Optimizing Corporate Branding: The Role of Artificial Intelligence In Business Transformation of IQOS
DOI:
https://doi.org/10.59653/jimat.v2i01.482Keywords:
artificial intelligence, business transformation, consumer behaviour, corporate branding, data-informed decisionsAbstract
Corporate branding stands as a pivotal facet of a company's identity, wielding substantial influence over consumer perceptions and confidence. In the contemporary digital landscape, the optimization of corporate branding demands innovative methodologies. Artificial intelligence (AI) assumes a pivotal role in revolutionizing how companies conceptualize and manage their brand image. Leveraging AI-powered tools and algorithms furnishes invaluable insights into consumer behaviors, preferences, and prevailing market trends, empowering companies to make data-informed decisions, tailor branding strategies, and elevate overall customer experiences. Furthermore, AI's integration augments brand management endeavors by expediting the analysis of feedback, enabling swift adaptation to dynamic market shifts and evolving consumer sentiments. This research significantly contributes to both academic discourse and industry practices by elucidating AI's transformative prowess and furnishing guidance on embedding strategic branding approaches aligned with the exigencies of the digital era. Through an amalgamation of case studies and theoretical frameworks, this article illuminates the symbiotic relationship between AI and corporate branding, underscoring their mutually advantageous alliance within the sphere of business transformation.
Downloads
References
Al-Surmi, A., Bashiri, M., & Koliousis, I. (2022). AI based decision making: combining strategies to improve operational performance. International Journal of Production Research, 60(14), 4464–4486. https://doi.org/10.1080/00207543.2021.1966540
Alpaydin, E. (2016). Machine Learning : The New AI. MIT Press.
Alshurideh, M., Gasaymeh, A., Ahmed, G., Alzoubi, H., & Kurd, B. Al. (2020). Loyalty program effectiveness: Theoretical reviews and practical proofs. Uncertain Supply Chain Management, 8(3), 599–612. https://doi.org/10.5267/j.uscm.2020.2.003
Anam, K. (2023). Pertama di Asia Tenggara, HMSP Luncurkan IQOS ILUMA Terbatas. CNBC Indonesia.
Balakrishnan, S. (2023). Chatbot, AI and its impact on marketing and customer experience. ETBrandEquity.
Berg, C. J., Abroms, L. C., Levine, H., Romm, K. F., Khayat, A., Wysota, C. N., Duan, Z., & Bar-Zeev, Y. (2021). IQOS marketing in the US: The need to study the impact of FDA modified exposure authorization, marketing distribution channels, and potential targeting of consumers. International Journal of Environmental Research and Public Health, 18(19). https://doi.org/10.3390/ijerph181910551
Brock, J. K.-U., & von Wangenheim, F. (2019). Demystifying AI: What Digital Transformation Leaders Can Teach You about Realistic Artificial Intelligence. California Management Review, 61, 110–134.
Brynjolfsson, E., & Mcafee, A. (2017). How AI Fits into Your Science Team: What it can and cannot do for your organization. Harvard Business Review, 1–20.
Buhmann, A., & Fieseler, C. (2021). Towards a deliberative framework for responsible innovation in artificial intelligence. Technology in Society, 64, 101475. https://doi.org/https://doi.org/10.1016/j.techsoc.2020.101475
Chatterjee, S., Ghosh, S. K., & Chaudhuri, R. (2020). Knowledge management in improving business process: an interpretative framework for successful implementation of AI-CRM-KM system in organizations. Bus. Process. Manag. J., 26, 1261–1281.
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2022). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), 100899. https://doi.org/https://doi.org/10.1016/j.hrmr.2022.100899
Christensen, D. (2023). IQOS Consumer centric transformation.
Collins, C., Dennehy, D., Conboy, K., & Mikalef, P. (2021). Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management, 60(July), 102383. https://doi.org/10.1016/j.ijinfomgt.2021.102383
Dhar, V. (2016). The future of artificial intelligence. Big Data, 4(1), 5–9. https://doi.org/10.1089/big.2016.29004.vda
Eshima, Y., & Anderson, B. S. (2017). Firm growth, adaptive capability, and entrepreneurial orientation. Strategic Management Journal, 38(3), 770–779. https://doi.org/10.1002/smj.2532
Fadler, M., & Legner, C. (2021). Toward big data and analytics governance: Redefining structural governance mechanisms. Proceedings of the Annual Hawaii International Conference on System Sciences, 2020-Janua(January), 5696–5705. https://doi.org/10.24251/hicss.2021.691
Feng, H., Morel, M. (2023). How AI-powered chatbots are transforming marketing and sales operations. IBM.
Grawal, A., Gans, J. S., & Goldfarb, A. (2017). What to Expect From Artificial Intelligence. MIT Sloan Management Review.
Harb, A. (2023). We are very pleased we got Transforming the IQOS Digital Shopping Experience: A Realtime 3D Visualization Case Study for Ten05. Modularcx.
