Understanding the Effect of Multi-Agent Collaboration on the Performance of Logistics Park Projects: Evidence from China

Abstract

With the rapid development of a new generation of information technology and data systems, there are more and more modern logistics park projects appearing in China. However, in the process of the construction and operation of a logistics park project, there is often a lack of coordination between the multiple parties (usually the government, the development enterprise and the entered enterprises), which leads to a series of problems such as low efficiency and disordered management and so on, finally affecting the project performance. However, few studies have focused on this phenomenon, and prior studies are unclear regarding the impact of multi-agent collaboration on logistics park project performance. Therefore, in this study, we investigate the link between multi-agent collaboration and the logistics park project performance based on survey data from Yunnan Province in China. The empirical analysis was conducted using the partial least squares (PLS)-based structural equation modeling with Smart PLS 2.0. The data analysis results suggest that the three dimensions of multi-agent collaboration (management, mechanism and information collaboration) have a significant positive impact on the performance of a logistics park project. Under different environmental dynamics conditions, different strategies should be adopted by a logistics park project to improve the performance. This study provides a new perspective for understanding the value of multi-agent collaboration in logistics park projects both in theory and practice.

1. Introduction

As a key component of the modern service industry, the current logistics industry is a cross-industry, cross-department, cross-region and highly permeable complex formed in the supply chain through the integration of the traditional logistics industry. As a basic and strategic industry supporting the development of China’s national economy [1,2], the modern logistics industry covers many areas, such as the road transportation industry, storage industry and information industry [3]. It involves a wide range of fields and a large number of employees, so the development of the modern logistics industry is very important. It can promote the adjustment and upgrading of the industrial structure. At the same time, its development degree can measure a country’s comprehensive national strength, which is an extremely important indicator [4]. In recent years, the operation quality and efficiency of China’s logistics industry have been greatly improved. At the same time, the demand for social logistics shows an overall growth trend. In addition, the ratio of the total social logistics expenses to GDP also dropped from 17.8% to 14.6% [5], which proves that logistics efficiency has improved. China’s logistics industry has made remarkable progress with rapid development.

Among various indicators, as an important functional carrier and component of the modern logistics system, the construction of logistics parks has also made rapid development in China. A logistics park refers to a place with a certain scale that stores, transports, packs, processes, loads and transports commodities based on large and medium-sized cities [6,7]. According to relevant survey reports, in 2020, there were 1638 large-scale logistics parks (covering an area of more than 1000 acre) including operation, construction in progress and planning, an increase of 428 from 1210 in 2020. Over the three years, the number of large-scale logistics parks in China increased by 10.7% annually.

Transportation, storage, distribution, loading and unloading, packaging, circulation processing, information processing, data integration and other businesses are included in the functional scope of the logistics park. The logistics park above this scale also has value-added services such as information transactions, big data mining, supply chain design and finance. A logistics park is generally equipped with an advanced logistics management information system and its main function is to facilitate the faster and more economic flow of goods. Centralized storage can improve the level of logistics regulation [8]. The construction of a logistics park project can organically connect the upstream and downstream of the supply chain, accelerate the logistics speed, shorten the circulation time and reduce the circulation cost. A logistics park usually has the function of circulation and processing and can carry out appropriate circulation and processing according to the needs. It can make rational and effective use of goods sources and improve the enterprise and social benefits. The supplier submits different kinds of goods to the logistics park for processing, and classifying, packaging, safekeeping, circulation processing and information processing for the purpose of completing the distribution and delivery according to customer needs [9].

The construction of a logistics park project is different from traditional engineering projects, which involve many stakeholders, usually the government, the development enterprise (the owner) and the settled enterprise, which often have different demands. For example, the cost and quality control requirements of the owner developer, the planning and guidance of the government, and the warehousing, loading and unloading, handling and other logistics operation requirements of the logistics and trade enterprises in the logistics park often make the construction of the logistics park project face a situation of low collaboration between the multi-agent entities. As a result, there will be a series of problems such as schedule confusion, non-standard quality, non-compliance with reality, repeated construction waste, etc., which will seriously affect the project performance. Therefore, the research problem of this paper is ‘how multi-agent collaboration of the logistics park project affects the project performance’. Through an in-depth study of this problem, we discuss the multi-agent collaboration mechanism of the project, so as to put forward corresponding countermeasures and suggestions to guide the logistics park to improve project performance. In order to address the research gaps, this paper will focus on the above structured research issues and systematically study the following main contents and key issues:

(1)

What is the definition, connotation and measurement of the multi-agent collaboration concept in logistics park project?

(2)

What is the definition, connotation and measurement of logistics park project performance?

(3)

What is the impact of multi-agent collaboration in logistics park projects on the performance of logistics park projects?

(4)

Under different environmental factors, how does multi-agent collaboration affect the project performance of a logistics park?

The study focuses on the impact of multi-agent collaboration on project performance, which has strong practical and theoretical significance, specifically reflected in the following two points. First, based on the theory of synergetics, we analyzed the impact of multi-agent collaboration on the project performance, and the corresponding synergy management system, and issued questionnaires to the managers of logistics park projects in Yunnan Province of China; we then used the structural equation method (SEM) to explore the impact of multi-agent collaboration on project performance. This study provides a comprehensive reference mechanism for the specific practice of the owners of the logistics park project construction, and also has a wide reference significance for the management of similar multi-agent projects. Second, the existing research on logistics park construction projects lacks the perspective of the multi-agent collaborative impact on project performance. This study innovatively combines the synergetic theory, logistics and project performance theory, to a certain extent, improves and expands the original theory, and also effectively expands the theoretical innovation of project management through integration, which has particular theoretical significance.

