Towards Directive Driving Automation: Nudging Driver’s Away From Overriding Using Social Norms

Abstract

We propose the concept of directive driving automation that positively influences drivers’ intentions to achieve shared goals. As a step toward directive driving automation, this survey study explored how social norms can persuade drivers to continue using driving automation. We tested social norm messages using a 2x2x2x2 factorial within-subject design: norm type (descriptive vs. injunctive), explanation (absent vs. present), spatial scale (local vs. global), and outcome criticality (safety-critical vs. non-safety-critical). The results suggest that framing messages as descriptive norms and providing explanations relevant to the driver can encourage them to continue using automation, especially with safety-critical outcomes. This study highlights the importance of considering message characteristics in persuasive interventions to promote the safe use of driving automation.

Keywords

Driving automation Automated vehicle Persuasive technology Social norm Nudge

Introduction

Driving automation has changed substantially over the past decade. Some production-level vehicles with Advanced Driver Assistance Systems can not only drive by themselves under the driver’s supervision but can also monitor the driver’s readiness to perform the driving task (e.g., eyes on the road, hands on the steering wheel, etc.). With further improvements in these advanced technologies, interacting with driving automation will soon become a more collaborative task, like driving with an intelligent co-driver (Lee et al., 2022). To support collaboration, automation observability and directablity—the ability to see automation’s behavior and intention and the ability to redirect automation behavior and intention—have been conceptualized to create an interdependent relationship (Christoffersen & Woods, 2002).

However, observable and directable automation may not fully represent a fundamental aspect of collaboration: a horizontal relationship, which refers to a relationship between individuals that are on the same level. The concept of observable and directable automation focuses solely on the human operator’s perspective and may consider automation as a subordinate system. We suggest the concepts of directive and observant automation, which are the complements of observable and directable automation. While directable automation allows a person to adjust the intentions of the automation, directive automation changes the intentions of the person (Chiou & Lee, 2021). In the context of driving, we can envision directive driving automation that alters the driver’s intention to achieve a shared goal. This could involve encouraging the use of driving automation for safer driving when automation is more capable of controlling the vehicle or for efficient travel when using driving automation can enhance traffic flow. Although the concept holds promise, implementing directive driving automation can pose challenges; how can driving automation persuade the human driver?

Persuasive Technology

Persuasion involves influencing someone’s beliefs, attitudes, behaviors, or decisions through communication (Gass & Seiter, 2011). Persuasion is a non-coercive method similar to nudging, which is a behavioral intervention strategy to guide individuals toward desired behaviors while preserving their freedom of choice (Thaler & Sunstein, 2009). However, persuasion emphasizes interaction and communication between a persuader and the persuadee (i.e., a person receiving the persuasive technique).

Persuasive technology is defined as a “computing system, device, or application intentionally designed to change a person’s attitudes or behavior in a predetermined way” (Fogg, 1999, p. 27). Persuasion techniques have been categorized in various ways. For instance, the persuasive systems design model includes four categories: (a) primary task support, (b) dialogue, (c) system credibility, or (d) social support. Primary task support strategies assist a user in carrying out a primary task. For example, reduction techniques reduce the effort needed to reach a target behavior. Dialogue strategies involve human-system communication. For example, reward techniques communicate incentive structures to the user for performing a target behavior. System credibility refers to strategies that prompt persuasion by increasing a system’s credibility. Examples include authority, which refers to leveraging authoritative figures to add credibility to a target behavior. Finally, social support strategies use social influence principles to prompt a target behavior. This paper explores social support strategies to promote a target behavior. Particularly, we focus on the two types of social norms: descriptive norms and injunctive norms.

Social Norms

In the focus theory of normative conduct, Cialdini et al. (1991) distinguished descriptive norms and injunctive norms based on their relevance to fundamental goals that people pursue. Descriptive norms refer to behaviors that are perceived to be prevalent or typical. These norms appeal to people’s desire to behave efficiently or accurately. By motivating people to follow what most people typically do, descriptive norms function as heuristic rules (Cialdini & Goldstein, 2004). In contrast, injunctive norms refer to behaviors perceived to be approved by others. Injunctive norms appeal to people’s desire to build and maintain social relationships. By motivating people to follow what most people approve of doing or expect them to do, injunctive norms provide people with information about how to behave appropriately in a certain social context (Cialdini & Goldstein, 2004). There has been substantial evidence demonstrating the effects of these two types of social norms on changing people’s behavior in various contexts, such as littering (Cialdini et al., 1990), energy conservation (Goldstein et al., 2008), and mobile phone use while driving (Nicolls et al., 2022).

