Kweku-Muata Osei-Bryson

Managing risks in information systems outsourcing: An approach to analyzing outsourcing risks and structuring incentive contracts

Information systems outsourcing is now almost standard practice for many companies. Outsourcing the information processing activities is a complex issue that entails considerable implications for the strategy of the firm. An important mechanism for managing the performance of outsourcing vendors is incentive contracts. But to develop an outsourcing contract the IS manager must quantify risks and benefits. However methods and tools for analyzing and quantifying outsourcing risks that IS managers have at their disposal are rudimentary. In this paper we offer a method and some mathematical models for analyzing risks and constructing incentive contracts for IS outsourcing. We are aware that most managers do not like to use mathematical models, consequently we have minimized the technical discussion and have illustrated how this model could be implemented using spreadsheet software for ease of use.

Evaluation of decision trees: a multi-criteria approach

Data mining (DM) techniques are being increasingly used in many modern organizations to retrieve valuable knowledge structures from organizational databases, including data warehouses. An important knowledge structure that can result from data mining activities is the decision tree (DT) that is used for the classification of future events. The induction of the decision tree is done using a supervised knowledge discovery process in which prior knowledge regarding classes in the database is used to guide the discovery. The generation of a DT is a relatively easy task but in order to select the most appropriate DT it is necessary for the DM project team to generate and analyze a significant number of DTs based on multiple performance measures. We propose a multi-criteria decision analysis based process that would empower DM project teams to do thorough experimentation and analysis without being overwhelmed by the task of analyzing a significant number of DTs would offer a positive contribution to the DM process. We also offer some new approaches for measuring some of the performance criteria.

Increasing the discriminatory power of DEA in the presence of the sample heterogeneity with cluster analysis and decision trees

Data envelopment analysis (DEA) is a widely used non-parametric data analytic tool discriminatory power of which is dependent on the homogeneity of the domain of the sample. In many real-life cases, however, the sample of the decision making units (DMU) could consist of two or more naturally occurring subsets, thus exhibiting clear signs of heterogeneity. In such situations, the discriminatory power of DEA is limited, for the nature of the relative efficiency of a DMU is likely to be influenced by its membership in a particular subset of the sample. In this study, we propose a three-step methodology allowing for increasing the discriminatory power of DEA in the presence of the heterogeneity of the sample. In the first phase, we use cluster analysis (CA) in order to test for the presence of the naturally occurring subsets in the sample. In the second phase DEA is used to calculate the relative efficiencies of the DMUs, as well as averaged relative efficiencies of each subset identified in the previous phase. Finally, we utilize decision tree (DT) induction in order to inquire into the subset-specific nature of the relative efficiencies of the DMUs in the sample. Illustrative example is provided.

Towards defining dimensions of knowledge systems quality

Knowledge management systems (KMS) are extremely important for organisations, primarily because they help to manage a key organisational resource - intellectual capital with the potential to produce a competitive advantage. The usefulness of this resource, however, is only as good as the quality of the knowledge that it contains. In order to improve the quality of KMS, a set of test measures is required. The purpose of this paper is to define some dimensions that can be used to measure the quality of the knowledge management system and to compare KMS quality across systems.

Building ontology based knowledge maps to assist business process re-engineering

Business Process Re-engineering (BPR) is being used to improve the efficiency of the organizational processes, however, a number of obstacles have prevented its full potential from being realised. One of these obstacles is caused by an emphasis on the business process itself at the exclusion of considering other important knowledge of the organization. Another is due to the lack of tools for identifying the cause of the inefficiencies and inconsistencies in BPR. In this paper we propose a methodology for BPR that overcomes these two obstacles through the use of a formal organizational ontology and knowledge structure and source maps. These knowledge maps are represented formally to facilitate an inferencing mechanism which helps to automatically identify the causes of the inefficiencies and inconsistencies. We demonstrate the applicability of this methodology through the use of a case study of a university domain.

