IT Transformation Decision Processes: A Conceptual Framework for Efficiency/Effectiveness Optimization

Decision processes are complex managerial challenges for long-term information technology (IT) initiatives with an organization-wide scope (in short: IT transformations). Costs, benefits, and risks of such IT investments are hard to capture, and the dozens ofprescriptive methods proposed for IT project evaluation have proved to contribute little to solve the dilemma in practice. Thus, as opposed to continue the search for another prescriptive evaluation methodology, this paper follows a new approach: it targets the development of a conceptual framework that allows for the improved management of holistic IT decision processes - as opposed to cook-book approaches that attempt optimizing the corresponding decisions. The paper introduces a descriptive conceptual framework capable of describing the key components (e.g. socio-political elements) of practical IT transformation decision processes, while at the same time providing enough structure (e.g. rational decision process structures) to allow for decision process effectiveness/efficiency optimization. The framework is then leveraged to deduce theoretical levers for the improvement of practical decision processes. It is suggested as a basis for future structured/holistic case study research ofpractical IT transformation decision processes. To allow for the latter, the paper introduces an approach for the approximation of the involved input and output parameters in the framework

On The Applicability of Data Envelopment Analysis for Multiple Attriliute Decision Making in the Context of Information Systems Appraisals

This article elaborates on the applicability of basic and extended data envelopment analysis (DEA) models for various information system (IS) decision use-cases including illustrative examples from an enterprise resource planning (ERP) software investment appraisal. The usage of data envelopment analysis models and their extensions for IS decisions remains limited. This omission seems critical in particular for two reasons. First, organizational studies have shown that in practice business management fails to appreciate the portfolio of investment appraisal techniques available. Second, DEA based methodologies, especially new extensions, promise valuable insights to support the complex IS decision problem. The results indicate DEA applicability in a number of use-cases, e.g. for structural analysis of system alternatives or validation of ranking outcomes

Analysis of Heuristic Validity, Efficiency and Applicability of the Profile Distance Method for Implementation in Decision Support Systems

This article seeks to enhance acceptance of the profile distance method (PDM) in decision support systems. The PDM is a multiple attributive based decision making as well as a multiple method approach to support complex decision making and uses a heuristic to avoid computationally complex global optimization. We elaborate on the usability of the method and question the heuristic used. We present a bisection algorithm, which efficiently supports the discovery of transition profiles needed in a user-friendly and practical application of the method. Additionally, we provide empirical evidence showing that the proposed heuristic is efficient and delivers results within 5% of the global optimizer for a wide range of data sets

Finding all maximal cliques in dynamic graphs

Clustering applications dealing with perception based or biased data lead to models with non-disjunct clusters. There, objects to be clustered are allowed to belong to several clusters at the same time which results in a fuzzy clustering. It can be shown that this is equivalent to searching all maximal cliques in dynamic graphs like G_t=(V,E_t), where E_(t-1) in E_t, t=1,... ,T; E_0=(). In this article algorithms are provided to track all maximal cliques in a fully dynamic graph. It is naturally to raise the question about the maximum clique, having all maximal cliques. Therefore this article discusses potentials and drawbacks for this problem as well. (author's abstract)Series: Working Papers on Information Systems, Information Business and Operation

Stochastic branch & bound applying target oriented branch & bound method to optimal scenario tree reduction

In this article a new branch & bound method is described. It uses an artificial target to improve its bounding capabilities. Therefore the new approach is faster compared to the classical one. It is applied to the stochastic problem of optimal scenario tree reduction. The aspects of global optimization are emphasized here. All necessary components for that problem are developed and some experimental results underline the benefits of the new approach. (author's abstract)Series: Working Papers on Information Systems, Information Business and Operation

Target oriented branch & bound method for global optimization

We introduce a very simple but efficient idea for branch & bound (B&B) algorithms in global optimization (GO). As input for our generic algorithm, we need an upper bound algorithm for the GO maximization problem and a branching rule. The latter reduces the problem into several smaller subproblems of the same type. The new B&B approach delivers one global optimizer or, if stopped before finished, improved upper and lower bounds for the problem. Its main difference to commonly used B&B techniques is its ability to approximate the problem from above and from below while traversing the problem tree. It needs no supplementary information about the system optimized and does not consume more time than classical B&B techniques. Experimental results with the maximum clique problem illustrate the benefit of this new method. (author's abstract)Series: Working Papers on Information Systems, Information Business and Operation

Explaining MCDM acceptance: a conceptual model of influencing factors

The number of newly developed Multi-Criteria Decision Making (MCDM) methods grew considerably in the last decades. Although their theoretical foundations are solid, there is still a lack of acceptance and application in the practical field. The objective of this research is the development of a conceptual model of factors that influence MCDM acceptance that serves as a starting point for further research. For this purpose, a broad diversified literature survey was conducted in the discipline of technology adoption and related topics (like human computer interaction) with special focus on MCDM acceptance. The constructs collected within the literature survey were classified based on a qualitative approach which yielded a conceptual model structuring the identified factors according to individual, social, technology-related, task-related and facilitating aspects. (author's abstract

Open Source Project Categorization Based on Growth Rate Analysis and Portfolio Planning Methods

Abstract. In this paper, we propose to arrive at an assessment and evaluation of open source projects based on an analysis of their growth rates in several aspects. These include code base, developer number, bug reports and downloads. Based on this analysis and assessment, a well-known portfolio planning method, the BCG matrix, is employed for arriving at a very broad classification of open source projects. While this approach naturally results in a loss of detailed information, a top-level categorization is in some domains necessary and of interest

Probabilistic subproblem selection in branch-and-bound algorithms

AbstractWe investigate the branch-and-bound method for solving nonconvex optimization problems. Traditionally, much effort has been invested in improving the quality of the bounds and in the development of branching strategies, whereas little is known about good selection rules. After summarizing several known selection methods, we propose to introduce a probabilistic element into the selection process. We describe conditions which guarantee that a branch-and-bound algorithm using our probabilistic selection rule converges with probability 1. This new method is a generalization of the well-known best-bound selection rule. Furthermore, we relate the corresponding probability measure to the distribution of the optimal solution in the bounding interval. We also show how information on the quality of the upper and lower bounds influences the choice of the subset selection rule and conclude with numerical experiments on the Maximum Clique Problem which show that probabilistic selection can speed up an algorithm in many cases

Introducing Complex Decision Models to the Decision Maker with Computer Software - The Profile Distance Method (PDM)

In this paper we demonstrate how the profile distance method was transformed into a software environment enabling the decision maker to utilize a complex decision making tool without any advanced knowledge of the underlying mathematical and technical features. We present theoretical and technical aspects as well as contextual and usage related information from the viewpoint of the decision maker. Preliminary empirical results suggest that the developed software component is effective in terms of platform independence, usability and intuitive interface design. The data showed a good rating for usefulness, which, however, was targeted as the main goal for further development