Return Policy Selection Analysis for Brands Considering MCN Click Farming and Customer Disappointment Aversion

There are a few research on live streaming e-commerce at present. Some of them focus on empirical research, including consumers’ viewing, consumption motivation, and how to improve consumers’ purchasing intention [14,15,16,17,18,19]. The others focus on supply chain via live streaming e-commerce, including influence of streamers sign contracts on decisions in service supply chain [20,21], influence of reference price and streamers’ personal ability on pricing [22,23].

Click farming is a common tactic used to attract consumers in e-commerce. Some literatures focus on click farming itself and investigate its characteristics and screening methods. In particular, Zhao et al. designed a new detection framework, which can detect click farming behaviors by inferring implicit behaviors of online users [24]. Li et al. used three-phase methodology to detect click farming and found that most click farmers are lowly rated [25]. Jiang et al. conducted an empirical analysis of click farming on Taobao and found that click farming is most likely to occur in clothing products [26]. In addition, some literatures study click farming from brands to analyze influence of click farming on competition of brands and discuss regulation measures of platforms. In particular, by establishing an evolutionary game model between a brand and an e-commerce platform, Fang built an evolutionary game model and payment matrix of platform and brand which uses click farming, and showed that negative effect brought by click farming and positive effect brought by controlling click farming may both affect strategic choice of the major interest subjects [27]. Bao et al. used a two-stage game model to analyze impact of click farming on competition between two brands in e-commerce platforms and revealed that brands would fall into prisoner’s dilemma in choosing click farming strategy at a low cost [11].

With the development of live streaming e-commerce, click farming from MCN has emerged in this industry and caused huge economic losses to brands. To the best of our knowledge, there is no literature studying click farming from this angle and investigating its influence on brands. This paper focus on this topic.

About return policies selection, relevant research mainly focus on the following three aspects so far: First, some research study return policy selection under traditional retail background by considering return guarantee and return deadline. In particular, McWilliams studied competition among high-quality and low-quality retailers when consumers are fully informed and risk neutral and showed that retailers could benefit from the use of return guarantee [28]. Xu et al. studied retailers’ return policy selection, pricing, and inventory strategy under the consideration of four return strategies with different return periods [29]. Second, in the context of e-commerce, some research on return policy selection is to consider return-freight insurance on the basis of traditional retail models. Especially, Fan and Chen considered a market composed of a manufacturer and a brand, and studied return policy selection of brands under three return-freight insurance policies [13]. Ren et al. studied product pricing, return policy, and return-freight insurance decisions of brands under the condition that return are not allowed, consumers bear freight, brands insure return-freight insurance, and consumers insure return-freight insurance [30]. Chen et al. studied a supply chain system composed of a brand, a platform, a manufacturer and studied whether the brand should insure return-freight insurance under four scenarios of resale and consignment modes [31]. Third, return policy selection based on online and offline channels mainly considers cross-channel return and other issues. For example, Chen et al. constructed an online and offline dual-channel sales model and showed that, as long as the net residual value of returned products is positive in this channel, retailers should provide return guarantees in this channel [32]. Radhi and Zhang studied return policy and pricing of dual-channel retailers and established four return strategies of same-channel and cross-channel return respectively [33]. Jin et al. studied non-cooperative game among retailers providing online to offline cross-channel return service in a duopoly market [34].

Different from the above studies, this paper proposes a new return policy in which brands and MCN jointly insure return-freight insurance so as to provide some practical management enlightenment for brands.

In order to explain behavior that violates conventional expected utility theory, disappointment has become a research direction of many psychologists and behavioral decision scientists. In terms of theoretical research, Bell thought that total expected utility perceived by customers is equal to the sum of economic benefits and psychological (dissatisfaction) satisfaction; if the actual result is worse (or better) than expected, the individual will experience a feeling of disappointment (or elation), which will reduce (or increase) the utility of the consumer, and a disappointment model is built based on this disappointment theory; as disappointment has stronger impact on perceived utility than elation, consumers usually avoid disappointment [1]. Furthermore, Loomes and Sugden [35], Delquie and Cillo [36], Koszegi and Rabin [37] further extended Bell’s disappointment model. However, there are only a few research on customer disappointment aversion in the field of economic management. Some of them studied influence of strategic consumers with disappointment aversion on firm pricing, capacity, and inventory decisions. For example, Liu and Shum established a two-stage model to study impact of disappointment aversion on customers’ strategic purchasing behavior and companies’ pricing decisions [38]. Zhang and Zhang studied impact of disappointment aversion on strategic consumer behavior and proved effectiveness of pricing commitment and best-customer protection in alleviating consumers’ strategic purchasing [39]. Wang et al. considered optimal pricing and inventory decision of a company under the condition that strategic consumers are disappointment-aversion and elation-seeking, respectively [40].

