# A Hybrid MCDM Model to Select Optimal Hosts of Variety Shows in the Social Media Era

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Methodology

#### 3.1. Conceptual Framework

#### 3.2. Representation of the Selection Model

#### 3.3. Fuzzy Delphi Method

_{i}denotes the importance rating of the criteria by the i-th expert (i = 1, 2,..., n). L

_{G}= geometric mean value.

#### 3.4. DEMATEL

_{ij}is the degree to which the decision-maker evaluation factor i influences factor j. For i = j, all the diagonal elements values are 0. For each decision-maker, a n $\times $ n positive matrix can be developed as ${X}^{k}=\left[{X}_{ij}^{k}\right]$, where k is the number of decision-makers with 1 $\le $ k $\le $ H, and n is the number of perspectives.

^{−1}), where I is an identity matrix with a diagonal value of 1.

#### 3.5. ANP

_{1}, a

_{2}, …, a

_{n}and the weights are indicated by w

_{1}, w

_{2}, …, w

_{n}. The pairwise comparisons can be shown by questionnaires with subjective perception as:

_{ij}is 1/a

_{ji}and a

_{ij}= a

_{ik}/a

_{jk}.

_{m}denotes the mth cluster, e

_{mn}is the nth factor in the mth cluster, and W

_{ij}is the principal eigenvector of the influence of the factors compared in the jth cluster to the ith cluster. If the jth cluster has no impact on the ith cluster, then W

_{ij}is 0. The eigenvector obtained in Step 2 is grouped and located in appropriate positions in the supermatrix based on the influences.

_{21}is a matrix that illustrates the weights of cluster 2 concerning cluster 1; W

_{33}shows that there is an inner dependence within cluster 3.

#### 3.6. TOPSIS

_{ij}is the i alternative under the j criterion to be measured.

^{−})

## 4. Empirical Study and Results

#### 4.1. Construct the Hierarchy

#### 4.2. Complete Pairwise Comparison

#### 4.3. Solve the Supermatrix

#### 4.4. Build a Normalized and a Weighted Evaluation Matrix

^{−}= (0.0478, 0.0390, 0.0610, 0.0567, 0.0382, 0.0511, 0.0448, 0.0346, 0.0511, 0.0307, 0.0198, 0.0352).

#### 4.5. Rank the Alternative

_{1}, A

_{3}, and A

_{2}. The case company selected the host per our suggestion.

_{1}is the best and A

_{2}is the worst.

## 5. Conclusions

_{1}as their variety show host. Through the interview, the performance of A

_{1}and the ratings of the program are better than expected. However, we are limited to compare the performance of the three alternatives, practically.

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A. The Pairwise Comparison Matrices

Person | Profession | Program | Promotion | Priority Weights | |
---|---|---|---|---|---|

λ_{max} = 4.1092, CR = 0.0368 | |||||

Person | 1.0000 | 1.5874 | 0.8355 | 1.4736 | 0.2874 |

Profession | 0.6300 | 1.0000 | 1.4422 | 2.0801 | 0.2850 |

Program | 1.1968 | 0.6934 | 1.0000 | 1.8171 | 0.2694 |

Promotion | 0.6786 | 0.4807 | 0.5503 | 1.0000 | 0.1582 |

Person | Profession | Program | Promotion | Priority Weights | |
---|---|---|---|---|---|

λ_{max} = 4.0175, CR = 0.0059 | |||||

Person | 1.0000 | 0.5754 | 0.5754 | 0.6114 | 0.1631 |

Profession | 1.7380 | 1.0000 | 1.4422 | 1.1447 | 0.3164 |

Program | 1.7380 | 0.6934 | 1.0000 | 1.1447 | 0.2635 |

Promotion | 1.6355 | 0.8736 | 0.8736 | 1.0000 | 0.2570 |

Person | Profession | Program | Promotion | Priority Weights | |
---|---|---|---|---|---|

