# A Multi-Criteria Group Decision-Making Method for Risk Assessment of Live-Streaming E-Commerce Platform

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*J. Theor. Appl. Electron. Commer. Res.*

**2023**,

*18*(2), 1126-1141; https://doi.org/10.3390/jtaer18020057

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

#### 2.1. The Risks of the Live-Streaming E-Commerce Platforms

#### 2.2. Risk Assessment Methods for Live-Streaming E-Commerce Platforms

## 3. Overview of Interval-Valued Intuitionistic Fuzzy Sets

**Definition**

**1.**

**Definition**

**2.**

**Definition**

**3.**

- (a)
- $\tilde{\alpha}+\tilde{\beta}=\left(\left[{a}_{1}+{a}_{2}-{a}_{1}{a}_{2},{b}_{1}+{b}_{2}-{b}_{1}{b}_{2}\right],\left[{c}_{1}{c}_{2},{d}_{1}{d}_{2}\right]\right)$;
- (b)
- $\tilde{\alpha}\cdot \tilde{\beta}=\left(\left[{a}_{1}{a}_{2},{b}_{1}{b}_{2}\right],\left[{c}_{1}+{c}_{2}-{c}_{1}{c}_{2},{d}_{1}+{d}_{2}-{d}_{1}{d}_{2}\right]\right)$;
- (c)
- $\lambda \tilde{\alpha}=\left(\left[1-{\left(1-{a}_{1}\right)}^{\lambda},1-{\left(1-{b}_{1}\right)}^{\lambda}\right],\left[{c}_{1}{}^{\lambda},{d}_{1}{}^{\lambda}\right]\right)$;
- (d)
- ${\tilde{\alpha}}^{\lambda}=\left(\left[{a}_{1}^{\lambda},{b}_{1}^{\lambda}\right],\left[1-{\left(1-{c}_{1}\right)}^{\lambda},1-{\left(1-{d}_{1}\right)}^{\lambda}\right]\right)$.

