Tourism demand forecasting based on user-generated images on OTA platforms

References

• Adhinugroho, Y., Putra, A. P., Luqman, M., Ermawan, G. Y., Takdir, Mariyah, S., & Pramana, S. (2020). Development of online travel Web scraping for tourism statistics in Indonesia. Information Research, 25(4), 885. https://doi.org/10.47989/irpaper885
   Web of Science ®Google Scholar
• Afzaal, M., Usman, M., & Fong, A. (2019). Predictive aspect-based sentiment classification of online tourist reviews. Journal of Information Science, 45(3), 341–363. https://doi.org/10.1177/0165551518789872
   Web of Science ®Google Scholar
• Amaro, S., Duarte, P., & Henriques, C. (2016). Travelers’ use of social media: A clustering approach. Annals of Tourism Research, 59, 1–15. https://doi.org/10.1016/j.annals.2016.03.007
   Web of Science ®Google Scholar
• Aydin, T. O., Smolic, A., & Gross, M. (2014). Automated aesthetic analysis of photographic images. IEEE Transactions on Visualization and Computer Graphics, 21(1), 31–42. https://doi.org/10.1109/TVCG.2014.2325047
   Web of Science ®Google Scholar
• Baek, J., & Ok, C. M. (2017). The power of design: How does design affect consumers’ online hotel booking? International Journal of Hospitality Management, 65, 1–10. https://doi.org/10.1016/j.ijhm.2017.05.001
   Web of Science ®Google Scholar
• Baloglu, S., & McCleary, K. W. (1999). A model of destination image formation. Annals of Tourism Research, 26(4), 868–897. https://doi.org/10.1016/S0160-7383(99)00030-4
   Web of Science ®Google Scholar
• Bangwayo-Skeete, P. F., & Skeete, R. W. (2015). Can Google data improve the forecasting performance of tourist arrivals? Mixed-data Sampling Approach. Tourism Management, 46, 454–464. https://doi.org/10.1016/j.tourman.2014.07.014.
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Botha, I., & Saayman, A. (2022). Forecasting tourism demand cycles: A Markov switching approach. International Journal of Tourism Research, 24(6), 759–774. https://doi.org/10.1002/jtr.2543
   Web of Science ®Google Scholar
• Cerqueira, V., Torgo, L., & Soares, C. (2019). Machine learning vs statistical methods for time series forecasting: Size matters. arXiv Preprint ArXiv, 1909, 13316. https://doi.org/10.48550/arXiv.1909.13316
   Google Scholar
• Chang, Y. M., Chen, C. H., Lai, J. P., Lin, Y. L., & Pai, P. F. (2021). Forecasting hotel room occupancy using long short-term memory networks with sentiment analysis and scores of customer online reviews. Applied Sciences, 11(21), 10291. https://doi.org/10.3390/app112110291
   Google Scholar
• Chen, L., Zaharia, M., & Zou, J. (2022). Efficient online ML API selection for multi-label classification tasks. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, & S. Sabato (Eds.), International conference on machine learning (pp. 3716–3746). PMLR.
   Google Scholar
• Chen, W., Xu, Z., Zheng, X., & Luo, Y. (2019). Geo-tagged photo metadata processing method for Beijing inbound tourism flow. ISPRS International Journal of Geo-Information, 8(12), 556. https://doi.org/10.3390/ijgi8120556
   Web of Science ®Google Scholar
• Cheung, R. (2014). The influence of electronic word-of-mouth on information adoption in online customer communities. Global Economic Review, 43(1), 42–57. https://doi.org/10.1080/1226508X.2014.884048
   Web of Science ®Google Scholar
• Childers, T. L., & Houston, M. J. (1984). Conditions for a picture-superiority effect on consumer memory. Journal of Consumer Research, 11(2), 643–654. https://doi.org/10.1086/209001
   Web of Science ®Google Scholar
• Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88, 2–9. https://doi.org/10.1111/j.1475-4932.2012.00809.x
   Web of Science ®Google Scholar
• Chowdhury, R. M., Olsen, G. D., & Pracejus, J. W. (2008). Affective responses to images in print advertising: Affect integration in a simultaneous presentation context. Journal of Advertising, 37(3), 7–18. https://doi.org/10.2753/JOA0091-3367370301
   Web of Science ®Google Scholar
• Chu, F. L. (2009). Forecasting tourism demand with ARMA-based methods. Tourism Management, 30(5), 740–751. https://doi.org/10.1016/j.tourman.2008.10.016
   Web of Science ®Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Erlbaum.