λ_{max} = 4.0226, CR = 0.0076 | |||||

Person | 1.0000 | 0.9565 | 1.0455 | 1.1006 | 0.2535 |

Profession | 1.0455 | 1.0000 | 1.4422 | 1.4422 | 0.3005 |

Program | 0.9565 | 0.6934 | 1.0000 | 1.4422 | 0.2448 |

Promotion | 0.9086 | 0.6934 | 0.6934 | 1.0000 | 0.2012 |

Person | Profession | Program | Promotion | Priority Weights | |
---|---|---|---|---|---|

λ_{max} = 4.0359, CR = 0.0121 | |||||

Person | 1.0000 | 3.8259 | 3.4760 | 3.3019 | 0.5393 |

Profession | 0.2614 | 1.0000 | 1.4422 | 1.2599 | 0.1739 |

Program | 0.2877 | 0.6934 | 1.0000 | 1.2599 | 0.1483 |

Promotion | 0.3029 | 0.7937 | 0.7937 | 1.0000 | 0.1385 |

**Table A5.**The interdependent matrix within the Profession perspective concerning the ‘Familiarity’ criteria.

Expression | Emotion | Response | Priority Weights | |
---|---|---|---|---|

λ_{max} = 3.0136, CR = 0.0103 | ||||

Expression | 1.0000 | 0.5228 | 0.4055 | 0.1871 |

Emotion | 1.9129 | 1.0000 | 1.1006 | 0.4022 |

Response | 2.4662 | 0.9086 | 1.0000 | 0.4107 |

## Appendix B. The Unweighted, Weighted, and Limiting Supermatrices

C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} | C_{7} | C_{8} | C_{9} | C_{10} | C_{11} | C_{12} | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

C_{1} | 0.2365 | 0.3921 | 0.3921 | 0.5098 | 0.2072 | 0.3869 | 0.2781 | 0.3856 | 0.4808 | 0.2920 | 0.3390 | 0.3143 |

C_{2} | 0.4035 | 0.2158 | 0.2158 | 0.2451 | 0.3964 | 0.2262 | 0.2475 | 0.2210 | 0.2696 | 0.3054 | 0.2195 | 0.2059 |

C_{3} | 0.3599 | 0.3921 | 0.3921 | 0.2451 | 0.3964 | 0.3869 | 0.4745 | 0.3935 | 0.2496 | 0.4026 | 0.4416 | 0.4798 |

C_{4} | 0.1871 | 0.4225 | 0.5294 | 0.5091 | 0.4736 | 0.3014 | 0.3856 | 0.2395 | 0.4600 | 0.2479 | 0.2836 | 0.3143 |

C_{5} | 0.4022 | 0.2153 | 0.1840 | 0.2643 | 0.3524 | 0.1727 | 0.2210 | 0.3802 | 0.2211 | 0.2593 | 0.1836 | 0.2059 |

C_{6} | 0.4107 | 0.3622 | 0.2866 | 0.2266 | 0.1739 | 0.5259 | 0.3935 | 0.3802 | 0.3189 | 0.4929 | 0.5328 | 0.4798 |

C_{7} | 0.1698 | 0.4483 | 0.3316 | 0.5098 | 0.4889 | 0.2438 | 0.3971 | 0.3333 | 0.4193 | 0.2920 | 0.3390 | 0.3143 |

C_{8} | 0.4454 | 0.1958 | 0.1282 | 0.2451 | 0.2556 | 0.2525 | 0.2067 | 0.3333 | 0.2351 | 0.3054 | 0.2195 | 0.2059 |

C_{9} | 0.3848 | 0.3558 | 0.5403 | 0.2451 | 0.2556 | 0.5037 | 0.3962 | 0.3333 | 0.3456 | 0.4026 | 0.4416 | 0.4798 |

C_{10} | 0.4099 | 0.3862 | 0.4587 | 0.4343 | 0.1617 | 0.2940 | 0.2918 | 0.3333 | 0.4457 | 0.3512 | 0.3390 | 0.5098 |

C_{11} | 0.2156 | 0.1968 | 0.2524 | 0.2088 | 0.3093 | 0.1719 | 0.1672 | 0.3333 | 0.2142 | 0.3584 | 0.2195 | 0.2451 |

C_{12} | 0.3745 | 0.4171 | 0.2889 | 0.3570 | 0.5290 | 0.5341 | 0.5410 | 0.3333 | 0.3401 | 0.2903 | 0.4416 | 0.2451 |

C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} | C_{7} | C_{8} | C_{9} | C_{10} | C_{11} | C_{12} | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

