Tourism demand forecasting models: a review of current studies
DOI:
https://doi.org/10.46661/rev.metodoscuant.econ.empresa.6191Keywords:
forecast, demand, tourism, review, bibliometrixAbstract
Tourism has gained vital importance in recent times as it is one of the economic activities that brings the greatest benefits to a country, both in the social, economic and environmental spheres. Consequently, demand forecasting models in the sector are adequate tools that support decision-making. In this sense, several authors have made important contributions in the field of science that help improve tourism management. This leads to the objective of analyzing current trends in tourism forecasting models using the R bibliometrix tool, covering 254 research articles published between 2017 and 2021. The main results show that the models for forecasting tourism demand they are constantly evolving and there is no single model that works well for all situations. It can also be seen that due to the COVID-19 pandemic, the forecast models for that year were unusable; however, it was the year with the most publications. Similarly, this research allowed to identify the main countries, scientific journals and authors who address the study of tourism demand.
Downloads
References
Aliyev, R., Salehi, S., & Aliyev, R. (2019). Development of fuzzy time series model for hotel occupancy forecasting. Sustainability (Switzerland), 11(3). 793. https://doi.org/10.3390/su11030793
Andreeski, C., & Mechkaroska, D. (2020). Modelling, forecasting and testing decisions for seasonal time series in tourism. Acta Polytechnica Hungarica, 17(10), 149-171. https://doi.org/10.12700/APH.17.10.2020.10.9
Antolini, F., & Grassini, L. (2019). Foreign arrivals nowcasting in Italy with Google Trends data. Quality and Quantity, 53(5), 2385-2401. https://doi.org/10.1007/s11135-018-0748-z
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
Bamel, U., Pereira, V., Del Giudice, M., & Temouri, Y. (2020). The extent and impact of intellectual capital research: A two decade analysis. Journal of Intellectual Capital.23 (2). 375-400. https://doi.org/10.1108/JIC-05-2020-0142
Bayliss, C. (2021). Machine learning based simulation optimisation for urban routing problems. Applied Soft Computing, 105. 107269. https://doi.org/10.1016/j.asoc.2021.107269
Bi, J.-W., Li, H., & Fan, Z.-P. (2021). Tourism demand forecasting with time series imaging: A deep learning model. Annals of Tourism Research, 90. 103255. https://doi.org/10.1016/j.annals.2021.103255
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
Cahlik, T. (2000). Comparison of the maps of science. Scientometrics, 49(3), 373-387.
https://doi.org/10.1023/A:1010581421990
Callon, M., Courtial, J.-P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry. Scientometrics, 22(1), 155-205. https://doi.org/10.1007/BF02019280
Camón Luis, E., & Celma, D. (2020). Circular Economy. A review and bibliometric analysis. Sustainability, 12(16), 6381. https://doi.org/10.3390/su12166381
Campra, M., Riva, P., Oricchio, G., & Brescia, V. (2021). Bibliometrix analysis of medical tourism. Health Services Management Research. 32(5). 172-188. https://doi.org/10.1177/09514848211011738
Chen, J. L., Li, G., Wu, D. C., & Shen, S. (2019). Forecasting Seasonal Tourism Demand Using a Multiseries Structural Time Series Method. Journal of Travel Research, 58(1), 92-103. https://doi.org/10.1177/0047287517737191
Chernbumroong, S., Nunti, C., & Somboon, K. (2020). Forecasting Chinese Tourism Demand for Thailand: Using Markov Switching Autoregressive Model. J. Phys. Conf. Ser., 1651(1). 012027. https://doi.org/10.1088/1742-6596/1651/1/012027
