How Does Port Logistics Service Innovation Enhance Cross-Border e-Commerce Enterprise Performance? An Empirical Study in Ningbo-Zhoushan Port, China

Abstract

The port logistics service innovation (PLSI) is closely associated with cross-border e-commerce (CBEC) enterprise performance, given that the port, as the spatial carrier and the joint point of goods, information, customs house affairs, etc., is essentially a key node of the CBEC logistics chain. However, the influence mechanism of PLSI on CBEC enterprise performance has still not yet been elaborated by consensus. To fill this gap, this study aims to figure out the effect mechanism integrating the probe into two variables (i.e., information interaction and environmental upgrade) in a moderated mediation model. Specifically, this study collects questionnaire survey data of logistics enterprises and CBEC enterprises in the Ningbo-Zhoushan Port of China by the Bootstrap method in the software SPSS 26.0. The results show the following: (1) PLSI can positively affect the CBEC enterprise performance; (2) information interaction plays an intermediary role between PLSI and CBEC enterprise performance; and (3) environmental upgrade can not only positively regulate the relationship between information interaction and CBEC enterprise performance, but also enhance the mediating role of information interaction with a moderated intermediary effect.

1. Introduction

The combination of global consumers’ online shopping habits and China’s strong commodity supply capacity has made cross-border e-commerce (CBEC) a new model for Chinese enterprises to “enter overseas markets” [1]. Ports are at the hub node in the logistics chain of CBEC; port logistics service enterprises that rely on ports to provide logistics services have become the key to promote the efficient operation of cross-border transportation at this node. Port logistics service enterprises are “specialized service organizations in the port economic circle”. They create value through symbiosis and evolution with port operators, shipping companies, CBEC, and other entities [2]. Refined cross-border market competition and personalized CBEC enterprise demand are increasingly challenging the era of reform and innovation of port logistics services. How the innovation of port logistics services affects the “black box” of CBEC business performance has gradually become the focus of scholars [3,4,5].

The existing research on port logistics service mainly focuses on two aspects: The first is to pay attention to the innovation mechanism of port logistics service. The research on the innovation mode of port logistics service enterprises shows a paradigm shift from linear innovation to networked collaboration. The main innovation mechanisms include the following: (1) Technology-driven innovation. Focusing on single-point breakthroughs in technology, such as building a path optimization model, it has been proven that algorithm-driven transportation route adjustment can increase the punctuality rate by 22% and reduce the cost by 15%. Such studies ignore the organizational adaptability [6]; (2) Institutional synergy innovation. Enterprises’ participation in the “single window” policy design can significantly improve the service efficiency, reduce the customs clearance time by 50%, and reduce compliance costs by 33%, revealing the leverage of government enterprise system co-creation on service innovation [7]; (3) Ecological-enabling innovation. The relevant research turned to the construction of innovation ecology. The 4PL platform integrated the resources of enterprises, governments, and R&D institutions through the triple-helix model, which increased the service iteration speed of small- and medium-sized logistics enterprises by 2.1 times and reduced the customer churn rate by 18% [8]. The second is to focus on the interaction mechanism between port logistics services and service objects. The research on the interaction of port logistics services presents a paradigm shift from one-way supply to ecological co-creation. Most scholars discuss how the quality of interaction between port owners and operators, as key subjects connecting the global supply chain and regional economy, and service objects (freight forwarders, cross-border e-commerce, manufacturers, etc.) affect the efficiency of international trade from the macro level [9,10,11]. The empirical research shows that the logistics timeliness is improved by 0.63 units for each unit of resource complementarity between port and cargo owner enterprises, which verifies the core proposition of “integrating Operand and Operant resources to create value” [12]; it also puts forward that the core feature of the third-generation port is to shift from “cargo handling” to “whole process value-added services”, and the service object participates in value creation through information sharing, process collaboration, etc. [13].

There are three disjunctions in the existing research, which restrict the depth of theoretical development. The first is the dislocation of the research object: 78% of the literature equated macro-level port operators with micro-level “port logistics service enterprises” (third-party enterprises that rely on ports to provide logistics services, such as freight forwarders, warehousing service providers, multimodal transport operators, etc., but not port operators and not port owners), confused port facility owners and port service providers, and lacked a deep description of the interaction between micro enterprises, resulting in the obscuration of the innovation mechanism of third-party port logistics service enterprises. The second is the weak theoretical foundation. There is a lack of academic explanation such as a value co-creation theory for “collaborative behavior” such as cost sharing and revenue sharing between port logistics enterprises and CBEC enterprises. The third is the lack of internalization of environmental factors. Most works of literature focus on static resource integration; emergency innovation in turbulent environments has not been theorized; and there is a lack of in-depth discussion on the “uncertainty of business environment” in cross-border logistics.

From a practical perspective, geographical conflicts such as the Red Sea crisis have led to a surge in the risk of traditional shipping routes. The interruption rate of international shipping routes has increased by 40%, and the inventory cost of CBEC has surged by 25% [14]. The real crisis of cross-border logistics forces theoretical innovation, further highlighting the urgency of revealing “the complex superposition effect of key exogenous process variables such as information interaction and environmental upgrade on the direct linkage mechanism between port logistics service enterprises and CBEC enterprises”.

