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.... more
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.
Keywords Big data, Neural network, Online reviews, Product demands, Valence,
Promotional marketing, Online marketplace
Paper type Research paper
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.
Keywords Big data, Neural network, Online reviews, Product demands, Valence,
Promotional marketing, Online marketplace
Paper type Research paper
Research Interests:
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... more
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.
Research Interests:
This research uses a Big Data methodology for under-standing geographic social-cultural indicators in Smart Cities that can be generalised to other topics, by investi-gating factors influencing sentiments of city dwellers via large-scale... more
This research uses a Big Data methodology for under-standing geographic social-cultural indicators in Smart Cities that can be generalised to other topics, by investi-gating factors influencing sentiments of city dwellers via large-scale social media. We adopted an important indica-tor as a proof of concept reported here – alcohol consump-tion and factors influencing sentiments of world-wide cities. Big Data methodological approaches were used for collecting, pre-processing, analysing and mapping senti-ments over time. Geo-referencing was used for mapping 24-hour activities in relation to density and volume of activities, together with sentiment analysis, and multino-mial logistic regression. The results demonstrate the feasi-bility of our Big Data approach in Smart City sentiment monitoring. Based on a dataset of more than 369,000 tweets, our work shows that for emotionally charged tweets, the count of followers, location, alcohol strength and the sentiment of self-description are significant influ-encing variables. The research has definite implications in Smart City governance of public health, safety, and the public monitoring and mapping of hotspot areas at differ-ent time of day in real-time, and therefore the potential to predict the spread of behaviours that could impact on pub-lic safety. The generic methodology could be adopted for sentiments of variable topics in heterogeneous datasets.
Research Interests:
With the rise of web 2.0 and subsequently electronic worth-of-mouth (eWOM), service encounter experiences and related issues are becoming increasingly visible to an unprecedented number of potential customers. Consequently, service... more
With the rise of web 2.0 and subsequently electronic worth-of-mouth (eWOM), service encounter experiences and related issues are becoming increasingly visible to an unprecedented number of potential customers. Consequently, service failure is also becoming more of a threat to companies in the era of ubiquitous social media usage and thus, choosing the appropriate service recovery strategies and managing responses online is very important. To tackle this issue, this study proposes a framework to test different service recovery strategies' effects using Twitter data. We plan to complete this study by collecting Twitter log data, classifying different service recovery strategies posted online and analysing the sentiment of tweets, sentiment of replies, number of favourites, and number of retweets. This study provides a new way to empirically test the roles of different service recovery strategies, and is useful for practitioners to formulate a better solution to service failures.
Research Interests:
Computer-mediated communication (CMC) tools, such as instant messenger and feedback system, provide great opportunities for service innovation and are important for vendor performance in e-commerce. Previous research suggests a positive... more
Computer-mediated communication (CMC) tools, such as instant messenger and feedback system, provide great opportunities for service innovation and are important for vendor performance in e-commerce. Previous research suggests a positive relationship between CMC and consumers' online repurchase intention; however, past studies mainly focus within the context of single vendor. This paper extends previous studies by taking consumers' perceptions towards e-commerce platform into consideration and examining the relationships between CMC, perceived effectiveness of e-commerce institutional mechanism (PEEIM)and repurchase intention. Here we propose that,the effective use of CMC tools increases consumers' sense of interactivity and presence, thereby increasing the trust in vendor andfurtherthe consumers' repurchase intentions; moreover, effective CMC also increases PEEIM and trust in e-commerce platform, which in turn diminishes the influence of trust in vendor on repurchase intention. To test the above conceptual framework, we propose to conduct surveys on consumers who have purchasing experience on Taobao.com, a well-known e-commerce platform in China. The findings can help vendor owners and e-commerce practitioners know better about the CMC's influencing mechanism on consumers' online repurchase intention in e-commerce platforms.
Research Interests:
Research Interests:
Remanufacturing has received increasing attention from researchers over the last decade. While many associated operational issues have been extensively studied, research into the prediction customer demand for, and the market development... more
Remanufacturing has received increasing attention from researchers over the last decade. While many associated operational issues have been extensively studied, research into the prediction customer demand for, and the market development of, remanufactured products is still in its infancy. The majority of the existing research into remanufactured product demand is largely based on conventional statistical models that fail to capture the non-linear behaviour of customer demand and market factors in real-world business environments, in particular e-marketplaces. Therefore, this paper aims to develop a comprehensible data-mining prediction approach, in order to achieve two objectives: (1) to provide a highly accurate and robust demand prediction model of remanufactured products; and (2) to shed light on the non-linear effect of online market factors as predictors of customer demand. Based on the real-world Amazon dataset, the results suggest that predicting remanufactured product demand is a complex, non-linear problem, and that, by using advanced machine-learning techniques, our proposed approach can predict the product demand with high accuracy. In terms of practical implications, the importance of market factors is ranked according to their predictive powers of demand, while their effects on demand are analysed through their partial dependence plots. Several insights for management are revealed by a thorough comparison of the sales impact of these market factors on remanufactured and new products.