Evolving strategies of e-commerce and express delivery enterprises with public supervision

Research in Transportation Economics xxx (xxxx) xxx

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Evolving strategies of e-commerce and express delivery enterprises with public supervision Xuemei Zhang a, Gengui Zhou a, Jian Cao a, b, *, Anqi Wu a, c a

School of Business, Zhejiang University of Technology, Hangzhou, 310023, China Center for Global & Regional Environmental Research, The University of Iowa, Iowa City, 52242, United States c Gies College of Business, University of Illinois Urbana-Champaign, IL 61820, United States b

A R T I C L E I N F O

A B S T R A C T

JEL classification: C73 L24 L81

This paper considers the e-commerce enterprises as the principal who outsource logistics services to the express delivery firms acting as the agent. The delivery enterprise faced with incomplete information provides either high- or low-quality logistics services, while the e-firm regulates either actively or passively. Using an evolu­ tionary game model, this paper seeks equilibrium strategies of the two parties under public supervision, with impacts of pertinent parameters on strategy selections illuminated. Analytical results indicate that the delivery firms rely mostly on comparative profit between high- and low-quality logistics services to make decisions, while the e-firms consult to monitoring cost rather than regulatory success rates to make selections between active and passive regulation. When public supervision stays at a relatively deficient level, passive supervision is preferred by the e-firm with the increase of consumer complaint rate. Still, it is possible to maintain the benefits of ecommerce corporations as well as enhance logistical performance in evolutionary games with the help of an operative supervision and punishment mechanism. Additional managerial insights are provided for discussion.

Keywords: E-commerce Logistics Public supervision Principal-agent Evolutionary game

1. Introduction The advent and especially most recent sophistication of the Internet has facilitated substantial progress of electronic commerce (e-com­ merce). It is estimated that e-commerce sales keep a steady rise across the globe, increasing to $1.92 trillion in 2016, and will reach a climax to $4 trillion by 2020, accounting for a fraction of 14.6%, compared to that of 8.7% in 2015, of total retail sales (Emarketer, 2016). During 2017, the total online retail sales in China reached ¥ 7.10 trillion ($ 1.09 trillion), increased by 32.2% compared to that number during 2016 (ECRC, 2018). Though online business origins from and prevails in developed countries, developing Asian regions have witnessed phenomenal expansion of the industry over the past decades. China, India and Malaysia have burgeoned to an average growth of 20% in e-business. Among these, China has been the most influential e-market possessing the most massive volume of online sales since 2013, with Alibaba, a giant e-commerce corporation that occupies the largest market share of 26.6%, stimulates continuous prosperity of Internet business across the nation. However, the situation is much more intricate than conventional

statistics could indicate, there remains implications to be concerned. Since most e-commerce firms entrust third-party express delivery en­ terprises for supplementary of regular operations regarding physical distribution, logistical support has been proved consequential. While ebusiness has escalated unremittingly, the matching delivery service comes to a halt in better satisfying its clients and consumers (Chen, 2017). Worse still, credibility crisis arises in the trustee-beneficiary relationship between e-commerce and third-party express delivery en­ terprises. The logistics firm, driven by interests, may take advantage of information asymmetry to provide unqualified services for its own profit maximization, which brings about consumer complaints and makes the e-firm suffered. Thus, it is imperative for the e-firm to establish a well-run incentive and supervisory mechanism to maintain its benefits and invigorate logistical advancements. In addition to a traditional monitoring mechanism between e-com­ merce enterprises and logistics service providers, public participation in the e-commerce environment is naturally considered as a third-party supervision. Owing to technology innovation and mass media advancement, e-commerce enterprises along with their logistics service providers are supervised by numerous online consumers with diverse

* Corresponding author. Wen’er Xincun 1-4-402, Hangzhou, 310012, China., E-mail addresses: [email protected] (X. Zhang), [email protected] (G. Zhou), [email protected], [email protected] (J. Cao), [email protected] (A. Wu). https://doi.org/10.1016/j.retrec.2019.100810 Received 13 March 2019; Received in revised form 30 October 2019; Accepted 18 December 2019 0739-8859/© 2020 Elsevier Ltd. All rights reserved.

Please cite this article as: Xuemei Zhang, Research in Transportation Economics, https://doi.org/10.1016/j.retrec.2019.100810

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requirements. Generally, the e-commerce enterprise autonomously regulates the logistics service provider with the help of the advanced ecommerce platform, where logistics operations could be tracked and evaluated. Besides, the advanced Internet has facilitated e-firms to accumulate feedbacks on logistics services from online buyers, in the form of consumer reviews and complaints. Once the logistics services provider is accused of performing inferiorly by consumers, the e-com­ merce enterprise then should take the responsibility to investigate such irregularities. According to “Chinese E-commerce User Experience and Complaint Report” published by China E-commerce Research Centre (CECRC), the year-over-year growth of complaint cases of inferior lo­ gistics in retail e-commerce accepted by the Public Service Platform of Ecommerce Complaints and Rights Protection arrives to 30.78% (including commodity distribution, delivery, exchange or return) in the first half year of 2017. The proportions of 2016 and 2015 are 30.77% and 29.56%, respectively (CECRC, 2017). The stubbornly high reporting rate justifies enhanced public consciousness of safeguarding their rights and interests. From the perspective of online buyers, the express delivery enterprise conducting logistical business as expected is acclaimed for providing high-quality services conveying fast delivery, traceable and accurate information, good consumer reflection and so on. When the express delivery enterprise operates irregularly, however, even its tiny transgressions are to blame for inferior performances. Statistics show that unsatisfied logistical services regarding delivery delay, commodity damage, missing parcel or items, poor service attitude, etc., have occupied a fixed position among the ten hot spots of online shopping complaints since 2011 (CECRC, 2017). Specific contribution of this paper is to show that within such con­ texts it is possible to maintain the benefits of e-commerce enterprises and to energize logistics industry in exchange for the delivery enter­ prises’ irregularities, better satisfying online consumers and progress ebusiness eventually. To achieve such an objective, we modeled an evolutionary game between firms of e-commerce and express delivery considering public supervision and attained evolutionary stable strate­ gies under various circumstances. In addition, associated numerical analyses are emerged to supplement theoretical exploration. The rest of the paper is organized as follows. Following the intro­ duction, a brief literature review provides insights into the issue of ecommerce operations and logistic performances. In Section 3, we set forth the basic model along with problem illustrations and assumptions. Proceeding to model formulation and discussion, Section 4 shows pref­ erential strategies of both stakeholders under distinct conditions. The numerical analyses follow in Section 5, which manifest how relevant parameters impact evolutionary processes. Section 6 concludes in­ vestigations with managerial insights and future orientations presented.

has been strengthened in e-business, and consumers generally reflect physical delivery as a crucial element in e-commerce implementations (Agatz, Fleischmann, & Nunen, 2008; Ramanathan, 2010). “Last mile” concept is frequently discussed in e-commercial supply chains (Amico & Hadjidimitriou, 2012; Kull, Boyer, & Calantone, 2007; Wang, Zhang, Liu, Shen, & Lee, 2016), and non-standard operations of many e-firms are generally attributed either to late arrival of commodity or parcel damage. On the other hand, the Internet has opened new avenues for logistics management and inspired firms to make breakthroughs, as Gunase­ karan, Marri, McGaughey, and Nebhwani (2002), Rutner, Gibson, and Williams (2003), Romero and Rodriguez (2010), Speranza (2018) have figured out that continuous growth of e-commerce would facilitate supply chain management, logistics integration and operational effi­ ciency. In response to the changed way of firms in conducting business, it’s essential to convert traditional logistics systems into electronic ones by collaborating logistical partners with the same intentions of better €zkan, serving consumers and accomplishing business excellence (Büyüko Feyzio� glu, & Nebol, 2008). Lu and Liu (2015) introduced single and dual channels in comparison to illuminate the effects of e-commerce channel entry on profitability and behavior of the distribution system. Shao, Yang, Xing, and Yang (2016) investigated the relations between distri­ bution strategies and traffic congestion in Internet environment, which concluded that e-commerce could reduce shopping costs and the firm might adopt a traditional, mixed, or electronic distribution with public recognition of e-commerce increase. A case study of parcel delivery operations in London was put forward by Allen et al. (2017), which demonstrates a range of undertakings of e-retailers and parcel carriers to reduce costs related with last mile delivery under e-commerce influences. It is a new type of challenge, as most e-commerce firms entrust thirdparty delivery enterprises to carry out physical distribution, that the moral hazard problem of logistics service providers triggered by incomplete information has been severed. The delivery firm may pro­ vide high quality services under information asymmetry, and the client merely has an access to the outcomes while the operation process is barely detectable (Cao, 2007). Adoptions of case study (Wagner & Sut­ ter, 2012; Yang, Zhao, Yeung, & Liu, 2016) and theoretical approaches involving cost-benefit analysis, risk compensation, contract models and game theory methods are emerged, hoping to establish supervisory and incentive mechanisms in the face of such a principal-agent dilemma. Zhao and Xi (1999) discussed two mathematical approaches in solving agent problems, namely first-order and cost-benefit methods, and clar­ ified their merits and drawbacks. Lim (2000) developed a game-theoretic model to investigate contract design problem faced by a third-party logistics service buyer in evaluating the quality of services. He proves that a contract without penalty is useless while the contract with a punishment or revenue-sharing scheme is productive. Strategic network theory was adopted by Rabinovich, Knemeyer, and Mayer (2007) to present Internet corporations’ attempts to incorporate logis­ tics service providers, which in turn makes the logistics enterprises more available to new clients and consumers. Gao, Wei, Li, and Gu (2008) employed an improved model considering risk compensation, in which both supervisory and incentive expenditures of outsourcing transactions between producers and suppliers can be reduced. Feng and Liu (2010) formulated a contract for Internet firms using a drive and monitory game analysis. Results show that an effective compensation mechanism should be established in accompany with e-commerce enterprise’s regulation, to enhance efforts and quality of physical distribution en­ terprises. Yang, Zhang, Wang, and Du (2012) introduced a discount factor in a sub-game perfect Nash equilibrium model to analyse e-commerce and logistics service. They suggest the express delivery companies should take full advantage of the gains to improve the service level during holiday, thus a win-win situation for both parties can be achieved. Xu, Cheng, and Huang (2015) used Vickrey-Clarke-Groves auction and primal-dual Vickrey auction to analyse the B2B

