Articles

Herding and Social Media Word-of-Mouth: Evidence from Groupon

X. LI, L. WU

MIS Quarterly

A paraître

Départements : Information Systems and Operations Management, GREGHEC (CNRS)

Mots clés : Herding, word-of-mouth, social media, interaction effect, complementarity


Modern online retailing practices provide consumers with new types of real-time information that can potentially increase demand. In particular, showing past product sales information can reduce uncertainty about product quality, inducing consumers to herd. This effect could be particularly salient for experience goods due to their inherent high uncertainty about product quality. Social media word-of-mouth (WOM) can increase product awareness as product information spreads via social media, increasing demand directly and also amplifying existing quality signals such as past sales. This study examines the mechanisms behind the strategy of facilitating herding and the strategy of integrating social media platforms to understand the potential complementarities between the two strategies. We conduct empirical analysis using data from Groupon.com which sells goods in a fast cycle format of “daily deals”. We find that facilitating herding and integrating social media platforms are complements in generating sales, supporting that it is beneficial to combine the two strategies on social media-driven platforms. Furthermore, we find that herding is more salient for experience goods, consistent with our hypothesized mechanisms, while the effect of social media WOM does not differ between experience goods and search goods

Impact of Average Rating on Social Media Endorsement: The Moderating Role of Rating Dispersion and Discount Threshold

X. LI

Information Systems Research

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Départements : Information Systems and Operations Management, GREGHEC (CNRS)


Social Presence in Virtual World Collaboration: An Uncertainty Reduction Perspective Using a Mixed Methods Approach

S. C. SRIVASTAVA, S. CHANDRA

MIS Quarterly

A paraître

Départements : Information Systems and Operations Management, GREGHEC (CNRS)

Mots clés : Virtual worlds, uncertainty reduction theory, institutional trust, sequential mixed methods

http://www.misq.org/skin/frontend/default/misq/pdf/Abstracts/11914_RA_SrivastavaAbstract.pdf


The life-like collaborative potential offered by virtual worlds (VWs) has sparked significant interest for companies to experiment with VWs in order to organize convenient, cost-effective virtual global workplaces. Despite the initial hype, recent years have witnessed a rather stagnant use of VWs for collaboration in organizations. Previous research recognizes that the inherent uncertainties within the VW environment are factors limiting their utilization by businesses. Hence, grounding this research in uncertainty reduction theory (URT), we aim to understand the modalities and mechanisms for mitigating the uncertainties and fostering user trust within VWs so that they can be effectively utilized as a workplace collaboration tool. With this end in view, we propose contextualizing and extending McKnight et al.’s (2002) institutional trust framework to the context of VWs by examining the significant role that social presence has in influencing the efficacy of the institution-based trust-building factors of situational normality and structural assurance in VWs. Using a sequential mixed methods approach (Venkatesh et al. 2013; Venkatesh et al. 2016), this research integrates results from a quantitative study with findings from a qualitative study to arrive at rich and robust inferences and meta-inferences, with the qualitative method first corroborating the inferences obtained from the quantitative research and then complementing them by identifying boundary conditions that may limit the use of VWs in organizations for workplace collaboration. The results together suggest not only the direct, but also the interactional (complementary and substitutive) influences of social presence on the relationships of the two institutional-trust-building factors to user trust in VWs


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