The Ningbo-Zhoushan port (Figure 1) is located in Ningbo and Zhoushan, Zhejiang Province, China (Latitude: 29.92959° N, Longitude: 121.844481° E), and comprises 19 port areas including Beilun, Yangshan, Liuheng, Qushan, and Chuanshan, etc. It is a first-class port opened to the outside world. For 15 consecutive years, its cargo throughput ranks first in the world, and its container throughput ranks among the first three in the world. It is also the fourth 30 million class port in the world after the Shanghai port, Singapore port, and Shenzhen port. Around the Ningbo-Zhoushan port, many well-known port logistics service enterprises and CBEC clusters have gathered, forming a well-established industrial chain and supply chain system. Therefore, it is of typical significance to explore how the service innovation of port logistics service enterprises can activate the business performance of CBEC based on the Ningbo-Zhoushan port.

This study takes port logistics service enterprises and CBEC enterprises in the Ningbo-Zhoushan port cross-border logistics chain as the research object, based on the service-oriented value co-creation theory, and introduces “information interaction” as the intermediary variable and “environmental upgrade“ as the moderator variable, so as to build a moderated intermediary model, and explore the impact mechanism of port logistics service innovation (PLSI) on the performance of CBEC enterprises. The possible contributions of this study are as follows: the first is to expand the research boundary of value co-creation theory, and reveal that port logistics service enterprises (non-port-operators and non-port-owners) create value with CBEC enterprises in a dynamic environment through the chain mechanism of “resource reorganization → information integration → environment adaptation → value output”, providing a theoretical paradigm for third-party port logistics service enterprises to break through resource constraints and strengthen core competitiveness. The second is to promote the development of a service-dominant logic process theory, and reveal that information resources reconstruct the path of value co-creation through “information interaction”, and service innovation needs to penetrate the organizational boundary through “information interaction”, modify the static cognition of “innovation directly drives performance”, and build a new theoretical base of dynamic competition and cooperation. The third is to deepen the innovation of service-oriented value co-creation theory, and reveal that value co-creation has contextual variability; “environmental upgrade” adjusts the efficiency of value co-creation by changing the original boundary conditions of enterprise operation and reconstructing the rules of resource integration, and promotes the refined development of a service-oriented value co-creation theory in the dimension of “information interaction–environmental upgrade”. The research framework is shown in Figure 2.

2. Theoretical Framework and Research Hypothesis

2.1. Theoretical Framework

The service-oriented value co-creation theory is the integration of the service-dominant logic and value co-creation theory, emphasizing that “economic value comes from service exchange”. The essence of co-creation is that value is not provided unilaterally by enterprises, but is defined and realized jointly by service providers and users in the interaction [15]. Its core is to integrate Operand and Operant resources to create value (

Table 1. Resource classification.

Resource Type	Essential Attribute	Performance in Port Logistics
Operand resources	Operated static entity resources	Port infrastructure (wharf, crane), goods, vehicles, etc.
Operant resources	Dynamic capability resources that can actively create value	Logistics algorithm, employee skills, customer data, collaboration process, etc.

); that is, the real innovation value does not lie in the number of port facilities (Operand), but in how to transform these facilities into service solutions to customer pain points through data, algorithms, and collaboration mechanisms (Operant).

Specifically, for port logistics service enterprises that rely on the Ningbo-Zhoushan port to carry out business, the essence of service innovation is to dynamically integrate two types of core resources: first, Operant resources (dynamic resources), including the enterprise’s digital capability (e.g., order forecasting algorithm), customer collaboration mechanism (e.g., sharing inventory data with cross-border e-commerce), and process optimization experience (e.g., LCL inspection scheme); and, second, Operand resources (object resources), including leased port facilities (e.g., Ningbo Meishan bonded warehouse), cross-border means of transport (e.g., the carriage of the China Europe Express), and customs resources (such as the right to use the places under customs supervision). When port logistics service enterprises reorganize Operant resources through service innovation, the potential value of Operand resources can be activated. Compared with the traditional commodity led logic, the theoretical applicability of this theory in the port logistics service scenario is shown in

Table 2. Adaptability of service-oriented value co-creation theory.

Dimension	Traditional Commodity Dominant Logic	Service-Oriented Value Co-Creation Logic
Subject of value creation	Unilateral creation by producers	Multi-party collaboration and co-creation
Resource role	Operand resources as the core	Operant resource drives Operand resource
Source of value creation	Value arising from displacement of goods (physical utility)	Value comes from solving the pain points of CBEC supply chain (Such as timeliness and cost)
Competitive focus	Port Facility Scale	Resource integration and responsiveness
Port logistics service cases	Port logistics service enterprises only provide standardized logistics services (loading and unloading services, etc.)	Port logistics service enterprises and CBEC enterprises jointly design service schemes (Such as “overseas warehouse + front warehouse” ecology, etc.)

.

Grounded in the theoretical framework of service-oriented value co-creation, value is co-created through “dynamic capabilities acting on physical resources”, and its value creation mechanism is as follows: port logistics service enterprises contribute Operant resources (such as intelligent scheduling algorithms), CBEC enterprises (service objects) contribute Operant resources (such as market demand data, collaborative feedback, etc.), and then both parties jointly activate Operand resources (such as port logistics facilities, goods, etc.), thus generating new value (such as logistics timeliness improvement, cost reduction, etc.), as shown in Figure 3.

This section logically demonstrates the underlying mechanism through which PLSI enhances CBEC enterprises performance. The analysis centers on core variables—PLSI, information interaction, environmental upgrade, and CBEC performance—examining their multilayered relationships through main effects, mediating effects, and moderating effects.

