1. Introduction
Over the last few decades, the platform business has received much attention, and it is already a significant part of the economy and continues to grow rapidly
[1]. In 2019, the General Office of the State Council of the People’s Republic of China stated that “the internet platform economy is a new way of organizing productive forces and a new driving force for economic development.” Recently, published data showed that the total value of China’s digital platforms is 2.02 trillion USD, accounting for 22.5% of the global total and ranking second. Platforms serve as venues for commerce, facilitating access to interactions among multiple parties
[2]. They increase efficiency by reducing transaction costs and duplication, thereby facilitating transactions between supply and demand and acting as intermediaries among different organizations
[3],
[4]. Strengthening the digital economy industrial system with a platform as the center of gravity is the main feature of the digital economy organizational approach, and it is also crucial for China to develop a digital, intelligent, and international supply chain. Alibaba’s report indicates that China’s platform economy has generated a new consumer market valued at 10 trillion CNY, which is expected to exceed 100 trillion CNY by 2030. The rapid emergence of the platform economy presents supply chain management with new opportunities and challenges
[5],
[6]. The supply chain has evolved into a new stage known as the platform supply chain (PSC), which is deeply integrated with the platform economy
[7].
Through a digital platform, the PSC engages and coordinates with various participants, including manufacturers, sellers, and consumers
[8]. Logistics engineering management, as an essential component of PSC management, refers to the systematic and scientific management and optimization of logistics systems aimed at enhancing logistics efficiency, reducing costs, and meeting customer demands
[9]. It involves the design, implementation, and improvement of logistics processes via data analytics, technology applications, and resource optimization to ensure the operational efficiency and flexibility of logistics activities. In this context, the selection of logistics service strategies becomes a crucial aspect of logistics engineering management. Specifically, logistics engineering management not only focuses on the efficiency of logistics activities themselves but also requires the development of appropriate logistics service strategies on the basis of market demand and customer preferences. Through systematic management and strategic selection, logistics engineering management can effectively enhance a firm’s competitiveness and customer satisfaction, ultimately achieving sustainable logistics development. According to the China Federation of Logistics and Purchasing, China’s logistics demand surged to unprecedented levels in 2021, with the nation’s total social logistics reaching 335.2 trillion CNY, marking an 11.70% increase from the previous year. The traditional supply chain model is no longer adequate for addressing the rapidly changing market demands and consumer expectations
[10]. Instead, the platform economy model has emerged as the mainstream, facilitating supply and demand matching and transactions through digital platforms
[11]. The development and execution of logistics service strategies for PSCs have become increasingly pivotal and complex.
There are three main types of logistics service strategies in PSCs:
self-built logistics (SBL), third-party logistics (3PL), and
logistics service sharing (LSS).
Fig. 1 simply shows the logical relationships among SBL, 3PL, and LSS.
Fig. 1 shows that SBL and 3PL are two independent logistics service strategies in stage 1, whereas LSS is an innovative model that follows the development of SBL and 3PL in stage 2, which emphasizes resource sharing among multiple companies.
SBL refers to the logistics system established and operated by the enterprise itself. For instance, JD.com, a platform-based firm in China, implements its own logistics system to ensure fast and efficient delivery
[12],
[13]. SBL enables JD.com to maintain control over the speed and quality of order delivery, thereby enhancing the user experience
[14].
3PL refers to the method by which enterprises entrust professional logistics service providers with carrying out logistics management and operations
[15]. Pinduoduo is a business-to-consumer (B2C) platform that utilizes 3PL
[16],
[17]. However, such a logistics service strategy does not allow Pinduoduo to directly control the operation of 3PL and thus leads to a passive position in service quality assurance
[18]. The China Association of Warehousing and Distribution indicated that 43% of production enterprises are engaged in SBL, 36% utilize a mixed mode of SBL and 3PL, whereas only 21% rely solely on 3PL.
LSS emerges as an innovative model after SBL and 3PL have developed to a certain stage which emphasizes resource sharing among multiple enterprises. For example, Deliv is a shared logistics platform that offers same-day delivery services for retailers within the same city. Through Deliv, merchants can share logistics resources to meet flexible delivery demands. LSS fosters improved collaboration within the supply chain by integrating logistics, information, and financial flows, which can enhance coordination among enterprises and improve overall efficiency. Unlike SBL or 3PL, LSS consolidates demand from various businesses, minimizing resource waste and optimizing logistics operations. Companies can adjust resource usage on the basis of demand fluctuations, making it particularly effective during peak periods, such as promotional events or unforeseen surges. Technologies such as the Internet of Things, big data, and artificial intelligence empower platforms such as the Cainiao Network to allocate resources dynamically, significantly enhancing logistics efficiency
[19],
[20],
[21]. A comparison of the three logistics service strategies is summarized in
Table 1.
Currently, studies on logistics service strategies in PSCs have garnered increasing attention. Platform firms relying on platform operations need flexible logistics service strategies to accommodate various business models, including SBL, 3PL, and LSS. Different logistics service strategies have distinct cost structures; for example, SBL requires a substantial capital investment to build a logistics network
[22], 3PL may be more flexible in terms of service fees
[23], and LSS highlights the cost-sharing benefits derived from resource sharing. Scholars have investigated how to optimize the cost structure and improved the profitability of firms through a rational selection of logistics service strategies
[24]. However, a comprehensive review that synthesizes the diverse perspectives presented in the research on this issue is lacking. Previous review studies on logistics service strategies have often focused on specific strategies within general supply chains, neglecting the unique context of PSCs. For example, studies of Selviaridis and Spring
[25] and Aguezzoul
[26] providing perceptive analyses have focused solely on 3PL in supply chains. Verdonck et al.
[27] reviewed studies related to LSS from the perspective of road transport firms. On the basis of the above background analyses and identified research gaps, this paper aims to address three research questions. First, what is the current research status of logistics service strategies in PSCs? Second, what factors influence PSC members’ choice between SBL and 3PL? Third, what factors influence PSC members’ decisions on whether to share logistics services?
