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This paper examines how (1) a crowdfunding campaign's prosociality (the production of a public versus private good), (2) the social network structure (embeddedness) among individuals advocating for the campaign on social media, and (3) the volume of social media activity around a campaign jointly determine fundraising from the crowd. Integrating the emerging literature on social media and crowdfunding with the literature on social networks and public goods, we theorize that prosocially, public-oriented crowdfunding campaigns will benefit disproportionately from social media activity when advocates' social media networks exhibit greater levels of embeddedness. Drawing on a panel dataset that combines campaign fundraising activity associated with more than 1,000 campaigns on Kickstarter with campaign-related social media activity on Twitter, we construct network-level measures of embeddedness between and amongst individuals initiating the latter, in terms of transitivity and topological overlap. We demonstrate that Twitter activity drives a disproportionate increase in fundraising for prosocially oriented crowdfunding campaigns when posting users' networks exhibit greater embeddedness. We discuss the theoretical implications of our findings, highlighting how our work extends prior research on the role of embeddedness in peer influence by demonstrating the joint roles of message features and network structure in the peer influence process. Our work suggests that when a transmitter's message is prosocial or cause-oriented, embeddedness will play a stronger role in determining influence. We also discuss the broader theoretical implications for the literatures on social media, crowdfunding, crowdsourcing, and private contributions to public goods. Finally, we highlight the practical implications for marketers, campaign organizers, and crowdfunding platform operators. [ABSTRACT FROM AUTHOR]

Online referral systems help firms attract new customers and expand their customer base by leveraging the social relationships of existing customers. We integrate ultimatum game theory, which focuses on fairness, with motivation theories to investigate the effects of social distance and monetary incentives on the performance of three competing designs for online referral systems: rewarding only or primarily the proposer, rewarding only or primarily the responder, and dividing the reward equally or fairly between the proposer and responder. A set of controlled laboratory and randomized field experiments were conducted to test how the fairness of the split of the reward (equal/fair versus unequal/unfair split of the referral bonus) and social distance (small versus large) between the proposer and the responder jointly affect the performance of online referral systems (the proposer sending an offer and the responder accepting the offer). For a large social distance (acquaintances or weak tie relationships), equally splitting the referral bonus results in the best performance. However, for a small social distance (friends or strong tie relationships), an equal split of the referral reward does not improve referral performance, which suggests that under a small social distance, monetary incentives may not work effectively. Face validity and external validity (generalizability) are ensured using two distinct measures of social distance across several contexts. Through the analysis of the interaction effects of fairness and social distance, our research offers theoretical and practical implications for social commerce by showing that the effectiveness of fairness on the success of online social referrals largely depends on social distance. [ABSTRACT FROM AUTHOR]

Assessment of risk due to product failure is important both for purposes of finance (e.g., warranty costs) and safety (e.g., potential loss of human life). In many applications a prediction of the number of future failures is an important input to such an assessment. Usually the field-data response used to make predictions of future failures is the number of weeks (or another unit of real time) in service. Use-rate information usually is not available (automobile warranty data are an exception, where both weeks in service and number of miles driven are available for units returned for warranty repair). With new technology, however, sensors and smart chips are being installed in many modern products ranging from computers and printers to automobiles and aircraft engines. Thus the coming generations of field data for many products will provide information on how the product was used and the environment in which it was used. This article was motivated by the need to predict warranty returns for a product with multiple failure modes. For this product, cycles-to-failure/use-rate information was available for those units that were connected to the network. We show how to use a cycles-to-failure model to compute predictions and prediction intervals for the number of warranty returns. We also present prediction methods for units not connected to the network. To provide insight into the reasons that use-rate models provide better predictions, we also present a comparison of asymptotic variances comparing the cycles-to-failure and time-to-failure models. This article has supplementary material online. [ABSTRACT FROM AUTHOR]

The failure probability of a product F(t), and the life time quantile tp, are commonly used metrics in reliability applications. Confidence intervals are used to quantify the s-uncertainty of estimators of these two metrics. In practice, a set of point- wise confidence intervals for F(t), or the quantiles tp, are often plotted on one graph, which we refer to as pointwise "confidence bands." These confidence bands for F(t) or can be obtained through s-normal approximation, maximum likelihood, or other procedures. In this paper, we compare s-normal approximation to likelihood methods, and introduce a new procedure to get the confidence intervals for F(t) by inverting the pointwise confidence bands of the quantile tp, function. We show why it is valid to interpret the set of pointwise confidence intervals for the quantile function as a set of pointwise confidence intervals for F(t), and vice- versa. Our results also indicate that the likelihood-based point- wise confidence bands have desirable statistical properties, beyond those that were known previously. [ABSTRACT FROM AUTHOR]