This study links self-concept and place attachment to generate a better understanding of travel behavior patterns by migrant populations, in this case, Western professional migrants who live in the Hong Kong and Macau Special Administrative Regions of China. Five discrete Western professional migrant groups are identified, each with different demographic profiles, travel patterns, propensity, and intensity. The findings challenge the view that migrant populations are homogenous and also challenge the widely held notion that home return travel is their dominant mobility pattern. Conceptual and managerial implications of migrant travel behavior for destination marketers are briefly outlined.
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The section - Travel Psychology - contains 3 chapters. Chapter 1 starts by listing different types of travel constraints facing all citizens. For travelers who negotiate their travel constraints and are able to travel, two seminal models for tourism motivations are reviewed. First, the pull and push factors are mentioned along with examples. In a second model, travel motivations are categorized into traveling to gain something and, travelling to get away from something.After reviewing various travel constraints and motivations, Chapter 1 continues by discussing how travelers’ mood and tendencies at any given time could affect the type of travel destination they pursue – historical vs. natural vs. manmade; warm vs. cold; urban vs. country; crowded vs. quiet; familiar vs. novel vs arousing; and, the type of experiences and activities travelers pursue on their vacation. Also, the relationship between less dynamic personality traits and travel decision-makings are discussed: who to travel with, where to stay, what to do; perceived risks; and information seeking behavior. Chapter 1 ends by discussing how travel service providers could play a significant role in helping customers make more informed and authentic decisions that would eventually feed their psychological needs, wants, and wellbeing. This wellbeing perspective to travel is contrasted with a service quality and money-driven perspective in tourism industry and research. Chapter 2 starts by reviewing the fundamentals of the science of positive psychology, defining wellbeing, happiness, and quality of life, and how tourism could be accounted as one element linked to all the above. A page is dedicated to memorable tourism experiences and its different dimensions such as hedonic and eudaimonic experiences, and how some of these memorable experiences positively impact travelers’ subjective wellbeing. In the core of chapter 2, travelers’ diverse needs are discussed under: (a) physiological needs such as quality and attractive local food and drinks, physical activity, and adequate sleep on vacations; (b) mental needs including topics such as expressing emotions before, during, and after vacation, causes and fluctuations of emotions; mood regulations on vacations; mindfulness; technology use; stress recovery mechanisms during vacations namely relaxation, detachment, control, mastery; and optimal challenge and flow states for individuals and group of travelers; (c) interpersonal needs of the traveler including interaction with host community, service providers, and other travelers, e.g., joint experiences of romantic partners and family members. Throughout chapter 2, how service providers and experience designers could more effectively monitor, identify, and address these physiological, mental, and social needs are thoroughly discussed. Moreover, evidence and research-based travel tips are offered to general travelers for observing, attending to, appreciating, and enhancing positive emotions during the anticipation phase of a vacation, during the actual trip, on the way home, and up to two weeks post-vacation. A small section at the end of Chapter 2 is devoted to the psychology of holidays and staycations for employees with stressful jobs. Chapter 3 discusses how small occasions during vacations can accumulate and sometimes have long-term psychological effects on travelers. This chapter reviews the psychological of souvenirs, savoring, and photography on vacations. It continues by talking about the concepts of self-awareness, learning, growth, meaning and transformation, related to vacations, using examples. Chapter 3 ends by encouraging travel planners and designers to invest in long-term benefits of vacations.This handbook contains a total of 42 chapters on a range of topics aimed at educating employees at tourism service providers in Iran. This book is in press and distribution, and will be the official source for the national exam for the national travel agency certification in Iran. Topics of this book include the following: tour design and operations, travel psychology, air travel, tour marketing, human resource management, accounting, travel technology, travel start-ups, strategic management, and ethics.
This paper presents the results of an experimental field study, in which the effects were studied of personalized travel feedback on car owners’ car habits, awareness of the environmental impact of their travel choices, and the intention to switch modes. For a period of six weeks, 349 car owners living in Amsterdam used a smart mobility app that automatically registered all their travel movements. Participants in the experiment group received information about travel distance, time, and CO2 emission. Results show that the feedback did not influence self-reported car habits, intention, and awareness, suggesting that personalized feedback may not be a one-size-fits-all solution to change travel habits.
In the Netherlands approximately 2 million inhabitants have one or more disabilities. However, just like most people they like to travel and go on holiday.In this project we have explored the customer journey of people with disabilities and their families to understand their challenges and solutions (in preparing) to travel. To get an understanding what ‘all-inclusive’ tourism would mean, this included an analysis of information needs and booking behavior; traveling by train, airplane, boat or car; organizing medical care and; the design of hotels and other accommodations. The outcomes were presented to members of ANVR and NBAV to help them design tourism and hospitality experiences or all.
Denim Democracy from the Alliance for Responsible Denim (ARD) is an interactive exhibition that celebrates the journey and learning of ARD members, educates visitors about sustainable denim and highlights how companies collaborate together to achieve results. Through sight, sound and tactile sensations, the visitor experiences and fully engages sustainable denim production. The exhibition launches in October 2018 in Amsterdam and travels to key venues and locations in the Netherlands until April 2019. As consumers, we love denim but the denim industry, like other sub-sectors in the textile, apparel and footwear industries, faces many complex sustainability challenges and has been criticized for its polluting and hazardous production practices. The Alliance for Responsible Denim project brought leading denim brands, suppliers and stakeholders together to collectively address these issues and take initial steps towards improving the ecological sustainability impact of denim production. Sustainability challenges are considered very complex and economically undesirable for individual companies to address alone. In denim, small and medium sized denim firms face specific challenges, such as lower economies of scale and lower buying power to affect change in practices. There is great benefit in combining denim companies' resources and knowledge so that collective experimentation and learning can lift the sustainability standards of the industry and lead to the development of common standards and benchmarks on a scale that matters. If meaningful, transformative industrial change is to be made, then it calls for collaboration between denim industry stakeholders that goes beyond supplier-buyer relations and includes horizontal value chain collaboration of competing large and small denim brands. However collaboration between organizations, and especially between competitors, is highly complex and prone to failure. The research behind the Alliance for Responsible Denim project asked a central research question: how do competitors effectively collaborate together to create common, industry standards on resource use and benchmarks for improved ecological sustainability? To answer this question, we used a mixed-method, action research approach. The Alliance for Responsible Denim project mobilized and facilitated denim brands to collectively identify ways to reduce the use of water and chemicals in denim production and then aided them to implement these practices individually in their respective firms.
The maximum capacity of the road infrastructure is being reached due to the number of vehicles that are being introduced on Dutch roads each day. One of the plausible solutions to tackle congestion could be efficient and effective use of road infrastructure using modern technologies such as cooperative mobility. Cooperative mobility relies majorly on big data that is generated potentially by millions of vehicles that are travelling on the road. But how can this data be generated? Modern vehicles already contain a host of sensors that are required for its operation. This data is typically circulated within an automobile via the CAN bus and can in-principle be shared with the outside world considering the privacy aspects of data sharing. The main problem is, however, the difficulty in interpreting this data. This is mainly because the configuration of this data varies between manufacturers and vehicle models and have not been standardized by the manufacturers. Signals from the CAN bus could be manually reverse engineered, but this process is extremely labour-intensive and time-consuming. In this project we investigate if an intelligent tool or specific test procedures could be developed to extract CAN messages and their composition efficiently irrespective of vehicle brand and type. This would lay the foundations that are required to generate big data-sets from in-vehicle data efficiently.