**Definition**

**4.**

**Definition**

**5.**

## 4. Risk Assessment Model of Live-Streaming E-Commerce Platform

#### 4.1. Problem Description

- (a)
- $A=\left\{{A}_{1},{A}_{2},\dots ,{A}_{m}\right\}$: The set of m alternative live-streaming e-commerce platforms concerned by decision-makers, where ${A}_{i}$ represents the i-th alternative live-streaming e-commerce platform, $i=1,2,\dots ,m$.
- (b)
- $U=\left\{{u}_{1},{u}_{2},\dots ,{u}_{n}\right\}$: The set of n risk criteria that decision-makers pay attention to when evaluating the risk of the live-streaming e-commerce platform, where ${u}_{j}$ represents the j-th risk criterion, $j=1,2,\dots ,n$.
- (c)
- $E=\left\{{e}_{1},{e}_{2},\dots ,{e}_{n}\right\}$: s decision-makers participating in the decision, where ${e}_{k}$ represents the k-th decision-maker, $k=1,2,\dots ,s$.
- (d)
- $\omega =\left\{{\omega}_{1},{\omega}_{2},\dots ,{\omega}_{j}\right\}$: Weight vector of risk criteria, where ${\omega}_{j}$ represents the weight or importance of the risk criterion, satisfying ${\omega}_{j}\ge 0$ and $\sum _{j=1}^{n}{\omega}_{j}=1,j=1,2,\dots ,n$. Here, the weight vector of the risk criterion can be given by the decision-maker.
- (e)
- ${\lambda}_{j}^{k}$: Weight of decision-maker ${e}_{k}$ for risk criterion ${u}_{j}$.
- (f)
- ${r}_{ij}^{k}=\left(\left[{a}_{ij}^{k},{b}_{ij}^{k}\right],\left[{c}_{ij}^{k},{d}_{ij}^{k}\right]\right)$: The evaluation value of the decision-maker ${e}_{k}$ on the risk criterion ${u}_{j}$ of the alternative live-streaming e-commerce platform ${A}_{i}$, which is an interval-valued intuition fuzzy number, where $\left[{a}_{ij}^{k},{b}_{ij}^{k}\right]$ and $\left[{c}_{ij}^{k},{d}_{ij}^{k}\right]$ represent the decision-maker’s membership degree and non-membership degree of the alternative live-streaming e-commerce platform ${A}_{i}$ on the risk criterion ${u}_{j}$, respectively. Further, $\left[{a}_{ij}^{k},{b}_{ij}^{k}\right]\subseteq \left[0,1\right],\left[{c}_{ij}^{k},{d}_{ij}^{k}\right]\subseteq \left[0,1\right],{b}_{ij}^{k}+{d}_{ij}^{k}\le 1$.
- (g)
- $\tilde{{R}^{k}}={\left(\tilde{{r}_{ij}^{k}}\right)}_{m\times n}$: The risk assessment matrix of decision-maker ${e}_{k}$.
- (h)
- $T=\left\{{T}^{1},{T}^{2},\dots ,{T}^{v}\right\}$: The set of evaluation scales about the decision-makers’ professionalism for risk criteria. Where ${T}^{\epsilon}$ represents the $\mathit{\epsilon}$-th evaluation scale, $\epsilon =1,2,\dots ,v$. Generally, the larger $\epsilon $, the corresponding evaluation level is higher. For instance, in the specific example in the fifth part of this article, regarding the decision-maker’ scoring of the professionalism for the risk criteria, the scale set used is in the form of a 5-point scale, namely $T=\left\{{T}^{1}=1,{T}^{2}=2,{T}^{3}=3,{T}^{4}=4,{T}^{5}=5\right\}$. Where 1 indicates the least professionalism, and 5 indicates the highest professionalism.
- (i)
- ${q}_{gj}^{k}={T}^{\epsilon}$: The professional score value of decision-maker ${e}_{g}$ on the risk criterion ${u}_{j}$ for decision-maker ${e}_{\mathrm{k}}$, where ${e}_{g}$ represents the g-th decision-maker, $g=1,2,\dots ,n$.