2. Literature review The related literature of e-commerce study considers three issues: (1) General interrelations between e-commerce enterprises and logistics service providers, (2) Methods and solutions to investigate supervisory and incentive measures in the e-commerce environment, (3) Applica­ tions of evolutionary game method in e-commerce problems. A review of the relevant studies is provided, novelty and insight of this paper along with the proposed model are then presented to clarify the main contri­ bution of this paper. An e-commerce company that intends to go through bottlenecks must address the importance of logistical support. From one perspective, logistics initiatives are principle factors of e-business firms for elevated consumer royalty and operational performance (Bai, Wei, & Yan, 2017; Huang & Yin, 2014; Liu, Zhang, Chen, Zhou, & Miao, 2018; Ram­ anathan, George, & Ramanathan, 2014). Aldin and Stahre (2003) explored how logistics could boost the development of e-marketing channels and improve flexibility of the electronic marketplace from the viewpoint of a wholesaler in a supply chain background. According to Rabinovich and Knemeyer (2006), the importance of logistical support 2

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e-commerce logistics problem, in which the e-commerce platform is the agent and the third-party logistics providers fulfil the online orders. It is concluded that quality services of logistics enterprises can reduce the potential risk and damage for the e-commerce platform. Zhu (2016) focused on risks management resulted from asymmetric information in an outsourcing contract of a buyer and a supplier, from which optimal contracts were derived by exploiting game-theoretic model and nu­ merical experiment. Based on fuzzy axiomatic design and extended regret theory, Chen, Gho, and Zou (2018) developed a new evaluation system for the logistics service providers selection problem in an e-commerce environment. Chen, Wu, and Hsu (2019) provided a reference for e-retailers and home delivery companies to determine appropriate logistics fees by developing a pricing model for alleviation of logistics congestion. According to the model, both parties can adjust their decision-making parameters based on actual business circumstances. Although there are many methods to solve the cooperation and competition of e-commerce and logistics service providers, most of them overlook the multi-period decision-makings, decision makers’ bounded rationality and continuous learning ability. Evolutionary game approach along with the concept of Evolutionary Stable Strategy (ESS) was introduced initially to focus on behavior analysis (Smith & Price, 1973). It has been widely adopted in the fields of supervision and strategy se­ lections of either relations between government and enterprises (Wu, Liu, & Xu, 2017), or those of different firms/consumers linked with contracts (Ji, Ma, & Li, 2015; Pedrielli, Lee, & Ng, 2015; Xiao & Chen, 2009; Yi & Yang, 2017). Unlike conventional game theories and ap­ proaches that assume participants behaving in complete rationality with complete information (Fudenberg; Tirole, 1991; Tadelis, 2013), evolu­ tionary game theory accentuates bounded rationality in the process of decision adjustments and progresses. Most recently, evolutionary theory and game models have been consequential in investigating e-commerce improvements. Huang, Hu, and Lu (2009) referred to the typical example of Alibaba Group to analyse e-business growth in China, and reveled four stages in the evolutionary path of the corporation with their respective traits identified. Li, Feng, Han, and Cheng (2015) proposed dynamic evolutionary game models and calculated replicated dynamic equations of three species in the e-business ecosystem, finally concluded that a collaborative state of three species in the group can be attained under a higher level of cooperative yield. Wang (2015) built up the evolutionary game between e-commerce enterprises and third-party logistics enterprises. Analytic results indicate the initial state of the system, the collaborative development cost and extra income of coor­ dinated development are main influencing factors of strategy selections. Bahbouhi and Moussa (2017) formalized an agent-based model to simulate various types of activities in online auction and depicted a co-evolutionary game between service buyers and sellers. Du, Guo, Lu, and Fu (2019) established an evolutionary game model to analyse the influencing factors of domestic e-firms and foreign logistics enterprises, and illustrate the willingness to cooperation between the two groups under different conditions. The game between the e-commerce and express delivery enterprises is the very case of coopetition between the two groups. Both groups fail to achieve the preferable strategy in the initial game for their distinct value orientations and rational knowledge. Their game is a process of continuous learning, adaption and improvement until a favorable status can be attained. Evolutionary game approach can delineate the process and strategy changes during the game and is more intuitive and comprehensible compared with static and complete information games that merely focus on the final equilibrium results. However, applications of the evolutionary game method are relatively singular in investigating strategy selections between the two groups of e-commerce enterprises and logistics service providers. Besides, public participation is frequently overlooked as third-party inspection. Wang (2015), Du et al. (2019) proposed evolutionary game models between e-firms and de­ livery enterprises but consumer responses are neglected. Lu, Wang, and

Luo (2016) considered public participation as one member of the game other than a third-party that is independent from the system. All in all, existing literature that explores relations of e-commerce implementation and logistics performance in expounding their mutual influences has been widely conducted, but there remain more thorough investigations. This paper extends the literature in the following main aspects. � Application of evolutionary game approach is relatively singular in analysing relations of e-commerce companies and express delivery enterprises. Fewer achievements have been made to accomplish evolutionary stable strategies between the two groups. Considering multi periods for decision-makings, and from the perspective of group game, the proposed model fills the gap by investigating evolving strategies of e-commerce and express delivery enterprises. � Public awareness is enhanced due to motivated participation of on­ line buyers, however associated issues in the academic realm over­ look consumers’ role. This paper captures online buyers’ role in supervising unqualified logistics services, which facilitates the ecommerce enterprise to establish an effective supervisory mechanism. � Strategy selections of the principal often include “supervision” and “no supervision” in previous researches. However, in an e-commerce context with online shopping evaluation platforms, the logistics service providers are inevitably supervised by numerous online buyers. The e-commerce enterprise as the principal therefore should always undertake the duty of “supervision”. The proposed model seeks to fill this gap by including e-firm’s responsibility of regulating logistics services, while the e-firm can make selections between “active regulation” and “passive supervision”. � Previous studies seldom consider the regulatory success rate of the efirms. Benefit-loss ratio which indicates the comparative profit of express delivery enterprise’s different strategies, is also absent in existing literature. All of these influencing factors along with their interrelations are addressed in this paper. This paper focuses on evolutionary analyses of the e-commerce and express delivery enterprises considering public supervision, to probe into game progresses and favorable strategies of system evolution. Considering distinct developing stages of e-commerce industry and public participation, evolutionary stable strategies in various contexts are discussed. Influencing factors involving regulatory costs, regulatory success rates, public participation rate, punishment of low-quality lo­ gistics services and so on, are investigated, along with their impacts on strategy evolvements figured out. We conclude the results of significant references for the enhancement of express delivery operations as well as e-commerce progress, owing to which further advancement of the ecommerce industry can be achieved. 3. Model descriptions, assumptions and the pay-off matrix E-commerce enterprises often entrust third-party logistics service providers to complete the task of physical distribution since outsourcing is generally thought to be able to reduce the risk and uncertainty in online business (Giri & Sarker, 2017; Wong, Tai, & Zhou, 2018). Under such an environment, a game relationship emerges between the two groups with the e-firm acting as a client and the supervisor while the express delivery enterprise representing an agent and the regulated member. Taking evolutionary game as the guiding method, this section brings about model descriptions and associated assumptions. 3.1. Problem characteristics and assumptions Assumption 1. For the considering express delivery enterprise faced 3

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with incomplete information in a principal-agent mode, it is assumed to provide either high- or low-quality logistics services.

operations of the logistics service provider, ω representing compensa­ tion for consumers if the criticized behavior is confirmed. f lE ω > 0 should be satisfied.

The express delivery enterprise conducting logistical business as expected is publicly acclaimed of providing high-quality services which refer to fast delivery, traceable and accurate information, good con­ sumer reflection and so on. While low-quality services of delivery delay, product damage, unfavorable service attitude and unreasonable charges, are frequently criticized by online buyers (Chen, Wu, & Xiong, L 2015; Jiao, 2016). πH D and π D are respectively defined as gains of the express delivery enterprise when providing high- and low-quality ser­ vices (all parameters and variables appearing in the paper are explained one by one, with relating notations listed in Appendix A for convenience of reference and understanding). When the logistics service provider is regulated by the e-commerce enterprise as a client and supervised by the public, loss of lD which is related to reputation corruption and potential clients churn is possibly caused by non-standard operations. On the contrary, rD which refers to long-term benefits regarding improved corporate reputation, elevated consumer loyalty, enhanced competitive power and so on, under regular behaviors would benefit the express delivery enterprise.

Assumption 4. For the costs of active regulation and passive super­ vision along with their regulatory success rates, cAE < cPE , μA < μP < 1 and cAE =μA ¼ cPE =μP should be satisfied. Online buyers are entitled to provide feedbacks and comments on logistics services with the help of advanced online complaint platforms established by the e-commerce enterprises, which in one respect facili­ tates public participation in supervising logistical service providers. In another respect, however, the e-commerce enterprise suffers costly expenditure caused by the establishment and maintenance of consumer complaint platforms. Besides, it takes time and efforts for the consumers to report unsatisfied logistics service, and the e-firm with passive su­ pervision takes over such monitoring costs by offering cash rewards or discounts coupons for next purchases. Once online buyers on Taobao of Alibaba report unqualified logistics services with convinced evidence, for instance, it is the e-firm who should take the responsibility and settle the complaints usually by refunding part of the commodity/service payment. After all, an extra expense is added to passive monitoring when such a behavior is considered as an outcome of public report based on consumers’ accusation of irregular distribution to be confirmed, so cAE < cPE . Since online buyers have direct accesses to express delivery services, irregular operations can be exposed in a greater chance with verifiable consumption experiences. Traceable or even documented evidences provided by consumers naturally raise the regulatory success rate of passive supervision, which leads to μA < μP < 1 in the model formula­ tion. The proposition relationship of cAE , cPE , μA and μP is borrowed from Zhang, Zhou, and Cao (2014), which takes the form of cAE =μA ¼ cPE =μP and justifies that improved success rate is a logical sequence to a higher regulatory cost.

Assumption 2. Respective profits resulted from high- and low-quality L logistical supports of the express delivery enterprise, π H D and π D , are < πLD . adopted to distinguish regular operations from inferior ones, πH D Frequently adopted performance measures on identifying the infe­ rior logistics services (Huang & Yin, 2015) are neglected in the model formulation so that the ultimate behavior of the logistics service pro­ vider, rather than its causes or measurements, can be highlighted in the game between the two groups. After all, it is the behaviors of the de­ livery firm that constitute the premise for further discussion of strategy changes and improvements. In practice, an express delivery company with good reputations often invests more to establish well-grounded infrastructure, to develop advanced technology, and offers competitive salaries, preferable incentives and applicable training plans to invigo­ rate its employees for better satisfying consumers: All of these in the short-term will put the company in a less remunerative status. Express delivery enterprises for cost-saving purposes, conversely, invest less and L perform irregularly to reap extra gains. Therefore, π H D < π D can be logically deduced, and possibility of this supposition arises in less-developed regions where Internet commerce and logistics business are growing in a state far from mature as residents share a penchant for less expensive purchases and services.

