The rapid evolution of technology used by drivers has increased the complexity of the driving task and introduced new sources of distraction, necessitating the development of distracted driving legislation that keeps pace with these changes. As such, this study examined drivers' views of a more comprehensive, technology-neutral approach to distracted driving rules, which extends beyond mobile phone use to include portable devices, in-built and mounted systems, and wearable devices. Guided by an extended Value-Belief-Norm theory, a policy acceptance model was developed and validated in assessing public support and acceptability of more comprehensive distracted driving legislation, examining how general and normative beliefs, as well as policy-specific perceptions, influence drivers’ acceptability, and identifying demographic differences in acceptability and its underlying factors. A sample of 494 drivers who reside in Queensland, Australia, participated in an online survey, which included both quantitative and open-ended questions. Findings revealed a relatively strong support for the proposed rules. Structural Equation Modelling identified personal norms, social norms, perceived fairness, and perceived effectiveness as significant predictors of acceptability, with the proposed policy acceptance model explaining 76.6% of the variance in acceptability. Group comparisons revealed that young drivers and males reported lower levels of perceived freedom, while those with greater familiarity with distracted driving reported higher levels of personal norms and problem awareness. Qualitative responses indicated that most participants viewed the proposed rules as more effective than current legislation, especially in addressing emerging technological distractions. However, concerns about enforcement, clarity, and the need for educational efforts were also raised.

Distracted driving remains a significant road safety concern. To address the issue, it is important to understand drivers' perceptions of distractions and the related road rules. Accordingly, this study has three objectives. The first was to investigate drivers' beliefs and behaviours regarding distracted driving by expanding the Susceptibility to Distracted Driving Questionnaire. The second was to explore drivers' perceptions of current distracted driving rules by using the extended Value-Belief-Norm Theory and open-ended questions and to examine how beliefs and behaviours about distractions shape their views on road rules. The third was to assess the extent to which drivers perceive a need for broader distracted driving legislation and how their perceptions of current road rules contribute to this perceived need. Data were collected from 494 participants (aged 17 to 83 years), residing in Queensland, Australia, through an online questionnaire. Findings indicated a moderate level of engagement with both technological and non-technological distractions, with no significant difference observed between their levels of engagement. However, participants reported more favourable attitudes and a higher sense of control over technology-related distractions than non-technological distractions. Results also showed that while distracted driving rules were seen as fair and allowing freedom while driving, they were viewed as moderately effective and somewhat complex. Further, most participants supported the need for improved distracted driving rules, with lower perceived effectiveness and higher complexity of current rules linked to greater support. Results indicated that higher engagement with non-technological distractions was negatively associated with perceived effectiveness of rules, while greater risk compensation was linked to lower perceived fairness, and higher perceived control over distractions was significantly associated with lower perceived freedom.

Distracted driving continues to be a significant risk to road safety. While effective legislation and education are essential for preventing distracted driving, there is evidence showing that current official information and road rules related to distracted driving may not provide drivers with a comprehensive understanding of the issue. Therefore, this study aimed to explore drivers’ perspectives on how official information and road rules could be improved to effectively address distracted driving. Semi-structured interviews were conducted with 35 drivers from Queensland, Australia, with thematic analysis employed to extract key themes from the discussions. The results showed that official information on distracted driving could benefit from more comprehensive content, including underrepresented distraction sources (e.g., using a smartwatch, interacting with passengers, and looking at advertisement billboards), the safety risks, and impacts on driving performance. Participants emphasised the need for distracted driving legislation to address the risks posed by a broader range of technological devices (e.g., infotainment systems, wearable devices), not just hand-held mobile phones. The results suggested a need for improved presentation of information, with future distracted driving content suggested to be presented visually, along with more targeted messaging for high-risk drivers such as young drivers. Further, social media, short training, and outdoor media were perceived by participants as the most effective delivery mechanisms for distracted driving resources. The findings provide valuable guidance for policymakers in establishing and communicating information and road rules for distracted driving.

Mobile phone distraction is a critical global road safety issue, contributing to crashes and subsequent injuries and fatalities. This issue has led to calls for effective interventions. Based on neuropsychological research indicating that colour stimuli play a significant role in driving phone engagement, one potential strategy to reduce road user phone use while on the road is activating greyscale on phones. By removing colour, the sensory reward associated with phone use may be diminished, potentially reducing usage. However, this approach has yet to be empirically tested. As such, the aim of this study is to investigate how greyscale influences phone use behaviours while driving and walking. Participants were asked to switch their phone interface from colour to greyscale for a duration of 2-weeks. A mixed-methods approach, including surveys and interviews, was employed to gather insights from participants regarding their perceptions of greyscale on their phone use behaviour while driving and walking. The quantitative results showed that greyscale decreased the frequency of participants glancing at their phone screens in a cradle while driving. However, using the greyscale feature did not lead to significant changes in the frequency of participants picking up the phone and looking at the screen while driving, nor did it increase participants’ use of other devices such as the in-vehicle infotainment system, smartwatches, or voice commands. Additionally, greyscale significantly reduced the probability of pedestrians using handsfree phones while walking, although greyscale did not influence the likelihood of looking at the screen of a handheld phone. The qualitative results revealed that the greyscale had a complex impact on road users’ phone behaviour. Greyscale altered how they used their phones, made them less appealing and enjoyable, and added complexity to phone use. However, some participants found work-around, though not everyone adopted them. Overall, the findings suggest that while greyscale effectively reduced some phone-related behaviours over a 2-week period, its impact on phone use behaviours while driving or walking was limited in scope, with mixed effectiveness across different contexts and with some users finding work-around.

