# Optimization of Pricing and Freshness-Keeping Effort in Fresh e-Commerce Supply Chain considering Online Comments

Authors: Quanli Wang; Bojun Gu; Yufang Fu
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Considering that online comments play an important role in operational decisions of a fresh e-commerce supply chain, we construct single-stage and two-stage models for fresh produce supply chain consisting of a supplier and a fresh e-commerce platform. Utilizing the Stackelberg game and two-stage repeated game, the profit functions of the supplier and platform are established, and then, the optimal retail price and freshness-keeping effort are derived. In addition, to improve the accuracy of models in describing practical problems, we construct an extended two-stage model and a third-party logistics model. Furthermore, we compare the optimal strategies under different models. Our results show that, firstly, the optimal retail price and freshness-keeping effort of supplier and platform vary in different models and online comments play a positive effect on supplier and platform’s profits. Secondly, compared with the single-stage model, the fresh e-commerce supply chain grabs more market share and profits under the two-stage model. Lastly, online comments vary in different dominance scenarios and may cause competition between supplier and platform for supply chain dominance.


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## Body

## 1. Introduction

The tremendous growth of the Internet has had a dramatic impact on the fresh produce industry. The market transaction size of China’s fresh produce e-commerce sector has reached CNY279.62 billion in 2019, which increases by 37% over the previous year. In 2020, the sector has further expanded to CNY458.49 billion, which is an increase of 64% year by year [1]. This rapid growth can be attributed to the COVID-19 pandemic, which has cultivated the consumer habit of buying fresh produce online. The Internet breaks the distance barrier, and consumers can buy fresh products from all over the world through it. However, the fresh products (such as fresh-cut flowers, fresh meats and seafoods, and fresh fruits and vegetables) inevitably suffer from quality and quantity loss due to the deterioration during the long distance between the production base and the target market. The freshness level of fresh products is closely related to the freshness-keeping effort of logistics provider, which in turn influences consumers’ purchasing behavior. The fresh produce e-commerce sector is beginning to show characteristics that differ from traditional business models, as is the behavior of consumers in the sector. These developments pose new problems and challenges, which is how to formulate management decisions for fresh produce enterprises.An online comment is a new medium for the spread of word-of-mouth information and also is an emerging market phenomenon that profoundly influences consumers’ online purchasing decisions. By necessity, fresh produce has a high purchasing frequency and a high repeat purchasing rate. In addition, compared with general commodities, fresh produce has a greater requirement for food safety. This motivates consumers to seek information from online comments to judge the reliability and authenticity of fresh produce. Many studies have shown that online comments can significantly reduce uncertainty in online consumers’ perceptions of produce and play an important role in consumers’ purchasing decisions [2, 3]. In fact, many fresh enterprises always ignore the importance of online comments. This is a key hindrance to the penetration of fresh produce e-commerce platforms and threatens their sustainable development.Continuous demand is essential for the sustainable development of the fresh produce e-commerce sector. Therefore, favorable purchase experiences are important means to improve consumer engagement and increase demand. While e-commerce is developing rapidly, its disadvantages are gradually emerging in terms of customer experience, quality assurance, after-sales service, and logistical efficiency [4]. Some researchers think consumers can better identify the quality characteristics of fresh food through quality grading [5]. To improve consumers’ shopping experience, it is important for fresh produce e-commerce enterprises to provide good pre-sales, in-sales, and after-sales services. As revealed by the “2020 Fresh Produce E-Commerce Consumer Complaint Data and Typical Cases Report,” delivery, refund, after-sales service, and commodity quality are the main problems mentioned in complaints regarding fresh produce e-commerce enterprises in 2020. Therefore, it is especially important to improve logistical services, after-sales services, and value-added services for e-commerce enterprises, to ensure produce freshness, solve disputes, and improve satisfaction, leading to a better shopping experience for consumers.From consumers’ perspective, the virtual context of online shopping means that they are unable to directly access produces and thus can only make purchasing decisions by gathering relevant information. However, consumers do not rely solely on the information provided by sellers to evaluate produce; online comments in fact seem to be their main source of produce information. From enterprises’ perspective, e-commerce not only broadens their sales channels but also enables them to collect more feedback from the market. Thus, fresh produce retailers pay close attention to consumers’ opinions of their produces because post-purchase opinions ultimately determine consumers’ satisfaction and loyalty and also influence other consumers who are hesitant to make a purchasing decision. Online comments offer fresh produce e-commerce enterprises the ability to gauge the market and adjust their sales strategies based on consumer opinion [6]. Consequently, these enterprises must design targeted service strategies based on the microchoice behavior of consumers to optimize consumers’ purchasing experience, increase consumer stickiness in the fresh produce e-commerce market, and improve consumption flow.Logistics capability has been found to be positively related to e-commerce development in the e-commerce market. Some mature e-commerce enterprises have established their self-operated logistics (e.g., amazon.com in USA and JD.com and Suning.Com in China). SF Express Co., Ltd., has established a fresh produce e-commerce platform based on its own cold-chain logistics, which has opened up the fresh produce e-commerce market in this way. The major fresh produce e-commerce platforms actively attract other fresh produce enterprises and share the cold-chain logistics with them. In this model, a fresh produce e-commerce platform acts as an intermediary between suppliers and consumers and is not directly involved in sales. In addition, the platform provides value-added services including commodity display, pre- and after-sales consultation, and cold-chain logistics. A fresh produce e-commerce platform can be seen as a place for consumers to exchange purchasing information and feedback on produce. Consequently, such platforms play an increasingly important role in the fresh produce supply chain, in some cases even the dominant role. Therefore, it is of great practical significance to analyze the optimal decision in an open model comprising a fresh produce supplier and a fresh produce e-commerce platform under different power structures.Generally, the key questions are how online comments affect the optimal decisions of supply chain members and then how the market power affects the optimal strategies of the e-commerce supply chain. As is well known, market power in supply chain has a great effect on the optimal decision and profits of enterprises. Zeng et al. [7] and Wang and Yu [8] both demonstrate that the profit of the dominant supply chain side will be always greater. Bai and Xie [9] and Tang and Peng [10] show fresh produce supplier plays a leading role when the fresh produce differentiation in online channel is high. These differentiations reflect certain brand and origin advantages, such as “Chu” orange sold by Benlai.com and coconut sold by Tootoo.com. Wu [11] considers that the e-commerce enterprise-oriented mode is the most common model and establishes the Stackelberg game model to analyze the decision-making behavior of the three-level agricultural produce supply chain consisting of producer, logistics enterprise, and e-commerce enterprise. For example, JD.com initiated the establishment of a strategic alliance of global fruit brands in 2018, and many renowned overseas fruit companies have joined the alliance such as Zespri, Capespan, and Driscoll’s. These fruit companies sell through JD.com and entrust JD’s self-operated logistics to deliver their fresh produce.Therefore, we consider a fresh produce e-commerce supply chain comprising a supplier and an online platform and establish two Stackelberg game models to analyze the influence of online comments. In each model, we also consider the market power of the supplier and the platform and set two game scenarios: a supplier-dominated scenario (SS) and a platform-dominated scenario (PS). In SS, the supplier first decides the retail price and then the platform decides how much effort to devote to keeping the produce fresh. In PS, the order of these two decisions is reversed.The remainder of this paper is organized as follows: In Section2, we review the relevant literature. In Section 3, we describe our model and its assumptions. In Section 4, we derive the optimal decisions of each model. In Section 5, we construct extended models to extend our research and conduct numerical analysis. Finally, in Section 6, we give concluding remarks and suggest directions for future work.


## 2. Literature Review

This study focuses on two main areas: the optimization of the fresh produce supply chain and the importance of online comments. We review studies in these areas in the following sections.


### 2.1. Fresh Produce Supply Chain Management

The supply chain management literature on fresh produce is particularly rich. Most of the previous research studies about the optimization of the fresh produce supply chain focus on pricing, freshness-keeping effort, and interactions between offline and online.Pricing and freshness-keeping effort have been major topics in offline channel of fresh produce supply chains and are often decided jointly with ordering and inventory. For example, Sun et al. [12] examine optimal pricing and ordering decisions of offline channel. Yang et al. [13] and Wen et al. [14] both analyze the optimal pricing policies for offline channel, and they all take the supplier as the leader in the Stackelberg game. Herbon and Khmelnitsky [15] and Hou et al. [16] focus on inventory management to determine the optimal replenishment schedule and dynamic prices of perishable produce over time, where both price and time affect demand. Huang et al. [17] study the optimal pricing, inventory, and preservations in a three-level food supply chain, in which the supplier is the leader in the Stackelberg game model. He et al. [18] present a literature review on quantitative models in food supply chains from the view of storage, distribution, marketing, food traceability, and safety. Other researchers [19–21] investigate fresh produce inventory management in the context of freshness-keeping and loss control. Chernonog and Avinadav [22] use the decision rights allocation framework to investigate the optimal pricing strategies for perishable products. In addition, many researchers investigate the structure of supply chain, such as Sana [23] and Cao et al. [24], which construct a centralized and decentralized system and investigate the optimal pricing strategies under different leaderships. In the online supply chain context, researchers consider the optimal pricing and freshness-keeping effort policies. Luo et al. [25] investigate optimal pricing and ordering decisions based on the Nash negotiation. Gu et al. [26] establish one centralized model and two decentralized models to investigate the optimal pricing and effort decisions in online channel. Liu et al. [27] consider a fresh produce supply chain consisting of an e-commerce platform and a supplier. The e-commerce platform is responsible for selling fresh produce and providing value-added service, while the supplier is responsible for providing freshness-keeping effort. They also construct a centralized and decentralized model based on the Stackelberg game to analyze the impacts of the elasticity of freshness and the elasticity of service on optimal strategies. However, online and offline optimal pricing decisions are limited to the fixed pricing model, ignoring the dynamic development of the market.The literature about single fresh produce online channel is limited, and there is more attention paid to the interactions between online and offline channels. For example, Tang and Peng [10] and Cao et al. [28] consider the freshness decay characteristics of fresh produce and design a joint contract to achieve coordination between traditional channels and online direct channels. Liang and He [29] regard the retailer as the leader of a Stackelberg game and construct three-channel structures—a single channel, a supplier-operated direct sales channel, and a retailer-operated direct sales channel—for analyzing the impact of different combinations of channel structures on a fresh produce supply chain, but Gu et al. [30] consider the supplier as the leader of the Stackelberg game and propose a revenue- and cost-sharing contract to coordinate the supply chain. He et al. [31] study the online presale model of fresh produce from a competitive perspective and identify two pricing strategies, penetration pricing and skimming pricing for the physical store and two pricing strategies for the online grocery. Taleizadeh et al. [32] investigate the pricing and the importance of carbon emissions in a dual supply chain where a producer uses green production as regulated by the cap and trade system.The above literature mainly studies the pricing, freshness-keeping effort, and coordination of fresh supply chain, and logistics is also an important part of fresh produce supply chain. For the “farmer-supermarket direct purchase” supply chain with an agricultural cooperative and a supermarket, Wang and Sun [33] investigate the optimal channel model for the agricultural cooperative, the supermarket, and the system of supply chain based on customers’ utility. While fresh produce retailers need to promote consumption, a concomitant issue is the online circulation of fresh produce. Generally, the performance of a fresh produce e-commerce platform depends not only on the volume of sales but also on the frequency of after-sales returns, the latter being the major determinant. Indeed, the development of fresh produce e-commerce is plagued by the problem of returns [34], and the improvement of circulation efficiency is central to the competitiveness of platforms. Online and offline integration has become an inevitable trend in the development of fresh produce retail, and Fan et al. [35] study three kinds of channel integrations in the dual-channel supply chain. In an online-to-offline fresh produce supply chain composed of e-commerce enterprises and community retail stores, Tian et al. [36] construct a game model under a wholesale pattern and a commission pattern and analyze the problem of the sales mode selection and coordination of the O2O fresh supply chain considering the freshness-keeping effort and value-added service. Other researchers such as Li et al. [37] and Zhou et al. [38] focus on the BOPS sales model and investigate the optimal pricing and freshness-keeping effort policies, respectively. Typically, the freshness-keeping effort is extremely costly. These new retailing methods, involving O2O and BOPS, save logistical costs for a fresh produce e-commerce platform and reduce the loss of fresh produce during long-distance transportation. There are many other researchers that show that third-party logistics (TPL) is significant for improving the circulation efficiency of fresh produce supply chain. Ye et al. [39] and Cai et al. [40] consider that the presence of the TPL provider in fresh produce supply chain has a significant impact on its performance and derive the optimal cold-chain service and pricing decisions in the long-distance and long-time cross-border journey of fresh produce. In addition to integration with offline markets and professional logistics providers, a variety of business models are available to fresh produce e-commerce platforms, including open, self-operated, and integrated models. Although the involvement of TPL service providers is of great practical importance for the efficiency of fresh produce supply chain circulation, Feng et al. [41] demonstrate that it also increases the complexity of supply chain contract. Each member of the supply chain makes decisions to maximize its own interests, which affects the profit distribution under different power structures. Wang et al. [42] argue that TPL providers may be incentivized to violate contracts, thereby generating moral risks, owing to information asymmetry between the two parties in the supply chain. To account for this, Wu [11] constructs a three-level game model to study the influence of power structure on the optimal decisions of agricultural produce supply chain members. Wu et al. [43] characterize the role of a TPL service provider in a fresh produce supply chain and design three contracts to examine the influence of power structure on contract designing, optimal decision-making, and supply chain performance, which depend on the service provider’s extent of freshness-keeping effort. In e-commerce platform development, some larger players exploit their advantages in capital and logistics to seize market share. One example is JD.com, which becomes a classic case of e-commerce-logistics integration [44]. Wei [45] summarizes that the existing logistical models of typical e-commerce enterprises engage in fresh produce sales and classifies them as “self-operated logistics,” “self-operated logistics + TPL,” and “self-operated logistics + consumer pickup/self-operated distribution.” The scope of application of these three logistical models is also discussed. He et al. [46] consider that logistics sharing is one of the most popular collaborative strategies, and they build and solve two hoteling models to examine the impacts of logistics sharing on the optimal prices and profits of two companies. At present, there are a variety of fresh e-commerce platforms with self-operated logistics in the market, such as the “MISSFRESH” of self-built distribution warehouse and “JD fresh” based on the traditional e-commerce platform. Self-operated logistics of e-commerce enterprises can effectively control logistics activities and better control marketing activities. The market power of such fresh e-commerce platform on supply chain is increasing. However, there are few articles studying the effect of integrated e-commerce platform on supply chain and less in fresh produce supply chain. These models fail to reflect the distinct characteristics of the fresh produce market and the core necessity of improving cold-chain logistics for fresh produce e-commerce platforms. Based on the previous studies, this study considers the situation of fresh produce e-commerce platforms providing cold-chain logistics services to suppliers and explores the optimal decisions under the dominance of different players with the aim of promoting deep cooperation between e-commerce platforms and suppliers, thereby optimizing the supply chain.


### 2.2. Effect of Online Comments on Consumers’ Purchase Decisions and Supply Chains

Online comments have received increasing academic attention as a major source of empirical information that can significantly reduce uncertainty in online consumers’ perceptions of goods. There are two kinds of literature studies based on online reviews. One is to study the role of online review volume. For example, Maslowska et al. [47] and Bao and Chau [48] both utilize empirical analysis to study the relationship between sales and the quantity of online reviews, and Guo et al. [49] establish a review-related pricing model on the basis of utility theory to capture and measure the relationship between price and product review. The other is based on text mining, which includes external features (such as text length and text timeliness) and deep mining of contents (such as text quality, text comparability, and affective tendencies). For example, Zhang et al. [50] review the three directions of online review sentiment analysis: subjectivity detection, sentiment classification, and the effects of online reviews on product sales. Li and Hitt [51] construct a theoretical model under the existence of self-selection bias and empirically test how idiosyncratic preferences of early buyers affect long-term consumer purchasing behavior using online book reviews posted on Amazon.com. You et al. [52] construct a two-path integration model to study the impact of review contents and reviewer characteristics on the usefulness of online reviews and explore the moderate effects of commodity types. Li et al. [53] point out that the number and length of comments, the prices of the products, and the volume of sales strongly affect consumers’ willingness to buy from an online channel. Conducting research using the convolutional neural network text mining based on the online reviews of fresh products on JD.com, Hong et al. [54] show that logistics reliability, convenience, timeliness, and other factors had a significant impact on customer satisfaction. Li et al. [55] quantify online product reviews of garments, extract the elements of goods and services, and study the factors affecting customer satisfaction. Mudambi and Schuff [56] develop and test a model of customer review helpfulness to analyze the helpfulness of review extremity, review depth, and product type. The literature studies above only investigate the effect on consumers’ decisions.Moreover, online comments not only assist consumers to make effective decisions but also guide companies to make strategic adjustments, thus improving market competitiveness. Wu et al. [57] show that consumers display various learning behaviors when reading comments and that learning behaviors in the context of quality-based comments can significantly influence marketing strategies in the restaurant industry. Some researchers investigate the effect of affective tendencies on pricing and sales. Chen and Xie [58] and Pekgün et al. [59] discuss the effect of positive and negative comments on consumer decisions and construct a dynamic pricing game model for a dual oligopolistic retailer. They find that negative comments will increase the price of low-quality products under certain conditions and can even make low-quality merchants more profitable than high-quality merchants. Guo et al. [60] find that the applause rate tells the reviewers’ overall emotional tendency. Positive reviews will increase consumers’ expectation of product value, while negative reviews will reduce the expectation. Crapis et al. [61] study the effect of online comments on the dynamic pricing strategy of a monopoly from the perspective of unilateral learning. Their numerical experiments show that a pricing strategy that takes into account social learning via online comments results in a substantially higher revenue than a strategy that does not. To address the issues of low-value density and fragmentation of online comments, Zhu et al. [62] propose a method for constructing a topic map of online comments to help consumers grasp product information and make rational decisions. The method also helps enterprises obtain more important produce features, so they can improve their produce, service quality, and sales strategies. Ta and Fu [63] use the panel data fixed-effects model to analyze the impact of online reviews on hospitals’ service volume. The influence of consumer perceptions and online comments varies by produce type. As can be seen from the aforementioned literature, research on online consumer comments has focused on industries such as restaurants, travel, and software development.In contrast to other produce types, fresh produce has the inherent characteristic of perishability, which makes timeliness key, and directly affects consumers’ personal safety. Thus, buyers of fresh produce typically put considerable thought into purchase decisions. Ma and Li [64] and Wang and Ni [65], in studies based on data collection and processing, find that the key factors influencing consumers’ purchase decisions include quality, logistical service, and price. Online comments, which reflect the factors that consumers are most concerned about, also have a significant impact on sellers. Li et al. [66] consider the situation that a retailer encounters a transportation disruption, taking into account the demand learning effect facilitated from negative word-of-mouth during disruption, and develop a model to maximize the retailer’s profit by identifying joint decision of the rational period and freshness-keeping effort. Hu and Lin [67] argue that online comments affect sales on e-commerce platforms and build a theoretical model to verify this effect, using fresh lamb products on e-commerce platforms as the research object. The above studies mainly focus on the textual content and influence of online comments on consumers’ decisions from the perspective of empirical and case studies and do not deeply explore and quantify the influence of online comments on e-commerce supply chain enterprises in the fresh produce market. Therefore, our research explores how online comments affect the operating decisions of e-commerce supply chain members based on the game model in this context.Table1provides a summary of related literature. In the aforementioned literature, no model is proposed to consider pricing, freshness-keeping effort, market power, and online comments simultaneously.Table 1
Summary of the related literature.
AuthorsMain focusLogistics providerChannel powerChannelSun et al. [12]Pricing, orderingOfflineYang et al. [13], Cao et al. [24]Pricing✓OfflineWen et al. [14]PricingSupplier✓OfflineHerbon and Khmelnitsky [15], Hou et al. [16], Bai and Kendall [19], Qin et al. [20]Pricing, inventoryOfflineWang et al. [21]Freshness-keeping effort, inventoryDistribution centerOfflineYe et al. [39], Feng et al. [41], Wu et al. [43]Pricing, logistics serviceThe TPL✓OfflineCai et al. [40]Pricing, freshness-keeping effortThe TPLOfflineLuo et al. [25]Pricing, orderingOnlineGu et al. [26]Pricing, freshness-keeping effort✓OnlineLiu et al. [27]Freshness-keeping effort, value-added serviceSupplier✓OnlineHe et al. [31], Wang and Sun [33]PricingOnline and offlineTang and Peng [10], Liang and He [29], Gu et al. [30]Pricing✓Online and offlineCao et al. [28]Freshness-keeping effortSupplier✓Online and offlineFan et al. [35]PricingRetailer or the TPLO2OTian et al. [36]Pricing, freshness-keeping effort✓O2OLi et al. [37]PricingBOPSZhou et al. [38]Freshness-keeping effortSupplier✓BOPSAuthorsResearch areasMethodsMaslowska et al. [47], Bao and Chau [48], Li and Hitt [51], You et al. [52], Li et al. [53], Hong et al. [54]General products such as clothes and digital productsEmpirical analysisMudambi and Schuff [56]MP3 players, music CDs, etc.Tobit regressionWu et al. [57]Catering industryEmpirical analysisTa and Fu [63]Digital health careFixed-effects modelZhu et al. [62]General productsStatistical analysisChen and Xie [58], Pekgün et al. [59]General productsGame theoryGuo et al. [49]General productsUtility theoryGuo et al. [60]General productsUtility theory and game theoryHong et al. [54]Logistics service of fresh productsConvolutional neural network text miningMa and Li [64]Fresh productsTF-IDF methodLi et al. [66]Agricultural productsLDA modelHu and Lin [67]Mutton productsOLS method with robust standard errorIn summary, this study constructs a two-stage game model consisting of a fresh produce supplier and a fresh produce e-commerce platform with self-support cold-chain logistics and explores the impact of online comments on the decision-making of supply chain members under the dominance of different players.


