Influence model and doubly extended TOPSIS with TOPSIS based matrix of interpersonal influences

The aim of this paper is to construct a matrix of interpersonal influences employing TOPSIS and then to apply the matrix in influence model and doubly extended TOPSIS. Entries of that matrix are obtained from coefficients of relative closeness. Such a systematically constructed matrix performs better than the direct influence matrix because of the consideration of alternatives under certain criteria/attributes. Implementation of such influence matrix improves an influence model and group decision process. In this paper, TOPSIS is used for individual as well as group decisions. Once the decisions are reached by individuals with the help of TOPSIS, then coefficients of relative closeness are obtained and matrix of interpersonal influences is constructed. This matrix is used in influence model and to construct the influenced decision matrices. These influenced decision matrices are aggregated to get the collective decision. This strategy is based on the fact that the decisions taken by individuals affect their collective decision in future.

EVALUATION OF GROUP DECISION MAKING BASED ON GROUP PREFERENCES UNDER A MULTI-CRITERIA ENVIRONMENT

Arrow’s impossibility theorem stated that no single group decision making (GDM) method is perfect, in other words, different GDM methods can produce different or even conflicting rankings. So, 1) how to evaluate GDM methods and 2) how to reconcile different or even conflicting rankings are two important and difficult problems in GDM process, which have not been fully studied. This paper aims to develop and propose a group decision-making consensus recognition model, named GDMCRM, to address these two problems in the evaluation of GDM methods under a multi-criteria environment in order to identify and achieve optimal group consensus. In this model, the ordinal and cardinal GDM methods are both implemented and studied in the process of evaluating the GDM methods. What’s more, this proposed model can reconcile different or even conflicting rankings generated by the eight GDM methods, based on empirical research on two real-life datasets: financial data of 12 urban commercial banks and annual report data of seven listed oil companies. The results indicate the proposed model not only can largely satisfy the group preferences of multiple stakeholders, but can also identify the best compromise solution from the opinion of all the participants involved in the group decision process.

A Capstone for the Capstone: An Experiential Exercise in Strategic Management

The content of most MBA capstone strategic management courses emphasizes general management theory and analytical skill development. Additionally, the design of the capstone is often tasked with providing practical application, integrative thinking, and experiential learning. A common critique of the capstone, however, is its insufficient delivery of these important, noncontent objectives. This article describes a “capstone to the capstone” exercise which helps address this challenge. In this exercise, students work in functional and cross-functional groups to experience the realities of applying what they have learned to address strategic issues of a company. The exercise increases awareness of the value of functional integration, exposes students to realistic pressures and common process errors, and encourages student growth through reflective thinking. Empirical analysis suggests the exercise enhances practical understanding of course concepts, improves abilities to apply course concepts in realistic contexts, and opens new perspectives on cross-functional and group decision–process thinking.

General Model for Adequate Cloud Service Selection using Decision Making Methods

Cloud Computing (CC) is a technology that surely brings innovations in today’s business world, more and more companies around the world are widely incorporating this technology into their businesses. From a technical, as well as organizational point of view transferring enterprise IT to the Cloud is a complex task. Various factors have to be taken into consideration in order to make a right choice when moving IT services to the Cloud. The goal of this paper is to identify and to discuss in detail all factors that influence organization’s decision to adopt Cloud. General model for Cloud adoption, introduced in Pantelic et al. [13]a, consists of the key factors driving the organizational benefits when moving to the Cloud. The purpose of the model is to support decision makers in evaluating the benefits, risks and costs of using Cloud Computing. In this paper the general model is extended with two new aggregation methods for harmonization of alternatives rankings in a group decision process. We present the results of two new methods using the method results from previous research [13], as rank inputs, into an aggregate (group) preference. The idea is to find consensus ranking that minimizes disagreement among previous methods results. There were no strong differences between the results of performed methods. The results have shown that Software as a service model and Storage as a service model dominated according to not just arithmetic-mean method, but also to geometric-mean method.