Digital business platforms (DBPs) such as eBay, Google, and Uber Technologies have seen enormous growth; this paper explores their salient characteristics, the role of marketing in helping DBPs succeed, and important research topics for theory and practice. A new conceptual framework based on insights from transaction cost analysis outlines the role and impact of marketing in DBPs. A key role for marketing is to increase the number and quality of interactions on a DBP while reducing transaction costs for users and production costs for the DPB. The DBPs' interactions and the data thus generated are key enablers of value creation and value appropriation on these platforms. However, there are several challenges to resolve in value creation and value appropriation because DBPs cater to the needs of many different types of users. Therefore, DBPs should carefully coordinate and manage interactions among users on different sides of a platform. For researchers, there are many opportunities to reconceptualize some of the traditional roles of marketing in the context of DBPs.

In a viral marketing campaign, an organization develops a marketing message and encourages customers to forward this message to their contacts. Despite its increasing popularity, there are no models yet that help marketers to predict how many customers a viral marketing campaign will reach and how marketers can influence this process through marketing activities. This paper develops such a model using the theory of branching processes. The proposed viral branching model allows customers to participate in a viral marketing campaign by (1) opening a seeding e-mail from the organization, (2) opening a viral e-mail from a friend, and (3) responding to other marketing activities such as banners and offline advertising. The model parameters are estimated using individual-level data that become available in large quantities in the early stages of viral marketing campaigns. The viral branching model is applied to an actual viral marketing campaign in which over 200,000 customers participated during a six-week period. The results show that the model quickly predicts the actual reach of the campaign. In addition, the model proves to be a valuable tool to evaluate alternative what-if scenarios.branching processes, forecasting, Markov processes, online marketing, viral marketing, word of mouth

Many firms try to leverage consumers’ interactions on social platforms as part of their communication strategies. However, information on online social networks only propagates if it receives consumers’ attention. This paper proposes a seeding strategy to maximize information propagation while accounting for competition for attention. The theory of exchange networks serves as the framework for identifying the optimal seeding strategy and recommends seeding people that have many friends, who, in turn, have only a few friends. There is little competition for the attention of those seeds’ friends, and these friends are therefore responsive to the messages they receive. Using a game-theoretic model, we show that it is optimal to seed people with the highest Bonacich centrality. Importantly, in contrast to previous seeding literature that assumed a fixed and nonnegative connectivity parameter of the Bonacich measure, we demonstrate that this connectivity parameter is negative and needs to be estimated. Two independent empirical validations using a total of 34 social media campaigns on two different large online social networks show that the proposed seeding strategy can substantially increase a campaign’s reach. The second study uses the activity network of messages exchanged to confirm that the effects are driven by competition for attention. This paper was accepted by Anandhi Bharadwaj, information systems.

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The Demand Side of Marketing Management Support Systems 1. Antecedentsof Marketing Problem-Solving Modes 2.4.1 Problem Characteristics 2.4.2 Decision Environment Characteristics 2.4.3 Decision Maker Characteristics vi Contents 2.4.4 Summary of the Effects of the Antecedents of the Marketing Problem-Solving Modes PART nThe Supply Side of Marketing Management Support Systems 3.The Components of Marketing Management Support Systems 3.1