Prediction in Economic Networks: Using the Implicit Gestalt in Product Graphs

We define an economic network as a linked set of products, where links are created by realizations of shared outcomes between entities. We analyze the predictive information contained in an increasingly prevalent type of economic network, a “product network” that links the landing pages of goods frequently co-purchased on e-commerce websites. Our data include one million books in 400 categories spanning two years, with over 70 million observations. Using autoregressive and neural-network models, we demonstrate that combining historical demand of a product with that of its neighbors improves demand predictions even as the network changes over time. Furthermore, network properties such as clustering and centrality contribute significantly to predictive accuracy. To our knowledge, this is the first large-scale study showing that a non-static product network contains useful distributed information for demand prediction, and that this information is more effectively exploited by integrating composite structural network properties into one’s predictive models

A Study of Search Attention and Stock Returns Cross Predictability

This study investigates a novel application of correlated online searches in predicting stock performance across supply chain partners. If two firms are economically dependent through supply-chain relationship and if information related to both firms diffuses in the market slowly (rapidly), then our ability to predict stock returns increases (vanishes). Using supply-chain data provided by Bloomberg and weekly co-search network of supply-chain partners from Yahoo! Finance, we find that when investors of a focal stock pay less attention to its supply-chain partners, we can use lagged partner returns to predict the future return of the focal stock. When investors’ co-attention to focal and partner stocks is high, the predictability is low. We contribute to the growing literature on aggregate search and economics of networks by demonstrating the inferential power and economic implications of search networks

Where Information Systems Research Meets Artificial Intelligence Practice: Towards the Development of an AI Capability Framework

Information systems (IS) research has always been one of the leading applied research areas in the investigation of technology-related phenomena. Meanwhile, for the past 10 years, artificial intelligence (AI) has transformed every aspect of society more than any other technological innovation. Thus, this is the right time for IS research to foster more quality and high-impact research on AI starting by organizing the cumulated body of knowledge on AI in IS research. We propose a framework called AI capability framework that would provide pertinent and relevant guidance for conducting IS research on AI. Since AI is a fast-evolving phenomenon, this framework is founded on the main AI capabilities that shape today’s fast-moving AI ecosystem. Thus, it is crucial that such a framework engages both AI research and practice into a continuous and evolving dialogue

Predicting Medication Prescription Rankings with Medication Relation Network

Medication prescription rankings and demands prediction could benefit both medication consumers and pharmaceutical companies from various aspects. Our study predicts the medication prescription rankings focusing on patients’ medication switch and combination behavior, which is an innovative genre of medication knowledge that could be learned from unstructured patient generated contents. We first construct two supervised machine learning systems for medication references identification and medication relations classification from unstructured patient’s reviews. We further map the medication switch and combination relations into directed and undirected networks respectively. An adjusted transition in and out (ATIO) system is proposed for medication prescription rankings prediction. The proposed system demonstrates the highest positive correlation with actual medication prescription amounts comparing to other network-based measures. In order to predict the prescription demand changes, we compare four predictive regression models. The model incorporated the network-based measure from ATIO system achieve the lowest mean square errors

Popperian Falsificationism in IS: Major Confusions and Harmful Influences

The current relationship between Popper’s philosophy of science and Information Systems (IS) is complex and often confused. On the one hand, many influential members of the IS community claim that much IS research follows Popper’s falsificationism. On the other hand, many assumptions underlying Popper’s falsificationism, including the nature of theories as exceptionless laws rejected by a singular unsupportive observation are inappropriate and misleading. Moreover, Popper also rejected all inductive inferences and inductive methods as unscientific which, alas, has led some influential IS scholars to dismiss inductive inferences in major IS methodologies. Such Popperian advice is harmful as virtually all statistical or qualitative IS research relies on inductive inferences – and there is nothing wrong with that. Finally, we offer a solution for how to deal with the scientific significance of the problem of induction. This solution is inductive fallibilism. This means recognizing that theories, rather than always being held as true or false simply, often contain varying inductive supportive and unsupportive evidence

Estimating attention flow in online video networks

© 2019 Association for Computing Machinery. Online videos have shown tremendous increase in Internet traffic. Most video hosting sites implement recommender systems, which connect the videos into a directed network and conceptually act as a source of pathways for users to navigate. At present, little is known about how human attention is allocated over such large-scale networks, and about the impacts of the recommender systems. In this paper, we first construct the Vevo network — a YouTube video network with 60,740 music videos interconnected by the recommendation links, and we collect their associated viewing dynamics. This results in a total of 310 million views every day over a period of 9 weeks. Next, we present large-scale measurements that connect the structure of the recommendation network and the video attention dynamics. We use the bow-tie structure to characterize the Vevo network and we find that its core component (23.1% of the videos), which occupies most of the attention (82.6% of the views), is made out of videos that are mainly recommended among themselves. This is indicative of the links between video recommendation and the inequality of attention allocation. Finally, we address the task of estimating the attention flow in the video recommendation network. We propose a model that accounts for the network effects for predicting video popularity, and we show it consistently outperforms the baselines. This model also identifies a group of artists gaining attention because of the recommendation network. Altogether, our observations and our models provide a new set of tools to better understand the impacts of recommender systems on collective social attention