C_{1} | 0.0680 | 0.1127 | 0.1127 | 0.0831 | 0.0338 | 0.0631 | 0.0705 | 0.0977 | 0.1219 | 0.1575 | 0.1828 | 0.1695 |

C_{2} | 0.1160 | 0.0620 | 0.0620 | 0.0400 | 0.0646 | 0.0369 | 0.0627 | 0.0560 | 0.0683 | 0.1647 | 0.1184 | 0.1110 |

C_{3} | 0.1034 | 0.1127 | 0.1127 | 0.0400 | 0.0646 | 0.0631 | 0.1203 | 0.0997 | 0.0633 | 0.2171 | 0.2381 | 0.2587 |

C_{4} | 0.0533 | 0.1204 | 0.1509 | 0.1611 | 0.1499 | 0.0954 | 0.1159 | 0.0720 | 0.1382 | 0.0431 | 0.0493 | 0.0547 |

C_{5} | 0.1146 | 0.0614 | 0.0524 | 0.0836 | 0.1115 | 0.0547 | 0.0664 | 0.1143 | 0.0665 | 0.0451 | 0.0319 | 0.0358 |

C_{6} | 0.1170 | 0.1032 | 0.0817 | 0.0717 | 0.0550 | 0.1664 | 0.1183 | 0.1143 | 0.0959 | 0.0857 | 0.0927 | 0.0834 |

C_{7} | 0.0457 | 0.1208 | 0.0893 | 0.1343 | 0.1288 | 0.0642 | 0.0972 | 0.0816 | 0.1026 | 0.0433 | 0.0503 | 0.0466 |

C_{8} | 0.1200 | 0.0527 | 0.0345 | 0.0646 | 0.0673 | 0.0665 | 0.0506 | 0.0816 | 0.0576 | 0.0453 | 0.0326 | 0.0305 |

C_{9} | 0.1036 | 0.0958 | 0.1455 | 0.0646 | 0.0673 | 0.1327 | 0.0970 | 0.0816 | 0.0846 | 0.0597 | 0.0655 | 0.0712 |

C_{10} | 0.0648 | 0.0611 | 0.0726 | 0.1116 | 0.0416 | 0.0755 | 0.0587 | 0.0671 | 0.0897 | 0.0486 | 0.0469 | 0.0706 |

C_{11} | 0.0341 | 0.0311 | 0.0399 | 0.0536 | 0.0795 | 0.0442 | 0.0336 | 0.0671 | 0.0431 | 0.0496 | 0.0304 | 0.0339 |

C_{12} | 0.0593 | 0.0660 | 0.0457 | 0.0917 | 0.1359 | 0.1373 | 0.1089 | 0.0671 | 0.0684 | 0.0402 | 0.0611 | 0.0339 |

C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} | C_{7} | C_{8} | C_{9} | C_{10} | C_{11} | C_{12} | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