2. Literature Review

2.1. Logistics Park Project

The concept of the logistics park comes from developed countries such as Germany and Japan [10]. It is called a logistics group in Japan and a freight village in Europe. Logistics parks refer to those logistics places that rely on large and medium-sized cities that have certain strength and scale to manage the storage, transportation, packaging, processing, loading and unloading, and handling of various commodities [11]. These logistics parks are usually equipped with a more advanced logistics management information system, which can not only promote the transportation and flow of goods more quickly and economically and make the storage centralized, so as to improve the level of logistics regulation, but also make the connection organic, so as to accelerate the speed of logistics, shorten the time of circulation, and reduce the costs incurred in the circulation process. In addition, the logistics management information system can be processed properly according to the needs, so as to make reasonable use of the source of goods, and fully improve the economic benefits. According to [12], there are eight different types of logistics parks: the warehouse, distribution center, container yard, inland container, intermodal terminal, inland port, freight village and main port terminal. In terms of function, the logistics park can be divided into several types: de-consolidation center, shipping center, distribution center, collection center, reserve center and processing center [13]. According to the need of customers, logistics parks can be divided into two types: public type and self-use type [8]. The public logistics park is more extensive than the private logistics park. In other words, any supply chain member in the supply chain system can become a user of a public logistics park. From the perspective of the service aspect, logistics parks can also be divided into normal temperature logistics park and cold chain logistics parks. The modern logistics park has the following important functions: transportation function, storage function, loading and unloading function, packaging function, circulation processing and information processing. The information management function of the logistics park ensures the interaction and information sharing among order processing, inventory control, etc., thus reducing costs and increasing value-added services [14]. The transportation management function of logistics parks mainly involves the planning and control of enterprise transportation activities and sharing information with sub-logistics management system [15]. In addition to the above basic functions, the modern logistics park also has the following value-added functions [8,16]: settlement function, demand forecast, design consultation and education and training.

At present, research on logistics park projects has been rich and in-depth, and the research topics mainly include the following categories. First, research on the location of logistics park projects [8,9,10]. Second, research on the management mode of logistics park projects [17,18]. Third, research on the operation efficiency evaluation of logistics park projects [19,20].

Through the relevant literature analysis of existing logistics park projects, it is found that there is sparse literature on logistics parks from the perspective of project management, especially in regard to engineering construction projects. Therefore, through the literature review, we find that it has certain theoretical and practical significance to study the construction of logistics park project from the perspective of project management.

In addition, combined with the construction practice in logistics park projects, it is found that the development, construction and operation involve many subjects, including the owner, the government, the enterprises settled in the center, and even some peripheral enterprises providing supporting services. The construction involves multi-agent interests, and different interest demands among these subjects will affect the progress, quality and operation of the whole project. Therefore, research on the synergic effect of logistics park construction projects and the establishment of coordination and management mechanisms are critical to the success of the whole project.

2.2. Logistics Park Project Performance

A logistics park project is a typical engineering management project. Its development, construction and operation also need scientific theory and tools for measurement and evaluation [21]. Among them, as a very important part of the theoretical system of project management, project performance can effectively measure and evaluate a logistics park project. Lyu [22] examined the relationship between platform, location and operation performance, and found that the logistics park platform and location have a positive impact on enterprise resource integration, which has a positive impact on operation performance. The platform can directly improve operational performance.

At present, research on the concept of project performance has reached a relatively mature stage and achieved fruitful results [23,24,25,26,27]. As an important theoretical tool, the concept of project performance has been successfully applied in many fields, such as traffic construction, government investment, software development, etc. [16,28,29]. The concept of project performance is widely used in the field of construction engineering, and many significant research results have been achieved. According to Demaj [30], performance information can more clearly highlight the expected results of budget changes. Therefore, the introduction of performance information into budget proposals during legislative consideration in parliament may have a positive impact on the distribution status quo. A logistics park project is also a typical project management project; its development, construction and operation also need scientific theory and tools for measurement and evaluation. Among them, project performance as an effective tool of project management evaluation can be used to assess the performance of logistics park projects.

Generally speaking, there are two kinds of performance evaluation in the construction period, the first one is to use it as a kind of facility, the second one is to regard it as a process. Generally, the first method is most commonly used in evaluating the performance of construction projects. In general, the construction of the project mainly comes from the purpose and demand of the owner, and the degree of satisfaction can be reflected by quality, progress, cost and other evaluation indicators, and in general, are called the “iron triangle”. Although the “iron triangle” can reflect the success or failure of a construction project to a certain extent, such judgment and evaluation often need to be made after the completion of the whole project. Extensive evidence has shown that an important problem existing in the project environment is that when construction project participants agree that performance evaluation should include some elements other than the “iron triangle”, there are no practical principles [31]. Lingard et al. [32] believe that some factors such as the quality and adaptability of the relationship between project participants have an impact on customer satisfaction, which may affect the success or failure of the whole construction project. They also pointed out that when reviewing the whole process of the construction of the project, what impresses is not the cost or whether the project is completed ahead of time, but the impression of solidarity, kindness and mutual trust of the participants, or the disputes, crisis of trust and various conflicts between them. The willingness and decision making of the owner when bidding for the next construction project may be more affected by these factors. Because of this, the traditional performance evaluation methods in construction projects often cannot reflect the “real” project performance. Therefore, in order to evaluate the performance of construction projects more comprehensively and accurately, many professionals at home and abroad have carried out a series of related research. For example, Lai et al. [33] conducted a questionnaire survey on 324 construction projects in Hong Kong and found that the nine evaluation indicators of project performance have different importance rankings’; top of the list was the timely completion of the project, and the last was about the innovation of architectural design.

Based on the existing research results, we construct a logistics park project performance evaluation index system. The evaluation index system mainly includes hard performance and soft performance. Hard performance tends to be quantitative data indicators, including cost performance, quality performance, progress performance and safety performance, while soft performance focuses on qualitative analysis, including the satisfaction of project participants, effective communication and mutual trust.

2.3. Multi-Agent Collaboration

Multi-agent collaboration mainly refers to the collaboration of two or more individuals to achieve the same goal. Among them, each individual will influence and cooperate with each other [34]. The synergy can break the barriers of different subjects in multi-agent collaboration and achieve a win–win in a real sense [35]. The theory of multi-agent collaboration is closely related to the synergetic concept [36], first proposed in 1969.

German scientist Haken published his Monograph on synergetics in 1977, which is considered to mark the birth of synergetics [37]. In the following years, Haken published a series of works related to synergetics. Since then, scholars all over the world have begun to study this field. Many scholars have introduced synergetic theory into the field of management with many new achievements and breakthroughs. Michael Porter and others first put forward the concept of the value chain, and explained synergy with their own concept of the value chain. In addition, he paid more attention to the impact of internal management on value creation. Furthermore, he also considered that enterprises can gain a certain degree of competitive advantage in the market by constructing internal business units and on the interrelation between enterprises. Gajda [38] applied synergy theory to the research and application of the concept of strategic alliances. Strategic alliance means achieving the purpose of creating business partners and end customers’ benefits through collaboration between different organizations. Stank and Keller [39] think that collaboration is an important tool and process when making decisions among alliance partners. If members need to distribute results or work, share knowledge, etc., collaboration is an important tool. Esper et al. [40] introduced collaborative theory into the field of transportation management research, and believed that the owners and shippers can form an alliance through collaboration to achieve the effect of knowledge sharing and cost reduction. Huang et al. [41] studied the complexity of cooperation in the supply chain network, especially the influence of horizontal cooperation on vertical cooperation. The analysis emphasized various cooperative behaviors and their interactions under different types of cooperation. Zhang et al. [42] proposed a new quantitative analysis method of synergistic effect, which combined the hierarchical index framework, collaborative evaluation and collaborative optimization, and established an evaluation and optimization method, which filled the gap in the relevant research fields.

