The Role of the Mangement Sciences in Research on Personalization

We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,

Determination of structural elements of synthesized silver nano-hexagon from X-ray diffraction analysis

Silver nano-hexagons (AgNHs) have been prepared by a chemical reduction method using poly-vinyl pyrrolidone (PVP) as a stabilizing agent. The XRD results exhibit the crystalline nature of the prepared sample, with a face centred cubic (fcc) phase. Transmission electron microscopic (TEM) results reveal that the silver nanoparticles are nearly hexagon in shape with an average size of 50 nm. Here, crystallite size has been calculated using Williamson-Hall (W-H) method, which is nearly matching with average size obtained from TEM analysis. Again, using W-H method, micro strain has been calculated, which is produced in the nano-hexagon due to dislocation of silver atoms. Further, the lattice constant of the nano-hexagons has also been estimated from the Nelson–Riley plot. Moreover, the appropriate structural parameters such as Lorentz factor, Lorentz polarization factor, dislocation density, number of atoms in a unit cell and morphological index have also been studied from the X-ray diffraction profile

Free Software Offer and Software Diffusion: The Monopolist Case

An interesting phenomenon often observed is the availability of free software. The benefits resulting from network externality have been discussed in the related literature. However, the effect of a free software offer on new software diffusion has not been formally analyzed. We show in this study that even if other benefits do not exist, a software firm can still benefit from giving away fully functional software at the beginning period of the marketing process. This is due to the accelerated diffusion process and subsequently the increased NPV of future cash flows. The analysis is based on the well-known Bass diffusion model

A Bayesian Framework for Modifications of Probabilistic Relational Data

The inherent uncertainty pervasive over the real world often forces business decisions to be made using uncertain data. The conventional relational model does not have the ability to handle uncertain data. In recent years, several approaches have been proposed in the literature for representing uncertain data by extending the relational model, primarily using probability theory. However, the aspect of database modification has been overlooked in these investigations. It is clear that any modification of existing probabilistic data, based on new information, amounts to the revision of one’s belief about real world objects. In this paper, we examine the aspect of belief revision and develop a generalized algorithm that can be used for modification of existing data in a probabilistic relational database

Efficient Supply Chain Contracting with Loss-averse Players in Presence of Multiple Plausible Breaches

The legal literature distinguishes between the liquidated damage and the penalty clauses in contracts, and holds that penalties designed for the prevention of breach are excessive compared to the liquidated damages. In an efficient supply chain contract, the penalty must satisfy the participation and incentive compatibility constraints of the signatories. Considering loss-averse players, we have calculated optimal penalties in a supply chain contract and compared those with the liquidated damages. Two possible breaches are considered – a breach in quality of the delivery and a breach in the process. In the absence of any penalty, a process breach reduces the supplier’s delivery risk and cost of delivery. Determining the parametric conditions for efficient contracts, numerically we show the effects of various variables on the zone of efficient contract. We show that the optimal penalties need not be excessive compared to the liquidated damages

Protecting Privacy Against Regression Attacks in Predictive Data Mining

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-mining technique, can be used to effectively reveal individuals\u27 sensitive data. This problem, which we call a regression attack, has been overlooked in the literature. 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 adopts a novel measure which considers the tradeoff between disclosure risk and data utility in a regression tree pruning process. We also propose a dynamic value-concatenation method, which overcomes the limitation of requiring a user-defined generalization hierarchy in traditional k-anonymity approaches. Our approach can be used for anonymizing both numeric and categorical data. An experimental study is conducted to demonstrate the effectiveness of the proposed approach

DATA CLUSTERING AND MICRO-PERTURBATION FOR PRIVACY-PRESERVING DATA SHARING AND ANALYSIS

Clustering-based data masking approaches are widely used for privacy-preserving data sharing and data mining. Existing approaches, however, cannot cope with the situation where confidential attributes are categorical. For numeric data, these approaches are also unable to preserve important statistical properties such as variance and covariance of the data. We propose a new approach that handles these problems effectively. The proposed approach adopts a minimum spanning tree technique for clustering data and a micro-perturbation method for masking data. Our approach is novel in that it (i) incorporates an entropy-based measure, which represents the disclosure risk of the categorical confidential attribute, into the traditional distance measure used for clustering in an innovative way; and (ii) introduces the notion of cluster-level microperturbation (as opposed to conventional micro-aggregation) for masking data, to preserve the statistical properties of the data. We provide both analytical and empirical justification for the proposed methodology

A Data Perturbation Approach to Privacy Protection in Data Mining

Advances in data mining techniques have raised growing concerns about privacy of personal information. Organizations that use their customers’ records in data mining activities are forced to take actions to protect the privacy of the individuals involved. A common practice for many organizations today is to remove the identity-reated attributes from customer records before releasing them to data miners or analysts. In this study, we investigate the effect of this practice and demonstrate that a majority of the records in a dataset can be uniquely identified even after identity related attributes are removed. We propose a data perturbation method that can be used by organizations to prevent such unique identification of individual records, while providing the data to analysts for data mining. The proposed method attempts to preserve the statistical properties of the data based on privacy protection parameters specified by the organization. We show that the problem can be solved in two phases, with a linear programming formulation in phase one (to preserve the marginal distribution), followed by a simple Bayes-based swapping procedure in phase two (to preserve the joint distribution). The proposed method is compared with a random perturbation method in classification performance on two real-world datasets. The results of the experiments indicate that it significantly outperforms the random method