## 2.1. Fresh Produce Supply Chain Management

The supply chain management literature on fresh produce is particularly rich. Most of the previous research studies about the optimization of the fresh produce supply chain focus on pricing, freshness-keeping effort, and interactions between offline and online.Pricing and freshness-keeping effort have been major topics in offline channel of fresh produce supply chains and are often decided jointly with ordering and inventory. For example, Sun et al. [12] examine optimal pricing and ordering decisions of offline channel. Yang et al. [13] and Wen et al. [14] both analyze the optimal pricing policies for offline channel, and they all take the supplier as the leader in the Stackelberg game. Herbon and Khmelnitsky [15] and Hou et al. [16] focus on inventory management to determine the optimal replenishment schedule and dynamic prices of perishable produce over time, where both price and time affect demand. Huang et al. [17] study the optimal pricing, inventory, and preservations in a three-level food supply chain, in which the supplier is the leader in the Stackelberg game model. He et al. [18] present a literature review on quantitative models in food supply chains from the view of storage, distribution, marketing, food traceability, and safety. Other researchers [19–21] investigate fresh produce inventory management in the context of freshness-keeping and loss control. Chernonog and Avinadav [22] use the decision rights allocation framework to investigate the optimal pricing strategies for perishable products. In addition, many researchers investigate the structure of supply chain, such as Sana [23] and Cao et al. [24], which construct a centralized and decentralized system and investigate the optimal pricing strategies under different leaderships. In the online supply chain context, researchers consider the optimal pricing and freshness-keeping effort policies. Luo et al. [25] investigate optimal pricing and ordering decisions based on the Nash negotiation. Gu et al. [26] establish one centralized model and two decentralized models to investigate the optimal pricing and effort decisions in online channel. Liu et al. [27] consider a fresh produce supply chain consisting of an e-commerce platform and a supplier. The e-commerce platform is responsible for selling fresh produce and providing value-added service, while the supplier is responsible for providing freshness-keeping effort. They also construct a centralized and decentralized model based on the Stackelberg game to analyze the impacts of the elasticity of freshness and the elasticity of service on optimal strategies. However, online and offline optimal pricing decisions are limited to the fixed pricing model, ignoring the dynamic development of the market.The literature about single fresh produce online channel is limited, and there is more attention paid to the interactions between online and offline channels. For example, Tang and Peng [10] and Cao et al. [28] consider the freshness decay characteristics of fresh produce and design a joint contract to achieve coordination between traditional channels and online direct channels. Liang and He [29] regard the retailer as the leader of a Stackelberg game and construct three-channel structures—a single channel, a supplier-operated direct sales channel, and a retailer-operated direct sales channel—for analyzing the impact of different combinations of channel structures on a fresh produce supply chain, but Gu et al. [30] consider the supplier as the leader of the Stackelberg game and propose a revenue- and cost-sharing contract to coordinate the supply chain. He et al. [31] study the online presale model of fresh produce from a competitive perspective and identify two pricing strategies, penetration pricing and skimming pricing for the physical store and two pricing strategies for the online grocery. Taleizadeh et al. [32] investigate the pricing and the importance of carbon emissions in a dual supply chain where a producer uses green production as regulated by the cap and trade system.The above literature mainly studies the pricing, freshness-keeping effort, and coordination of fresh supply chain, and logistics is also an important part of fresh produce supply chain. For the “farmer-supermarket direct purchase” supply chain with an agricultural cooperative and a supermarket, Wang and Sun [33] investigate the optimal channel model for the agricultural cooperative, the supermarket, and the system of supply chain based on customers’ utility. While fresh produce retailers need to promote consumption, a concomitant issue is the online circulation of fresh produce. Generally, the performance of a fresh produce e-commerce platform depends not only on the volume of sales but also on the frequency of after-sales returns, the latter being the major determinant. Indeed, the development of fresh produce e-commerce is plagued by the problem of returns [34], and the improvement of circulation efficiency is central to the competitiveness of platforms. Online and offline integration has become an inevitable trend in the development of fresh produce retail, and Fan et al. [35] study three kinds of channel integrations in the dual-channel supply chain. In an online-to-offline fresh produce supply chain composed of e-commerce enterprises and community retail stores, Tian et al. [36] construct a game model under a wholesale pattern and a commission pattern and analyze the problem of the sales mode selection and coordination of the O2O fresh supply chain considering the freshness-keeping effort and value-added service. Other researchers such as Li et al. [37] and Zhou et al. [38] focus on the BOPS sales model and investigate the optimal pricing and freshness-keeping effort policies, respectively. Typically, the freshness-keeping effort is extremely costly. These new retailing methods, involving O2O and BOPS, save logistical costs for a fresh produce e-commerce platform and reduce the loss of fresh produce during long-distance transportation. There are many other researchers that show that third-party logistics (TPL) is significant for improving the circulation efficiency of fresh produce supply chain. Ye et al. [39] and Cai et al. [40] consider that the presence of the TPL provider in fresh produce supply chain has a significant impact on its performance and derive the optimal cold-chain service and pricing decisions in the long-distance and long-time cross-border journey of fresh produce. In addition to integration with offline markets and professional logistics providers, a variety of business models are available to fresh produce e-commerce platforms, including open, self-operated, and integrated models. Although the involvement of TPL service providers is of great practical importance for the efficiency of fresh produce supply chain circulation, Feng et al. [41] demonstrate that it also increases the complexity of supply chain contract. Each member of the supply chain makes decisions to maximize its own interests, which affects the profit distribution under different power structures. Wang et al. [42] argue that TPL providers may be incentivized to violate contracts, thereby generating moral risks, owing to information asymmetry between the two parties in the supply chain. To account for this, Wu [11] constructs a three-level game model to study the influence of power structure on the optimal decisions of agricultural produce supply chain members. Wu et al. [43] characterize the role of a TPL service provider in a fresh produce supply chain and design three contracts to examine the influence of power structure on contract designing, optimal decision-making, and supply chain performance, which depend on the service provider’s extent of freshness-keeping effort. In e-commerce platform development, some larger players exploit their advantages in capital and logistics to seize market share. One example is JD.com, which becomes a classic case of e-commerce-logistics integration [44]. Wei [45] summarizes that the existing logistical models of typical e-commerce enterprises engage in fresh produce sales and classifies them as “self-operated logistics,” “self-operated logistics + TPL,” and “self-operated logistics + consumer pickup/self-operated distribution.” The scope of application of these three logistical models is also discussed. He et al. [46] consider that logistics sharing is one of the most popular collaborative strategies, and they build and solve two hoteling models to examine the impacts of logistics sharing on the optimal prices and profits of two companies. At present, there are a variety of fresh e-commerce platforms with self-operated logistics in the market, such as the “MISSFRESH” of self-built distribution warehouse and “JD fresh” based on the traditional e-commerce platform. Self-operated logistics of e-commerce enterprises can effectively control logistics activities and better control marketing activities. The market power of such fresh e-commerce platform on supply chain is increasing. However, there are few articles studying the effect of integrated e-commerce platform on supply chain and less in fresh produce supply chain. These models fail to reflect the distinct characteristics of the fresh produce market and the core necessity of improving cold-chain logistics for fresh produce e-commerce platforms. Based on the previous studies, this study considers the situation of fresh produce e-commerce platforms providing cold-chain logistics services to suppliers and explores the optimal decisions under the dominance of different players with the aim of promoting deep cooperation between e-commerce platforms and suppliers, thereby optimizing the supply chain.


## 2.2. Effect of Online Comments on Consumers’ Purchase Decisions and Supply Chains

Online comments have received increasing academic attention as a major source of empirical information that can significantly reduce uncertainty in online consumers’ perceptions of goods. There are two kinds of literature studies based on online reviews. One is to study the role of online review volume. For example, Maslowska et al. [47] and Bao and Chau [48] both utilize empirical analysis to study the relationship between sales and the quantity of online reviews, and Guo et al. [49] establish a review-related pricing model on the basis of utility theory to capture and measure the relationship between price and product review. The other is based on text mining, which includes external features (such as text length and text timeliness) and deep mining of contents (such as text quality, text comparability, and affective tendencies). For example, Zhang et al. [50] review the three directions of online review sentiment analysis: subjectivity detection, sentiment classification, and the effects of online reviews on product sales. Li and Hitt [51] construct a theoretical model under the existence of self-selection bias and empirically test how idiosyncratic preferences of early buyers affect long-term consumer purchasing behavior using online book reviews posted on Amazon.com. You et al. [52] construct a two-path integration model to study the impact of review contents and reviewer characteristics on the usefulness of online reviews and explore the moderate effects of commodity types. Li et al. [53] point out that the number and length of comments, the prices of the products, and the volume of sales strongly affect consumers’ willingness to buy from an online channel. Conducting research using the convolutional neural network text mining based on the online reviews of fresh products on JD.com, Hong et al. [54] show that logistics reliability, convenience, timeliness, and other factors had a significant impact on customer satisfaction. Li et al. [55] quantify online product reviews of garments, extract the elements of goods and services, and study the factors affecting customer satisfaction. Mudambi and Schuff [56] develop and test a model of customer review helpfulness to analyze the helpfulness of review extremity, review depth, and product type. The literature studies above only investigate the effect on consumers’ decisions.Moreover, online comments not only assist consumers to make effective decisions but also guide companies to make strategic adjustments, thus improving market competitiveness. Wu et al. [57] show that consumers display various learning behaviors when reading comments and that learning behaviors in the context of quality-based comments can significantly influence marketing strategies in the restaurant industry. Some researchers investigate the effect of affective tendencies on pricing and sales. Chen and Xie [58] and Pekgün et al. [59] discuss the effect of positive and negative comments on consumer decisions and construct a dynamic pricing game model for a dual oligopolistic retailer. They find that negative comments will increase the price of low-quality products under certain conditions and can even make low-quality merchants more profitable than high-quality merchants. Guo et al. [60] find that the applause rate tells the reviewers’ overall emotional tendency. Positive reviews will increase consumers’ expectation of product value, while negative reviews will reduce the expectation. Crapis et al. [61] study the effect of online comments on the dynamic pricing strategy of a monopoly from the perspective of unilateral learning. Their numerical experiments show that a pricing strategy that takes into account social learning via online comments results in a substantially higher revenue than a strategy that does not. To address the issues of low-value density and fragmentation of online comments, Zhu et al. [62] propose a method for constructing a topic map of online comments to help consumers grasp product information and make rational decisions. The method also helps enterprises obtain more important produce features, so they can improve their produce, service quality, and sales strategies. Ta and Fu [63] use the panel data fixed-effects model to analyze the impact of online reviews on hospitals’ service volume. The influence of consumer perceptions and online comments varies by produce type. As can be seen from the aforementioned literature, research on online consumer comments has focused on industries such as restaurants, travel, and software development.In contrast to other produce types, fresh produce has the inherent characteristic of perishability, which makes timeliness key, and directly affects consumers’ personal safety. Thus, buyers of fresh produce typically put considerable thought into purchase decisions. Ma and Li [64] and Wang and Ni [65], in studies based on data collection and processing, find that the key factors influencing consumers’ purchase decisions include quality, logistical service, and price. Online comments, which reflect the factors that consumers are most concerned about, also have a significant impact on sellers. Li et al. [66] consider the situation that a retailer encounters a transportation disruption, taking into account the demand learning effect facilitated from negative word-of-mouth during disruption, and develop a model to maximize the retailer’s profit by identifying joint decision of the rational period and freshness-keeping effort. Hu and Lin [67] argue that online comments affect sales on e-commerce platforms and build a theoretical model to verify this effect, using fresh lamb products on e-commerce platforms as the research object. The above studies mainly focus on the textual content and influence of online comments on consumers’ decisions from the perspective of empirical and case studies and do not deeply explore and quantify the influence of online comments on e-commerce supply chain enterprises in the fresh produce market. Therefore, our research explores how online comments affect the operating decisions of e-commerce supply chain members based on the game model in this context.Table1provides a summary of related literature. In the aforementioned literature, no model is proposed to consider pricing, freshness-keeping effort, market power, and online comments simultaneously.Table 1
Summary of the related literature.
AuthorsMain focusLogistics providerChannel powerChannelSun et al. [12]Pricing, orderingOfflineYang et al. [13], Cao et al. [24]Pricing✓OfflineWen et al. [14]PricingSupplier✓OfflineHerbon and Khmelnitsky [15], Hou et al. [16], Bai and Kendall [19], Qin et al. [20]Pricing, inventoryOfflineWang et al. [21]Freshness-keeping effort, inventoryDistribution centerOfflineYe et al. [39], Feng et al. [41], Wu et al. [43]Pricing, logistics serviceThe TPL✓OfflineCai et al. [40]Pricing, freshness-keeping effortThe TPLOfflineLuo et al. [25]Pricing, orderingOnlineGu et al. [26]Pricing, freshness-keeping effort✓OnlineLiu et al. [27]Freshness-keeping effort, value-added serviceSupplier✓OnlineHe et al. [31], Wang and Sun [33]PricingOnline and offlineTang and Peng [10], Liang and He [29], Gu et al. [30]Pricing✓Online and offlineCao et al. [28]Freshness-keeping effortSupplier✓Online and offlineFan et al. [35]PricingRetailer or the TPLO2OTian et al. [36]Pricing, freshness-keeping effort✓O2OLi et al. [37]PricingBOPSZhou et al. [38]Freshness-keeping effortSupplier✓BOPSAuthorsResearch areasMethodsMaslowska et al. [47], Bao and Chau [48], Li and Hitt [51], You et al. [52], Li et al. [53], Hong et al. [54]General products such as clothes and digital productsEmpirical analysisMudambi and Schuff [56]MP3 players, music CDs, etc.Tobit regressionWu et al. [57]Catering industryEmpirical analysisTa and Fu [63]Digital health careFixed-effects modelZhu et al. [62]General productsStatistical analysisChen and Xie [58], Pekgün et al. [59]General productsGame theoryGuo et al. [49]General productsUtility theoryGuo et al. [60]General productsUtility theory and game theoryHong et al. [54]Logistics service of fresh productsConvolutional neural network text miningMa and Li [64]Fresh productsTF-IDF methodLi et al. [66]Agricultural productsLDA modelHu and Lin [67]Mutton productsOLS method with robust standard errorIn summary, this study constructs a two-stage game model consisting of a fresh produce supplier and a fresh produce e-commerce platform with self-support cold-chain logistics and explores the impact of online comments on the decision-making of supply chain members under the dominance of different players.


## 3. The Model and Assumptions

### 3.1. Model Description

We consider a fresh produce supply chain consisting of a fresh supplier (hereafter referred to as supplier) and a fresh e-commerce platform (hereafter referred to as platform). As shown in Figure1, the e-channel is operated as an open pattern. In this pattern, the supplier is the actual retailer that sells fresh produce to consumers online at retail price ps through the platform. The platform provides value-added services and cold-chain logistics services. The value-added services include pre-sales and post-sales consulting services, which are influenced greatly by response time. We use parameter t to represent platform’s ability of value-added services; in the meantime, the platform also provides delivery services by self-built cold-chain logistics at the freshness-keeping effort ep. The supplier pays platform at the proportion φ of the sales revenue as commission.Figure 1
Structure of the fresh e-commerce supply chain.In the online shopping process, online comments play a significant role in influencing potential consumers’ perception of the real quality of produce and making purchase decisions. There are many researchers [58, 68, 69] that demonstrated that online comments have a significant leveraging effect on business sales. We take the one kilogram beef brisket pieces in the app of JD.com as an example, and we find that the higher the favorable rate, the higher the retail price. Li and Hitt [70] show that both quality and price have an impact on their decision process when consumers make purchase decisions, but consumer comments now provide a way of resolving the quality uncertainty. Therefore, it is of great significance to explore the relationship between online comments and fresh supply chain decisions.In practice, consumers who have already purchased the fresh produce are willing to share their purchasing experience, which is mainly based on the freshness of fresh produce through an online platform; for those who have not yet purchased, they prefer to comment on various information about the produce before making a purchasing decision to reduce the risk of purchase, including the number of returns and positive and negative comments. At the same time, e-commerce enterprises can also rely on consumers’ online comments to increase produce promotion, form good IWOM, and gain greater market share and sales.To investigate the impact of online comments on supplier’s and platform’s decision-making, two modes will be discussed: (I) without considering online comments, i.e., optimal pricing strategy for supplier and optimal freshness-keeping effort strategy for platform are only decided once before the start of sales, and we call it single-stage model; and (II) considering online comments, i.e., in the first stage, the supplier decides retail priceps1 and platform decides freshness-keeping effort ep at the beginning of sales, respectively. In the meantime, the platform receives online comments from consumers who have bought fresh produce in the first stage. In the second stage, according to online comments that have been received during the first stage, the supplier adjusts the retail price ps1 to ps2. We call this model the two-stage model.We construct the game model by utilizing the Stackelberg game and two-stage repeated game. Based on market power, we consider two game scenarios under each model: supplier-dominated scenario (SS) and platform-dominated scenario (PS).