The construction of the logistics park is also a typical project management project. The construction process is usually first carried out by the government so as to make a general plan, initially define the land use, function and positioning of the logistics park project, and then introduce a large leading enterprise as the owner to carry out the construction and development of the park. Finally, the owners of the enterprises themselves or in cooperation with the local government attract local third-party logistics enterprises, warehousing enterprises and trade enterprises to participate in the logistics park, forming an industrial cluster area [28]. The owner manages the whole park in a unified way. In addition to collecting property rent from the enterprises settled in the center, it also provides value-added services such as information consultation, investment and financing services and network services, etc. Therefore, the construction of a logistics park project is different from the traditional engineering project, which involves many stakeholders, usually including the government, the development enterprise (the owner) and the settled enterprises. However, the functions of these three parties are not the same in the construction project of a logistics park and as a result, there will be a series of problems such as schedule confusion, non-standard quality, non-compliance with reality, repeated construction waste, etc., which will seriously affect the project performance [10,43,44].

Therefore, in this paper we define the multi-agent main body of a logistics park project as the government, development enterprise and resident enterprise. According to the related theory of synergetics, founded by Haken, the synergetic effect of multi-agents is the result of a complex and dynamic process of collaborative interaction among the information subject, information and information environment. The main body and the information environment of the logistics park project realize the safe, efficient and intelligent operation of the multi-agent information chain of a smart city through the main body collaboration, information collaboration and mechanism collaboration, so as to improve the multi-agent collaboration effect. There are many similarities between the synergy mechanism of multi-agent of logistics park project and that of cross organization consortia, but because of the particularity of the logistics industry itself, the synergy mechanism also has particularities.

Based on synergetic theory, we combine the characteristics of the multi-agent of the logistics park project, and the multi-agent collaboration of the logistics park project is divided into three dimensions: management collaboration, mechanism collaboration and information collaboration.

The management collaboration among multi-agents in logistics park projects means that the strategic objectives of the multi-agents should be consistent during the development of the project, the transformation of strategic objectives in the process of collaboration should be holistic, and each department should be closely connected during the collaboration, so as to realize the optimal allocation of resources and make the division of labor clearer and more efficient between each department, that is, realize the strategic coordination and organizational coordination among multiple stakeholders in the logistics park project. The multi-agent mechanism collaboration mainly refers to the collaboration among the government, development enterprises and settled enterprises in terms of the mutual trust mechanism, conflict resolution mechanism and risk prevention mechanism [45]. This kind of collaboration can effectively improve the communication efficiency and stability of collaboration among the various stakeholders, reduce the probability of risk occurrence, and enable the multi-agent subjects to negotiate together to solve the problems. The multi-agent information collaboration refers to the effective sharing of information among all parties, the increase in information flow frequency among all parties, and the effective exchange of information technology among all parties. This can realize information sharing and collaboration, information exchange and collaboration, and information interactive learning, so as to promote the information and knowledge sharing among all parties, and improve project performance.

2.4. Environmental Dynamics

The construction of a logistics park project is closely related to the business, political and economic environment. When the business, policy and economic environment change, it will inevitably have a great impact from planning to construction. Jakobsen Siri [46] proposed that the use of environmental policies to promote R&D cooperation that can generate environmental innovation is increasingly important for sustainable development. For the main body of the logistics park project, the external environment mainly refers to a series of environments that affect the project from the outside, such as politics, economy, society and technology. These environmental factors will affect the technology commercialization performance of the enterprise. However, not only should the external environment be considered, but also the dynamic environment. The uncertainty and change frequency of various environmental factors constitute the environmental dynamics, which are characterized by instability. Kohli and Jaworski [47] classified environmental dynamics into technology dynamics and market dynamics. Technology dynamics make the industry’s technology change faster, and the path of technology evolution is difficult to predict. Market dynamics make the change in customer preference faster, which makes it difficult for enterprises to accurately grasp customer satisfaction in a short time. Simsek [48] believed that the dynamic change in external environment gives enterprises enough motivation to carry out technology innovation. A new and stable external environment will make the enterprise satisfied with the status quo. Wong et al. [49] extended previous studies on supply chains, constructed a theoretical model of the relationship between environmental uncertainty on the three dimensions of supply chain integration and the four dimensions of business performance, and conducted empirical tests on it. Javier et al. [50] argued that the degree of dynamic changes in the environment brings to the enterprise technology innovation activities and reduces the effect of the innovation of the enterprise. Stephane and Richard [51] found through research that the dynamic changes in the environment may be reflected in consumers’ desire for enterprises to design more innovative products, which sometimes increases the motivation of enterprises, but also increases the pressure on managers. Yang [52] evaluated the impact of learning ability on logistics service capability and organizational performance, and pointed out that future research can use environmental uncertainty as a moderator to evaluate the impact of environmental uncertainty on the relationship between logistics learning ability and organizational performance.

2.5. The Relationship between Multi-Agent Collaboration and Logistics Park Project Performance

As mentioned above, multi-agent collaboration based on synergetic theory has resulted in many achievements. Some scholars have introduced it into the field of logistics research. For example, Gajda [38] connected collaboration theory with strategic alliance, so that each business entity and terminal consumption in the alliance can achieve the maximum benefit goal. Sandberg [53] conducted an in-depth study on collaborative logistics operation by adopting a questionnaire survey and statistical data analysis. The result of the data analysis shows that the collaboration between the business and main body in logistics operation will directly affect the final performance. If the supply chain management is effective, it can enhance logistics collaboration. The inconsistency between the goals of suppliers and end consumers is one of the most important reasons for the ineffective implementation of logistics collaboration. Lehoux et al. [54] believed that because the development of enterprises is restricted by many factors, such as resources, environmental protection and other external conditions, it is necessary to promote enterprises to continuously explore new multi-agent collaborative business models in order to improve the operation efficiency of enterprises and reduce cost in the operation process. Park et al. [55] studied and established a CEP delivery model based on the last mile network to evaluate the impact of logistics collaboration in apartment communities. The results also show the financial and economic feasibility of the model.