To address these research questions, this paper adopts a multistep methodology designed to provide both quantitative and qualitative insights into logistics service strategies in PSCs. First, we collect a comprehensive sample of academic articles from leading databases and use bibliometric software, such as CiteSpace, to analyze trends in publication years, distribution across journals, co-cited authors, keywords, and geographical distribution of the sample articles from an overall perspective. Second, we conduct an in-depth qualitative review of the selected articles, categorizing them according to the three primary logistics service strategies: SBL, 3PL, and LSS. Finally, the factors influencing the selection of managers in PSCs between SBL and 3PL and the decision of LSS are summarized. By integrating both theoretical insights and real-world examples, we provide a comprehensive overview of how platform firms make strategic decisions in managing logistics, offering practical guidance for navigating among SBL, 3PL, and LSS.
The review reveals the following findings. First, two topics related to SBL and 3PL have emerged as particularly interesting to researchers. One is the choice of the optimal logistics service strategy for the members of PSCs, while the other is the impact of factors related to logistics service on PSCs, which include channel selection, platform entry, sales model, and so forth. Second, the main factors influencing PSC members’ choice between SBL and 3PL are the service cost and service level, followed by the channel, brand, market potential, and competition. Third, LSS is regarded as an important complement to SBL and 3PL, with key research hotspots, including the channel, partner selection, and service competition. Service cost is a major factor influencing LSS, with competition, consumers’ logistics preference, and market potential being secondary factors.
Our study highlights future research directions from the existing literature. The first direction is to explore collaboration and competition between SBL and 3PL in a multiplatform ecosystem. The second direction is to examine consumer preferences for green logistics services in PSCs. The final direction is to incorporate risk related and uncertainty factors when conducting research on logistics service strategies in PSCs. In light of the challenges and opportunities brought about by technological development, future research can encompass the impact of real-time data on logistics service strategies, the impact of intelligent robot technology on logistics service optimization, and the impact of green policies on logistics service strategies.
This paper makes several innovative contributions to the field of logistics engineering management. First, it provides a novel synthesis of three logistics service strategies, SBL, 3PL, and LSS, within the specific context of PSCs. Unlike previous reviews that focus on individual logistics strategies or traditional supply chains, this paper integrates these logistics service strategies under the framework of the platform economy, offering a new perspective on how these strategies interact with and complement each other in digital environments. Second, by employing a combination of bibliometric and qualitative analyses, this paper offers a dual methodological contribution. The bibliometric analysis identifies emerging trends and influential works, whereas the qualitative review provides deeper insights into the practical implications of each logistics service strategy. Finally, this paper contributes to managerial decision-making by providing a clear framework that assists platform-based firms in selecting the most appropriate logistics strategy while considering crucial factors such as service cost, competition, and market potential.
The remainder of the paper is arranged as follows. Section 2 describes the literature analysis. The current state of research regarding logistics service strategies in PSCs is examined in Section 3. Section 4 provides a summary of the factors that influence the selection of a logistics service strategy in PSCs. Future research directions are proposed in Section 5, and the study is concluded in Section 6.
2. Literature analysis
We conduct a literature search based on the Web of Science, Scopus, and Science Direct databases, using the keyword combinations “logistics and platform,” “logistics and e-commerce,” and “logistics and electronic commerce,” and apply some filters to narrow down the search results. First, only papers published after 2005 are included. Second, we exclude conference papers due to incompleteness. Third, to ensure the quality and credibility of the literature review, we exclude open access journals. Finally, on the basis of titles and abstracts, papers related to logistics service strategies in PSCs are selected. Following the aforementioned procedures and the elimination of duplicate articles, a total of 62 articles are acquired. Owing to the limited quantity of papers, we examine the references in the deduplicated papers and add an additional 15 relevant papers on the basis of the recommendations of experts for a more comprehensive literature review. Please note that, owing to relevance and space constraints, we include only a representative selection of these works.
2.1. Visualization of publication year and journal
Fig. S1 in Appendix A shows the number of selected papers published each year on logistics service strategies in PSCs. It is found that there has been a sharp increase in the past five years. Some journals published special issues about logistics in 2020, leading to a significant increase in the number of papers on logistics service strategies for PSCs, such as the special issues in Transportation Research Part E on logistics and supply chain management in the luxury industry and on sustainable logistics and supply chain management in emerging markets. Fig. S2 in Appendix A shows the peer-reviewed academic journals that published the sampled literature. Fig. S2 shows that among the 31 journals in our sample, seven journals accounted for more than 50% of the papers, including Transportation Research Part E (TRE), International Journal of Production Economics (IJPE), Omega, Computers & Industrial Engineering (CIE), Electronic Commerce Research and Applications (ECRA), European Journal of Operational Research (EJOR), and IEEE Transactions on Engineering Management (IEEE TEM). To improve readability, we classified the remaining 24 journals that published only one or two sample papers as “other.” This category includes, for example, Management Science, Production and Operations Management, and Manufacturing & Service Operations Management.
2.2. Visualization of co-cited authors
Fig. S3 in Appendix A is a co-cited author network from CiteSpace, which visualizes the relationships and frequency of citations among authors. Fig. S3(a) shows that the co-citation author map consists of several highly cited authors, with larger nodes such as Baozhuang Niu, Lin Tian, Vibhanshu Abhishkek, and Xu Chen, indicating their significant influence in the research field. The size of the nodes correlates with the frequency of citations, suggesting that these authors’ works have been widely recognized and referenced. The outer rings of the nodes are colored to represent the citation years: The purple nodes signify authors whose works were frequently cited in approximately 2019 whereas the yellow and orange nodes represent citations from 2023 and later indicating that these authors’ academic influence has increased recently. Moreover, the links between the nodes reflect co-citation relationships. Dense connections show that some authors’ works are frequently cited together, forming a closely related academic network. For example, Baozhuang Niu, Lin Tian, Xuelian Qin, and Ping He form a highly interconnected cluster, suggesting that their research topics are closely related, as shown in the Fig. S3(b). Additionally, authors such as Xu Chen and Vibhanshu Abhishkek, with larger node sizes and connections to multiple clusters, indicate that their research not only has significant influence within a specific domain but also serves as a bridge across various subfields, as illustrated in Fig. S3(c). The prominent clusters on the map reflect various research directions and academic schools within this field. The connections among authors such as Guo Li, Ping He, and Kaiying Cao highlight their shared focus on specific research issues as shown in Fig. S3(d), including platform strategies and logistics service models.
