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This research examines what motivates professional truck drivers to engage in eco-driving by linking their self-reports with objective driving scores.

Theory of Planned Behavior (TPB) is illustrated in an embedded, single-case study of a Finnish carrier with 17 of its truck drivers. Data are obtained through in-depth interviews with drivers, their fuel-efficiency scores generated by fleet telematics and a focus group session with the management.

Discrepancies between drivers’ intentions and eco-driving behaviors are illustrated in a two-by-two matrix that classifies drivers into four categories: ideal eco-drivers, wildcards, wannabes and non-eco-drivers. Attitudes, subjective norms and perceived behavioral control are examined for drivers within each category, revealing that drivers’ perceptions did not always align with the reality of their driving.

This study strengthens the utility of TPB through data triangulation while also revealing the theory’s inherent limitations in elucidating the underlying causes of its three antecedents and their impact on the variance in driving behaviors.

Managerial insights are offered to fleet managers and eco-driving solution providers to stipulate the right conditions for drivers to enhance fuel-efficiency outcomes of transport fleets.

This is one of the first studies to give a voice to professional truck drivers about their daily eco-driving practice.

The purpose of this study is to examine the drivers and barriers of travel behavior associated with tourist behavior during/post-COVID-19 pandemic to provide a knowledge base as well as an agenda for future research.

The authors utilized the Systematic Literature Review (SLR) technique for searching the articles published in Web of Science (WOS) and Scopus, to identify the main drivers and barriers affecting the tourist behavior during/post-COVID-19 pandemic. Eventually, 47 articles were chosen for the final analysis.

The findings are reported in three sections, (1) quantitative research profile, (2) qualitative synthesis and (3) future agenda. This study addresses the nuanced questions regarding the significant change in tourist travel behavior, emotional dynamics and a detailed understanding of mechanisms, such as which drivers and barriers affect tourist behavior in a particular destination. Drivers and barriers to tourist travel behavior were characterized in personal-related, destination-related, and health-related factors. Moreover, this study provides thought-provoking ideas in theory, policy and practice in the field of tourism and hospitality.

This study has three limitations, as follows. First, the authors searched only two databases, Scopus and Web of Science, due to which the authors might be missing some related studies existing on the other databases. Although these databases provide an extensive range of academic literature, further studies could extend the data collection from the other databases (e.g. via Taylor & Francis). However, our systematic literature review (SLR) coverage is quite extensive, since journals are listed on these three main databases. Second, the authors followed a main study search protocol based on the synonyms and related keywords, however, some of the studies that may be related to the tourist behavior towards the destination are missing on account of the lack of our keywords in there, title, author, keyword and abstract. Furthermore, future research could endeavor to add other keywords to expand the results of studies. Third, although the accurate analysis was conducted to reduce subjectivity in identifying themes for drivers and barriers of tourist behavior, future studies on categorization could work to ensure that other sub-themes categorize.

The recent study has some key practical implications. First, this study is valuable for all the stakeholders in a unique way, including destination managers, academicians and policymakers, because it provides insight into barriers and drivers that influence the development of tourist behavior towards the destination. Second, the current study also offers practical implications for people involved in tourism service industries including governments and private businesses. Policymakers and other leaders are increasingly interested in harnessing the economic potential of tourism. Therefore, identifying the barrier which is inhibiting the tourist traffic towards the destination is beneficial to understand and effectively develop strategies to minimize the effect of such factors. Moreover, drivers and barriers of tourist behavior towards the destination in the COVID-19 pandemic situation towards the destination may help to create a framework for the development of destinations according to the current vulnerable situation. Third, current findings suggest that tourism marketers understand the drivers and barrier constructs found in this study and tailor their marketing strategies for attracting existing and new tourists. For instance, marketers should understand the drivers and barriers of tourist behavior for effective strategy development to increase the positive effect of drivers and to reduce the negative effect of barriers.

This is the first systematic literature review on the impact of drivers and barriers of tourist travel behavior. This paper analyses the methods and approaches that have been used in the previous literature to examine the drivers and barriers of tourist travel behavior. The paper ends with the research implication and limitations of the studies.

