Drivers’ attitudes, preference, and acceptance of in-vehicle anger intervention systems and their relationships to demographic and personality characteristics

International Journal of Industrial Ergonomics 75 (2020) 102899

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International Journal of Industrial Ergonomics journal homepage: http://www.elsevier.com/locate/ergon

Drivers’ attitudes, preference, and acceptance of in-vehicle anger intervention systems and their relationships to demographic and personality characteristics Shuling Li a, Tingru Zhang b, *, Na Liu c, Wei Zhang a, Da Tao b, Ziqi Wang a a b c

State Key Laboratory of Automotive Safety and Energy, Department of Industrial Engineering, Tsinghua University, Beijing, China College of Mechatronics and Control Engineering, Institute of Human Factors and Ergonomics, Shenzhen University, Shenzhen, China School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Driving anger Anger intervention systems Personality traits

This research conducted focus group interviews and a questionnaire survey to investigate the potential demand of drivers for anger intervention systems (AISs) and explore the effects of demographic factors and personality traits on the preference and attitudes toward AISs. Results indicate that drivers prefer auditory intervention over tactile and visual interventions. Moreover, they favor emotion recording features but also have negative attitudes about accuracy and system security. In addition, age and some personality traits (i.e., types of driving anger and categories of driving anger expressions) play an important role in predicting the preference of intervention modalities or attitudes toward AISs and provide a new perspective on designing customized intervention systems. The outcome of this research provides practical implications regarding the design of in-vehicle anger inter­ vention systems for the automotive industry to reduce drivers’ anger and improve driving safety.

1. Introduction Driving anger is defined as frequent and intense anger that drivers experience while operating a vehicle (Deffenbacher et al., 1994). It exclusively refers to angry emotions in driving-related contexts. Driving anger has become an important concern that threatens traffic safety (Galovski and Blanchard, 2004). The American Automobile Association (AAA) reported that approximately 80% of drivers express severe anger and/or road rage in daily driving (AAA Foundation, 2016). Previous research indicated that anger could deteriorate drivers’ perception and decision-making process and induce unsafe driving, which can lead to vehicle damage or road accidents (Brewer, 2000; Deffenbacher et al., 2002; Li et al., 2019; Martin and Dahlen, 2005). Furthermore, drivers with high levels of driving anger have more negative driving incidents and violations, such as tailgating, speeding, and racing (Iversen and Rundmo, 2002; Ulleberg and Rundmo, 2003; Zhang and Chan, 2016). More importantly, some drivers have difficulty calming down when angry (Underwood et al., 1999). Hence, effective intervention methods or in-vehicle systems must be proposed to help drivers reduce driving anger and improve driving safety. The existing systems that related to drivers’ emotion mainly focus on

emotion recognition. Researchers often utilize physiological signals (e. g., facial expression, heart rate, skin resistivity, vocal intonation, pros­ ody, breathing patterns, and body posture) and vehicle motion data with machine learning and pattern recognition methods to identify drivers’ emotional states and degree of emotional arousal (Vasey et al., 2018; Frasson et al., 2014; Leng et al., 2007; Nadai et al., 2016; Rigas et al., 2011; Katsis et al., 2011). However, the use of in-vehicle anger inter­ vention systems (AISs) has been limited, partly due to that drivers’ at­ titudes and preference for AISs are unclear. Of the few available studies, some examined the effects of different intervention modality (e.g., vi­ sual, tactile and auditory) on anger reduction (Cackowski and Nasar, 2003; Harris, 2011; M. Jeon, Walker and Gable, 2015; Jonsson et al., 2004; Johnson and McKnight, 2009; Cunningham, 2016). For instance, as for visual interventions, researchers have used parkway design and roadside vegetation to help drivers reduce anger (Cackowski and Nasar, 2003). Recent studies showed that tactile interventions could be applied to tighten seat belts when drivers’ anger was detected (Cunningham, 2016; Ven and Rasch, 2017). Some auditory warnings have also been proved to be effective in reducing drivers’ anger (Graham, 1999; Jones and Furner, 1989; Meng and Spence, 2015). For instance, Jeon et al. (2015) reported that speech-based in-vehicle agents not only reduced

* Corresponding author. E-mail address: [email protected] (T. Zhang). https://doi.org/10.1016/j.ergon.2019.102899 Received 15 April 2019; Received in revised form 6 November 2019; Accepted 9 December 2019 Available online 17 December 2019 0169-8141/© 2019 Elsevier B.V. All rights reserved.

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the anger level of drivers and the perceived workload, but also enhanced their situation awareness and driving performance. These related studies have confirmed the effectiveness of some anger interventions, however, none of them have considered driver preferences or demands towards the interventions. Further research is required to explore the kinds of interventions that drivers prefer. While a range of factors might affect driver preference towards invehicle products/systems, the basic considerations in customization tend to be demographic factors such as age, gender, and driving expe­ rience (Eksioglu and Kızılaslan, 2008; B.H. Jeon, Ajovalasit and Giaco­ min, 2009; Kim et al., 2010; G. Li, Li and Cheng, 2015). For example, age has a considerable influence on preferences for audible safety warnings, with old drivers prefer loud warnings with a high tempo than young drivers do (Kim et al., 2010). Gender also affects system preferences. Previous research revealed that female drivers rated advanced driver assistance systems more positively than male drivers did (G. Li et al., 2015). In addition to demographic factors, personality traits should be considered in customized anger intervention design. Previous studies have suggested that people’s attitudes and perceptions on products are likely to be influenced by personality traits (Chang, 2001; Kyriakidis et al. 2015). For example, Chang (2001) pointed out that an extroverted person demonstrated a more positive attitude in product evaluation (e. g., higher preference for products), compared with an introverted per­ son. Moreover, individuals high in neuroticism (i.e., frequently experi­ ence negative emotions) showed more anxious and concerns about autonomous cars than those with other personality traits (Kyriakidis et al., 2015). As for the driving anger domain, driving anger-related traits were also observed, such as the propensity to be angry while driving or expressions of driving anger. The driving anger scale (DAS, Deffenbacher et al., 1994) has been widely used to measure an in­ dividual’s propensity to be angry across several driving situations (Sullman, 2006; Sullman et al., 2014; Yasak and Esiyok, 2009). DAS-measured trait anger has been shown as a reliable predictor of aggressive driving, risky driving, anger toward other drivers, and traffic violations (Dahlen et al., 2005; Zhang et al., 2018; Deffenbacher et al., 2003). Driving anger expression (DAX, Deffenbacher et al., 2002) is a measure of how people express their anger on the road. Previous studies indicate that drivers with high trait driving anger or aggressive anger expressions point out that cognitive-behavioral treatment is a more effective way to reduce their driving anger than relaxation intervention (Deffenbacher et al., 2002; Deffenbacher et al., 2000). It is possible to infer that the effect of anger interventions might differ among drivers with different levels of trait driving anger and different forms of anger expressions. Therefore, the types of trait driving anger and the cate­ gories of driving anger expressions could provide additional design implications for the intervention preference of angry drivers. To sum up, although anger detection techniques are available and some anger interventions are effective in reducing anger, little is known of the principles or customized design implications that could make intervention systems more effective and acceptable. This research con­ ducted two studies on Chinese drivers using qualitative and quantitative methods. The purpose of the research was as follows: First, the driving anger situation of drivers was explored to investigate whether a poten­ tial demand for AISs exists in the automobile market. Second, drivers’ opinions of AISs design were gathered, the effects of demographic fac­ tors and personality traits on preference and attitudes towards AISs were investigated, and implications for AISs design guidelines were provided based on the research outcomes. In Study 1, a focus group method was employed to explore drivers’ viewpoints on AISs design, and the results were used to develop a questionnaire. For Study 2, a questionnaire survey was conducted to acquire quantitative information and investi­ gate the effects of demographic factors and personality traits (i.e., types of trait driving anger and categories of driving anger expressions) on preference and attitudes towards AISs. To our best knowledge, this research is the first to investigate drivers’ preference for AISs from both qualitative and quantitative aspects. The findings can be used in the

