Maximilian Beichert, Andreas Bayerl, Jacob Goldenberg, and Andreas Lanz show that influencers with small followings, despite their limited reach, produce a higher return on investment in direct sales campaigns than their larger counterparts. By linking the strength of viewers’ engagement to actual revenue rather than vanity metrics, they provide the first large-scale evidence that, in influencer marketing, bigger is not always better.

Andreas Lanz, Jacob Goldenberg, Daniel Shapira, and Florian Stahl introduce a forward-looking influencer marketing framework that allows managers identify and engage still-unknown prospective influencers in order to buy future endorsements.

Jacob Goldenberg, Andreas Lanz, Florian Stahl, and Daniel Shapira show that managers and creators who build ties with nearby, low-status influencers outperform those who cultivate high-status influencers. This efficient means of expanding audiences uses the existing dynamics of social networks.

In this paper, we study whether and how behavior toward newcomers impacts their socialization outcomes in terms of retention and quality of contributions in online communities. By exploiting a natural experiment on a large deal-sharing platform, we find that an intervention that proactively reminds other community members to be more considerate of newcomers causes newcomer deals to receive 54% more comments with a more positive sentiment. The newcomers are 10% more likely to post another deal, suggesting an increase in retention. However, we do not observe any effect of the intervention on the quality of subsequent contributions. Our evidence suggests that the intervention merely caused a temporary shock to newcomers’ first contributions but did not improve their learning or motivate greater efforts. We draw implications on the design of socialization processes to help communities improve the retention and performance of newcomers.

As influencer marketing evolves into a dominant force in the marketing landscape, it necessitates a deeper theoretical exploration to understand its strategic implementations and impacts. This article examines the dynamics of influencer marketing within the growing creator economy, emphasizing the interactions among firms, influencers, followers, and digital platforms. We introduce a novel, equity-driven framework that analyzes how influencers contribute to customer equity, how influencers manage and leverage the value from their followers, and how platforms maximize the value from their users. We detail the complex relationships and value exchanges within the influencer marketing ecosystem, highlighting the challenges of measuring the return on investment and influencers’ strategic use of content to maintain authenticity and influence. By synthesizing diverse academic literature and current industry practices, this manuscript provides a comprehensive overview of the mechanisms of value creation and exchange in influencer marketing, offers strategic implications for marketers aiming to optimize their influencer engagements, and outlines future work in the form of the eleven “INFLUENCERS” research directions.

We study the estimation of controlled Markov processes if the states are only occasionally observed by the econometrician. We propose an extension to the recursive likelihood integration method of Reich (2018), to which we incorporate such occasional state observations in a numerically efficient and accurate way. To evaluate the performance of the proposed method, we assess the computational feasibility as well as the statistical efficiency by applying it to a counter-factual scenario of the widely known bus engine replacement model of Rust (1987): We assume that the mileage state is observed only at replacement, but unobserved in between. We demonstrate that - despite reducing the amount of mileage observations to about only 2% of the original data set - the distribution of the cost parameter estimator under the occasional observation regime is almost indistinguishable from its distribution using all mileage observations; hence there is no (additional) bias and comparable variance.

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Excessive monetary compensation and existing contractual agreements of influencers limit the ability of many firms to engage in effective influencer seeding. The authors suggest a forward-looking approach of targeting prospective influencers—while they are still largely unknown (e.g., a few months after their platform registration)—and signing them to endorse the firm in the future (e.g., more than a year later). This approach has the potential to significantly reduce costs. However, as only rarely do newly registered users ultimately become influencers (and as signals are weak), the authors propose a novel framework to cope with this rare-event problem. For empirical demonstration and application, the authors conduct data-based simulations using a data set from a worldwide leading audio platform. Every wave of newly registered users is associated with a profit potential stemming from future endorsements by prospective influencers. With knowledge about the order of magnitude of the return on successful influencer spend, managers applying the framework can extract around 20% of this profit potential (if the return is around three times the spend).

In influencer marketing, the differing incentives of advertisers and influencers necessitate a delicate balance between control and creativity. Investigating this trade-off, we explore the impact of contractual constraints on advertiser outcomes, addressing the empirical challenges posed by the fact that contracts are rarely observed by researchers and are strategically offered by advertisers and selectively accepted by influencers. Our analysis uses a unique dataset comprising a thousand contracts offered by hundreds of brands to thousands of influencers to examine the prevalent advertiser-imposed constraints. We relate this data to information on influencer participation and follower responses, assessing how contractual constraints influence these outcomes. Our findings demonstrate that influencers are significantly averse to contractual constraints, which create a relational cost for them. Additionally, the audiences respond less favorably in terms of the advertiser's outcomes to content produced under more restrictive conditions, implying a creativity suppression cost borne by the advertiser. A uniquely designed two-stage field experiment shows that the creativity suppression cost for advertisers outweighs the relational cost for influencers. Relaxing these constraints allows advertisers to triple their influencer retention rate within the same budget. We highlight the critical need for balancing managerial direction and influencer autonomy in designing influencer marketing campaigns.

On user-generated content platforms, individuals and firms alike seek to build and expand their follower base to eventually increase the reach of the content they upload. The bulk of the seeding literature in marketing suggests targeting users with a large follower base, that is, high-status influencers. In contrast, some recent studies find targeting lower-status influencers to be a more effective seeding policy. This multimethod article shifts the focus from the follower base of the seeding target to the focal content creator. The authors propose accelerating natural triadic closure by leveraging first-degree followers as interconnectors to target second-degree followers, that is, the nearby (low-status) influencers (who are interconnected with the focal content creator). Empirical studies document that this seeding target is much more effective for building and expanding the follower base, compared with targeting influencers who are not interconnected with the focal content creator-that is, the remote (both high- and low-status) influencers-by 2,300% and 46%, respectively. These studies on the acceleration of natural triadic closure are augmented by a preregistered field experiment to obtain convergent validity of the findings.