2.3. Visualization of keywords
CiteSpace uses a timeline as the analytical node to present a keyword time graph, which visually depicts the sequential evolution of keywords
[28],
[29], as shown in Fig. S4 in Appendix A. The timeline perspective provides a clearer depiction of historical findings, trends, and intra-cluster connections. Each node corresponds to a distinct keyword, with larger nodes indicating a greater frequency of occurrence. A trend in the keyword’s appearance over time is indicated by the line colors gradually shifting from cold to warm tones. Although the clusters on the vertical axis of a keyword timeline diagram have the same meaning as in a cluster map, the key innovation of the keyword timeline diagram is that the keywords from each category are simultaneously distributed along the time series (horizontal axis). Keywords from the same year are concentrated on the same vertical axis, highlighting the focus and attention of the research field in that particular year. Keywords from the same cluster are concentrated on the same horizontal axis, showing the historical research outcomes of that cluster.
From the timeline on the left side of Fig. S4, early themes such as “electronic commerce,” “decision making,” and “internet” dominate the initial period, reflecting foundational studies in platform commerce. Over time, the focus shifts toward more specialized topics such as “logistics service,” “delivery time,” and “logistics costs,” indicating a growing emphasis on operational efficiency and cost management in logistics engineering. Emerging concepts such as “logistics service sharing” in recent years highlight the rise of collaborative logistics models, driven by digital platforms and shared resources. Additionally, the graph shows the increasing relevance of technological integration, with terms such as “algorithm” and “computer simulation” connecting to logistics themes, highlighting the impact of big data and blockchain on logistics optimization.
2.4. Visualization of geographical distribution
The country co-citation networks illustrate the academic collaborations between different countries. In Fig. S5 in Appendix A, the nodes represent countries, and the size of each node is proportional to its citation frequency. The connecting lines indicate collaboration or co-citation relationships between countries, with thicker lines representing higher frequencies of collaboration or co-citation. China occupies a central position in Fig. S5 with the largest node, signifying its highest citation frequency in this field and its core academic influence. The United States is closely linked with China, suggesting frequent academic cooperation between the two countries. Other notable countries include the United Kingdom, Singapore, and Canada. Although these countries have smaller nodes, they still exhibit collaborative relationships with major research contributors such as China and the United States. The color of the nodes, ranging from yellow to blue, represents the time distribution of the research contributions. Nodes for China and the United States are closer to yellow, indicating more recent contributions in this field.
2.5. Research framework
This paper first reviews the research related to SBL and 3PL in PSCs. We then review the LSS strategy in PSCs as an innovative model that follows the evolution of SBL and 3PL, emphasizing the sharing of resources among multiple companies. Next, we summarize the factors that influence the PSC members’ choice between logistics service strategies: SBL and 3PL. Finally, the factors that influence whether a firm adopts LSS are also analyzed. The research framework of this paper is shown in
Fig. 2.
3. Overview of the logistics service strategy in PSCs
This section presents a detailed and systematic review of the relevant literature on the basis of the characteristics of different logistics service strategies and the logical relationships among these three logistics service strategies. Specifically, SBL and 3PL are two completely independent logistics service strategies. We first outline the advantages of these two logistics service strategies, review the related studies in 3.1 SBL, 3.2 3PL, and then summarize the existing studies involving both SBL and 3PL in Subsection 3.3. LSS is partially based on SBL and partly interacts with 3PL. We describe the advantages of LSS in Subsection 3.4, and then summarize the existing literature on LSS in terms of its relationship with SBL and 3PL. The number of annual papers on different logistics service strategies is shown in
Fig. 3. A surge in research on logistics services in PSCs occurred after 2019, with SBL and 3PL being the most abundant studies.
3.1. SBL
SBL refers to the practice of a firm creating and operating its own logistics system, including warehousing, transportation, distribution, and other links
[30]. Some e-commerce platforms, such as JD.com, have established their own logistics systems to facilitate product delivery and control over the speed and quality of order delivery, making online shopping more convenient for consumers
[31]. The advantages lie in providing fast logistics services to ensure logistics efficiency
[32]. However, constructing one’s own logistics system is costly and requires significant capital investment and resources.
SBL facilitates the efficient coordination of a platform’s sales, procurement, and logistics operations, enabling the flexible allocation of logistics resources. This flexibility enhances overall operational efficiency and improves consumer satisfaction. Moreover, SBL improves the stability and consistency of logistics services through standardized operational processes, thereby enhancing the overall consumer experience and strengthening the platform’s market competitiveness. For example, JD.com’s SBL system dynamically allocates logistics resources in response to promotional activities, ensuring the rapid and accurate delivery of goods to consumers
[33]. Li and Li
[12] further reported that e-commerce platforms derive the greatest benefit from adopting SBL when cross-price elasticity is moderate. Moreover, intense competition in the promised delivery time (PDT) tends to drive platforms toward internalized logistics management
[34].
In terms of service quality, SBL significantly enhances the stability and consistency of logistics services by standardizing processes such as packaging, transportation, and delivery, thereby improving the overall consumer experience
[35]. Ma et al.
[36] emphasized that platform-provided logistics services are the most effective strategy for improving service levels for online stores. Sun et al.
[37,
38] demonstrated that consumers show a clear preference for order fulfillment by Amazon, as fulfillment by seller is typically associated with slower response times and higher return costs, which diminishes consumer satisfaction.
3.2. 3PL
3PL refers to the outsourcing of logistics services to a specialized business that handles the planning and implementation of various logistics activities, including those related to platforms such as eBay and Tmall. In China, prior to 2013, Tmall outsourced logistics services to a number of 3PL providers such as YUNDA Express and STO Express. Implementing this strategy can reduce operating costs, improve efficiency, and enable platform enterprises to optimize supply chain management and focus on core business activities
[39].
