Marketing Strategy and Artificial Intelligence State of the Art and Research Agenda

Main Article Content

Hasna Koubaa El Euch https://orcid.org/0009-0005-6259-4574
Foued Ben Said https://orcid.org/0000-0001-6951-9490

Keywords

Marketing strategy/strategic marketing, artificial intelligence, bibliometric analysis

Abstract

The marketing literature highlights the growing integration of artificial intelligence (AI) into marketing strategies. Several publications show that this field is attracting increasing interest from researchers. The purpose of this article is to provide an overview of academic publications related to AI and marketing strategies, while also examining the lack of bibliometric analysis in this area. In this study, 1100 articles, published in the Scopus and Web of Science databases, were selected and, according to a consistent search procedure, were examined. A performance analysis, based on bibliometric indicators, revealed the most impactful journals, the most indexed authors according to H-index and the most cited papers. The thematic factorial map highlighted the typology of AI tools used in the field of strategic marketing, in this case the marketing strategy. It also provides a discussion, potential research avenues and recommendations for future investigations.


 

Downloads

Download data is not yet available.
Abstract 809 | 887-PDF-v12n1pp538-574 Downloads 17

References

Abakouy, R., En-Naimi, M., El Haddadi, A., & Elaachak, L. (2022). How marketers can increase the relevance of email marketing campaigns: data analysis with machine learning methods. Journal of Theoretical and Applied Information Technology. https://api.semanticscholar.org/CorpusID:264402162
Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. (2019, July). Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews. International Journal of Hospitality Management, 80, 52–77. https://doi.org/10.1016/j.ijhm.2019.01.003
Al-Ghalibi, M., Al-Azzawi, A., & Lawonn, K. (2019, March 15). NLP based sentiment analysis for Twitter’s opinion mining and visualization. Eleventh International Conference on Machine Vision (ICMV 2018). https://doi.org/10.1117/12.2522679
Amin, C. R., Hasin, M. F., Leon, T. S., Aurko, A. B., Tamanna, T., Rahman, M. A., & Parvez, M. Z. (2020, December 1). Consumer Behavior Analysis using EEG Signals for Neuromarketing Application. 2020 IEEE Symposium Series on Computational Intelligence (SSCI). https://doi.org/10.1109/ssci47803.2020.9308358
Argyris, Y. A., Wang, Z., Kim, Y., & Yin, Z. (2020, November). The effects of visual congruence on increasing consumers’ brand engagement: An empirical investigation of influencer marketing on Instagram using deep-learning algorithms for automatic image classification. Computers in Human Behavior, 112, 106443. https://doi.org/10.1016/j.chb.2020.106443
Aria, M., & Cuccurullo, C. (2017, November). 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
Arya, V., Paul, J., & Sethi, D. (2021, December 16). Like it or not! Brand communication on social networking sites triggers consumer‐based brand equity. International Journal of Consumer Studies, 46(4), 1381–1398. https://doi.org/10.1111/ijcs.12763
Bae, J. K., Kim, J., & Lee, J. (2007, November). Integration of Heterogeneous Models with Knowledge Consolidation. 2007 International Conference on Convergence Information Technology (ICCIT 2007). https://doi.org/10.1109/iccit.2007.32
Ballestar, M. T., Grau-Carles, P., & Sainz, J. (2018, December 14). Predicting customer quality in e-commerce social networks: a machine learning approach. Review of Managerial Science, 13(3), 589–603. https://doi.org/10.1007/s11846-018-0316-x
Bhade, K., Gulalkari, V., Harwani, N., & Dhage, S. N. (2018, July). A Systematic Approach to Customer Segmentation and Buyer Targeting for Profit Maximization. 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT). https://doi.org/10.1109/icccnt.2018.8494019
Boldt, L. C., Vinayagamoorthy, V., Winder, F., Schnittger, M., Ekran, M., Mukkamala, R. R., Lassen, N. B., Flesch, B., Hussain, A., & Vatrapu, R. (2016, December). Forecasting Nike’s sales using Facebook data. 2016 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata.2016.7840881
