Risk Assessment of Live-Streaming Marketing Based on Hesitant Fuzzy Multi-Attribute Group Decision-Making Method
Abstract
:1. Introduction
2. Literature Review
2.1. Live-Streaming E-Commerce Risk Research
2.2. Risk Identification Research
2.3. Risk Assessment Research
3. Live-Streaming Marketing Risk Analysis
3.1. Identification and Analysis of Risk Factors in Live-Streaming Marketing
3.1.1. Analysis of the “People” Factor Risks in Live-Streaming Marketing
3.1.2. Analysis of the “Product” Factor Risks in Live-Streaming Marketing
3.1.3. Analysis of the “Scene” Factor Risks in Live-Streaming Marketing
3.2. Construction of the Risk Indicator System for Live-Streaming Marketing
4. Methodologies
4.1. Delphi Method
4.2. Hesitant Fuzzy Multi-Attribute Group Decision-Making Method
4.2.1. The Definition of Hesitant Fuzzy Non-Probabilistic Entropy
- (1)
- Enp (α) = 0, if and only if α = {0}, or α = {1};
- (2)
- Enp (α) = 1, if and only if α = αc;
- (3)
- Enp (α) = Enp (αc);
- (4)
- Enp (α) ≤ Enp (β), then
4.2.2. Hesitant Fuzzy Multi-Attribute Group Decision-Making Method Based on Entropy Weight Method
4.3. Risk Identification and Evaluation Model for Live-Streaming Marketing
5. Empirical Analysis of the Evaluation of Live-Streaming Marketing Risks
5.1. Risk Factor Framework Based on the Delphi Method
5.2. Comprehensive Evaluation of Live-Streaming Marketing Risks Based on Hesitant Fuzzy Multi-Attribute Group Decision-Making Method
- -
- They have shopped in the live broadcast rooms of the above three anchors;
- -
- They have at least two years of online shopping experience;
- -
- They watch at least three hours of live streaming a week;
- -
- They make purchases through live streaming at least three times a month;
- -
- They shop online for at least three commodities every month.
- (1)
- Determine the hesitant fuzzy decision-making matrix.
- (2)
- Calculate the hesitant fuzzy non-probabilistic entropy.
- (3)
- Calculate the average non-probabilistic entropy.
- (4)
- Calculate the attribute weights.
- (5)
- Calculate the weighted distances to the positive and negative ideal solutions.
- (6)
- Calculate the comprehensive evaluation value.
5.3. Risk Analysis and Control Countermeasures
5.3.1. Anchor Risk Analysis and Control Countermeasures
5.3.2. Consumption Scenarios Risk Analysis and Control Countermeasures
5.3.3. Merchant and Supply Chain Risk Analysis and Control Countermeasures
5.3.4. Live-Streaming Platforms Risk Analysis and Control Countermeasures
5.3.5. MCN Institutions Risk Analysis and Control Countermeasures
6. Conclusions and Suggestions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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First-Level Risk Indicators | Second-Level Risk Indicators | Third-Level Risk Indicators | Risk Description |
---|---|---|---|
“People” Risks | Anchor | Anchor Reputation [37] | The social evaluation and trustworthiness of the anchor formed based on professionalism, credibility, and public feedback. |
Anchor Influence [38,39] | The guiding role and social effect of the anchor on the audience’s attitude, decision-making, and behavior through content dissemination, interactive behavior, and public image. | ||
