledger

Bennett

(2019) To buy or not to buy? Predicting willingness to pay for automated vehicles based on public opinion. Transportation Research Part F: Traffic Psychology and Behaviour 65: 418–438.

11.

Druckman

(2004) Political preference formation: Competition, deliberation, and the (ir)relevance of framing effects. American Political Science Review 98: 671–686.

12.

Eagly

Chaiken

(1993) The Psychology of Attitudes. Fort Worth, TX: Harcourt, Brace, & Janovich.

13.

Entman

(1993) Framing: Toward clarification of a fractured paradigm. Journal of Communication 43: 51–58.

14.

Fraedrich

Lenz

(2016) Societal and individual acceptance of autonomous driving. In: Maurer

Gerdes

Lenz

Winner

(eds) Autonomous Driving: Technical, Legal and Social Aspects. London: Springer, pp. 621–640.

15.

Gandia

Antonialli

Cavazza

Neto

Lima

DAd

Sugano

, et al. (2019) Autonomous vehicles: Scientometric and bibliometric review. Transport Reviews 39: 9–28.

16.

Hardman

Lee

Tal

(2019) How do drivers use automation? Insights from a survey of partially automated vehicle owners in the United States. Transportation Research Part A: Policy and Practice 129: 246–256.

17.

Hayes

(2018) Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-based Approach. London: The Guilford Press.

18.

Hohenberger

Spörrle

Welpe

(2016) How and why do men and women differ in their willingness to use automated cars? The influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94: 374–385.

19.

Horn

(1965) A rationale and test for the number of factors in factor analysis. Psychometrika 30: 179–185.

20.

Hryniewicz

Grzegorczyk

(2020) How different autonomous vehicle presentation influences its acceptance: Is a communal car better than agentic one? PLoS ONE 15: e0238714.

21.

Jang

Hart

(2015) Polarized frames on “climate change” and “global warming” across countries and states: Evidence from Twitter big data. Global Environmental Change 32: 11–17.

22.

Kankanhalli

Tan

BCY

Wei

K-K

(2005) Contributing knowledge to electronic knowledge repositories: An empirical investigation. MIS Quarterly 29: 113–143.

23.

Liu

Yang

Wang

Liu

(2019a) Evaluating initial public acceptance of highly and fully autonomous vehicles. International Journal of Human-Computer Interaction 35: 919–931.

24.

Liu

(2020) Public attitude toward self-driving vehicles on public roads: Direct experience changed ambivalent people to be more positive. Technological Forecasting and Social Change 151: 119827.

25.

Liu

Guo

Ren

Wang

(2019b) Willingness to pay for self-driving vehicles: Influences of demographic and psychological factors. Transportation Research Part C: Emerging Technologies 100: 306–317.

26.

Liu

Zuo

(2019c) Self-driving vehicles: Are people willing to trade risks for environmental benefits? Transportation Research Part A: Policy and Practice 125: 139–149.

27.

Liu

Zhao

(2019d) Road tests of self-driving vehicles: Affective and cognitive pathways in acceptance formation. Transportation Research Part A: Policy and Practice 124: 354–369.

28.

Liu

Yang

(2019e) Public acceptance of fully automated driving: Effects of social trust and risk/benefit perceptions. Risk Analysis 39: 326–341.

29.

Madigan

Louw

Wilbrink

Schieben

Merat

(2017) What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of automated road transport systems. Transportation Research Part F: Traffic Psychology and Behaviour 50: 55–64.

30.

Merk

Pönitzsch

(2017) The role of affect in attitude formation toward new technologies: The case of stratospheric aerosol injection. Risk Analysis 37: 2289–2304.

31.

Midden

CJH

Huijts

NMA

(2009) The role of trust in the affective evaluation of novel risks: The case of CO2 storage. Risk Analysis 29: 743–751.

32.

Moody

Bailey

Zhao

(2020) Public perceptions of autonomous vehicle safety: An international comparison. Safety Science 121: 634–650.

33.

Motamedi

Wang

Zhang

Chan

C-Y

(2020) Acceptance of full driving automation: Personally owned and shared-use concepts. Human Factors 62: 288–309.

34.

Myrick

Ahern

Shao

Conlin

(2019) Technology name and celebrity endorsement effects of autonomous vehicle promotional messages: Mechanisms and moderators. Science Communication 41: 38–65.

35.

National Highway Traffic Safety Administration (2016) Federal Automated Vehicles Policy: Accelerating the Next Revolution in Roadway Safety. Washington, DC: National Highway Traffic Safety Administration (NHTSA), U.S. Department of Transportation.