3PL is characterized by professional expertise, extensive industry experience, and advanced technological capabilities
[40]. Specializing in logistics services, such as warehousing, transportation, and distribution, 3PL enhances operational efficiency and improves customer satisfaction for platforms. Moreover, 3PL achieves economies of scale by consolidating the logistics needs of multiple clients and enabling bulk procurement of resources such as transportation equipment, thereby lowering per-unit logistics costs
[41].
Companies such as SF Express and YTO Express exemplify the efficiency and precision of 3PL networks, offering fast and accurate distribution for e-commerce platforms
[42]. Cao et al.
[43] suggested that when considering consumer returns, traditional enterprises are more likely to benefit consumers by entering the platform and adopting 3PL. When the platform in the online-to-offline (O2O) mode lacks self-scheduling delivery capacity, outsourcing home-delivery services to a delivery platform becomes a strategic necessity
[44]. Dong et al.
[45] underscored the benefits of outsourcing logistics even when greenwashing practices are present, whereas Yang et al.
[46] highlighted how variations in service levels and pricing influence manufacturers’ choices between an online freight platform and 3PL.
E-commerce platforms benefit from these cost efficiencies by outsourcing their logistics operations, reducing their own capital investments in infrastructure
[47]. The existing literature has shown that 3PL improves cost efficiency through mechanisms such as multimodal auctions
[48], revenue-sharing contracts
[49], and automated negotiation models that optimize logistics capabilities
[50]. Additionally, sorting centers at higher supply chain levels and the management of store-to-customer transportation by 3PL providers further contribute to reducing transportation costs
[51],
[52]. 3PL is particularly beneficial for remanufacturing firms entering e-commerce platforms, as it helps lower fees and associated production costs
[53].
Numerous studies have proposed tools and frameworks to support better decision-making in 3PL management. For example, Cochran and Ramanujam
[54] developed a tool to help manufacturers evaluate whether to switch 3PL in response to issues such as rising costs or service quality deficiencies. Other optimization tools include decision support systems designed to increase transportation network efficiency for 3PL
[55], indicator systems for selecting 3PL partners
[56], and heuristic models that minimize outbound logistics costs for online retailers
[57]. Further research has addressed integrated order picking and scheduling to maximize fulfillment efficiency when outsourcing to 3PL providers
[58],
[59]. Additionally, financing models that support 3PL innovation were explored by Fu et al.
[60] and Chang et al.
[61], with an emphasis on equity financing and secured financing services, respectively. Qin et al.
[62] proposed a mixed-integer programming model to optimize the deployment of multi-tote storage and retrieval systems via autonomous mobile robots.
3.3. SBL and 3PL
3.3.1. Interaction between logistics services and channel selection
Academia has conducted extensive research on the optimization strategies for logistics services and channel selection, exploring the optimal combinations of logistics services and channel decisions for various enterprises in the O2O mode, brand platform operations, and durable goods markets. Du et al.
[63] explored the optimal strategy for the O2O delivery mode of merchants. He et al.
[64] investigated when local brick-and-mortar (B&M) stores should adopt an O2O strategy and under what conditions the three logistics service models providing online ordering and 3PL. Xu et al.
[65] analyzed the dynamic joint strategy of a durable goods company with offline channels in providing channel encroachment and logistics choices for strategic consumers.
Wang et al.
[66] discussed whether the manufacturer introduces a direct sales channel and the selection between SBL and 3PL. Cao et al.
[67] explored the optimal channel and logistics service strategy for brands that operate on platforms. The decision of a brand to choose between SBL and 3PL is influenced by the difference in the service level between SBL and 3PL, the annual service fee of the platform, and the logistics fees charged by the platform and the 3PL providers. Liu et al.
[68] investigated the interaction between the logistics service strategy and platform channel selection in the B2C platform marketplace.
3.3.2. Interaction between logistics service and selling model
Scholars have examined the different cases of logistics service strategies and selling models adopted by suppliers and e-commerce platforms under different circumstances, and the key drivers behind these choices, such as logistics service efficiency, reputation, and cost. Qin et al.
[69] explored the interaction between the selling model and the logistics service strategy. They find that the supplier’s logistics service strategy preference is consistent with an increase in the logistics service level. Xu et al.
[70] used a principal-agent model to investigate the problems faced by e-tailers in a selecting selling model and logistics service strategy.
3.3.3. Interaction between logistics services and brands
Through multiperspective analyses of manufacturers, retailers, and the platforms themselves, scholars have revealed how key factors such as platform attractiveness, logistics service level, channel dominance, and the introduction of private labels on platforms collectively influence companies’ sales and operational strategy choices. These studies offer new insights into the interactions between brands and logistics in the platform economy. Liu et al.
[71] studied the impact of free brand spillover on the manufacturer’s logistics service selection and the retailer’s brand spillover preference. Xu et al.
[72] discussed the optimal logistics service strategy between platform logistics and 3PL chosen by the enterprise selling fresh products on the platform.
3.3.4. Interaction between logistics services and members’ benefits
Scholars have conducted in-depth explorations of membership-oriented marketing collaborations such as supply chain cooperation, free shipping, and membership-based logistics services. Yu et al.
[13] analyzed whether an online platform provides product delivery services through SBL or 3PL. Shen et al.
[73] explored the optimal decisions of e-tailers and manufacturers regarding logistics services. When the supply chain revenue sharing ratio is large, the e-tailer providing logistics service is the optimal choice. Wang et al.
[74] focused on the interaction between consumer behavior and free shipping. Sun et al.
[75] explored the strategic significance of membership-based free shipping programs on online retail platforms.
3.3.5. Interaction between logistics services and other factors
Logistics services interact with various other factors, creating complex relationships, such as sourcing strategies, market potential, and pricing strategies under different logistics distribution systems. Xu et al.
[76] examined the problem of sourcing strategies faced by e-tailers selling products online under different logistics distribution systems. Niu et al.
[77] investigated restaurants’ preferences for platform logistics and their own logistics services. Du et al.
[78] examined the pricing strategy and the choice of delivery mode between autonomous delivery and platform delivery for restaurants in O2O dual-channel sales. Yu et al.
[79] reported that the delivery strategy selected by e-tailers, SBL or 3PL, is determined by service fee and service cost.