Campbell, C., Sands, S., Ferraro, C., Tsao, H. Y. J., & Mavrommatis, A. (2020). From data to action: How marketers can leverage AI. Business horizons, 63(2), 227–243. https://doi.org/10.1016/j.bushor.2019.12.002
Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021, August). The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management, 97, 205–219. https://doi.org/10.1016/j.indmarman.2021.07.013
Chen, C. (2017, March 21). Science Mapping: A Systematic Review of the Literature. Journal of Data and Information Science, 2(2), 1–40. https://doi.org/10.1515/jdis-2017-0006
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. https://doi.org/10.2307/41703503
Chong, A. Y. L., Ch’ng, E., Liu, M. J., & Li, B. (2017). Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews. International Journal of Production Research, 55(17), 5142–5156. https://doi.org/10.1080/00207543.2015.1066519
Cuadros, A. J., & Domínguez, V. E. (2014, March 25). Customer segmentation model based on value generation for marketing strategies formulation. Estudios Gerenciales, 30(130), 25–30. https://doi.org/10.1016/j.estger.2014.02.005
Cuccurullo, C., Aria, M., & Sarto, F. (2016, May 21). Foundations and trends in performance management. A twenty-five years bibliometric analysis in business and public administration domains. Scientometrics, 108(2), 595–611. https://doi.org/10.1007/s11192-016-1948-8
Culotta, A., & Cutler, J. (2016, May). Mining Brand Perceptions from Twitter Social Networks. Marketing Science, 35(3), 343–362. https://doi.org/10.1287/mksc.2015.0968
Dai, Y., & Wang, T. (2021, April 16). Prediction of customer engagement behaviour response to marketing posts based on machine learning. Connection Science, 33(4), 891–910. https://doi.org/10.1080/09540091.2021.1912710
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2019, October 10). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319–340. https://www.jstor.org/stable/249008
de Bruyn, C., Ben Said, F., Meyer, N., & Soliman, M. (2023, August). Research in tourism sustainability: A comprehensive bibliometric analysis from 1990 to 2022. Heliyon, 9(8), e18874. https://doi.org/10.1016/j.heliyon.2023.e18874
Djebbi, M. A., & Ouersighni, R. (2022). TunTap: A Tunisian Dataset for Topic and Polarity Extraction in Social Media. International Conference on Computational Collective Intelligence, Computational Collective Intelligence, 507–519. https://doi.org/10.1007/978-3-031-16014-1_40
Ducange, P., Pecori, R., & Mezzina, P. (2017, March 11). A glimpse on big data analytics in the framework of marketing strategies. Soft Computing, 22(1), 325–342. https://doi.org/10.1007/s00500-017-2536-4
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Egebjerg, N. H., Hedegaard, N., Kuum, G., Mukkamala, R. R., & Vatrapu, R. (2017, June). Big Social Data Analytics in Football: Predicting Spectators and TV Ratings from Facebook Data. 2017 IEEE International Congress on Big Data (BigData Congress). https://doi.org/10.1109/bigdatacongress.2017.20
Egghe, L. (2006, October). Theory and practise of the g-index. Scientometrics, 69(1), 131–152. https://doi.org/10.1007/s11192-006-0144-7
Erevelles, S., Fukawa, N., & Swayne, L. (2016, February). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897–904. https://doi.org/10.1016/j.jbusres.2015.07.001
Fornell, C., & Larcker, D. F. (1981, February). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
Freeman, L. C. (1977, March). A Set of Measures of Centrality Based on Betweenness. Sociometry, 40(1), 35. https://doi.org/10.2307/3033543
Gandomi, A., & Haider, M. (2015, April). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
Garnier-Rizet, M. (2008). CallSurf: Automatic Transcription, Indexing and Structuration of Call Center Conversational Speech for Knowledge Extraction and Query by Content. https://www.semanticscholar.org/paper/CallSurf%3A-Automatic-Transcription%2C-Indexing-and-of-Garnier-Rizet-Adda/7f1185a1248433e2871491752cd4a43b33a37f77
Giglio, S., Bertacchini, F., Bilotta, E., & Pantano, P. (2019). Using social media to identify tourism attractiveness in six Italian cities. Tourism management, 72, 306–312. https://doi.org/10.1016/j.tourman.2018.12.007