Moreover, some scholars focus on influence of bounded rational consumers with disappointment-aversion on corporate profits and decision-making. For example, Simon thought that people’s thinking ability is not infinite, but limited rationality [41]. Psychological behaviors such as reference dependence, loss aversion, disappointment aversion and elation seeking are shown in consumers’ decision-making [42]. Cao et al. considered three behavioral characteristics of newsvendor, that is, reference dependence, disappointment aversion, and elation seeking, and they introduced them into the model of newspaper children to study the influence on decision-making [42]. They further constructed four kinds of joint ordering and sales effort decision models, respectively, and studied optimal ordering strategy and sales effort investment of retailers for temperature-sensitive products by taking into account influence of retailers’ disappointment aversion and elation seeking [43].

On the whole, most existing research related to disappointment aversion focus on the impact of consumer behavior on pricing, inventory, and other decisions of retailers; however, few of them consider impact on return policies selection of brands. In what follows, we focus on this problem.

Consider a market consist of one platform, one brand, and one MCN. Suppose that there are three return policies in the market, that is, the brand offers return-freight insurance (Model H), consumers purchase return-freight insurance (Model C), the brand and MCN jointly insure return-freight insurance (Model T). Let $I=\{\mathrm{H},\mathrm{C},\mathrm{T}\}$ represent the collection of return policies. We aim to study the band how to decide return policy by considering click farming from MCN and consumer disappointment aversion under live streaming e-commerce. The sales network is shown in Figure 1.

Before live streaming starts, the brand decides to pay pit fee $K$ to MCN and total commission rate $\theta \in [0,1]$ to MCN and platform, while MCN needs to pay entry fee $L$ to platform. After live streaming, the brand will distribute $\theta \mu$ sales of the unreturned order to platform and $\theta (1-\mu )$ sales to MCN, where $\mu \in [0,1]$ is the sharing ratio between platform and MCN.

The decision-making sequence of live streaming e-commerce sales network is as follows: first, the brand determines the unit price $p_i$ of product under the return policy $i\in I$. Second, at the beginning of live streaming, MCN determines its service level $s_{iM}$ and purchases click farming volume $f_i$ (if MCN does not use click farming, then $f_i=0$), which can increase the initial sales of live stream to attract potential consumers. At the same time, the platform determines its service level $s_{iE}$.

See Table 1 for descriptions of variables, parameters, and upper/lower indices involved in this paper.

Throughout, we make the following basic assumptions:

Suppose that, if product is returned, return-freight insurance is only insured by the brand. First, we consider consumers’ non-psychological utility. When a consumer is satisfied with the product, his/her utility is $v+{\alpha }_E{s}_{HE}+{\alpha }_M{s}_{HM}+\rho f_H-p_H$, where ${\alpha }_E,{\alpha }_M$ represent the sensitivity coefficient of consumers to the service level of platform and MCN, respectively, and $\rho$ represents the sensitivity coefficient of consumers to the sales volume via live stream [11]. When a consumer is dissatisfied with the product, he/she will return the product and get a full refund. Since the return freight will be paid by the insurance company, his/her utility is ${\alpha }_E{s}_{HE}+{\alpha }_M{s}_{HM}+\rho f_H$. Then, the non-psychological utility of the consumer is where $\beta$ represents the probability that the consumer is dissatisfied with the product.

Second, we consider consumers’ psychological utility. Bell showed in [1] that elation and disappointment are generated by comparison with previous expectations. According to Bell’s disappointment model in [1], when a consumer is satisfied with the product, he/she will feel elation. The elation coefficient is $e\in [0,1]$ and his/her utility is when a consumer is dissatisfied with the product, the disappointment coefficient is $d\in [0,1]$ and the utility is

Let $\xi =d-e$, known as disappointment aversion level, denote the difference value between the degrees of disappointment and elation. Since the impact of disappointment on utility of disappoint-averse consumers is greater than that of elation, thus $\xi >0$ [2]. By taking into account the influence of disappointment and elation, the consumer’s psychological utility is

The total utility of a consumer is equal to the sum of non-psychological utility and psychological utility, that is,

Only when the consumer expected utility $U_H\ge 0$, which means the consumer will buy the product. At this time, the sales volume of brand includes the real demand $D_H$ of consumers and the fake sales volume $f_H$ generated by click farming. Thus, the demand function is

The platform’s model is to decide its service level $s_{HE}$ by maximizing its profit, that is, where the objective consists of sales share, service cost, and entry fee. By the backward derivation method, the optimal service level of the platform is $s_{HE}^{*}(p_H)=\frac{\theta \mu {\alpha }_E{p}_H}{1-\xi \beta }$. Then, MCN decides its service level $s_{HM}$ and click farming volume $f_H$ to maximize its profit, that is, where the objective consists of sales share, service cost, click farming cost, pit fee, and entry fee. The optimal service level and the optimal click farming volume of MCN can be obtained as