### 3.2. Assumptions

#### 3.2.1. Demand Function

In the single-stage model, the market demand is affected by the retail priceps and response time t (e.g., Zhang and Xu [71] and Modak and Kelle [72]). Thus, we adopt a linear function to characterize the market demand, which is normalized as follows:(1)Q=a−bps−δt.Thereinto,a represents the market base and b and δ denote the measures of the sensitivity of the market demand to retail price and response time, respectively.In the two-stage model, the demand function in the first stage is the same as in the single-stage model, and we useps1 instead of ps to represent the retail price in this model. In the second stage, the demand function is also affected by online comments besides retail price and response time. The demand function in the two-stage model is shown as follows (to facilitate the description, subscripts i are set in the two-stage model to indicate the first- and second-stage decisions, respectively, i=1,2):(2)Q1=a−bps1−δt,Q2=a−bps2−δt+μθe−eEθ0.Thereinto,μ is the online comment sensitivity. To highlight the impact of freshness on online comments, we only consider online comments made by consumers based on freshness. Therefore, θep−eEθ0 denotes the gap between consumers’ fresh expectation and the actual produce freshness they receive it. Depending on the size of the gap, consumers make positive or negative online comments, which affect the subsequent market demand. Thereinto, θep represents the actual produce freshness that consumers finally receive after cold-chain logistics. θ0 is the initial produce freshness, and eE is the consumers’ freshness expectation. The produce freshness is affected by freshness-keeping effort. Referring to Cai et al. [73], we adopt θep=θ0ep to characterize produce freshness. For brevity, we further assume that θ0=1, which does not affect the optimal decisions. Therefore, the demand function at the second stage can be written as Q2=a−bps2−δt+μep−eE. In addition, to ensure that supplier and platform are profitable, the parameters should satisfy a>δt and a>δt+eEμ; thus, we let A=a−δt>0 and B=a−δt−eEμ>0.


#### 3.2.2. Return Function

Due to the perishable and time-sensitive nature of fresh produce, consumers are generally not allowed to return produce without reason upon receipt, and faulty goods usually do not need to be sent back. Therefore, we assume that the return of online fresh produce is affected by market demand and freshness.In the single-stage model, the return function of online fresh produce is normalized as follows:(3)Qr=αQ−βθep=αa−bps−δt−βep.Thereinto,α represents the sensitivity of the return to market demand and β represents the sensitivity of the return to produce freshness.In the two-stage model, the return function is the same as in the single-stage model and is normalized as follows (to facilitate the description, subscriptsi are set in the two-stage model to indicate the first- and second-stage decisions, respectively, i=1,2):(4)Qr1=αQ1−βθep=αa−bps1−δt−βepQr2=αQ2−βθep=αa−bps2−δt+μep−eE−βep.Considering that the return function in the second stage can be written asQr2=αQ2−βθep=αa−bps2−δt−μeE+αμ−βep, the parameters should satisfy αμ−β<0.


#### 3.2.3. Cost Function

The platform provides the freshness-keeping effort to preserve and deliver fresh produce, and more freshness-keeping effort will lead to higher costs. The freshness-keeping costcep=λep2/2, where λ is the freshness-keeping cost coefficient, and cep has the properties of ∂cep/∂ep>0 and ∂2cep/∂ep2>0, which reflect the diminishing returns of freshness-keeping effort. Several studies used this quadratic cost function to depict cost structure (e.g., Wu et al. [43], Ha et al. [74], and Cao et al. [75]).The platform also provides value-added services, which are measured by response time. The shorter the response time will lead to higher costs, regarding the service costct=L/t2, where L is the response time cost coefficient (e.g., Zhang and Xu [71]).We assume that there are no production costs and logistics costs for brevity.The notations to be used in this study are summarized in Table2.Table 2
List of notations.
DescriptionSubscriptiThei stage under the two-stage model, i=1,2sSupplierpPlatformSuperscript∗The optimal outcomesSSupplier as the leader of gameIIThe two-stage modelPPlatform as the leader of gameIThe single-stage modelIIIThe extended two-stage modelDecision variablesepThe freshness-keeping effort of platform in the two-stage model,0<ep<1psThe retail price of supplier in the single-stage model,ps>0psiThe retail price of supplier in the two-stage model,psi>0ParameterQiThe market demandaThe market base,a>0bThe sensitivity of the market demand to the retail price,b>0δThe sensitivity of the market demand to response time,δ>0LThe cost coefficient of response timeλThe cost coefficient of freshness-keeping effort,λ>0tThe response time of platform,t>0θepThe actual freshness of fresh produceθ0The initial freshness of fresh produceQriThe returncepThe cost of freshness-keeping effortαThe sensitivity of the return to market demand,0<α<1βThe sensitivity of the return to produce freshness,β>0φThe proportion of sales revenue for platform as commission,0<φ<1eEThe consumers’ expected freshness of fresh produce,0<eE<1ctThe cost of response time of platformμThe sensitivity of the market demand to online comments,μ>0rThe ratio of revenue sharing,0<r<1


## 3.1. Model Description

We consider a fresh produce supply chain consisting of a fresh supplier (hereafter referred to as supplier) and a fresh e-commerce platform (hereafter referred to as platform). As shown in Figure1, the e-channel is operated as an open pattern. In this pattern, the supplier is the actual retailer that sells fresh produce to consumers online at retail price ps through the platform. The platform provides value-added services and cold-chain logistics services. The value-added services include pre-sales and post-sales consulting services, which are influenced greatly by response time. We use parameter t to represent platform’s ability of value-added services; in the meantime, the platform also provides delivery services by self-built cold-chain logistics at the freshness-keeping effort ep. The supplier pays platform at the proportion φ of the sales revenue as commission.Figure 1
Structure of the fresh e-commerce supply chain.In the online shopping process, online comments play a significant role in influencing potential consumers’ perception of the real quality of produce and making purchase decisions. There are many researchers [58, 68, 69] that demonstrated that online comments have a significant leveraging effect on business sales. We take the one kilogram beef brisket pieces in the app of JD.com as an example, and we find that the higher the favorable rate, the higher the retail price. Li and Hitt [70] show that both quality and price have an impact on their decision process when consumers make purchase decisions, but consumer comments now provide a way of resolving the quality uncertainty. Therefore, it is of great significance to explore the relationship between online comments and fresh supply chain decisions.In practice, consumers who have already purchased the fresh produce are willing to share their purchasing experience, which is mainly based on the freshness of fresh produce through an online platform; for those who have not yet purchased, they prefer to comment on various information about the produce before making a purchasing decision to reduce the risk of purchase, including the number of returns and positive and negative comments. At the same time, e-commerce enterprises can also rely on consumers’ online comments to increase produce promotion, form good IWOM, and gain greater market share and sales.To investigate the impact of online comments on supplier’s and platform’s decision-making, two modes will be discussed: (I) without considering online comments, i.e., optimal pricing strategy for supplier and optimal freshness-keeping effort strategy for platform are only decided once before the start of sales, and we call it single-stage model; and (II) considering online comments, i.e., in the first stage, the supplier decides retail priceps1 and platform decides freshness-keeping effort ep at the beginning of sales, respectively. In the meantime, the platform receives online comments from consumers who have bought fresh produce in the first stage. In the second stage, according to online comments that have been received during the first stage, the supplier adjusts the retail price ps1 to ps2. We call this model the two-stage model.We construct the game model by utilizing the Stackelberg game and two-stage repeated game. Based on market power, we consider two game scenarios under each model: supplier-dominated scenario (SS) and platform-dominated scenario (PS).


## 3.2. Assumptions

### 3.2.1. Demand Function

In the single-stage model, the market demand is affected by the retail priceps and response time t (e.g., Zhang and Xu [71] and Modak and Kelle [72]). Thus, we adopt a linear function to characterize the market demand, which is normalized as follows:(1)Q=a−bps−δt.Thereinto,a represents the market base and b and δ denote the measures of the sensitivity of the market demand to retail price and response time, respectively.In the two-stage model, the demand function in the first stage is the same as in the single-stage model, and we useps1 instead of ps to represent the retail price in this model. In the second stage, the demand function is also affected by online comments besides retail price and response time. The demand function in the two-stage model is shown as follows (to facilitate the description, subscripts i are set in the two-stage model to indicate the first- and second-stage decisions, respectively, i=1,2):(2)Q1=a−bps1−δt,Q2=a−bps2−δt+μθe−eEθ0.Thereinto,μ is the online comment sensitivity. To highlight the impact of freshness on online comments, we only consider online comments made by consumers based on freshness. Therefore, θep−eEθ0 denotes the gap between consumers’ fresh expectation and the actual produce freshness they receive it. Depending on the size of the gap, consumers make positive or negative online comments, which affect the subsequent market demand. Thereinto, θep represents the actual produce freshness that consumers finally receive after cold-chain logistics. θ0 is the initial produce freshness, and eE is the consumers’ freshness expectation. The produce freshness is affected by freshness-keeping effort. Referring to Cai et al. [73], we adopt θep=θ0ep to characterize produce freshness. For brevity, we further assume that θ0=1, which does not affect the optimal decisions. Therefore, the demand function at the second stage can be written as Q2=a−bps2−δt+μep−eE. In addition, to ensure that supplier and platform are profitable, the parameters should satisfy a>δt and a>δt+eEμ; thus, we let A=a−δt>0 and B=a−δt−eEμ>0.


### 3.2.2. Return Function

Due to the perishable and time-sensitive nature of fresh produce, consumers are generally not allowed to return produce without reason upon receipt, and faulty goods usually do not need to be sent back. Therefore, we assume that the return of online fresh produce is affected by market demand and freshness.In the single-stage model, the return function of online fresh produce is normalized as follows:(3)Qr=αQ−βθep=αa−bps−δt−βep.Thereinto,α represents the sensitivity of the return to market demand and β represents the sensitivity of the return to produce freshness.In the two-stage model, the return function is the same as in the single-stage model and is normalized as follows (to facilitate the description, subscriptsi are set in the two-stage model to indicate the first- and second-stage decisions, respectively, i=1,2):(4)Qr1=αQ1−βθep=αa−bps1−δt−βepQr2=αQ2−βθep=αa−bps2−δt+μep−eE−βep.Considering that the return function in the second stage can be written asQr2=αQ2−βθep=αa−bps2−δt−μeE+αμ−βep, the parameters should satisfy αμ−β<0.


### 3.2.3. Cost Function

The platform provides the freshness-keeping effort to preserve and deliver fresh produce, and more freshness-keeping effort will lead to higher costs. The freshness-keeping costcep=λep2/2, where λ is the freshness-keeping cost coefficient, and cep has the properties of ∂cep/∂ep>0 and ∂2cep/∂ep2>0, which reflect the diminishing returns of freshness-keeping effort. Several studies used this quadratic cost function to depict cost structure (e.g., Wu et al. [43], Ha et al. [74], and Cao et al. [75]).The platform also provides value-added services, which are measured by response time. The shorter the response time will lead to higher costs, regarding the service costct=L/t2, where L is the response time cost coefficient (e.g., Zhang and Xu [71]).We assume that there are no production costs and logistics costs for brevity.The notations to be used in this study are summarized in Table2.Table 2
List of notations.
DescriptionSubscriptiThei stage under the two-stage model, i=1,2sSupplierpPlatformSuperscript∗The optimal outcomesSSupplier as the leader of gameIIThe two-stage modelPPlatform as the leader of gameIThe single-stage modelIIIThe extended two-stage modelDecision variablesepThe freshness-keeping effort of platform in the two-stage model,0<ep<1psThe retail price of supplier in the single-stage model,ps>0psiThe retail price of supplier in the two-stage model,psi>0ParameterQiThe market demandaThe market base,a>0bThe sensitivity of the market demand to the retail price,b>0δThe sensitivity of the market demand to response time,δ>0LThe cost coefficient of response timeλThe cost coefficient of freshness-keeping effort,λ>0tThe response time of platform,t>0θepThe actual freshness of fresh produceθ0The initial freshness of fresh produceQriThe returncepThe cost of freshness-keeping effortαThe sensitivity of the return to market demand,0<α<1βThe sensitivity of the return to produce freshness,β>0φThe proportion of sales revenue for platform as commission,0<φ<1eEThe consumers’ expected freshness of fresh produce,0<eE<1ctThe cost of response time of platformμThe sensitivity of the market demand to online comments,μ>0rThe ratio of revenue sharing,0<r<1


## 3.2.1. Demand Function

In the single-stage model, the market demand is affected by the retail priceps and response time t (e.g., Zhang and Xu [71] and Modak and Kelle [72]). Thus, we adopt a linear function to characterize the market demand, which is normalized as follows:(1)Q=a−bps−δt.Thereinto,a represents the market base and b and δ denote the measures of the sensitivity of the market demand to retail price and response time, respectively.In the two-stage model, the demand function in the first stage is the same as in the single-stage model, and we useps1 instead of ps to represent the retail price in this model. In the second stage, the demand function is also affected by online comments besides retail price and response time. The demand function in the two-stage model is shown as follows (to facilitate the description, subscripts i are set in the two-stage model to indicate the first- and second-stage decisions, respectively, i=1,2):(2)Q1=a−bps1−δt,Q2=a−bps2−δt+μθe−eEθ0.Thereinto,μ is the online comment sensitivity. To highlight the impact of freshness on online comments, we only consider online comments made by consumers based on freshness. Therefore, θep−eEθ0 denotes the gap between consumers’ fresh expectation and the actual produce freshness they receive it. Depending on the size of the gap, consumers make positive or negative online comments, which affect the subsequent market demand. Thereinto, θep represents the actual produce freshness that consumers finally receive after cold-chain logistics. θ0 is the initial produce freshness, and eE is the consumers’ freshness expectation. The produce freshness is affected by freshness-keeping effort. Referring to Cai et al. [73], we adopt θep=θ0ep to characterize produce freshness. For brevity, we further assume that θ0=1, which does not affect the optimal decisions. Therefore, the demand function at the second stage can be written as Q2=a−bps2−δt+μep−eE. In addition, to ensure that supplier and platform are profitable, the parameters should satisfy a>δt and a>δt+eEμ; thus, we let A=a−δt>0 and B=a−δt−eEμ>0.


## 3.2.2. Return Function

Due to the perishable and time-sensitive nature of fresh produce, consumers are generally not allowed to return produce without reason upon receipt, and faulty goods usually do not need to be sent back. Therefore, we assume that the return of online fresh produce is affected by market demand and freshness.In the single-stage model, the return function of online fresh produce is normalized as follows:(3)Qr=αQ−βθep=αa−bps−δt−βep.Thereinto,α represents the sensitivity of the return to market demand and β represents the sensitivity of the return to produce freshness.In the two-stage model, the return function is the same as in the single-stage model and is normalized as follows (to facilitate the description, subscriptsi are set in the two-stage model to indicate the first- and second-stage decisions, respectively, i=1,2):(4)Qr1=αQ1−βθep=αa−bps1−δt−βepQr2=αQ2−βθep=αa−bps2−δt+μep−eE−βep.Considering that the return function in the second stage can be written asQr2=αQ2−βθep=αa−bps2−δt−μeE+αμ−βep, the parameters should satisfy αμ−β<0.


## 3.2.3. Cost Function

The platform provides the freshness-keeping effort to preserve and deliver fresh produce, and more freshness-keeping effort will lead to higher costs. The freshness-keeping costcep=λep2/2, where λ is the freshness-keeping cost coefficient, and cep has the properties of ∂cep/∂ep>0 and ∂2cep/∂ep2>0, which reflect the diminishing returns of freshness-keeping effort. Several studies used this quadratic cost function to depict cost structure (e.g., Wu et al. [43], Ha et al. [74], and Cao et al. [75]).The platform also provides value-added services, which are measured by response time. The shorter the response time will lead to higher costs, regarding the service costct=L/t2, where L is the response time cost coefficient (e.g., Zhang and Xu [71]).We assume that there are no production costs and logistics costs for brevity.The notations to be used in this study are summarized in Table2.Table 2
List of notations.
DescriptionSubscriptiThei stage under the two-stage model, i=1,2sSupplierpPlatformSuperscript∗The optimal outcomesSSupplier as the leader of gameIIThe two-stage modelPPlatform as the leader of gameIThe single-stage modelIIIThe extended two-stage modelDecision variablesepThe freshness-keeping effort of platform in the two-stage model,0<ep<1psThe retail price of supplier in the single-stage model,ps>0psiThe retail price of supplier in the two-stage model,psi>0ParameterQiThe market demandaThe market base,a>0bThe sensitivity of the market demand to the retail price,b>0δThe sensitivity of the market demand to response time,δ>0LThe cost coefficient of response timeλThe cost coefficient of freshness-keeping effort,λ>0tThe response time of platform,t>0θepThe actual freshness of fresh produceθ0The initial freshness of fresh produceQriThe returncepThe cost of freshness-keeping effortαThe sensitivity of the return to market demand,0<α<1βThe sensitivity of the return to produce freshness,β>0φThe proportion of sales revenue for platform as commission,0<φ<1eEThe consumers’ expected freshness of fresh produce,0<eE<1ctThe cost of response time of platformμThe sensitivity of the market demand to online comments,μ>0rThe ratio of revenue sharing,0<r<1


## 4. Equilibrium Analysis

### 4.1. Single-Stage Model

As a benchmark, this section performs a single-stage decision without considering online comments (we call a single-stage model for simplicity and denote it as superscript I). The optimal retail price of supplier and the optimal fresh-keeping effort of platform are decided only once; the Stackelberg game model is constructed in SS and PS, respectively. The objective functions of supplier and platform are as follows:(5)∏sI=1−φpsQ−psrQr=ps1−φ−rαa−bps−δt+rβepps,(6)∏pI=φpsQ−cep−ct=φpsa−bps−δt−λep22−Lt2.To ensure that the supplier yields a positive profit, we assumeC=1−φ−rα>0.Utilizing backward induction, we calculate the optimal retail price and freshness-keeping effort strategies for SS and PS under the single-stage model, respectively.