#### 4.2. Risk Assessment Model of Live-Streaming E-Commerce Platform

## 5. Case Study

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Luo, H.; Cheng, S.; Zhou, W.; Su, M.Y.; Xu, D.L. A Study on the Impact of Linguistic Persuasive Styles on the Sales Volume of Live Streaming Products in Social E-Commerce Environment. Mathematics
**2021**, 9, 1576. [Google Scholar] [CrossRef] - Nie, W.; Greeven, M.J.; Feng, Y.; Wang, J. The Future of Global Retail: Learning from China’s Retail Revolution; Taylor and Francis: Milton Park, UK, 2021; pp. 5–18. [Google Scholar]
- Elmorshidy, A.; Mostafa, M.M.; El-Moughrabi, I. Factors influencing live customer support chat services: An empirical investigation in Kuwait. J. Theor. Appl. Electron. Commer. Res.
**2015**, 10, 63–76. [Google Scholar] [CrossRef][Green Version] - Guthrie, C.; Fosso-Wamba, S.; Arnaud, J.B. Online consumer resilience during a pandemic: An exploratory study of e-commerce behavior before, during and after a COVID-19 lockdown. J. Retail. Consum. Serv.
**2021**, 61, 102570. [Google Scholar] [CrossRef] - Pang, Q.; Meng, H.; Fang, M. Social distancing, health concerns, and digitally empowered consumption behavior under COVID-19: A study on livestream shopping technology. Front. Public Health
**2021**, 9, 748048. [Google Scholar] [CrossRef] [PubMed] - Lin, G.; Xu, W.; Li, Y. Return Policy Selection Analysis for Brands Considering MCN Click Farming and Customer Disappointment Aversion. J. Theor. Appl. Electron. Commer. Res.
**2022**, 17, 1543–1563. [Google Scholar] [CrossRef] - Guo, J.; Li, Y.; Xu, Y. How live streaming features impact consumers’ purchase intention in the context of cross-border E-commerce? A research based on SOR theory. Front. Psychol.
**2021**, 12, 767876. [Google Scholar] [CrossRef] - Chen, T.; Tong, C.; Bai, Y. Analysis of the Public Opinion Evolution on the Normative Policies for the Live Streaming E-Commerce Industry Based on Online Comment Mining under COVID-19 Epidemic in China. Mathematics
**2022**, 10, 3387. [Google Scholar] [CrossRef] - Van Droogenbroeck, E.; Van Hove, L. Are the Time-Poor Willing to Pay More for Online Grocery Services? When ‘No’ Means ‘Yes’. J. Theor. Appl. Electron. Commer. Res.
**2022**, 17, 253–290. [Google Scholar] [CrossRef] - Katarzyna, B.R.; Anna, D.O. E-commerce as the predominant business model of fast fashion retailers in the era of global COVID 19 pandemics. Procedia Comput. Sci.
**2021**, 192, 2479–2490. [Google Scholar] - Ma, Y. Elucidating determinants of customer satisfaction with live-stream shopping: An extension of the information systems success model. Telemat. Inform.
**2021**, 65, 101707. [Google Scholar] [CrossRef] - Saibene, A.; Assale, M.; Giltri, M. Expert systems: Definitions, advantages and issues in medical field applications. Expert Syst. Appl.
**2021**, 177, 114900. [Google Scholar] [CrossRef] - Koksalmis, E.; Kabak, Ö. Deriving decision makers’ weights in group decision making: An overview of objective methods. Inf. Fusion
**2019**, 49, 146–160. [Google Scholar] [CrossRef] - Deng, Z. Government Officials social influencer marketing: The mechanism Challenges and countermeasures of government livestreaming+ agriculture. Chin. Public Adm.
**2020**, 10, 80–85. [Google Scholar] - Wongkitrungrueng, A.; Assarut, N. The role of live streaming in building consumer trust and engagement with social commerce sellers. J. Bus. Res.
**2020**, 117, 543–556. [Google Scholar] [CrossRef] - Puška, A.; Stojanović, I. Fuzzy Multi-Criteria Analyses on Green Supplier Selection in an Agri-Food Company. J. Intell. Manag. Decis
**2022**, 1, 2–16. [Google Scholar] [CrossRef] - Liu, F.H.; Norden, L.; Spargoli, F. Does uniqueness in banking matter? J. Bank. Financ.
