References

• Abri, F., Gutiérrez, L. F., Namin, A. S., Jones, K. S., and Sears, D. R. (2020), “Linguistic features for detecting fake reviews”, In Proceedings of 2020 19th IEEE International Conference on machine learning and applications (ICMLA), Pasadena, CA, pp. 352-359.
   Google Scholar
• Akay, M. F. (2009). Support vector machines combined with feature selection for breast cancer diagnosis. Expert Systems with Applications, 36(2), 3240–3247. https://doi.org/10.1016/j.eswa.2008.01.009
   Web of Science ®Google Scholar
• Akhtar, N., Ahmad, W., Siddiqi, U. I., & Akhtar, M. N. (2019). Predictors and outcomes of consumer deception in hotel reviews: The roles of reviewer type and attribution of service failure. Journal of Hospitality and Tourism Management, 39, 65–75. https://doi.org/10.1016/j.jhtm.2019.03.004
   Web of Science ®Google Scholar
• Alsubari, S. N., Shelke, M. B., & Deshmukh, S. N. (2020). Fake reviews identification based on deep computational linguistic. International Journal of Advanced Science and Technology, 29(8s), 3846–3856.
   Google Scholar
• Ansari, S., & Gupta, S. (2021). Customer perception of the deceptiveness of online product reviews: A speech act theory perspective. International Journal of Information Management, 57, 102286. https://doi.org/10.1016/j.ijinfomgt.2020.102286
   Web of Science ®Google Scholar
• Baker, M. A., & Kim, K. (2019). Value destruction in exaggerated online reviews: The effects of emotion, language, and trustworthiness. International Journal of Contemporary Hospitality Management, 31(4), 1956–1976. https://doi.org/10.1108/IJCHM-03-2018-0247
   Web of Science ®Google Scholar
• Ball, L., & Elworthy, J. (2014). Fake or real? The computational detection of online deceptive text. Journal of Marketing Analytics, 2(3), 187–201. https://doi.org/10.1057/jma.2014.15
   Google Scholar
• Banerjee, S., Chua, A. Y., & Kim, J. J. (2015). Using supervised learning to classify authentic and fake online reviews. Proceedings of the 9th International Conference on ubiquitous information Management and communication, bali, Indonesia, pp. 1–7.
   Google Scholar
• Barbado, R., Araque, O., & Iglesias, C. A. (2019). A framework for fake review detection in online consumer electronics retailers. Information Processing & Management, 56(4), 1234–1244. https://doi.org/10.1016/j.ipm.2019.03.002
   Web of Science ®Google Scholar
• Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2020a). Robots or frontline employees? Exploring customers’ attributions of responsibility and stability after service failure or success. Journal of Service Management, 31(2), 267–289. https://doi.org/10.1108/JOSM-05-2019-0156
   Web of Science ®Google Scholar
• Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2020b). Service robot implementation: A theoretical framework and research agenda. The Service Industries Journal, 40(3-4), 203–225. https://doi.org/10.1080/02642069.2019.1672666
   Web of Science ®Google Scholar
• Belk, R. (2021). Ethical issues in service robotics and artificial intelligence. The Service Industries Journal, 41(13-14), 860–876. https://doi.org/10.1080/02642069.2020.1727892
   Web of Science ®Google Scholar
• Bradley, G. L., Sparks, B. A., & Weber, K. (2015). The stress of anonymous online reviews: A conceptual model and research agenda. International Journal of Contemporary Hospitality Management, 27(5), 739–755. https://doi.org/10.1108/IJCHM-01-2014-0005
   Web of Science ®Google Scholar
• Bradner, E., & Mark, G. (2002). Why distance matters: Effects on cooperation, persuasion and deception. Proceedings of the 2002 ACM Conference on computer supported cooperative work, New Orleans LA, pp. 226–235.
