Selling Stuff
Influencers: The Power of Comments
Cristina Nistor & Matthew Selove
Marketing Science, forthcoming
Abstract:
Many customers choose products based on information from social media influencers. Companies can pay these influencers to promote their products. We develop a model in which customers read an influencer’s sponsored post for a mix of entertainment and product information, and those who purchase the product can leave comments for future customers. We derive conditions in which a large celebrity influencer endorses all products, whereas a microinfluencer adopts a policy of endorsing only high-quality products. In equilibrium, the microinfluencer screens for high-quality products so his followers do not waste time reading informative comments about low-quality products. By contrast, the celebrity influencer attracts so many uninformative comments his followers do not use his comments as a source of product information, and the value of his endorsement arises solely from generating product awareness.
‘You Will:’ A Macroeconomic Analysis of Digital Advertising
Jeremy Greenwood, Yueyuan Ma & Mehmet Yorukoglu
Review of Economic Studies, forthcoming
Abstract:
An information-based model is developed where traditional and digital advertising finance the provision of free media goods and affect price competition. Digital advertising is directed toward consumers while traditional advertising is undirected. The equilibrium is suboptimal. Media goods, if valued by the consumer, are under provided with both types of advertising. Additionally, traditional advertising is excessive because it is undirected. The tax-cum-subsidy policy that overcomes these inefficiencies is characterized. The model is calibrated to the U.S. economy. Through the lens of the calibrated model, digital advertising increases welfare significantly. The welfare gain from the optimal policy is much smaller than the gain from digital advertising.
Promotional language and the adoption of innovative ideas in science
Hao Peng et al.
Proceedings of the National Academy of Sciences, 18 June 2024
Abstract:
How are the merits of innovative ideas communicated in science? Here, we conduct semantic analyses of grant application success with a focus on scientific promotional language, which may help to convey an innovative idea’s originality and significance. Our analysis attempts to surmount the limitations of prior grant studies by examining the full text of tens of thousands of both funded and unfunded grants from three leading public and private funding agencies: the NIH, the NSF, and the Novo Nordisk Foundation, one of the world’s largest private science funding foundations. We find a robust association between promotional language and the support and adoption of innovative ideas by funders and other scientists. First, a grant proposal’s percentage of promotional language is associated with up to a doubling of the grant’s probability of being funded. Second, a grant’s promotional language reflects its intrinsic innovativeness. Third, the percentage of promotional language is predictive of the expected citation and productivity impact of publications that are supported by funded grants. Finally, a computer-assisted experiment that manipulates the promotional language in our data demonstrates how promotional language can communicate the merit of ideas through cognitive activation. With the incidence of promotional language in science steeply rising, and the pivotal role of grants in converting promising and aspirational ideas into solutions, our analysis provides empirical evidence that promotional language is associated with effectively communicating the merits of innovative scientific ideas.
Competitive Model Selection in Algorithmic Targeting
Ganesh Iyer & Tony Ke
Marketing Science, forthcoming
Abstract:
We study how market competition influences the algorithmic design choices of firms in the context of targeting. Firms face a general bias-variance trade-off when choosing the design of a supervised learning algorithm in terms of model complexity or the number of predictors to accommodate. Each firm has a data analyst who uses the chosen algorithm to estimate demand for multiple consumer segments, based on which it devises a targeting policy to maximize estimated profits. We show that competition induces firms to strategically choose simpler algorithms that involve more bias but lower variance. Therefore, more complex/flexible algorithms may have higher value for firms with greater monopoly power.
The entry-deterring effects of synergies in complementor acquisitions: Evidence from Apple's digital platform market, the iOS app store
Yongzhi Wang et al.
Strategic Management Journal, forthcoming
Abstract:
We develop the following typology of four types of acquisition synergies by integrating the multisidedness feature of digital platforms with the mainstream strategy research: complementary-technology-side economies of scope, complementary-technology-side economies of scale, user-side economies of scope, and user-side economies of scale. We show that (1) acquisition synergies are entry-deterring, (2) synergies derived from economies of scope have stronger effects than those derived from economies of scale, and (3) synergies derived from the technology side have stronger effects than those derived from the user side. We highlight the significant competitive and regulatory implications of our findings. For example, one standard-deviation increase in technology-side economies of scope is associated with 55 deterred entries in 1 month or a $2.80 million potential loss in annual revenue.