However, the potential persuasive power of descriptive and injunctive norms in the context of driving automation has been underexplored. Previous research showed that the relative influences of descriptive norms and injunctive norms may depend on which norm is salient in each context (Kallgren et al., 2000), and the persuasive power can be the strongest when those two norms are consistent with each other (Cialdini, 2003). In the current research, we examined the effects of making descriptive or injunctive norms salient on the use of driving automation, as little work has investigated the effects of these norms in automated driving contexts. Thus, in the directive messages presented to participants, we described using driving automation as either what most people choose to do (descriptive norms) or what most people think people should choose to do (injunctive norms). We targeted the use of the automated driving mode because of its benefits, as suggested in the existing literature on driving automation, such as reducing traffic congestion (e.g., Chen et al., 2012; Fagnant & Kockelman, 2015; Zheng et al., 2011).

When framing directive messages to promote the use of driving automation, it is essential to consider additional factors unique to this context. While there are benefits that justify the motivation for promoting the use of driving automation, it is unlikely that such benefits are shared among members of the current society. This distinguishes promoting the use of driving automation from other choices like avoiding littering or not texting while driving. Therefore, promoting the use of automation may require additional explanation to encourage its adoption.

The orientation of the message is another factor to consider when framing the directive messages. Prior research suggests that a message can be more persuasive when framed to be self-oriented, highlighting its relevance to the recipient rather than other-oriented, highlighting its relevance to others (Burnkrant & Unnava, 1995; Scannell & Gifford, 2013). To test this, we varied the message orientation to explore whether emphasizing relevance to the self could increase the persuasive impact of the directive messages.

Although driving is strongly associated with safety, not all everyday driving situations involve serious safety concerns like car crashes. While highlighting a message’s relevance to safety may increase its persuasive power, it is important to compare it with messages that are not associated with safety-critical outcomes. To do this, we created and compared two sets of messages portraying driving situations: one set involving car crashes and another set involving traffic flow.

Research Questions

This study addresses four research questions with a survey study of social norm messages. We used four measures— preference, effectiveness, trustworthiness, and feeling of control—but research questions refer to one measure for simplicity. The independent variables that we manipulated in the survey are boldfaced:

RQ1. Which norm type (descriptive vs. injunctive) is preferred?

RQ2. Does the presence of an explanation affect participants’ preferences?

RQ3. When the explanation is present, which spatial scale (local vs. global) will be more preferred?

RQ4. Do people’s preferences for the messages vary between safety-critical and non-safety-critical situations?

To answer the questions, first, we examined whether directive messages framed as either descriptive or injunctive norms could persuade people to use the automated driving mode (RQ 1: effect of norm type). Second, we explored whether presenting explanations for why using the automated driving mode is beneficial would enhance the persuasiveness of the messages (RQ 2: effect of explanation). Third, we investigated whether describing the impact of the participants’ decisions on themselves (local impact) or other drivers (global impact) would influence the persuasiveness of the messages (RQ 3: effect of spatial scale). Lastly, we tested whether safety-related driving situations affected the persuasiveness of the messages (RQ 4: effect of outcome criticality).

Method

Participants

Twenty-four people participated in this study (Male = 9, female = 15, mean age = 32.7, SD of age = 2.9). Participants were recruited from the U.S. and compensated for their time ($10). Eligibility criteria for participants included: (a) between the ages of 25–55, (b) holding a valid U.S. driver’s license for at least two years, and (c) driving a minimum of 2,000 miles per year. The study lasted approximately 30 minutes for each participant.

Study Design

We used a within-subject design with a 2x2x2x2 factorial structure: norm type (descriptive vs. injunctive), explanation (absent vs. present), spatial scale (local vs. global), and outcome criticality (safety-critical vs. non-safety-critical). See Figure 1 for message samples and how each factor was manipulated.

Figure 1.

Illustration of the survey design with example messages.

