Decision making for the selection of cloud vendor: An improved approach under group decision-making with integrated weights and objective/subjective attributes

摘要

Cloud computing technology has become increasingly popular and can deliver a host of benefits. However, there are various kinds of cloud providers in the market and firms need scientific decision tools to judge which cloud computing vendor should be chosen. Studies in how a firm should select an appropriate cloud vendor have just started. However, existing studies are mainly from the technology and cost perspective, and neglect other influence factors, such as competitive pressure and managerial skills, etc. Hence, this paper proposes a multi-attribute group decision-making (MAGDM) based scientific decision tool to help firms to judge which cloud computing vendor is more suitable for their need by considering more comprehensive influence factors. It is argued that objective attributes, i.e., cost, as well as subjective attributes, such as TOE factors (Technology, Organization, and Environment) should be considered for the decision making in cloud computing services, and presents a new subjective/objective integrated MAGDM approach for solving decision problems. The proposed approach integrates statistical variance (SV), improved techniques for order preference by similarity to an ideal solution (TOPSIS), simple additive weighting (SAW), and Delphi-AHP to determine the integrated weights of the attributes and decision-makers (DMs). The method considers both the objective weights of the attributes and DMs, as well as the subjective preferences of the DMs and their identity differences, thereby making the decision results more accurate and theoretically reasonable. A numerical example is given to illustrate the practicability and usefulness of the approach and its suitability as a decision-making tool for a firm using of cloud computing services. This paper enriches the theory and methodology of the selection problem of cloud computing vendoring and MAGDM analysis. (C) 2016 Elsevier Ltd. All tights reserved.