#### 4.1.1. SS under the Single-Stage Model

In this part, we investigate the optimal retail price and freshness-keeping effort strategies where the supplier (he) is the leader and the platform (she) is the follower in the Stackelberg game under the single-stage model. The supplier’s objective is to maximize his profit by setting his optimal retail priceps, and then, the platform decides the freshness-keeping effort ep to maximize her profit after observing the supplier’s decision. Based on (5) and (6), we derive the optimal solutions, which are given in Theorem 1.Theorem 1.
epSI∗=0, psSI∗=A/2b, ∏sSI∗=A2C/4b, and ∏pSI∗=φt2A2−4bL/4bt2.Proof.
Proof of Theorem1 is available in Appendix.
Theorem1 means that platform is not willing to invest freshness-keeping effort in SS under the single-stage model. Note that ∂psSI∗/∂t<0, and we derive that the longer the response time of platform, the lower retail price supplier will set to attract consumers and reduce demand loss.


#### 4.1.2. PS under the Single-Stage Model

When the platform performs an act as a leader in the Stackelberg game, she decides the freshness-keeping effort first to maximize her profit, and then, the supplier’s objective is to maximize his profit by setting his optimal retail priceps after observing the platform’s decision. Based on (5) and (6), similar to the analysis in Section 4.1.1, we can derive the optimal solutions in PS as follows.Theorem 2.
epPI∗=0, psPI∗=A/2b, ∏sPI∗=A2C/4b, and ∏pPI∗=φt2A2−4bL/4bt2.Proof.
Proof of Theorem2 is available in Appendix.
Theorem2 states that the platform still has no incentive to input freshness-keeping effort. It is easy to verify that ∂psPI∗/∂t<0, which means that the supplier always decreases the retail price when the response time becomes longer.


### 4.2. Two-Stage Model

In this section, we consider the influence of online comments on the optimal decision of fresh e-commerce supply chain and construct a two-stage repeated game model (which we call a two-stage model for simplicity and denote it as superscript II). The decision sequence can be divided into two stages: in the first stage, the supplier’s and platform’s objectives are to maximize their profits ((7) and (8)) by setting an optimal retail price and an optimal freshness-keeping effort. In the second stage, in view of the online comments received in the first stage, the supplier determines the retail price to maximize the second-stage profit (9). At each stage, the supplier and the platform act as the leader and the follower of the Stackelberg game, respectively. Since the freshness-keeping cost mainly includes the expenses of renting refrigerated equipment, it cannot be easily changed within a short time once they are invested. Therefore, the platform only decides the optimal freshness-keeping effort once at the beginning of sale with the goal of maximizing the total profit. The same as in the single-stage model, we investigate the optimal decisions in SS and PS under the two-stage model, respectively. The objective functions of supplier and platform at each stage are as follows.The profit function of supplier and platform in the first stage is as follows:(7)∏s1II=1−φps1Q1−ps1rQr1+∏s2II∗=ps11−φ−rαa−bps1−δt+rβepps1+∏s2II∗,(8)∏p1II=φps1Q1−cep−ct+∏p2II∗=φps1a−bps1−δt−λep22−Lt2+∏p2II∗.The profit function of supplier and platform in the second stage is as follows:(9)∏s2II=1−φps2Q2−ps2rQr2=ps21−φ−rαa−bps2−δt+μep−eE+rβepps2,(10)∏p2II=φps2Q2−cep−ct=φps2a−bps2−δt+μep−eE−λep22−Lt2.


#### 4.2.1. SS under the Two-Stage Model

In this model, based on the Stackelberg game and two-stage repeated game, the objective of both supplier and platform in the first stage is to maximize their total profit. In the first stage, the supplier first sets the retail priceps1, and then, the platform sets the freshness-keeping effort ep. After that, the supplier decides the retail price ps2 in the second stage. The sequence of events is shown in Figure 2.Figure 2
Sequence of decisions in SS under the two-stage model.Given equations (7) to (10), we can derive that there exists the unique optimal strategy for supplier and platform utilizing backward induction, which are given in Theorem 3.Theorem 3.
(11)epSII∗=φμBC2D,ps1SII∗=AD+φrβμBC2bD,ps2SII∗=B4bλC2+φr2β2+φrβμC2bD,∏sSII∗=Cφ2r2β2μ2B2C2+2φrβμABCD+A2D2+B24bλC2+φrβμC+rβ24bD2,∏pSII∗=φt2B2D4bλC2+φr2β2−φt2φ2r2β2μ2B2C2−A2D2−8bLD24bt2D2.Proof.
Proof of Theorem3 is available in Appendix.
We can summarize the character of supplier’s and platform’s optimal strategies, which are given in Corollaries1 and 2.Corollary 1.
If0<μ<μ1¯, then ps1SII∗>ps2SII∗; otherwise, ps1II∗<ps2II∗.
Here,μ1¯=eE4bλC2+φr2β2/φAC2.Proof.
Proof of Corollary1 is available in Appendix.
Corollary1 indicates that the supplier will take a price-cutting strategy when online comment sensitivity is relatively low. If the online comment sensitivity is high, the retail price in the second stage will be higher than that in the first stage. It can be seen that the higher the online comment sensitivity, the more attention that consumers pay to freshness of fresh produce. Namely, freshness has a significant impact on demand. Therefore, the platform will strive to invest more freshness-keeping efforts to improve produce quality, and the supplier will increase retail prices accordingly.Corollary 2.
(a)
If0<eE<mineE1¯,1, then ∂epSII∗/∂μ>0 and ∂ps1SII∗/∂μ>0; if mineE1¯,1<eE<1, then ∂epSII∗/∂μ<0 and ∂ps1SII∗/∂μ<0. If 0<eE<mineE2¯,1, then ∂ps2SII∗/∂μ>0; otherwise, ∂ps2SII∗/∂μ<0.(b)
∂epSII∗/∂t<0, ∂ps1SII∗/∂t<0, and ∂ps2SII∗/∂t<0.Here, eE1¯=A4bλC2+φr2β2+φμ2C2/2μ4bλC2+φr2β2 and eE2¯=φrβμCD+2φμ2C+D4bλC2+φr2β2+φrβμ/φACrβD+2μ.Proof.
Proof of Corollary2 is available in Appendix.
Corollary2(a) shows that there is a threshold of consumers’ freshness expectation in SS under the two-stage model. Below this threshold, the optimal decisions of supplier and platform will both increase with the increase in the online comment sensitivity; above this threshold, the optimal decisions decrease with the increase in the online comment sensitivity. This can be explained that consumers tend to make positive online comments within the acceptable range of freshness expectations.
Therefore, when consumers’ freshness expectations are within a certain range, the platform has an incentive to increase freshness-keeping efforts to stimulate demand. Therefore, the optimal freshness-keeping effort and retail prices are also increased with the increase in online comment sensitivity. On the contrary, when consumers expect too much freshness, the benefits from increasing freshness-keeping efforts by platform do not compensate for the freshness-keeping costs. In this case, even if consumers are very sensitive to online comments, the platform is not willing to increase freshness-keeping effort, but rather to make supplier cut retail prices and implement a “low quality, low price” strategy.
Corollary2(b) states that the response time has an impact on supplier’s and platform’s optimal decisions. This is mainly because consumers’ purchasing decisions are influenced by the response time to some extent. If the platform cannot provide services to consumers in time, the consumers will leave. Therefore, the supplier will decrease the retail price to make up for the reduced demand caused by long response time of the platform. The decline in retail price means that the profit sharing of the platform is also reduced. To maintain profit margin, the platform will invest relatively less in freshness-keeping efforts.


#### 4.2.2. PS under the Two-Stage Model

In this model, based on the Stackelberg game and two-stage repeated game, each participant aims to maximize their total profits of two stages in the first stage. The platform first makes a decision on freshness-keeping effortep, and supplier sets retail price ps1 in the first stage after observing the decision of platform. In the second stage, the supplier decides the retail price ps2 according to the online comments in the first stage. The sequence of events is shown in Figure 3.Figure 3
Sequence of decisions in PS under the two-stage model.Given equations (7) to (10), utilizing backward induction, we can derive that there exist the optimal decisions of the supplier and the platform in PS under the two-stage model, which are given in Theorem 4.Theorem 4.
(12)epPII∗=φμBC2D+φr2β2,ps1PII∗=AD+φr2β2+φrβμBC2bD+φr2β2,ps2PII∗=B4bλC2+2φr2β2+φrβμC2bD+φr2β2,∏sPII∗=CφrβμBC+AD+φr2β22+B24bλC2+2φr2β2+φrβμC24bD+φr2β22,∏pPII∗=φt2A2−8bLD+φr2β2+2φt2B24bλC2+φr2β24bt2D+φr2β2.Proof.
Proof of Theorem4 is available in Appendix.
We can also capture the characteristics of the optimal retail prices and freshness-keeping effort, and the result is summarized in Corollary3 and Corollary 4.Corollary 3.
If0<μ<μ2¯, then ps1PII∗>ps2PII∗; otherwise, ps1PII∗<ps2PII∗.Here, μ2¯=2eE2bλC2+φr2β2/φAC2.Proof.
Proof of Corollary3 is available in Appendix.
Corollary3 indicates that when online comment sensitivity is low, the supplier will decrease the retail price in the second stage (and vice versa), which is basically consistent with the optimal decision in SS. This is mainly because consumers are less interested in produce freshness when online comment sensitivity is low. In this case, the positive effect of freshness-keeping effort improvement cannot make up for the negative effect of cost increase. Therefore, the influence of freshness-keeping effort increase is worse than the price reduction in market demand. So, the supplier will adopt a price reduction strategy as priority. However, when the online comment sensitivity is high, consumers are more sensitive to produce freshness than retail price, and the improvement of freshness-keeping effort is significant. As a result, with the improvement of produce freshness, the retail price will also increase.Corollary 4.
(a)
If0<eE<mineE3¯,1, then ∂epPII∗/∂μ>0 and ∂ps1PII∗/∂μ>0; if mineE3¯,1<eE<1, then ∂epPII∗/∂μ<0 and ∂ps1PII∗/∂μ<0. If 0<eE<mineE4¯,1, then ∂ps2PII∗/∂μ>0; if mineE4¯,1<eE<1, then ∂ps2PII∗/∂μ<0.(b)
∂epPII∗/∂t<0, ∂ps1PII∗/∂t<0, and ∂ps2PII∗/∂t<0.Here, eE3¯=A4bλC2+φμ2C2+2φr2β2/4μ2bλC2+φr2β2 and eE4¯=φACrβ+2μC4bλC2+2φr2β2+φrβμ2C2/4bλC2+2φr2β22+φμCμC+2rβ4bλC2+2φr2β2.Proof.
Proof of Corollary4 is available in Appendix.
Similar to Corollary2(a), Corollary 4(a) shows that there is a threshold of consumer freshness expectation in PS under the two-stage model. Below this threshold, both optimal freshness-keeping effort and retail price increase as online comment sensitivity rises; above this threshold, the optimal decision of fresh produce e-commerce firms decreases with the increase in online review sensitivity.
Generally, consumers will make positive online comments when the actual freshness of produce they receive is within the acceptable range, and the positive online comments will increase market demand. Thereby, when consumers’ freshness expectations are low, the higher the online comment sensitivity, the more significant the increasing freshness-keeping effort. Accordingly, the supplier will also raise retail prices to capture more profits. In this case, the strategy of “high quality, high price” is beneficial for both supplier and platform. On the contrary, when consumers’ freshness expectations are high, it is uneconomical for platform to increase freshness-keeping efforts with the increase in online comment sensitivity. To avoid excessive demand loss, the supplier correspondingly has to reduce retail price; for example,, the strategy of "low quality, low price" is suitable to the circumstance.
Corollary4(b) reveals the correlation between optimal decisions and response time: the longer the response time, the lower the optimal freshness-keeping effort and retail price. The reason is that long response time will lead to demand loss, and supplier should mitigate the demand loss by reducing retail price, while the decrease in retail price means a decrease in profits. To maintain the profit margin, the platform will reduce freshness-keeping effort accordingly. Therefore, the platform needs to improve the service capability and shorten response time. Otherwise, it will result in demand loss and have a cascading negative impact on retail price and freshness-keeping effort.


### 4.3. Comparison of the Optimal Decisions

To analyze the effect of different market power and different models on optimal results, we aggregate the equilibrium results of different fresh e-commerce supply chain models and get some managerial opinions.We first make a comparison of the optimal decisions in the supplier-dominated scenario, namely SS, between single-stage model and two-stage model. The results are shown in Proposition1.Proposition 1.
In SS,epSI∗<epSII∗ and psSI∗<ps1SII∗; if 0<eE<mineE5¯,1, then psSI∗<ps2SII∗, and if mineE5¯,1<eE<1, then psSI∗>ps2SII∗; and ΠsSI∗<ΠsSII∗; if response cost 0<L/t2<c1t¯, then ΠpSI∗<ΠpSII∗, and if L/t2>c1t¯, then ΠpSI∗>ΠpSII∗; and the total profit of the fresh e-commerce supply chain always exists in the relationship that ∏SI∗<∏SII∗.Here, eE5¯=φACrβ+μC/4bλC2+φr2β2+φrβμC and c1t¯=φB2D4bλC2+φr2β2−φ2r2β2μ2C2/4bD2.Accordingly, we can draw these observations as follows.Firstly, the optimal freshness-keeping effort under the single-stage model is lower than the optimal outcome under the two-stage model; the optimal retail price under the single-stage model is lower than that in the first stage under the two-stage model. What is more, when consumers’ freshness expectation is low, the optimal retail price under single-stage model is lower than that in the second stage under the two-stage model and vice versa. This is because the effect of restrain demand loss by increasing freshness-keeping effort is poor, when consumers’ freshness expectation is low. This can also be seen from the result of the previous section. Therefore, the supplier is more inclined to mitigate demand loss by decreasing prices; thus, the retail price in the second stage under the two-stage model is relatively higher than that under the single-stage model.Secondly, the optimal profit of supplier under single-stage model is lower than that under the two-stage model. That is, the two-stage model is more favorable to supplier. In the meantime, when the response cost of platform is lower than a certain threshold, the optimal profit of platform under the single-stage model is lower than that under the two-stage model. Above this threshold, the optimal profit of platform under the single-stage model is higher than that in the two-stage model. In other words, when the response cost of the platform is controlled within a certain range, the two-stage model is beneficial to platform; otherwise, the single-stage model is better.Finally, the total profits of fresh e-commerce supply chain under the two-stage model are higher than that in the single-stage model in SS. On the whole, the two-stage model is better than the single-stage model in SS for fresh e-commerce supply chain.Then, we turn to investigate the comparison of the optimal decisions in the platform-dominated scenario, namely PS, between single-stage model and two-stage model. The results are shown in Proposition2.Proposition 2.
In PS,epPI∗<epPII∗, psPI∗<ps1PII∗, and ΠsPI∗<ΠsII∗; if 0<eE<mineE6¯,1, then psPII∗<ps2PII∗, and if mineE6¯,1<eE<1, then psPI∗>ps2PII∗; if the response cost of platform L/t2<c2t¯, then ΠpPI∗<ΠpPII∗, and if L/t2>c2t¯, then ΠpPI∗>ΠpPII∗; and the total profit of the fresh e-commerce supply chain always exists in the relationship that ∏PI∗<∏PII∗.Here, eE6¯=φACrβ+μC/94bλC2+2φr2β2+φrβμC and c2t¯=φB24bλC2+φr2β2/2bD+φr2β2.Similar to Proposition1, we can draw these observations as follows.Firstly, we can see that, under the single-stage model, the optimal retail price and freshness-keeping effort are lower than those in the first stage under the two-stage model. When consumer freshness expectation is low, the optimal retail price under the single-stage model is lower than that in the second stage under the two-stage model; when consumer freshness expectation exceeds a certain threshold, the result is opposite. This is because the low freshness expectation weakens the positive effect of freshness improvement, and thus, the supplier tends to decrease retail price to mitigate demand loss. In the two-stage model, to attract more demand, the supplier and platform will set higher freshness-keeping effort and retail price in the first stage. To maintain reputation and profit margin, the retail price in the second stage cannot be too low, and thus, the optimal retail price in the single-stage model is necessarily lower than that in the two-stage model.Secondly, the supplier can gain more profits in the two-stage model; for example, the two-stage model is more favorable to the supplier. When the response cost of platform is lower than a certain threshold, the optimal profit of platform under the single-stage model is lower than that in the two-stage model; when the response cost of platform is higher than this threshold, the result is opposite. That is, when the response cost is controlled within a certain range, the two-stage model is beneficial to platform; otherwise, the single-stage model is better.Finally, in PS, the total profit of the supply chain under the two-stage model is better than that under the single-stage model. Therefore, from an overall perspective, the two-stage model in PS is superior to the single-stage model.Then, we discuss the comparison of the optimal decisions between SS and PS under the single-stage model and two-stage model, respectively. The results are shown in Proposition3.Proposition 3.
Under the single-stage model,epSI∗=epPI∗, psSI∗=psPI∗, ∏sSI∗=∏sPI∗, and ∏pSI∗=∏pPI∗. In other words, the change in leader will not affect the optimal freshness-keeping effort and the optimal retail price decisions in this model.
Under the two-stage model,epSII∗>epPII∗, ps1SII∗>ps1PII∗, ps2SII∗>ps2PII∗, and ∏pSII∗<∏pPII∗; if μ2>μ3¯, then ∏sSII∗<∏sPII∗, and if 0<μ2<μ3¯, then ∏sSII∗>∏sPII∗. Here, μ3¯=4bλC2+φr2β24bλC2+2φr2β2/φC28bλC2+3φr2β2.Proposition3 shows that the optimal strategies in SS and PS are the same under the single-stage model. It is evident that the market power will not affect the optimal decisions when the fresh supplier and fresh e-commerce platform are not considering the online comments.In the two-stage model, the optimal freshness-keeping effort and retail price in SS are higher than that in PS, but the optimal profit of fresh e-commerce platform in SS is lower than that in PS. In other words, market power has a significant impact on the optimal decision-making of fresh e-commerce platform, and the fresh e-commerce platform is more profitable as a leader than as a follower.When online comment sensitivity is high, the supplier and platform both obtain more profit in PS than SS under the two-stage model. Combined with the above analysis of optimal decisions, the result shows that the optimal freshness-keeping effort and retail price in PS are lower than that in SS, while supplier and platform can both gain more profits. It can be seen that this scenario is applicable to fresh produce with relatively low freshness requirements and low value, such as apples and oranges. When online comment sensitivity is relatively low, the supplier will gain more profit in SS than in PS, and the optimal freshness-keeping effort and retail price are higher in SS than that in PS. Therefore, the supplier prefers to be the leader of the fresh e-commerce supply chain. In summary, this circumstance is suitable for the supplier to sell fresh produce with high freshness requirements and high value, when the supplier is the leader of this supply chain.


