Digital Transformation: Evaluating Emerging Technologies. Группа авторов

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Digital Transformation: Evaluating Emerging Technologies - Группа авторов World Scientific Series In R&d Management

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*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

      3.Hierarchical Decision Model

      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

      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

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