# A Hybrid MCDM Model for Live-Streamer Selection via the Fuzzy Delphi Method, AHP, and TOPSIS
## 1 Introduction
The widespread adoption of mobile devices has enabled users to watch a livestream any time and from any location. Therefore, livestream shopping has become a method of sale adopted by a host of sellers [10]. Users and live-streamers can realize equal and real-time communication via livestreaming platforms; similarly, it is possible for users to communicate with each other. Hence, livestreaming reduces the distance between companies and individuals [11]. When a live-streamer demonstrates a product to consumers and proactively helps them solve their problems, the consumers are more likely to purchase the product [12]. Moreover, the success of livestream shopping has highlighted the importance of livestreamers in e-commerce, who play an important role in livestream shopping, distinguishing it from traditional shopping [13]. In 2019, the e-commerce livestreaming industry witnessed a rapid growth, with increased competition among live-streamers due to the arrival of many newcomers [14]. Similar to salespersons in traditional markets, live-streamers play an essential role in e-commerce livestreaming. Hence, e-commerce operators must make careful hiring decisions [15]. Therefore, as live-streamers play an essential role, their selection is an MCMD problem, in which the most optimal solution can be determined based on the appropriate selection criteria and screening procedures. Decision-making is a usual activity in daily life and plays a significant role. MCDM selects the optimal alternative under various criteria. To find the most preferred alternative, decision-makers provide alternative preferences [16]. However, each MCDM method has its pros and cons. There is no rule for identifying how to choose them [17]. [PERSON] and [PERSON] [18] also indicated that it is very hard to determine the best MCDM method. To obtain robust results, applying more than one method is recommended. Data should be collected via group decision-making and expert discussion among the MCDM methods. The Delphi method is widely used for this purpose [19]. However, there are several shortcomings in its application, such as lack of direct feedback and obstruction in understanding certain concepts [20]. Additionally, it is time-consuming and produces incorrect results when there are semantically unclear items in the questionnaire. These shortcomings were subsequently overcome by scholars by incorporating fuzzy theory [21]. Decision-making scenarios (e.g., decision-making information, numeric values, and decision-maker cognition) faced by individuals are usually fuzzy. To make suitable and high-quality decisions in a scenario, the fuzzy set theory plays an important role. [PERSON] [22] proposed the concept of \"fuzzy sets,\" emphasizing that individuals' thinking, inference, and perception of surroundings are quite fuzzy in essence. Accordingly, the analysis method of fuzzy mathematics is more effective than the traditional quantitative method in making decisions in fuzzy scenarios [23]. In many decision-making problems, decision-makers obtain vague or imprecise information. To adequately settle such situations, the fuzzy set theory has become a powerful implement [16]. The fuzzy Delphi method primarily focuses on uncertainty and linguistic variables; the triangular fuzzy number in the fuzzy theory is used to improve the shortcomings of the traditional Delphi method. Additionally, the new method also reduces the time spent on data collection [24]. Previous studies have shown that the fuzzy Delphi method can obtain valid and reliable criteria [25; 26]. Additionally, since the properties of the geometric mean can reduce the effects of extreme values and highlight the correlations of elements, it is used as a reference for the final selection of elements [21]. The fuzzy Delphi method has been successfully administered in many areas [27]. Furthermore, many studies [28; 29; 30; 31; 32] have applied the fuzzy Delphi method to screen the criteria for personnel selection.