Robot How Excites Tourism Industry Development
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| Author | : Johnny Ch LOK |
| Publisher | : |
| Total Pages | : 153 |
| Release | : 2020-10-16 |
| Genre | : |
| ISBN | : |
The future of retail for consumer industries' (AI) big data gather challenges are similar to future travelling industry's entertainment consumption challenges. Another challenge of (AI) big data gather is that how to shape the consumer behavior to let business owner to feel or know oe predict. It means that how it express it's conclusion or opinion for every consumer behavior after it had gather all big data in any data gather period, e.g. three months, half year or one year consumer shopping model data gather period.Because every kind of industry, consumers will continue to demand price and quality change , with a wide range of convenient fulfilment options among of different kinds of products or services supply. Overall, the (AI) big data gather procedure gives opinion concerns every time retail experience will become more exciting, simple and convenient, depending on the consumer's ever-changing needs. So, I believe that (AI) big data gather every conclusion or result will be different, due to consumer's price and quality demand will often change to every kind of product or service supply in retail industry. So, how to shape (AI) big data gathering's analytical conclusion or result more clear. I shall recommend organizations need to build great understanding of and a stronger connection to increasingly empowered consumers before they plan and implement how to apply (AI) big data gather tool to predict consumer behavior as below:Firstly, (AI) is empowered by technology, the consumer is redefining value. The traditional measures of cost, choice and convenience are still relevant, but not control and experience are also important. Globally, consumers have access to more than 2 billion different products choice by a wide range of traditional competitors and dynamic new entrants, all experimenting with new business models and methods of client engagement. As choice increases, loyalty becomes more difficult familiarity and the consumer becomes more empowered. Businesses will have no choice and constantly innovate and disrupt themselves by meeting new technologies of high standards and expectations of consumers. So, (AI) data gather tool will need to follow different target group of consumers' needs to follow their different kinds of product design or style choice preferable to gather data in order to conclude the different target groups of consumer behavior to give opinion more clear and accurate to let businessmen to understand more clear how its customers' behavioral choice trend in the future half month, even to two years period.Secondly, businessmen need to adopt changing technologies rapidly. Technology will be the key driver of this retail industry. Industry participants will only success if they have a clear prediction to focus on how to using technology to increase the value added to consumers. They must , however, do so will I realistic assessment of their costs and benefits. Hence, (AI) big data gather technological tools will need to design to help them to gather data efficiently by these ways, such as the internet of things ( IOT), artificial intelligence (AI) machine learning, augmented reality (AR)/virtual reality (VR), digital traceability. So, future (AI) big data gather tool are predicted to be most influential customer behavioral positive emotion changing tool for retail , due to their widespread applications , ability to drive efficiencies and impact on labor in order to impact consumer behavior changing effort from negative emotion to positive.
| Author | : Johnny Ch LOK |
| Publisher | : |
| Total Pages | : 758 |
| Release | : 2021-07-09 |
| Genre | : |
| ISBN | : |
⦁ How (AI)helps tourism industry to reduce cost ? In the future, robotics or artificial intelligence systems dominate the tourism industry, they may include chat bots, travel assistants, and service robots to any travel agents service providers. Their roles may include these several aspects to any travel agents service providers. They may include searching and being inspired, discovering , planning and booking , refining and improving, how excites the tourist experiencing to the journey service arrangement, reflecting and how raising his/her tourism leisure enjoyment feeling during the whole tourism journey from customer intelligence platform service. However, future travel agent intermediaries as well as inventory providers will start to focus on their quality of offering and journey arrangement service. Thus, they invest comparable less in operational excellence, e.g. concentration of digital systems at the search and booking phase and robots at the experience phase of the customer journey is almost self-explanatory, journey searching, planning and booking via internet is nowadays the norm. This, these are no hybrid systems necessary. However, during the experience phase, in which the traveler leaves the realms of the digital world, physical interaction, which can only be delivered by robots, regains