Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/97035
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Title: Driving product sales performance by analysing product prelaunch in a linguistics approach
Authors: Ng, W 
Cho, V 
Issue Date: Jun-2020
Source: International journal on natural language computing, June 2020, v. 9, no. 3
Abstract: This paper uses a natural linguistics analytic approach, by studying product prelaunch events’ script, to investigate the determinants of driving the product sales based on customer values framework as well as “Nextopia” consumer psychology. This research contributes to the theoretical framework of identifying the customer values, which have impacts on the product sales. Moreover, we investigate how product sales be driven by the optimism attitude and affective forecasting feeling, which are vocal during product prelease events. Through the study of analysing the essential words, which represent the underlying customer values from the script of Apple Inc. product prelaunch events, we found that product functional and experiential/hedonic of customer values drive product sales. Induced affective forecasting message negatively moderated the impact of cost/ sacrifices values on product sales. In addition to the theoretical contributions, this research provides practical guidelines of how to shape the product prelaunch speech to maximize the sales of the to-be-released products.
Keywords: Product preannouncement
Product sales
Signalling
Communications
Speech recognition
Publisher: AIRCC Publishing Corporation
Journal: International journal on natural language computing 
ISSN: 2319-4111
EISSN: 2278-1307
DOI: 10.5121/ijnlc.2020.9301
Rights: For all papers published in AIRCC journals, the copyright of the paper is retained by the author under Creative Commons (CC) Attribution license. This license authorizes unrestricted circulation and reproduction of the publication by anybody, as long as the original work is properly cited.
The following publication Ng, W, Cho, V (2020). Driving Product Sales Performance by Analysing Product Prelaunch in a Linguistics Approach. International Journal on Natural Language Computing (IJNLC), 9(3) is available at https://doi.org/10.5121/ijnlc.2020.9301.
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