**2020**, 120, 105941. [Google Scholar] [CrossRef] - Xu, P.; Cui, B.; Lyu, B. Influence of streamer’s social capital on purchase intention in live streaming E-commerce. Front. Psychol.
**2022**, 12, 6194. [Google Scholar] [CrossRef] - Pfeil, K.P.; Chatlani, N.; LaViola, J.J., Jr. Bridging the socio-technical gaps in body-worn interpersonal live-streaming telepresence through a critical review of the literature. Proc. ACM Hum. Comput. Interact.
**2021**, 5, 1–39. [Google Scholar] [CrossRef] - Hyun, Y.; Thavisay, T.; Lee, S.H. Enhancing the role of flow experience in social media usage and its impact on shopping. J. Retail. Consum. Serv.
**2022**, 65, 102492. [Google Scholar] [CrossRef] - Thorburn, E.D. Social media, subjectivity, and surveillance: Moving on from occupy, the rise of live streaming video. Commun. Crit./Cult. Stud.
**2014**, 11, 52–63. [Google Scholar] [CrossRef] - Zhu, L.; Liu, N. Game theoretic analysis of logistics service coordination in a live-streaming e-commerce system. Electron. Commer. Res.
**2021**, 23, 1049–1087. [Google Scholar] [CrossRef] - Mina, A.; Vahid, S.M.; Mariam, A. Risk assessment modeling for knowledge based and startup projects based on feasibility studies: A Bayesian network approach. Knowl. Based Syst.
**2021**, 222, 106992. [Google Scholar] [CrossRef] - Feng, J.; Yuan, B.; Li, X.; Tian, D.; Mu, W. Evaluation on risks of sustainable supply chain based on optimized BP neural networks in fresh grape industry. Comput. Electron. Agric.
**2021**, 183, 105988. [Google Scholar] [CrossRef] - Wang, W.; Ding, L.; Liu, X.; Liu, S. An interval 2-Tuple linguistic Fine-Kinney model for risk analysis based on extended ORESTE method with cumulative prospect theory. Inf. Fusion
**2022**, 78, 40–56. [Google Scholar] [CrossRef] - Karaşan, A.; Kaya, İ.; Erdoğan, M.; Çolak, M. A multicriteria decision making methodology based on two-dimensional uncertainty by hesitant Z-fuzzy linguistic terms with an application for blockchain risk evaluation. Appl. Soft Comput.
**2021**, 113, 108014. [Google Scholar] [CrossRef] - Jokar, E.; Aminnejad, B.; Lork, A. Assessing and Prioritizing Risks in Public-Private Partnership (PPP) Projects Using the Integration of Fuzzy Multi-Criteria Decision-Making Methods. Oper. Res. Perspect.
**2021**, 8, 100190. [Google Scholar] [CrossRef] - Dahooie, J.H.; Hajiagha, S.H.R.; Farazmehr, S.; Zavadskas, E.K.; Antucheviciene, J. A novel dynamic credit risk evaluation method using data envelopment analysis with common weights and combination of multi-attribute decision-making methods. Comput. Oper. Res.
**2021**, 129, 105223. [Google Scholar] [CrossRef] - Liu, P.; Li, Y. An improved failure mode and effect analysis method for in green logistics risk assessment. Reliab. Eng. Syst. Saf.
**2021**, 215, 107826. [Google Scholar] [CrossRef] - Zhang, H.; Mao, Z. Credit risk evaluation modeling based on fuzzy multi-attribute decision making of multi-dimensional time series. Inf. Control.
**2011**, 40, 692–697. [Google Scholar] - Huang, W.; Zhang, Y.; Yin, D. Using improved Group 2 and Linguistic Z-numbers combined approach to analyze the causes of railway passenger train derailment accident. Inf. Sci.
**2021**, 576, 694–707. [Google Scholar] [CrossRef] - Pan, X.; Wang, Y. Evaluation of renewable energy sources in China using an interval type-2 fuzzy large-scale group risk evaluation method. Appl. Soft Comput.
**2021**, 108, 107458. [Google Scholar] [CrossRef] - Keshavarz Ghorabaee, M.; Amiri, M.; Kazimieras Zavadskas, E. Assessment of third-party logistics providers using a CRITIC–WASPAS approach with interval type-2 fuzzy sets. Transport
**2017**, 32, 66–78. [Google Scholar] [CrossRef][Green Version] - Zhao, M.; Qin, S.; Xie, J.; Zhang, F.; Li, G. Interval-valued intuitionistic fuzzy multi-attribute group decision making considering risk preference of decision makers. Oper. Res. Manag. Sci.