   Google Scholar
• Brown, J. J., & Reingen, P. H. (1987). Social ties and word-of-mouth referral behavior. Journal of Consumer Research, 14(3), 350–362. https://doi.org/10.1086/209118
   Web of Science ®Google Scholar
• Buzova, D., Sanz-Blas, S., & Cervera-Taulet, A. (2019). Does culture affect sentiments expressed in cruise tours’ eWOM? The Service Industries Journal, 39(2), 154–173. https://doi.org/10.1080/02642069.2018.1476497
   Web of Science ®Google Scholar
• Casaló, L. V., Flavián, C., Guinalíu, M., & Ekinci, Y. (2015). Avoiding the dark side of positive online consumer reviews: Enhancing reviews’ usefulness for high risk-averse travelers. Journal of Business Research, 68(9), 1829–1835. https://doi.org/10.1016/j.jbusres.2015.01.010
   Web of Science ®Google Scholar
• Castillo, D., Canhoto, A. I., & Said, E. (2021). The dark side of AI-powered service interactions: Exploring the process of co-destruction from the customer perspective. The Service Industries Journal, 41(13-14), 900–925. https://doi.org/10.1080/02642069.2020.1787993
   Web of Science ®Google Scholar
• Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
   Web of Science ®Google Scholar
• Clemmensen, L., Hastie, T., Witten, D., & Ersbøll, B. (2011). Sparse discriminant analysis. Technometrics, 53(4), 406–413. https://doi.org/10.1198/TECH.2011.08118
   Web of Science ®Google Scholar
• Crawford, M., Khoshgoftaar, T. M., Prusa, J. D., Richter, A. N., & Al Najada, H. (2015). Survey of review spam detection using machine learning techniques. Journal of Big Data, 2(1), 1–24. https://doi.org/10.1186/s40537-015-0029-9
   Google Scholar
• Deng, R., Ruan, N., Jin, R., Lu, Y., Jia, W., Su, C., & Xu, D. (2018). Spamtracer: Manual fake review detection for O2O commercial platforms by using geolocation features. Proceedings of International Conference on information security and cryptology, Springer, pp. 384–403.
   Google Scholar
• Escobar, M. C. (2021). “Study: 67% of US consumers say fake online reviews a growing problem”, Hospitality Technology, available at: https://hospitalitytech.com/study-67-us-consumers-say-fake-online-reviews-growing-problem (accessed on February 10, 2022).
   Google Scholar
• Flavián, C., & Casaló, L. V. (2021). Artificial intelligence in services: Current trends, benefits and challenges. The Service Industries Journal, 41(13-14), 853–859. https://doi.org/10.1080/02642069.2021.1989177
   Web of Science ®Google Scholar
• Flavián, C., Gurrea, R., & Orús, C. (2016). Choice confidence in the webrooming purchase process: The impact of online positive reviews and the motivation to touch. Journal of Consumer Behaviour, 15(5), 459–476. https://doi.org/10.1002/cb.1585
   Web of Science ®Google Scholar
• Flavián, C., Gurrea, R., & Orús, C. (2020). Mobile word of mouth (m-WOM): analysing its negative impact on webrooming in omnichannel retailing. International Journal of Retail & Distribution Management, 49(3), 394–420. https://doi.org/10.1108/IJRDM-05-2020-0169
   Web of Science ®Google Scholar
• Frontline Systems. (2020). “Frontline solver XLMiner”, available at: https://www.solver.com/products-overview/ (accessed on February 10, 2022).
   Google Scholar
• Ghose, A., & Ipeirotis, P. G. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10), 1498–1512. https://doi.org/10.1109/TKDE.2010.188
   Web of Science ®Google Scholar
• Goldschmidt, C. (2019). “AI-generated reviews threaten business reputation”, Forbes, available at: https://www.forbes.com/sites/forbestechcouncil/2019/04/04/ai-generated-reviews-threaten-business-reputations/?sh=138b616e7eb1 (accessed on February 10, 2022).