C_{1} | 0.1015 | 0.1015 | 0.1015 | 0.1015 | 0.1015 | 0.1015 | 0.1015 | 0.1015 | 0.1015 | 0.1015 | 0.1015 | 0.1015 |

C_{2} | 0.0766 | 0.0766 | 0.0766 | 0.0766 | 0.0766 | 0.0766 | 0.0766 | 0.0766 | 0.0766 | 0.0766 | 0.0766 | 0.0766 |

C_{3} | 0.1152 | 0.1152 | 0.1152 | 0.1152 | 0.1152 | 0.1152 | 0.1152 | 0.1152 | 0.1152 | 0.1152 | 0.1152 | 0.1152 |

C_{4} | 0.1057 | 0.1057 | 0.1057 | 0.1057 | 0.1057 | 0.1057 | 0.1057 | 0.1057 | 0.1057 | 0.1057 | 0.1057 | 0.1057 |

C_{5} | 0.0706 | 0.0706 | 0.0706 | 0.0706 | 0.0706 | 0.0706 | 0.0706 | 0.0706 | 0.0706 | 0.0706 | 0.0706 | 0.0706 |

C_{6} | 0.0998 | 0.0998 | 0.0998 | 0.0998 | 0.0998 | 0.0998 | 0.0998 | 0.0998 | 0.0998 | 0.0998 | 0.0998 | 0.0998 |

C_{7} | 0.0855 | 0.0855 | 0.0855 | 0.0855 | 0.0855 | 0.0855 | 0.0855 | 0.0855 | 0.0855 | 0.0855 | 0.0855 | 0.0855 |

C_{8} | 0.0600 | 0.0600 | 0.0600 | 0.0600 | 0.0600 | 0.0600 | 0.0600 | 0.0600 | 0.0600 | 0.0600 | 0.0600 | 0.0600 |

C_{9} | 0.0931 | 0.0931 | 0.0931 | 0.0931 | 0.0931 | 0.0931 | 0.0931 | 0.0931 | 0.0931 | 0.0931 | 0.0931 | 0.0931 |

C_{10} | 0.0702 | 0.0702 | 0.0702 | 0.0702 | 0.0702 | 0.0702 | 0.0702 | 0.0702 | 0.0702 | 0.0702 | 0.0702 | 0.0702 |

C_{11} | 0.0445 | 0.0445 | 0.0445 | 0.0445 | 0.0445 | 0.0445 | 0.0445 | 0.0445 | 0.0445 | 0.0445 | 0.0445 | 0.0445 |

C_{12} | 0.0772 | 0.0772 | 0.0772 | 0.0772 | 0.0772 | 0.0772 | 0.0772 | 0.0772 | 0.0772 | 0.0772 | 0.0772 | 0.0772 |

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**Figure 4.**Hierarchy with a causal relationship to select the optimal hosts for television variety shows.

Year | Contributor(s) | Topic | Applied Method(s) |
---|---|---|---|

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2014 | Dadelo et al. [16] | Professional basketball player | Wisdom-of-crowds principles and TOPSIS |

2014 | Keršulienė and Turskis [17] | Chief accounting officer | Principles of fusion of fuzzy information, additive ratio assessment with fuzzy numbers (ARAS-F), fuzzy weighted-product model, and analytic hierarchy process (AHP) |

2015 | Chang [18] | Public relations personnel | The fuzzy Delphi method, ANP, and TOPSIS |

2015 | Chang et al. [19] | Tour guide | The fuzzy Delphi method, ANP, and TOPSIS |

2015 | Karabasevic et al. [20] | Mining engineer | Step-wise weight assessment ratio analysis (SWARA) and multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) |

2015 | Sang et al. [21] | System analyst engineer | Fuzzy TOPSIS (FTOPSIS) based on the Karnik–Mendel (KM) algorithm |

2016 | Asuquo and Onuodu [22] | Academic staff | FAHP |

2016 | Erdem [23] | Information technology personnel (Junior developer) | FAHP |

2016 | Karabasevic et al. [24] | Sales manager | SWARA and additive ratio assessment (ARAS) |

2016 | Kosareva et al. [25] | Security guard | KEmeny Median Indicator Rank Accordance (KEMIRA) |

2017 | Stanujkic et al. [26] | Promoter | New approach based on adapted weighted sum and SWARA |

2017 | Urosevic et al. [27] | Sales manager | SWARA and weighted aggregates sum product assessment (WASPAS) |

2018 | Deliktas and Üstun [28] | Industrial engineers | FAHP and FTOPSIS |

2018 | Ji et al. [29] | Sales supervisor | Projection-based an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) |

2019 | Ding et al. [30] | Middle manager | Fuzzy MCDM |

2019 | Liao et al. [31] | Celebrity endorser | The fuzzy Delphi method, DEMATEL, ANP, and TOPSIS |

2019 | Nabeeh et al. [32] | Manager | Neutrosophic AHP and TOPSIS |

2019 | Pehlivan et al. [33] | National football team player | FAHP, FTOPSIS, and integrated these two methods |

