We examine how social media plays the role of an attention driver for traditional media. Social media attracts and channels attention to a topic. This attention triggers people to seek further information that is reported professionally in traditional media. Specifically, the volume of social media posts about a stock influences the attention to this stock the next day, proxied by the viewership of news articles on the same stock published the next day. We test this hypothesis in the stock market context because social media is less likely than traditional media to diffuse fundamental information in the stock market. Analysing stock-related news articles and stock-related social media posts from Sina Finance and Sina Weibo, we find that the social media post volume of a stock at time t-1 is associated with the traditional media viewership of the same stock at time t. This effect is amplified when social media sentiment about the stock is more intense or positive, and with an increase in the volume of verified social media posts about the stock. Our results provide evidence that social media platforms act as attention drivers, which differ from the information channel functions discussed in prior literature.

This article introduces a large-scale virtual hackathon where we observed the way participants found collaborators and undertook innovation processes entirely in the virtual world. As an emerging social-technical practice, the virtual hackathon leverages the power of familiar strangers, the improvisation of low-cost digital services, and the crowdsourcing mechanism to enable open innovation under the constraint of physical distancing. This study contributes to the research by introducing and conceptualizing a modified artifact – virtual hackathon. The implication of and the lessons learnt from the virtual hackathon are applicable and generalizable to organizations when managing virtual collaborations, digital infrastructure, and open innovation.

Abstract Mass media sentiment of financial news significantly influences investment decisions of investors. Hence, studying how this sentiment emerges is important. In years past, this was straightforward, often dictated by journalists who cover financial news, but this has become more complex now. In this paper, we focus on how social media sentiment affects mass media sentiment. Using data from Sina Weibo and Sina Finance (around 60 million weibos and 6.2 million news articles), we show that social media does influence mass media sentiment emergence for financial news. The sentiment consistency between social media reaction and prior news articles amplifies the persistence of mass media sentiment over time. By contrast, we found limited evidence of social media reducing the persistence of mass media sentiment over time. The results have significant implications for understanding how 2 types of media, treated separately in the literature, may be connected.

Abstract Richer forms of online reviews such as videos or follow‐on reviews convey ‘additional information and can attract consumers’ attention. However, prior studies focused mostly on the relationship between aggregated online reviews and sales. This paper investigates the impact of online review richness (i.e., reviews containing videos or follow‐on reviews) on sales. Leveraging attribute substitution theory, we conjecture that online review richness can provide heuristic cues in the online shopping environment to help consumers make better purchase decisions. Using data from JD.com , we found that reviews containing either videos or follow‐on reviews positively affect sales. In addition, different product types can also serve as heuristic cues to replace target cues, which can further affect how different forms of online review richness affect sales. We found that the impact of online review richness on sales is stronger for utilitarian products than for hedonic products, and stronger for negatively commented products than for positively commented products. Moreover, we conducted two online experiments and confirmed that the causal relationship is from online review richness to sales. The research findings offer practical implications for online retailers and constitute one of the first steps toward a better understanding of the relationship between online review richness and sales.

Online reviews influence consumers’ purchase decisions. Product type – specifically, whether a product is utilitarian or hedonic – can help explain how consumers react to reviews. Characteristics of reviews – in particular, valence, measured by ratings, the arousal level of the language used in the text and the volume of the reviews – provide heuristics that consumers may use in making purchase decisions. Product type moderates the effect of these characteristics. Empirical evidence for this claim comes from multiple sources: a panel data analysis of 26,357 Amazon products and an online experiment with 541 participants. The findings of studies based on this evidence show that product type (hedonic or utilitarian) moderates the effect of the three heuristic attributes of online reviews (valence, volume, and arousal) on sales. The analysis uses OLS-fixed effects models and Granger causality tests. These findings explain why past studies have found that sometimes online review valence is more influential than volume and arousal with respect to sales and why sometimes this is reversed. Our findings have significant theoretical and practical implications for the design of choice architectures in online review systems.

When a crowd’s motivations are not triggered, they may not necessarily commit their best efforts, even if they have the knowledge to answer an open call. Drawing on the incentive theory, we introduce a top-down process model for an online crowdsourcing campaign that addresses the crowd’s motivations. This model is in contrast to the traditional bottom-up process model, where the crowd self-selects an open call based on their knowledge. We adopt a longitudinal case study method and examine two online crowdsourcing campaigns that represent both models. The findings suggest that the campaign that follows the top-down model generated high-quality ideas, while the bottom-up case was considered a failure. We further enrich the top-down model by developing a four-stage guidance model that addresses the crowd’s differing motivations in each stage. This research contributes to the crowdsourcing literature and helps better attract the qualifying crowd, thereby leading to greater campaign success likelihood.

This article examines the “chicken‐egg” two‐way relationships between online consumer reviews and sales, and assesses the dual influencer and indicator roles of online consumer reviews in relation to purchase behavior. Considering the time factor, we adopt the methodology of Granger causality test and track 3,390 products on Amazon.com over a 2‐month period. The results reveal that a causality loop exists between online consumer review volume and sales. Specifically, our findings indicate that the volume of negative consumer reviews drive consumers' purchasing decisions, but the volume of positive consumers reviews only marginally affects purchasing decisions. Also, consumers generate more positive reviews than negative reviews after sales. Our results highlight the importance of negative consumer reviews; negative reviews not only lead to sales, but sales, in turn, lead to higher volume of negative reviews. The findings suggest an alternative strategy for practitioners to address negative online consumer reviews and highlight the awareness effect of online consumer review postings that can later convert into purchase behaviors.