This thesis examines the integration of behavioural science terminology and artificial intelligence (AI) in tourism marketing. The primary objective of this study is to identify and analyse the potential synergies between these interdisciplinary disciplines and investigate novel strategies for enhancing marketing campaigns and promoting sustainable tourism. The study employs a multi-method approach, including a comprehensive literature review, in-depth interviews with industry experts, and case studies of effective marketing campaigns that engage AI and behavioural science principles.
Tourism is a critical contributor to the global economy, accounting for an estimated 1-in-10 jobs worldwide. In the post-Covid era, the sector has experienced significant growth, leading to intense competition among tourism organizations and marketers seeking innovative and sophisticated marketing strategies to attract and retain customers.
A promising approach to enhance customer experiences and maximize marketing efforts is integrating behavioural science and artificial intelligence (AI) in tourism marketing, optimizing marketing activities' effectiveness. Behavioural science involves systematically studying and researching decision-making processes, cognitive biases, and social influences to understand consumer behaviour better, promote environmentally friendly options, and influence purchasing decision. Similarly, AI technologies, such as machine learning and natural language processing, hold the potential to revolutionize business operations and customer engagement. When applied to tourism marketing, combining behavioural science and AI can improve personalization, targeting, and customer interactions, resulting in higher satisfaction and loyalty.
Table of Contents
Abstract
Acknowledgments
List ofTables
List of Abbreviations
Ontology Matrix
1.1 Background and Context
1.2. Research Aims and Objectives
1.3. Research Gaps
1.4. Scope and Limitations
Literature Review
2.2. The Role ofBehavioural Science Language in Tourism Marketing
2.3. Literature Review from a Behavioural Science Point ofView
2.4. Literature Review on Efficient Harnessing of AI in Tourism Marketing
2.5. Literature Review on the significance of AI and Behavioural Science
2.6. Literature Review on Language and Communication in Tourism Marketing
2.7. Literature Review on theoretical framework for the integration of Behavioural Science Language and AI in Tourism Marketing
2.8. Theoretical Framework
3.1. Research Survey Design
3.2. Data Collection and Analysis
3.3. Ethical Considerations
3.4. Validity and Reliability
Results
4.1. Overview of the Data
4.2. Behavioural Science Language Patterns in Tourism Marketing
4.3. The Role of Artificial Intelligence in Enhancing Behavioural Science Language
4.4. Impact on Customer Behaviour and Tourism Marketing Performance
4.5. Survey Conclusions
5.1. Interpretation of the Findings
5.2. Implications for Tourism Marketing Practice
5.3. Integration ofBehavioural Science Language and Artificial Intelligence
5.4. ComparisonwithPrevious Studies
5.5. Case Studies
5.6. Proposed New Theories
Conclusion
6.1. Summary of the Research
6.2. Contributions to Knowledge
6.3. Practical Implications
6.4. Limitations and Future Research Opportunities
Table of References
Declaration
I, Raymond Tinston, hereby declare that this thesis, submitted in fulfilment of the requirements for the Doctor of Business Administration degree, is entirely my own work. All information, data, and literature sources utilized in the preparation of this thesis have been accurately identified, acknowledged, and cited in accordance with the prescribed guidelines.
Raymond Eric Jonathan Tinston, Candidate
Number: 2019459865
Abstract
This thesis examines the integration of behavioural science terminology and artificial intelligence (AI) in tourism marketing. The primary objective of this study is to identify and analyse the potential synergies between these interdisciplinary disciplines and investigate novel strategies for enhancing marketing campaigns and promoting sustainable tourism. The study employs a multi-method approach, including a comprehensive literature review, in-depth interviews with industry experts, and case studies of effective marketing campaigns that engage AI and behavioural science principles.
This research provides insights into how Al-powered tools and behavioural science can effectively combine to create persuasive and targeted tourism marketing messages by investigating the underlying factors influencing consumer behaviour and decision-making. The findings indicate that combining behavioural science language and artificial intelligence can significantly transform the tourism marketing landscape.
By leveraging AI's predictive capabilities and behavioural science principles, marketers can create more nuanced and individualized communication strategies, ultimately nurturing positive engagement and driving sustainable tourism growth.
