The Effect of Artificial Intelligence Utilizing in Social Media Marketing

Authors

  • Yeyen Pratika Universitas Muhammadiyah Malang

DOI:

https://doi.org/10.37301/jmubh.v18i2.23211

Abstract

The development of Artificial Intelligence (AI) provides many opportunities in the current business structure. AI is used in various industries with different purposes, including creating engagement with customers. Financial technology (fintech) companies also utilize AI for giving product recommendations or targeted ads based on customers’ algorithms. Thus, this study aims to examine whether the use of AI in social media marketing can encourage consumers’ intention to use the products or services offered by the fintech companies. A total of 121 respondents were involved in this study with Structural Equation Modeling (SEM) as the analysis method. The result indicates that performance expectancy, utilitarian motivation and perceived valur co-creation influence the intention to invest.

References

Anderson, K. C., Knight, D. K., Pookulangara, S., & Josiam, B. (2014). Influence of hedonic and utilitarian motivations on retailer loyalty and purchase intention: A facebook perspective. Journal of Retailing and Consumer Services, 21(5), 773–779. https://doi.org/10.1016/j.jretconser.2014.05.007

Bagozzi, R. P., & Warshaw, P. R. (1990). Trying to Consume. Journal of Consumer Research, 17(2), 127. https://doi.org/10.1086/208543

Bank, S., Yazar, E. E., & Sivri, U. (2019). Can social media marketing lead to abnormal portfolio returns? European Research on Management and Business Economics, 25(2), 54–62. https://doi.org/10.1016/j.iedeen.2019.04.006

Basu, S. (2021). Personalized product recommendations and firm performance. Electronic Commerce Research and Applications, 48, 101074. https://doi.org/10.1016/j.elerap.2021.101074

Dilmperi, A., King, T., & Dennis, C. (2017). Toward a framework for identifying attitudes and intentions to music acquisition from legal and illegal channels. Psychology and Marketing, 34(4), 428–447. https://doi.org/10.1002/mar.20998

Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of Price, Brand, and Store Information on Buyers’ Product Evaluations. Journal of Marketing Research, 28(3), 307–319. https://doi.org/10.1177/002224379102800305

Duh, H. I., & Dabula, N. (2021). Millennials’ socio-psychology and blood donation intention developed from social media communications: A survey of university students. Telematics and Informatics, 58(November 2020), 101534. https://doi.org/10.1016/j.tele.2020.101534

Dumitriu, D., & Popescu, M. A. M. (2020). Artificial intelligence solutions for digital marketing. Procedia Manufacturing, 46(2019), 630–636. https://doi.org/10.1016/j.promfg.2020.03.090

Dwivedi, Y. K., Kapoor, K. K., & Chen, H. (2015). Social media marketing and advertising. The Marketing Review, 15(3), 289–309.

Dwivedi, Y. K., Rana, N. P., Tajvidi, M., Lal, B., Sahu, G. P., & Gupta, A. (2017). Exploring the role of social media in e-government: An analysis of emerging literature. ACM International Conference Proceeding Series, Part F1280(February 2019), 97–106. https://doi.org/10.1145/3047273.3047374

Fatima, A., & Niladri, D. (2019). Predictors of investment intention in Indian stock markets: Extending the theory of planned behaviour. International Journal of Bank Marketing, 37(1), 97–119. https://doi.org/10.1108/IJBM-08-2017-0167

Fosso Wamba, S., Bhattacharya, M., Trinchera, L., & Ngai, E. W. T. (2017). Role of intrinsic and extrinsic factors in user social media acceptance within workspace: Assessing unobserved heterogeneity. International Journal of Information Management, 37(2), 1–13. https://doi.org/10.1016/j.ijinfomgt.2016.11.004

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate Data Analysis (7th ed.). Essex: Pearson Education.

Hasan, G., & Lim, D. (2021). Menganalisis Efektivitas Ewom Pada Customer Purchase Intention Dengan Menggunakan Social Networking of Smartphone in Batam. Jurnal Manajemen Universitas Bung Hatta, 16(2), 87–95. https://doi.org/10.37301/jmubh.v16i2.19025

Honkanen, P., Verplanken, B., & Olsen, S. O. (2006). Ethical values and motives driving organic food choice. Journal of Consumer Behaviour, 5(October). https://doi.org/10.1002/cb

Huang, L. T. (2016). Exploring utilitarian and hedonic antecedents for adopting information from a recommendation agent and unplanned purchase behaviour. New Review of Hypermedia and Multimedia, 22(1–2), 139–165. https://doi.org/10.1080/13614568.2015.1052098

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

Jiwasiddi, A., Adhikara, C., Adam, M., & Triana, I. (2019). Attitude toward using Fintech among Millennials. (June 2020). https://doi.org/10.4108/eai.26-1-2019.2283199

Kompas. (2021). Riset Ungkap Lebih dari Separuh Penduduk Indonesia “Melek” Media Sosial. Retrieved August 30, 2021, from kompas.com website: https://tekno.kompas.com/read/2021/02/24/08050027/riset-ungkap-lebih-dari-separuh-penduduk-indonesia-melek-media-sosial

Lalicic, L., & Weismayer, C. (2021). Consumers’ reasons and perceived value co-creation of using artificial intelligence-enabled travel service agents. Journal of Business Research, (August). https://doi.org/10.1016/j.jbusres.2020.11.005