   Google Scholar
• Deng, N., & Li, X. R. (2018). Feeling a destination through the “right” photos: A machine learning model for DMOs’ photo selection. Tourism Management, 65, 267–278. https://doi.org/10.1016/j.tourman.2017.09.010
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Goldsmith, R. E. (2008). Electronic word-of-mouth. In S. A. Becker (Ed.), Electronic commerce: Concepts, methodologies, tools, and applications (pp. 2143–2149). IGI Global.
   Google Scholar
• Gunter, U., & Önder, I. (2021). An exploratory analysis of geotagged photos from Instagram for residents of and visitors to Vienna. Journal of Hospitality & Tourism Research, 45(2), 373–398. https://doi.org/10.1177/1096348020963689
   Web of Science ®Google Scholar
• Gunter, U., ¨Onder, 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
   Web of Science ®Google Scholar
• Hemphill, J. F. (2003). Interpreting the magnitudes of correlation coefficients. American Psychologist, 58(1), 78–79. https://doi.org/10.1037/0003-066X.58.1.78
   PubMed Web of Science ®Google Scholar
• Hong, S. K., Kim, J. H., Jang, H., & Lee, S. (2006). The roles of categorization, affective image and constraints on destination choice: An application of the NMNL model. Tourism Management, 27(5), 750–761. https://doi.org/10.1016/j.tourman.2005.11.001
   Web of Science ®Google Scholar
• Höpken, W., Eberle, T., Fuchs, M., & Lexhagen, M. (2020). 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
   Web of Science ®Google Scholar
• Hsieh, S. C. (2021). Tourism demand forecasting based on an LSTM network and its variants. Algorithms, 14(8), 243. https://doi.org/10.3390/a14080243
   Web of Science ®Google Scholar
• Hu, M., Li, H., Song, H., Li, X., & Law, R. (2022). Tourism demand forecasting using tourist-generated online review data. Tourism Management, 90, 104490. https://doi.org/10.1016/j.tourman.2022.104490
   Web of Science ®Google Scholar
• Hu, M., & Song, H. (2020). Data source combination for tourism demand forecasting. Tourism Economics, 26(7), 1248–1265. https://doi.org/10.1177/1354816619872592
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Hu, Y. C. (2021). Forecasting the demand for tourism using combinations of forecasts by neural network-based interval grey prediction models. Asia Pacific Journal of Tourism Research, 26(12), 1350–1363. https://doi.org/10.1080/10941665.2021.1983623
   Web of Science ®Google Scholar
• Huang, X., Zhang, L., & Ding, Y. (2017). The Baidu Index: Uses in predicting tourism flows –A case study of the Forbidden City. Tourism Management, 58, 301–306. https://doi.org/10.1016/j.tourman.2016.03.015
   Web of Science ®Google Scholar
• Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd ed.). OTexts.