future development of AISs for the automotive industry. 2. Study 1: focus group 2.1. Participants Three 1-h focus group interviews, with a number of 7, 7, and 8 participants, respectively, were carried out. The 22 participants consist of 8 females and 14 males and had a mean age of 32.77 years (SD ¼ 9.08). In terms of their educational level, 6 participants (27.3%) had a high school education or below, 10 (45.5%) had a bachelor’s degree, and 6 (27.3%) had a master’s degree or above. Their average driving experience was 8.5 years (SD ¼ 6.28), and the average annual driving distance was 23.39 thousand km (SD ¼ 21.43). 2.2. Procedure All participants first signed an informed consent form and completed a demographic questionnaire. Then, the facilitator stated that the objective of the focus group was to collect information on various as­ pects of designing an AIS to reduce driving anger and improve driving safety. The participants were informed that all comments were valued and equally welcomed and were encouraged to discuss any ideas pre­ sented in their group. All focus group sessions were audio-recorded. An experienced assistant facilitator presided over the focus groups, with an observer in each group keeping a record of the discussion. The discussion was in a semi-structured interview format, including a set of open-ended questions (see Table 1). All the questions were based on system design references and modified according to our purpose (Meng et al., 2016). Following the well-established pattern of focus group questions, the interview began with opening questions that gradually engaged participants into a discussion, and then continued to discuss the transition issues and key questions that focus on the research goals, and finally ended with the questions that tied the sessions together and brought closure (Newman, 2002). On the basis of this question pattern, the opening question (item 1 in Table 1) was designed to make participants feel comfortable and encourage them to join in the discussion. The following two questions (items 2 and 3 in Table 1) were intended to guide participants in dis­ cussing their driving anger and ways to relieve driving anger. Key questions followed to elicit the participants’ opinions and suggestions on AIS features (item 4 in Table 1) and preference of intervention modal­ ities (item 5 in Table 1) and their attitudes toward AISs (item 6 in Table 1). The final question (item 7 in Table 1) attempted to summarize the discussion and collect some missing information from the participants. 2.3. Data analysis All audio recordings of the focus groups’ sessions were transcribed Table 1 Set of questions for focus groups. (1) Please introduce yourself. What’s your name? How many years have you been a driver? (2) Describe the reasons why you get angry while driving and how you express this anger. (3) How do you relieve driving anger? (4) If an anger intervention system is developed, what functions or features do you expect? (5) Which intervention types do you prefer? (6) What are your attitudes toward the system? Please state from positive and negative perspectives. (7) Do you have any more suggestions or comments on the design of the anger intervention system?

2

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consequences (25%), and listen to the persuasion of others (5.56%).

verbatim and analyzed using an Excel file (Meng et al., 2016; Krueger and Casey, 2014). First, the original transcripts were broken into com­ plete sentences. Next, three research members separately sorted the sentences and identified the major categories in the statements. Ac­ cording to the seven questions in the focus group sessions, four cate­ gories were identified prior to the classification work, namely, “current situation of driving anger”, “features of AISs”, “intervention modalities” and “attitudes toward AISs”. The three members were instructed to sort the sentences from the focus group discussion into the four categories, and the sentence that could not be classified into any category was included in the “other suggestions”. Subsequently, they extracted the themes (i.e., subcategories) in each category. When all three members finished sorting, their results were merged into one data file. In this process, when the three members held different opinions about the sorting result of a sentence, they voted to determine the final sorting of the sentence. The sentences with inconsistent initial classification results accounted for 5% of the total number of sentences. The subcategories under each category are reported in the following section, and the fre­ quency of each subcategory was calculated and presented. Inter-rater reliability (IRR) for aforementioned categories and subcategories was calculated via Krippendorff’s alpha reliability coefficient (Krippendorff, 2004). An IRR coefficient ranging from 0.61 to 0.80 and from 0.81 to 1.0 indicate substantial and perfect agreement, respectively.

3.3. Features of AISs As for the features of AISs, our results showed that drivers mentioned three types of features, i.e., providing regular anger record reports, sharing anger records with family or friends, and car networking. Drivers thought that real-time emotion records and regular emotion reports could help them understand their emotional state while driving and then help improve driving safety (mentioned five times, 23.81% of the frequency of the features mentioned). In terms of sharing emotion records, some participants said they would like to share their emotion records with family or friends to monitor their driving (19.05%), while others said they do not want to disclose their emotion records to others (23.81%). Some drivers also proposed to show the emotional state of other drivers in the AISs using car networking so they can avoid angry drivers (28.57%). In addition, they suggested that receiving positive feedback when the system detects a decrease in the driver’s anger fre­ quency would be good (4.76%). Finally, participants expected the sys­ tem to be easy to use and to intervene in a timely manner when they become angry. 3.4. Intervention modalities

3. Results

From the analysis of interventions proposed by the participants, we found that the interventions could be distinguished by modality. Most of the interventions involved only one modality, i.e., auditory, tactile, and visual. Very few were a combination of different modalities (see Fig. 1). For example, participants mentioned that a simple voice reminder with flashing red light or gently pat on the back with the voice of family members could also be used when they were angry. The frequency of detailed interventions is listed in Table 3. Given that the frequency of multiple-modality based interventions is limited, we have only studied the three single-modality based interventions in study 2. The corre­ sponding frequencies are shown in Fig. 1. Auditory interventions included speech-based warnings and music or radio. For speech-based interventions, some drivers suggested recording voices of family mem­ bers in advance, such as a message from their children saying, “We are waiting for you to come home.” Others mentioned that a simple voice reminder or an AI agent that they could talk to could also help relieve anger. For tactile interventions, some suggested for them to be presented as an ordinary vibration or a gentle pat on the back, while others pro­ posed the use of a slight current stimulus. Finally, visual interventions could be in the form of funny videos or a flashing red light. The total number of interventions mentioned in the interview were 33 times. These results indicated that drivers mentioned more auditory in­ terventions (mentioned 20 times) over the other two modalities (i.e.,

