Unilateral Invasions of Privacy

Most people seem to agree that individuals have too little privacy, and most proposals to address that problem focus on ways to give those users more information about, and more control over, how information about them is used. Yet in nearly all cases, information subjects are not the parties who make decisions about how information is collected, used, and disseminated; instead, outsiders make unilateral decisions to collect, use, and disseminate information about others. These potential privacy invaders, acting without input from information subjects, are the parties to whom proposals to protect privacy must be directed. This Article develops a theory of unilateral invasions of privacy rooted in the incentives of potential outside invaders. It first briefly describes the different kinds of information flows that can result in losses of privacy and the private costs and benefits to the participants in these information flows. It argues that in many cases the relevant costs and benefits are those of an outsider deciding whether certain information flows occur. These outside invaders are more likely to act when their own private costs and benefits make particular information flows worthwhile, regardless of the effects on information subjects or on social welfare. And potential privacy invaders are quite sensitive to changes in these costs and benefits, unlike information subjects, for whom transaction costs can overwhelm incentives to make information more or less private. The Article then turns to privacy regulation, arguing that this unilateral-invasion theory sheds light on how effective privacy regulations should be designed. Effective regulations are those that help match the costs and benefits faced by a potential privacy invader with the costs and benefits to society of a given information flow. Law can help do so by raising or lowering the costs or benefits of a privacy invasion, but only after taking account of other costs and benefits faced by the potential privacy invader

When Little Things Mean a Lot: On the Inefficiency of Item Pricing Laws

We study item-pricing laws (which require that each item in a store be individually marked with a price sticker) and examine and quantify their costs and benefits. On the cost side, we argue that item-pricing laws increase the retailers’ costs, forcing them to raise prices. We test this prediction using data on retail prices from large supermarket chains in the Tri-State area of New York, New Jersey and Connecticut. The Tri-States offer a unique setting—a natural experiment—to study item-pricing laws because the States vary in their use of item-pricing laws, but otherwise offer similar markets and chains operating in a close proximity to each other in a relatively homogenous socioeconomic environment. We use two datasets, one emphasizing the breadth in coverage across products and the other across stores. We find consistent evidence across products, product categories, stores, chains, states, and sampling periods, that the prices at stores facing item-pricing laws are higher than the prices at stores not facing the item pricing laws by about 25¢ or 9.6% per item. We also have data from supermarket chains that would be subject to item-pricing laws but are exempted from item pricing requirement because they use costly electronic shelf label systems. Using this data as a control, we find that the electronic shelf label store prices fall between the item-pricing law and non-item- pricing law store prices: they are lower than the item-pricing law store prices by about 15¢ per item on average, but are higher than the non- item-pricing law store prices by about 10¢ per item on average. On the benefit side, we study the frequency and the magnitude of supermarket pricing errors, which the item-pricing laws are supposed to prevent. We quantify the benefits of the IPLs by conservatively assuming that they successfully accomplish their mission of preventing all price mistakes. Comparing the costs of item-pricing laws to their benefits, we find that the item-pricing law costs are at least an order of magnitude higher than the benefits.Item Pricing Laws, Costs of Item Pricing Laws, Benefits of Item Pricing Laws, Cost of Price Adjustment, Pricing Accuracy, Electronic Shelf Label System, Pricing Regulation, Cost of Pricing, Supermarket Chains

How predictable : patterns of human economic behavior in the wild

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 40-41).Shopping is driven by needs (to eat, to socialize, to work), but it is also a driver of where we go. I examine the transaction records of 80 million customers and find that while our economic choices predict mobility patterns overall, at the small scale we transact unpredictably. In particular, we bundle together multiple store visits, and interleave the order in which we frequent those stores. Individual predictability also varies with income level. I end with a description of how merchant composition emerges in US cities, as seen through the lens of credit card swipes.by Katherine (Coco) Krumme.S.M

Real-Time Purchase Prediction Using Retail Video Analytics

The proliferation of video data in retail marketing brings opportunities for researchers to study customer behavior using rich video information. Our study demonstrates how to understand customer behavior of multiple dimensions using video analytics on a scalable basis. We obtained a unique video footage data collected from in-store cameras, resulting in approximately 20,000 customers involved and over 6,000 payments recorded. We extracted features on the demographics, appearance, emotion, and contextual dimensions of customer behavior from the video with state-of-the-art computer vision techniques and proposed a novel framework using machine learning and deep learning models to predict consumer purchase decision. Results showed that our framework makes accurate predictions which indicate the importance of incorporating emotional response into prediction. Our findings reveal multi-dimensional drivers of purchase decision and provide an implementable video analytics tool for marketers. It shows possibility of involving personalized recommendations that would potentially integrate our framework into omnichannel landscape

Path Data in Marketing: An Integrative Framework and Prospectus for Model Building

Many data sets, from different and seemingly unrelated marketing domains, all involve paths—records of consumers\u27 movements in a spatial configuration. Path data contain valuable information for marketing researchers because they describe how consumers interact with their environment and make dynamic choices. As data collection technologies improve and researchers continue to ask deeper questions about consumers\u27 motivations and behaviors, path data sets will become more common and will play a more central role in marketing research. To guide future research in this area, we review the previous literature, propose a formal definition of a path (in a marketing context), and derive a unifying framework that allows us to classify different kinds of paths. We identify and discuss two primary dimensions (characteristics of the spatial configuration and the agent) as well as six underlying subdimensions. Based on this framework, we cover a range of important operational issues that should be taken into account as researchers begin to build formal models of path-related phenomena. We close with a brief look into the future of path-based models, and a call for researchers to address some of these emerging issues

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

3D Analytics: Opportunities and Guidelines for Information Systems Research

Progress in sensor technologies has made three-dimensional (3D) representations of the physical world available at a large scale. Leveraging such 3D representations with analytics has the potential to advance Information Systems (IS) research in several areas. However, this novel data type has rarely been incorporated. To address this shortcoming, this article first presents two showcases of 3D analytics applications together with general modeling guidelines for 3D analytics, in order to support IS researchers in implementing research designs with 3D components. Second, the article presents several promising opportunities for 3D analytics to advance behavioral and design-oriented IS research in several contextual areas, such as healthcare IS, human-computer interaction, mobile commerce, energy informatics and others. Third, we investigate the nature of the benefits resulting from the application of 3D analytics, resulting in a list of common tasks of research projects that 3D analytics can support, regardless of the contextual application area. Based on the given showcases, modeling guidelines, research opportunities and task-related benefits, we encourage IS researchers to start their journey into this largely unexplored third spatial dimension

Target Markets - International Terrorism Meets Global Capitalism in the Mall

This book explores the points of convergence between corporate capitalist and terrorist practice. Assessing an increase in the number of terrorist attacks directed at commercial entities in urban areas, with an emphasis on the shopping mall in general and Nairobi's Westgate Mall in particular, Suzi Mirgani offers a fascinating and disturbing perspective on the spaces where the most powerful forces of contemporary culture - the most mainstream and the most extreme - meet on common ground