   Google Scholar
• Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(1), 1–22. https://doi.org/10.18637/jss.v027.i03
   Google Scholar
• Joshi, K., Tripathi, V., Bose, C., & Bhardwaj, C. (2020). Robust sports image classification using InceptionV3 and neural networks. Procedia Computer Science, 167, 2374–2381. https://doi.org/10.1016/j.procs.2020.03.290
   Google Scholar
• Lai, K., & Li, X. (. (2016). Tourism destination image: Conceptual problems and definitional solutions. Journal of Travel Research, 55(8), 1065–1080. https://doi.org/10.1177/0047287515619693
   Web of Science ®Google Scholar
• Lee, K. N. (2011). Forecasting long-haul tourism demand for Hong Kong using error correction models. Applied Economics, 43(5), 527–549. https://doi.org/10.1080/00036840802599743
   Web of Science ®Google Scholar
• Leung, D., Law, R., Van Hoof, H., & Buhalis, D. (2013). Social media in tourism and hospitality: A literature review. Journal of Travel & Tourism Marketing, 30(1-2), 3–22. https://doi.org/10.1080/10548408.2013.750919
   Web of Science ®Google Scholar
• Li, C., Ge, P., Liu, Z., & Zheng, W. (2020). Forecasting tourist arrivals using denoising and potential factors. Annals of Tourism Research, 83, 102943. https://doi.org/10.1016/j.annals.2020.102943
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Li, H., Ji, H., Liu, H., Cai, D., & Gao, H. (2022). Is a picture worth a thousand words? Understanding the role of review photo sentiment and text-photo sentiment disparity using deep learning algorithms. Tourism Management, 92, 104559. https://doi.org/10.1016/j.tourman.2022.104559
   Web of Science ®Google Scholar
• Li, H., Zhang, L., Guo, R. A., Ji, H., & Bruce, X. B. (2022). Information enhancement or hindrance? Unveiling the impacts of user-generated photos in online reviews. International Journal of Contemporary Hospitality Management. https://doi.org/10.1108/IJCHM-03-2022-0291
   Web of Science ®Google Scholar
• Li, H., Zhang, L., & Hsu, C. H. (2023). Research on user-generated photos in tourism and hospitality: A systematic review and way forward. Tourism Management, 96, 104714. https://doi.org/10.1016/j.tourman.2022.104714
   Web of Science ®Google Scholar
• Li, S., Chen, T., Wang, L., & Ming, C. (2018). Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index. Tourism Management, 68, 116–126. https://doi.org/10.1016/j.tourman.2018.03.006
   Web of Science ®Google Scholar
• Li, X., Law, R., Xie, G., & Wang, S. (2021). Review of tourism forecasting research with internet data. Tourism Management, 83, 104245. https://doi.org/10.1016/j.tourman.2020.104245
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Lin, C. H., Morais, D. B., Kerstetter, D. L., & Hou, J. S. (2007). Examining the role of cognitive and affective image in predicting choice across natural, developed, and theme-park destinations. Journal of Travel Research, 46(2), 183–194. https://doi.org/10.1177/0047287506304049
   Google Scholar
• Liu, L. M., Hudak, G. B., Box, G. E., Muller, M. E., & Tiao, G. C. (1992). Forecasting and time series analysis using the SCA statistical system (Vol. 1, No. 2). Scientific Computing Associates.
   Google Scholar
• Liu, Y. Y., Tseng, F. M., & Tseng, Y. H. (2018). Big data analytics for forecasting tourism destination arrivals with the applied vector autoregression model. Technological Forecasting and Social Change, 130, 123–134. https://doi.org/10.1016/j.techfore.2018.01.018
   Web of Science ®Google Scholar
• Llodra-Riera, I., Martínez-Ruiz, M. P., Jiménez-Zarco, A. I., & Izquierdo-Yusta, A. (2015). A multidimensional analysis of the information sources construct and its relevance for destination image formation. Tourism Management, 48, 319–328. https://doi.org/10.1016/j.tourman.2014.11.012
   Web of Science ®Google Scholar
• Mackinnon, J. G., Haug, A. A., & Michelis, L. (1999). Numerical distribution functions of likelihood ratio tests for cointegration. Journal of Applied Econometrics, 14(5), 563–577. https://doi.org/10.1002/(SICI)1099-1255(199909/10)14:5<563::AID-JAE530>3.0.CO;2-R
   Web of Science ®Google Scholar
• Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PLoS One, 13(3), e0194889. https://doi.org/10.1371/journal.pone.0194889
   PubMed Web of Science ®Google Scholar
• Marder, B., Erz, A., Angell, R., & Plangger, K. (2021). The role of photograph aesthetics on online review sites: Effects of management-versus traveler-generated photos on tourists’ decision making. Journal of Travel Research, 60(1), 31–46. https://doi.org/10.1177/0047287519895125
   Web of Science ®Google Scholar
• Neves, G. A., Nunes, C. S., & Fernandes, P. O. (2022). Seasonal autoregressive integrated moving average time series model for tourism demand: The case of Sal Island, Cape Verde. In J. V. de Carvalho, P. Liberato, & A. Peña (Ed.), Advances in tourism, technology and systems (pp. 11–21). Springer.