Survey Implementation and Procedure

The survey was created using the Qualtrics platform. The survey included a consent form, demographic questions, driving history, use of driving automation, propensity to trust automation (Merritt, 2011), the main survey (26 items per set), and follow-up questions. To assess participant engagement, two attention check items were added, and only the surveys that passed both items were used in the analysis.

The survey presented a scenario of drivers using partially automated driving who intend to disengage from driving automation, and the vehicle displays a message to encourage the driver to continue using driving automation. Participants were asked to compare a pair of messages and choose the message that (a) they preferred, (b) was effective in changing their minds about overriding vehicle control, (c) was more trustworthy, and (d) gave them the feeling that they had more control over the vehicle (see Figure 2). The follow-up questions explored participants’ selections by requesting them to rank the importance of each reason for choosing a message. Reasons for choosing a message for each measure were extracted from previous literature: preference (Kaley, 2022; Munro, 2020; Nielsen, 1994, 2001), effectiveness (Meske & Potthof, 2017; Torning & Oinas-Kukkonen, 2009), trustworthiness (Bews & Martins, 2002; Roy & Shekhar, 2010), and feeling of control (Thompson et al., 1998).

Figure 2.

Survey item example. Participants were instructed to compare a pair of messages and select one for each measure.

Analytic Method

We used the choice-based conjoint (CBC) analysis to assess how the independent variables (IV) affect participants’ choice of one of the two messages. CBC is typically used in marketing research to assess how various attributes of products (e.g., price, design, etc.) affect consumers’ choices to purchase one product from several alternatives (Desarbo et al., 1995). For the survey analysis, we fit a multinomial logistic regression model using R (R Core Team, 2022).

Equation (1) shows the assumption of the multinomial logistic model from Kuhfeld (2010). Specifically, it assumes that the probability that a participant will choose one message (c_!) between the two messages (m) in a survey item (C) is:

p (c_{i} / C) = \frac{\exp (U (c_{i}))}{\sum_{j = 1}^{m} \exp (U (c_{j}))} = \frac{\exp (χ_{i} β)}{\sum_{j = 1}^{m} \exp (χ_{j} β)}

(1)

x_! is a vector of dummy-coded independent variables. x_! is a linear part-worth utility function determining the utility for the message c_! (U(c_!) ). From (1), we estimated each message’s choice probability ((c_!|C)). We also used the partworth utility function (x_!β) to calculate part-worth utilities for the IVs. In turn, the part-worth utilities provided a measure of the IVs’ impact on participants’ choices between the two messages in each survey item.

Results

The analysis was performed separately for the non-safetycritical set and the safety-critical set for simplicity, and the results were compared between the two sets.

Non-Safety-Critical Outcome

For messages related to non-safety-critical outcomes, participants selected descriptive norms over injunctive norms for all four measures, as shown in Figure 3 (left). The effect of explanations and spatial scale was only significant for trustworthiness, indicating that participants deemed messages with an explanation and messages with an explanation about the estimated time of arrival (ETA) to be more trustworthy.

Figure 3.

Beta weights and 95th percentile confidence intervals for factors in the non-safety-critical outcome set (left) and in the safety-critical outcome set (right).

Safety-Critical Outcome

Regarding messages related to safety-critical outcomes, norm type, explanation, and spatial scales were statistically significant for all measures, showing a consistent trend as depicted in Figure 3 (right). In general, messages using descriptive norms, providing an explanation, and using an explanation related to the crash probability of the participant’s vehicle (versus other drivers’ crash probability) had higher probabilities of being selected. The coefficients’ directions were consistent with the non-safety-critical messages, suggesting a general trend regardless of the outcome criticality. However, the impact of message characteristics was more pronounced when the outcome was safety-relevant.

Rank-Ordered Subdimensions of the Measures

Participants ranked the subdimensions (reasons) of each measure at the end of the survey. We analyzed the data to investigate the participants’ decision-making process. Only the top three reasons are listed in Table 1 for comparison, as the number of reasons varied across measures (6-11 reasons).

Table 1.

Top three reasons of each measure (the mean rank).