2019 | Yalçın and Pehlivan [34] | Blue-collar personnel | Fuzzy COmbinative Distance-based Assessment (CODAS) method using the fuzzy envelopes of the hesitant fuzzy linguistic term sets (HFLTSs) based on comparative linguistic expressions (CLEs) |

Criteria | Geometric Mean |
---|---|

Expertise | 6.4007 |

Experience | 7.4466 |

Education | 6.1803 |

Appearance | 6.2223 |

Ability to follow orders | 6.1803 |

Confidence | 6.1672 |

Expression | 7.2107 |

Response | 7.2381 |

Desire of performance | 5.8800 |

Living experience | 5.6806 |

Familiarity | 7.4449 |

Creativity | 6.2953 |

Adaptation to company | 5.3741 |

Teamwork | 6.2518 |

Interpersonal skill | 6.1682 |

Adaptation to environment | 6.0776 |

Attitude | 7.4172 |

Language | 5.6714 |

Likeability | 7.4570 |

Emotion | 7.3461 |

Cost | 7.4939 |

Match | 7.9214 |

YouTube (number of views) | 7.8596 |

YouTube (number of subscriptions) | 6.9637 |

Facebook (number of fans) | 7.3322 |

Facebook (number of posts shared) | 7.0743 |

7.6511 |

Perspective | Criteria | Definition | Contributors |
---|---|---|---|

Person | C_{1}: Familiarity | Familiarity to the target audience. | [3,55] |

C_{2}: Likeability | Likeability to the target audience. | Executive proposed | |

C_{3}: Attitude | Conscientious toward the work. | [55,56] | |

Profession | C_{4}: Expression | Expression ability. | [3,55,56,57] |

C_{5}: Emotion | Emotional steadiness. | Executive proposed | |

C_{6}: Response | React appropriately to the emergency. | [55,56,57] | |

Program | C_{7}: Experience | Hosting experience. | [55] |

C_{8}: Cost | Cost of acquiring the host. | Executive proposed | |

C_{9}: Match | Match between host and program. | Executive proposed | |

Promotion | C_{10}: YouTube | Views of the host’s YouTube channel. | Executive proposed |

C_{11}: Facebook | The number of people in the host’s Facebook fan page. | Executive proposed | |

C_{12}: Instagram | The number of fans in the host’s Instagram. | Executive proposed |

Perspectives | Person | Profession | Program | Promotion |
---|---|---|---|---|

Person | 0.000 | 4.000 | 3.000 | 3.333 |

Profession | 4.000 | 0.000 | 3.333 | 3.000 |

Program | 3.000 | 3.333 | 0.000 | 3.000 |

Promotion | 4.000 | 3.333 | 3.000 | 0.000 |

Perspectives | Person | Profession | Program | Promotion |
---|---|---|---|---|

Person | 11.4600 | 11.4977 | 10.3333 | 10.3574 |

Profession | 11.7174 | 11.1985 | 10.3333 | 10.3213 |

Program | 10.8056 | 10.6011 | 9.3333 | 9.5634 |

Promotion | 11.7402 | 11.4635 | 10.3333 | 10.1153 |

C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} | C_{7} | C_{8} | C_{9} | C_{10} | C_{11} | C_{12} | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

A_{1} | 0.5972 | 0.6466 | 0.6420 | 0.6099 | 0.6191 | 0.6206 | 0.6025 | 0.5774 | 0.6041 | 0.7665 | 0.7786 | 0.7647 |

A_{2} | 0.4708 | 0.5084 | 0.5541 | 0.5365 | 0.5408 | 0.5123 | 0.5235 | 0.5774 | 0.5489 | 0.4707 | 0.4438 | 0.4557 |

A_{3} | 0.6494 | 0.5687 | 0.5300 | 0.5833 | 0.5694 | 0.5936 | 0.6025 | 0.5774 | 0.5778 | 0.4369 | 0.4438 | 0.4557 |

C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} | C_{7} | C_{8} | C_{9} | C_{10} | C_{11} | C_{12} | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

