Knowledge that Transforms

In recent years, the world has witnessed a number of severe natural disasters, causing heavy losses to families, communities, and even nations. Natural disaster management (NDM) websites play an im...

In this paper, we develop a game theoretic model to study the pricing of e-books and e-readers under two pricing models: wholesale and agency. We analyze pricing strategies for a publisher and a retailer. We identify the complementary relationship between e-books and e-readers as the main reason for the retailer to set a low e-book price in the wholesale model. Comparing the wholesale and the agency models, we find, in a wide range of market conditions, the price for e-book readers is lower in the agency model, leading to a higher e-book market share. However, a higher e-book price in the agency model lowers e-book consumption. Overall social welfare is lower in the agency model than in the wholesale model. While total consumer surplus is slightly higher in the agency model, largely because of a lower e-reader price, business profit is lower. The publisher, surprisingly, is worse off under the agency model.

Research examining the relationship between IT-business strategic alignment (hereafter referred to as alignment) and firm performance (hereafter referred to as performance) has produced apparently ...

For the last two or three years, the field of "big data" has emerged as the new frontier in the wide spectrum of IT-enabled innovations and opportunities allowed by the information revolution.The ever-increasing creation of massive amounts of data through an extensive array of several new data generating sources has prompted organizations, consultants, scientists, and academics to direct their attention to how to harness and analyze big data.Businesses are looking for a technology-based competitive advantage, while the public, academic, and scientific sectors look for opportunities to understand the world in unprecedented ways.The expectations are high that big data will propel our society into an exciting era of across the board innovations.As with any new emerging technology wave, quotes and wild forecasts abound:"Information is the oil of the 21 st century, and analytics is the combustion engine."(Peter Sondergaard, Gartner Group) "Data is the new science.Big Data holds the answers."(Pat Gelsinger, EMC) "Data are becoming the new raw material of business."(The Economist 2010) Gartner's hype curve predicts big data will start making deep transformational impact in two to five years (Heudecker 2013).Aside from splashy quotes and predictions by consultants, company executives are actually trying to do something about the new wave, or at least thinking about it (IBM 2011), and even midmarket companies are gearing up for it (Dell Midmarket Research 2014).In my role as department head of a prominent IS department, which has always been active in interdisciplinary collaboration, sponsored research, and business partnerships, I have been part of extensive discussions about big data in the last two years both with potential industry partners as well as academic collaborators in my university.I find that in industry, there is a considerable gap in the understanding of the area, its challenges, and its potential.Except for a few large companies, especially informationintensive ones like Linkedin, Facebook, and Google, executives of most corporations and midmarket companies are struggling with understanding and deciding what to do.The confusion is exacerbated by the highly fragmented environment of solutions and applications that are intended to work in the big data realm.Universities have been moving to address the industry gaps.IS groups have been responding especially to the opportunity of delivering academic programs that specialize in data and business analytics, to form data scientists.Such programs are proliferating fast.As far as academic research, big data opportunities lure.Through its funding agencies, the federal government has identified big data as a strategic priority for funding.A quick search of grant opportunities in the U.S.

Although the vast majority of the manuscript proposals or complete manuscripts I receive pertain to endeavors that require extreme minutiae and consume vast amounts of brainpower, a number of them seem-borrowing from an anonymous reviewer-"to confuse hard work with hard thinking.There's a lot of reading

Regression techniques can be used not only for legitimate data analysis, but also to infer private information about individuals. In this paper, we demonstrate that regression trees, a popular data-analysis and data-mining technique, can be used to effectively reveal individuals’ sensitive data. This problem, which we call a regression attack, has not been addressed in the data privacy literature, and existing privacy-preserving techniques are not appropriate in coping with this problem. We propose a new approach to counter regression attacks. To protect against privacy disclosure, our approach introduces a novel measure, called digression, which assesses the sensitive value disclosure risk in the process of building a regression tree model. Specifically, we develop an algorithm that uses the measure for pruning the tree to limit disclosure of sensitive data. We also propose a dynamic value-concatenation method for anonymizing data, which better preserves data utility than a user-defined generalization scheme commonly used in existing approaches. Our approach can be used for anonymizing both numeric and categorical data. An experimental study is conducted using real-world financial, economic, and healthcare data. The results of the experiments demonstrate that the proposed approach is very effective in protecting data privacy while preserving data quality for research and analysis.

Despite growing interest in the economic and policy aspects of information security, little academic research has used field data to examine the development process of a security countermeasure provider. In this paper, we empirically examine the learning process a security software developer undergoes in resolving a malware problem. Using the data collected from a leading antivirus software company in Asia, we study the differential effects of experience on the malware resolution process. Our findings reveal that general knowledge from cross-family experience has greater impact than specific knowledge from within-family experience on performance in the malware resolution process. We also examine the factors that drive the differential effects of prior experience. Interestingly, our data show that cross-family experience is more effective than within-family experience in malware resolution when malware targets the general public than when a specific victim is targeted. Similar results—for example, the higher (lower) effect of cross-family (within-family) experience— were observed in the presence of information sharing among software vendors or during a disruption caused by a catastrophe. Our study contributes to a better understanding of the specific expertise required for security countermeasure providers to be able to respond under varying conditions to fast-evolving malware.

In recent years, we have witnessed an unprecedented growth in the security software market. This market is now fiercely competitive with hundreds of nearly identical products; yet, the price is high and coverage low. Although recent research has examined such idiosyncrasies and found the existence of a negative network effect as a possible explanation, several important questions still remain: (1) What possibly discourages product differentiation in such a competitive market? (2) Why is versioning absent here? (3) How does the presence of free alternatives in this market impact its structure? We develop a comprehensive oligopoly model, with endogenous quality and versioning decisions, to address these issues. Our analyses reveal that, although the presence of numerous competitors leads to a greater need to differentiate, the network effect in this market works as a counterweight, incentivizing vendors to sacrifice differentiation in favor of collocating in the top end of the quality spectrum. We explain the reasons and implications of this important finding. We further show that this result is robust and applicable even when versioning by competing vendors or the presence of free software is taken into consideration. Furthermore, given that the presence of free software actually intensifies competitive pressure and heightens the need to differentiate, the role of the network effect in abating differentiation becomes even more discernible.