Lin, C. A., & Kim, T. (2016). Predicting user response to sponsored advertising on social media via the technology acceptance model. Computers in Human Behavior, 64, 710–718. https://doi.org/10.1016/j.chb.2016.07.027

Manser Payne, E., Peltier, J. W., & Barger, V. A. (2018). Mobile banking and AI-enabled mobile banking: The differential effects of technological and non-technological factors on digital natives’ perceptions and behavior. Journal of Research in Interactive Marketing, 12(3), 328–346. https://doi.org/10.1108/JRIM-07-2018-0087

Mazambani, L., & Mutambara, E. (2019). Predicting FinTech innovation adoption in South Africa: the case of cryptocurrency. African Journal of Economic and Management Studies, 11(1), 30–50. https://doi.org/10.1108/AJEMS-04-2019-0152

Oh, J. C., & Yoon, S. J. (2014). Predicting the use of online information services based on a modified UTAUT model. Behaviour and Information Technology, 33(7), 716–729. https://doi.org/10.1080/0144929X.2013.872187

Okada, E. M. (2005). Justification effects on consumer choice of hedonic and utilitarian goods. Journal of Marketing Research, 42(1), 43–53. https://doi.org/10.1509/jmkr.42.1.43.56889

Overby, J. W., & Lee, E. J. (2006). The effects of utilitarian and hedonic online shopping value on consumer preference and intentions. Journal of Business Research, 59(10–11), 1160–1166. https://doi.org/10.1016/j.jbusres.2006.03.008

Parment, A. (2013). Generation Y vs. Baby Boomers: Shopping behavior, buyer involvement and implications for retailing. Journal of Retailing and Consumer Services, 20(2), 189–199. https://doi.org/10.1016/j.jretconser.2012.12.001

Paschen, J., Wilson, M., & Ferreira, J. J. (2020). Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel. Business Horizons, 63(3), 403–414. https://doi.org/10.1016/j.bushor.2020.01.003

Perez-Vega, R., Kaartemo, V., Lages, C. R., Borghei Razavi, N., & Männistö, J. (2020). Reshaping the contexts of online customer engagement behavior via artificial intelligence: A conceptual framework. Journal of Business Research, (August). https://doi.org/10.1016/j.jbusres.2020.11.002

Rana, N. P., Dwivedi, Y. K., Lal, B., Williams, M. D., & Clement, M. (2017). Citizens’ adoption of an electronic government system: towards a unified view. Information Systems Frontiers, 19(3), 549–568. https://doi.org/10.1007/s10796-015-9613-y

Rasmussen, E. E., Punyanunt-Carter, N., LaFreniere, J. R., Norman, M. S., & Kimball, T. G. (2020). The serially mediated relationship between emerging adults’ social media use and mental well-being. Computers in Human Behavior, 102, 206–213. https://doi.org/10.1016/j.chb.2019.08.019

Rita, P., Brochado, A., & Dimova, L. (2019). Millennials’ travel motivations and desired activities within destinations: A comparative study of the US and the UK. Current Issues in Tourism, 22(16), 2034–2050. https://doi.org/10.1080/13683500.2018.1439902

Schulz, F., Schlereth, C., Mazar, N., & Skiera, B. (2015). Advance payment systems: Paying too much today and being satisfied tomorrow. International Journal of Research in Marketing, 32(3), 238–250. https://doi.org/10.1016/j.ijresmar.2015.03.003

Shareef, M. A., Baabdullah, A., Dutta, S., Kumar, V., & Dwivedi, Y. K. (2018). Consumer adoption of mobile banking services: An empirical examination of factors according to adoption stages. Journal of Retailing and Consumer Services, 43(March), 54–67. https://doi.org/10.1016/j.jretconser.2018.03.003

Tian, L., Yang, B., Yin, X., & Su, Y. (2020). A Survey of Personalized Recommendation Based on Machine Learning Algorithms. ACM International Conference Proceeding Series, 602–610. https://doi.org/10.1145/3443467.3444711

Tiwari, R., Srivastava, S., & Gera, R. (2020). Investigation of Artificial Intelligence Techniques in Finance and Marketing. Procedia Computer Science, 173(2019), 149–157. https://doi.org/10.1016/j.procs.2020.06.019

To, P. L., Liao, C., & Lin, T. H. (2007). Shopping motivations on Internet: A study based on utilitarian and hedonic value. Technovation, 27(12), 774–787. https://doi.org/10.1016/j.technovation.2007.01.001

Tuten, T. L., & Solomon, M. R. (2017). Social Media Marketing. Thaosand Oaks, CA.: Sage.

Vargo, S. L., & Lusch, R. F. (2008). Service-Dominant Logic: Continuing the Evolution. Journal of the Academy of Marketing Science, (36), 1–10. Retrieved from http://dx.doi.org/10.1007/s11747-007-0069-6

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. https://doi.org/10.2307/30036540

Xu, F., Tan, J., Lu, L., Li, S., & Qin, L. (2021). How does value co-creation behavior affect destination loyalty? A role switching perspective. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1805–1826. https://doi.org/10.3390/jtaer16050101

Zeithaml, V. A., Parasuraman, A., & Malhotra, A. (2002). Service quality delivery through web sites: A critical review of extant knowledge. Journal of the Academy of Marketing Science, 30(4), 362–375. https://doi.org/10.1177/009207002236911

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Published

2023-07-28