Decision making on adoption of cloud computing in e-commerce using fuzzy TOPSIS

© 2017 IEEE. Cloud computing promises enhanced scalability, flexibility, and cost-efficiency. In practice, however, there are many uncertainties about the usage of cloud computing resources in the e-commerce context. As e-commerce is dependent on a reliable and secure online store, it is important for decision makers to adopt an optimal cloud computing mode (Such as SaaS, PaaS and IaaS). This study assesses the factors associated with cloud-based e-commerce based on TOE (technological, organizational, and environmental) framework using multi-criteria decision-making technique (Fuzzy TOPSIS). The results show that Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) approach proposes software-as-a-service (SaaS) as the best choice for e-commerce business

WHAT ARE THE MOST IMPORTANT CRITERIA FOR CLOUD SERVICE PROVIDER SELECTION? A DELPHI STUDY

Selecting an appropriate cloud service provider (CSP) is one of the most important challenges affecting sourcing performance. Although cloud computing (CC) relies on the principle of information technology outsourcing (ITO), it remains unclear if selection criteria for ITO provider hold true. Hence, the purpose of this research is to identify the most important criteria for the selection of cloud service providers (CSP). We do this by conducting a Delphi study which includes 16 cloud service decision makers across different cloud service models, company sizes, and industry types. Our results show consensus on CSP selection criteria and identify functionality, legal compliance, contract, geolocation of servers, and flex-ibility as top five CSP selection criteria. From a theoretical perspective, we demonstrate that results from ITO research hold true for CC research as differences in delivery model and arrangement between ITO and CC will be considered. Practitioners like CSP and cloud decision makers get guidance from our findings to conduct optimal cloud service investments. This is the first study which provides a com-prehensive view on relevant criteria for CSP selection

Comprehensive Framework for Selecting Cloud Service Providers (CSPs) Using Meta synthesis Approach

IntroductionNowadays, cloud computing has attracted the attention of many organizations. So many of them tend to make their business more agile by using flexible cloud services. Currently, the number of cloud service providers is increasing. In this regard, choosing the most suitable cloud service provider based on the criteria according to the conditions of the service consumer will be considered one of the most important challenges. Relying on previous studies and using a meta-synthesis approach, this research comprehensively searches past researches and provides a comprehensive framework of factors affecting the choice of cloud service providers including 4 main categories and 10 sub-areas. Then, using the opinions of experts who were selected purposefully and using the snowball method, and using the Lawshe validation method, the framework is finalized.Research Question(s)This research aims to complete the results of previous studies and answer the following questions with a systematic review of the subject literature:-What are the components of the comprehensive framework for choosing cloud service providers?-What are the effective criteria to choose a cloud service provider?-What is the selected framework of effective factors? Literature ReviewMany researchers have looked at the problem of choosing the best CSP from different aspects and have tried to provide a solution in this field. In this regard, we can refer to "Tang and Liu" (2015) who proposed a model called "FAGI" which defines the choice of a trusted CSP through four dimensions: security functions, auditability, management capability, and Interactivity helps. "Kong et al." (2013) presented an optimization algorithm based on graph theory to facilitate CSP selection. Some researchers have also provided a framework for CSP selection, such as "Gash" (2015) who provides a framework called "SelCSP" with the combination of trustworthiness and competence to estimate the risk of interaction. "Brendvall and Vidyarthi" (2014) suggest that in order to choose the best cloud service provider, a customer must first identify the indicators related to the level of service quality related to him and then evaluate different providers. Some researchers have focused on using different techniques for selection. For example: "Supraya et al." (2016) use the MCDM method to rank based on infrastructure parameters (agility, financial, efficiency, security, and ease of use). They investigate the mechanisms of cloud service recommender systems and divide them into four main categories and their techniques in four features of scalability, accessibility, accuracy, and trustIn this research, it has been tried to use the models and variables of the subject literature in developing a comprehensive framework. The codes, concepts, and categories related to the choice of cloud service providers are extracted from previous studies, and a comprehensive framework of the factors influencing the choice of cloud service providers is presented using the meta-composite method. MethodologyIn this research, based on the "Sandusky and Barroso" meta-composite qualitative research method, which is more general, a systematic review of the research literature was conducted, and the codes in the research literature were extracted. Then the codes, categories, and finally the proposed model are formed. The seven-step method of "Sandusky and Barroso" consists of: formulation of the research question, systematic review of the subject literature, search and selection of suitable articles, extraction of article information, analysis and synthesis of qualitative findings, quality control, and presentation of findings. Lawshe validation method has been used to validate the research findings. ResultsIn the meta-synthesis method, all the factors extracted from previous studies are considered as codes and concepts are obtained from the collection of these codes. Using the opinion of experts and considering the concept of each of these codes, codes with similar concepts were placed next to each other and new concepts were formed. This procedure was repeated in converting the concepts into categories and the proposed framework was identified. This framework consists of 27 codes, 10 concepts, and 4 categories (Table 1).Table 1: Codes, concepts, and categories extracted from the sourcescategoryConceptCodeNo.TrustSecurityHardware Security1Network Security2Software Security3Confidentiality4Control5Guarantee and AssuranceAccessibility6Stability7Facing ThreatsTechnical Risk8Center for Security Measures9TechnologyEfficiencyService Delivery Efficiency10Interactivity11Hardware and Network InfrastructureConfiguration and Change12Capacity (Memory, CPU, Disk)13Functionality Flexibility14Usability15Accuracy16Service Response Time17Ease of use18ManagerialMaintenanceEducation and Awareness19Customer Communication Channels20StrategicLegal Issues21Data Analysis22Service Level Agreement23CommercialCustomer SatisfactionResponsiveness24Customer Feedback25CostSubscription Fee26Implementation Cost27The lack of a common framework for evaluating cloud service providers is compounded by the fact that no two providers are the same, so that this issue complicates the process of choosing the right provider for each organization. Figure 1 shows the proposed comprehensive framework including 4 categories and 10 concepts covering the issue of choosing cloud service providers. These factors are useful in determining the provider that best matches the personal and organizational needs of the service recipient. The main categories are: trust building, technology, management, and business, which will be explained in the following.Figure 1: Cloud service provider selection framework 5- ConclusionBy comprehensively examining the factors affecting the choice, this research introduces specific areas such as trust building, technology, management, and business as the main areas of cloud service provider selection and add to the previous areas. The category of building trust between the customer, and the cloud service provider is of particular importance. In this research, the concepts related to trust building are: security (including hardware security, network security, software security, confidentiality and control), (availability, stability and stability), and facing threats (technical risk). In 36% of the articles, the concept of trust is mentioned, but in each study, only a limited number of factors affecting this category are discussed. This research takes a comprehensive look at the category of technology, the concepts of productivity (including service delivery efficiency, interactivity), hardware and network infrastructure (including configuration and repair, capacity (memory, processor, disk)), and performance (including flexibility, usability, accuracy of operation, service response time, ease of use). Considering the variety of services on different cloud platforms, service recipients must ensure that the provision of services is managed easily and in the shortest possible time by the cloud provider. The commercial aspect of service delivery deals with the two concepts of customer satisfaction (including responsiveness, customer feedback) and service rates (including: subscription cost and implementation cost), which are of interest to many businesses. The results of this research will help the decision makers of using the cloud space (both organizational managers and cloud customers) in choosing the best cloud service provider to have a comprehensive view of the effective factors before choosing and plan according to their needs