   Google Scholar
• Nguyen, Q. H. (2022). Outbound tourism demand modeling–region destination approach: The case of Vietnam’s data. Journal of China Tourism Research, 1–22. https://doi.org/10.1080/19388160.2022.2091699
   Web of Science ®Google Scholar
• Nikhitha, M., Sri, S. R., & Maheswari, B. U. (2019). Fruit recognition and grade of disease detection using inception v3 model. In 2019 3rd international conference on electronics, communication and aerospace technology (ICECA) (pp. 1040–1043). IEEE.
   Google Scholar
• Nwokike, C. C., Offorha, B. C., Obubu, M., Ugoala, C. B., & Ukomah, H. I. (2020). Comparing SANN and SARIMA for forecasting frequency of monthly rainfall in Umuahia. Scientific African, 10, e00621. https://doi.org/10.1016/j.sciaf.2020.e00621
   Google Scholar
• Önder, I., Gunter, U., & Gindl, S. (2020). Utilizing Facebook statistics in tourism demand modeling and destination marketing. Journal of Travel Research, 59(2), 195–208. https://doi.org/10.1177/0047287519835969
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Pan, B., Wu, D. C., & Song, H. (2012). Forecasting hotel room demand using search engine data. Journal of Hospitality and Tourism Technology, 3(3), 196–210. https://doi.org/10.1108/17579881211264486
   Google Scholar
• Pan, S., Lee, J., & Tsai, H. (2014). Travel photos: Motivations, image dimensions, and affective qualities of places. Tourism Management, 40, 59–69. https://doi.org/10.1016/j.tourman.2013.05.007
   Web of Science ®Google Scholar
• Park, E., & Kim, S. (2018). Are we doing enough for visual research in tourism? The past, present, and future of tourism studies using photographic images. International Journal of Tourism Research, 20(4), 433–441. https://doi.org/10.1002/jtr.2194
   Web of Science ®Google Scholar
• Park, E., Park, J., & Hu, M. (2021). Tourism demand forecasting with online news data mining. Annals of Tourism Research, 90, 103273. https://doi.org/10.1016/j.annals.2021.103273
   Web of Science ®Google Scholar
• Peng, G., Liu, Y., Wang, J., & Gu, J. (2017). Analysis of the prediction capability of web search data based on the HE-TDC method–prediction of the volume of daily tourism visitors. Journal of Systems Science and Systems Engineering, 26(2), 163–182. https://doi.org/10.1007/s11518-016-5311-7
   Web of Science ®Google Scholar
• Phau, I., Shanka, T., & Dhayan, N. (2010). Destination image and choice intention of university student travellers to Mauritius. International Journal of Contemporary Hospitality Management, 22(5), 758–764. https://doi.org/10.1108/09596111011053846
   Web of Science ®Google Scholar
• Pieters, R., & Wedel, M. (2004). Attention capture and transfer in advertising: Brand, pictorial, and text-size effects. Journal of Marketing, 68(2), 36–50. https://doi.org/10.1509/jmkg.68.2.36.27794
   Web of Science ®Google Scholar
• Safarov, B., Al-Smadi, H. M., Buzrukova, M., Janzakov, B., Ilieş, A., Grama, V., Ilieș, D. C., Csobán Vargáné, K., & Dávid, L. D. (2022). Forecasting the volume of tourism services in Uzbekistan. Sustainability, 14(13), 7762. https://doi.org/10.3390/su14137762
   Web of Science ®Google Scholar
• Sagheer, A., Hamdoun, H., & Youness, H. (2021). Deep LSTM-based transfer learning approach for coherent forecasts in hierarchical time series. Sensors, 21(13), 4379. https://doi.org/10.3390/s21134379
   PubMed Web of Science ®Google Scholar
• Scott, L. M. (1994). Images in advertising: The need for a theory of visual rhetoric. Journal of Consumer Research, 21(2), 252–273. https://doi.org/10.1086/209396
   Web of Science ®Google Scholar
• Shin, Y., Noone, B. M., & Robson, S. K. (2020). An exploration of the effects of photograph content, photograph source, and price on consumers’ online travel booking intentions. Journal of Travel Research, 59(1), 120–139. https://doi.org/10.1177/0047287518817399
   Web of Science ®Google Scholar
• Son, H. G., Ha, M. H., & Kim, S. (2012). A study on the tourism combining demand forecasting models for the tourism in Korea. The Korean Journal of Applied Statistics, 25(2), 251–259. https://doi.org/10.5351/KJAS.2012.25.2.251
   Google Scholar
• Song, H., Li, G., Witt, S. F., & Athanasopoulos, G. (2011). Forecasting tourist arrivals using time-varying parameter structural time series models. International Journal of Forecasting, 27(3), 855–869. http://doi.org/10.1016/j.ijforecast.2010.06.001
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Stylos, N., Vassiliadis, C. A., Bellou, V., & Andronikidis, A. (2016). Destination images, holistic images and personal normative beliefs: Predictors of intention to revisit a destination. Tourism Management, 53, 40–60. https://doi.org/10.1016/j.tourman.2015.09.006
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Sun, W., Schmöcker, J. D., & Nakao, S. (2022). Restrictive and stimulative impacts of COVID-19 policies on activity trends: A case study of Kyoto. Transportation Research Interdisciplinary Perspectives, 13, 100551. https://doi.org/10.1016/j.trip.2022.100551
   PubMedGoogle Scholar
• Sun, Y., Zhang, J., Li, X., & Wang, S. (2023). Forecasting tourism demand with a new time-varying forecast averaging approach. Journal of Travel Research, 62(2), 305–323. https://doi.org/10.1177/00472875211061206.
