Role of Internet of Things (IoT) in Retail Business and Enabling Smart Retailing Experiences
DOI:
https://doi.org/10.18034/abr.v11i2.579Keywords:
IoT, Retail business, Smart retailing, Artificial intelligenceAbstract
Internet of Things (IoT) is anticipated to be one of the primary megatrends up in innovation. Integrated with the current and upcoming mobility of digital gadgets, it offers ground to applications in numerous domains, including retail. The capability of sensors for setting applicable, customized, real-time, and intuitive communication with buyers and customers is considered to be a driving force of traffic and exchange, a facilitator of development along the way to elevate their purchasing experience. Simultaneously, IoT can serve to further develop relationships and foundations for more viable retail business and digital store management. Currently, digitally savvy customers expect an Omnichannel experience at each touchpoint. They need to track down the ideal data at the perfect time at the right location. Location-based innovation in a retail setting identifies the way that users take to arrive at specific areas of a retail store and helps upgrade the shopping experience. This is the reason the Internet of Things (IoT) is beginning to take the online business to a higher level, and will probably disrupt the conventional retail processes on a significant scale in the coming time. This paper surveys and arranges the most common applications of IoT and solutions for successful marketing at retail from the point of retailers and customers as well as from the point of manufacturers confronting framework or communication-related issues. We propose a model that demonstrates the potential that IoT has as compared to standard industry practices of retail to drive business results and gain an upper hand. In this paper, we’ve likewise talked about the new developments and new techniques for the organizations to accomplish competitive advantage brought about by the uses cases of IoT, particularly in the field of mobile sensors. Such developments are likely the most prominent factor in the coming years to make progress in the advanced economy.
Downloads
References
Ahmed, A. A. A.; Paruchuri, H.; Vadlamudi, S.; & Ganapathy, A. (2021). Cryptography in Financial Markets: Potential Channels for Future Financial Stability. Academy of Accounting and Financial Studies Journal, 25(4), 1–9. https://doi.org/10.5281/zenodo.4774829
Begum, R., Ahmed, A. A. A., Neogy, T. K. (2012). Management Decisions and Univariate Analysis: Effects on Corporate Governance in Bangladesh. Journal of Business Studies, 3(1), 87-115.
Bynagari, N. B. (2015). Machine Learning and Artificial Intelligence in Online Fake Transaction Alerting. Engineering International, 3(2), 115-126. https://doi.org/10.18034/ei.v3i2.566
Bynagari, N. B. (2019). GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. Asian Journal of Applied Science and Engineering, 8, 25–34. Retrieved from https://upright.pub/index.php/ajase/article/view/32
Bynagari, N. B., & Amin, R. (2019). Information Acquisition Driven by Reinforcement in Non-Deterministic Environments. American Journal of Trade and Policy, 6(3), 107-112. https://doi.org/10.18034/ajtp.v6i3.569
Chen, S., Xu, H., Liu, D., Hu, B., Wang, H. (2014). A Vision of IoT: Applications, Challenges, and Opportunities with China Perspective. IEEE Internet of Things Journal, 1(4). DOI: https://doi.org/10.1109/JIOT.2014.2337336
Donepudi, P. K., Ahmed, A. A. A., Hossain, M. A., & Maria, P. (2020). Perceptions of RAIA Introduction by Employees on Employability and Work Satisfaction in the Modern Agriculture Sector. International Journal of Modern Agriculture, 9(4), 486–497. https://doi.org/10.5281/zenodo.4428205
Donepudi, P. K., Banu, M. H., Khan, W., Neogy, T. K., Asadullah, ABM., & Ahmed, A. A. A. (2020a). Artificial Intelligence and Machine Learning in Treasury Management: A Systematic Literature Review. International Journal of Management, 11(11), 13–22. https://doi.org/10.5281/zenodo.4247297
Irish, C. (2017). The IoT Opportunity. Checkout, 43(12), 24-25.
Khan, W., Ahmed, A. A. A., Vadlamudi, S., Paruchuri, H., Ganapathy, A. (2021). Machine Moderators in Content Management System Details: Essentials for IoT Entrepreneurs. Academy of Entrepreneurship Journa, 27(3), 1-11. https://doi.org/10.5281/zenodo.4972587
Kolaric, B., Petrovic, R., Radojcic, S. (2011). Application of e-business in modern operation of public companies in Serbia. International Journal of Business Administration, 2(3), 32. DOI: https://doi.org/10.5430/ijba.v2n3p32
Li, X., Lu, R., Liang, X., Shen, X., Chen, J., Lin, X. (2011). Smart community: an internet of things application. IEEE Communications Magazine, 49(11). DOI: https://doi.org/10.1109/MCOM.2011.6069711
Manavalan, M. (2016). Biclustering of Omics Data using Rectified Factor Networks. International Journal of Reciprocal Symmetry and Physical Sciences, 3, 1–10. Retrieved from https://upright.pub/index.php/ijrsps/article/view/40
Manavalan, M. (2018). Do Internals of Neural Networks Make Sense in the Context of Hydrology?. Asian Journal of Applied Science and Engineering, 7, 75–84. Retrieved from https://upright.pub/index.php/ajase/article/view/41
Manavalan, M. (2020). Intersection of Artificial Intelligence, Machine Learning, and Internet of Things – An Economic Overview. Global Disclosure of Economics and Business, 9(2), 119-128. https://doi.org/10.18034/gdeb.v9i2.584 DOI: https://doi.org/10.18034/gdeb.v9i2.584
Manavalan, M., & Bynagari, N. (2021). Repurposing High-Throughput Imaging Tests for Drug Discovery Allows for Biological Activity Prediction. International Journal of Aquatic Science, 12(3), 2431-2443. http://www.journal-aquaticscience.com/article_136960.html
Manavalan, M., & Chisty, N. M. A. (2019). Visualizing the Impact of Cyberattacks on Web-Based Transactions on Large-Scale Data and Knowledge-Based Systems. Engineering International, 7(2), 95-104. https://doi.org/10.18034/ei.v7i2.578 DOI: https://doi.org/10.18034/ei.v7i2.578
Manavalan, M., & Ganapathy, A. (2014). Reinforcement Learning in Robotics. Engineering International, 2(2), 113-124. https://doi.org/10.18034/ei.v2i2.572 DOI: https://doi.org/10.18034/ei.v2i2.572
Nam, T., Pardo, T. A. (2011, June). Conceptualizing smart city with dimensions of technology, people, and institutions. In Proceedings of the 12th annual international digital government research conference: digital government innovation in challenging times (pp. 282-291). ACM. DOI: https://doi.org/10.1145/2037556.2037602
Neogy, T. K., & Bynagari, N. B. (2018). Gradient Descent is a Technique for Learning to Learn. Asian Journal of Humanity, Art and Literature, 5(2), 145-156. https://doi.org/10.18034/ajhal.v5i2.578
Paruchuri, H.; Vadlamudi, S.; Ahmed, A. A. A.; Eid, W.; Donepudi, P. K. (2021). Product Reviews Sentiment Analysis using Machine Learning: A Systematic Literature Review. Turkish Journal of Physiotherapy and Rehabilitation, 23(2), 2362-2368. https://doi.org/10.5281/zenodo.5534344
--0--
Published
How to Cite
Issue
Section
License
Asian Business Review is an Open Access journal. Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a CC BY-NC 4.0 International License that allows others to share the work with an acknowledgment 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 their work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal. We require authors to inform us of any instances of re-publication.
