The Trends of Potential User Research from 2014-2023 Based on Bibliometric and Bertopic
DOI:
https://doi.org/10.24857/rgsa.v18n9-068Palabras clave:
Prospective Users, Bibliometric Analysis, Topic Evolution, BERTopic Models, Text Data MiningResumen
Objective: Despite the increasing importance of lead generation research in increasing product or market share, cost and resource constraints have become a challenge for SMEs.Therefore, this study aims to explore and reveal research themes and market trends hidden in articles on lead generation over the past 10 years.
.
Theoretical Framework: In this study, qualitative and quantitative methods are combined, and three methods of bibliometrics, network analysis and BERTopic topic modeling are used to analyze the literature.
Method: A total of 7446 articles were analysed using bibliometrics, network analysis and BERTopic thematic modelling as the basis of a mixed method approach.
Results and Discussion: The study found that the field is currently experiencing a downward trend after a phase of rapid growth. During this period, the United States and China were the countries with the highest number of articles accounting for 77% of the total; the Journal of Cleaner Journal of Cleaner Production was the most cited journal. In addition, the potential user studies cover 43 mainstream topics, focusing on 6 aspects . In the in-depth analysis of the theme evolution, it was found that the potential user study gradually evolved from the initial multidimensional application to focus on open service, and was more oriented towards the public service field.
Research Implications: This provides a strong theoretical basis and practical guidance for identifying potential customers and increasing conversion rates and revenues.
Originality/Value: To our knowledge, this is the first study to use a mixed-methods approach to lead generation, which will help researchers to tackle more complex challenges and changes in the future.
Descargas
Citas
Slater, S. F., & Narver, J. C. (1998). Customer‐led and market‐oriented: let's not confuse the two. Strategic management journal, 19(10), 1001-1006. DOI: https://doi.org/10.1002/(SICI)1097-0266(199810)19:10<1001::AID-SMJ996>3.0.CO;2-4
Howard, J. A., & Sheth, J. N. (1969). The theory of buyer behavior. New York, 63, 145.
Read, S., Dew, N., Sarasvathy, S. D., Song, M., & Wiltbank, R. (2009). Marketing under uncertainty: The logic of an effectual approach. Journal of marketing, 73(3), 1-18. DOI: https://doi.org/10.1509/jmkg.73.3.1
DemandGen Report，2021，2021 Lead Nurturing & Acceleration Benchmark Survey: Moving Into The Modern Era With Highly Targeted, Account-Based LeadNurturing,DemandGenReport ,https://www.demandgenreport.com/resources/2021-lead-nurturing-acceleration-benchmark-survey-moving-into-the-modern-era-with-highly-targeted-account-based-lead-nurturing/6963/
Brandon Purcell，2023，Make Customer Lifetime Value Your Polaris For Long-TermGrowth,ForresterResearch ,https://www.forrester.com/report/make-customer-lifetime-value-your-polaris-for-long-term-growth/RES150315?ref_search=0_1712477309254
Hood, W. W., & Wilson, C. S. (2001). The literature of bibliometrics, scientometrics, and informetrics. Scientometrics, 52, 291-314. DOI: https://doi.org/10.1023/A:1017919924342
Prahalad, C. K., & Ramaswamy, V. (2004). The future of competition: Co-creating unique value with customers. Harvard Business Press. DOI: https://doi.org/10.1108/10878570410699249
Rogers, D. L. (2016). The digital transformation playbook: Rethink your business for the digital age. Columbia University Press. DOI: https://doi.org/10.7312/roge17544
Thelwall, M. (2008). Bibliometrics to webometrics. Journal of information science, 34(4), 605-621. DOI: https://doi.org/10.1177/0165551507087238
Thompson, D. F., & Walker, C. K. (2015). A descriptive and historical review of bibliometrics with applications to medical sciences. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy, 35(6), 551-559. DOI: https://doi.org/10.1002/phar.1586
Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.
Bai, Y., Li, H., & Liu, Y. (2021). Visualizing research trends and research theme evolution in E-learning field: 1999–2018. Scientometrics, 126, 1389-1414. DOI: https://doi.org/10.1007/s11192-020-03760-7
Chadegani, A. A., Salehi, H., Yunus, M. M., Farhadi, H., Fooladi, M., Farhadi, M., & Ebrahim, N. A. (2013). A comparison between two main academic literature collections: Web of Science and Scopus databases. arXiv preprint arXiv:1305.0377. DOI: https://doi.org/10.5539/ass.v9n5p18
Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications, 9(1), 12. DOI: https://doi.org/10.3390/publications9010012
Li, W., & Zhao, Y. (2015). Bibliometric analysis of global environmental assessment research in a 20-year period. Environmental Impact Assessment Review, 50, 158-166. DOI: https://doi.org/10.1016/j.eiar.2014.09.012
Park, H. W., Yoon, J., & Leydesdorff, L. (2016). The normalization of co-authorship networks in the bibliometric evaluation: the government stimulation programs of China and Korea. Scientometrics, 109, 1017-1036. DOI: https://doi.org/10.1007/s11192-016-1978-2
Meng, L., Wen, K. H., Brewin, R., & Wu, Q. (2020). Knowledge atlas on the relationship between urban street space and residents’ health—a bibliometric analysis based on VOSviewer and CiteSpace. Sustainability, 12(6), 2384. DOI: https://doi.org/10.3390/su12062384
Mazzei, D., & Ramjattan, R. (2022). Machine Learning for Industry 4.0: A Systematic Review Using Deep Learning-Based Topic Modelling. Sensors, 22(22), 8641. DOI: https://doi.org/10.3390/s22228641