2.2. Research Hypothesis

2.2.1. The Mutual Effect Between PLSI, Information Interaction, and CBEC Enterprise Performance

In terms of the interaction between PLSI and the performance of CBEC enterprises, firstly, the theoretical essence of service innovation is the strategic reorganization of Operant resources. The service-oriented value co-creation theory believes that economic value originates from the dynamic integration of Operant resources, which generates new value by acting on Operand resources. The essence of port logistics service innovation is the strategic reorganization of its Operant resources (such as digital capabilities, collaborative algorithms, process design knowledge, etc.) by port logistics service enterprises [16]. This reorganization activates the potential effectiveness of Operand resources (such as port facilities, transportation tools, etc.) through the reconstruction of the enterprise’s internal capabilities. Secondly, the value co-creation mechanism is embodied in resource integration. The service-oriented value co-creation theory requires that value creation must be realized through the resource integration of service providers and users [17]. The service innovation (Operant resource reorganization) of port logistics service enterprises provides a new resource integration interface for CBEC. CBEC injects its own resources (such as commodity flow and capital flow) into the interface, and is coupled with the Operant resources of port logistics service enterprises to form a collaborative resource bundle. The resource bundle generates indivisible collaborative value (such as shortening the logistics cycle, reducing the risk of cargo damage, etc.) through mutual adaptation. Thirdly, collaborative value is transformed into performance. The service-oriented value co-creation theory proposes that performance is the perception and realization of co-created value by beneficiaries [18]. As direct beneficiaries, CBEC enterprises have effectively improved their business performance under the causal chain of “Operant restructuring → resource integration → collaborative value → performance perception”, as shown in Figure 4.

To sum up, PLSI builds a resource integration interface through Operant resources reorganization, and generates collaborative value in the original resource coupling with CBEC, which is perceived by CBEC in the form of performance. Therefore, this study puts forward the following research hypothesis:

H1a: 

PLSI has a significant positive impact on the performance of CBEC enterprises.

In terms of the interaction between PLSI and information interaction, firstly, the essence of service innovation is Operant resources reorganization. The service-oriented value co-creation theory puts forward that the essence of all economic exchanges is service exchange, and services are driven by Operant resources (knowledge, skills, systems, etc.). In essence, PLSI is the strategic reorganization of its Operant resources (such as data algorithm, process design ability, collaborative knowledge, etc.) by port logistics service enterprises. The reorganization process expands the resource integration boundary of the enterprise [19] and creates a new interface for information interaction. Secondly, the reorganization of Operant resources has spawned new demands for information interaction. “Resource complementarity principle” believes that high-order Operant resources (such as an intelligent scheduling algorithm) need to be coupled with complementary resources to release value [20]. The service innovation of port logistics service enterprises (such as a dynamic position system) requires real-time order flow data (Operant resources of CBEC) to calibrate the model, while CBEC needs to obtain visual information of the shipping schedule (Operant resources of port logistics service enterprises) to optimize inventory decisions. Therefore, the efficiency release of Operant resources after service innovation depends on “two-way information interaction”; otherwise, the resource integration interface will fail. Thirdly, service innovation reconstructs the situational rules of information interaction. The context dependence principle of the service-oriented value co-creation theory emphasizes that value co-creation is defined by a specific context [21]. A context change forces participants to adjust the information interaction mode, while PLSI reconstructs the context through three paths: (1) the complexity is increased, new variables are introduced into service innovation, and finer grained data needs to be exchanged; (2) the timeliness is compressed, and service innovation accelerates the process, forcing the frequency of information interaction to upgrade; and (3) risk redistribution and service innovation change the responsibility boundary and require information transparency improvement, as shown in Figure 5.

To sum up, the PLSI expands the resource integration boundary and reconstructs the situational rules by reorganizing the Operant resources, thus endogeneously requiring the synchronous upgrading of information interaction in granularity, frequency, and depth. Thus, the following research hypothesis is formulated:

H1b: 

PLSI has a significant positive impact on information interaction.

In terms of the interaction between information interaction and the performance of CBEC enterprises, firstly, the essence of information interaction is the flow of Operant resources. The service-oriented value co-creation theory emphasizes that all economic exchanges are fundamentally service exchanges, and services transmit value through Operant resources (knowledge, skills, information, etc.). Information exchange is not simply data transmission, but the two-way flow of Operant resources: port logistics service enterprises output process knowledge (such as changes in customs clearance rules, etc.) and dynamic capabilities (such as class visualization algorithm, etc.); and CBEC enterprises input demand signals (such as promotion cycle) and feedback knowledge (such as return strategy). This process realizes the complementary integration of high-level Operant resources [22]. Secondly, resource integration has given birth to inseparable collaborative value. The inevitability principle of value co-creation holds that value is created jointly by service providers and users and realized through resource integration. Information interaction generates collaborative value through three paths: (1) the efficiency value, such as the real-time matching of shipping date data and order flow, reducing the timing friction of cross-border logistics links (such as the decline of port detention time); (2) the decision value, such as the change information of customs clearance policy, which drives CBEC enterprises to adjust procurement strategies and reduce bullwhip effect; and (3) the risk-hedging value, real-time sharing of abnormal events (such as weather delays), and activation of collaborative emergency response between the two sides (such as the relocation of the China Europe train). Thirdly, we have the transformation mechanism from collaborative value to performance. The value situational principle of service-oriented value co-creation theory holds that value is defined and realized by beneficiaries in specific situations. As a direct beneficiary, the business performance of CBEC enterprises has been improved in efficiency, cost, risk control, and many other aspects, as shown in Figure 6.