Assumption 5. Online consumers who suffer from low-quality logis­ tics services are assumed to report such irregularities at a proportion of p, which is considered as third-party supervision and assists to distin­ guish high with low qualities of delivery services. Online consumers are acquainted with low-quality services of ex­ press delivery enterprises, however a part of them are willing to provide feedbacks and safeguard their rights at a proportion of p. Remaining consumers opt out of supervision for either time- and effort-saving purposes or inability to censure inferior express delivery services. A restitution of ω will be granted to the consumer if the criticized behavior is confirmed by the e-firm. When the e-commerce industry emerges and is still developing at an immature stage, a high reporting rate is assumed to be induced by less satisfactory services. Comparatively weakened public participation would otherwise be captured in developed economics with the advanced e-commerce industry.

Assumption 3. The e-commerce enterprise that expects to maintain its interests by eliminating violations of the delivery firm makes selections between active regulation and passive supervision, with public partici­ pation accentuated in the latter mode. The e-firm behaves autonomously to monitor logistics performance under active regulation, with an evaluation system of logistics imple­ mentations established, or by requiring the express delivery enterprise to give an account of its regular operations. On the other hand, passive supervision is conducted after public report, and is generally considered more costly than active regulation. The advanced Internet has not only facilitated the e-firm to reach out to consumers, but also provides feedbacks on logistics services from online buyers. Once the express delivery enterprise is accused of performing inferiorly by consumers, the e-firm should therefore pay extra price for consumers’ complaints and is responsible for the following investigation, which makes passive su­ pervision more costly than active regulation. π E is defined as the basic revenue of the e-commerce enterprise. The cost of e-firm’s active regulation and passive supervision are deemed as cAE and cPE , with respective regulatory success rates of μA and μP . Addi­ tional income of penalty imposed on low-quality logistics service pro­ vider is denoted by f. Expenditures of the e-commerce enterprise encompass lE referring to degenerated public praise due to unqualified

Assumption 6. Strategy selection is a dynamic evolutionary process of both groups, since individual member in each group makes decisions with bounded rationality. Suppose a proportion of x of the express delivery members perform inferiorly and remaining part of 1-x provide standard services as ex­ pected. The same supposition applies to the other community of ecommerce enterprises, with a fraction of y regulating actively and 1-y supervising passively. Sensible decision-makings of the two groups ac­ cording to repeated games are modified and refined under uncertainties’ influences to attain an equilibrium solution. The e-commerce and express delivery enterprises often fail to ach­ ieve the preferable strategy in the initial game. Strategy selections for both groups are dynamic evolutionary processes which aim to reach an advanced state. During such adaptions and progresses, more detailed 4

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information will be accumulated with the level of rationality elevated, which orients to sensible behaviors and the preferable strategy eventu­ ally. For instance, actions of the e-commerce and express delivery en­ terprise, if devoid of public supervision, are frequently sequenced as “active regulation ‒ high-quality service ‒ passive supervision ‒ lowquality service ‒ active regulation”. Both two groups make strategy adjustments in response to behaviors and results in the previous period, which reflects strategy changes during their multi-period games. The evolutionary game model formulation and analysis with corresponding numerical simulations in the following sections are adopted to illustrate such system evolutionary processes under distinct scenarios logically and intuitively.

abandoned by participants (Samuelson, 1997; Xie, 2014). The replicator dynamics equation reflects the learning speed and direction of the members in the group. Only when the value of the equation is 0, which means the learning rate is 0, then the game reaches a stable state. In the replicator dynamics, when the express delivery enterprise adopts the strategy of “low-quality service”, the replicator dynamics equation can be expressed by the following dynamic differential equation. � FD ðxÞ ¼ dx=dt ¼ x U LD U D �� �� (4) ¼ xð1 xÞ πLD πHD rD pμP π LD þ f þ lD � �� � L ðμA pμP Þ πD þ f þ lD y where x is the proportion of express delivery enterprises that provides low-quality service in the group, ULD is the payoff under “low-quality service”, and UD is the average payoff of the whole group. Therefore, FD ðxÞ shows the dynamic convergence toward ESS of the express de­ livery enterprise. M ¼ π LD πH rD pμP ðπLD þf þlD Þ and N ¼ ðμA pμP ÞðπLD þf þlD Þ D are introduced to analyse evolutionary stable strategies of the express delivery enterprise. Observations of the pay-off matrix show that M is the comparative result of the express delivery enterprise’s profits ob­ tained from low- and high-quality services under e-firm’s passive su­ pervision. N denotes distinct profits under respective contexts of passive supervision and active regulation when the express delivery enterprise is performing inferiorly. Thus, M is regarded as extra gains in inferior services compared to that of qualified operations, while N-M in some sense signifies possible losses of the express delivery enterprise’s irreg­ ularities once exposed by the e-firm. M has a downward trend with the increase of public report rate p, success rate of passive supervision μP and penalty imposed on inferior services f. In the like manner, penalty and expected loss of irregular operations f and lD impact N positively. All of these indicate that enhanced public supervision with convinced evi­ dence and sever punishment will discourage the delivery firm from implementing inferior services. From Eq. (4), FD ðxÞ ¼ 0 can be deduced when y ¼ M=N. At this time, x 2 ½0; 1�. Any value of x within the range [0,1] can achieve evolutionary stable strategy of the express delivery enterprise. In the case of y 6¼ M=N, however, either x ¼ 0 or x ¼ 1 would be selected as the decision of the delivery firm to satisfy FD ðxÞ ¼ 0, under the prerequisite of Eq. (5).

3.2. Payoff matrix of e-commerce and express delivery enterprises Given the problem illustrations and assumptions above, the payoff matrix of e-commerce and express delivery enterprises considering public supervision is presented in Table 1 (see the Proof in Appendix B). 4. Evolutionary game model of e-commerce and express delivery enterprises 4.1. Evolutionary strategies of the e-commerce and express delivery enterprises According to problem illustrations in Section 3, expected revenues of the express delivery enterprise with low- and high-quality services are explicated as ULD and UH D , with UD indicating the average level of the whole group. U LD ¼ yπL;A yÞπL;P D þ ð1 D � � L ¼ y ð1 μA ÞπD μA ðf þ lD Þ þ ð1 � þ ð1 pÞπLD

� � yÞ p ð1

μP ÞπLD

�

μP ðf þ lD Þ

(1)

¼ yπH;A yÞπ H;P D þ ð1 D � � H ¼ y πD þ rD þ ð1 yÞ π HD þ rD

(2)

U D ¼ xU LD þ ð1 xÞU HD �� � ¼ x πLD πHD rD pμP π LD þ f þ lD � �� xy ðμA pμP Þ πLD þ f þ lD þ πHD þ rD

(3)

U HD

0

FD ðxÞ ¼ ð1

� When the condition of M > 0 indicating additional benefits reaped from inferior distribution is satisfied, the express delivery firm mainly provides low-quality services. When M < 0 on the contrary, the express delivery firm feels it obliged to comply with the standard due to disadvantages in profiting and enhanced monitoring by the efirm and online buyers. � When M > 0 and M < N, both possibilities of providing low- and high-quality services exist since the comparative profit of the two alternatives is uncertain. In particular, with the value of N M

Table 1 Payoff matrix of e-commerce and express delivery enterprises. E-commerce enterprises

1

Active regulation

Passive supervision

Lowquality service

πL;A ¼ ð1 μA ÞπLD D μA ðf þ lD Þ, πL;A ¼ E πE cAE þ μA ðf lE Þ

Highquality service

πH;A ¼ πH D þ rD , D πH;A ¼ πE cAE E

πL;P μP ÞπLD D ¼ p½ð1 μP ðf þ lD Þ� þ ð1 pÞπLD , πL;P ¼ πE þ p½μP ðf lE E ωÞ cPE �

(5)

NyÞ < 0

Proposition 1. The evolutionary stable strategies of the express de­ livery enterprise, shown in Table 2, are determined by relative values of 0 M and N that result in distinct results of FD ðxÞ.

Replicator dynamics is used to simulate the process of dynamic convergence toward ESS. When the fitness or payoff of a new emergence strategy is higher than the average fitness of the population, the new strategy would be accepted by more and more participants in the group. On the contrary, the underperforming strategy would gradually be

Express delivery enterprises

2xÞðM

Table 2 ESS of the express delivery enterprise.

H;P πH;P ¼ πH ¼ πE D þ rD , πE D

pcPE

πi;j K is deemed as the expected revenue of enterprise K in the scenario (i, j). i ¼ L,

H indicates that the express delivery enterprise is providing low- and highquality logistics, respectively, j ¼ A, P signifies that the e-commerce enterprise conducts active regulation and passive supervision, respectively, and K ¼ D, E represents either the express delivery enterprise or the e-firm. 5

Presupposition

ESS

Presupposition

ESS

M > 0, N < 0

x ¼1

M < 0, N > 0

x ¼0

M > 0, 0 < N < M

x ¼1

M < 0, M < N < 0

x ¼0

M > 0, 0 < M < N, y > M=N

x ¼0

M < 0, N < M < 0,y > M=N

x ¼1

M > 0, 0 < M < N, y < M=N

x ¼1

M < 0, N < M < 0, y < M=N

x ¼0

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becomes greater, the advantage in inferior distribution would be weakened while superior service eventually wins the edge in prof­ iting. Thus, the preferable strategy of the express delivery enterprise comes to x ¼ 0. � When M < 0 and N < M, similarly, the express delivery enterprise chooses either strategy, while the relatively insignificant N orients the logistics service provider to irregularities, presenting as x ¼ 1 in Table 2.

Table 3 ESS of the e-commerce enterprise.

