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With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

The physical internet (PI) is an emerging logistics and supply chain management (SCM) concept that draws on different technologies and areas of research, such as the Internet of Things (IoT) and key performance indicators, with the purpose of revolutionizing existing logistics and SCM practices. The growing literature on the PI and its noteworthy potential to be a disruptive innovation in the logistics industry call for a systematic literature review (SLR), which we conducted that defines the current state of the literature and outlines future research directions and approaches.

The SLR that was undertaken included journal publications, conference papers and proceedings, book excerpts, industry reports and white papers. We conducted descriptive, citation, thematic and methodological analyses to understand the evolution of PI literature.

Based on the literature review and analyses, we proposed a comprehensive framework that structures the PI domain and outlines future directions for logistics and SCM researchers.

Our research findings are limited by the relatively low number of journal publications, as the PI is a new field of inquiry that is composed primarily of conference papers and proceedings.

The proposed PI-based framework identifies seven PI themes, including the respective facilitators and barriers, which can inform researchers and practitioners on future potentially disruptive SC strategies.

The purpose of this paper is to analyse the metaverse platform in a social context to better understand the future of this tool in tourism cities and how this can help to improve the well-being of residents in both digital and physical scenarios.

In this paper, the current and probable developments in the metaverse, and its use in tourism cities and companies have been investigated. Moreover, this study develops, collects and examines the main metaverse definitions by expert authors and organizations as a methodology to ensure the transparency and credibility of the metaverse analysis.

Findings suggest that the fusion of the metaverse and tourism cities must create residents’ services and experiences in the new MetaTourPolis to help interact and connect citizens with the city’s institutions and companies, as well as make tourism cities more attractive, innovative, environmentally friendly and healthier places to live. Metaverse will bring new changes for residents and tourists, in fact, this virtual platform is already changing and improving the residents’ quality of life and people with disabilities in tourism cities. For instance, the metaverse platform has been implemented in Seoul, Santa Monica and Dubai MetaTourPolis to interact with their residents, including people with disabilities, to resolve bureaucratic and administrative problems, avoiding this group and the rest of the residents travelling by bus or car to the city’s institutions. In addition, several metaverse applications based on softbot tutors or metaverse virtual social centres have been developed to improve blind and impaired people, and elderly people’ quality of life, respectively.

A new concept called “MetaTourPolis” has been included to stage the relationship between tourism cities and the metaverse platform, where the fusion of metaverse and the new tourism polis of the 21st century will be at the service of citizens, tourists and companies, to create more sustainable, efficient, quantitative and environmental tourism cities.

This study aims to investigate the technology usage behaviour of the tourists in line with the modified unified theory of acceptance and use of technology (UTAUT) model.

Data were collected from a survey of 265 tourists using the random sampling technique. Partial least squares-based structural equation modelling (PLS-SEM) technique was used to analyze the data.

The findings revealed that performance expectancy, hedonic motivation and habit significantly influence the behavioural intention of tourists to use information and communication technology (ICT), while effort expectancy, social influence, and facilitating conditions do not have a significant influence. However, actual ICT usage behaviour largely depends on the behavioural intention of the tourists, and their habits, while the facilitating conditions do not have any influence in this case.

The findings uncover the core factors influencing tourists' actual ICT use behaviour that can assist the concerned stakeholders in designing tourism planning and sales. The study results also offer pathways for the world's tourism industry for a healthy recovery from the COVID-19 pandemic.

The findings have made robust contributions by extending the existing UTAUT-based literature by adding two new moderators in the relationship between behavioural intention and actual ICT usage behaviour.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

The study aims to analyse library user preferences in the willingness to read and pay for e-books, using a sample of both active readers (users of public library services) and non-users (the general population).

Two empirical surveys were conducted from August to November of 2019; the research sample consisted of 1,334 users from the Municipal Library of Prague and 1,101 non-users from the general Czech population. The research was focussed on e-book user preferences. The willingness to pay (WTP) for e-book services and the determinants that affect this willingness were also examined.

The results show the specific approach of Czech readers, whose main determinant of WTP is not the content, but the price and method of its payment (allocation). Some people prefer a cheaper annual lump sum, whereas others may prefer a charge of small regular fees. The decision to pay depends on their reading or payment habits.

This study also aims to clarify the demand for various types of digital media in Czech libraries and the preferred distribution models. Furthermore, the study determines the dependence of the preferences of library users in their WTP for e-books using different evaluation models. The originality of this study is in the evaluation of the determinants of WTP for e-books, which makes this study unique, and the findings should contribute to the expansion of existing knowledge in the field of information science.

As the world faces a new health crisis threatening people with the spread of Covid-19, this study aims to summarize the key information of Covid-19 related to disease characteristics, diagnosis, treatment and prevention along with the lessons learned from Thailand.

The narrative review was synthesized from various sources such as the World Health Organization; Centers for Disease Control and Prevention; Ministry of Public Health and other related news; articles in ScienceDirect, PubMed, Google Scholar; and the author's perspective regarding the lessons learned from Thailand with keywords of “Covid-19” and “Coronavirus” from January to August 2020. Google Trends was used to set common questions.

Covid-19 is the seventh family of coronaviruses that cause various symptoms related to respiratory systems. The disease can be treated through general and symptomatic treatment, by using antiviral drugs. As of July 2020, there are four potential vaccine candidates ChAdOx1 nCoV-19, mRNA-1273, Ad5-nCOV and BNT162b1. The recommendations for Covid-19 prevention are physical distancing, face masks, eye protection and hand washing. Thailand is now considered as low-risk for Covid-19 possibly because of (1) soft policy by government actions, (2) village health volunteers, (3) integration of technology and (4) fact-based communications.

This study summarized the key points about Covid-19, clarified some misunderstandings and shared strategic actions from Thailand, which can be adapted according to the different capacities and situations in other countries.