To sum up, information interaction generates indivisible collaborative value by driving the integration of Operant resources, which is transformed into efficiency, cost, risk control, and other performance improvements by CBEC enterprises in the context of decision-making. Therefore, this study puts forward the following research hypothesis:

H1c: 

Information interaction has a significant positive impact on the performance of CBEC enterprises.

2.2.2. The Mediating Effect of Information Interaction

As an important factor of production and operation, information resources can be obtained from the transmission and sharing of information to improve the level of service innovation, improve financial income, and formulate plans. Information exchange refers to the information exchange mode in which the owner of information resources communicates with stakeholders and jointly manages specific information resources for a certain purpose and within a certain period of time [23]. Value co-creation requires both sides to continuously exchange key resources. The essence of information interaction is the process of exchange of Operant resources between port logistics service enterprises and cross-border e-commerce enterprises: port logistics service enterprises provide shipping schedule dynamics, customs clearance status, position visibility, etc., and CBEC enterprises feed back order urgency, return strategy, promotion plan, etc. Only when the Operant resources (such as data, algorithms, etc.) of both sides are deeply integrated can PLSI be transformed into CBEC business performance.

According to the theory of limited resources, enterprises cannot obtain long-term benefits only by relying on their own specific resources. Active information exchange and sharing with other market participants can help enterprises create inimitable scarce resources in combination with their own characteristics. Especially in the market with rapidly changing external environment, information and knowledge from customers provide new space for enterprise innovation [24], but, at the same time, we should pay attention to the matching of innovative services and customer needs. Through the information exchange and communication in the early stage of service innovation, we can obtain in a timely manner customer information and perceive the market dynamics, so as to activate the first mover advantage of enterprises and occupy the market share first [25]. On the one hand, high-quality information interaction with CBEC enterprises can stimulate the willingness of port logistics enterprises to innovate services and improve the possibility of success. Smooth information exchange with CBEC enterprises provides port logistics enterprises with timely and accurate customer needs, enabling port logistics enterprises to formulate accurate customized services according to the market changes and consumption trends of CBEC, so as to maintain stable customer stickiness and market share. On the other hand, high-frequency information exchange and sharing with port logistics enterprises can enable CBEC enterprises to participate in the development process of new logistics services in real time, and accelerate the speed of PLSI [26]. The efficient logistics brought by service innovation will substantially improve the performance of CBEC enterprises. Therefore, with the help of the bridge of “information interaction” between port logistics enterprises and CBEC enterprises, PLSI has effectively improved the business performance of CBEC enterprises. Therefore, this study puts forward the following research hypothesis:

H2: 

Information interaction plays an intermediary role between PLSI and CBEC enterprise performance.

2.2.3. The Moderating Effect of Environmental Upgrade

Environmental upgrade is manifested as the external conditions affecting the pro-duction and operation of enterprises which have been in a dynamic process of unstable change degree and uncertain change scope, including the overall change in economic environment, the change of competitors’ business models, the emergence of new technology application scenarios, and the upgrade or downgrade of consumer preferences. The dynamic evolution of the environment promotes enterprises’ dynamic demand for information exchange [27,28]. Whether CBEC enterprises can strengthen the information interaction with port logistics enterprises, customers, and other stakeholders in the context of continuous upgrades of the environment will become the key to improving their business performance. When the environment changes relatively smoothly, the existing consumer trends, market structure, competitors, technology, equipment, and other external conditions change little, and the quantity and quality of the existing information sharing between CBEC and stakeholders can maintain the demand for cross-border e-commerce production and operation, and will not have a significant impact on business performance. With the continuous intensification of environmental changes, it means that the external conditions of the above impact on the e-commerce business have undergone quantitative or even qualitative changes, so it is urgent to grasp the latest information to adjust the production and operation mode and cope with the new situation. At this time, the information interaction between cross-border e-commerce and stakeholders becomes more urgent and important. By expanding the boundaries of information sharing and improving the quality of information sharing, cross-border e-commerce can obtain key information resources needed to re-establish the core competitive advantages in new scenarios. Therefore, environmental upgrade plays a regulating role in the impact of information interaction on the performance of CBEC enterprises. This study proposes the following hypothesis:

H3: 

Environment upgrade positively moderates the relationship between information interaction and CBEC enterprise performance.

In addition, based on the above assumptions, this study established a moderated mediation model; that is, environment upgrade moderates the mediating effect of PLSI on the CBEC enterprise performance through information interaction; that is, there is a moderated intermediary. Aprisma et al. (2020) explored the multiple relationships between environmental changes and firm performance and found that, in the influencing process of information interaction on firm performance, the final effect was significantly affected by external conditions such as market structure fluctuations and consumption trend changes [29]. Changes in the external environment will change the original boundary conditions of the enterprise operation, directly trigger enterprises to search for new business model parameter information, and further promote the information interaction between enterprises and the upstream and downstream of the industrial chain [30,31]. Specifically, when the environmental upgrade is weak, port logistics enterprises lack the motivation for innovation due to the relatively stable business environment. The existing logistics services can meet the needs of cross-border e-commerce, so they will not frequently exchange information with CBEC enterprises. At this time, information sharing between the two sides will not have a significant impact on the performance of CBEC enterprises. On the contrary, when the environmental upgrade is more intense, port logistics enterprises must develop new service models to respond to environmental changes, resulting in more and more innovation risks for port logistics enterprises during the development cycle. This will promote port logistics enterprises to strengthen the real-time information exchange with CBEC enterprises and understand in a timely manner customer needs, to hedge the uncertainty in the innovation process of port logistics services. In addition, high environment upgrades will accelerate the emergence and diffusion of new information, which not only provides pioneering ideas and opportunities for port logistics enterprises to accelerate the upgrading and updating of existing service models but also promotes the information interaction between port logistics enterprises and CBEC enterprises. The full communication between the two sides on new service ideas and processes will greatly improve the pertinence of port logistics innovation to cross-border e-commerce, thus effectively improving the CBEC business performance [32]. To sum up, the stronger the environmental upgrade, the greater the pressure of logistics service innovation, resulting in the port logistics enterprises and CBEC enterprises in the same cross-border logistics chain further strengthening information communication, improving the efficiency of information sharing, and, finally, achieving the business objectives of rapidly responding to customer needs to expand the market share and increase financial returns. Therefore, the following research hypothesis is proposed:

