An interactive approach for multi-attribute auctions

Auction processes are commonly employed in many environments. With rapid advances in Internet and computing technologies, electronic auctions have become very popular. People sell and buy a wide range of goods and services online. There is a growing need for the proper management of online auctions and for providing support to parties involved. In this paper, we develop an interactive approach supporting both the buyer and the bidders in a multi-attribute, single-item, multi-round, reverse auction environment. We demonstrate the algorithm on a number of problems.     

Mining churning behaviors and developing retention strategies based on a partial least squares (PLS) model

In a very competitive mobile telecommunication business environment, marketing managers need a business intelligence model that allows them to maintain an optimal (at least a near optimal) level of churners very effectively and efficiently while minimizing the costs throughout their marketing programs. As a first step toward optimal churn management program for marketing managers, this paper focuses on building an accurate and concise predictive model for the purpose of churn prediction utilizing a partial least squares (PLS)-based methodology on highly correlated data sets among variables. A preliminary experiment demonstrates that the presented model provides more accurate performance than traditional prediction models and identifies key variables to better understand churning behaviors. Further, a set of simple churn marketing programs—device management, overage management, and complaint management strategies—is presented and discussed.     

Performance of classification models from a user perspective

This paper proposes a complete framework to assess the overall performance of classification models from a user perspective in terms of accuracy, comprehensibility, and justifiability. A review is provided of accuracy and comprehensibility measures, and a novel metric is introduced that allows one to measure the justifiability of classification models. Furthermore, taxonomy of domain constraints is introduced, and an overview of the existing approaches to impose constraints and include domain knowledge in data mining techniques is presented. Finally, justifiability metric is applied to a credit scoring and customer churn prediction case.     

Using RFID for the management of pharmaceutical inventory — system optimization and shrinkage control

Motivated by a case study at a radiology practice, we analyze the incremental benefits of RFID technology over barcodes for managing pharmaceutical inventories. Unlike barcode technology, RFID enables accurate real-time visibility, which in turn enables several process improvements. We analyze the impact of automatic counting and discuss the system redesign critical to optimizing the inventory policy and eliminating shrinkage. We show that continuous review is superior to periodic review whenever accurate real-time information is available at no additional cost. We explain how RFID-enabled strategies vary with inventory parameters and provide a cost-benefit analysis for the implementation of RFID for the radiology practice.     

An elusive antecedent of superior firm performance: The knowledge management factor

Knowledge management (KM) concepts, principles, and technologies provide a foundation for understanding and building systems for acquiring, assimilating, selecting, generating, and emitting knowledge—a crucial resource of the firm. In the knowledge management community, it is commonly contended that knowledge, and capabilities for processing it, comprise a major resource that can differentiate one firm from another in the sense of yielding better performance or a competitive edge. However, aside from anecdotes, there has been little to substantiate this contention. Can any empirical link be discovered between a firm's KM success and that firm's financial performance? To develop an answer to this question, we use an independent research company's reports of firms judged to be highly successful in their KM initiatives, plus related data reported by COMPUSTAT. As an initial investigation of the linkage between KM performance and firm performance, as measured by financial ratios, this study uses the Matched Sample Comparison Group methodology to evaluate research hypotheses. The analysis reveals a heretofore elusive antecedent of firm performance—evidence that superior KM performance is indeed a predictor of superior bottom-line performance. This study contributes to the information systems (IS) literature by demonstrating that KM, a basic foundation for IS, is an important factor to consider from the standpoint of achieving strong financial performance. As such, it suggests that KM furnishes an important context for understanding designs, applications, and possibilities for IS with respect to achieving such performance. In the context of devising and executing KM initiatives, both technological and human treatments of knowledge need to be cultivated and integrated in ways that lead to superior KM performance. This study also contributes to the management literature by going beyond anecdotes and case studies in buttressing the proposition that a firm's KM competencies are an important ingredient in that firm's performance. It solidifies the raison d'etre for investigating KM phenomena and methods (computer-based and human), both within and across modern organizations. It gives practicing managers evidence that bottom-line benefits are indeed associated with superior KM strategy and execution.     

On the impact of analyzing customer information and prioritizing in a service system

This paper examines prioritization in a service system and analyzes whether, in the presence of heterogeneous customers who have different needs and a costly sorting process, it pays to prioritize. In particular, in our model, sorting is costly because the task of gathering information to prioritize jobs consumes resources. We investigate a stylized model in which there are two classes of jobs — one whose waiting cost is high, called urgent, and the other whose waiting cost is low, called non-urgent. There are two types of employees, sorters, who collect information on a job and then decide whether it is urgent or non-urgent, and processors, who execute the job or provide the service. We begin by assuming that sorters categorize customers perfectly, and we relax this assumption later in the paper. We optimize two performance metrics, waiting costs (under a given budget) and total costs, and find the conditions under which prioritization is beneficial for these two metrics.     

Bidding strategies for real-life small loan auctions

We define and identify bidding strategies in real-life small loan auctions (Prosper.com). In such auctions, lenders bid for borrowers' loan listings and the winners get to fund the loan at an interest rate determined by the auction. The exceptionally large empirical database provided by Prosper.com offers a unique opportunity to test and further develop the theory of online auctions. This study shows that bidding behavior is not homogeneous among bidders, as the traditional auction theory suggests. Instead, bidders use many different bidding strategies. Moreover, learning and bidders' consistency over time in different auctions is studied.     

Hybrid assessment method for software engineering decisions

During software development, many decisions need to be made to guarantee the satisfaction of the stakeholders' requirements and goals. The full satisfaction of all of these requirements and goals may not be possible, requiring decisions over conflicting human interests as well as technological alternatives, with an impact on the quality and cost of the final solution. This work aims at assessing the suitability of multi-criteria decision making (MCDM) methods to support software engineers' decisions. To fulfil this aim, a HAM (Hybrid Assessment Method) is proposed, which gives its user the ability to perceive the influence different decisions may have on the final result. HAM is a simple and efficient method that combines one single pairwise comparison decision matrix (to determine the weights of criteria) with one classical weighted decision matrix (to prioritize the alternatives). To avoid consistency problems regarding the scale and the prioritization method, HAM uses a geometric scale for assessing the criteria and the geometric mean for determining the alternative ratings.     

Adoption of Open Source Software: The role of social identification

While the benefits of incorporating Open Source Software (OSS) into personal and organizational systems have been widely touted, OSS must be adopted and used by end users before these benefits can be realized. Drawing on research in information systems and sociology, this study develops and evaluates an integrated model for the acceptance of OSS. In addition to the traditional technology adoption variables the findings stress the importance of social identification as a key driver of OSS adoption. The proposed model provides a useful decision support tool for assessing and proactively designing interventions targeted at successful OSS adoption and diffusion.     

Modeling the benefit of e-recruiting process integration

While e-recruiting has been widely adopted as one of the most successful e-business applications, it constitutes an under-researched area in e-business research. This study reviews the integration issues in e-recruiting and presents an e-recruiting integration decision model. The benefits of the investment in e-recruiting process integration are discussed in comparison to separate e-recruiting investments. We show that the optimal investment in the e-recruiting process integration results in a lower total cost than the separate e-recruiting investments. In addition, in light of the widely practiced resource constrained investments, we present the method of Lagrange multipliers which is used to find the optimal investment under a budget constraint.