The existing literature on SBL and 3PL has revealed intricate relationships that govern logistics service strategies within the realm of e-commerce. The research indicates that the channel selection, selling model, and branding strategy are profoundly influenced by the available logistics service options. Businesses encounter critical decisions regarding the adoption of diverse logistics models, including SBL, 3PL, and hybrid strategies, which are often shaped by factors such as service levels, cost efficiencies, and market dynamics. Furthermore, the interaction between logistics services and brand strategies highlights how competitive dynamics and brand positioning impact logistics choices. Consumer behavior, particularly incentives such as free shipping, adds another layer of complexity, as it influences loyalty and purchasing decisions. Overall, the integration of SBL and 3PL within e-commerce logistics creates a dynamic landscape that requires continuous research to adapt to the evolving demands of consumers and the competitive environment. An overview of the literature on SBL and 3PL is summarized in Table S1 in Appendix A.
3.4. LSS
For a firm that has adopted either the SBL strategy or 3PL strategy, LSS refers to the sharing of its logistics resources with other members in the PSC, such as logistics information, logistics infrastructure, and logistics distribution resources. The benefits of LSS include improving the seamless omnichannel shopping and service experience for consumers across industries, optimizing resource allocation, realigning supply and demand, reducing logistics costs and improving overall efficiency
[80]. LSS, however, may involve competition and conflicts of interest among partners, necessitating the establishment of good cooperation relationships and sharing mechanisms.
LSS offers significant advantages in optimizing resource utilization, reducing idleness and waste, and enhancing operational efficiency across multiple platforms and enterprises. By enabling the shared use of logistics facilities, equipment, and transportation tools, LSS allows businesses to streamline their logistics processes and allocate resources on the basis of specific business needs. This approach improves the efficiency of logistics operations, particularly in logistics parks where warehousing resources are jointly managed and utilized. The collaborative nature of LSS also promotes the integration of logistics information resources, facilitating real-time data exchange and optimizing the allocation of logistics resources to maximize transportation capacity and improve overall logistics performance.
Rabinovich et al.
[81] explained why internet commerce firms work with focused logistics service providers. The rationality behind this is that these primary logistics service providers provide a network of connections that combines numerous complementary logistics services. He et al.
[82] studied a dual-channel e-commerce supply chain consisting of a manufacturer and a retailer. Xu et al.
[83] established a logistics task-resource allocation model to address the multistage resource balancing problem in shared logistics networks. Xu et al.
[84] introduced collaborative logistics networks from a theoretical perspective. He et al.
[85] reached similar conclusions in their investigation of LSS for e-tailers and B&M stores.
Through the shared logistics information platform, different platforms can exchange logistics data, transportation needs, and other information to optimize the allocation of logistics resources. Zhou et al.
[86] propose a novel logistics service model, joint distribution, to address the bottlenecks in operational efficiency, service quality, and environmentally sustainable development. Lei et al.
[87] introduced an agricultural logistics collaboration mechanism based on platform information sharing and resource integration.
3.4.1. LSS based on SBL
Some studies have explored the dynamics of LSS in the context of SBL, particularly focusing on the relationships among e-commerce platforms, third-party sellers, and logistics providers. He et al.
[88] delved into the logistics resource-sharing dynamics between two B2C e-commerce companies, one equipped with SBL and the other lacking it. Qin et al.
[89] examined the implications of LSS where platforms and sellers utilize SBL. Guo et al.
[90] further investigated the influence of LSS on firms’ strategic decisions and profitability. He et al.
[91] explored the effects of manufacturer channel encroachment and logistics integration on e-commerce platforms. Lai et al.
[92] discussed the motivations behind Amazon’s provision of logistics services to potential competitors. Hu et al.
[93] investigated the rationale behind platforms sharing logistics services with adaptable third-party sellers. Li et al.
[94] conducted an in-depth analysis of equilibrium strategies in online fulfillment services.
Current research on LSS within the framework of SBL explores the complex dynamics among e-commerce platforms, third-party sellers, and logistics providers, highlighting both competitive and collaborative aspects. A key theme is that LSS allows platforms to manage competition while simultaneously generating new revenue streams, such as commissions and logistics fees. For instance, e-commerce platforms offering logistics services can alleviate price competition, improve service quality, and increase operational efficiency, ultimately leading to greater profitability for both the platform and third-party sellers. Several studies have further underscored the importance of LSS in creating “win–win” scenarios, especially in environments with moderate competition or lower logistics service levels. In these cases, LSS helps improve resource utilization and allows platforms and sellers to share logistical capabilities, reducing the need for independent investment in logistics infrastructure.
3.4.2. LSS based on 3PL
The interaction between 3PL and LSS is a critical area of research, highlighting various dynamics and implications for financial performance and operational efficiency. Büyüközkan et al.
[95] and Niu et al.
[96] focused on logistics service alliances (LSA). Büyüközkan et al.
[95] introduced a multicriteria decision-making method to assess potential e-logistics strategic alliance partners effectively. Niu et al.
[96] analyzed an e-commerce firm’s logistics service challenges, and evaluated the advantages and disadvantages of participating in a logistics service alliance, both with and without a promised delivery time. Ren et al.
[97] examined optimal pricing and service decisions for shared platforms, as well as coordination between manufacturers and recycling platforms. Ding and Liu
[98] explored the cooperation between business-to-business (B2B) platforms and 3PL providers within the context of platform-based logistics services. Liu et al.
[99] employed evolutionary game theory to analyze the multiperiod interactions between logistics platforms and suppliers.
Zhang and Ma
[100] investigated the potential for agreements between platforms and sellers regarding the integration of marketplace channels with logistics service strategies. Wang et al.
[101] investigated the impact of information sharing in 3PL on financial performance, and concluded that strategic information sharing significantly enhances financial outcomes. Wang et al.
[102] further explored LSS and carbon emission sharing within an e-commerce supply chain, revealing that sellers demonstrate a heightened willingness to share logistics services, thereby enhancing platform profitability. Zhu and Liu
[103] proposed a cost-sharing mechanism that effectively coordinates decentralized systems with subcooperative structures.