importance. Since char bots , travel assistants as well as service robots are all representatives of systems used at the travelling customer interface. However, supposing when the travelling agent decides to install (AI) systems, can not be explained by the trend of a rebound on customer journey experience. However, during the expert discussions, it was revealed that most of the tourism industry does not consider technology a core competency and often lack the capacities to self-develop and install such (AＩ) systems. However, it may also seem that AI can impact automatization tasks to tourism industry, chat bots or service robots are typical representatives in which artificial intelligence is used to automate a task previously done by humans. This can be called the automatization effect. Nevertheless, automatization through (AI) systems should not be equated with other forms of technology utilization which are called automatization too. Nowadays, the automatization through self-serving terminals, whether at airports through self-check terminals or at hotels through self check in systems is quite common in the tourism service industry. However, in travel agents service industry sector, the (AI) systems process itself is not automated, but rather changed from the airport check in / out organizations to the travel agent journey arrangement customer service organizations. Artificial intelligence on the other side truly automates these process. So that neither a human at the airport or travel agent journey service arrangement organizations nor the travelling customer much do it. A recent example are (AI) supermarkets , such as the " Amazon Go" store in which can artificial intelligence system, which can recognize persons and objects, tracks people and the objects which they put in their shopping-bad and automatically bills their purchases on the customers bank account (Ｓｔａｒｋ， ２０１７)． Another similar system is imaginable at hotels and airports in which a face-recognition system recognizes the arrival of guests and automatically checks them in. JetBlue is already testing such a system a Boston airport (Ｅｎｔｉｓ， ２０１７)．
| Author | : Johnny Ch Lok |
| Publisher | : |
| Total Pages | : 246 |
| Release | : 2020-06-21 |
| Genre | : |
| ISBN | : |
⦁How (AI)helps tourism industry to reduce cost ?In the future, robotics or artificial intelligence systems dominate the tourism industry, they may include chat bots, travel assistants, and service robots to any travel agents service providers. Their roles may include these several aspects to any travel agents service providers. They may include searching and being inspired, discovering, planning and booking, refining and improving, how excites the tourist experiencing to the journey service arrangement, reflecting and how raising his/her tourism leisure enjoyment feeling during the whole tourism journey from customer intelligence platform service.However, future travel agent intermediaries as well as inventory providers will start to focus on their quality of offering and journey arrangement service. Thus, they invest comparable less in operational excellence, e.g. concentration of digital systems at the search and booking phase and robots at the experience phase of the customer journey is almost self-explanatory, journey searching, planning and booking via internet is nowadays the norm. This, these are no hybrid systems necessary. However, during the experience phase, in which the traveler leaves the realms of the digital world, physical interaction, which can only be delivered by robots, regains importance. Since char bots, travel assistants as well as service robots are all representatives of systems used at the travelling customer interface. However, supposing when the travelling agent decides to install (AI) systems, can not be explained by the trend of a rebound on customer journey experience. However, during the expert discussions, it was revealed that most of the tourism industry does not consider technology a core competency and often lack the capacities to self-develop and install such (AＩ) systems.However, it may also seem that AI can impact automatization tasks to tourism industry, chat bots or service robots are typical representatives in which artificial intelligence is used to automate a task previously done by humans. This can be called the automatization effect. Nevertheless, automatization through (AI) systems should not be equated with other forms of technology utilization which are called automatization too. Nowadays, the automatization through self-serving terminals, whether at airports through self-check terminals or at hotels through self check in systems is quite common in the tourism service industry. However, in travel agents service industry sector, the (AI) systems process itself is not automated, but rather changed from the airport check in / out organizations to the travel agent journey arrangement customer service organizations. Artificial intelligence on the other side truly automates these process. So that neither a human at the airport or travel agent journey service arrangement organizations nor the travelling customer much do it.