The rise of ChatGPT has emphasized the need for an improved conceptual understanding of users’ agency when interacting with artificial intelligence (AI) systems for healthcare. Australian ChatGPT users (N = 216) completed a repeated measures online survey. Hierarchical regression analyses assessed the influence of demographic factors (age and gender), Technology Acceptance Model constructs (perceived usefulness and perceived ease of use), and extended variables (trust, privacy concerns) on users' behavioral intentions to use ChatGPT for physical and mental healthcare. The proposed model was partially supported: the findings emphasized the need to establish user trust in ChatGPT and its perceived usefulness in both areas of healthcare. Privacy concerns were a significant predictor of intentions to use ChatGPT for mental healthcare with perceived ease of use predicting intentions to use ChatGPT for physical healthcare. The findings indicate predictors of uses of AI cannot be generalized across healthcare types and unique drivers should be considered.

The rise of ChatGPT has prompted concerns over users’ agency when revealing personal data to artificial intelligence. This study examined users’ likelihood of disclosing their data to ChatGPT in physical and mental health scenarios. Participants (N = 216) completed a repeated measures survey where they viewed four vignettes of hypothetical scenarios and were asked to imagine disclosing health information (physical and mental health) at two sensitivity levels (low and high self-disclosure). A repeated measures ANOVA revealed participants were significantly more likely to provide their data when the information required low-disclosure than high-disclosure. Furthermore, participants were significantly more likely to report uploading their health information in the physical health scenario than in the mental health scenario. The findings suggest ChatGPT users exercise caution in disclosing data to the platform. Reluctance to upload information in sensitive scenarios reduces the training data for large language models, resulting in potential stagnation in technology development.

Distracted driving is a traffic safety issue worldwide. While the development of comprehensive information and road rules about distracted driving by governments is essential to address the issue, there is evidence showing that existing road rules and information may not always deter drivers from engaging in distractions while driving. Therefore, this study explored drivers’ views on government information and road rules concerning distracted driving, aiming to understand how these rules and information have shaped drivers’ perceptions and behaviour towards distractions. Interviews (n = 35) were conducted with Queensland drivers aged between 21 and 70 years and a thematic analysis was used to explore the data. Based on the findings, government information on distracted driving was believed by participants to be incomprehensive, not effectively communicated, and focused on mobile phone use. Road rules and enforcement measures often prioritise mobile phone use and contain grey areas that may confuse drivers about legal and illegal distractions. The results showed that the perceived risk of distractions varies among drivers, with some distractions not being considered as risky as other behaviours (e.g., using a smartwatch). Findings showed that government practices are believed to influence drivers’ perceptions and behaviour about distracted driving, encouraging an inaccurate perception about driver safety and with the potential to prompt drivers to engage with distracting behaviours without knowledge nor consideration of the risks. The results of this study offer important insights for policymakers in developing and disseminating comprehensive information and road rules for distracted driving.

The study applied the Theory of Planned Behaviour (TPB) to explore motorcycle riders’ underlying behavioural, normative, and control beliefs towards Advanced Rider Assistance Systems (ARAS). Each belief was explored in terms of three categories of technologies, (i) advanced technologies that help riders manage riding according to situations and conditions, (ii) advanced technologies that help riders to stop, and (iii) advanced technologies that help riders to corner. Eight focus groups were conducted with 39 motorcycle riders (Mage = 44.54 years, 27 males) who resided in Australia. First, participants completed a short online questionnaire which asked demographic information (e.g., age, gender, riding experience), before taking part in a 50-minute semi-structured online focus group. Participants’ knowledge of ARAS differed depending on the type of technology, with most participants reporting good to excellent knowledge of cruise control and standard anti-lock braking system (ABS) and a poor to fair understanding of selectable riding modes and cornering ABS. For behavioural beliefs, two common advantages reported for all three categories of technologies were safety and that the technologies would benefit new riders or riders with less experience. The three common disadvantages included concerns over riders’ reliance on the technologies, cost, and loss of skill or false sense of security. For normative beliefs, participants reported that their loved ones (i.e., partner, family, and friends) would approve of them using these technologies, with participants perceiving that ‘purists’ (i.e., riders who prefer to ride traditional motorcycles) would disapprove. For control beliefs, cost, lack of information on the safety of advanced technologies, and not being able to switch off systems were reported as barriers to use. Lowering insurance premiums, education/test rides, technologies as selectable options, and availability, were all identified as factors that would encourage use of ARAS. By providing information about ARAS, riders will become more informed about ARAS, which may enhance trust and user acceptance. Additionally, ongoing research and development are essential to ensure the evaluation and improvement of ARAS and mitigate any unintended consequences.