The current research on the interaction between 3PL and LSS highlights the complex dynamics of collaboration and its impact on operational efficiency and competitive strategy
[104]. A significant finding in this literature is the role of strategic information sharing, which substantially enhances financial outcomes in 3PL partnerships, unlike operational information sharing. This distinction underscores the importance of high-level strategic alignment in logistics collaborations. Moreover, the transition of platform logistics from a traditional reseller model to a marketplace model has reshaped pricing strategies and competition, particularly as platforms begin to earn revenue through commissions from 3PL services. The application of theoretical models, such as evolutionary game theory, provides valuable insights into the strategic decision-making processes between logistics platforms and their partners. An overview of the literature on LSS is summarized in Table S2 in Appendix A.
4. Factors influencing the selection of logistics service strategy in PSCs
4.1. Factors affecting the choice of SBL or 3PL
The factors affecting the choices of the members in the PSCs between SBL and 3PL include the
service cost,
service level,
channel,
brand,
market potential, and
competition.
Table 2 [11],
[12],
[34],
[35],
[37],
[43],
[44],
[46],
[64],
[66],
[67],
[68],
[69],
[70],
[71],
[72],
[73],
[77],
[78],
[79],
[104],
[105],
[106],
[107] shows the factors that influence decision-makers to choose between SBL and 3PL.
In terms of the
service cost factor, Chen et al.
[11] found that in situations where platform commissions are high and the cross-price elasticity coefficient is small, the e-tailer’s primary strategy is to utilize indicators that reflect the unit logistics service cost incurred by both the e-tailer and the 3PL provider. Chen et al.
[105] found that if the logistics service cost is low, the online platform adopts SBL. In terms of the logistics
service fees, Li and Li
[12] reported that if the platform lacks the ability to establish its own logistics services and the logistics cost is reasonable, both the platform and the retailer refrain from employing 3PL. Yu et al.
[79] indicated that the decision on the delivery strategy of an e-tailer is influenced by both the service fees and service costs associated with logistics. In terms of the
platform’s service fees, Cao et al.
[67] suggested that when there is a moderate difference in the logistics service level, a brand is more likely to collaborate and utilize the platform’s logistics service (3PL) if the platform’s logistics price is low (or high). Qin et al.
[69] used the cost performance of logistics services as a criterion to help the provider and the platform make the optimal choice.
Under the factor of the
service level, Cao et al.
[43] found that flagship stores use 3PL when the difference in the service level between the platform logistics and the 3PL provider is relatively small, owing to the relatively low annual service fees. Yang et al.
[46] reported that the manufacturer will not choose to outsource logistics services to an online freight platform if the transportation service level of the 3PL provider is very high and significantly higher than that of an online freight platform. Xu et al.
[70] explored the impact of differences in logistics service levels on the selection of logistics service strategies.
For the
channel factor, Liu et al.
[68] found that the channel choice of the platform affects the manufacturer’s logistics service. Du et al.
[78] demonstrated that the selection of a restaurant delivery mode for online channel sales is influenced by two factors: the number of prospective consumers in the online channel and the extent to which consumers are responsive to pricing differences between channels. Niu et al.
[106] concluded that greater channel advantages can encourage brands to sell through the platform.
In terms of the
brand factor, Liu et al.
[71] found that a manufacturer chooses to implement 3PL when the decline in brand attractiveness from implementing 3PL is low. Xu et al.
[72] reported that the willingness of third-party stores selling fresh product to adopt a platform’s logistics or 3PL is influenced by the introduction of the platform’s own brand.
For the
market potential factor, Niu et al.
[77] found that when the online market potential is modest, the restaurant tends to choose using the platform’s logistics. Wang et al.
[104] indicated that in cases where a product’s market potential is constrained, the manufacturer will select the platform despite the platform increasing logistics charges.
In terms of the
competition factor, Gong et al.
[34] found that under demand competition, the optimal logistics choice for e-commerce firms depends on the green technology readiness of 3PLs. Li et al.
[35] showed that the manufacturer should choose logistics services provided by platforms when the intensity of competition between the manufacturer’s products and the platform’s private label is low or when consumers are becoming more sensitive to logistics services.
In addition, some scholars have explored the impact of
other factors on the strategic choices of logistics services. With respect to
economic benefits, Sun et al.
[37] concluded that product sales volume and profitability affect the logistics choice of the seller who sells on an online platform. Shen et al.
[73] found that when revenue sharing is high, the e-tailer providing logistics services is the equilibrium choice. With respect to
strategic benefits, He et al.
[64] concluded that when the retail price is exogenous, the B&M store’s mode choice depends on market coverage. Qian et al.
[107] found that the logistics services undertaken by the e-tailer is in equilibrium when the e-tailer chooses reselling or an online marketplace with a medium to high fraction. With respect to
logistics and operational considerations, He et al.
[44] illustrated that an O2O platform can face incentive misalignment when it delegates its delivery service to a delivery platform without having any SBL capability. Wang et al.
[66] found that the manufacturer should use the e-tailer’s logistics when both the logistics gap and the self-demand sensitivity are large.
4.2. Factors affecting LSS
Factors that influence the PSC members’ choice of whether to share logistics services include the
service cost,
competition,
consumers’ logistics preference, and
market potential.
Table 3 [82],
[87],
[89],
[90],
[91],
[93],
[94],
[96],
[98],
[99],
[100],
[101],
[102] shows the factors that influence the members’ decision to share logistics services.
With respect to
service cost, Lei et al.
[87] found that when competition exists, it may not be as profitable to offer a fulfillment service if the degree of improvement in product valuation and cost heterogeneity is small. Guo et al.
[90] pointed out that the manufacturer benefits from LSS when having a higher level of logistics services and that there is a large difference in the costs of logistics services between the manufacturer and the e-tailer. Hu et al.
[93] highlighted that the platform sets a unique service level and service fee to allow it to control competitors’ responses and achieve desired outcomes. Liu et al.
[99] determined that the supplier’s decision to engage in eco-cooperation is influenced by the risk costs associated with data sharing and the agency fee charged by the platform.
From the perspective of
competition, He et al.
[82] argued that the impact of consumers’ logistics sensitivity on the retailer’s profitability depends on the intensity of competition. Lei et al.