Golpayegani et al. [34] described a collaborative P-MCTS CP (MCTS) model for electric cars, through consultation to actively influence the planning process, to solve their own conflicts, and use the consultation process of collective knowledge to optimize the final consumption patterns; the results show that during the rush hour, there is an obvious load transfer, and the load curve is more smooth, increasing the fees of fairness and flexibility. Hammes et al. [56] showed that civil buildings produce a large amount of garbage every year, which can be recycled through reverse logistics. It is necessary for managers to evaluate the performance of reverse logistics in order to understand the actual efficiency and effect of their actions and to avoid unnecessary expense and losses. However, such activities are still not widely practiced in developing countries. We propose a model to assess the performance of reverse engineering in civil construction in assisting developing countries in the practice of return activities. The purpose of the research by Beysenbaev [4] was to propose how to improve the current logistics performance indicators published by the World Bank. Due to the diversified nature of logistics, it is difficult to measure and demonstrate its efficiency in different countries. The author proposes a revised index based on international statistical data. This index objectively reflects the logistics systems and subsystems of 159 countries in both qualitative and quantitative terms, and can be used as a benchmark tool for national governments. Rashidi et al. [57] assessed the sustainability of operational logistics performance in an OECD country and compared it with the Logistics Performance Index, the most widely known national measure developed by the World Bank. The conclusion was that the SOLP approach provides useful information that complements the information provided by LPI, but it does a better job of facilitating performance improvement in a country’s logistics industry by helping to identify sources of inefficiency and countries with logistics industry performance benchmarks. However, through the literature review, we found that the impact of multi-agent collaboration on the project performance of logistics park was unclear.

3. Research Model and Hypothesis

On basis of the previous analysis, we propose the research model below (see Figure 1), in which the multi-agent collaboration (management, mechanism and information collaboration) effect on the logistics park project performance, and the relationship between them, is moderated by environmental dynamics. Furthermore, most studies treat the firm size and type as control variables, due to different types of operation modes and the fact that larger projects usually have more available resources. Hence, we also introduced size and type as control variables in the model.

3.1. Multi-Agent Collaboration and Logistics Park Project Performance

Management collaboration among multi-agents in a project means that the strategic objectives of the multi-agent should be consistent during the development of the project, and the transformation of the strategic objectives in the process of collaboration should be integrated. During the collaboration, all departments are closely connected so as to realize the optimal allocation of resources and enhance the collaboration among personnel, that is, the strategic collaboration and organizational collaboration among the multiple entities of the logistics park project. The government, the development enterprises and the entrants of the logistics park project can effectively improve the project performance through strategic collaboration and organizational collaboration. First of all, the government, the development enterprises and the settled enterprises usually have different strategic objectives. The government usually plans the project, and pays more attention to the long-term development vision and social benefits of the project. However, the development enterprises often pay more attention to the return on investment and income analysis of the project, and to the project details such as the financing, development cycle and development steps of the project development. Settled enterprises only pay attention to whether the functions can meet their needs and whether the cost is the lowest, and often do not pay attention to the social benefits of the whole project [10]. Therefore, whether the three parties in a logistics park project can achieve collaboration at the strategic level will affect whether the project can not only meet the social benefits of the government, but also meet the economic benefits of the development enterprises and the settled enterprises, effectively reducing the possibility of inconsistent interest demands in the later development and operation of the project, and in providing a certain guarantee for the development and management of the project from the strategic level can effectively improve project performance. Secondly, at the organizational level of project development, the multi-agent entities should be able to ensure close collaboration between departments in regard to business. The staff of both parties should be assigned posts and responsibilities, so that all parties can give full play to their functional advantages in project development and operation, learn from each other’s strengths and complement each other’s weaknesses, and realize the interest demands of multiple parties in business, so as to ensure the optimal allocation of resources of all parties [43], thereby effectively improving project performance. Therefore, based on the above analysis, the following hypotheses are proposed.

Hypothesis 1a (H1a).

Multi-agent management collaboration has a positive impact on the project performance of a logistics park.

The mechanism collaboration of the multi-agent subjects of the project mainly refers to the collaboration among the government, development enterprises and resident enterprises in terms of mutual trust, conflict resolution and risk prevention mechanisms, which can effectively improve the communication efficiency and collaboration stability between the various stakeholders [58], reduce the probability of risk occurrence, and enable the multi-agent stakeholders to jointly negotiate to solve the problems they face [9]. First of all, it is difficult to form an efficient communication mechanism among the multi-agent entities. Creating mutual trust between these parties is a key factor for the success of the project. The mutual trust mechanism encourages the multi-agent entities to trust each other, so as to effectively improve the communication efficiency and project performance. Secondly, due to the inconsistency of interest demands, the collaboration between the multi-agent entities often results in a certain degree of conflict, such as mutual shirking, falseness and insincerity, which leads to risks in the project from planning, design, construction and operation, and affects the project performance. Finally, the development and operation of a logistics park project will inevitably encounter a series of risks. The key points lie in mobilizing the enthusiasm of all parties and giving full play to their resources to jointly reduce the risks [59]. The risk prevention mechanism is helpful in reducing the probability of risk occurrence and improving the project performance. In this connection, we draw the following hypothesis.

Hypothesis 1b (H1b).

Multi-agent mechanism collaboration has a positive impact on the project performance of a logistics park.

The multi-agent information collaboration of the project refers to the effective sharing of information among all parties of the project through the comprehensive use of cloud computing, blockchain, big data and so on, increasing the frequency of information flow among the parties, and the effective exchange of information technology among the parties. This is in order to realize information sharing and collaboration, information and data exchange, and interactive learning, so as to promote information and knowledge sharing among all parties, and then improve project performance. First of all, in the process of development and operation, the government, the development enterprises and the settled enterprises need to effectively share data and information, so as to avoid blocked and distorted information exchange, which may lead to project risks. The collaboration of information sharing among multi-agent entities can effectively form a mechanism of sharing data and information among all parties. By creating information-sharing collaboration among all parties, the gap in information exchange can be reduced, and the efficiency of information sharing can be improved [60], so as to improve project performance. Secondly, the multi-agent entities of the logistics park project should strengthen the flow frequency of information and form a normal mechanism for the real-time sharing of data and information, so that all parties can grasp the progress and existing problems of the project in real time, so as to jointly negotiate to solve the problems as soon as possible, and then improve the project performance. Finally, all parties often have their own advantages and disadvantages in information systems and technology. For example, the government’s information technology and systems tend to be more public service functions, while the development enterprise is mainly the operation and management of the enterprises themselves. Because of the different types and scales of the enterprises, the information system of some small- and medium-sized enterprises may be relatively simpler [61]. Therefore, there may be a series of problems, such as inconsistent data interface and difficult data sharing between the parties. Therefore, all parties need to learn from each other’s information technology and systems, so that there is a certain degree of synergy between the information systems in the underlying technical interface and the top-level technical architecture; such technical collaboration will further improve the efficiency of information sharing and data systems integration between the parties, and then improve the project performance. Therefore, the following hypothesis is made.

