This research seeks to analyze how the use of smart data analytics solutions by tourism stakeholders can promote the prediction of responsible customer behavior. After a theoretical and conceptual framework, the theoretical study is based on an exploratory literature review. It revealed the adoption of a range of smart data analytics solutions by businesses and tourist destinations in order to better predict attitudes and concrete responsible actions reflecting the eco-responsible behavior of tourists. Despite the inherent limitations of the approach, the results show multiple good practices that can be adopted by industry players to better understand, anticipate, and even guide the responsible behavior of their customers and prospects.
TopIntroduction
In the current context of the abundance of big data on the customer or even on the prospect, companies are obliged to ensure the proper management for judicious exploitation to make relevant marketing decisions and be able to adapt continuously with the evolution of the needs of the customer, its attitudes and behaviours to update its value proposition, satisfy and streamline its customers, stand out from competitors, improve its performance, etc. Tourism is one of the sectors most affected by the digital revolution and digital transformation (Pencarelli, 2020 ; Lazić et al., 2023).
The rise of digital technologies such as data analytics, artificial intelligence (AI) and digital marketing has profoundly transformed tourism behaviours, marketing strategies and business models in the tourism industry. Tourists now have access to an unprecedented amount of information about tourist destinations, hotels, attractions, restaurants, and experiences.
In this sense, various studies have shown that the use of intelligent customer data management solutions is now essential to predict consumer behaviours, particularly in terms of responsible consumption, through dynamic interaction between responsible attitudes and behaviours.
The prediction of tourist behaviour is very complex given the specificities of the tourist activity (complexity and integration of the tourist product vs volatility of the activity). A second difficulty is added in terms of the unpredictability of tourist behavior marked by contradictions inherent to its ethical and responsible commitment and the maximization of pleasure and entertainment).
Also, research on the issue finds justification in many gaps that it tries to address, in this case, contribute to improving the understanding of the effectiveness of AI Solutions in predicting responsible customer behaviours (Puntoni et al., 2021) and reconsider the role of ethics in the use of AI Solutions to predict responsible customer behaviour (Martínez et al., 2022).
To what extent does the adoption of AI solutions by tourism businesses help improve the effectiveness of predicting customer needs? This is the central question to which our research attempts to provide answers.
To address this issue, we plan to conduct an exploratory literature review by examining results from a selection of empirical studies that have addressed the relationship between AI methods and the effectiveness of predicting responsible guest behaviours in tourism establishments.
Certainly, due to the innovative nature of the theme, the exploratory approach of the literature review is the most recommended. However, this methodological choice is not perfect and remains open to criticism. Indeed, the approach promotes the identification of emerging trends in the use of smart data and the analysis of responsible customer behavior in the tourism sector (Snyder, 2019; Munn et al., 2018), helps to better map research areas and thus help guide future research in this area (Tricco et al., 2018; Peters et al., 2015), identify gaps and guide researchers towards promising new avenues of research on the analysis of intelligent data and the prediction of responsible customer behaviour in tourism (Levac et al., 2010; Munn et al., 2018). While other authors highlight the lack of methodological rigor of the approach in terms of reliability and validity of the conclusions drawn (Tricco et al., 2018; Peters et al., 2015), or limited generalization deficit due to potential lack of depth and detail on specific aspects of smart data analysis and prediction of responsible customer behavior in tourism (Munn et al., 2018; Levac et al., 2010), or significant risk of bias (Tricco et al., 2018; Peters et al., 2015).
Our chapter is structured in three parts, highlighting successively the theoretical and conceptual framework of our research, the methodological approach adopted, the results of the study and finally, the discussion of the results and the contributions of the research.