Preference	Effectiveness	Trustworthi ness	Feeling of control
Conciseness (3.08)	Directness of message (2.25)	Competency (2.46)	Responsibility to make the final decision (2.5)
Usefulness (3.46)	Message form at & content (3.29)	History of int eractions (2.79)	Knowledge of driving behaviors & outcomes (4.54)
Simplicity (3.83)	Targeted beha vioral change (3.38)	Openness (2.79)	Obsession with vehicle control (4.71)

Participants preferred messages that were concise, useful, and simple. Message directness, format, content, and targeted behavioral change were associated with effectiveness. Trustworthiness was associated with the reasons such as competency, history of interactions, and openness. Regarding feeling of control, the most frequently selected reason was the responsibility to make the final decision, followed by the knowledge of driving behaviors and outcomes, and obsession with vehicle control.

Discussion and Conclusion

The current study explored how social norms might persuade drivers to continue using fully automated driving in situations where they intended to disengage the automation. To achieve this end, social norm messages were designed and tested. To understand how social norm messages influence the use of driving automation, four message characteristics (IVs) were manipulated: norm type, presence of explanation, spatial scale, and outcome criticality.

We found that messages framed as a descriptive norm were preferred and rated as more effective and trustworthy in both non-safety-critical and safety-critical situations (RQ1). The effect of explaining the benefits of using driving automation was significant for safety-critical outcomes (RQ2). Framing the explanation as relevant to the self was preferred (for both safety-critical and non-safety-critical situations), effective (for safety-critical situations), trustworthy (for safety-critical situations), and gave participants a greater sense of control (for safety-critical situations) (RQ3). Safety-critical situations made the effects of providing an explanation and framing the explanation relevant to the self significant for preference, effectiveness, and feeling of control, compared to non-safetycritical situations (RQ4). Follow-up questions showed that while participants preferred concise and simple messages, in safety-critical situations, they wanted explanations that increased message length. This presents a design challenge that might lead designers to consider alternative modalities beyond visual cues, such as auditory feedback, for both concise and informative messaging.

A previous meta-analysis of choice architecture interventions categorized choice architecture interventions into decision information, decision structure, and decision assistance (Mertens et al., 2022). They found decision structure interventions to be more effective in general, but decision information interventions (including social norms) should be used when persuasion depends on personal values and goals. This study found that social norms can enhance message effectiveness.

Future studies should validate the effect of social norms on driver behavior in a driving simulator. Additionally, combining social norms with other persuasion or nudging techniques (e.g., increase procedural friction to override) should be investigated to promote the appropriate use of driving automation. To enhance the effectiveness of persuasive messages, future research should consider personalizing messages based on user characteristics such as attitudes toward automation.

This study highlights the importance of message characteristics in persuasion interventions aimed at promoting the use of driving automation. The results suggest that framing messages as descriptive norms and providing explanations that are relevant to the driver might encourage drivers to continue using automation, especially in safety-critical situations.

Footnotes

Acknowledgements

This study was funded by Toyota Collaborative Safety Research Center (CSRC). The authors thank members of the Cognitive Systems Laboratory at the University of WisconsinMadison for their feedback on this manuscript.

ORCID iD

Joonbum Lee

References

Bews

Martins

(2002). An evaluation of the facilitators of trustworthiness. SA Journal of Industrial Psychology, 28(4), 14–19.

Burnkrant

R. E.

Unnava

H. R.

(1995). Effects of self-referencing on persuasion. Journal of Consumer Research, 22(1), 17–26.

Chen

Laval

J. A.

Ahn

Zheng

(2012). Microscopic traffic hysteresis in traffic oscillations: A behavioral perspective. Transportation Research Part B: Methodological, 46(10), 1440–1453.

Chiou

E. K.

Lee

J. D.

(2021). Trusting Automation: Designing for Responsivity and Resilience. Human Factors: The Journal of the Human Factors and Ergonomics Society, 001872082110099.

Christoffersen

Woods

D. D.

(2002). How to make automated systems team players. Advances in Human Performance and Cognitive Engineering Research, 2, 1–12.

Cialdini

R. B.

(2003). Crafting normative messages to protect the environment. Current Directions in Psychological Science, 12(4), 105–109.

Cialdini

R. B.

Goldstein

N. J.

(2004). Social influence: Compliance and conformity. Annu. Rev. Psychol., 55, 591–621.

Cialdini

R. B.

Kallgren

C. A.

Reno

R. R.

(1991). A focus theory of normative conduct: A theoretical refinement and reevaluation of the role of norms in human behavior. In Advances in experimental social psychology (Vol. 24, pp. 201–234). Elsevier.