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of informetrics, 5(1), 146-166. https://doi.org/10.1016/j.joi.2010.10.002
Crouch, G. I. (1994). The Study of International Tourism Demand: A Survey of Practice. Journal of Travel Research, 32(4), 41-55. https://doi.org/10.1177/004728759403200408
De Luca, G., & Rosciano, M. (2020). Quantile dependence in tourism demand time series: Evidence in the Southern Italy market. Sustainability (Switzerland), 12(8). 3243. https://doi.org/10.3390/SU12083243
Della Corte, V., Del Gaudio, G., Sepe, F., & Sciarelli, F. (2019). Sustainable tourism in the open innovation realm: A bibliometric analysis. Sustainability (Switzerland), 11(21). 6114. https://doi.org/10.3390/su11216114
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
Emili, S., Gardini, A., & Foscolo, E. (2020). High spatial and temporal detail in timely prediction of tourism demand. International Journal of Tourism Research, 22(4), 451-463. https://doi.org/10.1002/jtr.2348
Feng, Y., Li, G., Sun, X., & Li, J. (2019). Forecasting the number of inbound tourists with Google Trends. Procedia Computer Science, 162, 628-633. https://doi.org/10.1016/j.procs.2019.12.032
Gallego, I., & Font, X. (2021). Changes in air passenger demand as a result of the COVID-19 crisis: Using Big Data to inform tourism policy. Journal of Sustainable Tourism, 29(9), 1470-1489. https://doi.org/10.1080/09669582.2020.1773476
García-Madurga, M.-Á., Esteban-Navarro, M.-Á., & Morte-Nadal, T. (2021). Covid key figures and new challenges in the horeca sector: The way towards a new supply-chain. Sustainability (Switzerland), 13(12). 6884. https://doi.org/10.3390/su13126884
Gunter, U., Önder, I., & Gindl, S. (2019). Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria. Tourism Economics, 25(3), 375-401. https://doi.org/10.1177/1354816618793765
Han, S., Guo, Y., Cao, H., Feng, Q., & Li, Y. (2017). A cross-view model for tourism demand forecasting with artificial intelligence method. En Song X., Xie W., Lu Z., Zou B., Li M., & Wang H., Commun. Comput. Info. Sci. (Vol. 727, p. 582). Springer Verlag. https://doi.org/10.1007/978-981-10-6385-5_48
Hassani, H., Silva, E. S., Antonakakis, N., Filis, G., & Gupta, R. (2017). Forecasting accuracy evaluation of tourist arrivals. Annals of Tourism Research, 63, 112-127. https://doi.org/10.1016/j.annals.2017.01.008
Helgemeir, T., & Cenzano, C. H. (2019). Artificial intelligence in tourism software solutions: Opportunities and challenges until 2024. En Jain K., Sangle S., Gupta R., Persis J., & R. M. (Eds.), Manag. Technol. Incl. Sustain. Growth—Int. Conf. Int. Assoc. Manag. Technol., IAMOT (pp. 134-142). Excel India Publishers. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081108614&partnerID=40&md5=bc3aca23d37cba8b3b7d5a7466ba41b9
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 and Tourism, 21(1), 45-62. https://doi.org/10.1007/s40558-018-0129-4
Höpken, W., Eberle, T., Fuchs, M., & Lexhagen, M. (2021). Improving Tourist Arrival Prediction: A Big Data and Artificial Neural Network Approach. Journal of Travel Research, 60(5), 998-1017. https://doi.org/10.1177/0047287520921244
Hu, M., Xiao, M., & Li, H. (2021). Which search queries are more powerful in tourism demand forecasting: Searches via mobile device or PC? International Journal of Contemporary Hospitality Management. 33 (6). 2022-2043. https://doi.org/10.1108/IJCHM-06-2020-0559
Hu, Y.-C., & Jiang, P. (2020). Fuzzified grey prediction models using neural networks for tourism demand forecasting. Computational and Applied Mathematics, 39(3). 145. https://doi.org/10.1007/s40314-020-01188-6