3.1. Inter-rater reliability Table 2 presents the results of IRR coefficients for categories of “current situation of driving anger”, “features of AISs”, “intervention modalities” and “attitudes toward AISs”, and subcategories of each category. The Krippendorff’s alpha coefficients for all categories were over 0.8, indicating that the coders had a perfect agreement in the categorization of the themes. 3.2. Driving anger: triggers, expressions, and ways to relieve it There were four kinds of reasons to make drivers feel angry. Based on the frequency of each reason mentioned in the interview, the main reason for anger when driving was the violation of rules by others (53.66% of the frequency of the reasons mentioned), followed by bad mood (26.83%), poor road conditions (17.07%), and bad weather (2.44%). For driving anger expressions, drivers expressed their anger through body language (such as giving the middle finger) (41.38% of the frequency of the expressions mentioned) and words of discontent (24.14%); use vehicles to express anger (such as honking the car horn and beating the steering wheel) (27.59%); or take an adaptive response (such as taking deep breaths to calm down) (6.90%). To alleviate the harm caused by driving anger, drivers said that they usually tried to distract their attention (41.67% of the frequency of the interventions mentioned), make themselves alert (27.78%), think of risky Table 2 Inter-rater reliability coefficients on categorization (Krippendorff’s alpha). Categories

Subcategories

Alpha

Current situation of driving anger

Triggers of anger Anger Expression Way to relieve anger Features of AISs Auditory interventions Visual interventions Tactile interventions Multiple interventions Positive attitude of AISs Negative attitude of AISs Categories

.921 .901 .893 .954 .908 1.000 .862 1.000 .944 .925 .941

Features of AISs Intervention modalities

Attitudes towards AISs Categories

Fig. 1. Frequency of intervention modalities. 3

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how effective they think an intervention system is in helping relieve driving anger (from 1 “not at all” to 5 “very much”). This question was designed to exclude participants who did not think that AISs are useful. The last section is about AISs, which included acceptance of system features, preference of interventions, and attitudes toward AISs. The questions were developed from previous research (Meng et al., 2016) and the focus group interviews. The participants were required to rate their acceptance of the AISs features, the degree of preference of the AISs interventions, and their level of agreement with the positive and nega­ tive attitude of AISs using a five-point Likert scale (from 1 “not at all” to 5 “very much”). Table 4 shows how the results from Study 1 supported the development of the questions in Study 2.

Table 3 Frequency of detailed interventions. Anger Intervention Modality

Interventions

Frequency

Auditory interventions

simple voice reminder the voices of family members music or radio AI agent funny video flashing red light ordinary vibration gentle pat on the back slight current stimulus simple voice reminder and flashing red light the voices of family members and gentle pat on the back

3 8 5 4 1 2 3 4 1 1

Visual interventions Tactile interventions Multiple interventions

4.1.2. Procedures An online survey was conducted to collect data in this study because of the following reasons. First, geographical restrictions on data

1

tactile interventions were mentioned 8 times and visual interventions with 3 times) based on the frequencies presented in Fig. 1.

Table 4 Outcomes in Study 1 and corresponding questions in Study 2.

3.5. Attitudes regarding AISs

Topics of Study 1

Items

Corresponding questions in Study 2

All the participants stated their positive attitude toward AISs. They believed that the system would help them understand their driving emotional state, maintain safe driving, and improve their mood. How­ ever, some concerns about the function and usefulness of the system were brought up. The most important issue was system security, which was mentioned 15 times. Participants expressed concerns about sharing their emotion records with family or friends. Concerns about the accu­ racy of system detection were stated 7 times. Some of the other issues were about the complexity of system operation, the installation location, and the negative effect on driving performance, such as being distracting. In summary, participants reported triggers, expressions, and ways to relieve driving anger. Results demonstrated that drivers have a positive attitude toward AISs. The drivers also provided comments and sugges­ tions for the system functions and potential problems. On the basis of the discussion results, a corresponding questionnaire was designed to study the specific design of AISs further (see Study 2 for details).

Features

Regular emotion reports

Record driving emotions for a period and then give a staged report. Drivers can choose to share realtime records of driving emotions with people who are close to them for supervision. Drivers can see the emotional state of other drivers in the surrounding vehicles through the system. Give positive feedback if drivers have less frequency of angry driving (e.g., speech-based positive comments) Personalized voice warning, such as pre-recorded voice from family, friends, or oneself Play music, broadcasts, etc. that provide enjoyment Talk to an AI agent to get relief Simple voice warning (e.g., “You are angry now”) Gently pat the back, mounted on the seatback Vibration alerts (such as wristbands, seatbacks, etc.) Weak current stimulation alerts that are harmless to the body Visual alert system (e.g., a flashing red light) Play a funny video

Sharing anger records with family or friends Car networking

Positive feedback

Interventions

4. Study 2: questionnaire survey

Auditory: simple voice reminder, the voices of family members, music or radio or AI agent

Tactile: ordinary vibration, gentle pat on the back or slight current stimulus

4.1. Method 4.1.1. Materials The questionnaire with five sections was based on previous refer­ ences (Deffenbacher et al., 2002; Deffenbacher et al., 1994; Stephens and Sullman, 2014) and the outcomes of Study 1. The first section is the demographic background, which included gender, age, province, education level (“high school or below,” “bach­ elor’s degree,” or “master’s degree or above”), driving experience, annual driving distance, frequency of traffic citations, and collisions in the past three years. The second section is the short DAS developed by Deffenbacher et al. (1994). It describes 14 different driving scenarios, and the participants were required to rate the amount of anger they feel when encountering each condition on a five-point scale (1 ¼ not at all, 2 ¼ a little; 3 ¼ some; 4 ¼ much; 5 ¼ very much). The third section is the modified short DAX (see Appendix 1), which included questions from the short DAX (Deffenbacher et al., 2002; Sullman et al., 2014) and the focus group interviews. The participants were asked to report how often they express their driving anger using a four-point scale (1 ¼ almost never; 2 ¼ occasionally; 3 ¼ sometimes; 4 ¼ almost frequently). The fourth section is the driving anger situation, which included questions about awareness of driving anger and collisions related to driving anger, such as “how often do you feel angry while driving (from 1 “never” to 5 “very frequently”)?“. A question in the fourth section asked participants

Visual: a funny video or flashing red light Attitudes

Positive attitude Help to understand driving emotional state Help to maintain safe driving Help to improve their mood Negative attitude Accuracy of the system detection Accuracy of the system detection Complicated to use Privacy Negative effect on driving performance such as distracting the driver

4

AISs help me know my emotional state when driving. AISs help me improve driving safety. AISs help me stay in a positive mood while driving. I am worried that emotion monitoring is not accurate enough. I am worried that the system reminder is not timely enough. I am worried that the use of the system will be too complicated. I am worried about the privacy of this system, such as revealing my emotional state. I am worried that the system will be distracting.

