Krishnamurty Muralidhar

An Empirical Investigation of the Relationship Between Change in Corporate Social Performance and Financial Performance: A Stakeholder Theory Perspective

Stakeholder theory provides a framework for investigating the relationship between corporate social performance (CSP) and corporate financial performance. This relationship is investigated by examining how change in CSP is related to change in financial accounting measures. The findings provide some support for a tenet in stakeholder theory which asserts that the dominant stakeholder group, shareholders, financially benefit when management meets the demands of multiple stakeholders. Specifically, change in CSP was positively associated with growth in sales for the current and subsequent year. This indicates that there are short-term benefits from improving CSP. Return on sales was significantly positively related to change in CSP for the third financial period, indicating that long-term financial benefits may exist when CSP is improved.

The Development of a Systematic, Aggregate Measure of Corporate Social Performance

Individuals, groups, and government units have developed considerable interested in evaluating the social performance of corporations. Evaluating corporate social performance (CSP) is important for researchers investigating the relationship between different organizational measures and CSP and for stakeholders employing social performance information in their decision making models. To assess CSP, an aggregate measure of CSP which incorporates both an independent assessment of actual performance and the individual value judgements of the stakeholder is needed. In this study, we propose a methodology for the development of a systematic measure of CSP using the Analytic Hierarchy Process. This multi-criteria decision making technique allows for the conversion of a multidimensional scale to a unidimensional scale, enabling analysis/comparison of companies both within the same industry and across industries. The technique is illustrated by developing a measure of CSP using several types of managers. The results indicate that there are large rank changes when individual value judgements are incorporated using relative importance weights.

Using the analytic hierarchy process for information system project selection

Abstract Information system project selection plays an important role in planning for information systems. This paper presents an improved methodology for information system project selection using the analytic hierarchy process (AHP). The AHP method adopts a multicriteria approach to information system project selection unlike the single criteria approach used by existing methods. It also provides the capability to establish the relative importance of criteria in-line with organizational objectives. Existing methodologies fail to provide this capability. The application of the AHP method is illustrated through an example.

Data ShufflingA New Masking Approach for Numerical Data

This study discusses a new procedure for masking confidential numerical dataa procedure called data shufflingin which the values of the confidential variables are shuffled among observations. The shuffled data provides a high level of data utility and minimizes the risk of disclosure. From a practical perspective, data shuffling overcomes reservations about using perturbed or modified confidential data because it retains all the desirable properties of perturbation methods and performs better than other masking techniques in both data utility and disclosure risk. In addition, data shuffling can be implemented using only rank-order data, and thus provides a nonparametric method for masking. We illustrate the applicability of data shuffling for small and large data sets.

Security of random data perturbation methods

Statistical databases often use random data perturbation (RDP) methods to protect against disclosure of confidential numerical attributes. One of the key requirements of RDP methods is that they provide the appropriate level of security against snoopers who attempt to obtain information on confidential attributes through statistical inference. In this study, we evaluate the security provided by three methods of perturbation. The results of this study allow the database administrator to select the most effective RDP method that assures adequate protection against disclosure of confidential information.

A theoretical basis for perturbation methods

In this paper we discuss a new theoretical basis for perturbation methods. In developing this new theoretical basis, we define the ideal measures of data utility and disclosure risk. Maximum data utility is achieved when the statistical characteristics of the perturbed data are the same as that of the original data. Disclosure risk is minimized if providing users with microdata access does not result in any additional information. We show that when the perturbed values of the confidential variables are generated as independent realizations from the distribution of the confidential variables conditioned on the non-confidential variables, they satisfy the data utility and disclosure risk requirements. We also discuss the relationship between the theoretical basis and some commonly used methods for generating perturbed values of confidential numerical variables.

The Security of Confidential Numerical Data in Databases

Organizations are storing large amounts of data in databases for data mining and other types of analysis. Some of this data is considered confidential and has to be protected from disclosure. When access to individual values of confidential numerical data in the database is prevented, disclosure may occur when a snooper uses linear models to predict individual values of confidential attributes using nonconfidential numerical and categorical attributes. Hence, it is important for the database administrator to have the ability to evaluate security for snoopers using linear models. In this study we provide a methodology based on Canonical Correlation Analysis that is both appropriate and adequate for evaluating security. The methodology can also be used to evaluate the security provided by different security mechanisms such as query restrictions and data perturbation. In situations where the level of security is inadequate, the methodology provided in this study can also be used to select appropriate inference control mechanisms. The application of the methodology is illustrated using a simulated database.

Perturbing Nonnormal Confidential Attributes: The Copula Approach

Protecting confidential, numerical data in databases from disclosure is an important issue both for commercial organizations as well as data-gathering and disseminating organizations (such as the Census Bureau). Prior studies have shown that perturbation methods are effective in protecting such confidential data from snoopers. Perturbation methods have to provide legitimate users with accurate (unbiased) information, and also provide adequate security against disclosure of confidential information to snoopers. For databases described by nonnormal multivariate distributions, existing perturbation methods do not provide unbiased characteristics. In this study, we develop a copula-based perturbation method capable of maintaining the marginal distribution of perturbed attributes to be the same before and after perturbation. In addition, this method also preserves the rank order correlation between the confidential and nonconfidential attributes, thereby maintaining monotonic relationships between attributes. The method proposed in this study provides a high level of protection against inferential disclosure. An investigation of the new perturbation method for simulated databases shows that the method performs effectively. The methodology presented in this study represents a signicant step toward improving the practical applicability of data perturbation methods.

An Improved Security Requirement for Data Perturbation with Implications for E-Commerce

With the rapid increase in the ability to store and analyze large amounts of data, organizations are gathering extensive data regarding their customers, vendors, and other entities. There has been a concurrent increase in the demand for preserving the privacy of confidential data that may be collected. The rapid growth of e-commerce has also increased calls for maintaining privacy and confidentiality of data. For numerical data, data perturbation methods offer an easy yet effective solution to the dilemma of providing access to legitimate users while protecting the data from snoopers (legitimate users who perform illegitimate analysis). In this study, we define a new security requirement that achieves the objective of providing access to legitimate users without an increase in the ability of a snooper to predict confidential information. We also derive the specifications under which perturbation methods can achieve this objective. Numerical examples are provided to show that the use of the new specification achieves the objective of no additional information to the snooper. Implications of the new specification for e-commerce are discussed.

Secure and useful data sharing

Data sharing among organizations, both government and business, has increased with the advent of the Internet. The study is motivated by the need to address increased confidentiality and privacy concerns that arise with the use of the Internet, while realizing the benefits of data sharing. In this study, we develop a research issues framework based on a survey of existing literature. The framework is used to identify OR/MS research opportunities in disclosure prevention, record-linkage, and in the assessment of the impact of data sharing. Addressing these issues could enable organizations to securely share data.