Hu, Y.-C., Jiang, P., & Lee, P.-C. (2019). Forecasting tourism demand by incorporating neural networks into Grey–Markov models. Journal of the Operational Research Society, 70(1), 12-20. https://doi.org/10.1080/01605682.2017.1418150
Huang, B., & Hao, H. (2021). A novel two-step procedure for tourism demand forecasting. Current Issues in Tourism, 24(9), 1199-1210. https://doi.org/10.1080/13683500.2020.1770705
Huarng, K.-H., & Yu, T. H.-K. (2019). Application of Google trends to forecast tourism demand. Journal of Internet Technology, 20(4), 1273-1280. https://doi.org/10.3966/160792642019072004025
Jaipuria, S., Parida, R., & Ray, P. (2021). The impact of COVID-19 on tourism sector in India. Tourism Recreation Research, 46(2), 245-260. https://doi.org/10.1080/02508281.2020.1846971
Jiang, P., Yang, H., Li, R., & Li, C. (2020). Inbound tourism demand forecasting framework based on fuzzy time series and advanced optimization algorithm. Applied Soft Computing, 92, 106320. https://doi.org/10.1016/j.asoc.2020.106320
Jiao, E. X., & Chen, J. L. (2019). Tourism forecasting: A review of methodological developments over the last decade. Tourism Economics, 25(3), 469-492. https://doi.org/10.1177/1354816618812588
Keliwar, S., Putra, A. B. W., Hammad, J., & Haviluddin. (2018). Modeling time series data for forecasting the number of foreign tourists in east Kalimantan using fuzzy inference system based on ARX model. International Journal of Engineering and Technology(UAE), 7(2), 104-107. https://doi.org/10.14419/ijet.v7i2.2.12745
Kumar, S., Pandey, N., Burton, B., & Sureka, R. (2021). Research patterns and intellectual structure of Managerial Auditing Journal: A retrospective using bibliometric analysis during 1986-2019. Managerial Auditing Journal, 36(2), 280-313. https://doi.org/10.1108/MAJ-12-2019-2517
Law, R., Li, G., Fong, D. K. C., & Han, X. (2019). Tourism demand forecasting: A deep learning approach. Annals of Tourism Research, 75, 410-423. https://doi.org/10.1016/j.annals.2019.01.014
Li, G., Song, H., & Witt, S. F. (2005). Recent developments in econometric modeling and forecasting. Journal of Travel Research, 44(1), 82-99. https://doi.org/10.1177/0047287505276594
Li, H., Hu, M., & Li, G. (2020). Forecasting tourism demand with multisource big data. Annals of Tourism Research, 83, 102912. https://doi.org/10.1016/j.annals.2020.102912
Li, X., Li, H., Pan, B., & Law, R. (2021). Machine Learning in Internet Search Query Selection for Tourism Forecasting. Journal of Travel Research, 60(6), 1213-1231. https://doi.org/10.1177/0047287520934871
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
Lin, V. S., & Song, H. (2015). A review of Delphi forecasting research in tourism. Current Issues in Tourism, 18(12), 1099-1131. https://doi.org/10.1080/13683500.2014.967187
Liu, A., Lin, V. S., Li, G., & Song, H. (2020). Ex Ante Tourism Forecasting Assessment. Journal of Travel Research. 61 (1). 64-75. https://doi.org/10.1177/0047287520974456
Liu, A., Vici, L., Ramos, V., Giannoni, S., & Blake, A. (2021). Visitor arrivals forecasts amid COVID-19: A perspective from the Europe team. Annals of Tourism Research, 88.103182. https://doi.org/10.1016/j.annals.2021.103182
Liu, H., Liu, W., & Wang, Y. (2021). A Study On The Influencing Factors Of Tourism Demand From Mainland China To Hong Kong. Journal of Hospitality and Tourism Research, 45(1), 171-191. https://doi.org/10.1177/1096348020944435
Liu, H., Liu, Y., Wang, Y., & Pan, C. (2019). Hot topics and emerging trends in tourism forecasting research: A scientometric review. Tourism Economics, 25(3), 448-468.