H4: 

Environmental upgrade positively moderates the mediating role of information interaction in the relationship between PLSI and CBEC enterprise performance.

Therefore, based on the mediating effect of information interaction and the moderating effect of environmental upgrade, this study constructs a moderated mediation model of the impact of PLSI on the performance of CBEC enterprises, as shown in Figure 7:

3. Research Design

3.1. Sampling Method

This study adopts the method of stratified random sampling combined with systematic sampling. The specific implementation process is as follows:

The first step is to establish a sampling framework. Firstly, based on the type of enterprises, the sample enterprises to be surveyed in Ningbo-Zhoushan port are divided into four independent sub-populations (strata): warehousing logistics enterprises, transportation logistics enterprises, comprehensive logistics enterprises, and CBEC enterprises. Secondly, establish an employee sampling frame, and obtain a list of all employee numbers from the human resources department of each enterprise as the basic sampling frame to ensure that all functional departments (operations/customs/sales, etc.) are covered.

The second step is to perform random sampling. The systematic sampling method is adopted within each enterprise: firstly, calculate the sampling interval k (if the total number of employees in an enterprise is 500 and the planned sampling is 25, then k = 500 ÷ 25 = 20). Secondly, determine the random starting point, and use the random number generator to select the starting number between 1 and k (for example, the starting point is 7 when k = 20). Finally, take samples at equal intervals, and take 1 employee every k people from the starting point (e.g., sample No. = 7, 27, 47, 67, …).

The third step is quality control measures. The criteria for determining the invalid questionnaire were as follows: (1) the completeness of the questionnaire was <90%; (2) the options of 10 consecutive questions were the same; and (3) the logic was contradictory.

3.2. Questionnaire Design

The questionnaire design adopts a three-stage process: pilot testing → refinement → finalization.

Phase I: Pilot testing. Firstly, design the initial questionnaire. Based on the service-oriented value co-creation theory, this paper constructs the core potential variables—PLSI, information interaction, environmental upgrade, and CBEC enterprise performance—designs 20 items, and uses the Likert 5-point scale. Secondly, conduct small-sample pilot testing. In the informal survey, 12 enterprises (6 port logistics service enterprises + 6 CBEC enterprises) were selected, and 36 employees were selected according to the proportion of departments: (1) cognitive interview asks the subjects’ understanding of the core words one by one; (2) it takes 18.5 min on average (target ≤ 15 min) to complete the test, and 5 redundant items are deleted; and (3) open feedback was collected so that the dimension of “port logistics service innovation” lacks the index of “innovation mode and concept”, and added 2 questions.

Phase II: Refinement. The sample size was expanded to 200 questionnaires from 16 enterprises (182 were effectively recovered), and the statistical test was as follows (

Table 3. Reliability and validity test.

Inspection Index	Standard	Preliminary Test Results	Corrective Measures
Cronbach’s α	>0.7	Environmental upgrade scale α = 0.68	Deleted 2 items with factor loadings < 0.5
KMO	>0.7	KMO of PLSI = 0.67	Consolidated 3 semantically redundant items
Factor loading	>0.6	Four items exhibited factor loadings < 0.55	Revised semantically ambiguous items
AVE	>0.5	AVE = 0.48 for information interaction	Added measurement items

):

Phase III: Finalization. On the basis of strictly following the feedback in the pilot testing and refinement, the finalization process realizes the optimization of measurement tools through dual integration: firstly, the questionnaire framework is systematically reconstructed to strengthen the content validity; and, secondly, multi-dimensional deviation prevention mechanism is implanted to ensure data quality. Through optimization, this study finally formed a questionnaire with both theoretical rigor and practical applicability.

3.3. Data Sources

The data of this study are derived from a questionnaire survey conducted with 62 employees of Ningbo-Zhoushan port (including 28 logistics enterprises and 34 CBEC enterprises) in 2022. A total of 400 questionnaires were issued, 346 were recovered, and the recovery rate of the questionnaires was 86.5%. After excluding invalid questionnaires such as incomplete filling and completely consistent answers, 310 valid questionnaires were obtained, with an effective rate of 89.6%. Preliminary statistical analysis of the data was performed using SPSS 26.0, and the sample distribution characteristics are shown in

Table 4. Sample enterprises characteristics.

Statistical Variables	Class	Frequency	Percentage (%)
Years of Operation	Less than 2 years	9	14.5
	2–5 years	28	45.2
	5–10 years	21	33.9
	More than 10 years	4	6.4
Enterprises’ Size	Less than 50 people	5	8.1
	In the range of 50–100 persons	29	46.8
	Of 101–200 persons	24	38.7
	More than 200 people	4	6.4
Enterprises’ Types	Warehousing-type logistics enterprises	5	8.1
	Transportation and logistics enterprises	7	11.25
	Comprehensive logistics enterprise	16	25.85
	CBEC enterprises	34	54.8
Amount To		62	100

and

Table 5. Sample employee characteristics.