The brand’s profit consists of sales, returned salvage value, return-freight insurance cost and pit fee. Since MCN will return all products purchased by click farming, no commission will be shared for this kind products $f_H$. The product price should meet $p_H\ge \lambda$ to ensure the revenue and hence the leader-follower model H for the brand is as follows: where $r$ represents the unit return salvage value and $\lambda$ represents the unit return-freight insurance cost. Without loss of generality, we assume that the return salvage value is greater than the return-freight insurance price, namely, $r\ge \lambda$. In traditional e-commerce, the salvage value of returned products is generally lower than the product price [44,45,46]. However, due to the strong promotion ability of live streaming e-commerce [20], some brands are willing to conduct live streaming at a price lower than the market price to gain popularity [22], so there is a possibility that the salvage value is higher than the price. Therefore, we assume that the product price is higher than the return-freight insurance price, that is, $p\ge \lambda$. By the way, there is no size relationship between the product price and the returned salvage value.

We can derive the following equilibrium results for Mode H.

Suppose that return-freight insurance is insured by consumers. When a consumer is satisfied with the product, his/her utility is $v+{\alpha }_E{s}_{CE}+{\alpha }_M{s}_{CM}+\rho f_C-p_C-\lambda$. When he/she is dissatisfied with the product, his/her utility is ${\alpha }_E{s}_{CE}+{\alpha }_M{s}_{CM}+\rho f_C-\lambda$. Then, the non-psychological utility of the consumer is

On the other hand, the consumer gets utility of $e\beta (v-p_C)$ when he/she is satisfied with the product, and gets utility of $-d(1-\beta )(v-p_C)$ when he/she is dissatisfied with the product. Thus, the influence of elation and disappointment on psychological utility is

The total utility is

Only when the consumer expected utility $U_C\ge 0$, which means

The consumer will buy the product. In this case, the demand function is

The platform decides its service level $s_{CE}$ by maximizing its profit, that is,

By the backward derivation method, the optimal service level of the platform is $s_{CE}^{*}(p_C)=\frac{\theta \mu {\alpha }_E{p}_C}{1-\xi \beta }$. MCN sets its service level $s_{CM}$ and click farming volume $f_C$ by maximizing its profit, that is,

The optimal service level and the optimal click farming volume of MCN can be obtained as

The brand’s profit consists of sales, returned salvage value, and pit fee. Then, we give a leader-follower model C for the brand as follows:

We can derive the following equilibrium results for Mode C.

Suppose that the return-freight insurance is jointly insured by brand and MCN. Similarly, as in Section 4.1, the demand function is

The platform sets its service level $s_{TE}$ by maximizing its profit, that is,

By the backward derivation method, the optimal service level of the platform is $s_{TE}^{*}(p_T)=\frac{\theta \mu {\alpha }_E{p}_T}{1-\xi \beta }$. MCN sets its service level $s_{TM}$ and click farming volume $f_T$ by maximizing its profit, that is,

The optimal service level and the optimal click farming volume of MCN can be obtained as

The brand’s profit consists of sales, returned salvage value, return-freight insurance cost and pit fee. Then, we get a leader-follower model T for the brand as follows:

We can derive the following equilibrium results for Mode T.

By comparing the difference values of brand’s profits under three return policies, we analyzed the influence of consumer disappointment aversion level $\xi$, ability $a$ of MCN, commission rate $\theta$, and brand’s return-freight insurance purchasing ratio $\delta$ on brand’s return policy selection. By changing the values of $\theta$ and $\delta$, we further investigated the influence of $\xi$ and $a$ on brand’s return policy selection. Referring to Proposition 1–3, we chose the parameters $\xi \in [0,1],a\in (0,1],\delta \in [0.6,0.9]$. Since the commission rate $\theta$ generally does not exceed 30% [47], we discussed three cases of low commission rate ($\theta =0.1$), medium commission rate ($\theta =0.2$), and high commission rate ($\theta =0.3$), respectively.

Five important parameters are involved in this section, which are consumer disappointment aversion level $\xi$, ability $a$ of MCN, brand’s return-freight insurance purchasing ratio $\delta$, commission rate $\theta$, and consumer dissatisfaction $\beta$. We made some numerical experiments to analyze the influence of these parameters on each member in the system. The benchmark parameters were respectively taken as $\xi =0.5,a=0.5,\delta =0.75,\theta =0.2$.

