Cloud computing model selection for e-commerce enterprises using a new 2-tuple fuzzy linguistic decision-making method

Sohaib, Osama; Naderpour, Mohsen; Hussain, Walayat; Martínez, Luis

doi:10.1016/j.cie.2019.04.020

Cited by 115 publications

(60 citation statements)

References 40 publications

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“…Alfonso-Robaina et al [27] propose a system based on fuzzy decision rules for modelling enterprise architecture and strategic management viewpoint. Similarly, Sohaib et al [28] a 2-tuple fuzzy linguistic decision-making method to make decisions about IT infrastructure based on cloud computing services. In general, the majority of EA mining approaches in literature assume that viewpoints are populated from artefacts on a one to one basis [7].…”

Section: Input Artefacts and Viewpoint Modelling Decisionsmentioning

confidence: 99%

A decision-making support system for Enterprise Architecture Modelling

Pérez‐Castillo

Ruíz

Piattini

2020

Decision Support Systems

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Companies are increasingly conscious of the importance of Enterprise Architecture (EA) to represent and manage IT and business in a holistic way. EA modelling has become decisive to achieve models that accurately represents behaviour and assets of companies and lead them to make appropriate business decisions. Although EA representations can be manually modelled by experts, automatic EA modelling methods have been proposed to deal with drawbacks of manual modelling, such as error-proneness, time-consumption, slow and poor readaptation, and cost. However, automatic modelling is not effective for the most abstract concepts in EA like strategy or motivational aspects. Thus, companies are demanding hybrid approaches that combines automatic with manual modelling. In this context there are no clear relationships between the input artefacts (and mining techniques) and the target EA viewpoints to be automatically modelled, as well as relationships between the experts' roles and the viewpoints to which they might contribute in manual modelling. Consequently, companies cannot make informed decisions regarding expert assignments in EA modelling projects, nor can they choose appropriate mining techniques and their respective input artefacts. This research proposes a decision support system whose core is a genetic algorithm. The proposal first establishes (based on a previous literature review) the mentioned missing relationships and EA model specifications. Such information is then employed using a genetic algorithm to decide about automatic, manual or hybrid modelling by selecting the most appropriate input artefacts, mining techniques and experts. The genetic algorithm has been optimized so that the system aids EA architects to maximize the accurateness and completeness of EA models while cost (derived from expert assignments and unnecessary automatic generations) are kept under control.

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Section: Input Artefacts and Viewpoint Modelling Decisionsmentioning

confidence: 99%

A decision-making support system for Enterprise Architecture Modelling

Pérez‐Castillo

Ruíz

Piattini

2020

Decision Support Systems

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“…E-commerce platforms are facing great pressure to improve productivity and profitability; as a result, many e-commerce managers and sellers hope to increase product sales by improving their competitive advantages [36]. Whether in a traditional physical store or in an online store, price discounts are one of the most widely used marketing strategies employed by sellers.…”

Section: B Online Promotional Marketingmentioning

confidence: 99%

What Factors Influence Online Product Sales? Online Reviews, Review System Curation, Online Promotional Marketing and Seller Guarantees Analysis

et al. 2020

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This paper proposes an SFNN (a sales factor model using a neural network), which uses a backpropagation multilayer perceptron neural network and weight matrix operation, to study the mechanism of the influencing factors of online product sales in the e-commerce platform. To achieve this objective, this study analyzes the factors and relative strength of online product sales based on four aspects: online reviews, review system curation, online promotional marketing, and seller guarantees. The empirical analysis of the SFNN model based on the data of Taobao.com shows whether the 14 factors, in relation to the four aspects, have any impact on product sales. In addition, the findings indicate that the number of sentiment words greatly affects product sales. Other factors affecting online product sales significantly include the review volume, the number of uploaded pictures, the negative review rate, the discount rate, 7+ day returns and money-back guarantees, and the freight insurance. This study examines the interactions among the various factors affecting product sales on the e-commerce platform and provides management inspiration for ecommerce enterprises to manipulate online reviews, undertake effective promotion and fulfill after-sales promises.

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“…Nawaz, et al [40] developed an integrated Markov chain and a best-worst method-based MAGDM to select a cloud service. Sohaib, et al [41] proposed a new 2-tuple fuzzy linguistic MAGDM method combined with TOPSIS for cloud service selection by e-commerce enterprises.…”

Section: B Magdm (Multi-attribute Group Decision Making)mentioning

confidence: 99%

Blockchain Service Provider Selection Based on an Integrated BWM-Entropy-TOPSIS Method Under an Intuitionistic Fuzzy Environment

Liu

Zhang

et al. 2020

IEEE Access

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The emergence of blockchain technology has significantly changed the underlying infrastructure of existing information technology and will fundamentally affect the production modes of enterprises. However, because the application of blockchain is still in its infancy, it is difficult for an enterprise to develop a comprehensive assessment of various types of blockchain service providers in the market. Hence, enterprises need scientific decision tools to estimate which blockchain service provider is appropriate. However, few studies have focused on this phenomenon. Therefore, to address this challenge, this investigation proposes a novel integrated multi-attribute group decision-making (MAGDM) method to help enterprises estimate which blockchain vendor is more appropriate by considering more comprehensive influence factors. The proposed method is defined in an intuitionistic fuzzy environment and integrates entropy and the best-worst method (BWM) for comprehensive weighting of decision makers (DMs), subjective criteria and objective criteria in the decision-making process to make the decision results more reliable and reasonable. A numerical example and comparison are provided to illustrate the practicability and usefulness of the method. This study enriches the theory and methodology of blockchain technology and MAGDM analysis. INDEX TERMS Blockchain, BWM, entropy, intuitionistic fuzzy set, TOPSIS.

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Cloud computing model selection for e-commerce enterprises using a new 2-tuple fuzzy linguistic decision-making method

Cited by 115 publications

References 40 publications

A decision-making support system for Enterprise Architecture Modelling

A decision-making support system for Enterprise Architecture Modelling

What Factors Influence Online Product Sales? Online Reviews, Review System Curation, Online Promotional Marketing and Seller Guarantees Analysis

Blockchain Service Provider Selection Based on an Integrated BWM-Entropy-TOPSIS Method Under an Intuitionistic Fuzzy Environment

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