Statistical Variables	Class	Frequency	Percentage (%)
Sex	Man	232	74.8
	Woman	78	25.2
Age	Under 30	83	26.8
	30–39 years old	163	52.6
	Age 40–49	43	13.9
	Aged 50 and over	21	6.7
Record of Formal Schooling	Junior college	110	35.6
	Undergraduate course	166	53.4
	Master	30	9.8
	Other	4	1.2
Position	Grassroots staff	216	69.7
	Middle managers	79	25.5
	Senior managers	15	4.8
Amount To		62	100

.

Regarding enterprises’ operation years, startups (<2 years) constituted a minority (14.5%). The majority of enterprises had 2–5 years (45.2%) or 5–10 years (33.9%) of operation, while established enterprises (≥10 years) accounted for 6.4%. Regarding enterprises size, enterprises with fewer than 50 employees constituted 8.1% of the sample. Those with 50–100 employees formed the largest group (46.8%), followed by enterprises with 101–200 employees (38.7%). Enterprises exceeding 200 employees accounted for 6.4%. Regarding enterprises types, the sample included 28 port logistics enterprises (45.2%), with further breakdown: comprehensive logistics (25.85%), warehousing-based logistics (8.1%), and transportation-focused logistics (11.25%). CBEC enterprises constituted the remaining 54.8% (n = 34).

A total of 310 employees filled out the questionnaire, 232 males, 74.8%, and 78 women, 25.2%. In terms of age, 26.8% were under 30, and most of the respondents were 30–39 (52.6%), 13.9% were 40–49, and 6.7% were 50 and above. In terms of education, junior college accounted for 35.6%, with the largest number of undergraduates, accounting for 53.4%, Master’s degree for 9.8%, and others for 1.2%. In terms of positions, there are many grassroots employees, accounting for 69.7%, middle managers for 25.5%, and senior managers for 4.8%. The above data show that most of the research objects in this study are young and middle-aged grassroots employees and middle managers with higher education in port logistics enterprises and CBEC enterprises. They are active in thinking and are in the front line of business, and are more able to accept and perceive innovative things, which is in line with the research theme of this study.

3.4. Variable Measurement

There were four research variables in this study, including PLSI, information interaction, environmental upgrade, and the performance of CBEC enterprises. The measurement scales of variables are based on the mature scale of the existing literature; combined with the discussion and suggestions of experts and scholars and the recent research using the scale, and, after pilot testing and refinement, they finally form a questionnaire on the impact of PLSI on the performance of CBEC enterprises. Details are as follows:

Learn from the 4-dimensional service innovation model proposed by Pim et al. (2001) (i.e., new technology selection, new service concept, new delivery system, and new customer interface) [33]; combined with Calantone et al. (2002) proposed logistics service innovation scale [34] and applications of the scale in recent studies (Jing et al., 2020) [35], this study adopts the idea that PLSI is based on the new logistics service concept, with new logistics technology as the means, the service process, and management mode, and makes logistics service value appear in a new form to meet the diversified needs of customers. Therefore, PLSI is measured, including 4 items (Table 6).

Taking the conclusions of Ennew (1999) [36] and Yen et al. (2004) [37], and the measurement index developed by McEvily (2005) [38] and applications of the scale in recent studies (Bin et al., 2020) [39] into consideration, by screening the measurement dimensions of the indicators, and taking into account the current situation of information interaction between logistics service enterprises and CBEC enterprises in Ningbo-Zhoushan port, this study puts forward that information interaction is the Internet technology and customer information exchange behavior, including market dynamic sharing, business process communication, innovation, value experience feedback, etc. Therefore, information interaction is measured, concerning 4 items (Table 6).

Based on the research of Águeda et al. (2002) [40], and Jaworski et al. (1993) [41], and applications of the scale in recent studies (Aurora et al., 2024) [42], this paper analyzes the actual external business environment faced by logistics service enterprises and CBEC enterprises in Ningbo-Zhoushan port; this study believes that environmental upgrade mainly includes the changes in external conditions affecting technical equipment, service mode, customer demand, and competitor business strategy. Thus, the environmental upgrade is measured through four question items (Table 6).

According to the principle of Balanced Scorecard (BSC), the performance of CBEC enterprises should be considered comprehensively from four dimensions: finance, customer, internal process, and learning growth. This study refers to the scale designed by Stan et al. (1996) [43], and Storey et al. (2001) [44], and applications of the scale in recent studies (Engidaw et al., 2025) [45]; combined with the characteristics of CBEC enterprises, it designs five items from four aspects to measure enterprise performance (Table 6).

Table 6. Variable measures.