| Author | : Johnny Ch LOK |
| Publisher | : |
| Total Pages | : 153 |
| Release | : 2020-11-25 |
| Genre | : |
| ISBN | : |
Artificial intelligence refers to complex in vehicle market and travelling entertainment market which is very seem to be applied to predict consumer behaviors.(AI) machine learning that posses the same characteristics of human intelligence and that have all our sense, all our reason and think just like human vehicle buyer who prefer vehicle purchase choice or travelling consumer who prefer travelling package or travelling destination and airline choice. Besides, machine learning is the practice of using algorithms to collect and examine data, learn from it, and then make a determination or prediction about something in the world. So, it can be attempted to gather data concerns that travelling consumer past travelling destination choice and air ticket price choice and different travelling package, e.g. high, middle, or low class hotel and foods supply and entertainment places choice in their past travelling journeys.The machine is " trained" using large amounts of data and algorithms that give it the ability to learn how to automatically perform a task with increasing accuracy. Otherwise, deep learning is primarily based on artificial neural networks inspired by our understanding of the biology of human's brains. Thus, (AI) big data can gather all these past traveler consumption behavioral choice data to make reference to analyze whether how many travelers will choose to go to the specific travelling destination in any time by the past traveler number record to different travelling destinations, then it can gather the past air ticket sale price to different destinations and past travelling package design to different destinations in order to analyze whether it is the cheap airline ticket price factor or attractive travelling package factor or attractive travelling entertainment etc. in order to predict which factor is the most potential influential factor to they choose to go to the destination to travel in different time within one year. Then, traveler agent or airline can collect these big data to judge how to design their package to attract travelers to go to anywhere to travel or what the main factor influence most of them to choose to visit the destination to travel.For example, travel agents or airlines can apply "Deep learning" breaks down tasks in ways that enables machines to assist them to predict when travelling consumer choice will be changed and why their travelling choice will change and how their travelling choice will change with increasingly complex tasks. So, such as why (AI) technology can be applied to predict how travelling consumer behavior changes to bring to judge whether anywhere will be many travelling consumers who will prefer to choose travelling hot destinations next year or next month. Then, travel agents and airlines can gather overall past travelling consumer data to analyze and conclude the more accurate prediction of different travelling destinations to the number of traveler. Then, they can choose how much air ticket price is more reasonable to charge to the travelling destination or how to design the travelling package which can bring more attractive to the prediction number of different travelling destination travelers in order to achieve to raise the different travelling destination number next year.Thus, (AI) big data machine learning can help airlines or travel agents to solve how to design any attractive travelling package challenge. A travelling package is both one of the most important and carefully considered travelling entertainment consumption the majority of travelling people will ever make in their lifetime at least one travelling time.
| Author | : Craig Webster |
| Publisher | : Zangador |
| Total Pages | : 31 |
| Release | : 2020-02-17 |
| Genre | : Business & Economics |
| ISBN | : 9549278689 |
This report presents part of the results of the first global study on customers’ perceptions on robots in travel, tourism, and hospitality implemented by the authors in 2018-2019. Specifically, the report presents the methodology of the research and elaborates on the key results regarding the activities that customers perceive as appropriate for robotisation, their preferences to be served by robots, and their willingness-to-pay for robot-delivered services. Finally, the report provides recommendations to travel, tourism, and hospitality companies how to implement successfully robotic technologies in their operations.
| Author | : Stanislav Ivanov |
| Publisher | : Emerald Group Publishing |
| Total Pages | : 361 |
| Release | : 2019-10-14 |
| Genre | : Business & Economics |
| ISBN | : 1787566897 |
Using a combination of theoretical discussion and real-world case studies, this book focuses on current and future use of RAISA technologies in the tourism economy, including examples from the hotel, restaurant, travel agency, museum, and events industries.
| Author | : JOHN. LOK |
| Publisher | : |
| Total Pages | : 120 |
| Release | : 2022-02-23 |
| Genre | : |
| ISBN | : |
| Author | : Johnny Ch LOK |
| Publisher | : |
| Total Pages | : 86 |
| Release | : 2021-05-17 |
| Genre | : |
| ISBN | : |
What is push and pull factors to influence any traveler who chooses where is whose preferable travelling destination ?How to apply (AI) big data to predict individual traveler's behavioral intention of choosing a travel destination? Understanding why people travel and what factors influence their behavioral intention of choosing a travel destination is beneficial to tourism planning and marketing. In general, an individual's choice of a travel destination into two forces. The first force is the push factor that pushes an individual away from home and attempt to develop a general desire to go somewhere, without specifying where that may be. The other force is the pull factor that pull an individual toward in destination, due to a region-specific or perceived attractiveness of a destination. The respective push and pull factors illustrate that people travel because who are pushed by whose internal motives and pulled by external forced of a