Acknowledgments
I would like to acknowledge Natasha, my long-suffering wife, has been an inexhaustible source of encouragement and a great sounding board. She is also responsible for igniting my interest in behavioural science and how it is applied to the use oflanguage in marketing. I am grateful to my son Patric, a fellow learner possessing an exceptional intellect, for keeping my thought processes grounded and pushing me to stay competitive as he swiftly advances in his own educational journey. My supervisor, Prof. Dr. Dr. Sebastian Fuller, Academic Director of Apsley Business School, deserves my deepest gratitude for his good humour, guidance, and forbearance. He has always been helpful, sage, and, most significantly, accessible.
I am also indebted to all my Professors at GBSB, Barcelona who guided me through my MA in Tourism & Hospitality Management including Event Management and Professor Dr. Jorg Beier, Managing Director of ECE - Exhibition, Convention, and Event Management at Cooperative State University, Ravensburg, who awarded me my Exhibition Management Degree (EMD), for guiding me through crucial research areas which has led me directly to this body of research.
As I live and work in the Middle East, it has been challenging to publicly obtain information to help me build this thesis. Fortunately, the advent of OpenAI’s - GPT-4 and Grammarly has been a game changer for this research and making the thesis legible. It has helped me access information not publicly available, plus being able to adapt my language to be more relevant to the complex engineering and science which is applied to Leveraging Behavioural Science Language (BSL) and Artificial Intelligence (AI) in Tourism Marketing. Any generative AI has limitations in terms of output related to human needs and comprehension, but this is changing rapidly, and I greatly respect what the many scientists, linguists, psychologists, and engineers who specialise in this field are achieving. I remain a humble and respectful student of their endeavours, and I am privileged to now be a small part of the process.
Finally, I would like to thank my many new and old friends, clients and acquaintances in our global tourism, hospitality, and event industry. You have all been a part of myjourney toward enlightenment, so I hope that you will find something you can use in this research to benefit you, your business, and your communities.
List of Tables
Abbildung in dieser Leseprobe nicht enthalten
List of Abbreviations
- AI - Artificial Intelligence
- BSL- Behavioural Science Language
- TM - Tourism Marketing
- ML - Machine Learning
- NLP - Natural Language Processing
- NLP - Natural Language Generation
- NLU - Natural Language Understanding
- DL - Deep Learning
- ANN - Artificial Neural Networks
- RNN - Recurrent Neural Networks
- LSTM - Short-Term Long Memory
- CNN - Convolutional Neural Networks
- GAN - Generative Adversarial Networks
- RL - Reinforcement Learning
- B2C - Business to Consumer
- CRM - Customer Relationship Management
- SEM - Search Engine Marketing
- SEO - Search Engine Optimization
- SMM - Social Media Marketing
- UGC - User-Generated Content
- CTR - Click-Through Rate
- KPI - Key Performance Indicator
- ROI - Return on Investment
- A/B - A/B Testing
- CTA - Call to Action
- UX - User Experience
- GDPR - General Data Protection Regulation
- API - Application Programming Interface
- SaaS - Software as a Service
- BI - Business Intelligence
- CRO - Conversion Rate Optimization
- DMO - Destination Marketing Organization
These abbreviations cover various topics related to artificial intelligence, behavioural science, tourism marketing, and related fields.
Ontology Matrix
Below is an ontology matrix to assist with understanding of the main themes of this thesis on Artificial Intelligence and Behavioural Science Language in Tourism Marketing. The matrix categorizes key concepts and relationships within the field, along with their definitions and source references:
Table 1: OntologyMatrix
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Note that this ontology matrix is not exhaustive, but it should provide a solid foundation for understanding and exploring the key concepts and relationships in the domain of AI and Behavioural Science in Tourism Marketing.
Introduction
1.1 Background and Context
Tourism is a critical contributor to the global economy, accounting for an estimated 1-in-10 jobs worldwide. In the post-Covid era, the sector has experienced significant growth, leading to intense competition among tourism organizations and marketers seeking innovative and sophisticated marketing strategies to attract and retain customers.
