Mining Data to Catch Tax Cheats

This teaching case covers technical and non-technical concerns about data mining enabled by the creation of a data warehouse by the California Franchise Tax Board (CFTB). CFTB used data mining to analyze data collected from federal, state and municipal agencies and other organizations to identify residents who under-report income or fail to file tax returns. The case presents different stakeholders’ privacy, financial, technical and political concerns regarding the use of data obtained from an array of sources. The case is aimed at an undergraduate or MBA/MS course on IS Management, Data Management/Warehousing or Information Privacy. It could also be used to study IT and public policy, or E-government. It provides an opportunity for students to consider how social and political factors interact with technical challenges in inter-enterprise relationships. It also offers an opportunity to consider the value of data in relation to both the financial and non-financial costs of obtaining it

Promotions and Incentives in Nonprofit and For-Profit Organizations

[Excerpt] Using data from the 1992–95 Multi-City Study of Urban Inequality, an employer survey, the authors document a new empirical finding that workers are less likely to receive promotions in nonprofit organizations than in for-profit firms. The study also uncovers evidence that wage increases associated with promotion were of comparable magnitudes in the two sectors, as was the potential for within-job wage growth; nonprofits were less likely than for-profits to base promotions on job performance or merit; nonprofits were less likely to use output-contingent incentive contracts to motivate workers; and the observed difference in promotion rates between the nonprofit and for-profit sectors was more pronounced for high-skilled than for low-skilled workers. The authors also propose a theory, based on the idea that nonprofit workers are intrinsically motivated to a greater extent than are for-profit workers, that potentially explains the broad pattern of evidence they uncover

The contribution of data mining to information science

The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research

CONFLICT OF INTEREST: A CRITICAL ANALYSIS OF FRANCHISING BUSINESS

There are many multinational companies engaging with the franchising mechanisms in order to expand the business opportunity, to explore new opportunities, and to develop businesses domestically and internationally. Even though this is a good opportunity for multinational companies to expand their market to achieve the company target, but there is a problem attached with this mechanism. The main problem of franchising business is mainly conflict of interest between the parties involved. Hence, this paper attempts to critically evaluate the potential of conflict of interest that arises among the parties. Apart from that, this paper also outlined certain way on how to avoid this problem from occurs continuouslyconflict of interest, franchising

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision

Are NFL Athletes Receiving Over-Valued Contracts?

Many sport research studies have been conducted that examine the performance of professional athletes and their corresponding effect on franchise winning percentages, team revenues, economic repercussions, performance-based compensation, and much more. Research in the National Football League, however, has been found to be somewhat limited due to the numerous possible positions and resulting vastness of position-specific variables. The NFL lockout in 2011 caused many to question the specific relationship between professional athlete performance and salary distribution. This study’s purpose was to find a collection of variables with which all NFL athletes could be compared, and to identify relationships existing between a player’s performance and his value/salary. Data was collected from USAToday.com, Pro-football-reference.com, and AdvancedNFLStats.com. This data was then organized and manipulated into a format that allowed all players in the league during the 2009 season to be compared. Of the nine variables considered for this study, four were found to have a significant relationship with a player’s value/salary. These results were utilized to create a Player Valuation model and then analyze the overall salary distribution throughout the NFL. From this, it was observed while there are many athletes in the NFL that receive extravagant salaries well over their projected value, there is a much larger portion of the league that is undervalued and receive less than their projected value. It was then concluded that a super-star variable would be necessary to create a more accurate Player Valuation model, and the reason there is a larger proportion of NFL players receiving a lower salary than they deserve is due to franchise cap limits. These cap limits place pressure on franchises to push down the salaries of non-superstar athletes in order to compensate for the salaries required for the super-star athletes on their rosters