Compliance Degree of Marketing Behaviors [36] | The degree of compliance with norms and standards when the anchor is engaged in marketing activities such as product promotion and advertising. | ||
Ethical Risk of the AI Anchor [41] | Potential risks of AI anchors violating human values and moral norms in the live-streaming marketing process. | ||
MCN Institution | Intellectual Property Risk [43] | The risks arising from the violation of intellectual property laws due to the unauthorized use of others’ works, trademarks, or patents in live-streaming or promotions. | |
Content Creation Risk [43] | The risks faced due to the violation of laws and regulations by releasing false propaganda, illegal advertisements, or harmful information in live-streaming or content. | ||
“Product” Risks | Merchant | Merchant Credit [36] | The accumulated credit and reputation of the merchant. |
Degree of Account Standardization [1] | The degree of standardization of various types of information in the merchant’s account. | ||
After-sales Service Completeness [36] | The level of support and services provided by the merchant after consumers have purchased products. | ||
Supply Chain | Product Quality [36] | The level at which the goods offered by the merchant meet customer needs, expectations, and standards. | |
Logistics Distribution Service Quality [46] | The level of a series of logistics management and distribution services. | ||
“Scene” Risks | Live-streaming Platform | Completeness of Platform Services [37] | The degree to which the platform systematically meets user needs and continuously optimizes in aspects such as user experience and after-sales service. |
Privacy Leakage Risk [2] | Platforms rely on big data to analyze users’ behaviors, preferences, and consumption habits, but the collection and storage of a large amount of user data may trigger the risk of privacy leakage. | ||
Algorithmic Price Discrimination Risk [45] | Big data analysis may lead to price discrimination through algorithms. The use of algorithms to manipulate the background to offer differentiated prices and other services to different users will damage the rights and interests of consumers. | ||
Rationality of Interface Setting [2] | The degree to which the presentation of information and the operation process are optimized through scientific layout and intuitive design, so as to enhance the user experience. | ||
Consumption Scenario | Authenticity of Viewer Numbers [36] | The degree to which technical means and rule-based constraints are utilized to ensure the authenticity of the viewer data in the live-streaming room, the absence of false volume-brushing behaviors, and the accurate reflection of the actual viewer size. | |
Authenticity of Sales Data [36] | The degree to which technical monitoring and rule constraints are used to ensure the authenticity and credibility of transaction data (such as transaction volume and sales amount), and the absence of false order brushing or manipulation behaviors. | ||
VR Technology Promotion Risk [45] | The excessive beautification or false presentation of products using VR technology may mislead consumers about the actual effects of the products, resulting in negative impacts. |
Expert | Duty | Gender | Age | Specialist Topic | Location | Seniority |
---|---|---|---|---|---|---|