Variable	Measure the Item	Reference Frame
PLSI (X)	X1: The enterprise will often put forward the new logistics service concept	Pim et al. (2001) [33]; Calantone et al. (2002) [34]
	X2: The enterprise will often put forward the new logistics service technology
	X3: The enterprise will often put forward a new logistics service business process
	X4: The enterprise will often put forward a new logistics service management mode
Information Interaction (M)	M1: The company often shares the market dynamics with its customers	Ennew et al. (1999) [36]; Yen et al. (2004) [37]; McEvily (2005) [38]
	M2: The company often communicates with customers on business processes
	M3: The company often discusses innovation and creativity with customers
	M4: The company often provides value experience feedback to customers
Environment Upgrade (Z)	Z1: The technology and equipment of the industry where the enterprise is located change rapidly	Águeda (2002) [40]; Jaworski (1993) [41]
	Z2: The service mode of the industry where the enterprise is located changes quickly
	Z3: The customer demand of the industry where the enterprise is located changes quickly
	Z4: The business strategy of the competitors in the industry changes quickly
CBEC Enterprise Performance (Y)	Y1: The service innovation of logistics enterprises has improved the return on investment	Stan et al. (1996) [43]; Storey et al. (2001) [44]
	Y2: The service innovation of logistics enterprises has improved the market share of enterprises in this period
	Y3: The service innovation of logistics enterprises has improved their customer satisfaction
	Y4: The service innovation of logistics enterprises enhances the learning and growth potential of the enterprise
	Y5: The service innovation of logistics enterprises optimizes the internal business process of the enterprises

Table 6. Variable measures.

Variable	Measure the Item	Reference Frame
PLSI (X)	X1: The enterprise will often put forward the new logistics service concept	Pim et al. (2001) [33]; Calantone et al. (2002) [34]
	X2: The enterprise will often put forward the new logistics service technology
	X3: The enterprise will often put forward a new logistics service business process
	X4: The enterprise will often put forward a new logistics service management mode
Information Interaction (M)	M1: The company often shares the market dynamics with its customers	Ennew et al. (1999) [36]; Yen et al. (2004) [37]; McEvily (2005) [38]
	M2: The company often communicates with customers on business processes
	M3: The company often discusses innovation and creativity with customers
	M4: The company often provides value experience feedback to customers
Environment Upgrade (Z)	Z1: The technology and equipment of the industry where the enterprise is located change rapidly	Águeda (2002) [40]; Jaworski (1993) [41]
	Z2: The service mode of the industry where the enterprise is located changes quickly
	Z3: The customer demand of the industry where the enterprise is located changes quickly
	Z4: The business strategy of the competitors in the industry changes quickly
CBEC Enterprise Performance (Y)	Y1: The service innovation of logistics enterprises has improved the return on investment	Stan et al. (1996) [43]; Storey et al. (2001) [44]
	Y2: The service innovation of logistics enterprises has improved the market share of enterprises in this period
	Y3: The service innovation of logistics enterprises has improved their customer satisfaction
	Y4: The service innovation of logistics enterprises enhances the learning and growth potential of the enterprise
	Y5: The service innovation of logistics enterprises optimizes the internal business process of the enterprises

In this study, the variables were measured using the Likert 5-point scale: 1 represents “very disagree”, 2 means “more disagree”, 3 means “general”, 4 represents “more consent”, and 5 represents “very agree”.

3.5. Data Verification

3.5.1. Homology Deviation

As for the questionnaire data, since the respondents are completed by one person in the process of filling in the questionnaire, this may lead to common method variance (CMV). As a result, in terms of procedural control, this study adopted clear and concise items as much as possible, disrupted the order of the questions, reduced the respondent’s guess of the measurement purpose, and used anonymous methods when filling in the questionnaire. For statistical control, this study firstly used the Harman single-factorial method; the results explained 74.595% of the total variation, and principal component 1 explained 28.794% of the variance, and 38.600% of the cumulative variance, and did not exceed 40% of the total interpretation (

Table 7. Homology tests.

Ingredient	The Sum of the Rotating Load Squares
	Characteristic Root	Variance Interpretation Rate %	Accumulate %
1	8.466	28.794	28.794
2	1.926	20.338	49.132
3	1.376	20.090	69.222
4	0.914	5.373	74.595
Accumulated Variance Interpretation of The Principal Component 1 Occupancy Factor			38.600

). Secondly, the confirmatory factor analysis (CFA) is used to analyze all the items in one factor. The fitting indicators of the model are shown in

Table 8. Single-factor model fit indicators.

Model	X2	df	X2/df	RMSEA	TLI	CFI
Unifactor Model	426.465	97	4.397	0.126	0.804	0.853
Requirement	-	-	<3	<0.10	>0.9	>0.9

Note: X²/df < 3 indicates a good model fit; RMSEA < 0.10 indicates a good model fit; TLI > 0.9 indicates a good model fit; CFI > 0.9 is considered a good model fit.

: chi-square degrees of freedom X²/df is 4.397 greater than 3, the approximation error root mean square RMSEA is 0.126 greater than 0.1, and the Tucker–Lewis index TLI is 0.804 less than 0.9. The comparative fit index CFI is 0.853 less than 0.9. None of the fit indicators reached the standard, indicating poor model fit, indicating that the study scale data cannot be focused into a factor; that is, there is no serious common method bias in the study data.

3.5.2. Reliability and Validity Analysis

This study used SPSS 26.0 to test the reliability and validity of the questionnaire data. Reliability indicates the reliability, consistency, and stability of the measurements, usually measured using the Cronbach’s α coefficient. The value of this coefficient is between 0 and 1: when the value is above 0.9, the study reliability is excellent; between the interval [0.7 and 0.9], the study reliability is high; and, below 0.7, 0 is selected as the criterion for determining the reliability. The calculation results show that the Cronbach’s α coefficient values of PLSI, information interaction, environmental upgrade, and CBEC enterprise performance are all greater than 0.7, indicating that the questionnaire of this study has good reliability (

Table 9. Reliability and validity analysis.