   Google Scholar
• Hajek, P., Barushka, A., & Munk, M. (2020). Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining. Neural Computing and Applications, 32(23), 17259–17274. https://doi.org/10.1007/s00521-020-04757-2
   Web of Science ®Google Scholar
• Hancock, J. T., Curry, L. E., Goorha, S., & Woodworth, M. (2007). On lying and being lied to: A linguistic analysis of deception in computer-mediated communication. Discourse Processes, 45(1), No. 1, 1–23. https://doi.org/10.1080/01638530701739181
   Google Scholar
• Hao, K. (2018). “What is machine learning?”, available at: https://www.technologyreview.com/2018/11/17/103781/what-is-machine-learning-we-drew-you-another-flowchart/ (accessed on February 10, 2022).
   Google Scholar
• Huang, B., & Philp, M. (2021). When AI-based services fail: Examining the effect of the self-AI connection on willingness to share negative word-of-mouth after service failures. The Service Industries Journal, 41(13-14), 877–899. https://doi.org/10.1080/02642069.2020.1748014
   Web of Science ®Google Scholar
• Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
   Web of Science ®Google Scholar
• Jia, S., Zhang, X., Wang, X., and Liu, Y. (2018), “Fake reviews detection based on LDA”, In Proceedings of 2018 4th International Conference on Information Management (ICIM), pp. 280–283.
   Google Scholar
• Jindal, N., & Liu, B. (2008). Opinion spam and analysis. Proceedings of the 2008 International Conference on Web search and data mining, Palo Alto, CA, pp. 219–230.
   Google Scholar
• Kandampully, J., Bilgihan, A., & Li, D. K. (2022). Unifying technology and people: Revisiting service in a digitally transformed world. The Service Industries Journal, 42(1-2), 21–41. https://doi.org/10.1080/02642069.2021.1965578
   Web of Science ®Google Scholar
• Kesgin, M., & Murthy, R. S. (2019). Consumer engagement: The role of social currency in online reviews. The Service Industries Journal, 39(7-8), 609–636. https://doi.org/10.1080/02642069.2018.1553237
   Web of Science ®Google Scholar
• Kim, S. Y. (2011). Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression, and multivariate discriminant analysis. The Service Industries Journal, 31(3), 441–468. https://doi.org/10.1080/02642060802712848
   Web of Science ®Google Scholar
• Kumar, N., Venugopal, D., Qiu, L., & Kumar, S. (2019). Detecting anomalous online reviewers: An unsupervised approach using mixture models. Journal of Management Information Systems, 36(4), 1313–1346. https://doi.org/10.1080/07421222.2019.1661089
   Web of Science ®Google Scholar
• Lee, K., Ham, J., Yang, S.-B., & Koo, C. (2018). Can you identify fake or authentic reviews? An fsQCA approach. Proceedings of Information and Communication Technologies in tourism, Jönköping, Sweden, pp. 214–227.
   Google Scholar
• Lee, M., Jeong, M., & Lee, J. (2017). Roles of negative emotions in customers’ perceived helpfulness of hotel reviews on a user-generated review website. International Journal of Contemporary Hospitality Management, 29(2), 762–783. https://doi.org/10.1108/IJCHM-10-2015-0626
   Web of Science ®Google Scholar
• Lee, M., Kwon, W., & Back, K.-J. (2021). Artificial intelligence for hospitality big data analytics: Developing a prediction model of restaurant review helpfulness for customer decision-making. International Journal of Contemporary Hospitality Management, 33(6), 2117–2136. https://doi.org/10.1108/IJCHM-06-2020-0587
   Web of Science ®Google Scholar
• Lee, M., Lee, S. A., & Koh, Y. (2019). Multisensory experience for enhancing hotel guest experience: Empirical evidence from big data analytics. International Journal of Contemporary Hospitality Management, 31(11), 4313–4337. https://doi.org/10.1108/IJCHM-03-2018-0263
   Web of Science ®Google Scholar
• Li, J., Cardie, C., & Li, S. (2013). Topicspam: A topic-model based approach for spam detection. Proceedings of the 51st Annual Meeting of the Association for Computational linguistics, Sofia, Bulgaria, pp. 217–221.
