We define an economic network as a linked set of entities, where links are created by actual realizations of shared economic outcomes between entities. We analyze the predictive information contained in a specific type of economic network, namely, a product network, where the links between products reflect aggregated information on the preferences of large numbers of individuals to co-purchase pairs of products. The product network therefore reflects a simple “smoothed” model of demand for related products. Using a data set containing more than 70 million observations of a nonstatic co-purchase network over a period of two years, we predict network entities' future demand by augmenting data on their historical demand with data on the demand for their immediate neighbors, in addition to network properties, specifically, local clustering and PageRank. To our knowledge, this is the first study of a large-scale dynamic network that shows that a product network contains useful distributed information for demand prediction. The economic implications of algorithmically predicting demand for large numbers of products are significant.
Sponsored search is the mechanism whereby advertisers pay a fee to Internet search engines to be displayed alongside organic (nonsponsored) web search results. Based on prior literature, we draw an analogy between these markets and financial markets. We use the analogy as well as the key differences to present a theoretical framework consisting of a set of research questions about the pricing of keywords and design choices available to firms in sponsored search markets. These questions define an agenda for future research in sponsored search markets. They also have practical implications for advertisers and online marketplaces such as search engines and social media sites that support advertising.
How are business schools thinking about developing leaders for the emerging digital economy? Is there a set of core principles we can apply to thinking about the enabling potential of information technologies and their consequences for business and society? We present a business-centric framework and a technology-centric framework that together form a blueprint for answering these questions. The business-centric framework articulates three compelling reasons why information technology (IT) matters in business: (1) IT continually transform industry and society, (2) executive decisions about IT investments, governance, and strategy are critical to organizational success, and (3) deriving value from increasingly available data trails defines effective decision making in the digital economy. However, our conversations with the leadership of 45 business schools and our subsequent data indicate that business schools are challenged by effectively training future executives to think about these reasons and act on them as part of a forward-looking program of business education that is grounded in stable concepts. In response, the technology-centric framework provides a set of grounding concepts and stable principles about IT that have emerged over the last four decades, and leads to a natural set of consequences that can inform thinking about IT in business. We illustrate how these complementary frameworks--business and technology--can be combined to frame an educational program by outlining a set of key questions, by placing these questions in the context suggested by our frameworks, and by providing guidelines toward answering them. These questions also define a natural path for future research about IT in business and society that will lead to stronger intellectual foundations for the field and define future education that is better grounded in concepts and theories that emerge from academic research.
Over the last decade there have been several efforts at building knowledge-based "expert systems," mostly in the scientific and medical arenas. Despite the fact that almost all such systems are in their experimental stages, designers are optimistic about their eventual success. In the last few years, there have been many references to the possibility of expert systems in the management literature. However, what is lacking is a clear theoretical perspective on how various management problems differ in nature from problems in other domains and the implications of these differences for knowledge-based decision support systems for management. In this paper, I examine some of these differences, what they suggest in terms of the functionality that a computer-based system must have in order to support organizational decision making, and the scope of such a system as a decision aid. The discussion is grounded in the context of a computer-based system called PLANET that exhibits some of the desired functionality.