The Proof of Proposition 1 is provided in Appendix C. The e-commerce enterprise refers to strategies of either active regulation or passive supervision, expected revenues of respect selec­ tions UAE , UPE and the average level of the group UE can be proposed as U AE

U PE

¼ xπL;A þ ð1 xÞπH;A E �E � ¼ x πE cAE þ μA ðf lE Þ þ ð1 L;P E

H;P E

¼ xπ þ ð1 xÞπ � ¼ x πE þ pμP ðf lE

U E ¼ yU AE þ ð1 ¼ xy½μA ðf

xÞ πE

� pcPE þ ð1

ωÞ

pμP ðf

pcPE

�

�

and the replicator dynamics equation is expressed as � FE ðyÞ ¼ dy=dt ¼ y U AE U E � �� � A ¼ yð1 yÞ ½μA ðf lE Þ pμP ðf lE ωÞ�x cE pcPE

J

B B ð1 B B B ¼B B B B yð1 @ � ¼

ð1

yÞ½μA ðf 2xÞðM yð1

yÞS

lE Þ

pμP ðf

NyÞ

xð1 ð1

lE

ωÞ�

;

y ¼0

T < 0, S > 0

y ¼1

T > 0, 0 < T < S, x > T=S

y ¼1

T < 0, T < S < 0

y ¼1

T > 0, 0 < T < S, x < T=S

y ¼0

T < 0, S < T < 0, x > T=S

y ¼0

T < 0, S < T < 0, x < T=S

y ¼1

2yÞðSx

TÞ < 0

(10)

(7)

(8)

Proposition 2. The evolutionary stable strategies of the e-commerce enterprise, shown in Table 3, are determined by relative values of T and 0 S that lead to distinct results of FE ðyÞ. � When T > 0, the e-commerce enterprise frequently chooses passive supervision for benefiting and cost-saving purposes. When T > 0 and 0 < S < T in particular, active regulation is at an absolute disad­ vantage considering both mathematical and realistic implications of S and T. Conversely, the e-firm turns to active initiatives when T < 0, in which case proactive regulation possesses competitive edge. On occasions of T < S < 0 and T < 0 < S, active regulation is over­ whelmingly superior to passive supervision. � When T > 0 and T < S, both possibilities of active regulation and passive supervision exist, since the comparative profit of the two strategies is uncertain. With the value of S T becomes greater, the advantage in passive supervision reduces while active regulation turns out to be more beneficial. Thus, the preferable strategy of the express delivery enterprise comes to y ¼ 1. � When T < 0 and S < T, the e-firm chooses either strategy, while the relatively significant T guides all members to passive supervision, presenting as y ¼ 0.

(9)

We now turn to further discussions of the dynamic evolutionary game model based on replicator dynamics equations and equilibrium solutions obtained as ð0; 0Þ, ð0; 1Þ, ð1; 0Þ, ð1; 1Þ, ðx0 ;y0 Þ, where x0 ¼ T=S and y0 ¼ M=N respectively make all x 2 ½0; 1�and y 2 ½0; 1� of the e-firm and express delivery enterprise evolutionary stable strategies. The Ja­ cobian Matrix, showing as J in Eq. (11), is exploited for ESS selections among the five optimal points. The determinant and trace of the matrix are computed in Eqs. (12) and (13).

1 xð1

ð1

� xÞ ðμA 8 < ½μA ðf 2yÞ : �cA E

pμP Þ πLD þ f þ lD pμP ðf

lE Þ pcPE

�

xÞN

2yÞðSx

�9 rD =

T > 0, 0 < S < T

Further calculations to reach evolutionary stable strategies under different values of S and T are conducted, with the following Proposition 2 and Table 3 proposed (see Appendix D for Proof).

L;A toring. S ¼ πL;A πL;P πH;A ðπL;P πH;P E E þ T ¼ ðπ E E Þ E E Þ indicates the relative strength of active regulation when the express delivery is providing low-quality services. S-T signifies additional profits of the ecommerce enterprise when regulating actively in the face of express delivery firm’s inferior performance. Therefore, T can be regarded as ecommerce enterprise’s unfavorable situations of benefiting and costsaving caused by active regulation, and S signifies great rewards in regulating actively. With the increase of S and decrease of T, the e-firm would rely more on active regulation. All strategies of y 2 ½0; 1� are equilibrium solutions if FE ðyÞ ¼ 0 is

8� � < πLD π HD pμP πLD þ f þ lD 2xÞ �� : �ðμ pμP Þ π LD þ f þ lD y A

ESS

0

where y is the proportion of e-firms that regulate actively in the group, UAE is the payoff under “active regulation”, and UE is the average payoff of the whole group. When UAE is higher than UE , “active regulation” would be used by more and more e-firms in the group over time, and finally achieves the evolutionary stable strategy. Therefore, FE ðyÞ shows the dynamic convergence toward system stability. Suppose S ¼ μA ðf lE Þ pμP ðf lE ωÞ, T ¼ cAE pcPE , and recall the pay-off matrix in Subsection 3.2, we have T from one perspective representing distinct profits of the e-firm between passive supervision and active regulation when the express delivery enterprise performs as expected. Observing mathematical expression of T, on the other hand, we find it elaborating distinct costs between active and passive moni­

0

Presupposition

FE ðyÞ ¼ ð1

pcPE

ωÞ þ πE

lE

y cAE

ωÞ�

lE

pcPE

xÞ πE

ESS

satisfied when x ¼ T=S. Otherwise, only two preferential decisions of y ¼ 0 and y ¼ 1 turn out to be preconditions for the achievement of evolutionary stable strategy. The following condition being matched concurrently.

(6)

�

yÞU PE lE Þ

þ xpμP ðf

cAE

Presupposition

TÞ

6

�

��

lE

C C C C C 9C ωÞ�x = C C C ;A

(11)

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Table 4 ESS of e-commerce and express delivery enterprises. Scenario І

ESS

Scenario ІІ

ESS

Scenario ІІІ

ESS

(1) M > 0,S < T

(1,0)

(1) N < M < 0,S < T < 0

none

M < N,T < 0

(0,1)

(2) N < M,T < S

(1,1)

(2) 0 < M < N,0 < T < S

none

M < 0,T > 0

(0,0)

detJ ¼ ð1 trJ ¼ ð1

2xÞð1 2xÞðM

2yÞðM NyÞ þ ð1

NyÞðSx 2yÞðSx

TÞ þ xyð1

xÞð1

yÞNS

TÞ

attained with equilibrium solutions of the two groups presented in Table 5 (See Appendix F for Proof).

(12) (13)

Proposition 4. For the evolutionary stable strategies in Scenario І, the express delivery enterprise chooses to perform unqualified services, when faced with μA < pμP < λ and pμP < μA < λ.

detJ > 0 and trJ < 0 should be satisfied when the considering point is substituted into the matrix. When the trace of the matrix equals to zero, then the certain solution is called a saddle point (Yu & Liu, 2016).

When the value of benefit-loss ratio is greater than those of both regulatory success rates, the corresponding strategies are expressed as xІ;ð1Þ ¼ 1, yІ;ð1Þ ¼ 0 and xІ;ð2Þ ¼ 1 yІ;ð2Þ ¼ 1, which are the same in Sce­ nario І of Table 4. Fig. 1(a) and Fig. 1(b) depict Scenario І (1) and Scenario І (2) respectively, both of which show that the express delivery firm heads to providing low-quality services despite e-commerce enterprise’s regula­ tions. Apparently, equilibrium solutions in Scenario І reflect unfavor­ able situations of the e-commerce enterprise. The e-firm, thrown into passivity, refers to either active regulation or public supervision. To opt out of such plights, the e-firm is recommended to conduct stringent monitoring and take associated penalty measures to avoid aggravation of current situations. Increasing the punishment parameter f and diminishing the benefit-loss ratio λ will be applicable measures. Such endeavors lead the game between the stakeholders to evolve gradually from Fig. 1 into a stage as Fig. 2 delineates.

Proposition 3. For the evolutionary stable strategies of the e-com­ merce and express delivery enterprises in Table 4, additional gains reaped from inferior services noting as M ¼ πLD π H pμP ðπ LD þfþlD Þ D rD guide the express delivery enterprise to make decisions, and the ebusiness corporation essentially consults to T ¼ cAE pcPE representing the profit or cost variances between its two alternatives to make strategies. � In Scenario І, when extra benefits can be generated during inferior logistics operations (the value of M is considerably large), the express delivery enterprise takes little account of the e-firm’s behaviors and provides low-quality services. The e-firm, thrown into such passivity, acts either positively or passively depending on the value of S-T that suggests advantages of active regulation. Equilibrium solutions are expressed as xІ;ð1Þ ¼ 1, yІ;ð1Þ ¼ 0 in Scenario І(1) and xІ;ð2Þ ¼ 1, yІ;ð2Þ ¼ 1 in Scenario І(2) of the two groups. � In Scenario ІІ(1), benefits and losses of providing inferior services are uncertain since both M < 0 and N M < 0 are satisfied. S T < 0 and T < 0 make the e-firm’s strategy preference ambiguous. It is a much similar case under conditions of 0 < M < N and 0 < T < S in Scenario ІІ(2). Thus, Scenario ІІ cannot arrive at a stable stage, and the game between e-commerce and express delivery enterprises is in a transition period. Strategy selections of both groups are interdependable at this moment, and sensible incentives of punishments of the e-firm would behoove the delivery firm in providing superior services. � In Scenario ІІІ, inferior implementations of the express delivery enterprise are prevented by decreased benefit of operating irregu­ larly (a comparatively small value of M), which is the result of increased forfeit f and long-time interests rD . The logistics service provider will assume the liability to provide high-quality services. The Internet firm acts proactively when T < 0 showing its edge in cost-saving, or turns to passive supervision based on public super­ vision when T > 0. Stable strategies are expressed as xІІІ;ð1Þ ¼ 0, yІІІ;ð1Þ ¼ 1 in Scenario ІІІ(1) and xІІІ;ð2Þ ¼ 0, yІІІ;ð2Þ ¼ 0 in Scenario ІІІ(2).

Proposition 5. For the decisions-makings of both groups in Scenario ІІ, evolutionary stable strategies cannot be achieved. In the contexts of μA < λ < pμP and pμP < λ < μA in Scenario ІІ.The benefit-loss ratio stays at a relatively moderate value between distinct regulatory success rates, which results in a transition period of the game between the e-firm and logistics service provider. On such occasions as Fig. 2 shows, strategy selections of both groups are inter-dependable. Incentives of the e-firm would encourage the ex­ press delivery enterprise to provide high-quality logistics services. Taking the derivation of p to y0 , where L L y0 ¼ M =N ¼ ½πLD πH p μ ð π þf þl Þ r � = ½ð μ p μ Þð π þf þlD Þ� D D P D A P D D has been introduced in Subsection 4.1, following corollaries can be obtained. Corollary 1. ∂y0 =∂p > 0 is deduced under the condition of μA < λ < pμP in Scenario ІІ(1), which indicates that the growth of λ should be restrained by improving active regulatory success rate and escalating evolutionary regions G and H in Fig. 2(a). Challenge faced by the e-firm in such a circumstance is to take im­ mediate actions, regular inspection or random checking for instance to prevent the express delivery enterprise from transgression. The phase graph here steps forwards to Fig. 3(a) from Fig. 2(a).

The Proof of Proposition 3 is provided in Appendix E.

