Digital Popularity and Tourist Arrivals in Labuan Bajo and Raja Ampat: Evaluating Indonesia’s Super Priority Destination Policy

Authors

  • Shanti Pujilestari Sahid University, Food Technology Program, South Jakarta
  • Siti Aminatu Zuhria Sahid University, Industrial Engineering Program, South Jakarta
  • Farida Farida Sahid University, Management Program, South Jakarta

DOI:

https://doi.org/10.58258/fq23ee47

Abstract

This study examines the relationship between digital popularity and tourist arrivals in Labuan Bajo and Raja Ampat, focusing on differences between Indonesia’s Super Priority Destination (DSP) and non-priority destination policies. Monthly Google Trends data and annual tourist arrival statistics covering 2015–2025 were analyzed using geographic distribution analysis, Pearson correlation, and a multilevel modeling approach to compare tourism growth before and after DSP implementation. The results reveal different relationships between digital popularity and tourist arrivals across the two destinations. Labuan Bajo showed strong positive correlations between tourist arrivals and global (r = 0.8712) and domestic digital popularity (r = 0.8912), whereas Raja Ampat showed moderate correlations of 0.6543 and 0.5210, respectively. Tourist arrivals increased by 425.9% in Labuan Bajo and 75.8% in Raja Ampat over the study period. The multilevel model identified a positive and statistically significant interaction between DSP status and the post-policy period (γ₂₁ = 12.800; p < 0.05), indicating different post-policy growth patterns between the destinations. However, the significant pre-policy growth difference suggests that their tourism trajectories were already unequal before DSP implementation. The findings highlight the importance of integrating digital indicators and official tourism statistics in evaluating destination performance. They also suggest that tourism policies should consider destination readiness, accessibility, and balanced development to strengthen the conversion of digital interest into actual tourist visits.
Abstract Views 0PDF Downloads 0

References

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Andjelicus, P. J. (2021). Pariwisata super premium di Labuan Bajo [Super premium tourism in Labuan Bajo]. Warta Pariwisata, 19(2), 8–11. https://doi.org/10.5614/wpar.2021.19.2.03

Atmodjo, E., Lamers, M., & Mol, A. (2017). Financing marine conservation tourism: Governing entrance fees in Raja Ampat, Indonesia. Marine Policy, 78, 181–188. https://doi.org/10.1016/j.marpol.2017.01.023

Balderas, Y. (2022). Normalized data [Data set]. Figshare. https://doi.org/10.6084/m9.figshare.20076047.v1

Behnen, P., Kessler, R., Kruse, F., & Gómez, J. M. (2020). Experimental evaluation of scale and patterns of systematic inconsistencies in Google Trends data. In Experimental design and modeling in the social sciences (pp. 241–260). Springer. https://doi.org/10.1007/978-3-030-65965-3_25

Bhattacharjee, B., Poudel, A., Panta, S., Sharma, S., & Neupane, A. (2024). Forecasting the monthly tourist arrival from India to Nepal: An econometrics modeling approach. In Leveraging emerging technologies for tourism (pp. 121–138). Springer. https://doi.org/10.1007/978-981-96-2548-2_8

Bokelmann, B., & Lessmann, S. (2019). Spurious patterns in Google Trends data—An analysis of the effects on tourism demand forecasting in Germany. Tourism Management, 75, 1–12. https://doi.org/10.1016/j.tourman.2019.04.015

BPS-Statistics of Raja Ampat Regency. (2025). Tourism Statistics of Raja Ampat Regency 2025. Waisai: BPS-Statistics of Raja Ampat Regency. Retrieved from https://rajaampatkab.bps.go.id/id

BPS-Statistics of West Manggarai Regency. (2025). Tourism Statistics of West Manggarai Regency 2025. Labuan Bajo: BPS-Statistics of West Manggarai Regency. Retrieved from https://manggaraibaratkab.bps.go.id/id

Camacho, M., & Pacce, M. (2018). Forecasting travellers in Spain with Google's search volume indices. Tourism Economics, 24(4), 434–448. https://doi.org/10.1177/1354816617737227