[87] indicated that the mitigating competition and the strategic channel effect can incentivize the adoption of LSS by increasing sales margins. Li et al.
[94] demonstrated that the configuration of market competition significantly influences the costs of fulfillment and the corresponding benefits. Niu et al.
[96] found that without a promised delivery time, a firm’s profit performance will be impaired by joining a logistics service alliance when the degree of competition intensity in the market is either high or low.
From the perspective of
consumers’ logistics preferences, He et al.
[82] found that in the absence of LSS, the manufacturer’s profit initially decreases and subsequently increases in response to the consumer’s increased sensitivity to logistics. Compared with the full logistics integration strategy, He et al.
[91] suggested that partial logistics integration strategies outperform those of the manufacturer or the entire supply chain and underperform the platform. Ding and Liu
[98] demonstrated that collaboration is viable only when the proportion of platform loyalty demand to 3PL loyalty demand exceeds a certain threshold.
With respect to the
market potential, Lei et al.
[87] suggested that e-tailer partners under LSS are more appropriate for offline stores with smaller market potential. Qin et al.
[89] found that the e-tailer benefits from LSS when the 3PL provider’s level or the market potential is very high. In contrast, the seller benefits from the LSS only when both the 3PL provider’s level and the market potential are low. Liu et al.
[99] concluded that market size is one of the factors that influences suppliers’ choices of eco-partnerships.
In addition, scholars have explored
other factors affecting LSS. He et al.
[82] concluded that the manufacturer’s profit will increase following the implementation of LSS if the price of the shared services is either low or high. However, the opposite is true for the retailer’s profit. Qin et al.
[89] concluded that the presence of economies of scale enhances the probability of achieving LSS between the e-tailer and the seller. Guo et al.
[90] noted that when the logistics disadvantage of LSS is sufficiently large, LSS is unfavorable to the manufacturer. He et al.
[91] found that implementing a comprehensive logistics integration approach via a platform consistently yields advantages for all members of the supply chain without manufacturer encroachment. Zhang and Ma
[100] found that if the marketplace channel is not established, the platform providing logistics services will generate better logistics services, lower retail prices for products and higher demand. Wang et al.
[101] indicated that users’ specific assets do not directly stimulate either type of information sharing. Wang et al.
[102] indicated that the seller with her own logistics system is not always willing to share logistics services.
4.3. Summary of the influencing factors
The key factors affecting PSC members’ choice between SBL and 3PL include several aspects, such as the service cost, service level, channel, brand, market potential, and competition. This study finds that researchers have extensively examined the cost-effectiveness of logistics services, the expenses associated with 3PL providers, and the costs related to platform logistics. With respect to the service level, the opinions among researchers diverge, with some studies suggesting that a high logistics service level helps brands sell through the platform, whereas others point out that e-tailers and manufacturers do not choose 3PL at a low service level. These studies have identified the preferences of organizations in various cost scenarios. Furthermore, the study conducts a thorough analysis and concise summary of many elements, including channels, store brands, market potential, and competition. Overall, this study provides a comprehensive reference for the members of PSCs when formulating logistics service strategies, reveals the influence mechanisms of different factors on the choices of SBL and 3PL, and offers important theoretical support and practical guidance for enterprises in logistics decision-making.
The factors affecting PSC members’ behavior in sharing logistics services include the
service cost,
competition,
consumers’ logistics preference, and
market potential. The existing literature on LSS emphasizes how competitive intensity and market conditions determine the profitability of firms involved in logistics service agreements. Strategies to address competitive effects and channel efficiency play an important role in incentivizing firms to adopt LSS practices. The market’s competitive structure has a significant effect on cost considerations and competitive advantage, and it shapes the decision-making process of logistics service providers. Consumer sensitivity to logistics has a direct effect on manufacturers’ profitability, and the outcome of LSS varies according to the pricing strategy and market acceptance. The study emphasizes the complexity of LSS decisions among stakeholders, highlighting the need for strategic alignment and cost effectiveness.
Fig. 4 summarizes the factors influencing the choice of logistics service strategy.
5. Future research
Although much attention has been given to this topic, there is still room for further research (
Fig. 5). Subsection 5.1 illustrates future research directions from the perspective of possible extensions to the current literature. Subsection 5.2 presents challenges and opportunities arising from technological developments.
5.1. Possible extensions to the current literature
Collaboration and competition between SBL and 3PL in a multiplatform ecosystem. On the basis of the columns for PSC members in Tables S1 and S2, the existing research on collaboration and competition in platform ecosystems has focused predominantly on logistics choices within a single platform. In reality, there is multiparty competition in PSCs, such as competition among e-commerce platforms. Hence, future research could delve deeper into the collaboration and competition between SBL and 3PL in a multiplatform ecosystem. Meanwhile, 3PL providers, facing increasing competition from platform-owned logistics services, could carve out a niche through differentiated service offerings. This multilayered competition and cooperation present fertile ground for future research to explore how these dynamics affect broader supply chain performance and platform competition.
Consumer preferences for green logistics services in PSCs. In Tables S1 and S2, regarding the impact of consumer behavior on logistics strategy choices, the existing research reveals that consumers are sensitive to incentives such as membership-based free shipping
[75],
[108],
[109] and fast delivery. Future research can refine the analysis of consumer preferences for logistics services in PSCs and explore how personalized logistics services can improve customer satisfaction and loyalty in the context of diverse needs. For example, China has accelerated green science and technology innovation, popularized and applied advanced green technologies, and built a low-carbon and recycling economic system
[110],
[111],
[112]. Future studies could investigate how platforms, 3PL providers, and members of shared logistics services in PSCs can meet consumers’ environmental expectations through green logistics practices. Furthermore, the research on new quality productive forces is at the forefront of the field, so adding a research perspective on PSC logistics management in the context of new quality productive forces is also of research interest.