A | Professor | Male | 52 | Risk Management | Beijing | 20–25 |
B | Professor | Female | 47 | Live-streaming Marketing | Liaoning | 15–20 |
C | Associate Professor | Male | 42 | Marketing | Beijing | 10–15 |
D | Professor | Male | 57 | Multi-attribute Decision-making | Taiwan, China | 10–15 |
E | Research Fellow | Female | 36 | Live-streaming Marketing | Shandong | 10–15 |
F | Senior Manager | Male | 49 | Marketing | Shanghai | 15–20 |
Second-Level Risk Indicators | Third-Level Risk Indicators | Necessity Scoring | Mean Value | Standard Deviation | CDI | Whether to Eliminate | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
A | B | C | D | E | F | ||||||
Anchor | Anchor Reputation | 5 | 4 | 5 | 5 | 5 | 4 | 4.667 | 0.471 | 0.101 | No |
Anchor Influence | 3 | 4 | 4 | 4 | 4 | 3 | 3.667 | 0.471 | 0.129 | No | |
Compliance Degree of Marketing Behaviors | 5 | 5 | 5 | 5 | 5 | 4 | 4.833 | 0.373 | 0.077 | No | |
Ethical Risk of the AI Anchor | 5 | 5 | 5 | 4 | 5 | 5 | 4.833 | 0.373 | 0.077 | No | |
MCN Institution | Intellectual Property Risk | 4 | 4 | 5 | 5 | 4 | 4 | 4.333 | 0.471 | 0.109 | No |
Content Creation Risk | 4 | 3 | 4 | 4 | 4 | 3 | 3.667 | 0.471 | 0.129 | No | |
Merchant | Merchant Credit | 5 | 4 | 5 | 4 | 5 | 5 | 4.667 | 0.471 | 0.101 | No |
Degree of Account Standardization | 3 | 3 | 4 | 4 | 3 | 2 | 3.167 | 0.687 | 0.217 | No | |
After-sales Service Completeness | 4 | 4 | 5 | 5 | 5 | 4 | 4.500 | 0.500 | 0.111 | No | |
Supply Chain | Product Quality | 5 | 5 | 5 | 5 | 5 | 4 | 4.833 | 0.373 | 0.077 | No |
Logistics Distribution Service Quality | 5 | 4 | 4 | 5 | 4 | 4 | 4.333 | 0.471 | 0.109 | No | |
Live-streaming Platform | Completeness of Platform Services | 4 | 4 | 4 | 5 | 5 | 4 | 4.333 | 0.471 | 0.109 | No |
Privacy Leakage Risk | 5 | 5 | 5 | 5 | 5 | 5 | 5.000 | 0.000 | 0.000 | No | |
Algorithmic Price Discrimination Risk | 4 | 5 | 5 | 4 | 5 | 4 | 4.500 | 0.500 | 0.111 | No | |
Rationality of Interface Setting | 2 | 3 | 3 | 3 | 3 | 2 | 2.667 | 0.471 | 0.177 | Yes | |
Consumption Scenario | Authenticity of Viewer Numbers | 3 | 3 | 5 | 4 | 4 | 3 | 3.667 | 0.745 | 0.203 | No |
Authenticity of Sales Data | 4 | 3 | 4 | 5 | 5 | 3 | 4.000 | 0.816 | 0.204 | No | |
VR Technology Promotion Risk | 5 | 5 | 5 | 4 | 4 | 5 | 4.667 | 0.471 | 0.101 | No |
Second-Level Risk Indicators | Third-Level Risk Indicators | Necessity Scoring | Mean Value | Standard Deviation | CDI | Whether to Eliminate | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
A | B | C | D | E | F | ||||||
Anchor | Anchor Reputation | 5 | 4 | 5 | 5 | 5 | 4 | 4.667 | 0.471 | 0.101 | No |
Anchor Influence | 3 | 4 | 4 | 4 | 4 | 3 | 3.667 | 0.471 | 0.129 | No | |
Compliance Degree of Marketing Behaviors | 5 | 5 | 5 | 5 | 5 | 4 | 4.833 | 0.373 | 0.077 | No | |
Ethical Risk of the AI Anchor | 5 | 5 | 5 | 4 | 5 | 5 | 4.833 | 0.373 | 0.077 | No | |
MCN Institution | Intellectual Property Risk | 4 | 4 | 5 | 5 | 4 | 4 | 4.333 | 0.471 | 0.109 | No |
Content Creation Risk | 4 | 3 | 4 | 4 | 4 | 3 | 3.667 | 0.471 | 0.129 | No | |
Merchant | Merchant Credit | 5 | 4 | 5 | 4 | 5 | 5 | 4.667 | 0.471 | 0.101 | No |
Degree of Account Standardization | 3 | 3 | 3 | 3 | 3 | 2 | 2.833 | 0.373 | 0.132 | Yes | |
After-sales Service Completeness | 4 | 4 | 5 | 5 | 5 | 4 | 4.500 | 0.500 | 0.111 | No | |