Hypothesis 1c (H1c).

Multi-agent information collaboration has a positive impact on the project performance of a logistics park.

3.2. The Moderating Role of Environmental Dynamics

A logistics park project is a typical asset-heavy investment project, from planning and design, to project implementation, to operation and management. Development can take less than one or two years, or more than three or five years, so the project will face environmental changes in the implementation process. As mentioned above, there are many types of logistics parks, which are divided by different functions, including transfer type, storage type, circulation processing type and comprehensive type. From the logistics perspective, it can be divided into cold chain logistics, bulk logistics, urban distribution and so on. Therefore, different types of logistics park projects are faced with different market and policy environments. For example, in recent years, the Chinese government has vigorously supported the development of cold chain logistics, and issued a series of preferential policies to support the construction of cold chain logistics parks and other basic projects. Listed companies in China, such as JD, Shun Feng Express, Alibaba Group and other enterprises are setting up e-commerce cold chain logistics distribution centers in many cities with the support of the government. On the contrary, the logistics park of bulk materials such as steel, cement and building materials is greatly limited in urban construction due to environmental protection, transportation and other factors. Therefore, the more dynamic the logistics park project is in the changing environment, the more effective and sufficient collaboration of management, mechanisms and information among the multi-agent entities is needed, so that the government, development enterprises and resident enterprises can face the series of difficulties brought by the changes in the economic, policy and market environments [62]. The joint consultation and optimization of resource allocation enables an effective response, so as to improve project performance more effectively. Instead, if the logistics park project is located in a relatively stable environment in regard to policy, market and economy, the development and operation of the project can be carried out in accordance with the original plan, without much change. At this time, the collaboration degree of the multi-agents in management, mechanisms and information is not as high as in a dynamic environment. Therefore, based on the above analysis, the following hypotheses are made.

Hypothesis 2a (H2a).

The more the environment changes, the greater the impact of multi-agent management collaboration on the project performance of a logistics park.

Hypothesis 2b (H2b).

The more the environment changes, the greater the impact of multi-agent mechanism collaboration on the project performance of a logistics park.

Hypothesis 2c (H2c).

The more the environment changes, the greater the impact of multi-agent information collaboration on the project performance of a logistics park.

4. Research Method

4.1. Sample and Data Collection

We chose Yunnan Province in China for the research focus, through the collaboration with Yunnan Federation of Logistics and Purchasing, and randomly distributed 200 questionnaires and recycled 152 questionnaires to the development enterprises of typical logistics park projects in all regions and prefectures of Yunnan Province as sample frames. Among them, 11 invalid questionnaires with incomplete answers and 141 valid questionnaires were received, so the effective recovery rate was 70.5%. The specific characteristics of the participants are shown in

Table 1. Characteristics of sample enterprises.

Types	Number	Percent
Size
10–100 employees	28	19.9%
100–300 employees	68	48.2%
>300 employees	45	31.9%
Revenue (2019)
<50 million RMB	35	24.8%
10 million RMB to 50 million RMB	75	53.2%
>50 million RMB	31	22.0%
* Types
Cold chain logistics	29	20.6%
(including medicine, agricultural products and animal-husbandry-related industries)	38	26.9%
Urban distribution	31	22.0%
(including express delivery, logistics and related industries)	18	12.8%
Business logistics	19	13.5%
(including wholesale, retail, display and logistics integration industry)	6	4.3%

* Note: The classification standard according to logistics park standards developed by China Federation of Logistics and Purchasing.

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4.2. Measures

In the aspect of variable measurement, this paper gives priority to the mature scales in domestic and foreign journals to ensure the reliability and validity of the scale in the variable link. When it comes to the English scale, translation and back translation were used to ensure its accuracy. Combined with the actual investigation, the scale translated from English was revised. In addition, the authors also invited five master’s and doctoral candidates as well as three management and business personnel with many years of work experience in the survey industry to answer the questionnaire, and then modify the questionnaire according to their feedback, so as to ensure that these scales were still reliable in the Chinese language environment. The questionnaire took the form of a Likert seven-point scale, divided into seven numbers from 1 to 7, 1 indicating totally disagree to 7 indicating fully agree.

By referring to the relevant literature [63,64], we divide multi-agent collaboration into three dimensions: management, mechanism and information collaboration. The measurement items are shown as

Table 2. Measurement items of multi-agent collaboration.

Constructs	Survey Item	Source
Management collaboration of multi-agent	Our company has the same strategic objectives when cooperating with government departments and settled enterprises; The transformation of strategic objectives in the process of collaboration between our company and government departments and enterprises has integrity; When our company cooperates with government departments and settled enterprises, all departments are closely connected; Through collaboration, the resources between our company and the government departments and the settled enterprises are optimized; Through collaboration, the collaboration between our company and the government departments and the enterprises settled in realizes the collaboration of personnel and division of labor.	[63,64]
Mechanism collaboration of multi-agent	Our company and the government departments and the enterprises settled in the company have mutual trust and high communication efficiency; Our company and the government departments and the settled enterprises can solve difficulties through consultation; Our company has a common risk prevention mechanism with government departments and resident enterprises to help reduce the probability of risk occurrence; The collaboration between our company and government departments and settled enterprises is relatively stable.
Information collaboration of multi-agent	Information and data sharing has been realized between our company, government departments and resident enterprises; The information and data flow between our company and government departments and settled enterprises is relatively frequent; Our company, government departments and enterprises often learn from each other’s information systems and technologies.

.

Project performance is based on [65,66], taking into account both qualitative and quantitative factors. The quantitative performance indicators are mainly from the cost and efficiency aspects, including cost performance, quality performance, schedule performance and safety performance.

Qualitative performance indicators are mainly considered from the collaboration level of all participants, mainly including the satisfaction, effective communication and mutual trust of project participants. The specific measurement scale of logistics park project performance is shown in

Table 3. Measurement scale of logistics park project performance.