   Web of Science ®Google Scholar
• Wang, L., Law, R., Guillet, B. D., Hung, K., & Fong, D. K. C. (2015). Impact of hotel website quality on online booking intentions: eTrust as a mediator. International Journal of Hospitality Management, 47, 108–115. https://doi.org/10.1016/j.ijhm.2015.03.012
   Web of Science ®Google Scholar
• Wen, L., Liu, C., & Song, H. (2019). Forecasting tourism demand using search query data: A hybrid modelling approach. Tourism Economics, 25(3), 309–329. https://doi.org/10.1177/1354816618768317
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Wu, D. C., Zhong, S., Qiu, R. T., & Wu, J. (2022). Are customer reviews just reviews? Hotel forecasting using sentiment analysis. Tourism Economics, 28(3), 795–816. https://doi.org/10.1177/13548166211049865
   Web of Science ®Google Scholar
• Xia, X., Xu, C., & Nan, B. (2017). Inception-v3 for flower classification. In 2017 2nd international conference on image, vision and computing (ICIVC) (pp. 783–787). IEEE.
   Google Scholar
• Yang, S. B., Hlee, S., Lee, J., & Koo, C. (2017). An empirical examination of online restaurant reviews on Yelp. com: A dual coding theory perspective. International Journal of Contemporary Hospitality Management, 29(2), 817–839. https://doi.org/10.1108/IJCHM-11-2015-0643
   Web of Science ®Google Scholar
• Yang, X., Pan, B., Evans, J. A., & Lv, B. (2015). Forecasting Chinese tourist volume with search engine data. Tourism Management, 46, 386–397. https://doi.org/10.1016/j.tourman.2014.07.019
   Web of Science ®Google Scholar
• Ying, X. (2019). An overview of overfitting and its solutions. Journal of Physics: Conference Series, 1168(2), 022022. https://doi.org/10.1088/1742-6596/1168/2/022022
   Google Scholar
• Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0
   Web of Science ®Google Scholar
• Zhang, H., Wu, Y., & Buhalis, D. (2018). A model of perceived image, memorable tourism experiences and revisit intention. Journal of Destination Marketing & Management, 8, 326–336. https://doi.org/10.1016/j.jdmm.2017.06.004
   Web of Science ®Google Scholar
• Zhang, J., & Dong, L. (2021). Image monitoring and management of hot tourism destination based on data mining technology in big data environment. Microprocessors and Microsystems, 80, 103515. https://doi.org/10.1016/j.micpro.2020.103515
   Web of Science ®Google Scholar
• Zhang, Y., Choo, W. C., Ho, J. S., & Wan, C. K. (2022). Single or combine? Tourism demand volatility forecasting with exponential weighting and smooth transition combining methods. Computation, 10(8), 137. https://doi.org/10.3390/computation10080137
   Web of Science ®Google Scholar
• Zhang, Y., Li, G., Muskat, B., & Law, R. (2020). Tourism demand forecasting: A decomposed deep learning approach. Journal of Travel Research, 60(5), 981–997. https://doi.org/10.1177/0047287520919522
   Web of Science ®Google Scholar