2016
DOI: 10.1016/j.cie.2016.08.009
|View full text |Cite
|
Sign up to set email alerts
|

Predicting online e-marketplace sales performances: A big data approach

Abstract: 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 ro… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

2
34
0
4

Year Published

2017
2017
2023
2023

Publication Types

Select...
5
3

Relationship

0
8

Authors

Journals

citations
Cited by 47 publications
(40 citation statements)
references
References 35 publications
2
34
0
4
Order By: Relevance
“…Based on the relationship between visual esthetic product characteristics and user experience, Chien et al 47 proposed a data-driven product design framework for capturing the visual esthetic user experience of the product, so as to effectively identify the user preferences and reflect them into product conceptual design. To confirm the important interaction between the customer review function and other factors, Li et al 48 proposed a method for mining and analyzing product review data and realized the analysis and prediction of product sales and development trends.…”
Section: Data-driven Requirement Analysis For Preference Perceptionmentioning
confidence: 99%
“…Based on the relationship between visual esthetic product characteristics and user experience, Chien et al 47 proposed a data-driven product design framework for capturing the visual esthetic user experience of the product, so as to effectively identify the user preferences and reflect them into product conceptual design. To confirm the important interaction between the customer review function and other factors, Li et al 48 proposed a method for mining and analyzing product review data and realized the analysis and prediction of product sales and development trends.…”
Section: Data-driven Requirement Analysis For Preference Perceptionmentioning
confidence: 99%
“…Big data presents unparalleled opportunities to accelerate scientific discovery and innovation in key areas that impact organisations and economies ( Zhou, Chawla, Jin, & Williams, 2014 ). The utilisation of big data techniques is rampant within the private sector, with organisations successfully utilising them to offer personalised solutions ( Anshari, Almunawar, Lim, & Al-Mudimigh, 2018 ), market segmentation, creative marketing ( Erevelles, Fukawa, & Swayne, 2016 ), and predicting sales trends ( Li, Ch’ng, Chong, & Bao, 2016 ). With organisations reaping much benefit from Big Data Analytics, there is a growing urgency to adopt similar techniques in order to gain real-time insights into individuals’ well-being in order to target aid interventions to vulnerable groups ( UN, 2013 ).…”
Section: Un Sdgs Big Data and Well-being: A Review Of Literaturementioning
confidence: 99%
“…também pode ter sido resultado de uma classificação satisfatória atribuída aos mesmos, o que indica maior importância e poder ___________________________________________________________________________________________ , Maringá, v.26, n.2, jul.-dez./2018 de persuasão para vendas on-line nessas lojas virtuais específicas (LI et al, 2016), além de maior confiança de compra por parte dos seus e-consumidores (PAVLOU; GEFEN, 2004), fator diferencial do marketplace on-line.…”
Section: Figura 5 -Quantidade De Pedido Porunclassified
“…Dessa forma, tendo em vista a teoria dos sinais exposta por Li et al (2015), o que justifica o sucesso dos e-commerces nos emarketplaces pode ser a utilização de sinais como garantia, reputação, qualidade do site, preço e frete, assim como itens que tornam os anúncios mais atrativos aos compradores, complementando estudos já realizados (ex. : BAKOS, 1991;PAVLOU;GEFEN, 2004;CERIBELI et al, 2015;LI et al, 2016).…”
Section: Considerações Finaisunclassified