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collection could be avoided by an online survey at a lower cost than an offline survey (van Gelder et al., 2010; Zhang et al., 2018). Second, Chinese netizens increased to 829 million in 2018, with a penetration rate of 59.6% (China Internet Network Information Center, 2018), covering a wide range of drivers. Third, several studies have verified the effectiveness of Internet tools in assessing driving behaviors in China (Shi et al., 2010; Zhang et al., 2019). The questionnaire was designed and posted on Sojump (www. sojump.com), a professional online survey platform, where answers and completion time are recorded automatically. The survey was distributed in several driver chat groups on WeChat and QQ, both of which are popular networking apps in China. To ensure high involve­ ment and feedback authenticity, the participants were given the driving anger level rating (based on the DAS score) and a 10 RMB incentive after completing the survey.

evaluate the fit of the proposed model. Subsequently, the hierarchical multiple regressions could be helpful to test associations for more var­ iables (i.e., demographic variables, factors of the DAS and DAX) in detail. To investigate how the preference of intervention modalities are predicted by a combination of variables, hierarchical multiple regres­ sion was conducted with demographic variables entered in the first step, the DAS entered in the second step, and the DAX entered in the third step. As for the investigation of how different attitudes toward AISs were predicted by a combination of variables, hierarchical multiple re­ gressions were conducted, with the same three steps as above. The reason for choosing the hierarchical model over other multiple regres­ sion models, such as stepwise regressions, is as follows. First, in multiple regression models like stepwise, it would let the computer decide which terms to enter at what stage, telling the computer to base its decision on some criterion such as an increase in R2, AIC, BIC and so on (Lewis, 2007). While in hierarchical regression one could decide the order of variables entered into the analysis based on substantive knowledge and statistical expertise. Second, hierarchical regression is an appropriate tool for analysis when the dependent variable is being explained by predictor variables that are correlated with each other, which is commonly seen in social science research (Pedhazur, 1997). Moreover, hierarchical regression is a popular method used to analyze the effect of a predictor variable after controlling for other variables. The data satisfied the assumption of collinearity, indicating that multicollinearity was not a concern (variance inflation factors < 10) (Neter et al., 1996). These statistical analyses were performed using R, version 3.5.2, and the SPSS statistical software package, version 23 (SPSS, 2015).

4.1.3. Participants The survey was completed by 600 drivers. Several criteria were used to filter the participants. Three categories of participants were filtered out. Category 1: Participants with less than one year of driving experience (87 participants) Category 2: Participants with more than one year of driving experi­ ence but less than 1,000 km of annual driving distance (6 participants) Category 3: Participants who believe that AISs are useless (34 participants) Finally, 473 participants (mean age ¼ 32.81, SD ¼ 8.61) from 28 provinces remained, including 177 females (37.4%) and 296 males (62.6%). A total of 104 participants (22.0%) had high school education or below, 254 (53.7%) had a bachelor’s degree, and 115 (24.3%) had a master’s degree or above. The distributions of education level were comparable for the focus group sample and survey sample. Their average driving experience was 5.25 years (SD ¼ 5.42), while their average annual driving distance was 23.67 thousand km (SD ¼ 36.86). The participants reported an average of 2.96 (SD ¼ 4.15) traffic citations and 0.87 (SD ¼ 1.34) collisions in the past three years.

4.2. Results 4.2.1. Current situation of driving anger The participants reported their current driving anger situation from two aspects, namely, awareness of driving anger and citations or colli­ sions related to angry driving. In this regard, when asked how often they feel angry while driving (from 1 “never” to 5 “very frequently”), 334 participants (70.6%) reported a score higher than 2. A total of 418 participants (88.4%) complained that anger had adverse effects on driving, and 337 participants (71.2%) mentioned that they highly preferred to receive some intervention when they are angry while driving. Results indicated that 91 participants (19.2%) engaged in un­ safe driving when angry. The frequency of traffic citations while driving angry in the recent three years was 0.18 (SD ¼ 0.96), which was 6.1% of the total traffic citations frequency (M ¼ 2.96, SD ¼ 4.15). The fre­ quency of anger-related traffic collisions was 0.10 (SD ¼ 0.51), which accounted for 11.5% of the total traffic collisions frequency (M ¼ 0.87, SD ¼ 1.34). In sum, the participants were aware of their severe angry driving situation and would benefit from using intervention systems.

4.1.4. Data analysis One-way ANOVA was conducted to test the difference among intervention modalities. The Tukey posthoc analysis was used for mul­ tiple comparisons. The significance level was set at .05 for all analyses. Confirmatory factor analysis (CFA) was conducted to investigate the factor structures of the short DAS, the modified DAX, the preference of intervention modalities, and the attitudes toward AISs. As recommended by Schreiber et al. (2006), the goodness of fit of a model was evaluated with four indices, namely, the ratio of chi-square value to df (χ 2 = df), comparative fit index (CFI), standardized root mean square residual (SRMSR), and root mean square error of approximation (RMSEA), together with a 90% confidence interval (90% CI). In general, a high CFI value and low values of the other three indices correspond to an improved fit of the model. A model is considered a good fit when χ 2 = df < 5, CFI > .90, SRMSR < .08, and RMSEA < .06 (Hooper et al., 2008). A structural equation model was built to test the influential paths among DAS, DAX, the preference of intervention modalities, and the attitudes toward AISs. Based on previous studies, the DAS scores have a significant positive influence on the DAX scores (Dahlen et al., 2005). Moreover, the DAS and DAX might also have effects on the intervention modalities and attitudes. Then, a structural equation model was pro­ posed, which included the influential path from DAS to DAX, and also paths from DAS or DAX to the preference of intervention modalities and the attitudes toward AISs. The same goodness-of-fit criteria (i.e., χ 2 = df < 5, CFI > .90, SRMSR < .08, and RMSEA < .06) were applied to

4.2.2. General features of AISs 4.2.2.1. Features of the AISs. Table 5 summarizes the results of the users’ acceptance of AIS features. The participants showed the highest acceptance of positive feedback (e.g., speech-based positive comments) Table 5 Acceptance of features of the AISs.