Jarek, K., & Mazurek, G. (2019). Marketing and Artificial Intelligence. Central European Business Review, 8(2), 46–55.
Jarrahi, M. H. (2018). Artificial Intelligence and the Future of Work: Human-AI Symbiosis in Organizational Decision Making. Bus. Horiz., 61(577–586).
Jiang, Q. L., & Chen, Z. H. (2021). Active Aging of Gray Surfers: Research on the Mechanism of Internet Use to Improve Subjective Well-Being of the Elderly. Modern Communication (Journal of Communication University of China), 43, 41–48.
Kar, S., Kar, A., & Gupta, M. P. (2021). Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective. Intelligent Systems in Accounting, Finance and Management, 28, 217–238. https://doi.org/10.1002/isaf.1503
Kinkar, K. (2021). Product Recommendation System: A Systematic Literature Review. International Journal for Research in Applied Science and Engineering Technology, 9(VII), 3330–3339. https://doi.org/10.22214/ijraset.2021.37024
Lawton, G., & Pratt, M. K. (2022). Change management. Opgeroepen Op Maart 20.
Lieto, A., Bhatt, M., Oltramari, A., & Vernon, D. (2017). The role of cognitive architectures in general artificial intelligence. Cognitive Systems Research, 48, 1–3. https://doi.org/10.1016/j.cogsys.2017.08.003
Makowski, P., & Kajikawa, Y. (2021). Automation-driven innovation management? Toward Innovation-Automation-Strategy cycle. Technological Forecasting and Social Change, 168, July 2021, 120723. https://doi.org/10.1016/j.techfore.2021.120723
Mendling, J., Reijers, H. A., & van der Aalst, W. M. P. (2010). Seven process modeling guidelines (7PMG). Information and Software Technology, 52(2), 127–136. https://doi.org/10.1016/j.infsof.2009.08.004
Mishra, A. N., & Pani, A. K. (2021). Business value appropriation roadmap for artificial intelligence. VINE Journal of Information and Knowledge Management Systems, 51(3), 353–368. https://doi.org/10.1108/VJIKMS-07-2019-0107
Rhamadona, S., Sufa, S. A., Indrasari, M., Brumadyadisty, G., & Asnawi, A. (2023). Communication Audit of Digital Entrepreneurship Academy of Human Resources Research Program and Development Agency of the BPSDMP Kominfo Surabaya in Pamekasan Region. Jurnal Riset Multidisiplin Dan Inovasi Teknologi, 2(01 SE-Articles), 197–206. https://doi.org/10.59653/jimat.v2i01.422
Rubin, H. J., & Rubin, I. (2011). Qualitative interviewing : the art of hearing data. In TA - TT - (Third edit). SAGE Thousand Oaks, California. https://doi.org/LK - https://worldcat.org/title/722452581
Selimović, J., Martinović, D., & Hurko, D. (2020). Critical success factors in insurance companies. Management (Croatia), 25(1), 215–233. https://doi.org/10.30924/mjcmi.25.1.12
Shanahan, M. (Ed.). (2015). The Technology Singularity. In MIT Press (p. 0). The MIT Press. https://doi.org/10.7551/mitpress/10058.003.0003
Sterne, J. (2017). for Marketing Wiley & SAS Business.
Syam, N. B., & 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.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Knowledge and Strategy, 18(March), 509–533. https://doi.org/10.1093/oso/9780198781806.003.0019
Trunk, A., Birkel, H., & Hartmann, E. (2020). On the current state of combining human and artificial intelligence for strategic organizational decision making. Business Research, 13(3), 875–919. https://doi.org/10.1007/s40685-020-00133-x
van de Wetering, R., & Versendaal, J. (2021). Information Technology Ambidexterity, Digital Dynamic Capability, and Knowledge Processes as Enablers of Patient Agility: Empirical Study. JMIRx Med, 2(4), e32336. https://doi.org/10.2196/32336
Watson, R. T., & Webster, J. (2020). Analysing the past to prepare for the future: Writing a literature review a roadmap for release 2.0. Journal of Decision Systems, 29(3), 129–147. https://doi.org/10.1080/12460125.2020.1798591
Wierenga, B. (2010). Marketing and artificial intelligence: Great opportunities, reluctant partners. In In Marketing intelligent systems using soft computing.
Yigit, A., & Kanbach, D. (2021). THE IMPORTANCE OF ARTIFICIAL INTELLIGENCE IN STRATEGIC MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW. International Journal of Strategic Management, 21. https://doi.org/10.18374/IJSM-21-1.1
Yin, R. K. (2018). Publications Case Study Research and Applications: Design and Methods. Sage Publications.
Yu, J., & Moon, T. (2021). Impact of digital strategic orientation on organizational performance through digital competence. Sustainability (Switzerland), 13(17), 1–15. https://doi.org/10.3390/su13179766
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Denpharanto Agung Krisprimandoyo
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).