Department of Tourism of Raja Ampat Regency. (2025). Tourism Information System of Raja Ampat (SIPARI). Retrieved from https://sipari-rajaampat.id

Department of Tourism, Creative Economy, and Culture of West Manggarai Regency. (2025). Tourism Data of West Manggarai Regency. Retrieved from https://disparekrafbud.manggaraibaratkab.go.id

Dergiades, T., Mavragani, E., & Pan, B. (2018). Google Trends and tourists' arrivals: Emerging biases and proposed corrections. Tourism Management, 66, 108–120. https://doi.org/10.1016/j.tourman.2017.10.014

Dinis, G., Breda, Z., Costa, C., & Pacheco, O. (2019). Google Trends in tourism and hospitality research: A systematic literature review. Journal of Hospitality and Tourism Technology, 10(4), 747–763. https://doi.org/10.1108/JHTT-08-2018-0086

Goeldner, C. R., & Ritchie, J. B. (2011). Tourism: Principles, practices, philosophies (11th ed.). John Wiley & Sons.

Google. (2025). How Google Trends data works. https://support.google.com/trends/answer/4365533

Google Trends. (2025). Exploratory search data for "Labuan Bajo", "Raja Ampat", and "Destinasi Super Prioritas" (2015-2025). Retrieved from https://trends.google.com

Gulati, S., Rathod, M. J., Guntaj, J., & Agarwal, V. (2024). A novel approach for forecasting tourist arrivals using web search data and artificial intelligence. INTI Journal, 2024(1).

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.

Hall, C. M. (2011). Policy learning and policy failure in sustainable tourism governance: From first- and second-order to third-order change? Journal of Sustainable Tourism, 19(4–5), 649–671. https://doi.org/10.1080/09669582.2011.555555

Havranek, T., & Zeynalov, A. (2021). Forecasting tourist arrivals: Google Trends meets mixed-frequency data. Tourism Economics, 27(6), 1269–1284. https://doi.org/10.1177/1354816619879584

Heck, R. H., Thomas, S. L., & Tabata, L. N. (2013). Multilevel and longitudinal modeling with IBM SPSS (2nd ed.). Routledge. https://doi.org/10.4324/9780203701249

Höpken, W., Eberle, T., Fuchs, M., & Lexhagen, M. (2019). Google Trends data for analysing tourists' online search behaviour and improving demand forecasting: The case of Åre, Sweden. Information Technology & Tourism, 21(1), 45–62. https://doi.org/10.1007/s40558-018-0129-4

IBM Corp. (2019). IBM SPSS Statistics for Windows (Version 26.0) [Computer software]. IBM Corp.

Istanto, I., Ali, H., Patiro, S. P. S., & Damarwulan, L. M. (2023). Big data utilization on tourism marketing in post-pandemic recovery era: Future prospect for Indonesia. Jurnal Manajemen Indonesia, 23(2), 143–155. https://doi.org/10.25124/jmi.v23i2.5481

Kartiasih, F., Azhari, & Rinangku, R. E. B. I. (2025). Optimizing tourism development using Google Trends and nighttime light (NTL) data. Jurnal Ekonomi Indonesia, 14(3), 252–268. https://doi.org/10.52813/jei.v14i3.792

Kemenparekraf. (2020). Rencana strategis pengembangan destinasi super prioritas 2020–2024 [Strategic plan for the development of super priority tourism destinations 2020–2024]. Kementerian Pariwisata dan Ekonomi Kreatif Republik Indonesia.

Kusumawardani, R. P., Rahman, R. A., Wibowo, R. P., & Tjahjanto, A. (2024). Understanding fine-grained sentiments of super-priority destination visitors using multi-task learning for extraction of aspect terms and polarity classification on reviews. Procedia Computer Science, 234, 602–613. https://doi.org/10.1016/j.procs.2024.01.102

Li, X., Pan, B., Law, R., & Huang, X. (2017). Forecasting tourism demand with composite search index. Tourism Management, 59, 57–66. https://doi.org/10.1016/j.tourman.2016.07.005

Marine Conservation Area Management Unit of Raja Ampat. (2025). Raja Ampat Marine Protected Area (KKP Raja Ampat). Retrieved from https://kkprajaampat.id

Menard, S. W. (2002). Longitudinal research (Vol. 76). Sage.