## 4.1. Single-Stage Model

As a benchmark, this section performs a single-stage decision without considering online comments (we call a single-stage model for simplicity and denote it as superscript I). The optimal retail price of supplier and the optimal fresh-keeping effort of platform are decided only once; the Stackelberg game model is constructed in SS and PS, respectively. The objective functions of supplier and platform are as follows:(5)∏sI=1−φpsQ−psrQr=ps1−φ−rαa−bps−δt+rβepps,(6)∏pI=φpsQ−cep−ct=φpsa−bps−δt−λep22−Lt2.To ensure that the supplier yields a positive profit, we assumeC=1−φ−rα>0.Utilizing backward induction, we calculate the optimal retail price and freshness-keeping effort strategies for SS and PS under the single-stage model, respectively.


### 4.1.1. SS under the Single-Stage Model

In this part, we investigate the optimal retail price and freshness-keeping effort strategies where the supplier (he) is the leader and the platform (she) is the follower in the Stackelberg game under the single-stage model. The supplier’s objective is to maximize his profit by setting his optimal retail priceps, and then, the platform decides the freshness-keeping effort ep to maximize her profit after observing the supplier’s decision. Based on (5) and (6), we derive the optimal solutions, which are given in Theorem 1.Theorem 1.
epSI∗=0, psSI∗=A/2b, ∏sSI∗=A2C/4b, and ∏pSI∗=φt2A2−4bL/4bt2.Proof.
Proof of Theorem1 is available in Appendix.
Theorem1 means that platform is not willing to invest freshness-keeping effort in SS under the single-stage model. Note that ∂psSI∗/∂t<0, and we derive that the longer the response time of platform, the lower retail price supplier will set to attract consumers and reduce demand loss.


### 4.1.2. PS under the Single-Stage Model

When the platform performs an act as a leader in the Stackelberg game, she decides the freshness-keeping effort first to maximize her profit, and then, the supplier’s objective is to maximize his profit by setting his optimal retail priceps after observing the platform’s decision. Based on (5) and (6), similar to the analysis in Section 4.1.1, we can derive the optimal solutions in PS as follows.Theorem 2.
epPI∗=0, psPI∗=A/2b, ∏sPI∗=A2C/4b, and ∏pPI∗=φt2A2−4bL/4bt2.Proof.
Proof of Theorem2 is available in Appendix.
Theorem2 states that the platform still has no incentive to input freshness-keeping effort. It is easy to verify that ∂psPI∗/∂t<0, which means that the supplier always decreases the retail price when the response time becomes longer.


## 4.1.1. SS under the Single-Stage Model

In this part, we investigate the optimal retail price and freshness-keeping effort strategies where the supplier (he) is the leader and the platform (she) is the follower in the Stackelberg game under the single-stage model. The supplier’s objective is to maximize his profit by setting his optimal retail priceps, and then, the platform decides the freshness-keeping effort ep to maximize her profit after observing the supplier’s decision. Based on (5) and (6), we derive the optimal solutions, which are given in Theorem 1.Theorem 1.
epSI∗=0, psSI∗=A/2b, ∏sSI∗=A2C/4b, and ∏pSI∗=φt2A2−4bL/4bt2.Proof.
Proof of Theorem1 is available in Appendix.
Theorem1 means that platform is not willing to invest freshness-keeping effort in SS under the single-stage model. Note that ∂psSI∗/∂t<0, and we derive that the longer the response time of platform, the lower retail price supplier will set to attract consumers and reduce demand loss.


## 4.1.2. PS under the Single-Stage Model

When the platform performs an act as a leader in the Stackelberg game, she decides the freshness-keeping effort first to maximize her profit, and then, the supplier’s objective is to maximize his profit by setting his optimal retail priceps after observing the platform’s decision. Based on (5) and (6), similar to the analysis in Section 4.1.1, we can derive the optimal solutions in PS as follows.Theorem 2.
epPI∗=0, psPI∗=A/2b, ∏sPI∗=A2C/4b, and ∏pPI∗=φt2A2−4bL/4bt2.Proof.
Proof of Theorem2 is available in Appendix.
Theorem2 states that the platform still has no incentive to input freshness-keeping effort. It is easy to verify that ∂psPI∗/∂t<0, which means that the supplier always decreases the retail price when the response time becomes longer.


## 4.2. Two-Stage Model

In this section, we consider the influence of online comments on the optimal decision of fresh e-commerce supply chain and construct a two-stage repeated game model (which we call a two-stage model for simplicity and denote it as superscript II). The decision sequence can be divided into two stages: in the first stage, the supplier’s and platform’s objectives are to maximize their profits ((7) and (8)) by setting an optimal retail price and an optimal freshness-keeping effort. In the second stage, in view of the online comments received in the first stage, the supplier determines the retail price to maximize the second-stage profit (9). At each stage, the supplier and the platform act as the leader and the follower of the Stackelberg game, respectively. Since the freshness-keeping cost mainly includes the expenses of renting refrigerated equipment, it cannot be easily changed within a short time once they are invested. Therefore, the platform only decides the optimal freshness-keeping effort once at the beginning of sale with the goal of maximizing the total profit. The same as in the single-stage model, we investigate the optimal decisions in SS and PS under the two-stage model, respectively. The objective functions of supplier and platform at each stage are as follows.The profit function of supplier and platform in the first stage is as follows:(7)∏s1II=1−φps1Q1−ps1rQr1+∏s2II∗=ps11−φ−rαa−bps1−δt+rβepps1+∏s2II∗,(8)∏p1II=φps1Q1−cep−ct+∏p2II∗=φps1a−bps1−δt−λep22−Lt2+∏p2II∗.The profit function of supplier and platform in the second stage is as follows:(9)∏s2II=1−φps2Q2−ps2rQr2=ps21−φ−rαa−bps2−δt+μep−eE+rβepps2,(10)∏p2II=φps2Q2−cep−ct=φps2a−bps2−δt+μep−eE−λep22−Lt2.


### 4.2.1. SS under the Two-Stage Model

In this model, based on the Stackelberg game and two-stage repeated game, the objective of both supplier and platform in the first stage is to maximize their total profit. In the first stage, the supplier first sets the retail priceps1, and then, the platform sets the freshness-keeping effort ep. After that, the supplier decides the retail price ps2 in the second stage. The sequence of events is shown in Figure 2.Figure 2
Sequence of decisions in SS under the two-stage model.Given equations (7) to (10), we can derive that there exists the unique optimal strategy for supplier and platform utilizing backward induction, which are given in Theorem 3.Theorem 3.
(11)epSII∗=φμBC2D,ps1SII∗=AD+φrβμBC2bD,ps2SII∗=B4bλC2+φr2β2+φrβμC2bD,∏sSII∗=Cφ2r2β2μ2B2C2+2φrβμABCD+A2D2+B24bλC2+φrβμC+rβ24bD2,∏pSII∗=φt2B2D4bλC2+φr2β2−φt2φ2r2β2μ2B2C2−A2D2−8bLD24bt2D2.Proof.
Proof of Theorem3 is available in Appendix.
We can summarize the character of supplier’s and platform’s optimal strategies, which are given in Corollaries1 and 2.Corollary 1.
If0<μ<μ1¯, then ps1SII∗>ps2SII∗; otherwise, ps1II∗<ps2II∗.
Here,μ1¯=eE4bλC2+φr2β2/φAC2.Proof.
Proof of Corollary1 is available in Appendix.
Corollary1 indicates that the supplier will take a price-cutting strategy when online comment sensitivity is relatively low. If the online comment sensitivity is high, the retail price in the second stage will be higher than that in the first stage. It can be seen that the higher the online comment sensitivity, the more attention that consumers pay to freshness of fresh produce. Namely, freshness has a significant impact on demand. Therefore, the platform will strive to invest more freshness-keeping efforts to improve produce quality, and the supplier will increase retail prices accordingly.Corollary 2.
(a)
If0<eE<mineE1¯,1, then ∂epSII∗/∂μ>0 and ∂ps1SII∗/∂μ>0; if mineE1¯,1<eE<1, then ∂epSII∗/∂μ<0 and ∂ps1SII∗/∂μ<0. If 0<eE<mineE2¯,1, then ∂ps2SII∗/∂μ>0; otherwise, ∂ps2SII∗/∂μ<0.(b)
∂epSII∗/∂t<0, ∂ps1SII∗/∂t<0, and ∂ps2SII∗/∂t<0.Here, eE1¯=A4bλC2+φr2β2+φμ2C2/2μ4bλC2+φr2β2 and eE2¯=φrβμCD+2φμ2C+D4bλC2+φr2β2+φrβμ/φACrβD+2μ.Proof.
Proof of Corollary2 is available in Appendix.
Corollary2(a) shows that there is a threshold of consumers’ freshness expectation in SS under the two-stage model. Below this threshold, the optimal decisions of supplier and platform will both increase with the increase in the online comment sensitivity; above this threshold, the optimal decisions decrease with the increase in the online comment sensitivity. This can be explained that consumers tend to make positive online comments within the acceptable range of freshness expectations.
Therefore, when consumers’ freshness expectations are within a certain range, the platform has an incentive to increase freshness-keeping efforts to stimulate demand. Therefore, the optimal freshness-keeping effort and retail prices are also increased with the increase in online comment sensitivity. On the contrary, when consumers expect too much freshness, the benefits from increasing freshness-keeping efforts by platform do not compensate for the freshness-keeping costs. In this case, even if consumers are very sensitive to online comments, the platform is not willing to increase freshness-keeping effort, but rather to make supplier cut retail prices and implement a “low quality, low price” strategy.
Corollary2(b) states that the response time has an impact on supplier’s and platform’s optimal decisions. This is mainly because consumers’ purchasing decisions are influenced by the response time to some extent. If the platform cannot provide services to consumers in time, the consumers will leave. Therefore, the supplier will decrease the retail price to make up for the reduced demand caused by long response time of the platform. The decline in retail price means that the profit sharing of the platform is also reduced. To maintain profit margin, the platform will invest relatively less in freshness-keeping efforts.


### 4.2.2. PS under the Two-Stage Model

In this model, based on the Stackelberg game and two-stage repeated game, each participant aims to maximize their total profits of two stages in the first stage. The platform first makes a decision on freshness-keeping effortep, and supplier sets retail price ps1 in the first stage after observing the decision of platform. In the second stage, the supplier decides the retail price ps2 according to the online comments in the first stage. The sequence of events is shown in Figure 3.Figure 3
Sequence of decisions in PS under the two-stage model.Given equations (7) to (10), utilizing backward induction, we can derive that there exist the optimal decisions of the supplier and the platform in PS under the two-stage model, which are given in Theorem 4.Theorem 4.
(12)epPII∗=φμBC2D+φr2β2,ps1PII∗=AD+φr2β2+φrβμBC2bD+φr2β2,ps2PII∗=B4bλC2+2φr2β2+φrβμC2bD+φr2β2,∏sPII∗=CφrβμBC+AD+φr2β22+B24bλC2+2φr2β2+φrβμC24bD+φr2β22,∏pPII∗=φt2A2−8bLD+φr2β2+2φt2B24bλC2+φr2β24bt2D+φr2β2.Proof.
Proof of Theorem4 is available in Appendix.
We can also capture the characteristics of the optimal retail prices and freshness-keeping effort, and the result is summarized in Corollary3 and Corollary 4.Corollary 3.
If0<μ<μ2¯, then ps1PII∗>ps2PII∗; otherwise, ps1PII∗<ps2PII∗.Here, μ2¯=2eE2bλC2+φr2β2/φAC2.Proof.
Proof of Corollary3 is available in Appendix.
Corollary3 indicates that when online comment sensitivity is low, the supplier will decrease the retail price in the second stage (and vice versa), which is basically consistent with the optimal decision in SS. This is mainly because consumers are less interested in produce freshness when online comment sensitivity is low. In this case, the positive effect of freshness-keeping effort improvement cannot make up for the negative effect of cost increase. Therefore, the influence of freshness-keeping effort increase is worse than the price reduction in market demand. So, the supplier will adopt a price reduction strategy as priority. However, when the online comment sensitivity is high, consumers are more sensitive to produce freshness than retail price, and the improvement of freshness-keeping effort is significant. As a result, with the improvement of produce freshness, the retail price will also increase.Corollary 4.
(a)
If0<eE<mineE3¯,1, then ∂epPII∗/∂μ>0 and ∂ps1PII∗/∂μ>0; if mineE3¯,1<eE<1, then ∂epPII∗/∂μ<0 and ∂ps1PII∗/∂μ<0. If 0<eE<mineE4¯,1, then ∂ps2PII∗/∂μ>0; if mineE4¯,1<eE<1, then ∂ps2PII∗/∂μ<0.(b)
∂epPII∗/∂t<0, ∂ps1PII∗/∂t<0, and ∂ps2PII∗/∂t<0.Here, eE3¯=A4bλC2+φμ2C2+2φr2β2/4μ2bλC2+φr2β2 and eE4¯=φACrβ+2μC4bλC2+2φr2β2+φrβμ2C2/4bλC2+2φr2β22+φμCμC+2rβ4bλC2+2φr2β2.Proof.
Proof of Corollary4 is available in Appendix.
Similar to Corollary2(a), Corollary 4(a) shows that there is a threshold of consumer freshness expectation in PS under the two-stage model. Below this threshold, both optimal freshness-keeping effort and retail price increase as online comment sensitivity rises; above this threshold, the optimal decision of fresh produce e-commerce firms decreases with the increase in online review sensitivity.
Generally, consumers will make positive online comments when the actual freshness of produce they receive is within the acceptable range, and the positive online comments will increase market demand. Thereby, when consumers’ freshness expectations are low, the higher the online comment sensitivity, the more significant the increasing freshness-keeping effort. Accordingly, the supplier will also raise retail prices to capture more profits. In this case, the strategy of “high quality, high price” is beneficial for both supplier and platform. On the contrary, when consumers’ freshness expectations are high, it is uneconomical for platform to increase freshness-keeping efforts with the increase in online comment sensitivity. To avoid excessive demand loss, the supplier correspondingly has to reduce retail price; for example,, the strategy of "low quality, low price" is suitable to the circumstance.
Corollary4(b) reveals the correlation between optimal decisions and response time: the longer the response time, the lower the optimal freshness-keeping effort and retail price. The reason is that long response time will lead to demand loss, and supplier should mitigate the demand loss by reducing retail price, while the decrease in retail price means a decrease in profits. To maintain the profit margin, the platform will reduce freshness-keeping effort accordingly. Therefore, the platform needs to improve the service capability and shorten response time. Otherwise, it will result in demand loss and have a cascading negative impact on retail price and freshness-keeping effort.


## 4.2.1. SS under the Two-Stage Model

In this model, based on the Stackelberg game and two-stage repeated game, the objective of both supplier and platform in the first stage is to maximize their total profit. In the first stage, the supplier first sets the retail priceps1, and then, the platform sets the freshness-keeping effort ep. After that, the supplier decides the retail price ps2 in the second stage. The sequence of events is shown in Figure 2.Figure 2
Sequence of decisions in SS under the two-stage model.Given equations (7) to (10), we can derive that there exists the unique optimal strategy for supplier and platform utilizing backward induction, which are given in Theorem 3.Theorem 3.
(11)epSII∗=φμBC2D,ps1SII∗=AD+φrβμBC2bD,ps2SII∗=B4bλC2+φr2β2+φrβμC2bD,∏sSII∗=Cφ2r2β2μ2B2C2+2φrβμABCD+A2D2+B24bλC2+φrβμC+rβ24bD2,∏pSII∗=φt2B2D4bλC2+φr2β2−φt2φ2r2β2μ2B2C2−A2D2−8bLD24bt2D2.Proof.
Proof of Theorem3 is available in Appendix.
We can summarize the character of supplier’s and platform’s optimal strategies, which are given in Corollaries1 and 2.Corollary 1.
If0<μ<μ1¯, then ps1SII∗>ps2SII∗; otherwise, ps1II∗<ps2II∗.
Here,μ1¯=eE4bλC2+φr2β2/φAC2.Proof.
Proof of Corollary1 is available in Appendix.
Corollary1 indicates that the supplier will take a price-cutting strategy when online comment sensitivity is relatively low. If the online comment sensitivity is high, the retail price in the second stage will be higher than that in the first stage. It can be seen that the higher the online comment sensitivity, the more attention that consumers pay to freshness of fresh produce. Namely, freshness has a significant impact on demand. Therefore, the platform will strive to invest more freshness-keeping efforts to improve produce quality, and the supplier will increase retail prices accordingly.Corollary 2.
(a)
If0<eE<mineE1¯,1, then ∂epSII∗/∂μ>0 and ∂ps1SII∗/∂μ>0; if mineE1¯,1<eE<1, then ∂epSII∗/∂μ<0 and ∂ps1SII∗/∂μ<0. If 0<eE<mineE2¯,1, then ∂ps2SII∗/∂μ>0; otherwise, ∂ps2SII∗/∂μ<0.(b)
∂epSII∗/∂t<0, ∂ps1SII∗/∂t<0, and ∂ps2SII∗/∂t<0.Here, eE1¯=A4bλC2+φr2β2+φμ2C2/2μ4bλC2+φr2β2 and eE2¯=φrβμCD+2φμ2C+D4bλC2+φr2β2+φrβμ/φACrβD+2μ.Proof.
Proof of Corollary2 is available in Appendix.
Corollary2(a) shows that there is a threshold of consumers’ freshness expectation in SS under the two-stage model. Below this threshold, the optimal decisions of supplier and platform will both increase with the increase in the online comment sensitivity; above this threshold, the optimal decisions decrease with the increase in the online comment sensitivity. This can be explained that consumers tend to make positive online comments within the acceptable range of freshness expectations.
Therefore, when consumers’ freshness expectations are within a certain range, the platform has an incentive to increase freshness-keeping efforts to stimulate demand. Therefore, the optimal freshness-keeping effort and retail prices are also increased with the increase in online comment sensitivity. On the contrary, when consumers expect too much freshness, the benefits from increasing freshness-keeping efforts by platform do not compensate for the freshness-keeping costs. In this case, even if consumers are very sensitive to online comments, the platform is not willing to increase freshness-keeping effort, but rather to make supplier cut retail prices and implement a “low quality, low price” strategy.
Corollary2(b) states that the response time has an impact on supplier’s and platform’s optimal decisions. This is mainly because consumers’ purchasing decisions are influenced by the response time to some extent. If the platform cannot provide services to consumers in time, the consumers will leave. Therefore, the supplier will decrease the retail price to make up for the reduced demand caused by long response time of the platform. The decline in retail price means that the profit sharing of the platform is also reduced. To maintain profit margin, the platform will invest relatively less in freshness-keeping efforts.