5

Features

Mean

SD

Drivers can choose to share real-time records of their driving emotions with people who are close to them for supervision. Record driving emotions for a period and then give a staged report. Drivers can see the emotional state of other drivers in the surrounding vehicles through the system. Give positive feedback if drivers have less frequency of angry driving (e.g., speech-based positive comments)

2.97

1.14

3.57 3.16

.96 1.22

3.80

1.05

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(M ¼ 3.80). Emotion record and regular emotion reports (M ¼ 3.57) ranked second as the preferred feature, followed by the function of emotional states observations of other drivers (M ¼ 3.16). Sharing emotion records with family or friends obtained the lowest acceptance (M ¼ 2.97).

presented in Table 6. In terms of positive attitude, the participants said that AISs could help them understand their current emotional state (M ¼ 3.75), maintain positive emotions (M ¼ 3.79), and enjoy safe driving (M ¼ 3.88). However, the participants also pointed out some problems that may exist in AISs. The top three major negative attitudes were that the emotion monitoring might be inaccurate (M ¼ 3.69), system security (such as the leakage of personal emotions) (M ¼ 3.66), and the system warnings might not be timely enough (M ¼ 3.57). Moreover, the par­ ticipants were also worried that the system was complicated to use (M ¼ 3.49) and would distract them while driving (M ¼ 3.48).

4.2.2.2. Intervention modalities. Based on the information channel of interventions, three modalities of interventions were proposed (i.e., auditory interventions with items I1–I4 in Fig. 2, visual interventions with I6 and I8, and tactile interventions with I5, I7, and I9). Results indicated that there was a significant difference among the intervention modalities, F(2, 1416) ¼ 43.349, p < .001. Pairwise comparisons revealed that participants showed significantly higher preference for auditory interventions (M ¼ 2.98, SD ¼ 0.86) than visual (M ¼ 2.54, SD ¼ 1.01, p < .001) and tactile (M ¼ 2.42, SD ¼ 1.02, p < .001) in­ terventions. While the preference for visual interventions had no sig­ nificant difference with tactile interventions (p > .05). There was also a significant difference among the 9 interventions, F (8, 4248) ¼ 37.891, p < .001. Participants showed significantly higher preference on the personalized voice warning (M ¼ 3.31, SD ¼ 1.12) than others interventions, then the music or broadcast (M ¼ 3.03, SD ¼ 1.11), followed by talking to an AI agent (M ¼ 2.78, SD ¼ 1.24), and the simple voice warning (M ¼ 2.78, SD ¼ 1.16). There was no significant difference between an AI agent to talk with and the simple voice warning, but their scores were all significantly higher than the simple vibration alerts, funny videos, and weak current stimulation alerts. Similarly, no significant difference was found among gentle patting on the back (M ¼ 2.56, SD ¼ 1.14), visual alerts (M ¼ 2.56, SD ¼ 1.15), simple vibration alerts (M ¼ 2.53, SD ¼ 1.13), and funny videos (M ¼ 2.53, SD ¼ 1.28). Weak current stimulation alert obtained the signifi­ cantly lowest score (M ¼ 2.17, SD ¼ 1.18) and was thus the least popular intervention (see Fig. 2).

4.2.4. Hierarchical multiple regressions 4.2.4.1. Factor structures. To produce more detailed design implications to satisfy the needs of different user groups, this paper analyzed the influence of demographic factors, types of trait driving anger and cate­ gories of driving anger expressions on the attitudes toward AISs and the preference of intervention modalities. In this regard, CFA was first conducted to investigate the factor structure of the short DAS, the modified DAX, the attitudes toward AIS, and intervention preference. Previous research indicated that the short DAS fitted in a three-factor Table 6 Attitudes toward the AISs.

4.2.3. Attitudes toward the AISs The positive and negative attitudes toward AISs are summarized and

Attitudes

Mean

SD

AISs help me know my emotional state when driving. AISs help me to improve driving safety. AISs help me stay in a positive mood while driving. I am worried that emotion monitoring is not accurate enough. I am worried that the system reminder is not timely enough. I am worried that the system will be distracting. I am worried that the use of the system will be too complicated. I am worried about the privacy of this system, such as revealing my emotional state.

3.75 3.88 3.79 3.69 3.57 3.48 3.49 3.66

.76 .78 .74 .74 .82 .85 .83 .93

Fig. 2. Preference of interventions (error bars indicate the standard error of the mean values, *p < .05, **p < .01, ***p < .001). 6

International Journal of Industrial Ergonomics 75 (2020) 102899

S. Li et al.

structure (i.e., safety-blocking, arrival-blocking, and hostile gesture) from a sample of Chinese drivers (Zhang et al., 2018). The same factor structure was tested in our study: safety-blocking included scenarios that threatened driver safety (e.g., someone runs a red light or stop sign), arrival-blocking described scenarios that impeded driver progress (e.g., someone is slow in parking or the driver is stuck in a traffic jam), and hostile gesture contained hostile behaviors such as obscene gesture and honking from other drivers. The modified DAX was analyzed to deter­ mine whether it fitted the four-factor structure proposed in previous studies (Deffenbacher et al., 2002; Sullman et al., 2014). The four factors are the verbal aggressive expression, personal physical aggressive expression, use of a vehicle to express anger, and adaptive/constructive expression. A two-factor structure for attitudes toward AIS, with one factor being positive attitude (i.e., the top three items listed in Table 4) and another being negative attitude (i.e., the last five items listed in Table 4), was tested. Finally, a three-factor structure for the intervention modalities was tested (i.e., auditory, visual and tactile interventions). The CFA results of the goodness-of-fit indexes for the factor structures satisfied all the requirements (χ 2 =df < 5, CFI > .90, SRMSR < .08, and RMSEA < .06) and are presented in Table 7.