Hanafizadeh, P., & Mirzazadeh, M. (2011, January). Visualizing market segmentation using self-organizing maps and Fuzzy Delphi method – ADSL market of a telecommunication company. Expert Systems With Applications, 38(1), 198–205. https://doi.org/10.1016/j.eswa.2010.06.045
Hirsch, J. E. (2005, November 7). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572. https://doi.org/10.1073/pnas.0507655102
Hsieh, N. C. (2004, November). An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Systems With Applications, 27(4), 623–633. https://doi.org/10.1016/j.eswa.2004.06.007
Huang, M. H., & Rust, R. T. (2018, February 5). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
Hung, P. D., Lien, N. T. T., & Ngoc, N. D. (2019, March 16). Customer Segmentation Using Hierarchical Agglomerative Clustering. Proceedings of the 2019 2nd International Conference on Information Science and Systems. https://doi.org/10.1145/3322645.3322677
Hunt, S. D. (2015, November 13). The theoretical foundations of strategic marketing and marketing strategy: foundational premises, R-A theory, three fundamental strategies, and societal welfare. AMS Review, 5(3–4), 61–77. https://doi.org/10.1007/s13162-015-0069-5
Jabbar, J., Urooj, I., JunSheng, W., & Azeem, N. (2019, May). Real-time Sentiment Analysis On E-Commerce Application. 2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC). https://doi.org/10.1109/icnsc.2019.8743331
Kaplan, A., & Haenlein, M. (2019, January). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004
Kotler, P., Kotler, P. T., Armstrong, G., & Opresnik, M. O. (2017). Principles of marketing. Pearson
Kumar, V., Ramachandran, D., & Kumar, B. (2021, March). Influence of new-age technologies on marketing: A research agenda. Journal of Business Research, 125, 864–877. https://doi.org/10.1016/j.jbusres.2020.01.007
Lakshmi, S., Swadthi, N., Shree, V., & Swethamura, M. (2021, March 19). Investigation on Bitcoin Prognosis. 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). https://doi.org/10.1109/icaccs51430.2021.9441794
Lamrhari, S., Ghazi, H. E., Oubrich, M., & Faker, A. E. (2022, January). A social CRM analytic framework for improving customer retention, acquisition, and conversion. Technological Forecasting and Social Change, 174, 121275. https://doi.org/10.1016/j.techfore.2021.121275
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420
Li, S. (2000, January). The development of a hybrid intelligent system for developing marketing strategy. Decision Support Systems, 27(4), 395–409. https://doi.org/10.1016/s0167-9236(99)00061-5
Liu, X., Singh, P. V., & Srinivasan, K. (2016). A structured analysis of unstructured big data by leveraging cloud computing. Marketing Science, 35(3), 363–388. https://doi.org/10.1287/mksc.2015.0972
Lyu, F., & Choi, J. (2020, May 27). The Forecasting Sales Volume and Satisfaction of Organic Products through Text Mining on Web Customer Reviews. Sustainability, 12(11), 4383. https://doi.org/10.3390/su12114383
Mahdiraji, H. A., Kazimieras Zavadskas, E., Kazeminia, A., & Abbasi Kamardi, A. (2019, January 1). Marketing strategies evaluation based on big data analysis: a CLUSTERING-MCDM approach. Economic Research-Ekonomska Istraživanja, 32(1), 2882–2898. https://doi.org/10.1080/1331677x.2019.1658534
Min, C., Yinghui, X., XiaoGuang, Y., Hongmei, Y., & Comite, U. (2021, July 29). Innovation of Practical Teaching Mode of Marketing Major-Market Research and Forecast under the Background of Big Data. 2021 6th International Conference on Intelligent Information Processing. https://doi.org/10.1145/3480571.3480579
Morgan, N. A., Whitler, K. A., Feng, H., & Chari, S. (2018, August 18). Research in marketing strategy. Journal of the Academy of Marketing Science, 47(1), 4–29. https://doi.org/10.1007/s11747-018-0598-1
Overgoor, G., Chica, M., Rand, W., & Weishampel, A. (2019). Letting the Computers Take Over: Using AI to Solve Marketing Problems. California Management Review, 61(4), 156–185. https://doi.org/10.1177/0008125619859318