   Google Scholar
• Li, L., Lee, K. Y., Lee, M., & Yang, S.-B. (2020). Unveiling the cloak of deviance: Linguistic cues for psychological processes in fake online reviews. International Journal of Hospitality Management, 87, 102468. https://doi.org/10.1016/j.ijhm.2020.102468
   Web of Science ®Google Scholar
• Ligthart, A., Catal, C., & Tekinerdogan, B. (2021). Analyzing the effectiveness of semi-supervised learning approaches for opinion spam classification. Applied Soft Computing, 101, 107023. https://doi.org/10.1016/j.asoc.2020.107023
   Web of Science ®Google Scholar
• Liu, Y., Pang, B., & Wang, X. (2019). Opinion spam detection by incorporating multimodal embedded representation into a probabilistic review graph. Neurocomputing, 366, 276–283. https://doi.org/10.1016/j.neucom.2019.08.013
   Web of Science ®Google Scholar
• Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140–151. https://doi.org/10.1016/j.tourman.2014.09.020
   Web of Science ®Google Scholar
• Lo, A. S., & Yao, S. S. (2019). What makes hotel online reviews credible? International Journal of Contemporary Hospitality Management, 31(1), 41–60. https://doi.org/10.1108/IJCHM-10-2017-0671
   Web of Science ®Google Scholar
• Luca, M. (2016). Reviews, reputation, and revenue: The case of Yelp.com”, Harvard Business School NOM Unit. Working Paper No. 12-016, available at: https://ssrn.com/abstract=1928601 (accessed on November 1, 2021)
   Google Scholar
• Luca, M., & Zervas, G. (2016). Fake it till you make it: Reputation, competition, and Yelp review fraud. Management Science, 62(12), 3412–3427. https://doi.org/10.1287/mnsc.2015.2304
   Web of Science ®Google Scholar
• Malbon, J. (2013). Taking fake online consumer reviews seriously. Journal of Consumer Policy, 36(2), 139–157. https://doi.org/10.1007/s10603-012-9216-7
   Google Scholar
• Markham, I. S., & Rakes, T. R. (1998). The effect of sample size and variability of data on the comparative performance of artificial neural networks and regression. Computers & Operations Research, 25(4), 251–263. https://doi.org/10.1016/S0305-0548(97)00074-9
   Web of Science ®Google Scholar
• Martinez-Torres, M. R., & Toral, S. L. (2019). A machine learning approach for the identification of the deceptive reviews in the hospitality sector using unique attributes and sentiment orientation. Tourism Management, 75, 393–403. https://doi.org/10.1016/j.tourman.2019.06.003
   Web of Science ®Google Scholar
• Matsumoto, D., & Hwang, H. C. (2015). Differences in word usage by truth tellers and liars in written statements and an investigative interview after a mock crime. Journal of Investigative Psychology and Offender Profiling, 12(2), 199–216. https://doi.org/10.1002/jip.1423
   Web of Science ®Google Scholar
• Moon, S., Kim, M. Y., & Iacobucci, D. (2021). Content analysis of fake consumer reviews by survey-based text categorization. International Journal of Research in Marketing, 38(2), 343–364. https://doi.org/10.1016/j.ijresmar.2020.08.001
   Web of Science ®Google Scholar
• Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., & Ghosh, R. (2013a). Spotting opinion spammers using behavioral footprints. Proceedings of the 19th ACM SIGKDD International Conference on knowledge discovery and data mining, Chicago, IL, pp. 632–640.
   Google Scholar
• Mukherjee, A., Venkataraman, V., Liu, B., & Glance, N. (2013b). What Yelp fake review filter might be doing? Proceedings of the International AAAI Conference on Web and social media, Cambridge, MA, pp. 409–418.
   Google Scholar
• Munoz, A. (2014). “Machine learning and optimization”, available at: https://cims.nyu.edu/~munoz/files/ml_optimization.pdf/ (accessed on November 1, 2021).
