Topic Modeling Emerging Trends for Business Intelligence in Marketing: With Text Mining and Latent Dirichlet Allocation

Journal of System Management

مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 01 دی 1402

نوع مقاله: Research Paper

شناسه دیجیتال (DOI): 10.30495/jsm.2023.2000760.1901

نویسندگان

Rouhollah Bagheri^{* 1}؛ Nahid Entezarian²

¹Department of Management, Faculty of Administrative and Economic Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.

²Assistant Professor, Department of Management, Faculty of Administrative and Economic Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.

چکیده

This paper examines recent literature in the quest to uncover emerging patterns in the use of business intelligence in marketing. We conducted searches in pertinent academic journals and identified 1044 articles published between 2000 and 2023. To sift through this substantial body of work, we employed text mining techniques to extract pertinent terms in the realms of business intelligence and marketing. Additionally, we applied latent Dirichlet allocation modeling to categorize the articles into various pertinent topics. This analysis was performed within the domains of marketing and business intelligence. This approach enabled us to discover connections between terms and topics, which in turn allowed us to generate hypotheses regarding future research directions. To validate these hypotheses, we gathered and closely examined relevant articles. By pinpointing current research areas, this study underscores potential avenues for future investigation. The findings reveal that the predominant trend in business intelligence applications for marketing is the utilization of business intelligence systems, with a particular emphasis on marketing planning to enhance marketing strategies. Additionally, there is considerable interest in areas such as pricing models for marketing, enhancing brand value through effective social media marketing, employing predictive algorithms for customer data analysis, and harnessing big data for marketing analytics.

کلیدواژه‌ها

Marketing؛ Business intelligence؛ Text mining؛ Latent Dirichlet allocation؛ topic modeling

مراجع

Ahmadi, M. M., & Zare, S. (2021). Business Intelligence Technology in Research Organizations (Case Study of Academic Institutes in Tehran). Journal of System Management, 6(4), 69-101

Barrera, K. G., & Shah, D. (2023). Marketing in the Metaverse: Conceptual understanding, framework, and research agenda. Journal of Business Research, 155, 113420. https://doi.org/10.1016/j.jbusres.2022.113420

Bogoradnikova, D., Makhnytkina, O., Matveev, A., Zakharova, A., & Akulov, A. (2021, May). Multilingual Sentiment Analysis and Toxicity Detection for Text Messages in Russian. In 2021 29th Conference of Open Innovations Association (FRUCT) (pp. 55-64). IEEE. https://doi.org/10.23919/FRUCT52173.2021.9435584

Bremmer, I. (2014). The new rules of globalization. Harvard business review, 92(1), 103-107.

Buenano-Fernandez, D., Gonzalez, M., Gil, D., & Luján-Mora, S. (2020). Text mining of open-ended questions in self-assessment of university teachers: An LDA topic modeling approach. Ieee Access, 8, 35318-35330. 10.1109/ACCESS.2020.2974983

Chauhan, U., & Shah, A. (2021). Topic modeling using latent Dirichlet allocation: A survey. ACM Computing Surveys (CSUR), 54(7), 1-35. https://doi.org/10.1145/3462478

Chen, L., Wang, P., Ma, X., & Wang, X. (2021). Cancer communication and user engagement on Chinese social media: Content analysis and topic modeling study. Journal of Medical Internet Research, 23(11), e26310.  https://doi:10.2196/26310

Chen, Y., & Lin, Z. (2021). Business intelligence capabilities and firm performance: A study in China. International Journal of Information Management, 57, 102232. https://doi.org/10.1016/j.ijinfomgt.2020.102232

Daenekindt, S., & Huisman, J. (2020). Mapping the scattered field of research on higher education. A correlated topic model of 17,000 articles, 1991–2018. Higher Education, 80(3), 571-587. https://doi.org/10.1007/s10734-020-00500-x

Ghoushchi, S. J., Osgooei, E., Haseli, G., & Tomaskova, H. (2021). A novel approach to solve fully fuzzy linear programming problems with modified triangular fuzzy numbers. Mathematics, 9(22), 2937.  https://doi.org/10.3390/math9222937

Hadhoud, R., & Salameh, W. A. (2020). How business intelligence can help you to better understand your customers. International Journal of Business Intelligence Research (IJBIR), 11(1), 50-58.‏ https://doi.org/10.4018/IJBIR.2020010104

Haseli, G., & Sheikh, R. (2022). Base criterion method (BCM). In Multiple Criteria Decision Making: Techniques, Analysis and Applications (pp. 17-38). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-16-7414-3_2

Haseli, G., Ögel, İ. Y., Ecer, F., & Hajiaghaei-Keshteli, M. (2023). Luxury in female technology (FemTech): Selection of smart jewelry for women through BCM-MARCOS group decision-making framework with fuzzy ZE-numbers. Technological Forecasting and Social Change, 196, 122870. https://doi.org/10.1016/j.techfore.2023.122870

