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Tan, Su-lyn (2018) Disrupting the tobacco industry: exploring the motivations of E-Cigarette usage, using big data analytics. [Dissertation (University of Nottingham only)]

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Abstract

The tobacco industry is facing a digital disruption at an astonishing speed. The digitalization of conventional cigarettes into vaporizer or E-Cigarette has taken the industry by storm globally. The growth of E-Cigarette has been increasing year on year while tobacco cigarettes have been flat or declining. Sales of combustible tobacco cigarettes have also been partly affected by negative public sentiments, aggressive health campaigns, more stringent regulations and increasing price of cigarettes. This has resulted in tobacco users looking for a cheaper or safer alternative to cigarette smoking. E-Cigarette is a battery-operated device that heats up a coil to vaporize liquid which may or may not contain tobacco. The liquid also comes in various flavors which attracts both smokers and non-smokers alike. Big data analytics were applied in this research to identify sentiment drivers that E-Cigarette companies can use to enhance and grow their business. Data used in this research was obtained from internet forums using web scraper tool. The verbatim was then analyzed using SAS Text Analytic Studio Engine. Text clusters were formed and themes were assigned to each cluster. Microsoft power PI was used for analyzing the sentiments generated by SAS Text Analytic Studio Engine. The generated insights indicated that there are 5 positive sentiments towards vaping: Quit smoking / health related intentions, enjoyment purposes, cost related concerns, convenience, smoke & cloud. It is recommended to apply these sentiments in E-Cigarette marketing campaigns as these will create positive intentions towards E-Cigarette. The theoretical framework established is consistent with the theory of planned behavior. The results obtained are also consistent with previous studies conducted on vaping intentions. This proves that Big Data Analytics are accurate and efficient in analyzing large amount of data within a short period of time.

Item Type:	Dissertation (University of Nottingham only)
Depositing User:	Bujang, Rosini
Date Deposited:	13 Sep 2018 02:51
Last Modified:	08 Feb 2019 12:16
URI:	https://eprints.nottingham.ac.uk/id/eprint/54603

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