   Google Scholar
• Munzel, A. (2016). Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus. Journal of Retailing and Consumer Services, 32, 96–108. https://doi.org/10.1016/j.jretconser.2016.06.002
   Web of Science ®Google Scholar
• Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011). Finding deceptive opinion spam by any stretch of the imagination. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human language technologies-volume 1, Portland, OR, pp. 309–319.
   Google Scholar
• Öğüt, H., & Onur Taş, B. K. (2012). The influence of internet customer reviews on the online sales and prices in hotel industry. The Service Industries Journal, 32(2), 197–214. https://doi.org/10.1080/02642069.2010.529436
   Web of Science ®Google Scholar
• Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015. University of Texas at Austin.
   Google Scholar
• Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003). Psychological aspects of natural language use: Our words, our selves. Annual Review of Psychology, 54(1), 547–577. https://doi.org/10.1146/annurev.psych.54.101601.145041
   PubMed Web of Science ®Google Scholar
• Plotkina, D., Munzel, A., & Pallud, J. (2020). Illusions of truth—experimental insights into human and algorithmic detections of fake online reviews. Journal of Business Research, 109, 511–523. https://doi.org/10.1016/j.jbusres.2018.12.009
   Web of Science ®Google Scholar
• Polat, K., & Günes, S. (2007). Breast cancer diagnosis using least square support vector machine. Digital Signal Processing, 17(4), 694–701. https://doi.org/10.1016/j.dsp.2006.10.008
   Web of Science ®Google Scholar
• Post Modern Marketing. (2021). “Dealing with fake reviews on Yelp”, available at: https://www.postmm.com/social-media-marketing/dealing-fake-yelp-reviews/ (accessed on February 10, 2022).
   Google Scholar
• Salomon, L. (2019). “How TripAdvisor keeps fake reviews in check”, eHotelier, available at: https://insights.ehotelier.com/insights/2019/11/26/how-tripadvisor-keeps-fake-reviews-in-check/ (accessed on February 10, 2022).
   Google Scholar
• SAS. (2020). “Machine learning, what it is and why it matters”, available at: https://www.sas.com/en_ca/insights/analytics/machine-learning.html (accessed on November 1, 2021).
   Google Scholar
• Shah, T. (2017). “About train, validation and test sets in machine learning”, Towards Data Science, available at https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7, (accessed on February 10, 2022).
   Google Scholar
• Shan, G., Zhou, L., & Zhang, D. (2021). From conflicts and confusion to doubts: Examining review inconsistency for fake review detection. Decision Support Systems, 144, 113513. https://doi.org/10.1016/j.dss.2021.113513
   Web of Science ®Google Scholar
• Shmueli, G., Patel, N. R., & Bruce, P. C. (2016). Data mining for business analytics: Concepts, techniques, and applications with XLMiner. John Wiley & Sons.
   Google Scholar
• Siddiqi, U. I., Sun, J., & Akhtar, N. (2020). The role of conflicting online reviews in consumers’ attitude ambivalence. The Service Industries Journal, 40(13-14), 1003–1030. https://doi.org/10.1080/02642069.2019.1684905
   Web of Science ®Google Scholar
• Singh, J. P., Irani, S., Rana, N. P., & Dwivedi, Y. K. (2017). Predicting the “helpfulness” of online consumer reviews. Journal of Business Research, 70, 346–355. https://doi.org/10.1016/j.jbusres.2016.08.008
   Web of Science ®Google Scholar
• Stąpor, K. (2016). A critical comparison of discriminant analysis and SVM–based approaches to credit scoring. Studia Ekonomiczne, 4(288), 59–70.