Supply Chain | Product Quality | 5 | 5 | 5 | 5 | 5 | 4 | 4.833 | 0.373 | 0.077 | No |
Logistics Distribution Service Quality | 5 | 4 | 4 | 5 | 4 | 4 | 4.333 | 0.471 | 0.109 | No | |
Live-streaming Platform | Completeness of Platform Services | 4 | 4 | 4 | 5 | 5 | 4 | 4.333 | 0.471 | 0.109 | No |
Privacy Leakage Risk | 5 | 5 | 5 | 5 | 5 | 5 | 5.000 | 0.000 | 0.000 | No | |
Algorithmic Price Discrimination Risk | 4 | 5 | 5 | 4 | 5 | 4 | 4.500 | 0.500 | 0.111 | No | |
Rationality of Interface Setting | 2 | 3 | 3 | 3 | 3 | 2 | 2.667 | 0.471 | 0.177 | Yes | |
Consumption Scenario | Authenticity of Viewer Numbers | 3 | 4 | 4 | 4 | 4 | 3 | 3.667 | 0.471 | 0.129 | No |
Authenticity of Sales Data | 4 | 4 | 4 | 5 | 5 | 3 | 4.167 | 0.687 | 0.165 | No | |
VR Technology Promotion Risk | 5 | 5 | 5 | 4 | 4 | 5 | 4.667 | 0.471 | 0.101 | No |
Anchor | The Platform That the Anchor Has Settled in | Main Types of Goods for Live-Streaming Sales | Product Promotion Style |
---|---|---|---|
L | Taobao | Beauty products, maternal and infant products, pet products, etc. | Provide professional and detailed explanations, possess great infectiousness, and be proficient in using exaggerated emotions and language to drive the purchasing atmosphere. |
Y | Douyin | Food products, daily necessities, etc. | Mainly featuring humorous and funny, exaggerated performances, creating joyous scenes through interactions between brothers. |
D | Douyin | Agricultural products, food products, books, etc. | Knowledge-based live-selling. The anchor incorporates cultural knowledge and life insights while introducing products. |
A1 | A2 | A3 | A4 | |
L | {0.4, 0.5, 0.6, 0.7, 0.8, 0.9} | {0.4, 0.5, 0.6, 0.7, 0.8} | {0.4, 0.5, 0.6, 0.7} | {0.1, 0.2, 0.3} |
Y | {0.8, 0.9, 1.0} | {0.7, 0.8, 0.9, 1.0} | {0.5, 0.6, 0.7, 0.8, 0.9, 1.0} | {0.1, 0.2, 0.3} |
D | {0.1, 0.2, 0.3, 0.4, 0.6} | {0.2, 0.3, 0.4, 0.5, 0.6, 0.7} | {0.1, 0.2, 0.3, 0.4, 0.5} | {0.1, 0.2, 0.3} |
B1 | B2 | C1 | C2 | |
L | {0.2, 0.3, 0.4, 0.5, 0.6} | {0.3, 0.4, 0.6, 0.7, 0.8} | {0.2, 0.3, 0.4, 0.5} | {0.1, 0.2, 0.3, 0.4, 0.5} |
Y | {0.5, 0.6, 0.7, 0.8} | {0.3, 0.4, 0.5, 0.7, 0.8, 0.9} | {0.3, 0.4, 0.5, 0.6, 0.7} | {0.2, 0.3, 0.4, 0.5, 0.6, 0.7} |
D | {0.3, 0.4, 0.5, 0.6, 0.7, 0.9} | {0.2, 0.3, 0.4, 0.5, 0.6, 0.7} | {0.1, 0.2, 0.4, 0.5, 0.6} | {0.1, 0.2, 0.3, 0.4, 0.5} |
D1 | D2 | E1 | E2 | |
L | {0.1, 0.2, 0.3, 0.5} | {0.2, 0.3, 0.4, 0.5} | {0.1, 0.2, 0.3, 0.4, 0.5} | {0.2, 0.3, 0.4, 0.5} |
Y | {0.4, 0.5, 0.6, 0.7, 0.8, 0.9} | {0.2, 0.3, 0.4, 0.6} | {0.3, 0.4, 0.5, 0.6} | {0.2, 0.3, 0.4, 0.5, 0.6} |
D | {0.1, 0.2, 0.3, 0.4, 0.5} | {0.1, 0.2, 0.3, 0.4} | {0.3, 0.4, 0.5, 0.6} | {0.2, 0.3, 0.4, 0.5, 0.6} |
E3 | F1 | F2 | F3 | |
L | {0.2, 0.3, 0.4, 0.5} | {0.1, 0.3, 0.4, 0.5} | {0.1, 0.2, 0.3, 0.4, 0.5} | {0.1, 0.2, 0.3, 0.4} |
Y | {0.3, 0.4, 0.5, 0.6} | {0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0} | {0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0} | {0.1, 0.2, 0.3, 0.4, 0.6} |
D | {0.3, 0.4, 0.5, 0.6} | {0.1, 0.2, 0.3, 0.4, 0.5, 0.6} | {0.2, 0.3, 0.4, 0.5, 0.6} | {0.1, 0.2, 0.3} |
A1 | A2 | A3 | A4 | B1 | B2 | C1 | C2 | |
L | 0.57948 | 0.68825 | 0.82616 | 0.26864 | 0.68825 | 0.80943 | 0.55709 | 0.46442 |
Y | 0.14286 | 0.21822 | 0.39361 | 0.26864 | 0.55709 | 0.71288 | 1.00000 | 0.83576 |