## 4.2.2. PS under the Two-Stage Model

In this model, based on the Stackelberg game and two-stage repeated game, each participant aims to maximize their total profits of two stages in the first stage. The platform first makes a decision on freshness-keeping effortep, and supplier sets retail price ps1 in the first stage after observing the decision of platform. In the second stage, the supplier decides the retail price ps2 according to the online comments in the first stage. The sequence of events is shown in Figure 3.Figure 3
Sequence of decisions in PS under the two-stage model.Given equations (7) to (10), utilizing backward induction, we can derive that there exist the optimal decisions of the supplier and the platform in PS under the two-stage model, which are given in Theorem 4.Theorem 4.
(12)epPII∗=φμBC2D+φr2β2,ps1PII∗=AD+φr2β2+φrβμBC2bD+φr2β2,ps2PII∗=B4bλC2+2φr2β2+φrβμC2bD+φr2β2,∏sPII∗=CφrβμBC+AD+φr2β22+B24bλC2+2φr2β2+φrβμC24bD+φr2β22,∏pPII∗=φt2A2−8bLD+φr2β2+2φt2B24bλC2+φr2β24bt2D+φr2β2.Proof.
Proof of Theorem4 is available in Appendix.
We can also capture the characteristics of the optimal retail prices and freshness-keeping effort, and the result is summarized in Corollary3 and Corollary 4.Corollary 3.
If0<μ<μ2¯, then ps1PII∗>ps2PII∗; otherwise, ps1PII∗<ps2PII∗.Here, μ2¯=2eE2bλC2+φr2β2/φAC2.Proof.
Proof of Corollary3 is available in Appendix.
Corollary3 indicates that when online comment sensitivity is low, the supplier will decrease the retail price in the second stage (and vice versa), which is basically consistent with the optimal decision in SS. This is mainly because consumers are less interested in produce freshness when online comment sensitivity is low. In this case, the positive effect of freshness-keeping effort improvement cannot make up for the negative effect of cost increase. Therefore, the influence of freshness-keeping effort increase is worse than the price reduction in market demand. So, the supplier will adopt a price reduction strategy as priority. However, when the online comment sensitivity is high, consumers are more sensitive to produce freshness than retail price, and the improvement of freshness-keeping effort is significant. As a result, with the improvement of produce freshness, the retail price will also increase.Corollary 4.
(a)
If0<eE<mineE3¯,1, then ∂epPII∗/∂μ>0 and ∂ps1PII∗/∂μ>0; if mineE3¯,1<eE<1, then ∂epPII∗/∂μ<0 and ∂ps1PII∗/∂μ<0. If 0<eE<mineE4¯,1, then ∂ps2PII∗/∂μ>0; if mineE4¯,1<eE<1, then ∂ps2PII∗/∂μ<0.(b)
∂epPII∗/∂t<0, ∂ps1PII∗/∂t<0, and ∂ps2PII∗/∂t<0.Here, eE3¯=A4bλC2+φμ2C2+2φr2β2/4μ2bλC2+φr2β2 and eE4¯=φACrβ+2μC4bλC2+2φr2β2+φrβμ2C2/4bλC2+2φr2β22+φμCμC+2rβ4bλC2+2φr2β2.Proof.
Proof of Corollary4 is available in Appendix.
Similar to Corollary2(a), Corollary 4(a) shows that there is a threshold of consumer freshness expectation in PS under the two-stage model. Below this threshold, both optimal freshness-keeping effort and retail price increase as online comment sensitivity rises; above this threshold, the optimal decision of fresh produce e-commerce firms decreases with the increase in online review sensitivity.
Generally, consumers will make positive online comments when the actual freshness of produce they receive is within the acceptable range, and the positive online comments will increase market demand. Thereby, when consumers’ freshness expectations are low, the higher the online comment sensitivity, the more significant the increasing freshness-keeping effort. Accordingly, the supplier will also raise retail prices to capture more profits. In this case, the strategy of “high quality, high price” is beneficial for both supplier and platform. On the contrary, when consumers’ freshness expectations are high, it is uneconomical for platform to increase freshness-keeping efforts with the increase in online comment sensitivity. To avoid excessive demand loss, the supplier correspondingly has to reduce retail price; for example,, the strategy of "low quality, low price" is suitable to the circumstance.
Corollary4(b) reveals the correlation between optimal decisions and response time: the longer the response time, the lower the optimal freshness-keeping effort and retail price. The reason is that long response time will lead to demand loss, and supplier should mitigate the demand loss by reducing retail price, while the decrease in retail price means a decrease in profits. To maintain the profit margin, the platform will reduce freshness-keeping effort accordingly. Therefore, the platform needs to improve the service capability and shorten response time. Otherwise, it will result in demand loss and have a cascading negative impact on retail price and freshness-keeping effort.


## 4.3. Comparison of the Optimal Decisions

To analyze the effect of different market power and different models on optimal results, we aggregate the equilibrium results of different fresh e-commerce supply chain models and get some managerial opinions.We first make a comparison of the optimal decisions in the supplier-dominated scenario, namely SS, between single-stage model and two-stage model. The results are shown in Proposition1.Proposition 1.
In SS,epSI∗<epSII∗ and psSI∗<ps1SII∗; if 0<eE<mineE5¯,1, then psSI∗<ps2SII∗, and if mineE5¯,1<eE<1, then psSI∗>ps2SII∗; and ΠsSI∗<ΠsSII∗; if response cost 0<L/t2<c1t¯, then ΠpSI∗<ΠpSII∗, and if L/t2>c1t¯, then ΠpSI∗>ΠpSII∗; and the total profit of the fresh e-commerce supply chain always exists in the relationship that ∏SI∗<∏SII∗.Here, eE5¯=φACrβ+μC/4bλC2+φr2β2+φrβμC and c1t¯=φB2D4bλC2+φr2β2−φ2r2β2μ2C2/4bD2.Accordingly, we can draw these observations as follows.Firstly, the optimal freshness-keeping effort under the single-stage model is lower than the optimal outcome under the two-stage model; the optimal retail price under the single-stage model is lower than that in the first stage under the two-stage model. What is more, when consumers’ freshness expectation is low, the optimal retail price under single-stage model is lower than that in the second stage under the two-stage model and vice versa. This is because the effect of restrain demand loss by increasing freshness-keeping effort is poor, when consumers’ freshness expectation is low. This can also be seen from the result of the previous section. Therefore, the supplier is more inclined to mitigate demand loss by decreasing prices; thus, the retail price in the second stage under the two-stage model is relatively higher than that under the single-stage model.Secondly, the optimal profit of supplier under single-stage model is lower than that under the two-stage model. That is, the two-stage model is more favorable to supplier. In the meantime, when the response cost of platform is lower than a certain threshold, the optimal profit of platform under the single-stage model is lower than that under the two-stage model. Above this threshold, the optimal profit of platform under the single-stage model is higher than that in the two-stage model. In other words, when the response cost of the platform is controlled within a certain range, the two-stage model is beneficial to platform; otherwise, the single-stage model is better.Finally, the total profits of fresh e-commerce supply chain under the two-stage model are higher than that in the single-stage model in SS. On the whole, the two-stage model is better than the single-stage model in SS for fresh e-commerce supply chain.Then, we turn to investigate the comparison of the optimal decisions in the platform-dominated scenario, namely PS, between single-stage model and two-stage model. The results are shown in Proposition2.Proposition 2.
In PS,epPI∗<epPII∗, psPI∗<ps1PII∗, and ΠsPI∗<ΠsII∗; if 0<eE<mineE6¯,1, then psPII∗<ps2PII∗, and if mineE6¯,1<eE<1, then psPI∗>ps2PII∗; if the response cost of platform L/t2<c2t¯, then ΠpPI∗<ΠpPII∗, and if L/t2>c2t¯, then ΠpPI∗>ΠpPII∗; and the total profit of the fresh e-commerce supply chain always exists in the relationship that ∏PI∗<∏PII∗.Here, eE6¯=φACrβ+μC/94bλC2+2φr2β2+φrβμC and c2t¯=φB24bλC2+φr2β2/2bD+φr2β2.Similar to Proposition1, we can draw these observations as follows.Firstly, we can see that, under the single-stage model, the optimal retail price and freshness-keeping effort are lower than those in the first stage under the two-stage model. When consumer freshness expectation is low, the optimal retail price under the single-stage model is lower than that in the second stage under the two-stage model; when consumer freshness expectation exceeds a certain threshold, the result is opposite. This is because the low freshness expectation weakens the positive effect of freshness improvement, and thus, the supplier tends to decrease retail price to mitigate demand loss. In the two-stage model, to attract more demand, the supplier and platform will set higher freshness-keeping effort and retail price in the first stage. To maintain reputation and profit margin, the retail price in the second stage cannot be too low, and thus, the optimal retail price in the single-stage model is necessarily lower than that in the two-stage model.Secondly, the supplier can gain more profits in the two-stage model; for example, the two-stage model is more favorable to the supplier. When the response cost of platform is lower than a certain threshold, the optimal profit of platform under the single-stage model is lower than that in the two-stage model; when the response cost of platform is higher than this threshold, the result is opposite. That is, when the response cost is controlled within a certain range, the two-stage model is beneficial to platform; otherwise, the single-stage model is better.Finally, in PS, the total profit of the supply chain under the two-stage model is better than that under the single-stage model. Therefore, from an overall perspective, the two-stage model in PS is superior to the single-stage model.Then, we discuss the comparison of the optimal decisions between SS and PS under the single-stage model and two-stage model, respectively. The results are shown in Proposition3.Proposition 3.
Under the single-stage model,epSI∗=epPI∗, psSI∗=psPI∗, ∏sSI∗=∏sPI∗, and ∏pSI∗=∏pPI∗. In other words, the change in leader will not affect the optimal freshness-keeping effort and the optimal retail price decisions in this model.
Under the two-stage model,epSII∗>epPII∗, ps1SII∗>ps1PII∗, ps2SII∗>ps2PII∗, and ∏pSII∗<∏pPII∗; if μ2>μ3¯, then ∏sSII∗<∏sPII∗, and if 0<μ2<μ3¯, then ∏sSII∗>∏sPII∗. Here, μ3¯=4bλC2+φr2β24bλC2+2φr2β2/φC28bλC2+3φr2β2.Proposition3 shows that the optimal strategies in SS and PS are the same under the single-stage model. It is evident that the market power will not affect the optimal decisions when the fresh supplier and fresh e-commerce platform are not considering the online comments.In the two-stage model, the optimal freshness-keeping effort and retail price in SS are higher than that in PS, but the optimal profit of fresh e-commerce platform in SS is lower than that in PS. In other words, market power has a significant impact on the optimal decision-making of fresh e-commerce platform, and the fresh e-commerce platform is more profitable as a leader than as a follower.When online comment sensitivity is high, the supplier and platform both obtain more profit in PS than SS under the two-stage model. Combined with the above analysis of optimal decisions, the result shows that the optimal freshness-keeping effort and retail price in PS are lower than that in SS, while supplier and platform can both gain more profits. It can be seen that this scenario is applicable to fresh produce with relatively low freshness requirements and low value, such as apples and oranges. When online comment sensitivity is relatively low, the supplier will gain more profit in SS than in PS, and the optimal freshness-keeping effort and retail price are higher in SS than that in PS. Therefore, the supplier prefers to be the leader of the fresh e-commerce supply chain. In summary, this circumstance is suitable for the supplier to sell fresh produce with high freshness requirements and high value, when the supplier is the leader of this supply chain.


## 5. Model Extensions and Numerical Analysis

### 5.1. Model Extensions

#### 5.1.1. Model Extension I

In this section, we further extend the two-stage model by assuming that the freshness-keeping effort is adjustable, because the investment of refrigerated equipment also can be changed from the perspective of long period. That is to say, the platform also makes another round of decision on the freshness-keeping effort in the second stage (which we call the extended two-stage model for simplicity and denote it as superscript III). The decision models in SS and PS scenarios are both considered, and the objective functions of supplier and platform at each stage are shown as follows.The profit function of supplier and platform in the first stage is as follows:(13)∏s1III=1−φps1Q1−ps1rQr1+∏s2III∗=ps11−φ−rαa−bps1−δt+rβep1ps1+∏s2III∗,(14)∏p1III=φps1Q1−cep1−ct+∏p2III∗=φps1a−bps1−δt−λep122−Lt2+∏p2III∗.The profit function of supplier and platform in the second stage is as follows:(15)∏s2III=1−φps2Q2−ps2rQr2=ps21−φ−rαa−bps2−δt+μep1−eE+rβep2ps2,(16)∏p2III=φps2Q2−cep2−ct=φps2a−bps2−δt+μep1−eE−λep222−Lt2.The decision sequence in SS under the extended two-stage model is shown in Figure4. According to the Stackelberg game and two-stage repeated game, the supplier first decides retail price ps1, and platform decides freshness-keeping effort ep1 after observing the supplier's decision in the first stage. Their objective in the first stage is to maximize their own total profit. In the second stage, the supplier first decides retail price ps2, and platform then decides the freshness-keeping effort ep2 after obtaining online comments from the first stage.Figure 4
Sequence of decisions in SS under the extended two-stage model.The decision sequence in PS under the extended two-stage model is shown in Figure5. According to the Stackelberg game and two-stage repeated game, in the first stage, the platform first decides freshness-keeping effort ep1, and supplier decides retail price ps1 after observing the platform's decision, and their objective in the first stage is to maximize their own total profit; in the second stage, after obtaining online comments from the first stage, the platform first decides the freshness-keeping effort ep2, and supplier then decides retail price ps2.Figure 5
Sequence of decisions in PS under the extended two-stage model.The optimal decisions of supplier and platform in SS and PS under the extended two-stage model are shown in Appendix.


#### 5.1.2. Model Extension II

In this part, we relax the assumption of supply chain structure and consider the extended model of a fresh e-commerce supply chain consisting of one fresh supplier, one TPL provider, and one fresh e-commerce platform (this model is denoted by superscript IV). As shown in Figure6, the supplier is the actual retailer, which sells fresh produce through platform. The platform provides ordinary services and charges commissions to supplier. The cold-chain logistics service is provided by the TPL at the price pt.Figure 6
Structure of the model extension II.To investigate the impact of online comments on participants’ decision-making, there are still two models discussed: single-stage model and two-stage model. The freshness-keeping effort is provided by the TPL provider. As in model extension I, the freshness-keeping effort is adjustable in the two-stage model. Based on the Stackelberg game, the fresh supplier is the leader and TPL provider is the follower.The profit function of supplier, TPL provider, and platform in the single-stage model is as follows:(17)ΠsIV=1−φpsQ−ptQ−ps−ptrQr,ΠtIV=ptQ−cet−ptrQr,ΠpIV=φpsQ.We verify thatΠsIV and ΠtIV are jointly concave in ps and et when 1−φ−rα>0. To maximize the profits, the supplier’s optimal decision on retail price and the TPL provider’s optimal decision on freshness-keeping effort must satisfy ∂ΠsIV/∂ps=0 and ∂ΠtIV/∂et=0. Utilizing backward induction, we get the optimal retail price and freshness-keeping effort strategies under the single-stage model:(18)etIV∗=rβptλ,psIV∗=λaC+bpt1−rα+r2β2pt2bλC.The profit function of supplier, TPL provider, and platform in the two-stage model is as follows:(19)ΠsiIV=1−φpsiQi−ptQi−psi−ptrQri,ΠtiIV=ptQi−ceti−ptrQri,ΠpiIV=φpsiQi.Utilizing backward induction, the optimal retail price and freshness-keeping effort strategies under the two-stage model are as follows:(20)et1IV∗=ptμ1−rα+2rβ2λ,et2IV∗=rβptλ,ps1IV∗=2λaC+bpt1−rα+2ptr2β2+rβμpt1−rα4bλC,ps2IV∗=2ptrβμC+2ptr2β2+2λCa−eEμ+pt1−rαμ2C+2bλ4bλC.According to the optimal retail price and freshness-keeping effort from different models above, we get some conclusions and managerial insights. Firstly, the optimal freshness-keeping effortetIV∗ under the single-stage model is equal to et2IV∗ in the second stage under the two-stage model, but lower than et1IV∗ in the first stage under the two-stage model. It is clear that TPL is going to improve the freshness-keeping effort in the two-stage model. Secondly, the optimal retail price psIV∗ under the single-stage model is lower than ps1IV∗ in the first stage under the two-stage model. In this two-stage model, the TPL provider has the motivation to invest more freshness-keeping efforts to improve the quality of fresh produces, and the supplier will improve retail price to match the TPL provider’s freshness-keeping investment. It can be seen that the strategy of “high quality, high price” is more popular for fresh produce consumers.Next, we investigate the relationship between online comment sensitivity and the optimal decisions.Corollary 5.
∂et1IV∗/∂μ>0,∂ps1IV∗/∂μ>0;∂et1IV∗−etIV∗/∂μ>0,∂ps1IV∗−psIV∗/∂μ>0; and∂2ps1IV∗−ps2IV∗/∂μ2<0.Corollary5 indicates that the optimal retail price and freshness-keeping effort in the first stage are always positively correlated. As online comment sensitivity increases, the TPL provider will improve the investment of freshness-keeping effort; therefore, the supplier will also improve retail price. Besides, as online comment sensitivity increases, the greater the gap of optimal decisions between single-stage model and two-stage model. This is due to the fact that a larger online review sensitivity implies a greater impact of reduced freshness on demand loss and a more pronounced effect of increased freshness-keeping efforts on stimulating demand. To attract more consumers and get more positive online comments, the fresh produce supply chain participants are willing to take the “high quality, high price” strategy.What is more, the results mentioned above are in keeping with our original findings, and thus, it demonstrates that the model is robust.


### 5.2. Numerical Analysis

To provide a better understanding of the models and results above, we present numerical analysis to obtain more observations. Referring to the set of parameters of Cao et al. [75] and Xu and Song [76], and combining with the actual sales status of fresh e-commerce industry, model assumptions, and parameters’ constraints in this study, we choose the values of parameters as follows: a=10, b=3, α=0.2, β=5, λ=2, eE=0.6, δ=4, L=0.03, φ=0.3, and r=0.3. We get these numerical figures by MATLAB 2014b as follows.