A promising approach to enhance customer experiences and maximize marketing efforts is integrating behavioural science and artificial intelligence (AI) in tourism marketing, optimizing marketing activities' effectiveness. Behavioural science involves systematically studying and researching decision-making processes, cognitive biases, and social influences to understand consumer behaviour better, promote environmentally friendly options, and influence purchasing decisions (Rutz et al., 2021). Similarly, AI technologies, such as machine learning and natural language processing, hold the potential to revolutionize business operations and customer engagement. When applied to tourism marketing, combining behavioural science and AI can improve personalization, targeting, and customer interactions, resulting in higher satisfaction and loyalty.
Natural Language Processing in A.I. Tourism Marketing
Natural Language Processing (N.L.P.) is an A.I. technology that enables computers to understand, interpret, and generate human language. It plays a critical role in A.I. tourism marketing by providing personalized experiences and recommendations to tourists, enhancing customer service, and improving sentiment analysis.
I. Text Analysis: N.L.P. can extract relevant information from user reviews or feedback, enabling businesses to understand customer preferences and improve their services (Huang, Chang, & Wu, 2020).
II. Chatbots: N.L.P.-powered chatbots can provide real-time customer service, answering frequently asked questions and providing personalized recommendations based on the user's preferences and past interactions (Xu, et al., 2020).
III. Social Media Analysis: N.L.P. can analyse social media posts to identify trends, gauge public sentiment towards a destination or service, and provide businesses with valuable consumer insights (Hassanpour, etal.,2019).
Behavioural Science Language in A.I. Tourism Marketing
Behavioural science language, on the other hand, focuses on the psychological factors that influence the decisions and actions of tourists. A.I. in this context uses behavioural data to predict tourist behaviour and provide personalized marketing strategies.
I. Predictive Modelling: A.I. uses behavioural data to create models that can predict future tourist behaviour, such as their destination choices or spending patterns (Li, et al., 2018).
II. Personalized Marketing: Based on the insights gained from predictive modelling, A.I. can deliver targeted advertisements and personalized offers that match the individual preferences of tourists (Xiang, et al., 2017).
III. User Profiling: A.I. can analyse demographic and behavioural data to create comprehensive user profiles, which can then be used to further refine marketing strategies (Neidhardt & Werthner, 2019).
While both N.L.P. and behavioural science language have their roles in A.I. tourism marketing, they offer different perspectives. N.L.P. is primarily concerned with understanding and generating human language, while behavioural science language is more focused on understanding and predicting human behaviour. Both approaches can be complementary and, when used together, can provide a more comprehensive understanding of tourists, and enable more effective marketing strategies.
Natural Language Processing (N.L.P.) has a rich history that spans over six decades. Here's a timeline that captures some of the key milestones:
- 1950s:BirthofN.L.P.
- 1950: Alan Turing proposed the Turing Test to measure a machine's ability to exhibit intelligent behaviour.
- 1960s:EarlyN.L.P.Systems
- 1961: The first known operating N.L.P. program, the "General Problem Solver" (GPS), was developed by Newell and Simon.
- 1964: Daniel Bobrow's dissertation at MIT showed that computers can understand natural language to solve algebra word problems.
- 1970s:Rule-BasedN.L.P.
- 1970: SHRDLU, an early N.L.P. system that could understand and respond to commands, was created by Terry Winograd.
- 1980s: Rise ofMachine Learning
- 1980: The '80s saw a shift from rule-based systems to machine learning algorithms for N.L.P.
- 1990s:StatisticalN.L.P.
- 1997: IBM's DeepBlue beats World Chess Champion Garry Kasparov, demonstrating the potential of A.I.
- 1997: Message Understanding Conference (MUC-7) highlights the successful use of statistical N.L.P.
- 2000s: Advancements in Machine Learning
- 2001: First shared task on Statistical Machine Translation at the Workshop on Machine Translation.
- 2009: Google starts using statistical machine translation.
- 2010s: Deep Learning Revolution
- 2013: Word2Vec, a group of related models for generating word embeddings, was introduced by a team of researchers led by Tomas Mikolov at Google.
- 2015: The advent of sequence-to-sequence learning accelerates progress in language translation, question answering, and more.
- 2018: OpenA.I.'s GPT (Generative Pre-trained Transformer), an autoregressive language model, demonstrated the potential of transformer-based models forN.L.P.
- 2019: Google's BERT (Bidirectional Encoder Representations from Transformers) pushed the boundaries further by considering context from both directions.
