Product information given in purchase situations influences purchase behavior. In online purchase situations, the use of recommendation agents increases the value of product information as information becomes adaptive and thus more relevant to consumers' information needs. Correspondingly, mobile recommendation agents (MRAs) may also increase the value of product information in bricks-and-mortar stores. In this sense, product information is not only adaptive but can also be requested at any place such as in front of products consumers are interested in. Because unprecedented, we investigate the use of a MRA that is virtually bound to a physical product via an RFID-enabled mobile device and provides product information. Based on Theory of Planned Behavior, Innovation Diffusion Theory, and Technology Acceptance Model, we develop a model to better understand the impact of MRAs on usage intentions, product purchases and store preferences of consumers. This model is then tested in a lab experiment (n = 47). Among high usability scores, results indicate that perceived usefulness of a MRA influences product purchases, predicts usage intentions and store preferences of consumers. Thus, new business models for retail stores can be considered in which MRAs satisfy both the information needs of consumers and the communication needs of retailers.

The era of big data provides many opportunities for conducting impactful research from both datadriven and theory-driven perspectives. However, data-driven and theory-driven research have progressed somewhat independently. In this paper, we develop a framework that articulates important differences between these two perspectives and propose a role for information systems research at their intersection. The framework presents a set of pathways that combine the datadriven and theory-driven perspectives. From these pathways, we derive a set of challenges, and show how they can be addressed by research in information systems. By doing so, we identify an important role that information systems research can play in advancing both data-driven and theory-driven research in the era of big data. Medicine. His research investigates the relationship between conceptual modeling and AI/ML, AI in distributed environments (EdgeAI), and the design of smart services. Additionally he has a strong focus on knowledge transfer to industry through funded projects and spin-offs. 1273Carson Woo is Stanley Kwok Professor of Business, Sauder School of Business, University of British Columbia. His research interests include conceptual modeling, systems analysis and design, and requirements engineering. In particular, he is interested in using conceptual models to acquire relevant contextual information (e.g., business goals) and utilizing it to design new information systems, or aligning it to existing information systems design, so that changes can be more appropriate to business needs. At the University of British Columbia, he is a member of two research clusters: (1) Artificial Intelligence, and (2) Blockchain. Dr. Woo is editor of Information Technology and Systems Abstracts Journal at the Social Science Research Network, and serves or has served on several editorial boards, including ACM Transactions on Management Information Systems, Business & Information Systems Engineering Journal, Information and Management, and Requirements Engineering. He currently serves as the chair (2019-2020) of the Conceptual Modeling Conference steering committee and has served as the president of the

Abstract. Internet of Things (IOT) services provide new security and privacy challenges in our everyday life. But no empirical instrument has been developed for the class of IOT services that identifies privacy factors that predict usage intentions and individuals' willingness to provide personal information. The contribution of this paper is to address this lack of research. The proposed research model integrates the Extended Privacy Calculus Model and the Technology Acceptance Model and is pre-tested with 30 IOT experts. Results indicate that intentions to use IOT services are influenced by various factors such as perceived privacy risks and personal interest. It is further assumed that factors such as legislation, data security or transparency of information use influence the adoption of IOT services. Accordingly, further research must focus on a better understanding of these factors to increase the adoption of both useful and secure IOT services.

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BackgroundBiomedical research is set to greatly benefit from the use of semantic web technologies in the design of computational infrastructure. However, beyond well defined research initiatives, substantial issues of data heterogeneity, source distribution, and privacy currently stand in the way towards the personalization of Medicine.ResultsA computational framework for bioinformatic infrastructure was designed to deal with the heterogeneous data sources and the sensitive mixture of public and private data that characterizes the biomedical domain. This framework consists of a logical model build with semantic web tools, coupled with a Markov process that propagates user operator states. An accompanying open source prototype was developed to meet a series of applications that range from collaborative multi-institution data acquisition efforts to data analysis applications that need to quickly traverse complex data structures. This report describes the two abstractions underlying the S3DB-based infrastructure, logical and numerical, and discusses its generality beyond the immediate confines of existing implementations.ConclusionsThe emergence of the "web as a computer" requires a formal model for the different functionalities involved in reading and writing to it. The S3DB core model proposed was found to address the design criteria of biomedical computational infrastructure, such as those supporting large scale multi-investigator research, clinical trials, and molecular epidemiology.

BackgroundThe dramatic fall in the cost of genomic sequencing, and the increasing convenience of distributed cloud computing resources, positions the MapReduce coding pattern as a cornerstone of scalable bioinformatics algorithm development. In some cases an algorithm will find a natural distribution via use of map functions to process vectorized components, followed by a reduce of aggregate intermediate results. However, for some data analysis procedures such as sequence analysis, a more fundamental reformulation may be required.ResultsIn this report we describe a solution to sequence comparison that can be thoroughly decomposed into multiple rounds of map and reduce operations. The route taken makes use of iterated maps, a fractal analysis technique, that has been found to provide a "alignment-free" solution to sequence analysis and comparison. That is, a solution that does not require dynamic programming, relying on a numeric Chaos Game Representation (CGR) data structure. This claim is demonstrated in this report by calculating the length of the longest similar segment by inspecting only the USM coordinates of two analogous units: with no resort to dynamic programming.ConclusionsThe procedure described is an attempt at extreme decomposition and parallelization of sequence alignment in anticipation of a volume of genomic sequence data that cannot be met by current algorithmic frameworks. The solution found is delivered with a browser-based application (webApp), highlighting the browser's emergence as an environment for high performance distributed computing.AvailabilityPublic distribution of accompanying software library with open source and version control at http://usm.github.com. Also available as a webApp through Google Chrome's WebStore http://chrome.google.com/webstore: search with "usm".

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