Constructs	Survey Item	Source
Project performance of logistics park	Cost performance; Quality performance; Progress performance; Safety performance; Satisfaction of project participants; Effective communication; Mutual trust.	[20,66]

.

The dynamic environment reflects the dynamic changes in the market environment of the enterprises which include the native customers, the intensity of competition, market preference and other aspects, and these changes are unpredictable and uncertain. All of the above characteristics can be described by the dynamic environment. The dynamic environment reflects the changing characteristics of the external environment of the enterprise. Therefore, based on this feature, as a moderating variable and using the measurement scales of Pavlou and El Sawy [67], which mainly reflect the degree of change in market demand, the degree of change in customer preference, the frequency of new products in the market and the degree of competition incentive are shown in

Table 4. Measurement scale of environmental dynamic.

Constructs	Survey Item	Source
Environmental dynamic	In the business field of our company, customers’ preferences change greatly with time; In the business field of our company, the sales mode of products is constantly changing; In the business field of our company, new products frequently appear in the market; In our business field, there are many competitors in the market.	[67]

. The four indicators describe the dynamic changes in the external environment of the enterprise.

We also included size and type as control variables in the analysis. Size is measured by the number of employees and is shown in Table 1. We applied three dummy variables for four different types, as shown in Table 1, to control for the effect of type.

4.3. Validity and Reliability

The samples in this study were all enterprises, the sample size was small (141 questionnaires in this study), and the sample data did not obey the normal distribution; for these reasons, the data analysis method adopted in this paper was the structural equation model based on partial least squares (PLS), usually referred to as PLS-SEM. According to the literature, nearly one-third of the management research field uses the PLS-based structural equation model for analysis [68], and a large amount of research in the project management field also uses the PLS-SEM method for empirical analysis [69,70,71].

Compared with the traditional regression analysis method, SEM (structural equation model) has a number of advantages. (1) It allows the construction of the structural model of the relationship between latent variables. (2) It allows the dependent variables and independent variables to contain a certain degree of measurement error. (3) Multiple independent variables and dependent variables can be estimated and processed at the same time. (4) Compared with traditional factor analysis, the estimation steps in the structural equation are simpler, which allows latent variables to be composed of multiple indicators which can be estimated at the same time. In addition, the reliability and validity of the variables can be estimated simultaneously.

Cronbach’s alpha is used to measure the stability and internal consistency of the variables. As shown in Table 4 and

Table 5. Reliability and validity analysis of construct.

Constructs	Measurement Items	Factor Loading	AVE	Cronbach’s Alpha	Composite Reliability
Management collaboration of multi-agent	M1	0.843	0.532	0.742	0.812
	M2	0.839
	M3	0.812
	M4	0.724
	M5	0.754
Mechanism collaboration of multi-agent	S1	0.703	0.520	0.768	0.843
	S2	0.782
	S3	0.761
	S4	0.783
Information collaboration of multi-agent	I1	0.738	0.536	0.712	0.824
	I2	0.746
	I3	0.713
Environmental dynamics	D1	0.782	0.584	0.697	0.856
	D2	0.789
	D3	0.759
	D4	0.772
Project performance of logistics park	P1	0.787	0.562	0.714	0.876
	P2	0.761
	P3	0.714
	P4	0.802
	P5	0.779
	P6	0.752
	P7	0.706

, all Cronbach’s alpha values are greater than 0.7, indicating that the measurement of the construction of the model has a good reliability. Yue and Barnes [72] pointed out a Cronbach’s alpha value higher than 0.7, indicating that the scale has a good reliability. In addition, this paper also uses the method of the composite reliability value for further testing, which is a stricter reliability evaluation index [73]. From the data analysis results, the CR index of each variable is greater than 0.8, which shows that the scale has a strong reliability. Therefore, in conclusion, the measurement scale in this paper has a good reliability.

It can be seen from Table 5 that the AVE of all variables is higher than 0.5, indicating that the scale has a good aggregation validity. In addition, the factor load method was also used for testing. As can be seen from

Table 6. Correlation coefficient between latent variables.

Constructs	M	S	I	D	P
Multi-agent management collaboration (M)	0.753
Mechanism collaboration of multi-agents (S)	0.431	0.762
Multi-agent information collaboration (I)	0.423	0.427	0.741
Environmental dynamics (D)	0.511	0.541	0.545	0.725
Logistics park project performance (P)	0.526	0.483	0.552	0.558	0.734

Note: the value on the diagonal in the table is the square root of the AVE value of each variable, and other values are the correlation coefficient.

, these coefficients are all greater than 0.7. Ordanini and Rubera [74] pointed out that if the factor load is greater than 0.5, the variable has a good aggregation validity.

According to the research results of Fonell and Larcker [73], the AVE of variables in the structural model needs to be greater than the square of the correlation coefficient between variables, which indicates that the scale has a good discrimination validity. As shown in Table 6, the square root of AVE of each variable is greater than the correlation coefficient of all other variables, indicating that the scale has a good discrimination validity.

4.4. Non-Response Bias and Common Method Biases Test

In this study, the method of Yue and Barnes [72] was used to detect the non-response bias. The questionnaire collection was divided into two parts: early recovery (n = 120) and late recovery (n = 21). Late recovery refers to the questionnaires recovered in the later stage of the survey, regarded as non-response questionnaires. The data analysis results show that there is no significant difference between the early questionnaires and the later questionnaire in terms of the number of employees, capital scale, etc., so the non-response bias of this survey can be ignored. In recent years, many studies have used this method to test non-response bias [72,75].

In addition, due to the limitations of the survey conditions, the questionnaire was completed by the same respondent, which may have led to common method bias. We adopted the method of Podsakoff et al. [76] to analyze common method bias, and Harmon’s single factor analysis method was used to test the influence of common method bias. The final analysis results show that five factors with eigenvalues greater than 1, 36% of which are explained by the factor with maximum variance, and no more than 50%, which shows that the influence of common method bias can be ignored [77].

5. Data Analysis and Results

5.1. Hypotheses and Testing

The research model was examined by the PLS-SEM model. We calculated the path significance levels and t-statistics for each hypothesized relationship by making use of the bootstrapping method with 5000 resamples. The R² value and path coefficients were acquired by using Smart PLS 2.0 software to test the structural model in

Table 7. The main effects of the tested hypotheses.