#### 5.2.1. Numerical Analysis of Optimal Decisions

In this subsection, we explore the impacts of online comment sensitivityμ and response time t on the optimal freshness-keeping efforts and retail prices, which are shown in Figures 7 and 8.Figure 7
Impacts ofμ on optimal freshness-keeping efforts and retail prices.
(a)(b)Figure 8
Impacts oft on optimal decisions.
(a)(b)Firstly, Figure7(a) shows that the freshness-keeping efforts under the two-stage model and extended two-stage model increase with the increase in online comment sensitivity. Besides, online comment sensitivity has a more significant impact on optimal freshness-keeping effort in SS under the extended two-stage model than that under the two-stage model. While in PS, the observation is the opposite. Moreover, the freshness-keeping effort in SS is higher than that in PS, under both two-stage model and extended two-stage model.Secondly, Figure7(b) shows that the impact of online comment sensitivity on optimal retail price of the same stage is always consistent under the two-stage model and extended two-stage model. The optimal retail prices in the first stage increase with the increase in online comment sensitivity; the optimal retail prices in the second stage first decline and then rise with the increase in online comment sensitivity. The optimal retail price in the second stage is always lower than that in the first stage.Finally, high freshness-keeping effort corresponds to the high retail price. Combining with Figures7(a) and 7(b), we can observe that the freshness-keeping effort and retail price in SS are always higher than those in PS, which is consistent with the findings of Corollary 2(a) and Corollary 4(a). When online comment sensitivity is not high, the supplier will implement retail price reduction strategy in the two-stage model, and the platform will also reduce freshness-keeping effort. However, when online comment sensitivity is high, the supplier prefers to raise retail price, even if the corresponding freshness-keeping effort is still low. This behavior will affect the sustainable development of enterprises. In the long run, more negative comments will be formed, which is not conducive to establishing a good reputation.Figure8(a) reveals that the impact of response time on the optimal freshness-keeping effort is always negative under the two-stage model and extended two-stage model. The optimal freshness-keeping effort in SS under the extended two-stage model is higher than that under the two-stage model. However, the optimal freshness-keeping effort under the extended two-stage model is more sensitive to response time. Figure 8(b) shows the relationship between response time and optimal retail price under each model, respectively. The optimal retail prices always decrease with the response time prolonging. Thus, Corollary 2(b) and Corollary 4(b) are verified. It can be concluded that the longer customers wait for platform’s response, the more likely they are dissatisfied or even give up buying. In response, the supplier and platform will improve freshness-keeping effort or reduce retail price to prevent demand loss. Therefore, the platform should strive to improve the response efficiency for a better value-added service to consumers. Otherwise, the prolonged response time will have a negative impact on its own profits and reputation.


#### 5.2.2. Numerical Analysis of Market Demands

In this subsection, we will discuss the impact of online comment sensitivity and response time on market demands, which is shown in Figure9.Figure 9
Impacts ofμ and t on market demands.
(a)(b)Figure9(a) shows the relationship between online comment sensitivity and market demands. We can see that market demands will decline with the increase in online comment sensitivity. Combining with Figure 7, on one hand, freshness-keeping effort and retail price both increase with the increase in online comment sensitivity. As a result, the supplier may lose some customers who are price-sensitive in response. On the other hand, the promotion of freshness-keeping efforts will effectively reduce transportation loss. Therefore, consumers can receive high freshness produce and decrease returns. In addition, it can be found that the demands in the two-stage model are higher than that in the single-stage model. It indicates that considering online comments in decisions can stimulate market demands.As shown in Figure9(b), market demands decrease with the increase in response time, which verifies Theorem 1 and Theorem 2, as well as Corollary 2(b) and Corollary 4(b). Therefore, the platform should improve service quality and shorten response time to promote market demands. In addition, it also can be found that market demands in the two-stage model and extended two-stage model are always higher than that in the single-stage model. This means that supplier can boost demands by considering the impact of online comments on the decision-making process.


#### 5.2.3. Numerical Analysis of Profits

In this subsection, we further explore the impact of online comment sensitivity and response time on profits of supplier and platform. The numerical results are shown in Figures10 and 11.Figure 10
Impacts ofμ on supplier’s profit and platform’s profit.
(a)(b)Figure 11
Impacts oft on supplier’s profit and platform’s profit.
(a)(b)It can be seen from Figure10(a) that supplier’s profit in the single-stage model has nothing to do with online comment sensitivity. However, it roughly increases with the increase in online comment sensitivity under the two-stage model and extended two-stage model. Under the two-stage model, the platform’s profit in PS is higher than that in SS. Under the extended two-stage model, when online comment sensitivity is low 0<μ<1.5, the supplier’s profit in PS is higher than that in SS. Beyond this range, SS is better for supplier. Combining with the previous analysis, when consumers are more sensitive to online comments, the optimal freshness-keeping effort and retail price are higher, which means the “high quality, high price” strategy is suitable for this circumstance. Although market demand decreased, the benefits brought by the “high quality, high price” strategy still effectively make up for the negative effects of the loss of demand.Figure10(b) reveals that platform’s profit in the single-stage model also has no relationship with online comment sensitivity. However, the platform’s profit decreases with the increase in online comment sensitivity in the two-stage model and extended two-stage model, which is apparently different from the trend of supplier’s profit. The reason for this difference can be summarized as follows: when online comment sensitivity is higher, the platform will invest more in freshness-keeping efforts to attract consumers and positive online comments, which will lead to high freshness-keeping cost and relative profit reduction. However, profits under the two-stage model are higher than the single-stage model and extended two-stage model in both SS and PS. On the whole, the two-stage model is the best choice for platform.In a word, both supplier and platform obtain higher profits in the two-stage model. Therefore, the supplier and platform both prefer the two-stage model. The above observations also indicate that the strategy of “high quality, high price” is suitable for produce with high freshness requirements.Figure11(a) shows that supplier’s profit decreases with response time prolonging. According to the previous analysis, it is clear that optimal freshness-keeping effort, retail price, and market demand are negatively correlated with response time. Therefore, the supplier’s optimal profit also decreases. In Figure 11(b), when response time is lower than a certain threshold (0<t<0.4), the platform’s profit increases with the increase in response time. However, when response time exceeds this threshold, the platform’s profit will rapidly decrease as response time continuously increases. Therefore, the platform must control the response time within a certain range; otherwise, its own profits will be eroded. Furthermore, within the three models, the platform’s profit under the two-stage model is the highest. Under the extended two-stage model, the platform’s optimal profit in PS is lower than that in SS. Combining with Figure 10, the platform should not change fresh-keeping effort in the second stage.


## 5.1. Model Extensions

### 5.1.1. Model Extension I

In this section, we further extend the two-stage model by assuming that the freshness-keeping effort is adjustable, because the investment of refrigerated equipment also can be changed from the perspective of long period. That is to say, the platform also makes another round of decision on the freshness-keeping effort in the second stage (which we call the extended two-stage model for simplicity and denote it as superscript III). The decision models in SS and PS scenarios are both considered, and the objective functions of supplier and platform at each stage are shown as follows.The profit function of supplier and platform in the first stage is as follows:(13)∏s1III=1−φps1Q1−ps1rQr1+∏s2III∗=ps11−φ−rαa−bps1−δt+rβep1ps1+∏s2III∗,(14)∏p1III=φps1Q1−cep1−ct+∏p2III∗=φps1a−bps1−δt−λep122−Lt2+∏p2III∗.The profit function of supplier and platform in the second stage is as follows:(15)∏s2III=1−φps2Q2−ps2rQr2=ps21−φ−rαa−bps2−δt+μep1−eE+rβep2ps2,(16)∏p2III=φps2Q2−cep2−ct=φps2a−bps2−δt+μep1−eE−λep222−Lt2.The decision sequence in SS under the extended two-stage model is shown in Figure4. According to the Stackelberg game and two-stage repeated game, the supplier first decides retail price ps1, and platform decides freshness-keeping effort ep1 after observing the supplier's decision in the first stage. Their objective in the first stage is to maximize their own total profit. In the second stage, the supplier first decides retail price ps2, and platform then decides the freshness-keeping effort ep2 after obtaining online comments from the first stage.Figure 4
Sequence of decisions in SS under the extended two-stage model.The decision sequence in PS under the extended two-stage model is shown in Figure5. According to the Stackelberg game and two-stage repeated game, in the first stage, the platform first decides freshness-keeping effort ep1, and supplier decides retail price ps1 after observing the platform's decision, and their objective in the first stage is to maximize their own total profit; in the second stage, after obtaining online comments from the first stage, the platform first decides the freshness-keeping effort ep2, and supplier then decides retail price ps2.Figure 5
Sequence of decisions in PS under the extended two-stage model.The optimal decisions of supplier and platform in SS and PS under the extended two-stage model are shown in Appendix.


### 5.1.2. Model Extension II

In this part, we relax the assumption of supply chain structure and consider the extended model of a fresh e-commerce supply chain consisting of one fresh supplier, one TPL provider, and one fresh e-commerce platform (this model is denoted by superscript IV). As shown in Figure6, the supplier is the actual retailer, which sells fresh produce through platform. The platform provides ordinary services and charges commissions to supplier. The cold-chain logistics service is provided by the TPL at the price pt.Figure 6
Structure of the model extension II.To investigate the impact of online comments on participants’ decision-making, there are still two models discussed: single-stage model and two-stage model. The freshness-keeping effort is provided by the TPL provider. As in model extension I, the freshness-keeping effort is adjustable in the two-stage model. Based on the Stackelberg game, the fresh supplier is the leader and TPL provider is the follower.The profit function of supplier, TPL provider, and platform in the single-stage model is as follows:(17)ΠsIV=1−φpsQ−ptQ−ps−ptrQr,ΠtIV=ptQ−cet−ptrQr,ΠpIV=φpsQ.We verify thatΠsIV and ΠtIV are jointly concave in ps and et when 1−φ−rα>0. To maximize the profits, the supplier’s optimal decision on retail price and the TPL provider’s optimal decision on freshness-keeping effort must satisfy ∂ΠsIV/∂ps=0 and ∂ΠtIV/∂et=0. Utilizing backward induction, we get the optimal retail price and freshness-keeping effort strategies under the single-stage model:(18)etIV∗=rβptλ,psIV∗=λaC+bpt1−rα+r2β2pt2bλC.The profit function of supplier, TPL provider, and platform in the two-stage model is as follows:(19)ΠsiIV=1−φpsiQi−ptQi−psi−ptrQri,ΠtiIV=ptQi−ceti−ptrQri,ΠpiIV=φpsiQi.Utilizing backward induction, the optimal retail price and freshness-keeping effort strategies under the two-stage model are as follows:(20)et1IV∗=ptμ1−rα+2rβ2λ,et2IV∗=rβptλ,ps1IV∗=2λaC+bpt1−rα+2ptr2β2+rβμpt1−rα4bλC,ps2IV∗=2ptrβμC+2ptr2β2+2λCa−eEμ+pt1−rαμ2C+2bλ4bλC.According to the optimal retail price and freshness-keeping effort from different models above, we get some conclusions and managerial insights. Firstly, the optimal freshness-keeping effortetIV∗ under the single-stage model is equal to et2IV∗ in the second stage under the two-stage model, but lower than et1IV∗ in the first stage under the two-stage model. It is clear that TPL is going to improve the freshness-keeping effort in the two-stage model. Secondly, the optimal retail price psIV∗ under the single-stage model is lower than ps1IV∗ in the first stage under the two-stage model. In this two-stage model, the TPL provider has the motivation to invest more freshness-keeping efforts to improve the quality of fresh produces, and the supplier will improve retail price to match the TPL provider’s freshness-keeping investment. It can be seen that the strategy of “high quality, high price” is more popular for fresh produce consumers.Next, we investigate the relationship between online comment sensitivity and the optimal decisions.Corollary 5.
∂et1IV∗/∂μ>0,∂ps1IV∗/∂μ>0;∂et1IV∗−etIV∗/∂μ>0,∂ps1IV∗−psIV∗/∂μ>0; and∂2ps1IV∗−ps2IV∗/∂μ2<0.Corollary5 indicates that the optimal retail price and freshness-keeping effort in the first stage are always positively correlated. As online comment sensitivity increases, the TPL provider will improve the investment of freshness-keeping effort; therefore, the supplier will also improve retail price. Besides, as online comment sensitivity increases, the greater the gap of optimal decisions between single-stage model and two-stage model. This is due to the fact that a larger online review sensitivity implies a greater impact of reduced freshness on demand loss and a more pronounced effect of increased freshness-keeping efforts on stimulating demand. To attract more consumers and get more positive online comments, the fresh produce supply chain participants are willing to take the “high quality, high price” strategy.What is more, the results mentioned above are in keeping with our original findings, and thus, it demonstrates that the model is robust.


## 5.1.1. Model Extension I

In this section, we further extend the two-stage model by assuming that the freshness-keeping effort is adjustable, because the investment of refrigerated equipment also can be changed from the perspective of long period. That is to say, the platform also makes another round of decision on the freshness-keeping effort in the second stage (which we call the extended two-stage model for simplicity and denote it as superscript III). The decision models in SS and PS scenarios are both considered, and the objective functions of supplier and platform at each stage are shown as follows.The profit function of supplier and platform in the first stage is as follows:(13)∏s1III=1−φps1Q1−ps1rQr1+∏s2III∗=ps11−φ−rαa−bps1−δt+rβep1ps1+∏s2III∗,(14)∏p1III=φps1Q1−cep1−ct+∏p2III∗=φps1a−bps1−δt−λep122−Lt2+∏p2III∗.The profit function of supplier and platform in the second stage is as follows:(15)∏s2III=1−φps2Q2−ps2rQr2=ps21−φ−rαa−bps2−δt+μep1−eE+rβep2ps2,(16)∏p2III=φps2Q2−cep2−ct=φps2a−bps2−δt+μep1−eE−λep222−Lt2.The decision sequence in SS under the extended two-stage model is shown in Figure4. According to the Stackelberg game and two-stage repeated game, the supplier first decides retail price ps1, and platform decides freshness-keeping effort ep1 after observing the supplier's decision in the first stage. Their objective in the first stage is to maximize their own total profit. In the second stage, the supplier first decides retail price ps2, and platform then decides the freshness-keeping effort ep2 after obtaining online comments from the first stage.Figure 4
Sequence of decisions in SS under the extended two-stage model.The decision sequence in PS under the extended two-stage model is shown in Figure5. According to the Stackelberg game and two-stage repeated game, in the first stage, the platform first decides freshness-keeping effort ep1, and supplier decides retail price ps1 after observing the platform's decision, and their objective in the first stage is to maximize their own total profit; in the second stage, after obtaining online comments from the first stage, the platform first decides the freshness-keeping effort ep2, and supplier then decides retail price ps2.Figure 5
Sequence of decisions in PS under the extended two-stage model.The optimal decisions of supplier and platform in SS and PS under the extended two-stage model are shown in Appendix.


## 5.1.2. Model Extension II

In this part, we relax the assumption of supply chain structure and consider the extended model of a fresh e-commerce supply chain consisting of one fresh supplier, one TPL provider, and one fresh e-commerce platform (this model is denoted by superscript IV). As shown in Figure6, the supplier is the actual retailer, which sells fresh produce through platform. The platform provides ordinary services and charges commissions to supplier. The cold-chain logistics service is provided by the TPL at the price pt.Figure 6
Structure of the model extension II.To investigate the impact of online comments on participants’ decision-making, there are still two models discussed: single-stage model and two-stage model. The freshness-keeping effort is provided by the TPL provider. As in model extension I, the freshness-keeping effort is adjustable in the two-stage model. Based on the Stackelberg game, the fresh supplier is the leader and TPL provider is the follower.The profit function of supplier, TPL provider, and platform in the single-stage model is as follows:(17)ΠsIV=1−φpsQ−ptQ−ps−ptrQr,ΠtIV=ptQ−cet−ptrQr,ΠpIV=φpsQ.We verify thatΠsIV and ΠtIV are jointly concave in ps and et when 1−φ−rα>0. To maximize the profits, the supplier’s optimal decision on retail price and the TPL provider’s optimal decision on freshness-keeping effort must satisfy ∂ΠsIV/∂ps=0 and ∂ΠtIV/∂et=0. Utilizing backward induction, we get the optimal retail price and freshness-keeping effort strategies under the single-stage model:(18)etIV∗=rβptλ,psIV∗=λaC+bpt1−rα+r2β2pt2bλC.The profit function of supplier, TPL provider, and platform in the two-stage model is as follows:(19)ΠsiIV=1−φpsiQi−ptQi−psi−ptrQri,ΠtiIV=ptQi−ceti−ptrQri,ΠpiIV=φpsiQi.Utilizing backward induction, the optimal retail price and freshness-keeping effort strategies under the two-stage model are as follows:(20)et1IV∗=ptμ1−rα+2rβ2λ,et2IV∗=rβptλ,ps1IV∗=2λaC+bpt1−rα+2ptr2β2+rβμpt1−rα4bλC,ps2IV∗=2ptrβμC+2ptr2β2+2λCa−eEμ+pt1−rαμ2C+2bλ4bλC.According to the optimal retail price and freshness-keeping effort from different models above, we get some conclusions and managerial insights. Firstly, the optimal freshness-keeping effortetIV∗ under the single-stage model is equal to et2IV∗ in the second stage under the two-stage model, but lower than et1IV∗ in the first stage under the two-stage model. It is clear that TPL is going to improve the freshness-keeping effort in the two-stage model. Secondly, the optimal retail price psIV∗ under the single-stage model is lower than ps1IV∗ in the first stage under the two-stage model. In this two-stage model, the TPL provider has the motivation to invest more freshness-keeping efforts to improve the quality of fresh produces, and the supplier will improve retail price to match the TPL provider’s freshness-keeping investment. It can be seen that the strategy of “high quality, high price” is more popular for fresh produce consumers.Next, we investigate the relationship between online comment sensitivity and the optimal decisions.Corollary 5.
∂et1IV∗/∂μ>0,∂ps1IV∗/∂μ>0;∂et1IV∗−etIV∗/∂μ>0,∂ps1IV∗−psIV∗/∂μ>0; and∂2ps1IV∗−ps2IV∗/∂μ2<0.Corollary5 indicates that the optimal retail price and freshness-keeping effort in the first stage are always positively correlated. As online comment sensitivity increases, the TPL provider will improve the investment of freshness-keeping effort; therefore, the supplier will also improve retail price. Besides, as online comment sensitivity increases, the greater the gap of optimal decisions between single-stage model and two-stage model. This is due to the fact that a larger online review sensitivity implies a greater impact of reduced freshness on demand loss and a more pronounced effect of increased freshness-keeping efforts on stimulating demand. To attract more consumers and get more positive online comments, the fresh produce supply chain participants are willing to take the “high quality, high price” strategy.What is more, the results mentioned above are in keeping with our original findings, and thus, it demonstrates that the model is robust.


## 5.2. Numerical Analysis

To provide a better understanding of the models and results above, we present numerical analysis to obtain more observations. Referring to the set of parameters of Cao et al. [75] and Xu and Song [76], and combining with the actual sales status of fresh e-commerce industry, model assumptions, and parameters’ constraints in this study, we choose the values of parameters as follows: a=10, b=3, α=0.2, β=5, λ=2, eE=0.6, δ=4, L=0.03, φ=0.3, and r=0.3. We get these numerical figures by MATLAB 2014b as follows.