   Google Scholar
• Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24–54. https://doi.org/10.1177/0261927X09351676
   Web of Science ®Google Scholar
• Toma, C. L., & D’Angelo, J. D. (2015). Tell-tale words: Linguistic cues used to infer the expertise of online medical advice. Journal of Language and Social Psychology, 34(1), 25–45. https://doi.org/10.1177/0261927X14554484
   Web of Science ®Google Scholar
• Toma, C. L., & Hancock, J. T. (2012). What lies beneath: The linguistic traces of deception in online dating profiles. Journal of Communication, 62(1), 78–97. https://doi.org/10.1111/j.1460-2466.2011.01619.x
   Web of Science ®Google Scholar
• Van Laer, T., Edson Escalas, J., Ludwig, S., & Van Den Hende, E. A. (2019). What happens in vegas stays on TripAdvisor? A theory and technique to understand narrativity in consumer reviews. Journal of Consumer Research, 46(2), 267–285.
   Web of Science ®Google Scholar
• Viglia, G., Minazzi, R., & Buhalis, D. (2016). The influence of e-word-of-mouth on hotel occupancy rate. International Journal of Contemporary Hospitality Management, 28(9), 2035–2051. https://doi.org/10.1108/IJCHM-05-2015-0238
   Web of Science ®Google Scholar
• Wahyuni, E. D., & Djunaidy, A. (2016). Fake review detection from a product review using modified method of iterative computation framework. Proceedings of MATEC Web of conferences, EDP Sciences, p. 03003.
   Google Scholar
• Witts, S. (2019). “TripAdvisor removed over one million fake reviews in 2018”, BigHospitality, available at: https://www.bighospitality.co.uk/Article/2019/09/17/TripAdvisor-removed-over-one-million-fake-reviews-in-2018 (accessed on February 10, 2022).
   Google Scholar
• Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82. https://doi.org/10.1109/4235.585893
   Google Scholar
• Wu, Y., Ngai, E. W., Wu, P., & Wu, C. (2020). Fake online reviews: Literature review, synthesis, and directions for future research. Decision Support Systems, 132, 113280. https://doi.org/10.1016/j.dss.2020.113280
   Web of Science ®Google Scholar
• Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51–65. https://doi.org/10.1016/j.tourman.2016.10.001
   Web of Science ®Google Scholar
• Xiang, Z., Schwartz, Z., GerdesJr.J. H., & Uysal, M. (2015). What can big data and text analytics tell us about hotel guest experience and satisfaction? International Journal of Hospitality Management, 44, 120–130. https://doi.org/10.1016/j.ijhm.2014.10.013
   Web of Science ®Google Scholar
• Yang, S.-B., Shin, S., Joun, Y., & Koo, C. (2017). Exploring the comparative importance of online hotel reviews’ heuristic attributes in review helpfulness: A conjoint analysis approach. Journal of Travel & Tourism Marketing, 34(7), 963–985. https://doi.org/10.1080/10548408.2016.1251872
   Web of Science ®Google Scholar
• Yelp. (2020). “An introduction to Yelp metrics”, available at: https://www.yelppress.com/company/fast-facts/default.aspx (accessed on November 1, 2021).
   Google Scholar
• Yoo, K.-H., & Gretzel, U. (2009). Comparison of deceptive and truthful travel reviews. Proceedings of Information and Communication Technologies in Tourism 2009, pp. 37–47.
   Google Scholar
• Zhang, D., Zhou, L., Kehoe, J. L., & Kilic, I. Y. (2016). What online reviewer behaviors really matter? Effects of verbal and nonverbal behaviors on detection of fake online reviews. Journal of Management Information Systems, 33(2), 456–481. https://doi.org/10.1080/07421222.2016.1205907
   Web of Science ®Google Scholar
• Zhou, S., & Guo, B. (2017). The order effect on online review helpfulness: A social influence perspective. Decision Support Systems, 93, 77–87. https://doi.org/10.1016/j.dss.2016.09.016
   Web of Science ®Google Scholar
• Zhu, Y., Sun, L. Y., & Leung, A. S. (2014). Corporate social responsibility, firm reputation, and firm performance: The role of ethical leadership. Asia Pacific Journal of Management, 31(4), 925–947. https://doi.org/10.1007/s10490-013-9369-1
   Web of Science ®Google Scholar