D | 0.51797 | 0.83576 | 0.46442 | 0.26864 | 0.79349 | 0.83576 | 0.60776 | 0.46442 |
D1 | D2 | E1 | E2 | E3 | F1 | F2 | F3 | |
L | 0.42200 | 0.55709 | 0.46442 | 0.55709 | 0.55709 | 0.51674 | 0.46442 | 0.36116 |
Y | 0.57948 | 0.62765 | 0.82616 | 0.68825 | 0.82616 | 0.49526 | 0.49526 | 0.51797 |
D | 0.46442 | 0.36116 | 0.82616 | 0.68825 | 0.82616 | 0.57948 | 0.68825 | 0.26864 |
Second-Level Risk Indicators | Weight | Third-Level Risk Indicators | Weight |
---|---|---|---|
Anchor | 0.31775 | Anchor Reputation (A1) | 0.08566 |
Anchor Influence (A2) | 0.06123 | ||
Compliance Degree of Marketing Behaviors (A3) | 0.06405 | ||
Ethical Risk of the AI Anchor (A4) | 0.10681 | ||
MCN Institution | 0.07804 | Intellectual Property Risk (B1) | 0.04679 |
Content Creation Risk (B2) | 0.03125 | ||
Merchant | 0.10079 | Merchant Credit (C1) | 0.04066 |
After-sales Service Completeness (C2) | 0.06014 | ||
Supply Chain | 0.14547 | Product Quality (D1) | 0.07468 |
Logistics Distribution Service Quality (D2) | 0.07079 | ||
Live-streaming Platform | 0.13340 | Completeness of Platform Services (E1) | 0.04300 |
Privacy Leakage Risk (E2) | 0.05191 | ||
Algorithmic Price Discrimination Risk (E3) | 0.03849 | ||
Consumption Scenario | 0.22455 | Authenticity of Viewer Numbers (F1) | 0.06857 |
Authenticity of Sales Data (F2) | 0.06582 | ||
VR Technology Promotion Risk (F3) | 0.09017 |
The Live Broadcast Room of the Anchor | Distance to the Positive Ideal Solution (d+) | Distance to the Negative Ideal Solution (d−) |
---|---|---|
L | 1.32390 | 0.84150 |
Y | 1.03439 | 1.26531 |
D | 1.44586 | 0.79683 |
The Live Broadcast Room of the Anchor | Comprehensive Evaluation Value | Risk Level |
---|---|---|
L | 0.38861 | medium-low risk |
Y | 0.55021 | medium risk |
D | 0.35530 | medium-low risk |
Anchor Risk | MCN Institution Risk | Merchant Risk | Supply Chain Risk | Platform Risk | Consumption Scenario Risk | |
---|---|---|---|---|---|---|
L | 0.49220 | 0.46661 | 0.33228 | 0.32608 | 0.34350 | 0.30420 |
Y | 0.62647 | 0.61505 | 0.47216 | 0.52607 | 0.43363 | 0.55192 |
D | 0.32702 | 0.51704 | 0.34205 | 0.29354 | 0.43363 | 0.32925 |
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Zhang, C.; Wang, Y.; Zhang, J. Risk Assessment of Live-Streaming Marketing Based on Hesitant Fuzzy Multi-Attribute Group Decision-Making Method. J. Theor. Appl. Electron. Commer. Res. 2025, 20, 120. https://doi.org/10.3390/jtaer20020120
Zhang C, Wang Y, Zhang J. Risk Assessment of Live-Streaming Marketing Based on Hesitant Fuzzy Multi-Attribute Group Decision-Making Method. Journal of Theoretical and Applied Electronic Commerce Research. 2025; 20(2):120. https://doi.org/10.3390/jtaer20020120
Chicago/Turabian StyleZhang, Changlu, Yuchen Wang, and Jian Zhang. 2025. "Risk Assessment of Live-Streaming Marketing Based on Hesitant Fuzzy Multi-Attribute Group Decision-Making Method" Journal of Theoretical and Applied Electronic Commerce Research 20, no. 2: 120. https://doi.org/10.3390/jtaer20020120
APA StyleZhang, C., Wang, Y., & Zhang, J. (2025). Risk Assessment of Live-Streaming Marketing Based on Hesitant Fuzzy Multi-Attribute Group Decision-Making Method. Journal of Theoretical and Applied Electronic Commerce Research, 20(2), 120. https://doi.org/10.3390/jtaer20020120