Liu, Y., Hsiao, A., & Ma, E. (2020). Segmenting Tourism Markets Based on Demand Growth Patterns: A Longitudinal Profile Analysis Approach. Journal of Hospitality and Tourism Research. 45 (6). 967-997. https://doi.org/10.1177/1096348020962564
Lizano, H., & Sánchez, P. P. (2020). Technological evolution in tourism: A bibliometric analysis. RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao, 2020(E36), 480-495.
López, R. F., Alonso, J. A. V., Silverio, A. Q., & González, L. D. (2021). The tourist demand of the hotel chain. Time series for a forecast model. Visión de Futuro, 25(1), Article 1. https://visiondefuturo.fce.unam.edu.ar/index.php/visiondefuturo/article/view/453
Mach, L., Ponting, J., Brown, J., & Savage, J. (2018). Riding waves of intra-seasonal demand in surf tourism: Analysing the nexus of seasonality and 21st century surf forecasting technology [Article in Press]. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049604323&doi=10.1080%2f11745398.2018.1491801&partnerID=40&md5=789a59c11c81fa358897f1255c54258a
Mobarakeh, N. A., Shahzad, M. K., Baboli, A., & Tonadre, R. (2017). Improved Forecasts for uncertain and unpredictable Spare Parts Demand in Business Aircraft’s with Bootstrap Method. International Federation of Automatic Control (IFAC). https://doi.org/10.1016/j.ifacol.2017.08.2379
Niamjoy, P., & Phumchusri, N. (2020). Forecasting Inbound Tour Daily Demand with Multi Seasonality Pattern: A Case Study of a Tour Operator in Thailand. IEEE Int. Conf. Ind. Eng. Appl., ICIEA, 1044-1048. https://doi.org/10.1109/ICIEA49774.2020.9101918
Önder, I. (2017). Forecasting tourism demand with Google trends: Accuracy comparison of countries versus cities. International Journal of Tourism Research, 19(6), 648-660. https://doi.org/10.1002/jtr.2137
Önder, I., Gunter, U., & Scharl, A. (2019). Forecasting tourist arrivals with the help of web sentiment: A mixed-frequency modeling approach for big data. Tourism Analysis, 24(4), 437-452. https://doi.org/10.3727/108354219X15652651367442
Padmaja, N., & Sudha, T. (2019). A systematic review of application of big data analytics in tourism sector. Journal of Computational and Theoretical Nanoscience, 16(5-6), 1832-1838. https://doi.org/10.1166/jctn.2019.8107
Palácios, H., de Almeida, M. H., & Sousa, M. J. (2021). A bibliometric analysis of trust in the field of hospitality and tourism. International Journal of Hospitality Management, 95. 102944. https://doi.org/10.1016/j.ijhm.2021.102944
Prilistya, S. K., Permanasari, A. E., & Fauziati, S. (2020). Tourism Demand Time Series Forecasting: A Systematic Literature Review. ICITEE - Proc. Int. Conf. Inf. Technol. Electr. Eng., 156-161. https://doi.org/10.1109/ICITEE49829.2020.9271732
Qiu, R. T. R., Wu, D. C., Dropsy, V., Petit, S., Pratt, S., & Ohe, Y. (2021). Visitor arrivals forecasts amid COVID-19: A perspective from the Asia and Pacific team. Annals of Tourism Research, 88, 103155. https://doi.org/10.1016/j.annals.2021.103155
Ramos-Carrasco, R., Galvez-Diaz, S., Nunez-Ponce, V., & Alvarez-Merino, J. (2019). Artificial neural networks to estimate the forecast of tourism demand in Peru. SHIRCON - IEEE Sci. Humanit. Int. Res. Conf. 2019 IEEE Sciences and Humanities International Research Conference, SHIRCON 2019. https://doi.org/10.1109/SHIRCON48091.2019.9024873
Rice, W. L., Park, S. Y., Pan, B., & Newman, P. (2019). Forecasting campground demand in US national parks. Annals of Tourism Research, 75, 424-438. https://doi.org/10.1016/j.annals.2019.01.013