**2018**, 27, 7–16. [Google Scholar] - Peng, Y.; Liu, X.; Sun, J. Interval-valued intuitionistic fuzzy Research on Multi- attribute group decision making approach based on hesitancy degrees and correlation coefficient. Chin. J. Manag. Sci.
**2021**, 29, 229–240. [Google Scholar] - Dezert, J.; Tchamova, A.; Han, D. The SPOTIS Rank Reversal Free Method for Multi-Criteria Decision-Making Support. In Proceedings of the 2020 IEEE 23rd International Conference on Information Fusion (FUSION), Rustenburg, South Africa, 6–9 July 2020; pp. 1–8. [Google Scholar]
- Rei, R.; Stewart, C.; Farinha, A.C. COMET: A neural framework for MT evaluation. arXiv
**2020**, arXiv:2009.09025. [Google Scholar] - Stoilova, S.; Munier, N. A novel fuzzy SIMUS multicriteria decision-making method. An application in railway passenger transport planning. Symmetry
**2021**, 13, 483. [Google Scholar] [CrossRef] - Wątróbski, J.; Bączkiewicz, A.; Ziemba, E. Sustainable cities and communities assessment using the DARIA-TOPSIS method. Sustain. Cities Soc.
**2022**, 83, 103926. [Google Scholar] [CrossRef] - Krohling, R.A.; Pacheco, A.G.C. A-TOPSIS—An approach based on TOPSIS for ranking evolutionary algorithms. Procedia Comput. Sci.
**2015**, 55, 308–317. [Google Scholar] [CrossRef][Green Version] - Harish, G.; Krishankumarb, R.; Ravichandranc, K.S. Decision framework with integrated methods for group decision-making under probabilistic hesitant fuzzy context and unknown weights—ScienceDirect. Expert Syst. Appl.
**2022**, 200, 117082. [Google Scholar] - Liu, B.; Jiao, S.; Shen, Y. A dynamic hybrid trust network-based dual-path feedback consensus model for multi-criteria group decision-making in intuitionistic fuzzy environment. Inf. Fusion
**2022**, 80, 266–281. [Google Scholar] [CrossRef] - Zhao, M.; Shen, X.; He, Y.; Bai, M. Probabilistic linguistic entropy and cross-entropy measures for multiple criteria decision making. Syst. Eng.-Theory Pract.
**2018**, 38, 2679–2689. [Google Scholar] - Qiao, J.; Li, W.; Zhao, X.; Ma, S. TOPSIS method for interval-valued intuitionistic fuzzy multiple attribute decision making with preference information on alternatives. Math. Pract. Theory
**2020**, 50, 322–328. [Google Scholar] - You, T.; Zhang, J.; Fan, Z. Method for selecting desirable product(s) based on online rating information and customer’s aspirations. Chin. J. Manag. Sci.
**2017**, 25, 94–102. [Google Scholar] - Liu, X.; Walsh, J. Study on development strategies of fresh agricultural products e-commerce in China. Int. Bus. Res.
**2019**, 12, 61–70. [Google Scholar] [CrossRef] - Zeng, Y.; Jia, F.; Wan, L. E-commerce in agri-food sector: A systematic literature review. Int. Food Agribus. Manag. Rev.
**2017**, 20, 439–460. [Google Scholar] [CrossRef] - Zhu, Z.; Bai, Y.; Dai, W. Quality of e-commerce agricultural products and the safety of the ecological environment of the origin based on 5G Internet of Things technology. Environ. Technol. Innov.
**2021**, 22, 101462. [Google Scholar] [CrossRef] - Zeng, Z.Y.; Chen, A.G. Rigorous assessment of delphi method in the course of application. Inf. Stud. Theory Appl.
**2016**, 39, 64–68. [Google Scholar] - Yin, Z.; Li, B.; Li, S.; Ding, J.; Zhang, L. Key influencing factors of green vegetable consumption in Beijing, China. J. Retail. Consum. Serv.
**2022**, 66, 102907. [Google Scholar] [CrossRef] - Sharmaa, A.; Jain, R.; Pajni, N.S. Risk Identification Techniques in Retail Industry: A case study of Tesco Plc. J. Corp. Gov. Insur. Risk Manag.
**2022**, 9, 201–214. [Google Scholar]