destination. However, the decision making process leading to the choice of a travel destination is a very complex process. For example, a Taiwanese traveler who might either choose new travel destination of Hong Kong or another old travel Asia destinations again or who also might choose any one of Western country, as a new travel destination. The travel agents can predict where who will have intention to choose to travel from whose past behavior and attitude, subjective and perceived behavioral control model. When (AI) big data gather past every country traveler number who chose to go to which countries to travel in order to judge where destinations will be the country travelers' travelling choice destinations in the future.The factors influence where is the traveler choice, include personal safety, scenic beauty, cultural interest, climate changing, transportation tools, friendliness of local people, price of trip, trip package service in hotels and restaurants, quality and variety of food and shopping facilities and services etc. needs. So, whose factors will influence where is the individual travel's choice. It seems every traveler whose choice of travel process, will include past behavior. e.g. travelling experience, travelling habit, then to choose the best seasoned travelling action to satisfy whose travel needs. This process is the individual traveler's psychological choice process, who must need time to gather information to compare concerning of different travel packages, destination scene, climate change, transportation tools available to the destination, air ticket price etc. these factors, then to judge where is the best right destination to travel in the right time. Hence, (AI) big data can gather past different countries' climate changing data, transportation tool changing data, destination scene environment changing etc. different data to give opinions to travelling businesses whether any country's these above factors will influence about how many traveler number will be increase or decrease in the future.2.3Why can expectation, motivation and attitude factor influence travelling behavior?Social psychology is concerned with gaining insight into the psychological of socially relevant behaviors and the processes. For instance, on a global level bad influence to global warming, it influences some countries extreme cold or hot bad climate changing occurrence, then it ought influence some travelers' behavioral decision to change their mind to choose some countries to go to travel at the moment which do not occur extreme hot or cold climate ( temperature). e.g. above than 40 degree in summer or below than 0 degree in winter. Due to the extreme climate changing environment in the countries, it will cause them to feel uncomfortable to play during their trips. So, the global warming causes to climate changing factor will influence the numbers of travel consumption to be reduced possibly.
| Author | : Vicky Katsoni |
| Publisher | : Springer Nature |
| Total Pages | : 1032 |
| Release | : 2022-04-27 |
| Genre | : Business & Economics |
| ISBN | : 3030924912 |
This book features the proceedings of the 8th International Conference of the International Association of Cultural and Digital Tourism (IACuDiT). Held on the Hydra Island in Greece in September 2021, the conference's lead theme was “Transcending Borders in Tourism through Innovation and Cultural Heritage”. Highlighting the contributions made by numerous writers to the advancement of tourism research, this book presents a critical academic discourse evolving tourism products and services. It also deals with strategies that help stimulate economic innovation and growth, and promote knowledge transfer. Selected chapters also deal with innovation, creativity, and change management in all aspects of tourism, culture, and heritage. A crucial focus is also placed on embracing ICT as a powerful development tool along with strategies and campaigns for smart tourism. It offers numerous examples from the whole spectrum of cultural and heritage tourism, including art, innovations in museum interpretation and collections management, cross-cultural visions, gastronomy, film tourism, dark tourism, sports tourism, and wine tourism.
| Author | : Stephen J Marshall |
| Publisher | : Stephen J Marshall |
| Total Pages | : 215 |
| Release | : 2022-06-18 |
| Genre | : Technology & Engineering |
| ISBN | : |
Since the 18th century, automation has driven progress in manufacturing, transforming the workplace and contributing to the massive increase in living standards enjoyed by most people in the developed world over this period. A robot is a specific example of automation technology, one that can perform a complex sequence of actions automatically without manual intervention. Originally confined to manufacturing, robotic technology is now entering many other areas of our lives, from our domestic appliances to medical devices, and recent developments in self-driving vehicles are bringing some of the most sophisticated examples of the technology onto our roads. The Story of the Robot examines the history of the robot within the wider context of automation, thereby allowing the reader to fully appreciate the origins and evolution of robotic systems. It begins by tracing the historical roots of robotics through the development of automata and mechanical toys. The next four chapters guide the reader on a whistle-stop tour across more than 300 years of automation history. Chapter 6 charts the rise of humanoid robots, beginning with their first appearance in science fiction stories to their physical realisation at the end of the 20th century. In Chapter 7, the use of autonomous control technology in mobility applications is surveyed, from the earliest self-steering vehicles to autonomous robots and self-driving cars. The final chapter brings the story up to date with the new industrial revolution now taking place as a result of the application of digitalisation and interconnectivity technologies to manufacturing. Each chapter contains a selection of carefully chosen images and includes a further reading list for those readers who wish to delve deeper into the technical details.