Despite ample research on the adoption of online banking, the post-adoption phase remains largely neglected. The purpose of this paper is to develop a new conceptual model to investigate drivers, attitudes and behaviours in the post-adoption phase of the e-postbox, a co-creative online banking feature.

Research from bank marketing, services marketing, information systems and relationship management informs the proposed post-adoption model. Empirical tests rely on structural equation modelling and a sample of 750 current customers of the e-postbox of a large German bank.

The proposed model provides a multifaceted view of the post-adoption phase, including task-related, organisation-related and interpersonal communication-related drivers. This study reveals the importance of integrating dual interpersonal communication as a post-adoption driver and a post-adoption behaviour. It also extends the technology acceptance model by applying it to the post-adoption phase. Significant effects of age further suggest that younger customers express the most favourable attitudes towards and highest intentions to continue using the e-postbox; interestingly, older customers use it more and share more word-of-mouth.

This paper develops a post-adoption model that highlights the importance of continued usage for successful co-creation between the bank and its customers.

Managers can encourage continued usage during the post-adoption phase of a co-creative, digitalised service, which determines the retention of current customers and opportunities to attain new customers.

This study defines and establishes constructs for the post-adoption phase and categorises them according to post-adoption drivers, attitudes and behaviours.

This paper aims to provide a driving behavior scoring model to decide the personalized automobile premium for each driver.

Driving behavior scoring model.

The driving behavior scoring model could effectively reflect the risk level of driver’s safe driving.

A driving behavior scoring model for UBI.

This paper aims to explore whether drivers would adapt their behavior when they drive among automated vehicles (AVs) compared to driving among manually driven vehicles (MVs).Understanding behavioral adaptation of drivers when they encounter AVs is crucial for assessing impacts of AVs in mixed-traffic situations. Here, mixed-traffic situations refer to situations where AVs share the roads with existing nonautomated vehicles such as conventional MVs.

A driving simulator study is designed to explore whether such behavioral adaptations exist. Two different driving scenarios were explored on a three-lane highway: driving on the main highway and merging from an on-ramp. For this study, 18 research participants were recruited.

Behavioral adaptation can be observed in terms of car-following speed, car-following time gap, number of lane change and overall driving speed. The adaptations are dependent on the driving scenario and whether the surrounding traffic was AVs or MVs. Although significant differences in behavior were found in more than 90% of the research participants, they adapted their behavior differently, and thus, magnitude of the behavioral adaptation remains unclear.

The observed behavioral adaptations in this paper were dependent on the driving scenario rather than the time gap between surrounding vehicles. This finding differs from previous studies, which have shown that drivers tend to adapt their behaviors with respect to the surrounding vehicles. Furthermore, the surrounding vehicles in this study are more “free flow'” compared to previous studies with a fixed formation such as platoons. Nevertheless, long-term observations are required to further support this claim.

The purpose of this study is to investigate the influence of institutional dynamics on road accidents and whether this relationship is moderated by information and communication technology (ICT).

The study adopted a quantitative approach with 133 respondents. Research hypotheses were tested in AMOS version 21. In addition, moderated regression analysis was used to test the moderating role of ICT on the relationship between institutional dynamics and road accidents.

The results show that vehicle maintenance, policy enforcement, safety culture, driver training and driver management positively influence road accidents. Moreover, the study established that ICT moderates the relationship between institutional dynamics and road accidents.

The results of this study serve as a practical guideline for policymakers in the road haulage sector. Managers may gain insights on how to design effective interventions to reduce road accidents.

This research contributes to the existing body of knowledge by exploring previously unexplored moderating paths in the relationship between institutional dynamics and road accidents. By highlighting the moderating role of ICT, the study sheds new light on the institutional dynamics that influence road accidents in the context of road haulage companies.

Advanced driving assistance system (ADAS) has been applied in commercial vehicles. This paper aims to evaluate the influence factors of commercial vehicle drivers’ acceptance on ADAS and explore the characteristics of each key factors. Two most widely used functions, forward collision warning (FCW) and lane departure warning (LDW), were considered in this paper.

A random forests algorithm was applied to evaluate the influence factors of commercial drivers’ acceptance. ADAS data of 24 commercial vehicles were recorded from 1 November to 21 December 2018, in Jiangsu province. Respond or not was set as dependent variables, while six influence factors were considered.