Corollary 2. ∂y0 =∂p < 0 is derived from pμP < λ < μA in Scenario ІІ(2). Enhanced passive regulatory success rate and public report would

4.2. Strategy evolvements from the perspective of benefit-loss comparison Strategy evolvements of e-commerce and express delivery enter­ prises based on benefit-loss comparison are proposed in following in­ L vestigations. Let λ ¼ ðπLD π H D rD Þ =ðπ D þf þlD Þ be the benefit-loss ratio, ðπ LD πH r Þ represents extra benefits of the express delivery enterprise D D by providing low-quality services in the face of distinct regulatory strategies, and ðπLD þf þlD Þ indicates avoidable losses if the delivery firm performs regularly. Combining the Jacobian Matrix in Subsection 4.1 with prerequisites for ESS achievements, following propositions can be

Table 5 ESS of e-commerce and express delivery enterprises considering benefit-loss ratio. Scenario І

ESS

Scenario ІІ

ESS

Scenario ІІІ

ESS

(1) μA < pμP < λ

(1,0)

(1) μA < λ < pμP

none

λ < μA < pμP

(0,1)

(2) pμP < μA < λ

7

(1,1)

(2) pμP < λ < μA

none

(2)λ < pμP < μA

(0,0)

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Fig. 1. Evolutionary games faced with frequent irregularities of the express delivery enterprise.

Fig. 2. Transition period of the evolutionary game.

Fig. 3. Evolutionary games faced with frequent regularities of the express delivery enterprise.

facilitate the expansion of regions I and J in Fig. 2(b).

the express delivery enterprises insist on providing high-quality services regardless e-firm’s surveillances.

Motivational measures are suggested for the e-firm to improve suc­ cess rate of passive inspection. Reasonable arrangements of manpower and material resources, or inspired public with attentive supervisions and accurate testimonies will facilitate the progress of Fig. 3(b) from Fig. 2(b). Proposition 6.

When λ < μA < pμP and λ < pμP < μA , values of benefit-loss ratios are showing disadvantages compared to those of regulatory success rates. Stable strategies are presented as xІІІ;ð1Þ ¼ 0, yІІІ;ð1Þ ¼ 1 in Scenario ІІІ(1) and xІІІ;ð2Þ ¼ 0, yІІІ;ð2Þ ¼ 0 in Scenario ІІІ(2), which are the same in Scenario ІІІ of Table 4.

For the evolutionary stable strategies in Scenario ІІІ, 8

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Fig. 3 depicts such situations when it is impeccably favorable to access high-quality logistics services since unqualified services would be detrimental in profiting. Rational express delivery enterprises will operate in accordance with normal standards. The e-firm is thought to devote itself to making decisions sensibly by conducting either active regulation or passive supervision. Profit maxi­ mization could be accentuated if acting proactively, and efforts in inhibiting inferior delivery services eventually turn out to be applicable and productive. When supervising passively, the e-commerce enterprise could embrace its preferable moment, as the logistics service provider is discouraged to behave irregularly and online buyers are committed to public inspections. Thus, improved regulatory success rate and reduced monitoring expenditures can be achieved. According to Figs. 1–3, the following observation can be obtained. Observation 1. � When the initial stage is μA < pμP < λ in Scenario І (1), the e-com­ merce enterprise is recommended monitoring actively to deter de­ livery firm’s irregularities. The corresponding path of evolvement is expressed as Fig. 1(a) → Fig. 2(a) → Fig. 3(a). � When pμP < μA < λ in Scenario І(2) is observed as the elementary situation, intensified regulation is suggested by increasing forfeit of inferior services, in order to reach a declined benefit-loss ratio in providing unqualified services. The corresponding path of strategy evolvements is delineated as Fig. 1(b) → Fig. 2(b) → Fig. 3(b). Otherwise, the parallel evolutionary process of Fig. 1(b) → Fig. 2(a) → Fig. 3(a) is accessible, if public participation is enlivened in management and control of low-quality logistics services.

Fig. 5. Impacts of μA on strategy evolution.

5.1. Impacts of pertinent parameters on strategy evolution Impacts of relevant parameters regarding penalty imposed on lowquality logistics service f, regulatory success rate μA , regulatory costs cAE and cPE , and public report rate p are analysed. Influences of penalty f caused by inferior logistics services imposed on the express delivery enterprise are illuminated in Fig. 4. Considering that the e-commerce industry is just emerging and still develops at its early age, relevant parameters are assigned as π LD ¼ 5, πH D ¼ 1, μA ¼ 0:6, μP ¼ 0:8 lE ¼ 0:15, lD ¼ 0:3, ω ¼ 0:35, rD ¼ 0:2, p ¼ 0:8.

5. Numerical analysis In this section, numerical analyses are emerged to probe into the evolutionary game between the e-commerce and express delivery en­ terprises, with online consumers participating as the third-party super­ visor. Subsection 5.1 delineates influences of relevant parameters in the evolutionary processes. Relations of public report proportion, benefitloss ratio and regulatory success rates are illuminated in Subsection 5.2.

Observation 2. Realizing that a high penalty will be imposed on unqualified services, the express delivery enterprise behaves regularly and x ¼ 0 can be

Fig. 4. Impacts of f on strategy evolution. 9

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πHD ¼ 1, cPE ¼ 1:33, μP ¼ 0:8, lE ¼ 0:15, lD ¼ 0:3, ω ¼ 0:35, rD ¼ 0:2, f ¼ 0:8, p ¼ 0:8. When the e-commerce industry is just emerging and still developing, superior logistics services turn out to be less remunerative for the ex­ press delivery enterprise, which in some sense indicates that providing low-quality services is overwhelmingly profitable than high-quality services. Therefore, the express delivery firm is more inclined to oper­ ate irregularly in the pursuit of profit maximization, which brings frequent complaints of online buyers who suffer from unsatisfied services. Observation 3. Faced with comparatively greater profits in providing low-quality logistics services, the express delivery enterprise directs to inferior performance at an increasing rate with the decrease of active regulatory success rate μA , and ends up at x ¼ 1 (Fig. 5). Strategy selection of the efirm is convergent to y ¼ 1 at an accelerating rate since the comparative cost between passive supervision and active regulation increases (Fig. 6). Combining the proportional relationship between regulatory costs and success rates, we find that the monitoring cost, other than the regulatory success rate, is the most critical influencing element in ecommerce strategy. When the e-commerce industry is still at its immature stage, the efirm suffers from the relatively low success rate of regulation, and the express delivery enterprise takes such chance to reap additional benefits by performing low-quality services. In the face of unqualified logistics services that frequently show up, the e-commerce enterprise should conduct monitoring proactively despite the deficient success rate of active regulation. Monitoring costs should be reduced by establishing information tracking system for logistical performance to participate actively in ameliorating irregularities of the express delivery enterprise. As the e-commerce industry develops unremittingly, comparative profit between low- and high-quality logistics services reduce, while long-term benefits of superior distributions would be improved. Perti­ nent parameters are showing as πLD ¼ 3, πH D ¼ 2, μA ¼ 0:6, μP ¼ 0:8, lE ¼ 0:15, lD ¼ 0:3, ω ¼ 0:35, rD ¼ 0:5, f ¼ 0:8. Different values of public report rate p are set to delineate Fig. 7.

Fig. 6. Impacts of cAE =cPE on strategy evolution.

attained as t increases in Fig. 4(a). On the contrary, inferior performance occurs due to weakened punishment intensity, and the evolutionary stable strategy reaches x ¼ 1 at an increased convergence rate as f goes up. For the e-commerce enterprise in Fig. 4(b) faced with a relatively high rate of public report (p ¼ 0:8), its stable strategy gradually arrives at y ¼ 1 with the growth of penalty at a decreasing rate, and all members conduct active regulation eventually. When μA is 0.3, 0.4, 0.5, 0.6 respectively, which implies the gradual progress of the e-commerce industry with improved monitoring success rate of active regulation. Impacts of the regulatory success rate on strategy evolutions of the express delivery enterprise are delineated in Fig. 5. Considering continuous efforts of the e-firm in reducing regula­ tory costs with the development of the e-commerce industry, cAE is respectively assigned at 1, 0.83, 0.67, 0.5. Fig. 6 shows evolutionary process of strategy selections of the e-firm from a monitoring cost comparison view. Remaining parameters are set as follows, π LD ¼ 5,

Observation 4.

Fig. 7. Impacts of p on strategy evolution. 10

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In the presence of a relatively lower public supervision rate p, the ex­ press delivery enterprise tends to provide low-quality services and the efirm regulates passively at an increasing rate with the decrease of p. On the contrary, when online buyers actively participate in supervising unqualified logistics services, presenting as a greater value of p, enhanced report rate would impede low-quality logistics services of the express delivery enterprise and facilitate the e-firm to conduct active regulation ultimately. In Fig. 7(a), with the increase of p, the express delivery enterprise makes strategy changes from performing inferiorly to operating regu­ larly. In Fig. 7(b), p has a positive impact on active regulation of the efirm. When the public report rate is at a lower level, p ¼ 0:3 and 0.4 here, the e-firm tends to passive supervision at a decreasing rate with the increase of p, and finally reaches to y ¼ 0 which indicates that all members supervise passively. When the public report rate achieves a higher degree, however, the e-commerce enterprise heads to active regulation at a growing rate as p rises and eventually arrives at the stable strategy of y ¼ 1. We find evidence in practical operations that the efirm in the initial stage of the e-commerce industry is suffered from excessive costs when dealing with consumer complaints, which could be caused by frequently irregular operations of the express delivery en­ terprises. Moreover, either misunderstandings between consumers and delivery firms, or unreasonable requirements made by customers to obtain bonus for online shopping would also raise the public report rate. Faced with such a dilemma, the e-commerce enterprise prefers to conduct active regulation for cost-saving purposes. As the e-commerce industry develops continuously, reduced public report rate that in­ dicates ameliorated irregularities of the express delivery enterprise, would guide the e-firm to passive supervision.

Fig. 9. Relations of y0 μA , μP and p.

0.45, 0.50, 0.55. According to pμP < λ < μA , a presupposition of 0 < p < 0:5625 should be satisfied in advance. Relations between p and y0 are illuminated in Fig. 8. Fig. 9 explains correlations of public report pro­ portion p, regulatory success rates μA , μP and y0 , when λ ¼ 0:45 is set constantly.

5.2. Relations of regulatory success rates and the benefit-loss ratio

Observation 5. When the public report proportion stays within a certain range, motivated public participation in supervising lowquality logistics services ensures a higher success rate of passive supervision for the e-commerce to rely on public participation.