- 2020s: Large-Scale Language Models
- 2020: OpenA.I.'s GPT-4, a transformer-based language model with 175 billion parameters, showcased the power of large-scale language models in generating coherent, contextually relevant sentences.
N.L.P. has evolved from simple rule-based systems to sophisticated machine learning models that can understand and generate natural language with impressive accuracy. The field continues to advance rapidly, with ongoing research focusing on areas such as interpretability, multilingual models, and more efficient training methods.
Behavioural Science Language (B.S.L.) is a relatively new field, especially in the context of A.I., and draws from various disciplines such as psychology, economics, and cognitive science. Here's a brief timeline of the key developments in behavioural science and the application of its principles in A.I.:
- 1879: The birth of psychology
- 1879: Wilhelm Wundt established the first laboratory for psychological research at the University ofLeipzig, marking the birth of psychology as a formal discipline.
- 1900s: Early behavioural studies
- 1904: Ivan Pavlov's work on classical conditioning marked a significant step towards understanding behaviour.
- 1938: B.F. Skinner's work on operant conditioning further expanded our understanding ofbehaviour and its determinants.
- 1950s-1960s: Cognitiverevolution
- 1956: The term "cognitive psychology" was coined by Ulric Neisser, marking a shift from behaviourism to understanding the internal mental processes.
- 1970s: Behavioural economics emerges.
- 1979: Kahneman and Tversky's prospect theory challenged traditional economics, introducing psychological insights into economic theory.
- 1980s-1990s: Early applications of A.I.
- 1980s: Expert systems, an early form of A.I., started incorporating behavioural rules based on expert knowledge.
- 1995: Richard Thaler's work on "mental accounting" further advanced behavioural economics, influencing A.I. approaches to decision-making.
- 2000s: Advancements in machine learning and A.I.
- Early 2000s: The rise of machine learning and big data analytics allowed for more complex analysis ofbehavioural data.
- 2008: Behavioural insights started being used in public policy, with the establishment of the Behavioural Insights Team in the UK.
- 2010s: Integration ofbehavioural science in A.I.
- 2010s: A.I. started incorporating behavioural science language, for example, in predictive modelling and personalization algorithms.
- 2020s: Ongoing research and applications
- 2020s: The field continues to evolve, with ongoing research in behavioural A.I., neuro symbolic A.I., and ethical A.I., among other areas.
The integration of behavioural science principles into A.I. has allowed for a deeper understanding of human behaviour and decision-making, enabling more personalized and effective A.I. systems.
Merging the timelines of Natural Language Processing (N.L.P.) and Behavioural Science Language (B.S.L.) together:
- 1879: The birth of psychology
- Wilhelm Wundt established the first laboratory for psychological research at the University ofLeipzig.
- 1900s: Early behavioural studies and Birth ofN.L.P.
- 1904: Ivan Pavlov's work on classical conditioning.
- 1938: B.F. Skinner's work on operant conditioning.
- 1950: Alan Turing proposed the Turing Test to measure a machine's ability to exhibit intelligent behaviour.
- 1950s-1960s: Cognitive revolution and Early N.L.P. Systems
- 1956: The term "cognitive psychology" was coined by Ulric Neisser.
- 1961: The first known operating N.L.P. program, the "General Problem Solver" (GPS), was developed by Newell and Simon.
- 1964: Daniel Bobrow's dissertation at MIT showed that computers can understand natural language to solve algebra word problems.
- 1970s: Behavioural economics emerges and Rule-Based N.L.P.
- 1970: SHRDLU, an early N.L.P. system that could understand and respond to commands, was created by Terry Winograd.
- 1979: Kahneman and Tversky's prospect theory introduced psychological insights into economic theory.
- 1980s-1990s: Early applications of A.I. and Rise ofMachine Learning
- 1980s: Expert systems, an early form of A.I., started incorporating behavioural rules based on expert knowledge.
- 1980: The '80s saw a shift from rule-based systems to machine learning algorithms for N.L.P.
- 1995: Richard Thaler's work on "mental accounting" advanced behavioural economics, influencing A.I. approaches to decision-making.
- 1997: IBM's DeepBlue beats World Chess Champion Garry Kasparov. Message Understanding Conference (MUC-7) highlights the successful use of statistical N.L.P.