${\mathit{u}}_{1}$ | ${\mathit{u}}_{2}$ | ${\mathit{u}}_{3}$ | ${\mathit{u}}_{4}$ | ${\mathit{u}}_{5}$ | ||
---|---|---|---|---|---|---|

${e}_{1}$ | ${A}_{1}$ | ([0.6,0.8], [0.1,0.2]) | ([0.6,0.75], [0.05,0.2]) | ([0.6,0.65], [0.05,0.3]) | ([0.3,0.45], [0.35,0.4]) | ([0.4,0.5], [0.35,0.4]) |

${A}_{2}$ | ([0.7,0.8], [0.05,0.1]) | ([0.5,0.65], [0.25,0.3]) | ([0.45,0.6], [0.05,0.3]) | ([0.35,0.5], [0.3,0.4]) | ([0.45,0.5], [0.3,0.45]) | |

${A}_{3}$ | ([0.4,0.6], [0.15,0.3]) | ([0.45,0.6], [0.2,0.35]) | ([0.7,0.85], [0.05,0.1]) | ([0.35,0.6], [0.25,0.3]) | ([0.4,0.45], [0.5,0.65]) | |

${A}_{4}$ | ([0.3,0.5], [0.35,0.4]) | ([0.35,0.5], [0.35,0.45]) | ([0.6,0.75], [0.05,0.2]) | ([0.65,0.7], [0.15,0.5]) | ([0.25,0.3], [0.4,0.6]) | |

${A}_{5}$ | ([0.3,0.45, [0.35,0.5]) | ([0.65,0.8], [0.05,0.15]) | ([0.3,0.45], [0.45,0.5]) | ([0.55,0.6], [0.3,0.35]) | ([0.3,0.4], [0.3,0.55]) | |

${e}_{2}$ | ${A}_{1}$ | ([0.5,0.8], [0.05,0.2]) | ([0.65,0.75], [0.05,0.2]) | ([0.5,0.65], [0.05,0.3]) | ([0.3,0.55], [0.25,0.3]) | ([0.3,0.45], [0.45,0.5]) |

${A}_{2}$ | ([0.5,0.7], [0.15,0.3]) | ([0.65,0.7], [0.15,0.3]) | ([0.5,0.65], [0.15,0.3]) | ([0.25,0.4], [0.3,0.45]) | ([0.55,0.6], [0.3,0.4]) | |

${A}_{3}$ | ([0.45,0.7], [0.05,0.2]) | ([0.4,0.55], [0.2,0.45]) | ([0.65,0.7], [0.15,0.25]) | ([0.4,0.55], [0.25,0.4]) | ([0.35,0.5], [0.4,0.45]) | |

${A}_{4}$ | ([0.35,0.6], [0.15,0.3]) | ([0.55,0.7], [0.15,0.25]) | ([0.55,0.7], [0.15,0.2]) | ([0.55,0.8], [0.05,0.1]) | ([0.25,0.3], [0.4,0.7]) | |

${A}_{5}$ | ([0.35,0.6], [0.35,0.4]) | ([0.65,0.7], [0.15,0.2]) | ([0.45,0.5], [0.2,0.35]) | ([0.45,0.8], [0.1,0.15]) | ([0.25,0.3], [0.45,0.7]) | |

${e}_{3}$ | ${A}_{1}$ | ([0.45,0.7], [0.15,0.2]) | ([0.55,0.6], [0.15,0.25]) | ([0.45,0.6], [0.25,0.3]) | ([0.45,0.5], [0.25,0.3]) | ([0.35,0.4], [0.45,0.5]) |

${A}_{2}$ | ([0.55,0.6], [0.15,0.2]) | ([0.55,0.7], [0.15,0.25]) | ([0.55,0.6], [0.15,0.4]) | ([0.35,0.5], [0.3,0.4]) | ([0.4,0.6], [0.35,0.4]) | |

${A}_{3}$ | ([0.4,0.65], [0.15,0.3]) | ([0.35,0.45], [0.4,0.55]) | ([0.6,0.75], [0.2,0.25]) | ([0.45,0.5], [0.2,0.45]) | ([0.35,0.5], [0.4,0.45]) | |

${A}_{4}$ | ([0.55,0.6], [0.15,0.3]) | ([0.55,0.65], [0.05,0.2]) | ([0.55,0.7], [0.15,0.3]) | ([0.55,0.7], [0.05,0.2]) | ([0.15,0.2], [0.4,0.75]) | |

${A}_{5}$ | ([0.45,0.5], [0.3,0.45]) | ([0.5,0.7], [0.15,0.25]) | ([0.35,0.4], [0.25,0.4]) | ([0.65,0.8], [0.05,0.1]) | ([0.05,0.2], [0.55,0.7]) | |

${e}_{4}$ | ${A}_{1}$ | ([0.55,0.7], [0.2,0.25]) | ([0.45,0.7], [0.15,0.3]) | ([0.55,0.6], [0.3,0.35]) | ([0.55,0.6], [0.2,0.35]) | ([0.35,0.4], [0.4,0.55]) |