To investigate how the preference of the different intervention mo­ dalities was predicted by various variables, hierarchical multiple re­ gressions were conducted, with the demographic variables (i.e., age, gender, driving experience, and annual driving distance) entered in the first step, the three DAS subscales in the second step, and the four DAX subscales in the third step. All assumptions of the hierarchical multiple regressions were satisfied by the data in this study, and the results are shown in Table 8. First, the four demographic variables explained a significant 2.7% of the total variance in preference of auditory interventions (p < .05). The driving experience was the only demographic variable that showed a significant influence (β ¼ 0.112, p < .05). After the entry of DAS vari­ ables in Step 2, the model was statistically insignificant. Subsequently, the final model with the entry of DAX variables accounted for a signif­ icant 11.4% of the total variance in preference of auditory interventions (p < .001). Moreover, the adaptive expression revealed a positive effect on preference of auditory interventions (β ¼ 0.263, p < .001). Second, the demographic variables did not show a significant influ­ ence on preference of tactile interventions, whereas the DAS variables did, which explained a significant 5.1% of the total variance (p < .001). In this aspect, the arrival-blocking subscale (β ¼ 0.240, p < .001) and hostile gesture (β ¼ .215, p < .001) showed significant effects on preference of tactile interventions. Furthermore, the DAX variables explained 12.8% variance in preference of tactile interventions after controlling the demographic and DAS variables (p < .001). Verbal expression (β ¼ .237, p < .001), adaptive expression (β ¼ 0.187, p < .001), and physical expression (β ¼ 0.162, p < .01) exhibited an in­ fluence on the prediction of preference of tactile interventions. As for the preference of visual interventions, the effects of de­ mographic variables, based on conventions in interpreting p-values (Yoccoz, 1991), could only be reported as marginally significant (p < .1). Following this result, driving distance showed a significant positive effect on preference of visual interventions (β ¼ 0.120, p < .05). However, after the entry of the DAS variables in Step 2, the model showed significance (p < .05). In this regard, the arrival-blocking sub­ scale (β ¼ 0.233, p < .001) and hostile gesture (β ¼ .150, p < .05) showed significant effects on preference of visual interventions. In the final model, the DAX variables explained an additional 7.8% of the total variance significantly. Thus, three out of four predictor variables were statistically significant, with verbal expression recording a negative value (β ¼ .275, p < .001) and adaptive expression (β ¼ 0.176, p < .001) and physical expression (β ¼ 0.158, p < .01) yielding positive values. To investigate how different attitudes toward AISs were predicted by a combination of variables, hierarchical multiple regressions were conducted, with the demographic variables entered in the first step, the three DAS subscales in the second step, and the four DAX subscales in the third step. All assumptions of the hierarchical multiple regressions were satisfied by the data in this study, and the results are shown in Table 9. For positive attitudes, the results showed that neither demographic variables nor the three types of driving anger showed any significance. After the entry of the four types of driving anger expressions in Step 3, the total variance explained by the model was 6.9% (p < .001). The introduction of DAX variables explained an additional 4.7% of the variance in positive attitudes. Adaptive expression showed a signifi­ cantly positive effect on positive attitude (β ¼ 0.209, p < .001). The results indicated that a driver’s strong adaptive expression ability cor­ responds to positive attitudes of the driver toward intervention systems. In terms of negative attitudes, age showed a significant effect (β ¼ .143, p < .05), while gender, driving experience, and annual driving distance did not. After the effects of demographic variables were controlled, the three types of driving anger, which explained another 1.9% of the total variance, predicted negative attitudes to a statistically significant degree (p < .05). A subsequent inspection of the results revealed that the arrival-blocking subscale (β ¼ .131, p < .05) and the hostile gesture subscale (β ¼ 0.140, p < .05) were significant

4.2.4.2. Structural equation model. The goodness-of-fit indexes of the structural equation model satisfied all the requirements (χ 2 = df ¼ 2.25, CFI ¼ 0.911, SRMSR ¼ 0.058, and RMSEA ¼ 0.052), indicating that the proposed model was a good representation of the relationships. The structural equation model is represented in Fig. 3, with the solid lines indicating significant paths and the dotted lines indicating nonsignificant paths. Results showed that the DAS was a positive predic­ tor of the DAX (β ¼ 0.525, p < .001). Moreover, the DAX had positive effects on the preference of auditory (β ¼ 0.317, p < .001), tactile (β ¼ 0.196, p < .001), visual interventions (β ¼ 0.245, p < .001) and the positive attitude (β ¼ 0.231, p < .001). The DAS showed positive effects on the preference of tactile (β ¼ 0.117, p < .05) and visual interventions (β ¼ 0.219, p < .01). The DAS showed no direct effects on the preference of auditory intervention (β ¼ 0.008, p > .05) and positive attitude (β ¼ ​ .020, p > .05), but it can indirectly influence those latent variables via DAX. Results indicated that both the DAX (β ¼ 0.073, p > .05) the DAS (β ¼ 0.051, p > .05) had no significant effect on negative attitude. 4.2.4.3. Regressions. Previous studies found that demographic factors and personality traits had influences on the evaluation of products, which could provide design implications for products. The present study aimed to provide personalized and effective interventions for drivers when they are angry while driving. To achieve this goal, it is necessary to conduct an investigation on associations among demographic factors, driving anger-related traits like DAS or DAX and evaluation of AIS (i.e., attitude and preference). Based on the associations, the system could give proper interventions for drivers with various traits. By doing this, a mechanism could be built, i.e., AIS could mitigate the link connecting drivers’ encounter of angry cases and their reaction effectively, which may change their reactions from dangerous to safe ones. Thus, the re­ sults of the hierarchical regressions could help the mechanism work better. Table 7 Summary of goodness-of-fit indices for the factor structures. Model

χ2 =df

CFI

SRMSR

RMSEA

90% CI for RMSEA

Three-factor model for DAS Four-factor model for DAX Two-factor model for attitudes Three-factor model for interventions

3.521

.916

.050

.073

.063–.083

3.406

.914

.057

.071

.065–.078

3.797

.957

.054

.077

.059–.096

2.699

.980

.033

.060

.041–.079

7

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International Journal of Industrial Ergonomics 75 (2020) 102899

Fig. 3. Results of the structural model. The solid lines indicate significant paths and the dotted lines indicate non-significant paths. Note: ***p < 0.001; **p < 0.01; *p < 0.05.

Second, in terms of system features, emotion records, sharing emotions with family or friends, receiving positive feedback, and knowing the surrounding drivers’ emotion were the primarily discussed features. Real-time emotion recording and stage reporting are accepted by par­ ticipants because they perceive them as ways to understand their emotional state and to improve driving safety. In the focus groups, some participants expressed that they preferred to be informed of other drivers’ emotions rather than share their emotions with their family or friends, which was consistent with the results of the questionnaire sur­ vey. To solve the contradiction, an option to choose to share emotion or not could be included in the system. The last feature was about receiving positive feedback when the system detects a decrease in anger, which ranked first among all features in the questionnaire survey. This result was in line with reinforcement theory, behaviors are shaped by their consequences and that, accordingly, individual behaviors can be improved through positive feedback (Slade and Owens, 1998; Kaplan and Anderson, 1973). As Li et al. (2019) said, positive comments help drivers reduce anger and improve driving performance. Finally, on at­ titudes toward AISs, participants mentioned that the biggest advantage of AISs is enhancing driving safety, but they also worried about accuracy and system security. As for accuracy, the user experience will be greatly affected by frequent incorrect detection. Hence, the detection and time of interventions should be highly accurate. Some demographic factors contributed to the customized design implications for AISs. Older participants expressed fewer negative atti­ tudes toward AISs than younger participants did. Consistent with pre­ vious studies, young drivers might get annoyed by in-vehicle warnings and reject such a system even when the system can improve driving performance and safety (M. Jeon et al., 2015). Subsequent results demonstrated that drivers with long driving experience preferred