CPA firm’s cloud auditing provider for performance evaluation and improvement: an empirical case of China

While CPA (Certified Public Accountant) firms utilize cloud auditing technologies to generate auditing reports and convey information to their clients in the Internet of Things (IoT) Era, they often cannot determine whether cloud auditing is a secure and effective form of communication with clients. Strategies related to cloud auditing provider evaluation and improvement planning are inherently multiple attribute decision making (MADM) issues and are very important to the auditor industry. To overcome these problems, this paper proposes an evaluation and improvement planning model to be a reference for CPA firms selecting the best cloud auditing provider, and illustrates an application of such a model through an empirical case study. The DEMATEL (decision-making trial and evaluation laboratory) approach is first used to analyze the interactive influence relationship map (IIRM) between the criteria and dimensions of cloud auditing technology. DANP (DEMATEL-based ANP) is then employed to calculate the influential weights of the dimensions and criteria. Finally, the modified VIKOR method is utilized to provide improvement priorities for performance cloud auditing provider satisfaction. Based on expert interviews, the recommendations for improvement priorities are privacy, security, processing integrity, availability, and confidentiality. This approach is expected to support the auditor industry to systematically improve their cloud auditing provider selection

Deploying application modules over multiple clouds

Deploying and managing, in an efficient and adaptive way, complex multi-service applications over technologically dissimilar cloud environments is one of the problems that have emerged with the cloud revolution. In this work, we have studied suitable techniques to determine the distribution of application modules onto multiple available clouds while respecting QoS (Quality of Service) properties and technology requirements specified for individual application modules. For this purpose, we have proposed parametric allocation algorithm based on three selection criteria, i.e., Cost, QoS and Hybrid (Cost and QoS). In order to maximize the performance of the whole application—when the performance of the whole application is dominated by the performance of communicating modules—we have proposed the allocation of intensively communicating modules on a single provider using the same selection criteria (Cost, QoS and Hybrid)