${A}_{2}$ | ([0.45,0.6], [0.15,0.3]) | ([0.5,0.7], [0.15,0.25]) | ([0.45,0.6], [0.25,0.4]) | ([0.3,0.55], [0.3,0.45]) | ([0.3,0.6], [0.35,0.4]) | |

${A}_{3}$ | ([0.45,0.6], [0.15,0.3]) | ([0.35,0.4], [0.4,0.6]) | ([0.6,0.7], [0.2,0.3]) | ([0.4,0.55], [0.35,0.4]) | ([0.35,0.6], [0.25,0.3]) | |

${A}_{4}$ | ([0.55,0.7], [0.15,0.4]) | ([0.5,0.65], [0.05,0.35]) | ([0.55,0.7], [0.15,0.2]) | ([0.55,0.7], [0.15,0.2]) | ([0.2,0.4], [0.5,0.6]) | |

${A}_{5}$ | ([0.4,0.55], [0.35,0.4]) | ([0.5,0.75], [0.15,0.25]) | ([0.35,0.5], [0.45,0.5]) | ([0.65,0.8], [0.05,0.2]) | ([0.05,0.3], [0.55,0.7]) |

${\mathit{u}}_{1}$ | ${\mathit{u}}_{2}$ | ${\mathit{u}}_{3}$ | ${\mathit{u}}_{4}$ | ${\mathit{u}}_{5}$ | ||
---|---|---|---|---|---|---|

${e}_{1}$ | ${e}_{1}$ | 2 | 1 | 3 | 2 | 4 |

${e}_{2}$ | 2 | 3 | 4 | 2 | 5 | |

${e}_{3}$ | 3 | 4 | 1 | 4 | 3 | |

${e}_{4}$ | 4 | 5 | 2 | 3 | 2 | |

${e}_{2}$ | ${e}_{1}$ | 1 | 3 | 4 | 2 | 4 |

${e}_{2}$ | 3 | 2 | 3 | 1 | 5 | |

${e}_{3}$ | 4 | 4 | 3 | 3 | 3 | |

${e}_{4}$ | 4 | 3 | 2 | 5 | 2 | |

${e}_{3}$ | ${e}_{1}$ | 2 | 1 | 4 | 1 | 5 |

${e}_{2}$ | 2 | 3 | 5 | 2 | 3 | |

${e}_{3}$ | 4 | 3 | 2 | 5 | 3 | |

${e}_{4}$ | 4 | 3 | 2 | 3 | 1 | |

${e}_{4}$ | ${e}_{1}$ | 1 | 3 | 3 | 2 | 4 |

${e}_{2}$ | 2 | 1 | 5 | 1 | 3 | |

${e}_{3}$ | 4 | 4 | 1 | 3 | 3 | |

${e}_{4}$ | 4 | 5 | 1 | 4 | 2 |

${\mathit{u}}_{2}$ | ${\mathit{u}}_{2}$ | ${\mathit{u}}_{3}$ | ${\mathit{u}}_{4}$ | ${\mathit{u}}_{5}$ | |
---|---|---|---|---|---|

${e}_{1}$ | 3 | 4 | 7 | 3.5 | 8.5 |

${e}_{2}$ | 4.5 | 4.5 | 8.5 | 3 | 8 |

${e}_{3}$ | 7.5 | 7.5 | 3.5 | 7.5 | 6 |

${e}_{4}$ | 8 | 8 | 3.5 | 7.5 | 3.5 |

${\mathit{u}}_{1}$ | ${\mathit{u}}_{2}$ | ${\mathit{u}}_{3}$ | ${\mathit{u}}_{4}$ | ${\mathit{u}}_{5}$ | |
---|---|---|---|---|---|

