Boying Li

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 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.

Predicting online product sales via online reviews, sentiments, and promotion strategies

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 ...

Repurchase intention in the Chinese e-marketplace: Roles of interactivity, trust and perceived effectiveness of e-commerce institutional mechanisms

Purpose Retaining customers is very important for the survival of e-commerce sellers. The purpose of this paper is to investigate the roles of computer-mediated communication (CMC) tools, interactivity, trust and perceived effectiveness of e-commerce institutional mechanisms (PEEIM) in influencing customer’s repurchase intention in the Chinese online e-commerce marketplace. Design/methodology/approach The research model is empirically tested using survey data analyzed with partial least squares structural equation modeling. Findings This study confirms the positive relation between customer satisfaction and trust in the seller, which further contributes to repurchase intention. Results also support the positive influences of effective use of an instant messenger and feedback system on customer perceived interactivity, which helps enhance trust in the seller. PEEIM demonstrates interesting results regarding its moderating effects. Research limitations/implications In the future, researchers can extend the study to other e-commerce platforms and take trust transfer effects and product categories into consideration. Practical implications This study highlights the importance to manage trust, PEEIM, interactivity and CMC tools in e-commerce platforms, assisting practitioners to develop appropriate business strategies and processes to retain customers. Originality/value This study extends previous investigations by integrating trust and PEEIM with interactivity and testing the model in the context of the Chinese online marketplace.

Predicting online e-marketplace sales performances

Confirming the predictive power of product review volume and rating on sales.Examining product type, answers, discount and information usefulness as moderators.Using big data architecture to collect data for model testing. 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 2939 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.

Understanding and predicting what influence online product sales? A neural network approach

Abstract Understanding the factors that influence sales is important for online sellers to manage their supply chains. This study aims to examine the roles of online reviews and reviewer characteristics in predicting product sales. With Amazon.com data captured using our big data architecture, this study performs sentiment analysis to measure the sentiment strength and polarity of review content. The predicting powers of sentiment together with other variables are then examined using neural network analysis. The results indicate that all the proposed variables are important predictors of online sales, and among them helpful votes of reviewer and picture of reviewer are the most influential ones. The findings of this study can be helpful for online sellers to manage their businesses, and the big data architecture and methodology can be generalised into other research contexts.