Purpose The purpose of this paper is to investigate what factors can affect people’s continuous watching and consumption intentions in live streaming. Design/methodology/approach This research conducted a mixed-methods study. The semi-structured interview was deployed to develop a research model and a live streaming typology. A survey was then used for quantitative assessment of the research model. Survey data were analyzed using partial least squares-structural equation modeling. Findings The results suggest that sex and humor appeals, social status display and interactivity play considerable roles in the viewer’s behavioral intentions in live streaming and their effects vary across different live streaming types. Research limitations/implications This research is conducted in the Chinese context. Future research can test the research model in other cultural contexts. This study can also be extended by incorporating the roles of viewer gender and price sensitivity in the future. Practical implications This study provides managerial insights into how live streaming platforms and streamers can improve their popularity and profitability. Originality/value The paper introduces a novel form of social media and a new business model. It illustrates what will affect people’s behavioral intentions in such a new context.

This study extends literature on e-commerce trust and re-purchase intentions by exploring the role of swift guanxi and perceived effectiveness of institutional mechanisms (PEEIM) in the context of a Chinese e-marketplace-Taobao. We explore how Taobao's social media technologies (online reviews and instant messenger) can improve swift guanxi and PEEIM by increasing online interactivity and presence. We find that buyers' PEEIM negatively moderates trust in online sellers and repurchase intentions. We show that swift guanxi, created by social media's interactivity and presence, enhances trust, which further increases repurchase intentions. Theoretical and managerial implications and future research directions are discussed.

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.

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.

No abstract