Direct-to-consumer firms increasingly believe that influencer marketing is an effective option for seeding. However, the current managerially relevant question for direct-to-consumer firms of whether to target low- or high-followership influencers to generate immediate revenue is still unresolved. In this article, the authors’ goal is to answer this question by considering for the first time the whole influencer-marketing funnel, that is, from followers on user-generated content networks (e.g., on Instagram), to reached followers, to engagement, to actual revenue, while accounting for the cost of paid endorsements. The authors find that low-followership targeting outperforms high-followership targeting by order of magnitude across three performance (return on investment) metrics. A mediation analysis reveals that engagement can explain the negative relationship between the influencer followership levels and return on investment. This is in line with the rationale based on social capital theory that with higher followership levels of an influencer, the engagement between an influencer and their followers decreases. These two findings are derived from secondary sales data of 1,881,533 purchases and results of three full-fledged field studies with hundreds of paid influencer endorsements, establishing the robustness of the findings.

Direct-to-consumer (DTC) firms increasingly believe that influencer marketing is an effective option for seeding. However, the current managerially relevant question for DTC firms of whether to target low- or high-followership influencers to generate immediate revenue is still unresolved. In this article, the authors’ goal is to answer this question by considering for the first time the whole influencer-marketing funnel, i.e., from followers on user-generated content networks (e.g., on Instagram), to reached followers, to engagement, to actual revenue, while accounting for the cost of paid endorsements. The authors find that low-followership targeting outperforms high-followership targeting by order of magnitude across three performance (ROI) metrics. A mediation analysis reveals that engagement can explain the negative relationship between the influencer followership levels and ROI. This is in line with the rationale based on social capital theory that with higher followership levels of an influencer, the engagement between an influencer and his/her followers decreases. These two findings are derived from secondary sales data of 1,881,533 purchases and results of three full-fledged field studies with hundreds of paid influencer endorsements, establishing the robustness of the findings.

This paper develops a method to flexibly adapt interpolation grids of value function approximations in the estimation of dynamic models using either NFXP (Rust, Econometrica: Journal of the Econometric Society, 55, 999–1033, 1987 ) or MPEC (Su & Judd, Econometrica: Journal of the Econometric Society, 80, 2213–2230, 2012 ). Since MPEC requires the grid structure for the value function approximation to be hard-coded into the constraints, one cannot apply iterative node insertion for grid refinement; for NFXP, grid adaption by (iteratively) inserting new grid nodes will generally lead to discontinuous likelihood functions. Therefore, we show how to continuously adapt the grid by moving the nodes, a technique referred to as r -adaption. We demonstrate how to obtain optimal grids based on the balanced error principle, and implement this approach by including additional constraints to the likelihood maximization problem. The method is applied to two models: (i) the bus engine replacement model (Rust, 1987 ), modified to feature a continuous mileage state, and (ii) to a dynamic model of content consumption using original data from one of the world’s leading user-generated content networks in the domain of music.

Work-related social media networks (SMNs) like LinkedIn introduce novel networking opportunities and features that promise to help individuals establish, extend, and maintain social capital (SC). Typically, work-related SMNs offer access to advanced networking features exclusively to premium users in order to encourage basic users to become paying members. Yet little is known about whether access to these advanced networking features has a causal impact on the accumulation of SC. To close this research gap, we conducted a randomized field experiment and recruited 215 freelancers in a freemium, work-related SMN. Of these recruited participants, more than 70 received a randomly assigned voucher for a free 12-month premium membership. We observe that individuals do not necessarily accumulate more SC from their ability to access advanced networking features, as the treated freelancers did not automatically change their online networking engagement. Those features only reveal their full utility if individuals are motivated to proactively engage in networking. We found that freelancers who had access to advanced networking features increased their SC by 4.609% for each unit increase on the strategic networking behavior scale. We confirmed this finding in another study utilizing a second, individual-level panel dataset covering 52,392 freelancers. We also investigated the dynamics that active vs. passive features play in SC accumulation. Based on these findings, we introduce the ???theory of purposeful feature utilization???: essentially, individuals must not only possess an efficacious ???networking weapon??????they also need the intent to ???shoot??? it.

Influencer marketing is an ever-growing topic in today’s marketing strategies. We consider influencers as individuals with a high follower count and social media activity, who share content including product placements and advertising for commercial purposes on their social media channels. This three-sided study covers the triangle of marketing managers (N = 218), influencers (N = 124), and consumers of the millennial segment (N = 1007, representative Swiss sample of 13-30 year-olds) in order to determine the dissemination, relevance, and perception of influencer marketing in Switzerland. Our results reveal the high potential of influencer marketing and the impact of influencers on their followers. We find that 60% of Swiss Millennials follow influencers on social media and 53% of these active followers have already acquired a product or service inspired by an influencer. Those marketing managers who already use influencer marketing (nearly one third) agree with these promising results: The majority of them evaluates the return on in investment of their influencer campaigns as more efficient than alternative advertising formats. However, our results also reveal some potential for improvement, as there exist important misunderstandings between marketing managers and millennials with respect to the influencer topic. This concerns particularly diverging opinions regarding the most popular (Swiss) influencers, the most important influencer platforms, as well as which content topics (e.g., beauty, sports…) of influencers they consider most interesting. Further insights of the study address the influencer profession itself – such as influencers’ relationship building with followers and preferred contractual conditions in cooperations with firms. All in all, this study gives a broad overview over the influencer topic and provides insights of how firms should implement influencer marketing in their organization in order to grasp its full potential as a powerful marketing tool.