A typical numerical analysis is emerged based on the presupposition of pμP < λ < μA in Scenario ІІ(2), where the value of the benefit-loss ratio is respectively greater and less than the success rates of passive supervision and active regulation. Relations of public participation rate p, benefit-loss ratio λ, and regulatory success rates μA , μP are depicted to investigate their impacts on enterprises’ decision-makings. Relevant parameters are set as μA ¼ 0:6, μP ¼ 0:8, πLD ¼ 3, f ¼ 0:8 and lD ¼ 0:3 in analyzing influences of public report rate on strategy selections in the presence of diverse benefit-loss ratios defined as λ ¼

Fig. 8 suggests that y0 and p are concavely and negatively related. Therefore, when public supervision is at a relatively deficient level, 0 < p < 0:5625 here, the raised public participation rate leads to greater possibility of passive regulation. Meanwhile, y0 is more responsive to p under less consequential λ, and the e-firm is more inclined to conduct passive supervision. Observation 6. Improved regulatory success rates and public report proportion facilitate the e-commerce enterprise in profiting and ameliorate delivery irregularities of the logistics service provider. Pairwise comparisons of the three curves in Fig. 9 show that in either circumstance of the increase of success rate under active regulation (passive supervision) or the rise of public report proportion p, y0 will diminish as a result, on the condition of a constant value of the success rate of passive supervision (active regulation). In addition, increased μA and μP lead to a descendant movement of the curves, which implies that advanced regulatory success rates help to prevent the express delivery enterprise from behaving irregularly and benefit the e-firm in costsaving through passive supervision. 6. Conclusions and implications Notwithstanding the thriving of e-commerce, corresponding logistics service providers are frequently accused of behaving irregularly in providing unqualified delivery services, which inhibits further expan­ sion of both e-commerce and logistics developments. This paper in­ vestigates evolving strategies of the e-commerce and express delivery enterprises considering public participation. Using evolutionary game

Fig. 8. Relations of y0 , p and λ. 11

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method, we analyse strategy selections and adaptations to illuminate equilibrium solutions of system stability. Main concluding remarks are summarized as follows.

business, mobile terminal techs are applicable to maintain information transformations and exchanges, which ensures bidirectional logistics and information between e-firms and delivery companies. To reduce regulatory cost, the e-firm could build an e-commerce supply chain where the transaction process is fully electronic. Within this context, e-commerce platform is the core of the supply chain, which connects information, logistics, business in an integrated fashion. The logistics performance, thereby, could be managed through the e-com­ merce platform without any intermediate costs. The e-commerce plat­ form would facilitate the free flow of management resources of the efirm in supervising express delivery enterprise, and in all aspects within the supply chain, thus reducing regulatory cost. With continuous progress of the e-commerce industry, comparative profit between low- and high-quality logistics services reduces. The express delivery enterprise is more inclined to perform regularly, and the complaint rate of online consumers dramatically declines. The e-commerce enterprise should rely on online consumers to conduct passive supervision. Therefore, productive measures should be taken to invigorate public participation. Primary, with the help of advanced IT architecture and strong data analysis capabilities, the aftersales platform should be launched and maintained constantly, for example in the form of encouraging consumer reviews and recommen­ dations. Then the e-firm could track relevant feedbacks involving lo­ gistics operations, and provide better consumer services. Moreover, public supervision would be motivated if cash rewards or discounts coupons for next purchases are offered for online consumers if they report unqualified logistics services. Increased compensation for con­ sumers who suffer from and report low-quality logistics services that are confirmed by the e-firm, would also helpful. Once online buyers on Taobao of Alibaba report unqualified logistics services with convinced evidence, for instance, the e-firm normally refund part of the purchases. For the express delivery enterprise, it’s high time to further improve logistics service and reduce cost in the e-commerce context. To enhance quality of logistics services, the express delivery enterprise should con­ nect its general services to e-commerce business. Information of regular shopping time and consumption areas is traceable in the e-commerce context, which helps the delivery company to operate in a flexible manner and provide better services. Based on the information accu­ mulated, the express delivery enterprise should consider providing various delivery options, for example, to allow consumers to select the most convenient delivery form, time and location. Such personalized services would turn to new profits of the logistics providers as well as further progress e-commerce development. With the development of IOT and big data technology, the express delivery enterprise could integrate logistics resources and information based on the idea of sharing economy, building a sharing information platform to cut down operational costs. Through such a platform that is fully electronic, idle resources involving logistics capability and infra­ structure could be integrated and allocated in a cost-saving fashion. In particular, the platform should provide information regarding logistics service orders, regional distribution centers, available staff to dispatch parcels, delivery options including home delivery, self-pick up or thirdparty collection, to facilitate and economize logistics operations. In such a context, joint distribution and crowdsourcing distribution, as well as home delivery and self-pick up, are selectable, so that resources can be used precisely and economically. It is beyond the scope of the current paper to consider adaptive changes that should be made if risk preferences of both stakeholders are considered and, therefore, corresponding risk coefficients turn out to be influential in strategy selections. Besides, the progressed Internet will surely give rise to advanced online review platforms, which will facili­ tate public supervision and reduce monitoring costs of passive supervi­ sion eventually. Thus, the proportional relationship between regulatory costs and success rates of cAE =μA ¼ cPE =μP in the proposed model may not hold in future cases. Considering individual differences within the same

� The express delivery enterprise mainly relies on additional profits obtained from inferior performances M, rewards in providing supe­ rior logistics service N and the benefit-loss ratio λ, to make strategies. Yet, both possibilities of providing low- and high-quality services exist when the comparative profit of the two alternatives is uncer­ tain. In particular, when the value of N-M is considerably large, the express delivery enterprise tends to provide high-quality logistics services even if extra benefits can be reaped from low-quality operations. � For the e-commerce enterprise, regulatory cost advantage of passive supervision T and competitive profit of active regulation S, rather than the regulatory success rates μA and μP , play a decisive role in strategy selections. With the increase of T, the e-firms tend to su­ pervise passively at a higher proportion. The e-firms choose active regulation or passive supervision when the comparative cost of the two strategies are uncertain. Active regulation would be preferred if the value of S-T is relatively greater, though it is costly. � When online consumers participate at a deficient level (i.e., value of p is small), a growing number of e-firms in the group conduct passive supervision with the increase of public report rate. Otherwise, active regulation is preferred for the group to reach an evolutionary stable strategy. � When benefits and losses of providing low-quality services are equivocal, the value of benefit-loss ratio λ stays at a relatively moderate level. Evolutionary strategies of the e-commerce and ex­ press delivery enterprises fail to achieve a stable state. From above, we find that two parameters (i.e., comparative profit of providing low- and high-quality services M, and benefit-loss ratio λ) are most critical in strategy selections of the express delivery enterprise. Comparative regulatory cost T and public report rate p, play a decisive role for the e-firm to choose active regulation or passive supervision. Based on the conclusions, we find some managerial insights for e-com­ merce and express delivery enterprises in distinct development stages of e-commerce industry, mainly from three perspectives: (1) To strengthen active regulation and decrease regulatory cost for the e-firm in the immature stage of e-commerce industry (to reduce the value of T); (2) To enhance public participation and passive supervision for the e-firm in the mature stage of e-commerce development (to raise the value of p); (3) To improve logistics service and reduce cost for the express delivery enterprise (to reduce the values of M and λ). We clarify the details as follows. When the e-commerce industry is still developing and stays at its immature stage, extra benefits can be reaped from providing low-quality services due to high operational cost of superior services. This possibility arises when consumers in less-developed regions share a penchant for cheaper goods and services. At this time, the express delivery enterprise frequently conducts irregular operations. Online consumers suffering from low-quality logistics services report such irregularities in a higher possibility. Therefore, the e-commerce enterprise should respond forcefully to conduct active regulation, as well as reduce monitoring costs for cost-saving and profit-optimizing purposes. To effectively supervise the express delivery enterprise, the e-firm is suggested to monitor logistics performance by establishing a real-time evaluation platform. The e-commerce platform must upgrade its after­ sales tracking service constantly. It is proven that cloud computing, Internet of Things (IOT) and big data technology (Fernandez-Gago, Moyano, & Lopez, 2017; Persico, Pescap�e, Picariello, & Sperlí, 2018) could be adopted to enhance tracking services of e-commerce platforms in multiple decision-making levels. In addition to enhance its own operational platform, the e-firm should use the advanced mobile ter­ minal techniques. In response to continuous movement of logistics 12

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Acknowledgement

group, extended evolutionary game theory along with typical extended replication dynamics equations can be used to include various evolving strategies within the group. Finally, governmental involvement via laws and regulations also deserve studying as either an external influence or a part of the game.

We thank the anonymous reviewers and the editor for their valuable comments that have significantly improved the paper. This work was supported by the National Natural Science Foundation of China [71371169, 71874159, U1509220], the Natural Science Foundation of Zhejiang [LY18G020020].

Declaration of competing interest None.

Appendix A. Notations and definitions

Table A.1 The express delivery firm

Notations

Definitions

lD rD f

Reputation corruption and potential clients churn of the express delivery enterprise due to low-quality services Long-term benefits of the express delivery enterprise providing high-quality services Penalty imposed by the e-commerce enterprise on low-quality logistics service Profits of the express delivery enterprise in providing low-quality logistics services

πLD πHD

x ULD

UH D

The e-firm

Proportion of the express delivery enterprises who provide low-quality logistics services Expected revenue of the express delivery enterprises when providing low-quality logistics services

Expected revenue of the express delivery enterprises when providing high-quality logistics services

UD cAE

The average revenue of express delivery enterprises

μA μP

Regulatory success rate of e-commerce enterprises’ active regulation Regulatory success rate of e-commerce enterprises’ passive supervision Degenerated public praise of the e-firm due to low-quality services of the express delivery enterprise Compensation for customers if the criticized behavior is confirmed by the e-commerce enterprise Proportion of the e-commerce enterprises who conduct active regulation Basic revenue of the e-commerce enterprise Expected revenue of the e-commerce enterprises when regulating actively

cPE lE

ω y

πE

UAE

Other notations

Profits of the express delivery enterprise in providing high-quality logistics services

Cost of e-commerce enterprises’ active regulation

Cost of e-commerce enterprises’ passive supervision

UPE

Expected revenue of the e-commerce enterprises when supervising passively

πi;j K

The proportion of customer who report low-quality logistics services Benefit-loss ratio of the express delivery enterprise when providing low-quality services x0 ¼ T=S, which makes all selections of the e-firm evolutionary stable strategies y0 ¼ M=N, which makes all selections of the express delivery enterprise evolutionary stable strategies The Jacobian Matrix i ¼ L, H, where L and H represent low- and high- quality logistics services of the express delivery enterprise j ¼ A, P, where A and P represent active regulation and passive supervision of the e-commerce enterprise K ¼ D, E, where D and E represent the express deliver firm and e-commerce enterprise, respectively The expected revenue of enterprise K when the express delivery enterprise performs i and the e-firm regulates j

N

Comparative profit of the express delivery enterprise under passive supervision and active regulation,N ¼ πL;P D

UE p λ x0 y0 J i j K

The average revenue of e-commerce enterprises

M

Extra gains in low-quality services compared to that of high-quality operations,M ¼ πL;P D

T S

πH;P D

Comparative revenue of the e-firm under passive supervision and active regulation, T ¼ πH;P E

πH;A E

Relative strength of active regulation when the express delivery is providing low-quality services, S ¼ πL;A E

πL;A D

πL;P E þ T

Appendix B. The payoff matrix of the e-commerce and express delivery enterprises i;j

i;j

Deem πD and πE as expected revenues of the express delivery enterprise and e-firm respectively. � On the occasion of (low-quality service, active regulation), which signifies that the express delivery enterprise provides low-quality logistics service L and the e-commerce enterprise regulates actively, expected revenues of the two stakeholders are presented as πL;A D ¼ ð1 μA Þπ D

π L;A ¼ πE E

cAE þ μA ðf

lE Þ.