Cialdini

R. B.

Reno

R. R.

Kallgren

C. A.

(1990). A focus theory of normative conduct: Recycling the concept of norms to reduce littering in public places. Journal of Personality and Social Psychology, 58(6), 1015.

10.

Desarbo

W. S.

Ramaswamy

Cohen

S. H.

(1995). Market segmentation with choice-based conjoint analysis. Marketing Letters, 6(2), 137–147.

11.

Fagnant

D. J.

Kockelman

(2015). Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167–181.

12.

Fogg

B. J.

(1999). Persuasive technologie301398. Communications of the ACM, 42(5), 26–29.

13.

Gass

R. H.

Seiter

J. S.

(2011). Persuasion, social influence, and compliance gaining (4th ed). Allyn & Bacon.

14.

Goldstein

N. J.

Cialdini

R. B.

Griskevicius

(2008). A room with a viewpoint: Using social norms to motivate environmental conservation in hotels. Journal of Consumer Research, 35(3), 472–482.

15.

Kaley

(2022). UX writing: Study guide. Nielsen Norman Group. https://www.nngroup.com/articles/ux-writing-study-guide/

16.

Kallgren

C. A.

Reno

R. R.

Cialdini

R. B.

(2000). A focus theory of normative conduct: When norms do and do not affect behavior. Personality and Social Psychology Bulletin, 26(8), 1002–1012.

17.

Kuhfeld

(2010). Marketing research methods in SAS: experimental design, choice, conjoint, and graphical techniques. SAS. https://support.sas.com/techsup/technote/mr2010.pdf

18.

Lee

Rheem

Lee

J. D.

Szczerba

J. F.

Tsimhoni

(2022). Teaming with your car: Redefining the driver–automation relationship in highly automated vehicles. Journal of Cognitive Engineering and Decision Making, 17(1), 155534342211326.

19.

Merritt

S. M.

(2011). Affective processes in human–automation interactions. Human Factors, 53(4), 356–370.

20.

Mertens

Herberz

Hahnel

U. J.

Brosch

(2022). The effectiveness of nudging: A meta-analysis of choice architecture interventions across behavioral domains. Proceedings of the National Academy of Sciences, 119(1), e2107346118.

21.

Meske

Potthof

(2017). The DINU-model—A process model for the design of nudges. Proceedings of the 23rd European Conference on Information Systems (ECIS), 2587–2597.

22.

Munro

(2020). UX writing guidelines. Adobe XD Ideas. https://xd.adobe.com/ideas/process/information-architecture/uxwriting-guidelines/

23.

Nicolls

Truelove

Stefanidis

K. B.

(2022). The impact of descriptive and injunctive norms on engagement in mobile phone use while driving in young drivers: A systematic review. Accident Analysis & Prevention, 175, 106774.

24.

Nielsen

(1994). 10 Usability heuristics for user interface design. Nielsen Norman Group. https://www.nngroup.com/articles/ten-usabilityheuristics/

25.

Nielsen

(2001). Error message guidelines. Nielsen Norman Group. https://www.nngroup.com/articles/error-message-guidelines

26.

R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.rproject.org/

27.

Roy

S. K.

Shekhar

(2010). Dimensional hierarchy of trustworthiness of financial service providers. International Journal of Bank Marketing, 28(1), 47–64.

28.

Scannell

Gifford

(2013). Personally relevant climate change: The role of place attachment and local versus global message framing in engagement. Environment and Behavior, 45(1), 60–85.

29.

Thaler

R. H.

Sunstein

C. R.

(2009). Nudge: Improving decisions about health, wealth, and happiness (Rev. and expanded ed). Penguin Books.

30.

Thompson

S. C.

Armstrong

Thomas

(1998). Illusions of control, underestimations, and accuracy: A control heuristic explanation. Psychological Bulletin, 123(2), 143–161.

31.

Torning

Oinas-Kukkonen

(2009). Persuasive system design: State of the art and future directions. Proceedings of the 4th International Conference on Persuasive Technology, 1–8.

32.

Zheng

Ahn

Chen

Laval

(2011). Freeway traffic oscillations: Microscopic analysis of formations and propagations using wavelet transform. Procedia-Social and Behavioral Sciences, 17, 702–716.