Online Roadshow: A New Model for the Next-Generation Digital Marketing


Leow, Kang Ren and Leow, Meng Chew and Ong, Lee Yeng (2021) Online Roadshow: A New Model for the Next-Generation Digital Marketing. Lecture Notes in Networks and Systems, 358. pp. 994-1005. ISSN 2367-3370

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This paper proposed an Interactive Online Roadshow Model that comprises three components which are Roadshow Core, Delivery Agent and Digital Interface. The model is developed in action to the impact of physical engagement difficulties during COVID-19, the global pandemic that forced individuals to keep social distance between each other. Thus, it aims to lower the business impact from movement control order that significantly reduces the level of engagement towards the public physically while enhancing stability, flexibility and usability by using digital devices. The proposed model complies essential criteria of physical roadshow in the Roadshow Core component as the fundamental elements of a roadshow. By leveraging the VARK Learning Model, the proposed framework urges a higher degree of engagement for better understanding and deeper impression to overcome the inefficient of non-face-to-face interaction in an Online Roadshow with dynamic advertising and interactive content delivered by Delivery Agent. The Digital Interface in the proposed model allows participants without physical contact, eliminating them from constraints of specific geographical location and time, alternatively lays the foundation for any Online Roadshow implementations. This launches the next generation marketing era that leverages the rising capability of the Internet and big data waves.

Item Type: Article
Uncontrolled Keywords: Internet marketing, roadshow, online roadshow, engagement,
Subjects: H Social Sciences > HF Commerce > HF5001-6182 Business > HF5410-5417.5 Marketing. Distribution of products
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 04 Feb 2022 02:24
Last Modified: 04 Feb 2022 02:24


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