H;A � On the occasion of (high-quality service, active regulation), πH;A ¼ πH ¼ πE D þ rD , π E D

μA ðf þlD Þ and

cAE .

Public participation is absent on above two occasions, when the e-commerce enterprise conducts active regulation. So parameters relevant to

H;A public report rate p and compensation for consumers if the criticized behavior is confirmed by the e-firm ω don’t exist in the expression of πL;A E and π E . L L � On the occasion of (low-quality service, passive supervision), πL;P D ¼ ð1 pÞπ D þ p½ð1 μP Þπ D

� On the occasion of (high-quality service, passive supervision),

πH;P D

¼

πHD

The Proof of Table 1 is completed. 13

þ

rD , πH;P E

¼ πE

μP ðf þ lD Þ�, πL;P ¼ πE þ pμp ðf E

pcAE .

lE

ωÞ

pcAE .

X. Zhang et al.

Research in Transportation Economics xxx (xxxx) xxx

Appendix C. The evolutionary stable strategies of the express delivery enterprise The evolutionary stable strategy (ESS) of the express delivery enterprise is obtained by satisfying Eq. (5), with diverse values of M and N. � When M > 0 and N < 0, we have y > M=N, therefore x ¼ 1 is the solution to satisfy FD ðxÞ ¼ ð1 2xÞðM NyÞ < 0. � When M > 0 and 0 < M < N, we have 0 < M=N < 1. Both possibilities of y > M=N and y < M=N comes into notice, and respective solutions of x ¼ 0 and x ¼ 1 can be attained. � When M > 0 and 0 < N < M, we have M=N > 1 resulting to y < M=N, so that x ¼ 1 is the solution. 0

In the like manner, remaining solutions can be achieved when M < 0 with distinct values of N. The Proof of Table 2 is completed. Equilibrium solutions in Table 2 show that most (3 of 4) results happen to be x ¼ 1 when M > 0, while x ¼ 0 occupies most (3 of 4) positions when M < 0, so that Proposition 1 can be deduced. The Proof of Proposition 1 and Table 2 is completed. Appendix D. The evolutionary stable strategies of the e-commerce enterprise The evolutionary stable strategy (ESS) of the e-commerce enterprise is obtained by satisfying Eq. (10), with diverse values of S and T. When T > 0, we have p < cAE =cPE , so that p < ½cAE ðf lE Þ� =½cPE ðf lE ωÞ� ¼ ½μA ðf lE Þ� = ½μP ðf lE ωÞ�since a proportion relationship of cAE =μA ¼ cPE = μP has been brought out, therefore S > 0 is extrapolated. � When T > 0 and 0 < S < T, we have x < T=S, and y ¼ 0 should be the only solution to meet the prerequisite of FD ðyÞ ¼ ð1 � When T > 0 and 0 < T < S, respective situations of x > T=S and x < T=S orient to y ¼ 1 and y ¼ 0. 0

2yÞðSx

TÞ < 0.

Similarly, solutions in the context of T < 0 with different S can be attained, as Table 3 presented. Equilibrium solutions in Table 3 indicate that most results (2 of 3) in the context of T > 0 are y ¼ 0, while T < 0 facilitates the generation of y ¼ 1 (3 of 4). Thus, Proposition 2 can be deduced. The Proof of Proposition 2 and Table 3 is completed. Appendix E. ESS of the e-commerce and express delivery enterprises The evolutionary stable strategy (ESS) of the system can be achieved by satisfying detJ > 0 and trJ < 0, plug all possible solutions into Eqs. (12) and (13), we have the following table providing stability investigations. Table E.1 Stability investigations of equilibrium solutions. Presupposition

Equilibrium solutions

(1)

M > 0, N < M T > 0, S < T

(2)

M > 0, N < M T > 0, T < S

(3)

M > 0, N < M T < 0, S < T

(4)

M > 0, N < M T < 0, T < S

(5)

M > 0, M < N T > 0, S < T

(6)

M > 0, M < N T > 0, T < S

(7)

M > 0, M < N T < 0, S < T

(8)

M > 0, M < N T < 0, T < S

(9)

M < 0, N < M T > 0, S < T

(10)

M < 0, N < M T > 0, T < S

(11)

M < 0, N < M

detJ trJ Results detJ trJ Results detJ trJ Results detJ trJ Results detJ trJ Results detJ trJ Results detJ trJ Results detJ trJ Results detJ trJ Results detJ trJ Results detJ

(0,0)

(0,1)

(1,0)

(1,1)

– �

þ þ

– �

– �

þ þ

þ – ESS – �

þ þ

– �

þ þ

– �

– �

– �

– �

– �

þ þ

þ – ESS þ – ESS þ þ

þ – ESS – �

þ þ

– �

– �

þ þ

þ þ

–

–

þ – ESS –

þ þ þ – ESS þ – ESS –

þ – ESS – � þ – ESS – �

þ – ESS – � þ – ESS þ þ – �

– �

ðx0 ; y0 Þ 0 Saddle point 0 Saddle point 0 Saddle point 0 Saddle point 0 Saddle point 0 Saddle point 0 Saddle point 0 Saddle point 0 Saddle point 0 Saddle point (continued on next page)

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X. Zhang et al.

Table E.1 (continued ) Presupposition

Equilibrium solutions

T < 0, S < T

1

(12)

M < 0, N < M T < 0, T < S

(13)

M < 0, M < N T > 0, S < T

(14)

M < 0, M < N T > 0, T < S

(15)

M < 0, M < N T < 0, S < T

(16)

M < 0, M < N T < 0, T < S

trJ Results detJ trJ Results detJ trJ Results detJ trJ Results detJ trJ Results detJ trJ Results

(0,0)

(0,1)

(1,0)

(1,1)

ðx0 ; y0 Þ

�

�

�

�

0 Saddle point

– �

– �

þ þ

þ – ESS þ – ESS – �

– �

þ �

þ – ESS þ þ

– �

þ þ

– �

þ – ESS þ – ESS

– �

þ þ

þ þ

– �

– �

0 Saddle point 0 Saddle point 0 Saddle point 0 Saddle point 0 Saddle point

’þ’, ’ –’ respectively show positive and negative values of detJ and trJ, and ’�’ indicates uncertainty of positive or negative results.

According to the above Table E.1, the point (1,0) will be the ESS in situations of (1), (4), (5) and (8), therefore the final presupposition of (1,0) can be refined as M > 0, S < T. All presuppositions of each equilibrium solution can be deduced in the same way. The Proof of Proposition 3 and Table 4 is completed. Appendix F. Strategy evolvements from the perspective of benefit-loss comparison Proof of Proposition 4.

Combining λ ¼ ðπLD

πHD rD Þ =ðπLD þf þlD Þ with Eqs. (5) and (10), following calculations can be implemented.

� When x ¼ 1 and y ¼ 0, we have FD ðxÞ =ðπLD þf þlD Þ ¼ λ þ pμP < 0 and FE ðyÞ ¼ ðf lE Þ ðμA pμP Þ þ pμP ω cAE þ pcPE < 0, therefore μA < pμP < λ. 0 0 Recall M, N, S and T, FD ðxÞ ¼ M < 0 and FE ðyÞ ¼ S T < 0 can be attained, thus the presupposition of Scenario I(1) in Table 5 is equally converted into μA < pμP < λ. 0 0 � When x ¼ 1 and y ¼ 1, we have FD ðxÞ =ðπLD þf þlD Þ ¼ λ þ μA < 0 and FE ðyÞ ¼ ðf lE Þ ðμA pμP Þ pμP ω cAE pcPE < 0, therefore pμP < μA < λ. 0 0 Recall M, N, S and T, FD ðxÞ ¼ ðM NÞ < 0 and FE ðyÞ ¼ ðS TÞ < 0, thus the presupposition in Scenario I(2) in Table 5 is equally converted into pμP < μA < λ. 0

Proof of Proposition 4 is completed. Proof of Proposition 5.

Proof of Proposition 5 is similar to that of Proposition 4, so we omit the proof process.

Proof of Corollary 1. Combiningy0 ¼ ½πLD πH D

pμP ðπ LD þf þlD Þ rD � =½ðμA pμP ÞðπLD þf þlD Þ� with λ ¼ ðπLD πH D 2

rD Þ = ðπLD þ f þ lD Þ, we have the first order condition

ofy0 to p representing as ∂y0 =∂p ¼ ½μP ðλ μA Þ�=ðμA pμP Þ . When μA < λ < pμP is satisfied, ∂y0 =∂p > 0 can be deduced. The Proof of Corollary is completed. Proof of Corollary 2. Proof of Corollary 2 is similar to that of Corollary 1, so we omit the proof process. Proof of Proposition 6. Proof of Proposition 6 is similar to that of Proposition 4, so we omit the proof process.

References

Cao, Y. (2007). An analysis of principal- agent relationship under asymmetric information in third party logistics. Journal of Industrial Engineering & Engineering Management, 21(2), 74–77. CECRC (China E-commerce Research Centre). (2017). Chinese E-commerce user experience and complaint report. http://www.100ec.cn/zt/17tsjc/ Accessed 18 February 2018. Chen, B. (2017). A SWOT Analysis of trans-border e-commerce logistics mode of ’one belt one road’ economic zone. Logistics Technology, 36(2), 26–32. Chen, W., Gho, M., & Zou, Y. (2018). Logistics provider selection for omni-channel environment with fuzzy axiomatic design and extended regret theory. Applied Soft Computing, 71, 353–363. Chen, M., Wu, P., & Hsu, Y. (2019). An effective pricing model for the congestion alleviation of e-commerce logistics. Computers & Industrial Engineering, 129, 368–376. Chen, M., Wu, P., & Xiong, W. (2015). Explicating the trends of China’s logistics services for electronic commerce. Industrial Engineering, Management Science and Applications, 871–880. Du, Z., Guo, C., Lu, W., & Fu, S. (2019). Cooperation analysis of cross-border e-commerce logistics alliance based on evolutionary game theory. Mathematics in Practice and Theory, 49(10), 22–32. ECRC (E-Commerce Research Center). (2018). Report on online retail market in China2017. http://www.100ec.cn Accessed 24 October 2019. Emarketer. (2016). Worldwide retail ecommerce sales: The eMarketer forecast for. http s://www.emarketer.com/Report/Worldwide-Retail-Ecommerce-Sales-eMarketer -Forecast-2016/2001849/ Accessed 12 July 2017.