- 2000s: Advancements in Machine Learning and A.I.
- Early 2000s: The rise of machine learning and big data analytics allowed for more complex analysis ofbehavioural data.
- 2001: First shared task on Statistical Machine Translation at the Workshop on Machine Translation.
- 2008: Behavioural insights started being used in public policy, with the establishment of the Behavioural Insights Team in the UK.
- 2009: Google starts using statistical machine translation.
- 2010s: Deep Learning Revolution and Integration ofbehavioural science in A.I.
- 2010s: A.I. started incorporating behavioural science language, for example, in predictive modelling and personalization algorithms.
- 2013: Word2Vec, a group of related models for generating word embeddings, was introduced by a team of researchers led by Tomas Mikolov at Google.
- 2015: The advent of sequence-to-sequence learning accelerates progress in language translation, question answering, and more.
- 2018: OpenA.I.'s GPT (Generative Pre-trained Transformer), an autoregressive language model, demonstrated the potential of transformer-based models forN.L.P.
- 2019: Google's BERT (Bidirectional Encoder Representations from Transformers) pushed the boundaries further by considering context from both directions.
- 2020s: Large-Scale Language Models and Ongoing research and applications
- 2020s: The field continues to evolve, with ongoing research in behavioural A.I., neuro symbolic A.I., and ethical A.I., among other areas.
- 2020: OpenA.I.'s GPT-4, a transformer-based language model with 175 billion parameters, showcased the power of large-scale language models in generating coherent, contextually relevant sentences.
The combined timeline of N.L.P. and B.S.L. shows the parallel progression of these two fields and their eventual convergence in modern A.I. systems. From the early studies of human behaviour and cognition to the development of machine learning and A.I., both N.L.P. and B.S.L. have evolved significantly over the decades. Today, they work hand in hand in many A.I. applications, providing a more comprehensive understanding of human language and behaviour to create more personalized and effective A.I. systems.
The union of Natural Language Processing (N.L.P.) and Behavioural Science Language (B.S.L.) has paved the way for more sophisticated A.I. systems. The insights from behavioural science have helped in understanding the nuances of human language and behaviour, which in turn have been used to make A.I. more adaptable, personalized, and effective. Today, A.I. systems are capable of understanding and even mimicking human language, as well as predicting human behaviour based on past patterns.
As we look to the future, the integration of N.L.P. and B.S.L. in A.I. has immense potential. As A.I. systems become more advanced, they are expected to become better at understanding not just what we say, but also what we mean, taking into account the context, our past behaviour, and even our emotions. This will lead to more intuitive and helpful A.I. systems, whether they're virtual assistants on our phones, recommendation systems on e-commerce websites, or advanced diagnostic tools in healthcare.
Moreover, the insights from behavioural science can also help in making A.I. more ethical and fairer. By understanding how biases can creep into our decisions and actions, we can design A.I. systems that minimize these biases, leading to fairer outcomes.
The progress in both N.L.P. and B.S.L. over the past decades has been remarkable, and the fusion of these two fields in A.I. is a promising area for future research and development.
Natural Language Generation (N.L.G.) is a subfield of Natural Language Processing (N.L.P.) that focuses on generating coherent and contextually relevant text from structured data. N.L.G. is used in a wide array of applications, from report generation to chatbots and virtual assistants.
The main facets ofN.L.G. include:
I. Content Determination: This is the process of deciding what information to include in the generated text. It requires the system to understand the context and user requirements in order to select the most relevant and useful information.
II. Text Planning: Once the content has been determined, the system needs to decide on the structure of the text. This involves organizing the information into a logical sequence and deciding on the overall flow of the text.
III. Sentence Aggregation: This involves grouping related pieces of information into sentences. It requires the system to understand the rules of grammar and syntax, as well as the relationships between different pieces of information.
IV. Lexicalization: This is the process of converting structured data into natural language. The system needs to select appropriate words and phrases to represent the information in a way that is understandable to the user.
V. Referring Expression Generation: This involves deciding how to refer to entities in the text. For example, the system might need to decide whether to use a pronoun or a full noun phrase, or whether to use a definite or indefinite article.
VI. Realization: This is the final step where the system generates the actual text. This involves applying the rules of grammar and syntax to produce a coherent and grammatically correct piece of text.