	Hypothesis	Path Coefficient	T Statistics	Results
H1a	Multi-agent management collaboration has a positive impact on the project performance of a logistics park	0.518 ***	5.668	Support
H1b	Multi-agent mechanism collaboration has a positive impact on the project performance of a logistics park	0.663 ***	8.479	Support
H1c	Multi-agent information collaboration has a positive impact on the project performance of a logistics park	0.325 **	2.041	Support

Note: ** means p < 0.01, *** means p < 0.001.

. Mehmetoglu [78] proposed that the value R² of the structural equation model is 0.67, 0.33 and 0.19, indicating that the interpretation is sufficient, general and weak. The value R² of the project performance was 0.615, indicating that the model has strong explanatory power.

From the data analysis results demonstrated in Table 7, it can be seen that H1a, H1b and H1c are all supported. Referring to the control variables, the results indicate that size and type have no significant effect on the performance outcomes.

5.2. Moderating Effect of Environment Dynamics

To analyze the moderating effect of the environmental dynamics, we added the interaction terms to the primary effects model. After standardizing the management collaboration (M), mechanism collaboration (S), information collaboration (I) and environment dynamics (D), the measurement items of management collaboration (M), mechanism collaboration (S) and information collaboration (I) were multiplied by environment dynamics (D) as interaction variables (M * D, S * D and I * D), respectively. The variables were added to the study model for verification, and the PLS analysis results are shown in Figure 2 and

Table 8. The moderating effect of environment dynamics.

Hypothesis	Path	Path Coefficient	T Statistics	Significant or Not
H2a	Multi-agent management collaboration * environmental dynamic → logistics park project performance	0.215 *	2.203	Yes
H2b	Multi-agent mechanism collaboration * environmental dynamic → logistics park project performance	0.052	0.757	No
H2c	Multi-agent information collaboration * environmental dynamic → logistics park project performance	0.304 **	2.520	Yes

Note: * means p < 0.05, ** means p < 0.01.

.

It can be seen from the above table that there is no significant difference (t = 0.757) between the number of multi-agent mechanism collaboration * environmental dynamics → project performance path, assuming that H2b is not supported; others assume that H2a and H2b are supported, as shown in

Table 9. The results of hypotheses on environmental dynamic.

Hypothesis	Results
H2a: the more the environment changes, the greater the impact of multi-agent management collaboration on the project performance of a logistics park.	Support
H2b: the more the environment changes, the greater the impact of multi-agent mechanism collaboration on the project performance of a logistics park	Non-support
H2c: the more the environment changes, the greater the impact of multi-agent information collaboration on the project performance of a logistics park.	support

.

6. Discussion

6.1. Main Findings

Based on the empirical test results, the hypothesis test results of the theoretical model proposed in Section 3 are given in

Table 10. Hypothesis tests of general research.

Hypothesis	Hypothetical Relationship Description	Results
H1	H1a: multi-agent management collaboration has a positive impact on the project performance of a logistics park	Support
	H1b: multi-agent mechanism collaboration has a positive impact on the project performance of a logistics park	Support
	H1c: multi-agent information collaboration has a positive impact on the project performance of a logistics park	Support
H2	H2a: the more the environment changes, the greater the impact of multi-agent management collaboration on the project performance of a logistics park.	Support
	H2b: the more the environment changes, the greater the impact of multi-agent mechanism collaboration on the project performance of a logistics park	Non-support
	H2c: the more the environment changes, the greater the impact of multi-agent information collaboration on the project performance of a logistics park.	Support

.

The results in Table 10 show that: first of all, the three dimensions of multi-agent collaboration (management, mechanism and information collaboration) have a significant positive impact on the performance of a logistics park project, indicating that as long as the multi-agents involved in the logistics park project, such as the government, development enterprises and settled enterprises, can form a collaborative mechanism to achieve management collaboration, mechanism collaboration and information collaboration, then they can effectively avoid a series of problems caused by inconsistent interest demands, improper department connection, and poor information communication among multiple subjects, and thus effectively improve project performance.

This is also consistent with our previous hypothesis, which proves the positive significance of multi-agent collaboration on a logistics park project. The collaboration of multi-agents can effectively promote information sharing and data sharing between multi-agents, so as to promote knowledge exchange and generate new value. In addition, multi-agent collaboration can increase the ability of different subjects to work together. Through collaboration, the collaboration of all departments is closer, and some waste and low efficiency caused by inadequate collaboration are eliminated. Finally, the multi-agent collaboration of the project can further eliminate the inconsistency between the interests and demands of all parties, and realize the value sharing and goal unification of all parties through close collaboration.

In addition, this study regards the environmental dynamics as a moderating variable. Firstly, it was found that environmental dynamics positively moderate the relationship between the management collaboration and project performance of multi-agent logistics park projects, which is also consistent with previous assumptions. It shows that in the dynamic environment of rapidly changing product demand and market competition becoming more intense, the multi-agent entities achieve strategic consistency. Close cooperation between departments and the reasonable optimal allocation of resources can effectively help the project cope with changes in the economy, market and policy environment, thus improving project performance.

The change in environment has a great impact on enterprises. The change in the economic and policy environment means that enterprises need to constantly change their products and operation modes to meet the changes in customers’ needs. In a changing environment, the multi-agent subjects of the project need to cooperate more, so as to learn from each other’s strengths and make up for their weaknesses, face the changes together, and put forward countermeasures and suggestions with concerted effort, so as to make full use of the resources of all parties to solve problems and gain a competitive advantage.

Secondly, the research also found that the dynamic environment positively moderates the relationship between multi-agent information collaboration and logistics park project performance, which also conforms to the previous assumptions. It shows that in the dynamic environment, it is necessary to improve the flow of data, information and knowledge between the multi-agent entities through information sharing, information exchange frequency and mutual learning of information systems and technologies, so as to effectively avoid the long decision-making cycle and unscientific decision making caused by information concealment and distortion among the multi-agent entities, and other situations, so as to more effectively face environmental changes and improve project performance.

The results also show that the moderating effect of environmental dynamics on the relationship between multi-agent mechanism collaboration and logistics park project performance was not significant, and does not support the previous hypothesis. It is pointed out that in the process of the development and operation of a logistics park project, whether it is in a fast changing or competitive market or not, the development enterprises, the enterprises and the government of the project need to trust each other, negotiate and solve the relevant problems together, as well as have a long-term stable cooperative relationship and a common risk prevention mechanism to effectively avoid project development and operation issues.

The mitigation of possible safety, quality, progress and other problems will further reduce the probability of risk occurrence and ensure the smooth implementation of the project, and will not be affected by the external dynamic environment of the project.