Exploring managerial factors affecting ERP implementation: an investigation of the Klein‐Sorra model using regression splines

Abstract. Predicting successful implementation of enterprise resource planning (ERP) systems is still an elusive problem. The cost of ERP implementation failures is exceedingly high in terms of quantifiable financial resources and organizational disruption. The lack of good explanatory and predictive models makes it difficult for managers to develop and plan ERP implementation projects with any assurance of success. In this paper we investigate the Klein & Sorra theoretical model of implementation effectiveness. To test this model we develop and validate a data collection instrument to capture the appropriate data, and then use multivariate adaptive regression splines to examine the assertions of the model and suggest additional significant relationships among the factors of their model. Our research offers new dimensions for studying managerial interventions in IT implementation and insights into factors that can be managed to improve the effectiveness of ERP implementation projects.

Using ontologies to facilitate post-processing of association rules by domain experts

Data mining is used to discover hidden patterns or structures in large databases. Association rule induction extracts frequently occurring patterns in the form of association rules. However, this technique has a drawback as it typically generates a large number of association rules. Several methods have been proposed to prune the set of extracted rules in order to present only those which are of interest to the domain experts. Some of these methods involve subjective analysis based on prior domain knowledge, while others can be considered to involve objective, data-driven analysis based on numerical measures that provide a partial description of the interestingness of the extracted association rules. Recently it has been proposed that ontologies could be used to guide the data mining process. In this paper, we propose a hybrid pruning method that involve the use of objective analysis and subjective analysis, with the latter involving the use of an ontology. We demonstrate the applicability of this hybrid method using a medical database.

Exploring the characteristics of Internet security breaches that impact the market value of breached firms

The impact of Internet security breaches on firms has been a concern to both researchers and practitioners. One measure of the damage to the breached firm is the observed cumulative abnormal stock market return (CAR) when there is announcement of the attack in the public media. To develop effective Internet security investment strategies for preventing such damage, firms need to understand the factors that lead to the occurrence of CAR. While previous research have involved the use of regression analysis to explore the relationship between firm and attack characteristics and the occurrence of CAR, in this paper we use decision tree (DT) induction to explore this relationship. The results of our DT-based analysis indicate that both attack and firm characteristics determine CAR. While each of our results is consistent with that of at least one previous study, no previous single study has provided evidence that both firm and attack characteristics are determinants of CAR. Further, the DT-based analysis provides an interpretable model in the form of understandable and actionable rules that may be used by decision makers. The DT-based approach thus provides additional insights beyond what may be provided by the regression approach that has been employed in previous research. The paper makes methodological, theoretical and practical contribution to understanding the predictors of damage when a firm is breached.

Post-pruning in decision tree induction using multiple performance measures

The decision tree (DT) induction process has two major phases: the growth phase and the pruning phase. The pruning phase aims to generalize the DT that was generated in the growth phase by generating a sub-tree that avoids over-fitting to the training data. Most post-pruning methods essentially address post-pruning as if it were a single objective problem (i.e. maximize validation accuracy), and address the issue of simplicity (in terms of the number of leaves) only in the case of a tie. However, it is well known that apart from accuracy there are other performance measures (e.g. stability, simplicity, interpretability) that are important for evaluating DT quality. In this paper, we propose that multi-objective evaluation be done during the post-pruning phase in order to select the best sub-tree, and propose a procedure for obtaining the optimal sub-tree based on user provided preference and value function information.

Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis, DEA and Neural Networks

Data Envelopment Analysis (DEA) is a powerful data analytic tool that is widely used by researchers and practitioners alike to assess relative performance of Decision Making Units (DMU). Commonly, the difference in the scores of relative performance of DMUs in the sample is considered to reflect their differences in the efficiency of conversion of inputs into outputs. In the presence of scale heterogeneity, however, the source of the difference in scores becomes less clear, for it is also possible that the difference in scores is caused by heterogeneity of the levels of inputs and outputs of DMUs in the sample. By augmenting DEA with Cluster Analysis (CA) and Neural Networks (NN), we propose a five-step methodology allowing an investigator to determine whether the difference in the scores of scale heterogeneous DMUs is due to the heterogeneity of the levels of inputs and outputs, or whether it is caused by their efficiency of conversion of inputs into outputs. An illustrative example demonstrates the application of the proposed methodology in action.