A_{1} | 0.0606 | 0.0496 | 0.0740 | 0.0645 | 0.0437 | 0.0619 | 0.0515 | 0.0346 | 0.0563 | 0.0538 | 0.0347 | 0.0590 |

A_{2} | 0.0478 | 0.0390 | 0.0638 | 0.0567 | 0.0382 | 0.0511 | 0.0448 | 0.0346 | 0.0511 | 0.0331 | 0.0198 | 0.0352 |

A_{3} | 0.0659 | 0.0436 | 0.0610 | 0.0617 | 0.0402 | 0.0592 | 0.0515 | 0.0346 | 0.0538 | 0.0307 | 0.0198 | 0.0352 |

${\mathit{S}}_{\mathit{i}}^{\ast}$ | ${\mathit{S}}_{\mathit{i}}^{-}$ | ${\mathit{C}}_{\mathit{i}}^{\ast}$ | Rank | |
---|---|---|---|---|

A_{1} | 0.0053 | 0.0453 | 0.8953 | 1 |

A_{2} | 0.0452 | 0.0037 | 0.0747 | 3 |

A_{3} | 0.0395 | 0.0223 | 0.3607 | 2 |

Weights From ANP | ANP-TOPSIS | |||
---|---|---|---|---|

Min | Max | Range | ||

C_{1} | 0.10 | 0.00 | 0.32 | 0.32 |

C_{2} | 0.08 | 0.00 | 0.57 | 0.57 |

C_{3} | 0.12 | 0.00 | 0.45 | 0.45 |

C_{4} | 0.11 | 0.00 | 0.55 | 0.55 |

C_{5} | 0.07 | 0.00 | 0.51 | 0.51 |

C_{6} | 0.10 | 0.00 | 0.54 | 0.54 |

C_{7} | 0.09 | 0.00 | 0.53 | 0.53 |

C_{8} | 0.06 | 0.00 | 0.50 | 0.50 |

C_{9} | 0.09 | 0.00 | 0.53 | 0.53 |

C_{10} | 0.07 | 0.00 | 0.40 | 0.40 |

C_{11} | 0.04 | 0.00 | 0.48 | 0.48 |

C_{12} | 0.08 | 0.00 | 0.52 | 0.52 |

Weights From AHP | AHP-TOPSIS | |||
---|---|---|---|---|

Min | Max | Range | ||

C_{1} | 0.05 | 0.00 | 0.38 | 0.38 |

C_{2} | 0.03 | 0.00 | 0.36 | 0.36 |

C_{3} | 0.09 | 0.00 | 0.31 | 0.31 |

C_{4} | 0.12 | 0.00 | 0.45 | 0.45 |

C_{5} | 0.07 | 0.00 | 0.40 | 0.40 |

C_{6} | 0.12 | 0.00 | 0.45 | 0.45 |

C_{7} | 0.10 | 0.00 | 0.43 | 0.43 |

C_{8} | 0.05 | 0.00 | 0.38 | 0.38 |

C_{9} | 0.10 | 0.00 | 0.43 | 0.43 |

C_{10} | 0.13 | 0.00 | 0.24 | 0.24 |

C_{11} | 0.06 | 0.00 | 0.39 | 0.39 |

C_{12} | 0.06 | 0.00 | 0.39 | 0.39 |

Approach | Rank |
---|---|

AHP | A_{1} > A_{3} > A_{2} |

AHP and TOPSIS | A_{1} > A_{3} > A_{2} |

ANP | A_{1} > A_{3} > A_{2} |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Wu, L.-C.; Chang, K.-L.; Liao, S.-K.
A Hybrid MCDM Model to Select Optimal Hosts of Variety Shows in the Social Media Era. *Symmetry* **2020**, *12*, 125.
https://doi.org/10.3390/sym12010125

**AMA Style**

Wu L-C, Chang K-L, Liao S-K.
A Hybrid MCDM Model to Select Optimal Hosts of Variety Shows in the Social Media Era. *Symmetry*. 2020; 12(1):125.
https://doi.org/10.3390/sym12010125

**Chicago/Turabian Style**

Wu, Lee-Chun, Kuei-Lun Chang, and Sen-Kuei Liao.
2020. "A Hybrid MCDM Model to Select Optimal Hosts of Variety Shows in the Social Media Era" *Symmetry* 12, no. 1: 125.
https://doi.org/10.3390/sym12010125