Noel Bryson

Making the information systems outsourcing decision: A transaction cost approach to analyzing outsourcing decision problems

Abstract During the last several years outsourcing has emerged as a major issue in information systems management. As competitive forces impinge on business firms, senior managers are re-structuring their organizations with an eye on attaining or maintaining competitive advantage. Various strategies to IS outsourcing have emerged, for example, some outsourcers contract with a sole vendor while others contract with several. To date no studies have been done to determine which strategies are appropriate under what conditions. And while some firms have achieved varying degrees of success with any of these strategies, many have encountered significant difficulties. How then are managers to choose from a set of options that which is most appropriate for their firm? Outsourcing problems are complex and entail considerable implications for the strategy of the firm. A wrong decision can result in loss of core competencies and capabilities, and exposure to unexpected risks. Although many articles have appeared on outsourcing, few have extended the discussion beyond simple cost–benefit analysis. In this paper we discuss a transaction cost theory approach to the analysis of outsourcing decision making. Our approach provides managers with a strategy and techniques for analyzing some of the more subtle issues they may face when dealing with complex outsourcing decisions problems.

Group decision-making and the analytic hierarchy process: exploring the consensus-relevant information content

Abstract The Analytic Hierarchy Process (AHP) is being increasingly applied to managerial and technical group decision-making problems in which it is necessary to determine the preferences of the group as a whole. In such contexts unless there is an acceptable level of group consensus, it is premature to use mathematical techniques to generate “consensus” preference vectors. Hence it is necessary to assess the current level of group consensus before applying mathematical techniques to generate a “consensus” preference vector. In this paper, we explore possibilities that could result from the use of consensus relevant information embedded in the preference data that arise in group decision-making contexts. We offer a set of similarity measures and consensus indicators for assessing the level of group consensus that could also be used by the group facilitator to develop strategies for increasing the level of group consensus.

An action learning evaluation procedure for multiple criteria decision making problems

Abstract In this paper we focus on an extension of the Analytic Hierarchy Process (AHP) that accommodates ambiguity on the part of the decision maker (DM), and facilitates the exploration of the decision domain. We propose a systematic action learning process that builds confidence as it converges from numeric interval estimates to numeric point estimates. Our Multiple Criteria Decision Making (MCDM) problem procedure structures the problem as a hierarchy, evaluates all objects using pairwise comparisons that accommodate vagueness and ambiguity, uses interval prioritization techniques, and does synthesis using the linear additive value function. This action learning process facilitates the understanding of key stakeholders, which is imperative for the successful implementation of the subsequent decision.

Supporting facilitation in group support systems: techniques for analyzing consensus relevant data

In this paper, we present a set of techniques and an approach to support the facilitator in building consensus during group decision making in computer supported group work. The approach utilizes data about each participants expressed preferences (scores and ranks) for a set of decision alternatives under consideration. The data are analyzed to provide the facilitator with information about the level of group consensus, coalescing of subgroups, and areas of strong disagreement. An illustration from a real-world case situation demonstrates the approach.

Modelling pairwise comparisons on ratio scales

Abstract Many ‘real world’ decision problems have multiple criteria on qualitative domains. Given the expectation that more and more of such problems will be modelled as MCDM problems involving scoring on subjective/qualitative domains, then there is a class of significant problems for which the evaluator will require an evaluation framework which handles occurrences of seeming intransitivity and inconsistency. These phenomena are likely to appear in qualitative/subjective domains where the decision-making environment is characterized by ambiguity and vagueness. In this paper we present a model for exploring the implications of pairwise comparisons on ratio scales. This model provides a mathematical explanation for the phenomena of intransitivity and inconsistency that sometimes appear in situations involving scoring on a subjective/qualitative domain. Using this framework we develop methods for generating consistent intervals on ratio scales. We also present a two-stage action learning process for generating consistent points on ratio scales that provides both behavioral and mathematical convergence.

Eliciting and mapping qualitative preferences to numeric rankings in group decision making

Abstract Group work is becoming the norm in organizations. From strategy planning committees to quality management teams, organizational members are collaborating on problem solving. One area of team support that is often desired is the scoring and ranking of decision alternatives on qualitative/subjective domains, and the aggregation of individual preferences into group preferences. In this paper we present a new conceptual approach to qualitative preference elicitation and aggregation. This approach is based on well established decision analysis techniques. It significantly advances the state of the art of group decision making by addressing four common limitations: (1) the inability to deal with vagueness of human decision makers in articulating preferences; (2) difficulties in mapping qualitative evaluation to numeric estimates; (3) problems in aggregating individual preferences into meaningful group preference; and (4) the lack of simple user friendly techniques for dealing with a large number of decision alternatives. Our approach is easy to implement in stand alone personal computers and groupware. We illustrate this with a real-world problem.

Generating consensus priority interval vectors for group decision‐making in the AHP

Many practical and important decision-making problems are complicated by at least two factors: (1) the qualitative/subjective nature of some criteria often results in uncertainty in the individual ratings; and (2) group decision-making is involved and some means of aggregating individual ratings is required. Traditionally, both individual and group priorities have been represented as point estimates, but this approach presents severe limitations for accommodating imprecision in the decision-making process. This paper examines the group decision-making problem in the context where priorities are represented as numeric intervals. A set of techniques that could be used at some of the phases of an analytic hierarchy process (AHP)-based group decision-making process, which has the objective of generating a ‘consensus’ priority that represents the groups opinion with regards to the relative importance of a set of N objects (e.g. criteria, alternatives), is presented. Copyright

An approach to using the analytic hierarchy process for solving multiple criteria decision making problems

Abstract In this paper we present a procedure for solving multiple criteria decision making problems. Our procedure, which is an extension of the Analytic Hierarchy Process, addresses the issue of fuzziness in the weighing information provided by the decision maker, the consequent stability of the AHP ranking of the alternatives and the sensitivity of such rankings to small perturbations. These issues are especially important when the concept of optimality incorporates parameters or weights that reflect subjective preferences. The procedure provides a consistent and systematic method for carrying out goal-seeking sensitivity analysis that captures the decision makers preference structure using his/her indifference region.

Generating consensus fuzzy cognitive maps

The Fuzzy Cognitive Maps (FCM) of B. Kosko (1986) are useful tools for exploring the impacts of inputs to fuzzy dynamical systems. The development of an FCM often occurs within a group context because it is felt that the variety of perspectives on the given dynamical system improves the effort to identify the relevant concepts and the causal relationships between the concepts. The assumption is that combining incomplete, conflicting opinions of different experts may cancel out the effects of oversight, ignorance and prejudice. There is also then need to accommodate the inherent fuzziness of the problem. We present an integrated process for rating of the intensity of causal relationships, generating mean FCMs, assessing group consensus, and supporting the building of group consensus.

Supporting consensus formation in Group Support Systems using the Qualitative Discriminant Process

Group Support Systems (GSS) are software systems that support group work, providing facilities for idea generation, idea organization, rating and ranking, communication, and recording of ideas. Because preference tasks occur frequently in organizational work, the rating and ranking facility is one of the most popular features of GSS. Nevertheless, this area has not received the attention it deserves in GSS research. Ratings in group decision-making contexts require techniques for synthesizing individual scores into group scores. Because the application of these techniques is only meaningful when there exists a relatively high level of consensus that is obtained through human interaction, there is the need for techniques to assess the level of group consensus, and also for supporting consensus build-ing. In this paper, an integrated method for rating and ranking, consensus assessment and consensus building is presented. This approach permits ambiguity in the ratings and yet provides appropriate interval and point numeric estimates; it offers interpretable similarity measures and consensus indicators.