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Evaluating the effectiveness of tourist advertising to improve the competitiveness of destinations

Author

Listed:
  • Jackson de Souza

    (State University of Bahia (UNEB), Brazil)

  • Luiz Mendes-Filho

    (Federal University of Rio Grande do Norte (UFRN), Brazil)

  • Dimitrios Buhalis

    (Bournemouth University, UK)

Abstract

This study focuses on the evaluation of the tourist destination advertising effectiveness. The destination advertising response DAR model was used to analyze data on the effectiveness of destination promotional campaigns on visitor expenditure, in six trip facets: destination, accommodations, attractions, restaurants, events, and shopping. Independent sample t -tests were conducted to identify any differences in total destination spending among the groups of those visitors influenced for each trip facet. A multiple regression analysis was performed to discriminate the performance of the travel facets expenditures in the estimation of total expenditures. Significant results indicate that the “destination,†“accommodations,†and “restaurants†facets directly influence the total expenditures. Self-planners had the highest variance, explaining in total visitor expenditure compared to the regression analysis results of the other two groups (i.e. travel agencies and online travel agencies). The study also explores how destinations can improve their competitiveness on tourist advertising by using technologies.

Suggested Citation

  • Jackson de Souza & Luiz Mendes-Filho & Dimitrios Buhalis, 2020. "Evaluating the effectiveness of tourist advertising to improve the competitiveness of destinations," Tourism Economics, , vol. 26(6), pages 1001-1020, September.
    • Handle: RePEc:sae:toueco:v:26:y:2020:i:6:p:1001-1020
    DOI: 10.1177/1354816619846748
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    References listed on IDEAS

    as
    1. Park, Sangwon & Nicolau, Juan L., 2015. "Differentiated effect of advertising: Joint vs. separate consumption," Tourism Management, Elsevier, vol. 47(C), pages 107-114.
    2. Katerina Volchek & Anyu Liu & Haiyan Song & Dimitrios Buhalis, 2019. "Forecasting tourist arrivals at attractions: Search engine empowered methodologies," Tourism Economics, , vol. 25(3), pages 425-447, May.
    3. Molinillo, Sebastian & Liébana-Cabanillas, Francisco & Anaya-Sánchez, Rafael & Buhalis, Dimitrios, 2018. "DMO online platforms: Image and intention to visit," Tourism Management, Elsevier, vol. 65(C), pages 116-130.
    4. Li, Yunpeng & Hu, Clark & Huang, Chao & Duan, Liqiong, 2017. "The concept of smart tourism in the context of tourism information services," Tourism Management, Elsevier, vol. 58(C), pages 293-300.
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    Cited by:

    1. Shaowen Li & Shuyun Du, 2021. "An Empirical Study on the Coupling Coordination Relationship between Cultural Tourism Industry Competitiveness and Tourism Flow," Sustainability, MDPI, vol. 13(10), pages 1-14, May.
    2. Liping Fu & Jie Yang & Yongqing Dong & Tong Pei, 2025. "How does information and communication technology promote tourism development? Evidence from the e-commerce pilot city policy in China," Tourism Economics, , vol. 31(2), pages 332-358, March.
    3. Can, Ali Selcuk & Ekinci, Yuksel & Pino, Giovanni, 2021. "Joint brand advertising for emerging heritage sites," Annals of Tourism Research, Elsevier, vol. 91(C).
    4. Skowronek Ewa & Brzeziñska-Wójcik Teresa & Stasiak Andrzej, 2023. "How to Effectively Build the Image of an Emerging Destination," Quaestiones Geographicae, Sciendo, vol. 42(4), pages 143-156, December.
    5. Greta Piliponyte & David Massimo & Francesco Ricci, 2024. "Simulation of recommender systems driven tourism promotion campaigns," Information Technology & Tourism, Springer, vol. 26(3), pages 407-448, September.

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