These facets of N.L.G. work together to transform structured data into human-readable text. Modern N.L.G. systems often use machine learning techniques to perform these tasks more effectively.
Natural Language Understanding (N.L.U.) is a subfield of Natural Language Processing (N.L.P.) that focuses on the comprehension of human language by machines. It involves the use of computational linguistics and machine learning techniques to interpret and make sense ofhuman language in a valuable way.
The main facets ofN.L.U. include:
I. Syntactic Analysis (Parsing): This involves analyzing words in a sentence for grammar and arranging words in a manner that shows the relationship among the words. The sentence "The cat sat on the mat" is parsed to identify "the cat" as the subject, "sat on" as the relation, and "the mat" as the object.
II. Semantic Analysis: This facet is concerned with understanding the meaning of sentences. It involves tasks like word sense disambiguation (understanding the correct meaning of a word based on context), and semantic role labelling (identifying the semantic relationships between the elements of a sentence).
III. Pragmatic Analysis: Pragmatic analysis goes beyond the literal meaning of text to understand the purpose of a statement, the speaker's intent, or the context in which the conversation is happening. For instance, understanding that the sentence "Can you pass the salt?" is a request rather than a question about ability.
IV. Discourse Analysis: This involves understanding how the immediate sentence fits into the larger conversation or text. It includes tasks like anaphora resolution (identifying what a pronoun or a noun phrase refers to) and coherence and cohesion for better understanding of the text.
V. Sentiment Analysis: This facet of N.L.U. involves determining the sentiment or emotion expressed in a piece of text. This can be used to identify whether a review is positive or negative, or to understand the overall mood of a social media post.
VI. Named Entity Recognition (NER): This process involves identifying and classifying named entities in text into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, etc.
These facets ofN.L.U. work together to allow machines to understand, interpret, and generate a response that is relevant to the input.
Natural Language Processing (N.L.P.) is a multidisciplinary field that uses computational methods to enable computers to understand, interpret, and generate human language. N.L.P. combines aspects of computer science, artificial intelligence, and linguistics. The main facets ofN.L.P. include:
I. Natural Language Understanding (N.L.U.): This involves enabling machines to understand and interpret human language. It includes tasks like semantic analysis (understanding meaning), syntactic analysis (understanding grammar), and pragmatic analysis (understanding context and speaker's intent).
II. Natural Language Generation (N.L.G.): This is the process of generating coherent and contextually relevant text from structured data. It involves tasks like content determination (deciding what information to include), sentence planning (organizing the information), and text realization (generating the actual text).
III. Automatic Speech Recognition (ASR): ASR involves converting spoken language into written text. It's used in applications like voice assistants, transcription services, and voice-controlled systems.
IV. Speech Synthesis: This involves generating spoken language from written text. It is used in applications like text-to-speech systems and voice assistants.
V. Machine Translation: This involves automatically translating text from one language to another. It's a complex task that requires understanding the syntax, semantics, and even cultural context ofboth the source and target languages.
VI. Information Retrieval: This involves finding relevant information in response to a query. It's used in search engines and document retrieval systems.
VII. Information Extraction: This involves identifying and extracting structured information from unstructured text, such as named entities, facts, and relationships.
VIII. Sentiment Analysis: This involves determining the sentiment expressed in a piece of text, such as whether a review is positive or negative.
IX. Text Summarization: This involves automatically generating a concise summary of a longer text.
X. Question Answering: This involves building systems that can automatically answer questions posed by humans in a natural language.
These facets of N.L.P. are often interconnected, with advancements in one area often leading to improvements in others.
Table 2: Mediating Role of Customer Engagement in the Relationship between Behavioural Science Language, AI, and Marketing Outcomes
Abbildung in dieser Leseprobe nicht enthalten
Tourism marketing increasingly leverages concepts and techniques from behavioural sciences to exert more significant influence over customer decisions. According to Mikalef et al. (2021), the language ofbehavioural science is often used to craft persuasive messages. Social proof, a psychological phenomenon, can sway consumers' purchasing decisions. AI systems can also utilize behavioural science language to tailor marketing messages and recommendations by analyzing consumer behaviour and preference data to identify individual needs and desires.
