Predicting online e-marketplace sales performances: a big data approach
To manage supply chain efficiently, e-business organizations need to understand their sales effectively. Previous research has shown that product review plays an important role in influencing sales performance, especially review volume and rating. However, limited attention has been paid to understa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2016
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/48878/ |
| _version_ | 1848797869308379136 |
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| author | Li, Boying Ch’ng, Eugene Chong, Alain Yee-Loong Bao, Haijun |
| author_facet | Li, Boying Ch’ng, Eugene Chong, Alain Yee-Loong Bao, Haijun |
| author_sort | Li, Boying |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | To manage supply chain efficiently, e-business organizations need to understand their sales effectively. Previous research has shown that product review plays an important role in influencing sales performance, especially review volume and rating. However, limited attention has been paid to understand how other factors moderate the effect of product review on online sales. This study aims to confirm the importance of review volume and rating on improving sales performance, and further examine the moderating roles of product category, answered questions, discount and review usefulness in such relationships. By analyzing 2,939 records of data extracted from Amazon.com using a big data architecture, it is found that review volume and rating have stronger influence on sales rank for search product than for experience product. Also, review usefulness significantly moderates the effects of review volume and rating on product sales rank. In addition, the relationship between review volume and sales rank is significantly moderated by both answered questions and discount. However, answered questions and discount do not have significant moderation effect on the relationship between review rating and sales rank. The findings expand previous literature by confirming important interactions between customer review features and other factors, and the findings provide practical guidelines to manage e-businesses. This study also explains a big data architecture and illustrates the use of big data technologies in testing theoretical framework. |
| first_indexed | 2025-11-14T20:10:44Z |
| format | Article |
| id | nottingham-48878 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:10:44Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-488782019-08-21T04:30:12Z https://eprints.nottingham.ac.uk/48878/ Predicting online e-marketplace sales performances: a big data approach Li, Boying Ch’ng, Eugene Chong, Alain Yee-Loong Bao, Haijun To manage supply chain efficiently, e-business organizations need to understand their sales effectively. Previous research has shown that product review plays an important role in influencing sales performance, especially review volume and rating. However, limited attention has been paid to understand how other factors moderate the effect of product review on online sales. This study aims to confirm the importance of review volume and rating on improving sales performance, and further examine the moderating roles of product category, answered questions, discount and review usefulness in such relationships. By analyzing 2,939 records of data extracted from Amazon.com using a big data architecture, it is found that review volume and rating have stronger influence on sales rank for search product than for experience product. Also, review usefulness significantly moderates the effects of review volume and rating on product sales rank. In addition, the relationship between review volume and sales rank is significantly moderated by both answered questions and discount. However, answered questions and discount do not have significant moderation effect on the relationship between review rating and sales rank. The findings expand previous literature by confirming important interactions between customer review features and other factors, and the findings provide practical guidelines to manage e-businesses. This study also explains a big data architecture and illustrates the use of big data technologies in testing theoretical framework. Elsevier 2016-11 Article PeerReviewed application/pdf en cc_by_nc_nd https://eprints.nottingham.ac.uk/48878/1/6Predicting%20online%20e-marketplace%20sales%20performances_%20A%20big%20data%20approach.pdf Li, Boying, Ch’ng, Eugene, Chong, Alain Yee-Loong and Bao, Haijun (2016) Predicting online e-marketplace sales performances: a big data approach. Computers & Industrial Engineering, 101 . pp. 565-571. ISSN 0360-8352 E-business product reviews moderation effect big data architecture https://www.sciencedirect.com/science/article/pii/S0360835216302753 doi:10.1016/j.cie.2016.08.009 doi:10.1016/j.cie.2016.08.009 |
| spellingShingle | E-business product reviews moderation effect big data architecture Li, Boying Ch’ng, Eugene Chong, Alain Yee-Loong Bao, Haijun Predicting online e-marketplace sales performances: a big data approach |
| title | Predicting online e-marketplace sales performances: a big data approach |
| title_full | Predicting online e-marketplace sales performances: a big data approach |
| title_fullStr | Predicting online e-marketplace sales performances: a big data approach |
| title_full_unstemmed | Predicting online e-marketplace sales performances: a big data approach |
| title_short | Predicting online e-marketplace sales performances: a big data approach |
| title_sort | predicting online e-marketplace sales performances: a big data approach |
| topic | E-business product reviews moderation effect big data architecture |
| url | https://eprints.nottingham.ac.uk/48878/ https://eprints.nottingham.ac.uk/48878/ https://eprints.nottingham.ac.uk/48878/ |