A Comparative Analysis of Factor Effecting the Buying Judgement of Smart Phone

Bhalla, Rajni; Amandeep, Amandeep; Jain, Prateek

doi:10.11591/ijece.v8i5.pp3057-3066

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Filieri and Lin [38] Qualitative face-to-face interview and Partial Least Square-Based Structural Equation Modeling Bhalla and Jain [40] Factor Analysis and Descriptive Analysis Laohakosol and Sharma [41] Correlation and Logistics Regression Bringula et al [42] Hierarchical Regression Analysis Martins et al [45] Partial Least Square-Based Structural Equation Modeling Redda and Shezi [44] Descriptive Analysis and Logistics Regression Isa et al [46] Partial Least Square-Based Structural Equation Modeling Mishra et al [22] IFS-MABAC Kim et al [47] Mixed Logit Mao et al [48] Partial Least Square-Based Structural Equation Modeling Sawaftah et al [49] Multiple Linear Regression and Analysis of Variance…”

Section: Contributor(s) Methodology Usedmentioning

confidence: 99%

“…Shieh and Lai [39] understood the importance of brand experience in establishing the brand loyalty among the consumers. Bhalla and Jain [40] reported the influence of the demographic variables on consumers' choices, and the physical attributes and features were major influences, followed by brand, price, serviceability, and social status. Exploring the influencing factors affecting the selection of smartphones in Nepal, Laohakosol and Sharma [41] concluded that their compatibility to various platforms and apps, the features of the product, and social pressure are given more importance by the consumers.…”

Section: Smartphone Selectionmentioning

confidence: 99%

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A New Integrated FUCOM–CODAS Framework with Fermatean Fuzzy Information for Multi-Criteria Group Decision-Making

et al. 2021

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Smartphones have become an inevitable part of every facet of modern society. The selection of a particular smartphone brand from multiple options that are available is a complex and dynamic decision-making problem, involving multiple conflicting criteria that are associated with imprecise asymmetric information imposed by the uncertainty of the consumers. In this paper, we propose a novel hybrid full consistency method (FUCOM) and a combinative distance based assessment (CODAS) based on the multi-criteria group decision-making (MAGDM) framework in the Fermatean fuzzy (FF) domain for smartphone brand selection. We derive the criteria using the UTAUT2 (unified theory of acceptance and ese of technology) model. A group of 15 decision makers (DMs) participated in our study. We compare 14 leading smartphone brands in India and find that the brands having superior features of a good quality and selling a brand image at a affordable price outperform other smartphones. To check the validity of our framework, we compare the results using extant multi-criteria decision-making (MCDM) models. We observe our model provides a consistent solution. Furthermore, we carry out a sensitivity analysis for ascertaining the robustness and stability of the results generated by our model. The results of the sensitivity analysis show that our proposed framework delivers a stable and robust solution.

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Section: Contributor(s) Methodology Usedmentioning

confidence: 99%

Section: Smartphone Selectionmentioning

confidence: 99%

A New Integrated FUCOM–CODAS Framework with Fermatean Fuzzy Information for Multi-Criteria Group Decision-Making

et al. 2021

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“…Calisir used [20] Morlet Wavelet Support vector machine along with Linear discriminant analysis for diabetes classification. Their achieved classification accuracy of 89.74%.For classification one of supervised powerful techniques naïve bayes can also be used for prediction of diabetes.Naive bayes already used for prediction of mobile phone [21]. Other powerful data mining algorithms have been used for prediction of hear diseases [22].…”

Section: Related Workmentioning

confidence: 99%

RB-Bayes algorithm for the prediction of diabetic in Pima Indian dataset

Rajni

Amandeep

2019

IJECE

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<p>Diabetes is a major concern all over the world. It is increasing at a fast pace. People can avoid diabetes at an early stage without any test. The goal of this paper is to predict the probability of whether the person has a risk of diabetes or not at an early stage. This would lead to having a great impact on their quality of human life. The datasets are Pima Indians diabetes and Cleveland coronary illness and consist of 768 records. Though there are a number of solutions available for information extraction from a huge datasets and to predict the possibility of having diabetes, but the accuracy of their mining process is far from accurate. For achieving highest accuracy, the issue of zero probability which is generally faced by naïve bayes analysis needs to be addressed suitably. The proposed framework RB-Bayes aims to extract the required information with high accuracy that could survive the problem of zero probability and also configure accuracy with other methods like Support Vector Machine, Naive Bayes, and K Nearest Neighbor. We calculated mean to handle missing data and calculated probability for yes (positive) and no (negative). The highest value between yes and no decide the value for the tuple. It is mostly used in text classification. The outcomes on Pima Indian diabetes dataset demonstrate that the proposed methodology enhances the precision as a contrast with other regulated procedures. The accuracy of the proposed methodology large dataset is 72.9%.</p>

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“…Thus, in the measurement of user satisfaction, there are five variables measured, that is content, accuracy, format, ease of use, and timeliness [23]. We also used a five-level Likert scale as in [24]. The calculation result of the average percentage score of each variable in the satisfaction test of WSM method implementation in UMN Bulletin application for recommendation of information based on student interest shown in Table 3.…”

Section: System Evaluationmentioning

confidence: 99%

UMN Bulletin: User Interest Based Information Application

Tangerang¹

2019

IJATCSE

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The dissemination of information through technology promises an opportunity to increase speed and work efficiency. However, with the abundance of information does not necessarily make it easier to get information. Therefore, an application to provide information based on user interest was studied in this research. Information recommendation is derived using the Weighted Sum Model (WSM) method which has the ability to assess more precisely based on predetermined criteria and preference weights. Interest theory used in this research is the Holland Codes which states that humans have six types of interests, namely realistic, investigative, artistic, social, enterprising, and conventional. The application built had been tested for Universitas Multimedia Nusantara students and administrators (i.e. Student Affairs and Public Relations of Student Executive Board) based on End-User Computing Satisfaction (EUCS) method. Based on the research results, the value of user satisfaction is 79.87% which means that it can satisfy the users' needs.

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A Comparative Analysis of Factor Effecting the Buying Judgement of Smart Phone

Cited by 7 publications

References 17 publications

A New Integrated FUCOM–CODAS Framework with Fermatean Fuzzy Information for Multi-Criteria Group Decision-Making

A New Integrated FUCOM–CODAS Framework with Fermatean Fuzzy Information for Multi-Criteria Group Decision-Making

RB-Bayes algorithm for the prediction of diabetic in Pima Indian dataset

UMN Bulletin: User Interest Based Information Application

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