Digital Transformation: Evaluating Emerging Technologies. Группа авторов

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СКАЧАТЬ *Portland State University, Portland, Oregon, USA

       ^† Higher School of Economics, Moscow, Russia

       ^‡ Chaoyang University of Technology, Taiwan

      Abstract

      This project used a Hierarchical Decision Model (HDM) approach by dividing the model hierarchies into Mission, Objectives, Goals, Strategies and Actions, also called MOGSA. The fundamental criteria used for assessment are: Innovation Factor, Technological Factor, Usability Factor and Economic Factor. These four primary evaluation criteria were further divided into seven subcriteria: Complexity, Compatibility, Security, Architecture, Usefulness, Ease of Use and Cost. These criteria were evaluated using a pairwise comparison method. Four cloud computing platforms were considered for the project: Amazon Web Services, Google Cloud Platform, IBM Bluemix and Microsoft Azure. A group of experts was used to measure and compare results of the HDM. This group includes application developers working in different domains and had been using cloud computing platforms. The range of inconsistency recorded was between 0.02 and 0.04, whereas the disagreement between the judgments was 0.055. Despite individual responses by some of the evaluators, Amazon Web Services was the preferred cloud computing platform, thus making the HDM a better methodology to quantify and counterbalance all individual preferences while making complex decisions.

      Keywords: Technology assessment, cloud computing, web services.

      1.Introduction

      Cloud service is an integral part of today’s business. With rapidly increasing amounts of data, Internet of Things (IoT) and applications, their presence in our lives today demand high storage and computing power. Cloud computing makes it easier for businesses by providing them high computing power as an alternative to investing in costly infrastructure. Using cloud computing, people and enterprises can operate any application on a plug and play basis without really investing on hardware. Organizations not only get save a lot of money, maintenance also becomes easier since the platform provider takes care of its speed and technical abilities.

      Hence, it becomes very important for any business to carefully choose the appropriate cloud service provider that can provide the desired speed and computing power required for the business.

      An application developer wanted to choose the best cloud computing platform for one of the application he had developed. He was undecided which cloud service provider he should choose. Many possible decisions he had to make were discussed, such as choosing a cloud computing platform and the type of hardware that would be compatible with it. After much discussion without any results, he decided to use the HDM because it would help solve the problem in a better way. While going through the process it was observed that many application developers face the same problem in choosing a cloud service provider. Instead of using the HDM for just one developer, he decided to use it to help other developers to choose between Amazon Web Services, Microsoft Azure, Google Cloud Platform and IBM Bluemix.

      To make the HDM and research more vigorous, the panel of experts would be expanded to 13 application developers from different domains, where each would give their valued assessments.

      2.Methodology

The methodology that has been used in this project was the Hierarchical Decision Model (HDM), which was developed by David Cleland and Dundar Kocaoglu. For any HDM, the basic structure of the hierarchy is presented in the MOGSA form [1]. This model consists of five levels—Mission, Objectives, Goals, Strategies and Actions. Each of these levels has a specific function for the model [2]. Nevertheless, it is not essential to have all five levels in a model, though it needs to have at least three levels, which are Mission, Objectives (criteria) and Actions (decisions).

      3.Hierarchical Decision Model

The HDM—a multilayered method for studying complex decisions—was developed in 1979 using a similar concept as the Analytical Hierarchy Process (AHP) methodology, but with a different pairwise comparison scale and judgmental quantification technique [3]. Depending on how simple or complex the decision-making problem is, the number of hierarchical levels is determined.

HDM is a methodology that breaks down a problem into different hierarchies or sublevels. The approach an HDM takes is that it considers any problem as an association of sub-problems, which can be broken down into hierarchies or levels. The most common approach in a HDM consists of three important decision hierarchies: Impact or Mission level, Target or Objective level and Operational or Action level [4]. Each level comprises of multidimensional components [4].

      The top level which is the objective, leads to benefits. The bottom level, which is the alternative, results from multiple actions. Each decision element at every level has an impact on different elements at the next higher level. A hierarchy can be determined as a completed hierarchy if each element of the given hierarchy is evaluated with respect to each element in the next hierarchy [2]. Any complex decision problem can be expressed as an analytical hierarchical decision.

      3.1.Pairwise comparison

      Decision elements at every level are compared with each other. The expert panel assigns weights to each element, which contributes to the decision element in the next level. A total of 100 points is allocated between two decision elements. The formula for the pairwise comparison is given by [3]

      N = (n − 1)/2,

      where N is the number of pairwise comparisons, and n is the decision elements at every level.

      3.2.Inconsistency

Inconsistency occurs when there is an intentional or unintentional error while performing a pairwise comparison by an expert. There are two types of inconsistency: ordinal and cardinal [6]. In ordinal inconsistency, the ranking order of elements should be upheld. For example, if someone likes apples more than oranges, and oranges more than grapes, then that person should like apples more than grapes. However, if that person prefers grapes over apples, then that is accounted as ordinal inconsistency. Cardinal inconsistency occurs when the element’s proportion is not upheld. For example, if someone regards apples as being two times more valuable than oranges and oranges as being three times more valuable than grapes, then that person should regard apples as being six times more valuable than grapes, or else cardinal inconsistency could be observed. It is observed that an inconsistency of 10% (or 0.10) is considered as an acceptable inconsistency [3].

      3.3.Disagreement

      Unlike inconsistency, disagreement is calculated based on the differences between the opinions or evaluations of the expert panel. If the disagreement among the expert panel is beyond a certain range (which is considered to be 10%), then СКАЧАТЬ