### 5.2.1. Numerical Analysis of Optimal Decisions

In this subsection, we explore the impacts of online comment sensitivityμ and response time t on the optimal freshness-keeping efforts and retail prices, which are shown in Figures 7 and 8.Figure 7
Impacts ofμ on optimal freshness-keeping efforts and retail prices.
(a)(b)Figure 8
Impacts oft on optimal decisions.
(a)(b)Firstly, Figure7(a) shows that the freshness-keeping efforts under the two-stage model and extended two-stage model increase with the increase in online comment sensitivity. Besides, online comment sensitivity has a more significant impact on optimal freshness-keeping effort in SS under the extended two-stage model than that under the two-stage model. While in PS, the observation is the opposite. Moreover, the freshness-keeping effort in SS is higher than that in PS, under both two-stage model and extended two-stage model.Secondly, Figure7(b) shows that the impact of online comment sensitivity on optimal retail price of the same stage is always consistent under the two-stage model and extended two-stage model. The optimal retail prices in the first stage increase with the increase in online comment sensitivity; the optimal retail prices in the second stage first decline and then rise with the increase in online comment sensitivity. The optimal retail price in the second stage is always lower than that in the first stage.Finally, high freshness-keeping effort corresponds to the high retail price. Combining with Figures7(a) and 7(b), we can observe that the freshness-keeping effort and retail price in SS are always higher than those in PS, which is consistent with the findings of Corollary 2(a) and Corollary 4(a). When online comment sensitivity is not high, the supplier will implement retail price reduction strategy in the two-stage model, and the platform will also reduce freshness-keeping effort. However, when online comment sensitivity is high, the supplier prefers to raise retail price, even if the corresponding freshness-keeping effort is still low. This behavior will affect the sustainable development of enterprises. In the long run, more negative comments will be formed, which is not conducive to establishing a good reputation.Figure8(a) reveals that the impact of response time on the optimal freshness-keeping effort is always negative under the two-stage model and extended two-stage model. The optimal freshness-keeping effort in SS under the extended two-stage model is higher than that under the two-stage model. However, the optimal freshness-keeping effort under the extended two-stage model is more sensitive to response time. Figure 8(b) shows the relationship between response time and optimal retail price under each model, respectively. The optimal retail prices always decrease with the response time prolonging. Thus, Corollary 2(b) and Corollary 4(b) are verified. It can be concluded that the longer customers wait for platform’s response, the more likely they are dissatisfied or even give up buying. In response, the supplier and platform will improve freshness-keeping effort or reduce retail price to prevent demand loss. Therefore, the platform should strive to improve the response efficiency for a better value-added service to consumers. Otherwise, the prolonged response time will have a negative impact on its own profits and reputation.


### 5.2.2. Numerical Analysis of Market Demands

In this subsection, we will discuss the impact of online comment sensitivity and response time on market demands, which is shown in Figure9.Figure 9
Impacts ofμ and t on market demands.
(a)(b)Figure9(a) shows the relationship between online comment sensitivity and market demands. We can see that market demands will decline with the increase in online comment sensitivity. Combining with Figure 7, on one hand, freshness-keeping effort and retail price both increase with the increase in online comment sensitivity. As a result, the supplier may lose some customers who are price-sensitive in response. On the other hand, the promotion of freshness-keeping efforts will effectively reduce transportation loss. Therefore, consumers can receive high freshness produce and decrease returns. In addition, it can be found that the demands in the two-stage model are higher than that in the single-stage model. It indicates that considering online comments in decisions can stimulate market demands.As shown in Figure9(b), market demands decrease with the increase in response time, which verifies Theorem 1 and Theorem 2, as well as Corollary 2(b) and Corollary 4(b). Therefore, the platform should improve service quality and shorten response time to promote market demands. In addition, it also can be found that market demands in the two-stage model and extended two-stage model are always higher than that in the single-stage model. This means that supplier can boost demands by considering the impact of online comments on the decision-making process.


### 5.2.3. Numerical Analysis of Profits

In this subsection, we further explore the impact of online comment sensitivity and response time on profits of supplier and platform. The numerical results are shown in Figures10 and 11.Figure 10
Impacts ofμ on supplier’s profit and platform’s profit.
(a)(b)Figure 11
Impacts oft on supplier’s profit and platform’s profit.
(a)(b)It can be seen from Figure10(a) that supplier’s profit in the single-stage model has nothing to do with online comment sensitivity. However, it roughly increases with the increase in online comment sensitivity under the two-stage model and extended two-stage model. Under the two-stage model, the platform’s profit in PS is higher than that in SS. Under the extended two-stage model, when online comment sensitivity is low 0<μ<1.5, the supplier’s profit in PS is higher than that in SS. Beyond this range, SS is better for supplier. Combining with the previous analysis, when consumers are more sensitive to online comments, the optimal freshness-keeping effort and retail price are higher, which means the “high quality, high price” strategy is suitable for this circumstance. Although market demand decreased, the benefits brought by the “high quality, high price” strategy still effectively make up for the negative effects of the loss of demand.Figure10(b) reveals that platform’s profit in the single-stage model also has no relationship with online comment sensitivity. However, the platform’s profit decreases with the increase in online comment sensitivity in the two-stage model and extended two-stage model, which is apparently different from the trend of supplier’s profit. The reason for this difference can be summarized as follows: when online comment sensitivity is higher, the platform will invest more in freshness-keeping efforts to attract consumers and positive online comments, which will lead to high freshness-keeping cost and relative profit reduction. However, profits under the two-stage model are higher than the single-stage model and extended two-stage model in both SS and PS. On the whole, the two-stage model is the best choice for platform.In a word, both supplier and platform obtain higher profits in the two-stage model. Therefore, the supplier and platform both prefer the two-stage model. The above observations also indicate that the strategy of “high quality, high price” is suitable for produce with high freshness requirements.Figure11(a) shows that supplier’s profit decreases with response time prolonging. According to the previous analysis, it is clear that optimal freshness-keeping effort, retail price, and market demand are negatively correlated with response time. Therefore, the supplier’s optimal profit also decreases. In Figure 11(b), when response time is lower than a certain threshold (0<t<0.4), the platform’s profit increases with the increase in response time. However, when response time exceeds this threshold, the platform’s profit will rapidly decrease as response time continuously increases. Therefore, the platform must control the response time within a certain range; otherwise, its own profits will be eroded. Furthermore, within the three models, the platform’s profit under the two-stage model is the highest. Under the extended two-stage model, the platform’s optimal profit in PS is lower than that in SS. Combining with Figure 10, the platform should not change fresh-keeping effort in the second stage.


## 5.2.1. Numerical Analysis of Optimal Decisions

In this subsection, we explore the impacts of online comment sensitivityμ and response time t on the optimal freshness-keeping efforts and retail prices, which are shown in Figures 7 and 8.Figure 7
Impacts ofμ on optimal freshness-keeping efforts and retail prices.
(a)(b)Figure 8
Impacts oft on optimal decisions.
(a)(b)Firstly, Figure7(a) shows that the freshness-keeping efforts under the two-stage model and extended two-stage model increase with the increase in online comment sensitivity. Besides, online comment sensitivity has a more significant impact on optimal freshness-keeping effort in SS under the extended two-stage model than that under the two-stage model. While in PS, the observation is the opposite. Moreover, the freshness-keeping effort in SS is higher than that in PS, under both two-stage model and extended two-stage model.Secondly, Figure7(b) shows that the impact of online comment sensitivity on optimal retail price of the same stage is always consistent under the two-stage model and extended two-stage model. The optimal retail prices in the first stage increase with the increase in online comment sensitivity; the optimal retail prices in the second stage first decline and then rise with the increase in online comment sensitivity. The optimal retail price in the second stage is always lower than that in the first stage.Finally, high freshness-keeping effort corresponds to the high retail price. Combining with Figures7(a) and 7(b), we can observe that the freshness-keeping effort and retail price in SS are always higher than those in PS, which is consistent with the findings of Corollary 2(a) and Corollary 4(a). When online comment sensitivity is not high, the supplier will implement retail price reduction strategy in the two-stage model, and the platform will also reduce freshness-keeping effort. However, when online comment sensitivity is high, the supplier prefers to raise retail price, even if the corresponding freshness-keeping effort is still low. This behavior will affect the sustainable development of enterprises. In the long run, more negative comments will be formed, which is not conducive to establishing a good reputation.Figure8(a) reveals that the impact of response time on the optimal freshness-keeping effort is always negative under the two-stage model and extended two-stage model. The optimal freshness-keeping effort in SS under the extended two-stage model is higher than that under the two-stage model. However, the optimal freshness-keeping effort under the extended two-stage model is more sensitive to response time. Figure 8(b) shows the relationship between response time and optimal retail price under each model, respectively. The optimal retail prices always decrease with the response time prolonging. Thus, Corollary 2(b) and Corollary 4(b) are verified. It can be concluded that the longer customers wait for platform’s response, the more likely they are dissatisfied or even give up buying. In response, the supplier and platform will improve freshness-keeping effort or reduce retail price to prevent demand loss. Therefore, the platform should strive to improve the response efficiency for a better value-added service to consumers. Otherwise, the prolonged response time will have a negative impact on its own profits and reputation.


## 5.2.2. Numerical Analysis of Market Demands

In this subsection, we will discuss the impact of online comment sensitivity and response time on market demands, which is shown in Figure9.Figure 9
Impacts ofμ and t on market demands.
(a)(b)Figure9(a) shows the relationship between online comment sensitivity and market demands. We can see that market demands will decline with the increase in online comment sensitivity. Combining with Figure 7, on one hand, freshness-keeping effort and retail price both increase with the increase in online comment sensitivity. As a result, the supplier may lose some customers who are price-sensitive in response. On the other hand, the promotion of freshness-keeping efforts will effectively reduce transportation loss. Therefore, consumers can receive high freshness produce and decrease returns. In addition, it can be found that the demands in the two-stage model are higher than that in the single-stage model. It indicates that considering online comments in decisions can stimulate market demands.As shown in Figure9(b), market demands decrease with the increase in response time, which verifies Theorem 1 and Theorem 2, as well as Corollary 2(b) and Corollary 4(b). Therefore, the platform should improve service quality and shorten response time to promote market demands. In addition, it also can be found that market demands in the two-stage model and extended two-stage model are always higher than that in the single-stage model. This means that supplier can boost demands by considering the impact of online comments on the decision-making process.


## 5.2.3. Numerical Analysis of Profits

In this subsection, we further explore the impact of online comment sensitivity and response time on profits of supplier and platform. The numerical results are shown in Figures10 and 11.Figure 10
Impacts ofμ on supplier’s profit and platform’s profit.
(a)(b)Figure 11
Impacts oft on supplier’s profit and platform’s profit.
(a)(b)It can be seen from Figure10(a) that supplier’s profit in the single-stage model has nothing to do with online comment sensitivity. However, it roughly increases with the increase in online comment sensitivity under the two-stage model and extended two-stage model. Under the two-stage model, the platform’s profit in PS is higher than that in SS. Under the extended two-stage model, when online comment sensitivity is low 0<μ<1.5, the supplier’s profit in PS is higher than that in SS. Beyond this range, SS is better for supplier. Combining with the previous analysis, when consumers are more sensitive to online comments, the optimal freshness-keeping effort and retail price are higher, which means the “high quality, high price” strategy is suitable for this circumstance. Although market demand decreased, the benefits brought by the “high quality, high price” strategy still effectively make up for the negative effects of the loss of demand.Figure10(b) reveals that platform’s profit in the single-stage model also has no relationship with online comment sensitivity. However, the platform’s profit decreases with the increase in online comment sensitivity in the two-stage model and extended two-stage model, which is apparently different from the trend of supplier’s profit. The reason for this difference can be summarized as follows: when online comment sensitivity is higher, the platform will invest more in freshness-keeping efforts to attract consumers and positive online comments, which will lead to high freshness-keeping cost and relative profit reduction. However, profits under the two-stage model are higher than the single-stage model and extended two-stage model in both SS and PS. On the whole, the two-stage model is the best choice for platform.In a word, both supplier and platform obtain higher profits in the two-stage model. Therefore, the supplier and platform both prefer the two-stage model. The above observations also indicate that the strategy of “high quality, high price” is suitable for produce with high freshness requirements.Figure11(a) shows that supplier’s profit decreases with response time prolonging. According to the previous analysis, it is clear that optimal freshness-keeping effort, retail price, and market demand are negatively correlated with response time. Therefore, the supplier’s optimal profit also decreases. In Figure 11(b), when response time is lower than a certain threshold (0<t<0.4), the platform’s profit increases with the increase in response time. However, when response time exceeds this threshold, the platform’s profit will rapidly decrease as response time continuously increases. Therefore, the platform must control the response time within a certain range; otherwise, its own profits will be eroded. Furthermore, within the three models, the platform’s profit under the two-stage model is the highest. Under the extended two-stage model, the platform’s optimal profit in PS is lower than that in SS. Combining with Figure 10, the platform should not change fresh-keeping effort in the second stage.


## 6. Conclusions and Managerial Implications

### 6.1. Results and Managerial Implications

This study has demonstrated the importance of considering online comments in the operational decision-making of fresh produce e-commerce enterprises, especially with regard to the optimal retail price and freshness-keeping effort. The results have several management implications.First, the supply chain members of fresh produce e-commerce should devote themselves to obtain more positive online comments. We find that a two-stage model is more profitable than a single-stage model under both an SS and a PS. The results also show that online comments play a positive role in increasing the profits and improving the market share of both supplier and platform. There are conclusions that can be drawn from the two-stage model. Either a “high quality, high price” strategy or a “low quality, low price” strategy may be suitable for supplier and platform, which is depending on consumers’ freshness expectation. When freshness expectation is low, the optimal quality from the perspective of supply chain members is positively related to the online comment sensitivity coefficient. That is, consumers’ attention to online comments can effectively stimulate platform to improve freshness-keeping effort of produce, which will drive up retail prices. Therefore, a “high quality, high price” strategy is appropriate. However, when consumer freshness expectation is high, the utility of increasing fresh-keeping efforts to stimulate demand is negligible: the platform does not benefit from increasing its fresh-keeping effort, but benefits more by reducing its retail prices. Therefore, the “low quality, low price” strategy is more suitable for this situation. In addition, consumers’ online comment sensitivity has a significant impact on suppliers’ and platform’s profits. When the online comment sensitivity is low, the effect of price on demand is more significant than the effect of freshness-keeping effort. Therefore, a supplier is incentivized to reduce retail prices in the second-stage model, as this helps to expand its market share.Second, the supplier and platform should compete for market leadership towards different fresh produces. Although the two-stage model remains more profitable than the single-stage model overall, low comment sensitivity partly erodes the platform’s profit. Therefore, it may cause competition between supplier and platform for supply chain dominance. Based on the optimal strategies, when consumers are more sensitive to online comments, the supplier and platform will gain greater profits in the platform-dominated scenario, although the optimal retail price and freshness-keeping effort in this scenario are lower than those in the supplier-dominated scenario. Therefore, the platform-dominated scenario is applicable to fresh produce with relatively low freshness requirements and low value. In contrast, when consumers are not sensitive to online comments, the optimal retail price and freshness-keeping effort in the supplier-dominated scenario are higher than those in the platform-dominated scenario, and the supplier obtains greater profits in the supplier-dominated scenario than in the platform-dominated scenario.Last, the platform should attempt to improve its value-added service capability by shortening the response time. While a prolonged response time reduces costs, it also leads to the loss of consumers. For both platform and supplier, the prolonged response time adversely affects the enterprises’ reputation and leads to lower profits. Otherwise, the gain was outweighed by the loss of profit due to customer loss. Prolonging the response time will reduce the profits of supplier, but a balance of the response time cost and freshness-keeping cost is also required by a platform.


### 6.2. Limitations

This study has some limitations. First, the model considers certain market demand and risk-neutral consumer, while consumer purchasing decision in reality is more complex. In future work, we will study the fresh produce e-commerce supply chain under the condition of uncertain market demand. Second, this study does not consider the situation in which the platform shares the return cost with a supplier. Thus, in future work, we will also explore whether a contractual cooperation mechanism between a supplier and a platform can effectively share the return cost. We do not have sales data associated with online reviews, and hence, it is a limitation of this study.


## 6.1. Results and Managerial Implications

This study has demonstrated the importance of considering online comments in the operational decision-making of fresh produce e-commerce enterprises, especially with regard to the optimal retail price and freshness-keeping effort. The results have several management implications.First, the supply chain members of fresh produce e-commerce should devote themselves to obtain more positive online comments. We find that a two-stage model is more profitable than a single-stage model under both an SS and a PS. The results also show that online comments play a positive role in increasing the profits and improving the market share of both supplier and platform. There are conclusions that can be drawn from the two-stage model. Either a “high quality, high price” strategy or a “low quality, low price” strategy may be suitable for supplier and platform, which is depending on consumers’ freshness expectation. When freshness expectation is low, the optimal quality from the perspective of supply chain members is positively related to the online comment sensitivity coefficient. That is, consumers’ attention to online comments can effectively stimulate platform to improve freshness-keeping effort of produce, which will drive up retail prices. Therefore, a “high quality, high price” strategy is appropriate. However, when consumer freshness expectation is high, the utility of increasing fresh-keeping efforts to stimulate demand is negligible: the platform does not benefit from increasing its fresh-keeping effort, but benefits more by reducing its retail prices. Therefore, the “low quality, low price” strategy is more suitable for this situation. In addition, consumers’ online comment sensitivity has a significant impact on suppliers’ and platform’s profits. When the online comment sensitivity is low, the effect of price on demand is more significant than the effect of freshness-keeping effort. Therefore, a supplier is incentivized to reduce retail prices in the second-stage model, as this helps to expand its market share.Second, the supplier and platform should compete for market leadership towards different fresh produces. Although the two-stage model remains more profitable than the single-stage model overall, low comment sensitivity partly erodes the platform’s profit. Therefore, it may cause competition between supplier and platform for supply chain dominance. Based on the optimal strategies, when consumers are more sensitive to online comments, the supplier and platform will gain greater profits in the platform-dominated scenario, although the optimal retail price and freshness-keeping effort in this scenario are lower than those in the supplier-dominated scenario. Therefore, the platform-dominated scenario is applicable to fresh produce with relatively low freshness requirements and low value. In contrast, when consumers are not sensitive to online comments, the optimal retail price and freshness-keeping effort in the supplier-dominated scenario are higher than those in the platform-dominated scenario, and the supplier obtains greater profits in the supplier-dominated scenario than in the platform-dominated scenario.Last, the platform should attempt to improve its value-added service capability by shortening the response time. While a prolonged response time reduces costs, it also leads to the loss of consumers. For both platform and supplier, the prolonged response time adversely affects the enterprises’ reputation and leads to lower profits. Otherwise, the gain was outweighed by the loss of profit due to customer loss. Prolonging the response time will reduce the profits of supplier, but a balance of the response time cost and freshness-keeping cost is also required by a platform.


## 6.2. Limitations

This study has some limitations. First, the model considers certain market demand and risk-neutral consumer, while consumer purchasing decision in reality is more complex. In future work, we will study the fresh produce e-commerce supply chain under the condition of uncertain market demand. Second, this study does not consider the situation in which the platform shares the return cost with a supplier. Thus, in future work, we will also explore whether a contractual cooperation mechanism between a supplier and a platform can effectively share the return cost. We do not have sales data associated with online reviews, and hence, it is a limitation of this study.



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*Source: 1473331-2022-05-17.xml*