predictors of negative attitudes. The coefficient of the hostile gesture factor was positive in the regression model, suggesting that more anger provoked by the hostile gesture corresponds to more negative attitudes of a driver. By contrast, age and the arrival-blocking anger trait had negative coefficients, indicating that older drivers and those with higher scores in the arrival-blocking subscale had fewer negative attitudes to­ ward AISs than younger drivers and those with lower scores in the arrival-blocking subscale. The DAX didn’t show a significant effect on negative attitudes toward AISs (p > .05). 5. Discussion This research gathered drivers’ opinions regarding AISs design and then proposed detailed implications for design guidelines based on qualitative and quantitative studies. The overall results indicated that drivers have potential demand for AISs in the automobile market. More importantly, some of the personality traits were found to be associated with the preference of intervention modalities or attitudes toward AISs, providing a new perspective on designing AISs. The results of the focus group sessions and the questionnaire survey revealed some general design implications for AISs. First, with regard to the intervention modalities, the auditory modality was the most favorite one in both studies (Graham, 1999; Jones and Furner, 1989). Partici­ pants preferred the personalized voice of their family members because they believe it would help them improve driving safety. Previous studies also reported that drivers felt safe and had better driving when they listened to their familiar voices (Jonsson, 2009). More importantly, auditory interventions could help angry drivers reduce anger, increase situation awareness, become less distracted, and have fewer aggressive driving behaviors (M. Jeon et al., 2015; S. Li, Zhang et al., 2019). 8

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International Journal of Industrial Ergonomics 75 (2020) 102899

Table 8 Hierarchical multiple regressions on the preference of intervention modalities. DV

Model

Standardized coefficients

t

Sig

β Auditory Interventions

Step 1

Step 2

Step 3

Tactile Interventions

Step 1

Step 2

Step 3

Visual Interventions

Gender Age Driving experience Driving distance Safety-blocking Arrival-blocking Hostile gesture

.074 .025 .112 .035 .114 .014 .083

Verbal expression

ΔR2

ΔF

Sig.

.114 .651 .048 .445 .043 .830 .163

.027

.027

3.235

.012

.040

.013

2.033

.108

.114

.075

9.751

< .001

.013

.013

1.550

.187

.051

.038

6.252

< .001

.128

.077

10.142

< .001

.020

.020

2.360

.053

.098

1.626

.105

.092 .034

1.536 0.510

.125 .611

Adaptive expression Gender Age Driving experience Driving distance Safety-blocking

.263 .057 .041 .019 .076 .022

5.869 1.225 .741 .330 1.634 .390

< .001 .221 .459 .741 .103 .697

Arrival-blocking Hostile gesture

.240 .215

3.703 3.663

< .001 < .001

3.969

< .001

.237

R2 1.585 .453 1.980 .765 2.033 .215 1.398

Physical expression Vehicle expression

Verbal expression

Model summary

Physical expression Vehicle expression Adaptive expression Gender Age

.162 .092 .187 .027 .013

2.724 1.379 4.209 .580 .230

.007 .169 < .001 .562 .819

Step 2

Driving experience Driving distance Safety-blocking Arrival-blocking Hostile gesture

.057 .120 .004 .233 .150

1.003 2.598 .076 3.613 2.557

.316 .010 .940 < .001 .011

.054

.034

5.622

.001

Step 3

Verbal expression

4.621

< .001

.132

.078

10.323

< .001

Step 1

.275

Physical expression Vehicle expression Adaptive expression

.158 .016 .176

2.656 .244 3.959

.008 .807 < .001

Table 9 Hierarchical multiple regressions on attitudes toward AISs. DV

Model

Standardized coefficients

t

Sig

β Positive attitudes

R2

ΔR2

ΔF

Sig.

Step 1

Gender Age Driving experience Driving distance

.017 .048 .043 .035

.363 .851 .758 .756

.717 .395 .449 .450

.008

.008

.937

.442

Step 2

Safety-blocking Arrival-blocking

.053 .133

.935 2.022

.350 .044

.022

.014

2.204

.087

Hostile gesture Verbal expression Physical expression

.134 .039 .076

2.242 .630 1.243

.025 .529 .214

.069

.047

5.797

< .001

.023

.023

2.719

.029

.041

.019

3.021

.029

.055

.014

1.686

.152

Step 3

Vehicle expression Negative attitudes

Model summary

Step 1

Adaptive expression Gender

Step 3

.173

.863

4.550 1.879

.143

2.581

.010

Driving experience Driving distance

.071 .054

1.263 1.169

.207 .243

Safety-blocking Arrival-blocking

.090 .131

1.607 2.010

.109 .045

Hostile gesture Verbal expression Physical expression

.140 .117 .089

2.380 1.878 1.436

.018 .061 .152

Age

Step 2

.012 .209 .088

Vehicle expression Adaptive expression

.027

.383

.051

1.101

auditory interventions and those with long annual driving distance preferred visual interventions, that is, driving-related factors other than gender and age showed significant effects on predicting intervention

< .001 .061

.702 .272

modalities. The results indicated that design principles that have been known to be based on age, gender, and so on (Gilbert et al., 2003; Hodges and McBride, 2012; Hsu et al., 1999; Lin and Hsieh, 2016) may 9

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International Journal of Industrial Ergonomics 75 (2020) 102899

not be applicable here, and various factors related to driving might be more appropriate to satisfy customized design requirements. Driving personality traits also played an important role in predicting preferences of intervention modalities or attitudes toward AISs. The structural equation model indicated that driving anger traits had direct effects on tactile and visual interventions. Moreover, it also had an in­ direct impact on auditory interventions and positive attitudes through the mediation of driving anger expression traits. Specifically, drivers with “arrival-blocking” anger showed preference on tactile and visual interventions and had fewer negative comments on AISs. By contrast, drivers who were victims of hostile gestures had opposite results compared with those who were affected by arrival-blocking. Previous studies indicated that “arrival-blocking” anger and “hostile gestures” were significant predictors of aggressive driving behavior (Zhang et al., 2018). It is reasonable that individuals who became angry because of arrival-blocking felt their safety was threatened, and thought that in­ terventions could be taken to prevent further aggressive driving be­ haviors. However, drivers who paid more attention to hostile gestures might be easily affected by non-traffic factors and exhibit aggressive driving (Zhang et al., 2018), hence tactile and visual interventions could be ignored. Similar contradicting results were obtained regarding different kinds of driving anger expressions. The overall DAX had effects on all the in­ terventions and positive attitudes. The driving anger trait was a signif­ icant predictor of the DAX (Dahlen et al., 2005), and the DAX showed a mediation effect between the driving anger trait and interventions or attitudes. As for the types of DAX, drivers who preferred to express anger verbally did not favor tactile and visual interventions. These individuals favored verbal expression and would like to communicate with others, and might want to listen to a voice such as the in-vehicle agents, which felt like someone convince him/her not to take risky actions. Conversely, those who used physical expression preferred tactile and visual in­ terventions. A previous study indicated that drivers who exhibit strong physical anger expression have a high propensity to experience boredom in driving, which might lead to unsafe behavior (Dahlen et al., 2005). They would like to receive tactile and visual interventions to relieve their anger and focus on driving. The participants of the focus group sessions also mentioned that they hope to receive some tactile in­ terventions, such as fastening seat belts, to prevent them from taking dangerous actions. Results also demonstrated that drivers with adaptive expression showed a positive association with the preference of all the intervention modalities (i.e., auditory, tactile, and visual) and positive attitudes toward AISs. This finding was interesting because individuals with adaptive traits might be tolerant of interventions and prefer to receive interventions to help them become more adaptive (Snyder and Lopez, 2001). The summary of findings and design implications for AISs are listed in Table 10. To summarize, anger-related personality traits provided customized design implications for AISs from a new viewpoint. There­ fore, AISs should be formulated based on driving anger traits and anger expression traits to reduce dangerous or aggressive driving. Further­ more, corresponding interventions could be proposed when systems identify different driving scenarios, which is a good way to provide in­ terventions intelligently.