${e}_{1}$ | 0.13 | 0.17 | 0.30 | 0.16 | 0.33 |

${e}_{2}$ | 0.20 | 0.19 | 0.38 | 0.14 | 0.31 |

${e}_{3}$ | 0.32 | 0.31 | 0.16 | 0.35 | 0.23 |

${e}_{4}$ | 0.35 | 0.33 | 0.16 | 0.35 | 0.13 |

${\mathit{u}}_{1}$ | ${\mathit{u}}_{2}$ | ${\mathit{u}}_{3}$ | ${\mathit{u}}_{4}$ | ${\mathit{u}}_{5}$ | |
---|---|---|---|---|---|

${A}_{1}$ | ([0.52,0.75],[0.10,0.22]) | ([0.55,0.70],[0.10,0.25]) | ([0.55,0.65],[0.15,0.34]) | ([0.45,0.57],[0.27,0.36]) | ([0.37,0.46],[0.43,0.51]) |

${A}_{2}$ | ([0.53,0.68],[0.10,0.19]) | ([0.55,0.69],[0.16,0.27]) | ([0.49,0.64],[0.15,0.40]) | ([0.32,0.54],[0.30,0.43]) | ([0.46,0.58],[0.33,0.41]) |

${A}_{3}$ | ([0.43,0.67],[0.11,0.30]) | ([0.38,0.48],[0.32,0.51]) | ([0.65,0.78],[0.15,0.24]) | ([0.43,0.55],[0.25,0.38]) | ([0.37,0.52],[0.35,0.44]) |

${A}_{4}$ | ([0.49,0.65],[0.20,0.36]) | ([0.50,0.64],[0.08,0.30]) | ([0.57,0.74],[0.12,0.25]) | ([0.57,0.75],[0.08,0.25]) | ([0.22,0.32],[0.43,0.66]) |

${A}_{5}$ | ([0.40,0.55],[0.34,0.45]) | ([0.56,0.74],[0.13,0.22]) | ([0.38,0.50],[0.34,0.45]) | ([0.61,0.81],[0.11,0.20]) | ([0.20,0.32],[0.48,0.69]) |

${\mathit{A}}_{1}$ | ${\mathit{A}}_{2}$ | ${\mathit{A}}_{3}$ | ${\mathit{A}}_{4}$ | ${\mathit{A}}_{5}$ | |
---|---|---|---|---|---|

$d\left({A}_{i},\tilde{{X}^{+}}\right)$ | 0.38 | 0.37 | 0.38 | 0.39 | 0.47 |

$d\left({A}_{i},\tilde{{X}^{-}}\right)$ | 0.62 | 0.63 | 0.62 | 0.61 | 0.53 |

${\mathit{A}}_{1}$ | ${\mathit{A}}_{2}$ | ${\mathit{A}}_{3}$ | ${\mathit{A}}_{4}$ | ${\mathit{A}}_{5}$ | |
---|---|---|---|---|---|

$r\left({A}_{i}\right)$ | 0.3825 | 0.3749 | 0.3753 | 0.3931 | 0.4715 |

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## Share and Cite

**MDPI and ACS Style**

Su, J.; Wang, D.; Zhang, F.; Xu, B.; Ouyang, Z.
A Multi-Criteria Group Decision-Making Method for Risk Assessment of Live-Streaming E-Commerce Platform. *J. Theor. Appl. Electron. Commer. Res.* **2023**, *18*, 1126-1141.
https://doi.org/10.3390/jtaer18020057

**AMA Style**

Su J, Wang D, Zhang F, Xu B, Ouyang Z.
A Multi-Criteria Group Decision-Making Method for Risk Assessment of Live-Streaming E-Commerce Platform. *Journal of Theoretical and Applied Electronic Commerce Research*. 2023; 18(2):1126-1141.
https://doi.org/10.3390/jtaer18020057

**Chicago/Turabian Style**

Su, Jiafu, Dan Wang, Fengting Zhang, Baojian Xu, and Zhiguang Ouyang.
2023. "A Multi-Criteria Group Decision-Making Method for Risk Assessment of Live-Streaming E-Commerce Platform" *Journal of Theoretical and Applied Electronic Commerce Research* 18, no. 2: 1126-1141.
https://doi.org/10.3390/jtaer18020057