Rodríguez-Soler, R., Uribe-Toril, J., & De Pablo Valenciano, J. (2020). Worldwide trends in the scientific production on rural depopulation, a bibliometric analysis using bibliometrix R-tool. Land Use Policy, 97, 104787. https://doi.org/10.1016/j.landusepol.2020.104787
Saayman, A., & Botha, I. (2017). Non-linear models for tourism demand forecasting. Tourism Economics, 23(3), 594-613. https://doi.org/10.5367/te.2015.0532
Saayman, A., & de Klerk, J. (2019). Forecasting tourist arrivals using multivariate singular spectrum analysis. Tourism Economics, 25(3), 330-354. https://doi.org/10.1177/1354816618768318
Sánchez-Lozano, G., Pereira, L. N., & Chávez-Miranda, E. (2021). Big data hedonic pricing: Econometric insights into room rates’ determinants by hotel category. Tourism Management, 85. 104308. https://doi.org/10.1016/j.tourman.2021.104308
Shabri, A., & Samsudin, R. (2019). Application of improved GM(1, 1) models in seasonal monthly tourism demand forecast. Proc. - Int. Conf. Artif. Intell. Data Sci., AiDAS, 186-190. https://doi.org/10.1109/AiDAS47888.2019.8970945
Sharma, P., Singh, R., Tamang, M., Singh, A. K., & Singh, A. K. (2021). Journal of teaching in travel &tourism: A bibliometric analysis. Journal of Teaching in Travel and Tourism, 21(2), 155-176. https://doi.org/10.1080/15313220.2020.1845283
Silva, E. S., Ghodsi, Z., Ghodsi, M., Heravi, S., & Hassani, H. (2017). Cross country relations in European tourist arrivals. Annals of Tourism Research, 63, 151-168. https://doi.org/10.1016/j.annals.2017.01.012
Silva, E. S., Hassani, H., Heravi, S., & Huang, X. (2019). Forecasting tourism demand with denoised neural networks. Annals of Tourism Research, 74, 134-154. https://doi.org/10.1016/j.annals.2018.11.006
Singh, R., Sibi, P. S., & Sharma, P. (2021). Journal of ecotourism: A bibliometric analysis. Journal of Ecotourism. 21 (1). 37-53. https://doi.org/10.1080/14724049.2021.1916509
Singh, R., Sibi, P. S., Sharma, P., Tamang, M., & Singh, A. K. (2021). Twenty Years of Journal of Quality Assurance in Hospitality & Tourism: A Bibliometric Assessment. Journal of Quality Assurance in Hospitality and Tourism. https://doi.org/10.1080/1528008X.2021.1884931
Song, H., & Li, G. (2008). Tourism demand modelling and forecasting—A review of recent research. Tourism Management, 29(2), 203-220. https://doi.org/10.1016/j.tourman.2007.07.016
Song, H., Liu, A., Li, G., & Liu, X. (2021). Bayesian bootstrap aggregation for tourism demand forecasting. International Journal of Tourism Research. 23 (5). 914-927. https://doi.org/10.1002/jtr.2453
Song, H., Qiu, R. T. R., & Park, J. (2019). A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting. Annals of Tourism Research, 75, 338-362. https://doi.org/10.1016/j.annals.2018.12.001
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
Sun, S., Wei, Y., Tsui, K.-L., & Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, 1-10. https://doi.org/10.1016/j.tourman.2018.07.010
Vergori, A. S. (2017). Patterns of seasonality and tourism demand forecasting. Tourism Economics, 23(5), 1011-1027. https://doi.org/10.1177/1354816616656418
Volchek, K., Liu, A., Song, H., & Buhalis, D. (2019). Forecasting tourist arrivals at attractions: Search engine empowered methodologies. Tourism Economics, 25(3), 425-447. https://doi.org/10.1177/1354816618811558
Wang, M., & Song, H. (2010). Air Travel Demand Studies: A Review. Journal of China Tourism Research, 6(1), 29-49. https://doi.org/10.1080/19388160903586562
Wen, L., Liu, C., Song, H., & Liu, H. (2021). Forecasting Tourism Demand with an Improved Mixed Data Sampling Model. Journal of Travel Research, 60(2), 336-353. https://doi.org/10.1177/0047287520906220