36.

Nees

(2018) Drivers’ perceptions of functionality implied by terms used to describe automation in vehicles. In: Proceedings of the human factors and ergonomics society annual meeting 2018, Philadelphia, PA, 1–5 October.

37.

Nordhoff

de Winte

Madigan

Merat

van Arem

Happee

(2018a) User acceptance of automated shuttles in Berlin-Schöneberg: A questionnaire study. Transportation Research Part F: Traffic Psychology and Behaviour 58: 843–854.

38.

Nordhoff

de Winter

Kyriakidis

van Arem

Happee1

(2018b) Acceptance of driverless vehicles: Results from a large cross-national questionnaire study. Journal of Advanced Transportation 2018: 5382192.

39.

Panagiotopoulos

Dimitrakopoulos

(2018) An empirical investigation on consumers’ intentions towards autonomous driving. Transportation Research Part C: Emerging Technologies 95: 773–784.

40.

Pilkington

(2019) Definitions of scientific terminology in popular science books: An examination of definitional chains. Science Communication 41: 580–601.

41.

Revelle

(2018) How to: Use the psych package for Factor Analysis and data reduction. Available at: http://ww.personality-project.org/r/psych/HowTo/factor.pdf (accessed 16 May 2019).

42.

Rosenberg

Hovland

(1960) Cognitive, affective, and behavioral components of attitude. In: Rosenberg

Hovland

McGuire

Abelson

Brehm

(eds) Attitude Organization and Change: An Analysis of Consistency Among Attitude Components. New Haven: Yale University Press, pp. 1–14.

43.

Schmitt

(2016) German government asks Tesla to rename Autopilot, and drivers to read their owner’s manual. Forbes. Available at: https://www.forbes.com/sites/bertelschmitt/2016/10/14/german-government-asks-tesla-to-rename-autopilot-and-drivers-to-read-their-owners-manual/#592c4cfc51d2 (accessed 26 August 2019).

44.

Schoettle

Sivak

(2014) A Survey of Public Opinion About Autonomous and Self-Driving Vehicles in the U.S., the U.K., and Australia. Ann Arbor, MI: Transportation Research Institute, University of Michigan.

45.

Schuldt

Konrath

Schwarz

(2011) “Global warming” or “climate change”?: Whether the planet is warming depends on question wording. Public Opinion Quarterly 75: 115–124.

46.

Shariff

Bonnefon

J-F

Rahwan

(2017) Psychological roadblocks to the adoption of self-driving vehicles. Nature Human Behaviour 1: 694–696.

47.

Shladover

(2018) Connected and automated vehicle systems: Introduction and overview. Journal of Intelligent Transportation Systems 22: 190–200.

48.

Society of Automotive Engineers (SAE) (2018) Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems. Washington, DC: SAE International.

49.

Society of Automotive Engineers (SAE) (2020) Clearing the Confusion: Recommended Common Naming for Advanced Driver Assistance Technologies. Washington, DC: SAE International.

50.

Teoh

(2020) What’s in a name? Drivers’ perceptions of the use of five SAE Level 2 driving automation systems. Journal of Safety Research 72: 145–151.

51.

Vishwanath

(2009) From belief-importance to intention: The impact of framing on technology adoption. Communication Monographs 76: 177–206.

52.

Ward

Raue

Lee

Ambrosio

Coughlin

(2017) Acceptance of automated driving across generations: The role of risk and benefit perception, knowledge, and trust. In: 19th international conference on human-computer interaction, Vancouver, BC, Canada, 9–14 July.

53.

Whitmarsh

(2009) What’s in a name? Commonalities and differences in public understanding of “climate change” and “global warming.” Public Understanding of Science 18: 401–420.

54.

Zhang

Min

Wang

Zhao

Liu

(2018) What drives people to accept automated vehicles? Findings from a field experiment. Transportation Research Part C: Emerging Technologies 95: 320–334.

55.

Zahry

Besley

(2019) Genetic engineering, genetic modification, or agricultural biotechnology: Does the term matter? Journal of Risk Research 22: 16–31.

56.

Zhang

Tao

Zhang

Lin

Zhang

(2019) The roles of initial trust and perceived risk in public’s acceptance of automated vehicles. Transportation Research Part C: Emerging Technologies 98: 207–220.

57.

Zhao

Lynch

Jr Chen

(2010) Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research 37: 197–206.