Risk-related and uncertainty factors in PSCs. By analyzing the “research questions” columns in Tables S1 and S2, the research question analysis highlights a notable gap in the existing literature regarding the integration of risk and uncertainty factors into logistics service strategies for PSCs. Future research should delve into how these risk and uncertainty factors can be incorporated into strategic frameworks for logistics services. First, it is essential to consider the relationship between risk preferences among various stakeholders. Research could explore how different members of PSCs adjust logistics service choices (SBL, 3PL, and LSS) when facing high-risk situations. Second, the impact of these risk preferences on the demand for logistics services warrants exploration. Finally, exploring risk-sharing mechanisms in logistics service strategy choices is crucial.
5.2. Challenges and opportunities due to technological developments
Technological developments have significantly affected logistics and supply chain management. The Ministry of Industry and Information Technology of the People’s Republic of China, along with three other departments, jointly issued the “Guide to Improve the Supply Chain Management Level of Manufacturing Enterprises,” which points out that accelerating the digital transformation of the enterprise supply chain is necessary. This requires relying on a new generation of information technology such as the Internet of Things, blockchain, big data, the industrial internet, and artificial intelligence. Logistics enterprises based on the PSC continue to explore the important application of the above information technologies in online data management, logistics optimization, and so forth.
Technological innovations in logistics, while offering significant benefits, also introduce some negative implications that warrant consideration. On one hand, the integration of advanced technologies increases the complexity of logistics networks, necessitating substantial investments in training and systems management, which may not yield immediate returns. On the other hand, the rise of automation and intelligent systems, may lead to job displacement within the logistics sector, raising concerns about workforce stability, and highlighting the need for retraining initiatives to adapt to this evolving landscape. On the basis of the aforementioned industrial practices, future research on logistics service strategies in PSCs can consider the following areas.
Impact of real-time data on the logistics service strategy. Real-time online data enhance visibility across the entire supply chain, enabling PSC members to make informed decisions on the basis of accurate and up-to-date information. This improved visibility allows for better demand forecasting, inventory management, and resource allocation, which are crucial for selecting the most suitable logistics model. For example, if real-time data indicate fluctuating demand, members can choose a flexible 3PL solution that can quickly adapt to changes in order volume. As logistics optimization technologies evolve, the integration of online data into these systems enhances decision-making processes for PSC members. This dynamic capability enables members to remain agile and responsive to market changes, ultimately influencing their choice of SBL, 3PL, or LSS on the basis of real-time conditions and strategic objectives.
Impact of intelligent robot technology on logistics service optimization. The use of intelligent robots improves the efficiency and accuracy of logistics operations. By automating processes such as warehousing, sorting and delivery, intelligent robots can significantly reduce labor costs and operating time. As a result, PSC members can rely on the high performance of robots when choosing SBL to meet customers’ demands for fast delivery and high accuracy. If 3PL and LSS providers can integrate intelligent robotics technology, they can offer more competitive services in the market and attract companies to improve their operational efficiency.
Impact of green policies on logistics service strategy. As companies increasingly prioritize environmental sustainability, PSC members will be more inclined to choose logistics solutions that can effectively reduce carbon emissions and meet regulatory requirements. The carbon tax compels enterprises to focus on their carbon emission levels, driving them to adopt more environmentally friendly transportation and warehousing solutions. In this context, 3PL and LSS are typically more effective at reducing carbon emissions because of their ability to share resources. In contrast, SBL may face greater challenges in terms of emissions reduction, as it must bear all operating costs and environmental responsibilities independently. For PSC members, when evaluating SBL, 3PL, and LSS, they may prefer to choose service providers that can offer carbon emission reports and optimization recommendations.
6. Conclusions
This paper conducts a systematic literature review of logistics service strategies in PSCs on the basis of sample papers collected from the Web of Science, Science Direct, and Scopus databases. The status of logistics service strategies in PSCs and the factors influencing members’ logistics service strategy choices are identified, and some future research directions are proposed. By reviewing the relevant literature, we find that scholars have focused on the following issues in the research related to SBL and 3PL in PSCs. One is the choice of the optimal logistics service strategy for the members in the PSCs, whereas the other is the impact of factors related to logistics service on PSCs. LSS is regarded as an important complement to SBL and 3PL with key research hotspots including the channel, partner selection, and service competition. The studies in these intersecting areas reflect the complexity and diversity of PSCs and logistics management.
Furthermore, we summarize the key factors influencing the logistics service strategy selection of PSC members. First, the main factors influencing the choice between SBL and 3PL for PSC members include the service cost, service level, channel, brand, market potential, and competition. Regarding the service level factor, some studies argue that a high logistics service level enhances brand performance on platforms, whereas others contend that e-tailers and manufacturers are unlikely to select 3PL when the service level is low. Second, the factors influencing PSC members’ behavior in LSS include the service cost, competition, consumers’ logistics preference, and market potential. Furthermore, strategies to address competitive effects and improve channel efficiency play an important role in motivating firms to adopt LSS in practice. Overall, we reveal how different factors influence the choices of SBL, 3PL and LSS, and provide theoretical support and practical guidance for firms in logistics decision-making.
On the basis of the review of research on logistics service strategies in PSCs, some directions for future research are identified. Future research could focus on collaboration and competition between SBL and 3PL in a multiplatform ecosystem, consumer preferences for green logistics services in PSCs, and the impacts of risk-related and uncertainty factors on the logistics service strategy selection of PSC members. Considering challenges and opportunities due to technological developments, future research could investigate the impact of real-time data on logistics service strategies, the impact of intelligent robot technology on logistics service optimization, and the impact of green policies on logistics service strategies.
CRediT authorship contribution statement
Lin Chen: Writing – review & editing, Writing – original draft, Methodology, Investigation, Funding acquisition, Conceptualization. Ting Dong: Writing – review & editing, Writing – original draft, Software, Resources, Data curation. Xiang Li: Writing – review & editing, Writing – original draft, Supervision, Investigation, Funding acquisition, Conceptualization. Xiaofeng Xu: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Investigation.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This research was supported by research grant from the National Natural Science Foundation of China (72102171, 71931001, U2469202, and W2411066), the Humanities and Social Sciences Youth Foundation, Ministry of Education of the People’s Republic of China (21YJC630006), and the Graduate Innovative Fund of Wuhan Institute of Technology (CX2023368).
Appendix A. Supplementary data
Supplementary data to this article can be found online at
https://doi.org/10.1016/j.eng.2024.12.032.
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