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The purpose of this paper is to describe the core of an advanced destination management system, which uses a series of data matching techniques and business analytics.

This study first proposes the conceptual framework for an advanced destination management system and then illustrates the core components of the proposed system using real-world data from Northern Indiana. In this study, search interests, devices used and other forms of website use derived from online clickstream data were merged with visitor demographic and tripographic information obtained from an online survey to develop an analytic model used to describe the core market structure.

Key demographic factors (e.g. gender, age and income), search interests, referred websites, the number of total sessions, temporal aspects and spatial aspects of visitor travel provide essential information defining the structure and dynamics of the visitor marketing in Northern Indiana.

The process and data used in this study provide a “proof of concept” for developing highly personalized marketing systems, which can substantially improve the competitiveness of a destination management organization.

Destination recommender systems need to become truly human‐centric in their design and functionality. This requires a profound understanding of human interactions with technology as well as human behavior related to information search and decision‐making in the context of travel and tourism. This paper seeks to review relevant theories that can support the development and evaluation of destination recommender systems and to discuss how quantitative research can inform such theory building and testing.

Based on a review of information search and decision‐making literatures, a framework for the development of destination recommender systems is proposed and the implications for the design and evaluation of human‐centric recommender systems are discussed.

A variety of factors that influence the information search and processing strategies that influence interactions with a destination recommender system are identified. This reveals a great need for data‐driven models to inform recommender system processes.

The proposed framework provides a basis for future research and development in the area of destination recommender systems. The paper concludes that the success of a specific destination recommender system will depend largely on its ability to anticipate and respond creatively to transformations in the personal and situational needs of its users. Such system intelligence needs to be based on empirical data analyzed with sophisticated quantitative methods. The importance of recommender systems in tourism marketing is also discussed.

Tourism is leisure commodified. As a commodity, the actuality of tourism has been suggested as less important than its expressive representations. What is depicted or not depicted in destination image advertising, and on whose authority it is selected, involves a more complex question of what comprises the destination and who has the power to define its identity. Recently, there has been a shift in the promotional role of destination marketing organizations where it has broadened to include product (attraction) development and industry relations. This role extension enables a matching of authoritative voice and reconstructed reality in order to verify official imaging. The symbolism and meaning of destination image can be interpreted through a deconstructionist analysis of tourist destination advertising. This paper explores significance of tourism promotion on tourist culture as it packages touristic representations.

The purpose of this study is to estimate the extent (mean and range) of non‐response bias in online travel advertising conversion studies for 24 destinations located throughout the USA.

The method uses two weighting procedures (i.e. post stratification and propensity score weighting) to estimate the extent of non‐response bias by adjusting the estimates provided by respondents to more closely represent the total target sample.

The results of this analysis clearly indicate that the use of unweighted data to estimate advertising effectiveness may lead to substantial over estimation of conversion rates, but there is limited “bias” in the estimates of median visitor expenditures. The analyses also indicate that weighting systems have substantially different impact on the estimates of conversion rates.

First, the likelihood to answer a survey varies substantially depending on the degree of the familiarity with the mode (i.e. paper, telephone versus internet). Second, the competition‐related variables (i.e. the number and competitiveness of alternative nearby destinations) and various aspects of the campaign (i.e. amount of investment in a location) should be considered.

This study of 24 different American tourism campaigns provides a useful understanding in the nature (mean and range) of impact of non‐response bias in tourism advertising conversion studies. Additionally, where there is difficulty obtaining a reference survey in the advertising study, the two weighting methods used in this study are shown to be useful for assessing the errors in response data, especially in the case of propensity score weighting, where the means to develop multivariate‐based weights is straightforward.

The study aims to review how the use of technology enhances the authentic tourist experience. Technology and digitalization have enhanced tourist experiences. However, critiques comment on its ability to over-commercialize activity and lack of authenticity. Thus, there is a need to synthesize knowledge of technology usage to increase authentic tourist experience.

The study carries out a bibliometric review of the studies focusing on the use of technology in enhancing tourist experiences. Two hundred journal articles, published between 1997 and 2023 were retrieved from the Web of Science (WoS) database to carry out descriptive and network analysis using the Gephi, VOSviewer and Science of Science (Sci2) software. The components of authentic tourism experience are identified from the literature through a content analysis.

The findings of the study are broadly classified into two: first, the most frequently used keywords in the study include tourist experience and satisfaction, co-creation, virtual reality, smart tourism, technology, authenticity and heritage tourism. Second, the five major themes studied in the topic include virtual reality and tourist experience; media, tourist experience and encounters; technology, smart tourism and tourist experience; digital transformation, social media and tourist experience; and virtual reality and tourist experience which are still relevant in the literature because of the presence of study gaps.

The findings are used to develop a conceptual framework for the role of technology in enhancing authenticity in tourism typologies where authenticity is critical.