Table 10 Summary of findings and design implications for AISs. Items and factors

Summary of findings

Design implications

Interventions

■ Auditory intervention modality was the most favorite one. ■ Drivers had a higher preference for the personalized voice warning, the music or broadcast, talking to an AI agent and the simple voice warning. ■ Tactile and visual interventions were not highly favored by drivers ■ Weak current stimulation alert was the least popular intervention ■ Emotion records and stage reporting ■ Sharing emotions with family or friends ■ Receiving positive feedback ■ Knowing the surrounding drivers’ emotion

■ Include different kinds of intervention methods ■ Provide more auditory interventions than visual and tactile interventions ■ Provide the personalized voices of their family members or friends

System features

Attitudes

■ Enhancing driving safety ■ Concerns on accuracy and system security

Demographics

■ Older drivers expressed fewer negative attitudes toward AISs than younger drivers did. ■ Drivers with long driving experience preferred auditory interventions ■ Drivers with long annual driving distance preferred visual interventions ■ Driving anger trait had direct effects on tactile and visual interventions. ■ The driving anger trait was a significant predictor of the DAX ■ Driving anger traits had an indirect impact on auditory interventions and positive attitudes through the mediation of driving anger expression traits. ■ “Arrival-blocking” anger was positively associated with the preference of tactile and visual interventions and negatively associated with negative attitudes on AISs. ■ Drivers who were victims of hostile gestures had opposite results compared with those who were affected by arrivalblocking. ■ The overall DAX had effects on all the interventions and positive attitudes. ■ The DAX showed a mediation effect between the driving anger trait and interventions or attitudes.

Driving anger traits

6. Conclusion This research conducted focus group sessions to investigate drivers’ potential demands, such as features of, interventions from, or attitudes about AISs. Then, the subsequent questionnaire survey obtained more detailed design implications for AISs. Results indicated that some per­ sonality traits (i.e., types of trait driving anger and categories of driving anger expressions) play an important role in predicting preference of intervention modalities or attitudes toward AISs, thereby providing a new perspective on designing customized intervention systems. The outcome of this research could be used to propose suggestions for AISs

Driving anger expression traits

■ Provide emotion records and stage reporting ■ Include an option to choose to share emotion or not ■ Provide positive feedback (e.g., positive comments) when drivers’ anger has reduced ■ The detection of anger emotion should be precise ■ The time to provide interventions should be accurate ■ Ensure the system security ■ Provide more information about accuracy and system security for younger drivers ■ Various factors related to driving might be more appropriate to satisfy customized design requirements ■ Include the driving anger trait assessment based on the DAS ■ Identify specific scenarios that induce anger ■ Provide different kinds of interventions based on the types of driving anger trait

■ Include the driving anger expression trait assessment based on the DAX ■ Identify aberrant driving anger expression behaviors (continued on next page)

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International Journal of Industrial Ergonomics 75 (2020) 102899

other factors that could influence the preference of interventions or the attitudes towards AIS, which could account for a higher value of R2. This phenomenon is very common in social or behavioral sciences since re­ searchers cannot propose expected models to include all the relevant predictors to explain an outcome variable (Itaoka, 2012; Zhang et al., 2018). In future work, we would like to provide more detailed infor­ mation of the interventions as well as the intervention system. Mean­ while, we plan to investigate more demographic factors such as education level, and also other influential factors in order to improve the overall value of R2 in the regression model and provide detailed design implications. At last, multiple intervention modalities could also be taken into consideration and help the design of AISs.

Table 10 (continued ) Items and factors

Summary of findings

Design implications

■ Drivers who prefer to express anger verbally do not favor tactile and visual interventions. ■ Drivers who use physical expression favor tactile and visual interventions. ■ Drivers with adaptive expression showed a positive association with the preference of all the intervention modalities and positive attitudes toward AISs.

■ Provide different kinds of interventions based on the types of driving anger expression trait

Funding This research was funded by National Natural Science Foundation of China (Grant Number: 71771132, 71801156), the Shenzhen Peacock Program (Grant Number: 827000343), and the Start-up Grant of Shenzhen University (Grant Number: 85304-00000132).

development and hence improve driving safety. The study has limitations on the distributions of education level since a quarter of the participants in the questionnaire survey had a master’s degree or above. Moreover, the participants may have a different un­ derstanding of some descriptions in the questionnaire survey. For instance, ‘I3 talking to an AI agent’ may be interpreted differently ac­ cording to participants’ understanding of an AI agent. The value of R2 was relatively low in the present study, it might because there were

Declaration of competing interest The authors declare no conflict of interest.

Appendix 1. Modified DAX Factor

No.

Item

Reference

Verbal aggressive expression

1

I glare at the other driver.

Deffenbacher et al. (2002b)

Personal physical aggressive expression

2 3 4

Make negative comments about the driver under breath Swear at the other driver aloud I bump the other driver’s bumper with mine.

Deffenbacher et al. (2002b) Deffenbacher et al. (2002b) Deffenbacher et al. (2002b)

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Try to get out of the car and tell the other driver off Try to get out and have a physical fight Beat the steering wheel Honk the car horn to warn other drivers. Leave my lights on full in their mirrors Drive a lot faster Follow right behind for a long time I try to cut in front of the other driver. Do to drivers what they did to me Tell myself it’s not worth getting involved in Tell myself it’s not worth getting all mad about Think of positive solutions to deal with the situation I turn on the radio or music to calm down. I do things like take deep breaths to calm down. I think about the advice from family and friends to calm down. I think of dangerous consequences to remind myself not to be impulsive.

Deffenbacher et al. Deffenbacher et al. Study 1 Study 1 Deffenbacher et al. Deffenbacher et al. Deffenbacher et al. Deffenbacher et al. Deffenbacher et al. Deffenbacher et al. Deffenbacher et al. Deffenbacher et al. Deffenbacher et al. Deffenbacher et al. Study 1 Study 1

Use of vehicle to express anger

Adaptive/constructive expression

References

(2002b) (2002b) (2002b) (2002b) (2002b) (2002b) (2002b) (2002b) (2002b) (2002b) (2002b) (2002b)

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