A factor analysis approach for analyzing the service quality of banks

Singh, Amandeep; Singla, Babita

doi:10.5958/0973-9343.2019.00026.7

Cited by 1 publication

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“…By analyzing the intrinsic dependencies in the data of many indicators, the correlation and overlap between the indicators are excluded, and the original multidimensional variable indicators are reorganized to find the common factors. [22,23] The common factor is used to represent the main information of the original indicator system. Mathematical model is:…”

Section: Evaluation Methods Selectionmentioning

confidence: 99%

Research on Comprehensive Competitive Evaluation of P2P Network Lending Platforms Based on BP Neural Network Model

Deng¹,

Liu²

2020

RCES

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Based on many key factors affecting the comprehensive competitiveness of P2P online lending platform that include transaction time, registered capital, popularity points, dispersion points and transparency points, this paper constructs evaluation index system of P2P network lending platform. The 12 secondary indicators of the evaluation system were processed through factor analysis, and BP neural network model was used to quantitatively evaluate comprehensive competitiveness of P2P online lending platforms. The results show that in the selected P2P network lending platform, the highest score of comprehensive competitiveness evaluation is only 0.62 which indicates that the overall competitiveness of China's P2P industry platforms is not strong. The non-equilibrium development pattern of P2P network lending platform seems to be obvious, at least there were regional differences and background differences. Most of the top platforms for comprehensive competitiveness evaluation scores are located in China's first-tier cities or have a background of listed and state-owned capital.

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Section: Evaluation Methods Selectionmentioning

confidence: 99%