6.2. Contributions

We contribute to the existing literature in three aspects. (1) Based on the synergy theory, a new concept is proposed to explain the management characteristics of multi-agent collaboration on logistics park projects. In the past, few studies have focused on the impact of the relationships among the government, development enterprises, resident enterprises and other multi-agent entities in the construction, development and operation of logistics park projects; in particular, no relevant empirical research has been conducted by scholars. Based on synergy theory, we put forward the concept of “multi-agent collaboration of logistics park project”, and divide it into three dimensions: management collaboration, mechanism collaboration and information collaboration. It effectively reveals the management characteristics of multi-agent collaboration in the process of logistics park project development and operation, which is not only conducive to logistics park project management, but also for further construction on the basis of relationship and empirical research, and it makes a useful attempt to promote the theoretical research of logistics management and project management.

(2) Empirical analysis was carried out of the impact of the logistics park project multi-agent collaborative on the project performance mechanism. As mentioned above, the development, construction and operation will be affected by the relationships among multiple entities, which has also been recognized in many articles and reports, and also by enterprises [61,79]. However, most of these opinions and conclusions are only qualitative logical reasoning, not explained by scientific and complete theories, and there is a lack of rigorous research methods and empirical data as support. In theory, there has been a lack of in-depth research in this area. Therefore, this study proposes and empirically analyzes the management mechanism and information collaboration of multi-agent subjects of logistics park projects from the perspective of theory and practice, and will affect the mechanism of project performance, and provide a more comprehensive and detailed framework for understanding multi-agent collaboration on logistics park projects.

(3) This paper introduces the influencing factors based on the dynamic environment, and discusses the relationship between multi-agent collaboration and project performance under different environmental factors. After discussing the mechanism of the multi-agent collaborative effect on project performance, this paper also describes environmental dynamics as a regulating variable to explore the impact differences of logistics park projects in different environmental situations. As mentioned above, a logistics park is influenced by policy, economy and the market environment because it involves a wide range of logistics objects. In practice, we also see many cases where the multi-agent interests of the logistics park project are inconsistent due to environmental changes, resulting in a huge impact on safety, quality and progress.

Therefore, this paper introduces environmental dynamics as a moderating variable, an empirical test for different situations and moderating factors, the logistics park project multi-agent collaborative impact on project performance differences, and answers questions and expands the existing research to a certain extent.

6.3. Management Implications

The research results of this paper also have some guiding significance for the practice of a logistics park project, including the following main points.

(1) To help enterprises, governments and all sectors of society to fully clarify the multi-agent subjects and roles involved in the logistics park project. First of all, this paper combs the definition, background, nature and characteristics of a logistics park project, combs the connotation and concepts, and deeply analyzes the main multi-agent subjects involved in the process of the development, construction and operation management, such as the planning and guiding role of the government, the construction role of developers and the commercial value of the settled enterprise. This will help enterprises, the government and all sectors of the society fully realize the multi-agent subjects and functions involved in the logistics park project, so as to better help the planning, design, construction, development and operation management of logistics park projects.

(2) The enterprises should recognize and pay attention to the important strategic role of multi-agent collaboration. The previous research shows that the three dimensions of multi-agent collaboration (management collaboration, mechanism collaboration and information collaboration) can effectively improve project performance and create value. Therefore, for the practical aspects of China’s logistics park project, we should recognize and pay attention to the important strategic role of logistics park projects in building a collaborative mechanism to realize collaboration among multiple subjects in management, mechanisms and information, so as to effectively improve project performance and enhance competitive advantage.

(3) A logistics park project is developed and operated according to its own characteristics and market environment. On the basis of the previous research, we also introduce the environmental dynamics to investigate the different effects of multi-agent collaboration on project performance under different environmental impacts. The results show that in the face of an increasingly changing environment, the multi-agent agents need to pay more attention to management collaboration and information collaboration between the agents, so as to more effectively promote the collaboration between the multi-agent agents and the sharing of knowledge and information, and then jointly negotiate to face difficulties and improve performance. At the same time, no matter whether the external environment changes or not, the multi-agent entities of the logistics park project need to achieve mechanism collaboration in order to effectively reduce the project risk.

Therefore, in the development, construction and operation management of a logistics park project, a multi-agent collaboration mechanism should be implemented according to the environment to maximize the value.

6.4. Research Limitations and Future Works

This paper also has some limitations. (1) Sample size. The sample collection in this paper was only for Yunnan Province in China, and the sample subjects selected in this paper were all the development enterprises of a logistics park project whose multi-agent collaborative measurement was not distributed to the government, which may have had an impact on the universality of the conclusions of this study. In future research, it is necessary to expand the sample size to other provinces and related government subjects and conduct more in-depth research. (2) Sample data. On the one hand, the data obtained by the questionnaire are all sectional data. However, the development and operation of the sample enterprise’s logistics park project lasted for a period of time, which shows that the cross-section data we obtained may not be able to reflect the real situation. On the other hand, because the sample data in this paper are all enterprise-level data, and the number of logistics park projects in Yunnan Province is limited, it also increases the difficulty of data collection. In view of the limited sample size selected in this paper, a follow-up study may consider expanding the sample size to enhance the universality of the research results. (3) Limitations of variable measurement. Although we chose the measurement scale put forward by the authoritative literature at home and abroad, these scales are relatively mature. However, even if the Likert seven-point scale is used, it cannot guarantee the absolute accuracy of the measurement results, and questionnaire filling is subjective, so deviation of the measurement results may still occur due to some subjective factors of the respondents themselves. This calls the authenticity and reliability of the research data collected through the form of questionnaire into question. All of the above problems need to be solved in the follow-up study.

7. Conclusions

With the rapid development of the logistics industry in China, more and more logistics parks have appeared. The construction of logistics park projects usually involves multiple parties, which are the government, the development enterprise and entered enterprises. A lack of harmony among the multiple parties in a logistics park project will lead to a series of problems such as low efficiency and disordered management and so on, which then impact on project performance. However, the existing research has not paid attention to this phenomenon. We believe that the collaboration of multiple parties in a logistics park project will effectively promote project performance. Thus, this study investigates how multi-agent collaboration influences logistics park project performance under different dynamic market environments based on synergy theory. The results suggest that the three dimensions of multi-agent collaboration (management, mechanism and information collaboration) have a significant positive impact on the performance of a logistics park project. Under different environmental dynamics conditions, different strategies should be adopted by a logistics park project to improve performance. This study offers a new perspective on understanding the effect of multi-agent collaboration on logistics park project performance.