Wu, D. C., Song, H., & Shen, S. (2017). New developments in tourism and hotel demand modeling and forecasting. International Journal of Contemporary Hospitality Management, 29(1), 507-529. https://doi.org/10.1108/IJCHM-05-2015-0249
Xie, G., Li, X., Qian, Y., & Wang, S. (2021). Forecasting tourism demand with KPCA-based web search indexes. Tourism Economics, 27(4), 721-743. https://doi.org/10.1177/1354816619898576
Xie, G., Qian, Y., & Wang, S. (2021). Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach. Tourism Management, 82, 104208. https://doi.org/10.1016/j.tourman.2020.104208
Yu, H. (2021). Development of tourism resources based on fpga microprocessor and convolutional neural network. Microprocessors and Microsystems, 82. 103795. https://doi.org/10.1016/j.micpro.2020.103795
Zhang, B., Huang, X., Li, N., & Law, R. (2017). A novel hybrid model for tourist volume forecasting incorporating search engine data. Asia Pacific Journal of Tourism Research, 22(3), 245-254. https://doi.org/10.1080/10941665.2016.1232742
Zhang, B., Li, N., Shi, F., & Law, R. (2020). A deep learning approach for daily tourist flow forecasting with consumer search data. Asia Pacific Journal of Tourism Research, 25(3), 323-339. https://doi.org/10.1080/10941665.2019.1709876
Zhang, C., Jiang, F., Wang, S., & Sun, S. (2021). A new decomposition ensemble approach for tourism demand forecasting: Evidence from major source countries in Asia-Pacific region. International Journal of Tourism Research. 23 (5). 832-845. https://doi.org/10.1002/jtr.2445
Zhang, H., Song, H., Wen, L., & Liu, C. (2021). Forecasting tourism recovery amid COVID-19. Annals of Tourism Research, 87. https://doi.org/10.1016/j.annals.2021.103149
Zhang, Y., Li, G., Muskat, B., & Law, R. (2021). Tourism Demand Forecasting: A Decomposed Deep Learning Approach. Journal of Travel Research, 60(5), 981-997. https://doi.org/10.1177/0047287520919522
Zhang, Y., Li, G., Muskat, B., Law, R., & Yang, Y. (2020). Group pooling for deep tourism demand forecasting. Annals of Tourism Research, 82, 102899. https://doi.org/10.1016/j.annals.2020.102899
Zhang, Y., Li, G., Muskat, B., Vu, H. Q., & Law, R. (2021). Predictivity of tourism demand data. Annals of Tourism Research, 89, 103234. https://doi.org/10.1016/j.annals.2021.103234
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Reinier Fernàndez Lòpez, José Alberto Vilalta-Alonso, Deisy Alonso Porraspita, Yankiel Blanco Zamora, Saray Núñez González

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Submission of manuscripts implies that the work described has not been published before (except in the form of an abstract or as part of thesis), that it is not under consideration for publication elsewhere and that, in case of acceptance, the authors agree to automatic transfer of the copyright to the Journal for its publication and dissemination. Authors retain the authors' right to use and share the article according to a personal or instutional use or scholarly sharing purposes; in addition, they retain patent, trademark and other intellectual property rights (including research data).
All the articles are published in the Journal under the Creative Commons license CC-BY-SA (Attribution-ShareAlike). It is allowed a commercial use of the work (always including the author attribution) and other derivative works, which must be released under the same license as the original work.
Up to Volume 21, this Journal has been licensing the articles under the Creative Commons license CC-BY-SA 3.0 ES. Starting from Volume 22, the Creative Commons license CC-BY-SA 4.0 is used.