Agatz, N., Fleischmann, M., & Nunen, J. (2008). E-fulfillment and multi-channel distribution-A review. European Journal of Operational Research, 187(2), 339–356. Aldin, N., & Stahre, F. (2003). Electronic commerce, marketing channels and logistics platforms––a wholesaler perspective. European Journal of Operational Research, 144, 270–279. Allen, J., Piecyk, M., Piotrowska, M., McLeod, F., Cherrett, T., Ghali, K., et al. (2017). Understanding the impact of e-commerce on last-mile light goods vehicle activity in urban areas: The case of London. Transportation Research part D: Transpart and Environment. https://doi.org/10.1016/j.trd.2017.07.020. published online in 2017. Amico, M. D., & Hadjidimitriou, S. (2012). Innovative logistics model and containers solution for efficient last mile delivery. Procedia-Social and Behavioral Sciences, 48, 1505–1514. Bahbouhi, J. E., & Moussa, N. (2017). Prisoner’s dilemma game model for e-commerce. Applied Mathematis & Computation, 292, 128–144. Bai, J. F., Wei, X., & Yan, J. (2017). Research on the selection of business-to-customer ecommerce logistics model based on analytic hierarchy process method. In Proceedings of the 23rd International Conference on Industrial Engineering and Engineering Management, 2016 pp. 11–15). Büyük€ ozkan, G., Feyzio� glu, O., & Nebol, E. (2008). Selection of the strategic alliance partner in logistics value chain. International Journal of Production Economics, 113(1), 148–158.

15

X. Zhang et al.

Research in Transportation Economics xxx (xxxx) xxx Ramanathan, R. (2010). The moderating roles of risk and efficiency on the relationship between logistics performance and customer loyalty in e-commerce. Transportation Research Part E: Logistics and Transportation Review, 46, 950–962. Ramanathan, R., George, J., & Ramanathan, U. (2014). The Role of logistics in ecommerce transactions: An exploratory study of customer feedback and risk. In U. Ramanathan, & R. Ramanathan (Eds.), Supply chain strategies, issues and models (pp. 221–233). London: Springer. Romero, C. Q., & Rodriguez, D. R. (2010). E-commerce and efficiency at the firm level. International Journal of Production Economics, 126, 299–305. Rutner, S. M., Gibson, B. J., & Williams, S. R. (2003). The impacts of the integrated logistics systems on electronic commerce and enterprise resource planning systems. Transportation Research Part E: Logistics and Transportation Review, 39, 83–93. Samuelson, L. (1997). Evolutionary games and equilibrium selection. Cambridge: MIT Press. Shao, J., Yang, H., Xing, X., & Yang, L. (2016). E-commerce and traffic congestion: An economic and policy analysis. Transportation Research Part B: Methodological, 83, 91–103. Smith, J. M., & Price, G. R. (1973). The logic of animal conflict. Nature, 246, 15–18. Speranza, M. G. (2018). Trends in transportation and logistics. European Journal of Operational Research, 264, 830–846. Tadelis, S. (2013). Game theory: An introduction. Princeton: Princeton University Press. Wagner, S. M., & Sutter, R. (2012). A qualitative investigation of innovation between third-party logistics providers and customers. International Journal of Production Economics, 140, 944–958. Wang, Z. (2015). Analysis on evolutionary game of coordination development between e–commerce enterprises and third–party logistics enterprises. International Business, 3, 102–111. Wang, Y., Zhang, D., Liu, Q., Shen, F., & Lee, L. H. (2016). Towards enhancing the lastmile delivery: An effective crowd-tasking model with scalable solutions. Transportation Research Part E: Logistics and Transportation Review, 93, 279–293. Wong, E. Y. C., Tai, A. H., & Zhou, E. (2018). Optimising truckload operations in thirdparty logistics: A carbon footprint perspective in volatile supply chain. Transportation Research Part D: Transport and Environment, 63, 649–661. Wu, B., Liu, P., & Xu, X. (2017). An evolutionary analysis of low-carbon strategies based on the government–enterprise game in the complex network context. Journal of Cleaner Production, 141, 168–179. Xiao, T., & Chen, G. (2009). Wholesale pricing and evolutionarily stable strategies of retailers with imperfectly observable objective. European Journal of Operational Research, 196(3), 1190–1201. Xie, S. Y. (2014). Economic game theory. Shanghai: Fudan University Press. Xu, S. X., Cheng, M., & Huang, G. Q. (2015). Efficient intermodal transportation auctions for B2B e-commerce logistics with transaction costs. Transportation Research Part B: Methodological, 80, 322–337. Yang, H., Zhang, M., Wang, J., & Du, X. (2012). Game model for “shortage of logistics” in online shopping in service engineering. Systems Engineering Procedia, 5, 207–212. Yang, Q., Zhao, X., Yeung, H. Y. J., & Liu, Y. (2016). Improving logistics outsourcing performance through transactional and relational mechanisms under transaction uncertainties: Evidence from China. International Journal of Production Economics, 175, 12–23. Yi, Y., & Yang, H. (2017). Wholesale pricing and evolutionary stable strategies of retailers under network externality. European Journal of Operational Research, 259(1), 37–47. Yu, T., & Liu, C. (2016). The analysis of evolution game model and simulation between governments and the third-party in product quality regulation. Chinese Journal of Management Science, 24(6), 90–96. Zhang, W., Zhou, G., & Cao, J. (2014). Analysis on evolutionary game of government regulation mode and enterprise pollution emissions. China Population, Resources and Environment, 24(11), 108–113. Zhao, W., & Xi, Y. (1999). Comparison of two mathematical approach of agent problem. Journal of Industrial Engineering & Engineering Management, 13(2), 57–62. Zhu, X. (2016). Managing the risks of outsourcing: Time, quality and correlated costs. Transportation Research Part E: Logistics and Transportation Review, 90, 121–133.

Feng, Q., & Liu, T. (2010). The drive and supervisory game analysis of the electronic commerce enterprise outsourcing the physical distribution service. Proceedings of 2010 International Conference on Management Science and Engineering, 173–176. Fernandez-Gago, G., Moyano, F., & Lopez, J. (2017). Modelling trust dynamics in the Internet of Things. Information Sciences, 396, 72–82. Fudenberg, D., & Tirole, J. (1991). Game theory. Cambridge: MIT Press. Gao, J., Wei, J., Li, X., & Gu, D. (2008). An analysis of principal- agent relationship of outsourcing incentive contract on the risk compensative. Chinese Journal of Management Science, 16(5), 164–170. Giri, B. C., & Sarker, B. T. (2017). Improving performance by coordinating a supply chain with third party logistics outsourcing under production disruption. Computers & Industrial Engineering, 103, 168–177. Gunasekaran, A., Marri, H. B., McGaughey, R. E., & Nebhwani, M. D. (2002). Ecommerce and its impact on operations management. International Journal of Production Economics, 75, 185–197. Huang, L., Hu, G., & Lu, X. (2009). E-business ecosystem and its evolutionary path: The case of Alibaba group in China. Pacific Asia Journal of the Association for Information Systems, 1(4), 25–36. Huang, Y., & Yin, K. (2014). Research on the evaluation and selection of third-party logistics providers in B2C e-commerce mode. Journal of Electronic Commerce in Organizations, 12(2), 74–88. Huang, Y., & Yin, K. (2015). Evaluation indexes of the third-party logistics providers’ logistics capability in B2C e-commerce mode. In Proceedings of 3rd International Conference on Logistics, Informatics and Service Science, 2013 pp. 181–189). Jiao, Z. (2016). Modes and development characteristics of China’s cross-border ecommerce logistics. In B. Liu, L. Wang, S. Lee, J. Liu, F. Qin, & Z. Jiao (Eds.), Contemporary logistics in China. Current Chinese economic report series (pp. 211–232). Berlin, Heidelberg: Springer. Ji, P., Ma, X., & Li, G. (2015). Developing green purchasing relationships for the manufacturing industry: An evolutionary game theory perspective. International Journal of Production Economics, 166, 155–162. Kull, T. J., Boyer, K., & Calantone, R. (2007). Last-mile supply chain efficiency: An analysis of learning curves in online ordering. International Journal of Operations & Production Management, 27(4), 409–434. Li, C., Feng, L., Han, F., & Cheng, Y. (2015). Dynamic evolutionary game analysis of the e-business. Chinese Journal of Systems Science, 23(4), 75–78. Lim, W. S. (2000). A lemons market? An incentive scheme to induce truth-telling in third party logistics providers. European Journal of Operational Research, 125, 519–525. Liu, X., Zhang, K., Chen, B., Zhou, J., & Miao, L. (2018). Analysis of logistics service supply chain for the one belt and one road initiative of China. Transportation Research Part E: Logistics and Transportation Review, 117, 23–39. Lu, Q., & Liu, N. (2015). Effects of e-commerce channel entry in a two-echelon supply chain: A comparative analysis of single- and dual- channel distribution systems. International Journal of Production Economics, 165, 100–111. Lu, F., Wang, D., & Luo, D. (2016). Design of incentive mechanism for service outsourcing under client company participation. Operations Research & Management Science, 25(3), 274–282. Pedrielli, G., Lee, L. H., & Ng, S. H. (2015). Optimal bunkering contract in a buyer-seller supply chain under price and consumption uncertainty. Transportation Research Part E: Logistics and Transportation Review, 77, 77–94. Persico, V., Pescap�e, A., Picariello, A., & Sperlí, G. (2018). Benchmarking big data architectures for social networks data processing using public cloud platforms. Future Generation Computer Systems, 89, 98–109. Rabinovich, E., & Knemeyer, A. M. (2006). Logistics service providers in Internet supply chains. California Management Review, 48(4), 84–108. Rabinovich, E., Knemeyer, A. M., & Mayer, C. M. (2007). Why do Internet commerce firms incorporate logistics service providers in their distribution channels? The role of transaction costs and network strength. Journal of Operations Management, 25, 661–681.

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