Oztekin, A. (2017, May 2). Creating a marketing strategy in healthcare industry: a holistic data analytic approach. Annals of Operations Research, 270(1–2), 361–382. https://doi.org/10.1007/s10479-017-2493-4
Palmatier, R. W., & Crecelius, A. T. (2019). The “first principles” of marketing strategy. Ams Review, 9, 5–26. https://doi.org/10.1007/s13162-019-00134-y
Park, S. B., Ok, C. M., & Chae, B. K. (2015, September 4). Using Twitter Data for Cruise Tourism Marketing and Research. Journal of Travel & Tourism Marketing, 33(6), 885–898. https://doi.org/10.1080/10548408.2015.1071688
Pranckutė, R. (2021, March 12). Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World. Publications, 9(1), 12. https://doi.org/10.3390/publications9010012
Rong, J., Vu, H. Q., Law, R., & Li, G. (2012, August). A behavioral analysis of web sharers and browsers in Hong Kong using targeted association rule mining. Tourism Management, 33(4), 731–740. https://doi.org/10.1016/j.tourman.2011.08.006
Rownd, M., & Heath, C. (2008). The American Marketing Association releases new definition for marketing. Chicago IL: AMA, 1-3. https://gyansanchay.csjmu.ac.in/wp-content/uploads/2022/04/MM-L-1-AMA-Definitions-of-Marketing.pdf
Sakas, D. P., & Giannakopoulos, N. T. (2021). Big data contribution in desktop and mobile devices comparison, regarding airlines’ digital brand name effect. Big data and cognitive computing, 5(4), 48. https://doi:10.3390/bdcc5040048
Salehinejad, H., & Rahnamayan, S. (2016, December). Customer shopping pattern prediction: A recurrent neural network approach. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). https://doi.org/10.1109/ssci.2016.7849921
Saura, J. R., Ribeiro-Soriano, D., & Palacios-Marqués, D. (2021, October). Setting B2B digital marketing in artificial intelligence-based CRMs: A review and directions for future research. Industrial Marketing Management, 98, 161–178. https://doi.org/10.1016/j.indmarman.2021.08.006
Serrano, E., & Iglesias, C. A. (2016, May). Validating viral marketing strategies in Twitter via agent-based social simulation. Expert Systems With Applications, 50, 140–150. https://doi.org/10.1016/j.eswa.2015.12.021
Shankar, V. (2018). How artificial intelligence (AI) is reshaping retailing. Journal of retailing, 94(4), vi-xi. https://doi.org/10.1016/S0022-4359(18)30076-9
Slater, S. F., Hult, G. T. M., & Olson, E. M. (2010, May). Factors influencing the relative importance of marketing strategy creativity and marketing strategy implementation effectiveness. Industrial Marketing Management, 39(4), 551–559. https://doi.org/10.1016/j.indmarman.2008.03.007
Soguero-Ruiz, C., Gimeno-Blanes, F. J., Mora-Jiménez, I., Martínez-Ruiz, M. P., & Rojo-Álvarez, J. L. (2012, December). On the differential benchmarking of promotional efficiency with machine learning modeling (I): Principles and statistical comparison. Expert Systems With Applications, 39(17), 12772–12783. https://doi.org/10.1016/j.eswa.2012.04.017
Sozuer, S., Carpenter, G. S., Kopalle, P. K., McAlister, L. M., & Lehmann, D. R. (2020). The past, present, and future of marketing strategy. Marketing Letters, 31(2-3), 163–174. https://doi.org/10.1007/s11002-020-09529-5
Srivastava, P. R., Sharma, D., Kaur, I., & Wamba, S. F. (2021). Intellectual Structure and Publication Pattern in Journal of Global Information Management: A Bibliometric Analysis During 2002-2020. Journal of Global Information Management (JGIM), 29(4), 1–31. https://doi.org/10.4018/JGIM.20210701.oa1
Steinhoff, L., Arli, D., Weaven, S., & Kozlenkova, I. V. (2018, December 19). Online relationship marketing. Journal of the Academy of Marketing Science, 47(3), 369–393. https://doi.org/10.1007/s11747-018-0621-6
Sundsøy, P., Bjelland, J., Iqbal, A. M., Pentland, A. S., & de Montjoye, Y. A. (2014). Big Data-Driven Marketing: How Machine Learning Outperforms Marketers’ Gut-Feeling In Kennedy, W. G., Agarwal, N., Yang, S. J. (eds), Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2014. Lecture Notes in Computer Science, vol. 8393 (pp. 367–374). Springer, Cham. https://doi.org/10.1007/978-3-319-05579-4_45
Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial marketing management, 69, 135–146. https://doi.org/10.1016/j.indmarman.2017.12.019