Introduction: Speeding behaviour contributes significantly to road crashes and subsequent injuries and fatalities. The purpose of this study was to examine which traditional countermeasures (i.e., police enforcement and on-road signs) and technology-based countermeasures (i.e., advanced driver assistance systems [ADAS] and in-vehicle speed audio alerts) drivers perceived as effective in assisting them to comply with posted speed limits. Methods: Participants (N = 680; Mage = 49.34 years) who held a current driver's licence completed a 20-minute online survey. Participants in the experimental condition were randomly assigned to read one of four scenarios which differed based on location (urban or regional) and posted speed limit (60 km/hr or 100 km/hr) or to the control condition (no scenario), before answering questions about the perceived effectiveness of the traditional and technology-based interventions. For the experimental conditions, participants were instructed to respond to these questions based on how they would drive in the situation outlined in the scenario. Results: Low-level speeding behaviour was common, with 40.7 % reporting regularly driving 5 km over the posted speed limit in a 60 km/hr speed zone and 50.4 % reporting regularly driving 5 km over the posted speed limit in a 100 km/hr speed zone. A mixed ANOVA revealed that participants perceived police enforcement activities to be the most effective at assisting them to comply with posted speed limits when compared to other traditional and technology-based approaches. Further, ADAS was rated by participants as significantly more effective at assisting them to comply with the posted speed limit in the 100 km/hr urban condition compared to the 60 km/hr urban condition. Conclusions: Low-level speeding behaviour remains common practice and there is a need for continued roadside police presence to discourage this behaviour. Drivers with ADAS-equipped vehicles could also be encouraged to use systems, such as adaptative cruise control, to assist with speed management on high-speed roads.

Empirical data demonstrates that distracted driving is a leading cause of crashes even in countries with sophisticated road safety systems. As such, a paradigm shift is needed to prevent driver distraction. This study aims to contribute to this paradigm shift by critically investigating the official distraction-related information and road rules for drivers in Australia, while gaining an understanding of how distraction is specifically addressed in these materials. Using a multistage content analysis, it was identified that official information focuses on three major categories including overview of distraction, sources of distraction, and prevention of distraction. The findings suggested ways that State Governments could improve the available information and road rules, as some of these materials were insufficient or ambiguous. For instance, several sources of distraction, particularly internal distractions (e.g., medical impairments) and external distractions (e.g., advertisement billboards) have been overlooked or received limited attention in the information. Additionally, the information does not address the specific needs of certain road users, such as young and inexperienced drivers. Further, the guidelines for safe interaction with certain in-vehicle distractions such as smartwatches, advanced driving assistance systems, and pets are insufficient or inconsistent across jurisdictions. The rules concerning some distraction types are ambiguous and contain uncertainties. Furthermore, general rules involving distracted driving such as those related to careless driving were found to lack specificity. The results of this investigation provide guidance for policymakers worldwide in developing road rules for distracted driving and the need to change the approach to a more holistic management of distractions.

Participatory data stewardship (PDS) empowers individuals to shape and govern their data via responsible collection and use. As artificial intelligence (AI) requires massive amounts of data, research must assess what factors predict consumers’ willingness to provide their data to AI. This mixed-methods study applied the extended Technology Acceptance Model (TAM) with additional predictors of trust and subjective norms. Participants’ data donation profile was also measured to assess the influence of individuals’ social duty, understanding of the purpose and guilt. Participants (N = 322) completed an experimental survey. Individuals were willing to provide data to AI via PDS when they believed it was their social duty, understood the purpose and trusted AI. However, the TAM may not be a complete model for assessing user willingness. This study establishes that individuals value the importance of trusting and comprehending the broader societal impact of AI when providing their data to AI. Practitioner summary: To build responsible and representative AI, individuals are needed to participate in data stewardship. The factors driving willingness to participate in such methods were studied via an online survey. Trust, social duty and understanding the purpose significantly predicted willingness to provide data to AI via participatory data stewardship.

Artificial intelligence (AI) chatbots are set to be the defining technology of the next decade due to their ability to increase human capability at a low cost. However, more research is required to assess individuals' behavioural intentions to use this technology when it becomes publicly available. This study applied an extended Technology Acceptance Model (TAM), with additional predictors of trust and privacy concerns, to assess individuals' behavioural intentions to use AI chatbots across three industries: mental health care, online shopping, and online banking. These services were selected due to the current popularity of regular chatbots in these fields. Participants (N=360, 202 females) aged between 17 and 85 years (M=38.17, SD=17.66) completed a 71-item online, cross-sectional survey. As hypothesised, perceived usefulness and trust were significant positive predictors of behavioural intentions across all three behaviours. However, the influence of the perceived ease of use and privacy concerns on behavioural intentions differed across the three behaviours. These findings highlight that the combination of predictors within the extended TAM have different influences on behavioural intentions to use AI chatbots for mental health care, online shopping, and online banking. This research contributes to the literature by demonstrating that the influence of the variables in one field cannot be generalised across all uses of AI chatbots.