This article discusses the diffusion process in an online social network given the individual connections between members. The authors model the adoption decision of individuals as a binary choice affected by three factors: (1) the local network structure formed by already adopted neighbors, (2) the average characteristics of adopted neighbors (influencers), and (3) the characteristics of the potential adopters. Focusing on the first factor, the authors find two marked effects. First, an individual who is connected to many adopters has a greater adoption probability (degree effect). Second, the density of connections in a group of already adopted consumers has a strong positive effect on the adoption of individuals connected to this group (clustering effect). The article also records significant effects for influencer and adopter characteristics. For adopters, specifically, the authors find that position in the entire network and some demographic variables are good predictors of adoption. Similarly, in the case of already adopted individuals, average demographics and global network position can predict their influential power on their neighbors. An interesting counterintuitive finding is that the average influential power of individuals decreases with the total number of their contacts. These results have practical implications for viral marketing, a context in which a variety of technology platforms are increasingly considering leveraging their consumers' revealed connection patterns. The model performs particularly well in predicting the next set of adopters.

T he Internet has increased the flexibility of retailers, allowing them to operate an online arm in addition to their physical stores. The online channel offers potential benefits in selling to customer segments that value the convenience of online shopping, but it also raises new challenges. These include the higher likelihood of costly product returns when customers' ability to "touch and feel" products is important in determining fit. We study competing retailers that can operate dual channels ("bricks and clicks") and examine how pricing strategies and physical store assistance levels change as a result of the additional Internet outlet. A central result we obtain is that when differentiation among competing retailers is not too high, having an online channel can actually increase investment in store assistance levels (e.g., greater shelf display, more-qualified sales staff, floor samples) and decrease profits. Consequently, when the decision to open an Internet channel is endogenized, there can exist an asymmetric equilibrium where only one retailer elects to operate an online arm but earns lower profits than its bricks-only rival. We also characterize equilibria where firms open an online channel, even though consumers only use it for research and learning purposes but buy in stores. A number of extensions are discussed, including retail settings where firms carry multiple product categories, shipping and handling costs, and the role of store assistance in impacting consumer perceived benefits.

The increasing demand for high-rate broadcast and multicast services over satellite networks has pushed for the development of High Throughput Satellites characterized by a large number of beams (e.g., more than 100). This, together with the variable distribution of data traffic request across beams and over time, has called for the design of a new generation of satellite payloads, able to flexibly allocate bandwidth and power. In this context, this paper studies the problem of radio resource allocation in the forward link of multibeam satellite networks adopting the Digital Video Broadcasting -Satellite -Second Generation (DVB-S2) standard. We propose a novel objective function with the aim to meet as close as possible the requested traffic across the beams while taking fairness into account. The resulting non-convex optimization problem is solved using a modified version of the simulated annealing algorithm, for which a detailed complexity analysis is presented. Simulation results obtained under realistic conditions confirm the effectiveness of the proposed approach and shed some light on possible payload design implications.

Paid placements on search engines reached sales of nearly $11 billion in the United States last year and represent the most rapidly growing form of online advertising today. In its classic form, a search engine sets up an auction for each search word in which competing websites bid for their sponsored links to be displayed next to the search results. We model this advertising market, focusing on two of its key characteristics: (1) the interaction between the list of search results and the list of sponsored links on the search page and (2) the inherent differences in attractiveness between sites. We find that both of these special aspects of search advertising have a significant effect on sites' bidding behavior and the equilibrium prices of sponsored links. Often, sites that are not among the most popular ones obtain the sponsored links, especially if the marginal return of sites on clicks is quickly decreasing and if consumers do not trust sponsored links. In three extensions, we also explore (1) heterogeneous valuations across bidding sites, (2) the endogenous choice of the number of sponsored links that the search engine sells, and (3) a dynamic model where websites' bidding behavior is a function of their previous positions on the sponsored list. Our results shed light on the seemingly random order of sites on search engines' list of sponsored links and their variation over time. They also provide normative insights for both buyers and sellers of search advertising.Internet marketing, position auctions, game theory

Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research.