The acceptance rate for FCW and LDW systems was 69.52% and 38.76%, respectively. The accuracy of random forests model for FCW and LDW systems is 0.816 and 0.820, respectively. For FCW system, vehicle speed, duration time and warning hour are three key factors. Drivers prefer to respond in a short duration during daytime and low vehicle speed. While for LDW system, duration time, vehicle speed and driver age are three key factors. Older drivers have higher respond probability under higher vehicle speed, and the respond time is longer than FCW system.

Few research studies have focused on the attitudes of commercial vehicle drivers, though commercial vehicle accidents were proved to be more severe than passenger vehicles. The results of this study can help researchers to better understand the behavior of commercial vehicle drivers and make corresponding recommendations for ADAS of commercial vehicles.

This paper aims to use active fine lane management methods to solve the problem of congestion in a weaving area and provide theoretical and technical support for traffic control under the environment of intelligent connected vehicles (ICVs) in the future.

By analyzing the traffic capacities and traffic behaviors of domestic and foreign weaving areas and combining them with field investigation, the paper proposes the active and fine lane management methods for ICVs to optimal driving behavior in a weaving area. The VISSIM simulation of traffic flow vehicle driving behavior in weaving areas of urban expressways was performed using research data. The influence of lane-changing in advance on the weaving area was evaluated and a conflict avoidance area was established in the weaving area. The active fine lane management methods applied to a weaving area were verified for different scenarios.

The results of the study indicate that ICVs complete their lane changes before they reach a weaving area, their time in the weaving area does not exceed the specified time and the delay of vehicles that pass through the weaving area decreases.

Based on the vehicle group behavior, this paper conducts a simulation study on the active traffic management control-oriented to ICVs. The research results can optimize the management of lanes, improve the traffic capacity of a weaving area and mitigate traffic congestion on expressways.

Basic safety message (BSM) is a core subset of standard protocols for connected vehicle system to transmit related safety information via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Although some safety prototypes of connected vehicle have been proposed with effective strategies, few of them are fully evaluated in terms of the significance of BSM messages on performance of safety applications when in emergency.

To address this problem, a data fusion method is proposed to capture the vehicle crash risk by extracting critical information from raw BSMs data, such as driver volition, vehicle speed, hard accelerations and braking. Thereafter, a classification model based on information-entropy and variable precision rough set (VPRS) is used for assessing the instantaneous driving safety by fusing the BSMs data from field test, and predicting the vehicle crash risk level with the driver emergency maneuvers in the next short term.

The findings and implications are discussed for developing an improved warning and driving assistant system by using BSMs messages.

The findings of this study are relevant to incorporation of alerts, warnings and control assists in V2V applications of connected vehicles. Such applications can help drivers identify situations where surrounding drivers are volatile, and they may avoid dangers by taking defensive actions.

Analysis of characteristic driving operations can help develop supports for drivers with different driving skills. However, the existing knowledge on analysis of driving skills only focuses on single driving operation and cannot reflect the differences on proficiency of coordination of driving operations. Thus, the purpose of this paper is to analyze driving skills from driving coordinating operations. There are two main contributions: the first involves a method for feature extraction based on AdaBoost, which selects features critical for coordinating operations of experienced drivers and inexperienced drivers, and the second involves a generating method for candidate features, called the combined features method, through which two or more different driving operations at the same location are combined into a candidate combined feature. A series of experiments based on driving simulator and specific course with several different curves were carried out, and the result indicated the feasibility of analyzing driving behavior through AdaBoost and the combined features method.

AdaBoost was used to extract features and the combined features method was used to combine two or more different driving operations at the same location.

A series of experiments based on driving simulator and specific course with several different curves were carried out, and the result indicated the feasibility of analyzing driving behavior through AdaBoost and the combined features method.

There are two main contributions: the first involves a method for feature extraction based on AdaBoost, which selects features critical for coordinating operations of experienced drivers and inexperienced drivers, and the second involves a generating method for candidate features, called the combined features method, through which two or more different driving operations at the same location are combined into a candidate combined feature.