The experimental results related to the influence of consumer disappointment aversion level $\xi \in [0,1]$, ability of MCN $a\in (0,1]$, and commission rate $\theta \in [0,0.3]$ on each member are shown in Figure 5. In particular, Figure 5a–c, Figure 5d–f, Figure 5g–i, and Figure 5j–l show the trends of optimal price, service level, click farming volume, and optimal profit of each member with the increase of $\xi$, $a$, and $\theta$, respectively.

From Figure 5a–i, we can observe that under three return policies, optimal product price, service level, and click farming volume rise with the increase of disappointment aversion level, ability of MCN, and commission rate. This shows that, when brands sell products with high level of consumer disappointment aversion and cooperate with MCN with high ability and platform with high commission rate, the higher the product price is set to be, the higher the service levels are provided to be and the greater the amount of click farming is.

The trends of brand’s profit curve in Figure 5j,h correspond to Figure 3b, while the trend of brand’s profit curve in Figure 5l corresponds to Figure 2b, Figure 3b and Figure 4b. It can be seen from these figures that, only when consumer disappointment aversion level and MCN’s ability are both high or commission rate is low, the brand should choose return policy H, which is consistent with the conclusions in Section 5.1. Moreover, only when the brand chooses return policy H, both platform and MCN can get the maximum profits. Therefore, the whole system can be coordinated only when the brand cooperates with MCN with strong ability and sells with high consumer disappointment aversion level or with low commission rate. In other cases, because the brand does not choose return policy H, it is disadvantageous to both platform and MCN. In addition, with the increase of disappointment aversion level and the ability of MCN, the optimal profit of each member under three return policies increases, but the changes of optimal profits of platform and MCN are not significant. However, with the increase of commission rate, the optimal profit of the brand decreases, while the optimal profits of both platform and MCN increase significantly. This shows that commission rate is the main factor affecting the optimal profits of platform and MCN.

The experimental results related to the influence of return-freight insurance purchasing ratio $\delta \in [0.6,0.9]$ on each member are shown in Figure 6 and Figure 7a. From Figure 6a–c, it can be seen that $p_T^{*},s_{TM}^{*},f_T^{*}$ rise with the increase of brand’s return-freight insurance purchasing ratio. This indicates that, under return policy T, the higher brand’s return-freight insurance purchasing ratio is, the higher the values of product price, service level provided by MCN, and click farming volume from MCN are.

The trend of brand’s profit curve in Figure 7a corresponds to Figure 3a–c. It can be seen that under the condition of $\xi =0.5,a=0.5,\theta =0.2$, when brand’s return-freight insurance purchasing ratio is low, the brand should choose return policy H; otherwise, it should choose return policy T. Only when the brand chooses return policy H, the platform and MCN can get the maximal profits. Therefore, the whole system can realize the coordination only when brand’s return-freight insurance purchasing ratio is relatively low.

The experimental results related to the influence of consumer dissatisfaction on each member are shown in Figure 7b and Figure 8. According to Figure 8a, both $p_H^{*}$ and $p_C^{*}$ go up with the increase of consumer dissatisfaction with a trend of decreasing first and increasing later. From Figure 8b,c, it can be seen that the maximal service level and the optimal volume of click farming under three return policies rise with the increase of consumer dissatisfaction. From Figure 7b, it can be seen that, in the case of $\xi =0.5,a=0.5,\theta =0.2$, when consumers are less satisfied, the brand should choose return policy C; otherwise, it should choose return policy H. Only when the brand chooses return policy H, the platform and MCN can get the maximal profits. Therefore, the whole system can be coordinated only when consumers are highly dissatisfied.

Based on the above analysis, no matter how the parameters of consumer disappointment aversion level, ability of MCN, commission rate, brand’s return-freight insurance purchasing ratio, and consumer dissatisfaction change, the brand’s optimal prices under three return policies always satisfy $p_H^{*}>p_T^{*}>p_C^{*}$. This is because, when a brand needs to buy return-freight insurance, it will transfer part of the purchase cost to product price. Accordingly, the optimal service levels of platform and MCN under three return policies always satisfy $s_{HE}^{*}>s_{TE}^{*}>s_{CE}^{*}$, $s_{HM}^{*}>s_{CM}^{*}>s_{TM}^{*}$ and, in most cases, the service level provided by MCN is greater than that provided by the platform. The optimal click farming volumes of MCN under three return policies always satisfy $f_H^{*}>f_C^{*}>f_T^{*}$ and $f_T^{*}$ is significantly lower than $f_H^{*}$ and $f_C^{*}$, which indicates that return policy T can effectively suppress click farming of MCN. Furthermore, when consumer disappointment aversion level, MCN’s ability, consumer dissatisfaction are all high or commission rate, brand’s return-freight insurance purchasing ratio are both low, the whole system can achieve coordination. In other cases, the optimal return policies of brand are not conducive to platform and MCN.