An Empirical Study of the Impact of Social Media Data Analytics on Marketing Strategy Which Social Media Data Analytics Metrics to Select?
Main Article Content
Keywords
Social Media Data Analytics (SMDA), Marketing Strategies, Conceptual framework, Empirical Study, PLS-SEM
Abstract
Social Media Data Analytics (SMDA) has emerged as a dynamic and growing field across various disciplines, including marketing. However, practitioners and researchers in the marketing domain have realized that harnessing the full potential of SMDA for guiding marketing strategies necessitates a clear understanding of the relevant SMDA metrics. A significant challenge lies in the lack of clear guidance on which SMDA metrics are most relevant for enhancing marketing strategies. This study aims to empirically evaluate the impact of SMDA on marketing strategies. To achieve this goal, the study carried out a questionnaire for data collection and employs an empirical investigation using the PLS-SEM methodology. The results show that the impact of SMDA on marketing strategy depends on SMDA metrics (data type, platforms and analysis methods) and also on marketing strategy type. The results suggest a valid conceptual model introducing novel metrics for the SMDA concept. These results present a broader perspective on how SMDA affects marketing strategies and suggest that future research should focus on a specific type of marketing strategy and study SMDA metrics in a different and more in-depth way.
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References
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Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388. https://doi.org/10.1177/002224378101800313
Galetsi, P., Katsaliaki, K., & Kumar, S. (2020). Big data analytics in health sector: Theoretical framework, techniques and prospects. International Journal of Information Management, 50, 206–216. https://doi.org/10.1016/j.ijinfomgt.2019.05.003
Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101–107. https://doi.org/10.2307/2334290
Ghani, N. A., Hamid, S., Hashem, I. A. T., & Ahmed, E. (2019). Social media big data analytics: A survey. Computers in Human Behavior, 101, 417–428. https://doi.org/10.1016/j.chb.2018.08.039
Gupta, S., Drave, V. A., Dwivedi, Y. K., Baabdullah, A. M., & Ismagilova, E. (2020). Achieving superior organizational performance via big data predictive analytics: A dynamic capability view. Industrial Marketing Management, 90, 581–592. https://doi.org/10.1016/j.indmarman.2019.11.009
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064. https://doi.org/10.1016/j.im.2016.07.004
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. Journal of the academy of marketing science, 45(5), 616–632. https://doi.org/10.1007/s11747-017-0517-x
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Sinkovics, R.R. and Ghauri, P.N. (Ed.) New challenges to international marketing (pp. 277–319). Emerald Group Publishing Limited. https://doi.org/10.1108/S1474-7979(2009)0000020014
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Jabado, R., & Jallouli, R. (2023). A Systematic Literature Review on CRM Critical Success Factors. In: Jallouli, R., Bach Tobji, M.A., Belkhir, M., Soares, A.M., Casais, B. (eds), Digital Economy. Emerging Technologies and Business Innovation. ICDEc 2023. Lecture Notes in Business Information Processing, vol. 485. Springer, Cham. https://doi.org/10.1007/978-3-031-42788-6_15
Jallouli, R., & Kaabi, S. (2022). Mapping top strategic e-commerce technologies in the digital marketing literature. Journal of Telecommunications and the Digital Economy, 10(3), 149–164. https://doi.org/10.18080/jtde.v10n3.554
Jayachandran, S., Sharma, S., Kaufman, P., & Raman, P. (2005). The Role of Relational Information Processes and Technology Use in Customer Relationship Management. Journal of Marketing, 69(4), 177–192. https://doi.org/10.1509/jmkg.2005.69.4.177
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Lee, I. (2018). Social media analytics for enterprises: Typology, methods, and processes. Business Horizons, 61(2), 199–210. https://doi.org/10.1016/j.bushor.2017.11.002
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Mar 27, 2024
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Benslama, T., & Jallouli, R. (2024). An Empirical Study of the Impact of Social Media Data Analytics on Marketing Strategy: Which Social Media Data Analytics Metrics to Select?. Journal of Telecommunications and the Digital Economy, 12(1), 575–594. https://doi.org/10.18080/jtde.v12n1.830
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Special Issue: Emerging Technologies and Innovation for Digital Economy and Transformation
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