Table 3: Recommendations for Tourism Marketing Professionals
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This can be achieved by employing language from behavioural science research. A language that emphasizes the negative consequences of inaction or evokes a fear of missing out can create a sense of urgency in customers (Han et al., 2021). In recent years, Artificial Intelligence (AI) has been transforming various industries, including tourism marketing. In this context, AI utilizes behavioural science language as a crucial element to comprehend customers' needs and preferences better. By applying language from behavioural sciences, AI-powered tourism marketing can provide prospective clients with customized and relevant content catered to their interests.
Table 4: Correlation Analysis between Behavioural Science Language, AI Tools, and Marketing Outcomes
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1.2. Research Aims and Objectives
Aims
- This thesis aims to study the possibilities of merging behavioural science language (BSL) and artificial intelligence (AI) in creating and implementing more successful and focused tourism marketing strategies.
- This study aims to uncover this method's significant components, problems, and benefits and assess its application and effectiveness in various tourism situations. Specifically, the research will focus on the following:
Objectives
- Carry out a detailed literature review to gain an understanding of the current state of knowledge in behavioural science, artificial intelligence, and tourism marketing. This will entail determining essential theories, principles, and methodologies pertinent to the research's focus.
- To gain a deeper understanding of how AI-driven marketing strategies might exploit these strategies, it is essential to investigate the underlying psychological elements and behavioural drivers that influence tourists' decision-making processes and behaviours.
- Conduct research into the currently utilised applications of AI in the tourism industry, focusing on identifying best practices, case studies demonstrating success, and potential roadblocks to implementation.
- Develop a conceptual framework that merges the language of behavioural science and the technologies of artificial intelligence in order to improve tourism marketing techniques, with a particular emphasis on personalisation, targeting, and engagement.
- Conceive several experiments or case studies and carry them out to assess the efficacy of the suggested framework in various tourism marketing situations, such as event marketing, destination marketing, and hospitality marketing. This may involve comparing the performance of marketing initiatives driven by AI and combining language from behavioural research with the performance of marketing campaigns driven by more traditional marketing tactics.
- We are evaluating the ethical issues and potential hazards connected with using AI and behavioural science language in tourism marketing and proposing guidelines to ensure responsible and honest implementation of any recommendations made as a result of this analysis.
- Develop actionable advice for tourism marketers, policymakers, and AI developers regarding successfully including behavioural science language and AI into their marketing campaigns while addressing potential problems and barriers.
- Present the findings, insights, and recommendations that have been gained from this research to contribute to the academic literature on tourism marketing, behavioural science, and artificial intelligence.
This study investigates a variety of facets of the incorporation of AI and linguistic behavioural science into tourism marketing, including the following:
Table 5. Overview of Behavioural Science Theories and Concepts Relevant to Tourism Marketing
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- Theoretical Foundations: To lay a robust theoretical framework for the research, we will first survey the pertinent literature on behavioural science, AI, and tourism marketing. This will allow us to develop a firm theoretical foundation for the study.
- Personalization and Targeting: This thesis examines how artificial intelligence (AI) can evaluate significant consumer behaviour and preferences datasets to provide more personalised and targeted marketing messages informed by behavioural science principles.
- Nudging and Choice Architecture: The project will investigate how behavioural psychology terminology may be applied to marketing for tourism to effectively develop nudges and choice architecture that encourage tourists to engage in desirable and environmentally responsible behaviours.
- Natural Language Processing and Sentiment Analysis: This thesis investigates how AI- powered natural language processing and sentiment analysis can be used to analyse customer feedback and reviews. This will allow tourism marketers to understand consumer preferences better and adjust their marketing strategies accordingly.
- The research will involve the development and execution of creative marketing tactics that integrate the language of behavioural science and AI. This will be followed by evaluating these strategies' effects on consumer decision-making, satisfaction, and loyalty.
Table 6: Key Artificial Intelligence Technologies in Tourism Marketing
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[...]
- Citation du texte
- Ray Tinston (Auteur), 2023, Leveraging Behavioural Science Language and Artificial Intelligence in Tourism Marketing, Munich, GRIN Verlag, https://www.grin.com/document/1371928
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X. -
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Téléchargez vos propres textes! Gagnez de l'argent et un iPhone X.