Neuman, W. L. (2014). Social research methods: Qualitative and quantitative approaches (7th ed.). Pearson.

Nuti, S. V., Wayda, B., Ranasinghe, I., Wang, S., Dreyer, R. P., Chen, S. I., & Murugiah, K. (2014). The use of Google Trends in health care research: A systematic review. PLoS ONE, 9(10), e109583. https://doi.org/10.1371/journal.pone.0109583

Pratt, S. (2015). The economic impact of tourism in SIDS. Annals of Tourism Research, 52, 148–160. https://doi.org/10.1016/j.annals.2015.03.005

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Sage Publications.

Redha, M. R. M., Purnomo, E. P., & Khairunnisa, T. (2025). Sustainable tourism management strategy using dynamic system approach in Indonesia's super priority destinations. IOP Conference Series: Earth and Environmental Science, 1566(1), 012004. https://doi.org/10.1088/1755-1315/1566/1/012004

Rovetta, A. (2021). Reliability of Google Trends: Analysis of the limits and potential of web infoveillance during COVID-19 pandemic and for future research. Frontiers in Research Metrics and Analytics, 6, Article 670226. https://doi.org/10.3389/frma.2021.670226

Sousa, S. R., Soares, S. R., Moreira, N., Severis, R. M., & de Santa-Eulalia, L. A. (2021). Internal normalization procedures in the context of LCA: A simulation-based comparative analysis. Environmental Modeling & Assessment, 26(3), 271–281. https://doi.org/10.1007/s10666-021-09767-5

Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). Sage Publications.

Starosta, K., Budz, S., & Krutwig, M. (2019). The impact of German-speaking online media on tourist arrivals in popular tourist destinations for Europeans. Applied Economics, 51(14), 1558–1573. https://doi.org/10.1080/00036846.2018.1527463

Suharyanto, Fauzi, A., Adrianto, L., & Wahyudin, Y. (2024). Developing marine tourism policy pathways: The case of super priority marine tourism destinations in Labuan Bajo, Indonesia. International Journal of Sustainable Development and Planning, 19(10), 3809–3819. https://doi.org/10.18280/ijsdp.191010

Teguh, F. (2020). Destinasi pariwisata bahari berstandar internasional: Prospek, pembelajaran, tata kelola dan model bisnis [International standard marine tourism destinations: Prospects, lessons learned, governance, and business models]. Kementerian Pariwisata dan Ekonomi Kreatif Republik Indonesia.

Utari, B. A., Arsallia, S., Ramdani, M. A., Rahmaftria, F., Belgiawan, P. F., Dirgahayani, P., & Nasution, R. A. (2024). Tourist destination choice on five priority destinations of Indonesia during health crisis. Journal of Destination Marketing & Management, 32, Article 100880. https://doi.org/10.1016/j.jdmm.2024.100880

West, B. T., Welch, K. B., & Galecki, A. T. (2022). Linear mixed models: A practical guide using statistical software. Chapman and Hall/CRC. https://doi.org/10.1201/b17198

Wulandari, S., Sutrisno, J., Yusuf, E. S., & Komalawati, K. (2024). Developing industry clusters based on 4As to support agritourism competitiveness. IOP Conference Series: Earth and Environmental Science, 1364(1), 012033. https://doi.org/10.1088/1755-1315/1364/1/012033

Downloads

Published

2026-11-11

How to Cite

Digital Popularity and Tourist Arrivals in Labuan Bajo and Raja Ampat: Evaluating Indonesia’s Super Priority Destination Policy. (2026). Business Management, 5(4), 1283-1300. https://doi.org/10.58258/fq23ee47