Journal
INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT
Volume 36, Issue 4, Pages 358-+Publisher
EMERALD GROUP PUBLISHING LTD
DOI: 10.1108/IJOPM-03-2015-0151
Keywords
Big data; Neural network; Online reviews; Product demands; Valence; Promotional marketing; Online marketplace
Categories
Funding
- National Natural Science Foundation of China (NSFC)
- International Doctoral Innovation Centre
- Ningbo Education Bureau
- Ningbo Science and Technology Bureau
- China's MoST
- University of Nottingham
- NSFC [71402076]
- NBSTB [2012B10055]
Ask authors/readers for more resources
Purpose - The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales. Design/methodology/approach - The authors designed a big data architecture and deployed Node. js agents for scraping the Amazon.com pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales. Findings - This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume. Originality/value - This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available