Thomaz, F., Salge, C., Karahanna, E., & Hulland, J. (2019, November 11). Learning from the Dark Web: leveraging conversational agents in the era of hyper-privacy to enhance marketing. Journal of the Academy of Marketing Science, 48(1), 43–63. https://doi.org/10.1007/s11747-019-00704-3
Tian, J., Zhang, Y., & Zhang, C. (2018, March). Predicting consumer variety-seeking through weather data analytics. Electronic Commerce Research and Applications, 28, 194–207. https://doi.org/10.1016/j.elerap.2018.02.001
Timoshenko, A., & Hauser, J. R. (2019, January). Identifying Customer Needs from User-Generated Content. Marketing Science, 38(1), 1–20. https://doi.org/10.1287/mksc.2018.1123
Tong, S., Luo, X., & Xu, B. (2019, October 16). Personalized mobile marketing strategies. Journal of the Academy of Marketing Science, 48(1), 64–78. https://doi.org/10.1007/s11747-019-00693-3
Trupthi, M., Pabboju, S., & Narasimha, G. (2017, January). Sentiment Analysis on Twitter Using Streaming API. 2017 IEEE 7th International Advance Computing Conference (IACC). https://doi.org/10.1109/iacc.2017.0186
Tupikovskaja-Omovie, Z., & Tyler, D. (2020, May 4). Clustering consumers’ shopping journeys: eye tracking fashion m-retail. Journal of Fashion Marketing and Management: An International Journal, 24(3), 381–398. https://doi.org/10.1108/jfmm-09-2019-0195
Vaishanvi, S., Rajkaran, Y. P., Rahul, V., & Nirmal, L. (2022, October 27). Product Recommendation Using Sentiment Analysis. 2022 International Conference on Engineering and Emerging Technologies (ICEET). https://doi.org/10.1109/iceet56468.2022.10007234
Varadarajan, R. (2009, October 28). Strategic marketing and marketing strategy: domain, definition, fundamental issues and foundational premises. Journal of the Academy of Marketing Science, 38(2), 119–140. https://doi.org/10.1007/s11747-009-0176-7
Varadarajan, R. (2015). Strategic marketing, marketing strategy and market strategy. AMS review, 5, 78–90. https://doi.org/10.1007/s13162-015-0073-9
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021, April). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002. https://doi.org/10.1016/j.jjimei.2020.100002
Wang, C., & Si, L. (2023). A Bibliometric Analysis of Digital Literacy Research from 1990 to 2022 and Research on Emerging Themes during the COVID-19 Pandemic. Sustainability, 15(7), 5769. https://doi.org/10.3390/su15075769
Wang, S. C., Tsai, Y. T., & Ciou, Y. S. (2020, December). A hybrid big data analytical approach for analyzing customer patterns through an integrated supply chain network. Journal of Industrial Information Integration, 20, 100177. https://doi.org/10.1016/j.jii.2020.100177
Wang, Z., Tu, L., Guo, Z., Yang, L. T., & Huang, B. (2014, July). Analysis of user behaviors by mining large network data sets. Future Generation Computer Systems, 37, 429–437. https://doi.org/10.1016/j.future.2014.02.015
Xiao, D. (2012, October). Statistics and Analysis of Bank Customers’ Financial Consumption Behaviors. 2012 Fifth International Symposium on Computational Intelligence and Design. https://doi.org/10.1109/iscid.2012.72
Yang, J., Liu, C., Teng, M., Liao, M., & Xiong, H. (2016, December). Buyer targeting optimization: A unified customer segmentation perspective. 2016 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata.2016.7840730
Yoseph, F., & Heikkila, M. (2018, December). Segmenting Retail Customers with an Enhanced RFM and a Hybrid Regression/Clustering Method. 2018 International Conference on Machine Learning and Data Engineering (ICMLDE). https://doi.org/10.1109/icmlde.2018.00029
Zhang, M., & Ma, X. (2022, March 26). Online Shopping Brand Sales Based on IoT Big Data Processing. Computational Intelligence and Neuroscience, 2022, 1–14. https://doi.org/10.1155/2022/3833583
Zhou, W., Chen, Q., & Meng, S. (2019). Knowledge mapping of credit risk research: Scientometrics analysis using CiteSpace. Economic Research-Ekonomska Istraživanja, 32(1), 3457–3484. https://doi.org/10.1080/1331677X.2019.1660202
Zhu, J., & Liu, W. (2020, February 22). A tale of two databases: the use of Web of Science and Scopus in academic papers. Scientometrics, 123(1), 321–335. https://doi.org/10.1007/s11192-020-03387-8