Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews
This study aims to investigate the contributions of online promotional marketing and online reviews as predictors of consumer product demands. Using electronic data from Amazon.com, we attempt to predict if online review variables such as valence and volume of reviews, the number of positive and neg...
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| Format: | Article |
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Taylor & Francis
2017
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| Online Access: | https://eprints.nottingham.ac.uk/51933/ |
| _version_ | 1848798606621933568 |
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| author | Chong, Alain Yee Loong Ch’ng, Eugene Liu, Martin J. Li, Boying |
| author_facet | Chong, Alain Yee Loong Ch’ng, Eugene Liu, Martin J. Li, Boying |
| author_sort | Chong, Alain Yee Loong |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This study aims to investigate the contributions of online promotional marketing and online reviews as predictors of consumer product demands. Using electronic data from Amazon.com, we attempt to predict if online review variables such as valence and volume of reviews, the number of positive and negative reviews, and online promotional marketing variables such as discounts and free deliveries, can influence the demand of electronic products in Amazon.com. A Big Data architecture was developed and Node.JS agents were deployed for scraping the Amazon.com pages using asynchronous Input/Output calls. The completed Web crawling and scraping data-sets were then preprocessed for Neural Network analysis. Our results showed that variables from both online reviews and promotional marketing strategies are important predictors of product demands. Variables in online reviews in general were better predictors as compared to online marketing promotional variables. This study provides important implications for practitioners as they can better understand how online reviews and online promotional marketing can influence product demands. Our empirical contributions include the design of a Big Data architecture that incorporate Neural Network analysis which can used as a platform for future researchers to investigate how Big Data can be used to understand and predict online consumer product demands. |
| first_indexed | 2025-11-14T20:22:27Z |
| format | Article |
| id | nottingham-51933 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:22:27Z |
| publishDate | 2017 |
| publisher | Taylor & Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-519332020-05-04T19:03:11Z https://eprints.nottingham.ac.uk/51933/ Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews Chong, Alain Yee Loong Ch’ng, Eugene Liu, Martin J. Li, Boying This study aims to investigate the contributions of online promotional marketing and online reviews as predictors of consumer product demands. Using electronic data from Amazon.com, we attempt to predict if online review variables such as valence and volume of reviews, the number of positive and negative reviews, and online promotional marketing variables such as discounts and free deliveries, can influence the demand of electronic products in Amazon.com. A Big Data architecture was developed and Node.JS agents were deployed for scraping the Amazon.com pages using asynchronous Input/Output calls. The completed Web crawling and scraping data-sets were then preprocessed for Neural Network analysis. Our results showed that variables from both online reviews and promotional marketing strategies are important predictors of product demands. Variables in online reviews in general were better predictors as compared to online marketing promotional variables. This study provides important implications for practitioners as they can better understand how online reviews and online promotional marketing can influence product demands. Our empirical contributions include the design of a Big Data architecture that incorporate Neural Network analysis which can used as a platform for future researchers to investigate how Big Data can be used to understand and predict online consumer product demands. Taylor & Francis 2017-09-01 Article PeerReviewed Chong, Alain Yee Loong, Ch’ng, Eugene, Liu, Martin J. and Li, Boying (2017) Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews. International Journal of Production Research, 55 (17). pp. 5142-5156. ISSN 0020-7543 Product demands; online reviews; promotional marketing; online marketplace; big data; neural network https://www.tandfonline.com/doi/full/10.1080/00207543.2015.1066519 doi:10.1080/00207543.2015.1066519 doi:10.1080/00207543.2015.1066519 |
| spellingShingle | Product demands; online reviews; promotional marketing; online marketplace; big data; neural network Chong, Alain Yee Loong Ch’ng, Eugene Liu, Martin J. Li, Boying Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews |
| title | Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews |
| title_full | Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews |
| title_fullStr | Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews |
| title_full_unstemmed | Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews |
| title_short | Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews |
| title_sort | predicting consumer product demands via big data: the roles of online promotional marketing and online reviews |
| topic | Product demands; online reviews; promotional marketing; online marketplace; big data; neural network |
| url | https://eprints.nottingham.ac.uk/51933/ https://eprints.nottingham.ac.uk/51933/ https://eprints.nottingham.ac.uk/51933/ |