Smith, B. P., Hoots, B., DePadilla, L., Roehler, D. R., Holland, K. M., Bowen, D. A., & Sumner, S. A. (2023). Using Transformer-Based Topic Modeling to Examine Discussions of Delta-8 Tetrahydrocannabinol: Content Analysis. Journal of Medical Internet Research, 25(1), e49469. DOI: https://doi.org/10.2196/49469
Church, K. W. (2017). Word2Vec. Natural Language Engineering, 23(1), 155-162. DOI: https://doi.org/10.1017/S1351324916000334
Di Gennaro, G., Buonanno, A., & Palmieri, F. A. (2021). Considerations about learning Word2Vec. The Journal of Supercomputing, 1-16. DOI: https://doi.org/10.1007/s11227-021-03743-2
Wang, H., Tong, L., Takeuchi, R., & George, G. (2016). Corporate social responsibility: An overview and new research directions: Thematic issue on corporate social responsibility. Academy of Management journal, 59(2), 534-544. DOI: https://doi.org/10.5465/amj.2016.5001
Yu, Q., Shao, H., He, P., & Duan, Z. (2013). World scientific collaboration in coronary heart disease research. International journal of cardiology, 167(3), 631-639. DOI: https://doi.org/10.1016/j.ijcard.2012.09.134
Han, P., Shi, J., Li, X., Wang, D., Shen, S., & Su, X. (2014). International collaboration in LIS: global trends and networks at the country and institution level. Scientometrics, 98, 53-72. DOI: https://doi.org/10.1007/s11192-013-1146-x
Deng, X., Zhai, J., Lv, T., & Yin, L. (2017). Efficient vector influence clustering coefficient based directed community detection method. IEEE Access, 5, 17106-17116. DOI: https://doi.org/10.1109/ACCESS.2017.2740962
Sun, W., Huang, P., Song, H., & Feng, D. (2020). Bibliometric analysis of acute pancreatitis in Web of Science database based on CiteSpace software. Medicine, 99(49). DOI: https://doi.org/10.1097/MD.0000000000023208
Elazhary, H. (2019). Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions. Journal of network and computer applications, 128, 105-140. DOI: https://doi.org/10.1016/j.jnca.2018.10.021
Small, H. (1973). Co‐citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for information Science, 24(4), 265-269. DOI: https://doi.org/10.1002/asi.4630240406
Osareh, F. (1996). Bibliometrics, citation analysis and co-citation analysis: A review of literature I. DOI: https://doi.org/10.1515/libr.1996.46.3.149
Pouder, R., & St. John, C. H. (1996). Hot spots and blind spots: Geographical clusters of firms and innovation. Academy of management review, 21(4), 1192-1225. DOI: https://doi.org/10.5465/amr.1996.9704071867
Bornmann, L., Haunschild, R., & Hug, S. E. (2018). Visualizing the context of citations referencing papers published by Eugene Garfield: A new type of keyword co-occurrence analysis. Scientometrics, 114, 427-437. DOI: https://doi.org/10.1007/s11192-017-2591-8
Gottschalk, S., & Demidova, E. (2019). EventKG–the hub of event knowledge on the web–and biographical timeline generation. Semantic Web, 10(6), 1039-1070. DOI: https://doi.org/10.3233/SW-190355
Selvin, E., Parrinello, C. M., Sacks, D. B., & Coresh, J. (2014). Trends in prevalence and control of diabetes in the United States, 1988–1994 and 1999–2010. Annals of internal medicine, 160(8), 517-525. DOI: https://doi.org/10.7326/M13-2411
Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.
Egger, R., & Yu, J. (2022). A topic modeling comparison between lda, nmf, top2vec, and bertopic to demystify twitter posts. Frontiers in sociology, 7, 886498. DOI: https://doi.org/10.3389/fsoc.2022.886498
Weißer, T., Saßmannshausen, T., Ohrndorf, D., Burggräf, P., & Wagner, J. (2020). A clustering approach for topic filtering within systematic literature reviews. MethodsX, 7, 100831. DOI: https://doi.org/10.1016/j.mex.2020.100831
Matheron, G., Perrin, N., & Sigaud, O. (2020, September). PBCS: Efficient exploration and exploitation using a synergy between reinforcement learning and motion planning. In International Conference on Artificial Neural Networks (pp. 295-307). Cham: Springer International DOI: https://doi.org/10.1007/978-3-030-61616-8_24
Rong, X. (2014). word2vec parameter learning explained. arXiv preprint arXiv:1411.2738.
Jang, B., Kim, I., & Kim, J. W. (2019). Word2vec convolutional neural networks for classification of news articles and tweets. PloS one, 14(8), e0220976. DOI: https://doi.org/10.1371/journal.pone.0220976
Shinde, A. V., Patil, D. D., & Tripathi, K. K. (2024). A Comprehensive Survey on Recommender Systems Techniques and Challenges in Big Data Analytics with IOT Applications. Revista De Gestão Social E Ambiental, 18(2), e05195. https://doi.org/10.24857/rgsa.v18n2-097 DOI: https://doi.org/10.24857/rgsa.v18n2-097
Almustafa, E., Assaf, A., & Allahham, M. (2023). Implementation of Artificial Intelligence for Financial Process Innovation of Commercial Banks. Revista De Gestão Social E Ambiental, 17(9), e04119. DOI: https://doi.org/10.24857/rgsa.v17n9-004
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2024 Liu Kun , Hassan Alli, Khairul Aidil Azlin Abd Rahman
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
•The author(s) authorize(s) the publication of the article in the journal; • The author(s) guarantee the contribution is original and novel, and is not under evaluation in any other publication(s); • The editorial team is not responsible for the opinions, ideas and concepts emitted in the published texts, these being the full responsibility of the author(s); • It is reserved to the editors the right to proceed with adjustments related to the text and compliance with publication standards.