Haseli, G., Sheikh, R., & Sana, S. S. (2020). Base-criterion on multi-criteria decision-making method and its applications. International journal of management science and engineering management, 15(2), 79-88. https://doi.org/10.1080/17509653.2019.1633964

Hosseini Astaraei, F., Shojaie, S., Saeidi, P., & Mostaghimi, M. R. (2019). Providing a Model of the Enterprise Market Capabilities with an Emphasis on Organizational Entrepreneurship. Journal of System Management, 5(4), 103-112.‏

Huang, Z. X., Savita, K. S., Dan-yi, L., & Omar, A. H. (2022). The impact of business intelligence on the marketing with emphasis on cooperative learning: Case-study on the insurance companies. Information Processing & Management, 59(2), 102824. https://doi.org/10.1016/j.ipm.2021.102824

Kafilaleh, Y., Bodaghi Khajeh Noubar, H., Motemani, A., & Peyvasteh, A. (2021). Validation of the pattern of brand marketing efforts on social media with customers in the dermato-cosmetic industry. Journal of System Management, 7(3), 311-331.

Kim, K. Y. (2014). Business intelligence and marketing insights in an era of big data: The q-sorting approach. KSII Transactions on Internet & Information Systems, 8(2). https://doi.org/10.3837/tiis.2014.02.014

Malek Shirabadi, F., Karimi Zand, M., & Kabaran Zad Ghadim, M. R. (2022). Designing and validating a new integrated digital marketing model. Journal of System Management, 8(1), 127-144. https://doi.org/10.30495/JSM.2022.1944114.1565

Kongthanasuwan, T., Sriwiboon, N., Horbanluekit, B., Laesanklang, W., & Krityakierne, T. (2023). Market Analysis with Business Intelligence System for Marketing Planning. Information, 14(2), 116.‏ https://doi.org/10.3390/info14020116

Manian, A., & Ronaghi, M. H. (2015). A comprehensive framework for e-marketing implementation by meta-synthesis method. Journal of Business management, 7(4), 901-920. https://doi.org/10.22059/JIBM.2015.57097

Moro, S., Cortez, P., & Rita, P. (2015). Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Systems with Applications, 42(3), 1314-1324. https://doi.org/10.1016/j.eswa.2014.09.024

Moro, S., Pires, G., Rita, P., & Cortez, P. (2019). A text mining and topic modelling perspective of ethnic marketing research. Journal of Business Research, 103, 275-285. https://doi.org/10.1016/j.jbusres.2019.01.053

Peres, R., Schreier, M., Schweidel, D. A., & Sorescu, A. (2023). Blockchain meets marketing: Opportunities, threats, and avenues for future research. International Journal of Research in Marketing, 40(1), 1-11. https://doi.org/10.1016/j.ijresmar.2022.08.001

Rahchamani, S. M., Heydariyeh, S. A., & Zargar, S. M. (2022). A Model for Identification of Factors Affecting Services Intelligent Supply Chains: A Meta-Synthesis Approach. Journal of System Management, 8(3), 13-33.‏ https://doi.org /10.30495/JSM.2022.1957013.1641

Shao, C., Yang, Y., Juneja, S., & GSeetharam, T. (2022). IoT data visualization for business intelligence in corporate finance. Information Processing & Management, 59(1), 102736. https://doi.org/10.1016/j.ipm.2021.102736

Skålén, P., Cova, B., Gummerus, J., & Sihvonen, A. (2023). Marketing-as-practice: A framework and research agenda for value-creating marketing activity. Marketing Theory, 23(2), 185-206. https://doi.org/10.1177/14705931221123949

Sprong, N., Driessen, P. H., Hillebrand, B., & Molner, S. (2021). Market innovation: A literature review and new research directions. Journal of Business Research, 123, 450-462. https://doi.org/10.1016/j.jbusres.2020.09.057

Stone, M. D., & Woodcock, N. D. (2014). Interactive, direct and digital marketing: A future that depends on better use of business intelligence. Journal of research in interactive marketing, 8(1), 4-17. https://doi.org/10.1108/JRIM-07-2013-0046

Tilak, G. (2020). A Review of Using Business Intelligence (BI) in Digital Marketing.

Vayansky, I., & Kumar, S. A. (2020). A review of topic modeling methods. Information Systems, 94, 101582. https://doi.org/10.1016/j.is.2020.101582

Webster, F. E., & Lusch, R. F. (2013). Elevating marketing: marketing is dead! Long live marketing!. Journal of the Academy of Marketing Science, 41, 389-399. https://doi.org/10.1007/s11747-013-0331-z