Variable	Item	Factor Loading	Cronbach’s α	CR	AVE	(X)	(M)	(Z)	(Y)
PLSI (X)	X1	0.720	0.911	0.877	0.641	0.801
	X2	0.825
	X3	0.848
	X4	0.803
Information Interaction (M)	M1	0.698	0.895	0.806	0.510	0.639 **	0.714
	M2	0.727
	M3	0.715
	M4	0.716
Environmental Upgrade (Z)	Z1	0.815	0.876	0.834	0.557	0.586 **	0.710 **	0.746
	Z2	0.694
	Z3	0.777
	Z4	0.692
CBEC Enterprise Performance (Y)	Y1	0.690	0.873	0.877	0.591	0.477 **	0.499 **	0.520 **	0.769
	Y2	0.783
	Y3	0.656
	Y4	0.867
	Y5	0.826

Note: ** for p < 0.01.

).

Validity mainly indicates the accuracy of the measurement indicators, which is di-vided into content validity, convergence validity, and differentiation validity. KMO and Bartlett sphere tests yielded a KMO value of 0.877 greater than 0.7 and a significance p < 0.001, indicating that the questionnaire data were available for factor analysis (

Table 10. KMO and Bartlett Sphere Test.

KMO	Bartlett Sphere Test
	Approximate Chi-Square	Degree of Freedom	Significance
0.877	3997.242	136	0.000

). In this study, the confirmatory factor analysis of the model showed that the standardized factor load was greater than 0.6, indicating that the combined reliability CR of each factor was greater than 0.7, and the average extraction variance AVE of all constructs was above 0.5, indicating that the square root of the average extraction variance AVE of each factor was greater than the absolute value of the correlation coefficient between this factor and other factors, indicating that the study had good discriminatory validity (Table 9). In conclusion, the variable scales in this study have high reliability and validity.

4. Empirical Analysis

4.1. Descriptive Statistical Analysis and Correlation Analysis

The mean value and standard deviation of the four variables, PLSI, information interaction, environmental upgrade, and CBEC enterprise performance, as well as the Pearson correlation coefficients between the variables, are shown in

Table 11. Descriptive statistics and correlation analysis (N = 310).

Variable	Mean	Standard Error	(X)	(M)	(Z)	(Y)
PLSI (X)	4.104	0.785	1
Information Interaction (M)	4.052	0.743	0.639 **	1
Environment Upgrade (Z)	3.923	0.792	0.585 **	0.710 **	1
CBEC Enterprise Performance (Y)	4.093	0.644	0.477 **	0.499 **	0.520 **	1

Note: ** for p < 0.01.

. The results show that the correlation coefficients of all variables are all significant at the 0.01 level (two-tailed), and the PLSI, information interaction, and environment upgrade are all significantly and positively correlated with the performance of CBEC enterprises. This provides support for the validation of H1.

4.2. Collinearity Test

The regression results may be affected by the “high overlap or similarity between variables in the regression model”; that is, the conclusion is distorted due to the presence of multicollinearity between variables. Therefore, before testing the research hypothesis, it is also necessary to test whether there is multicollinearity between the variables. In this study, a multicollinearity test was conducted through SPSS26.0, and the results are shown in

Table 12. Collinearity test.

	B	Standard Error	β	t	Tolerance	VIF
(Constant)	1.917	0.183		10.455
PLSI (X)	0.170	0.051	0.207 ***	3.301	0.558	1.791
Information Interaction (M)	0.145	0.063	0.167 **	2.285	0.413	2.422
Environmental Upgrade (Z)	0.227	0.056	0.279 ***	4.031	0.458	2.184

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

.

When judging multicollinearity, it can be identified by observing the value of tolerance or VIF (tolerance and VIF are reciprocal). The higher the VIF value, the more serious the collinearity, and, when the VIF value is greater than 10, it indicates that there is a serious multicollinearity problem, and there are many overlapping parts between variables, which will affect the authenticity of the conclusion. The tolerance is between 0 and 1, and, when it is less than or equal to 0.1, it also indicates that there is a collinearity problem between variables. As can be seen from Table 12, the maximum value of VIF is 2.422, much less than 10, indicating that there is no multicollinearity problem among the variables in this study.

4.3. Hypothesis Test

4.3.1. Main Effect Test

This study uses SPSS26.0 to test the main effects of PLSI, information interaction, and CBEC enterprise performance. Taking PLSI as the predictive variable, and CBEC enterprise performance and information interaction as the outcome variable, with information interaction as the predictive variable and CBEC enterprise performance as the result variable, regression analysis was conducted, respectively, and the results were shown in

Table 13. Main effects test of PLSI, information interaction, and CBEC enterprise performance (N = 310).

Predictor Variable	Outcome Variable	R2	Adj R2	F	β	t
PLSI (X)	CBEC Enterprise Performance (Y)	0.228	0.225	90.845	0.392 ***	14.475
PLSI (X)	Information Interaction (M)	0.408	0.406	212.155	0.605 ***	14.566
Information Interaction (M)	CBEC Enterprise Performance (Y)	0.249	0.247	102.290	0.433 ***	10.114

Note: *** indicates p < 0.001.

.

The results in Table 13 show that the standardized regression coefficients of PLSI on CBEC enterprise performance, PLSI on information interaction, and information interaction on CBEC enterprise performance are 0.392, 0.605, and 0.433, respectively, and the regression coefficients are significant at the 0.001 level. The results show that PLSI has a positive impact on the performance of CBEC enterprises, and the correlation degree is significant. PLSI has a positive impact on information interaction, and the correlation degree is significant. Information interaction has a positive impact on the performance of CBEC enterprises, and the correlation degree is significant. Therefore, H1a, H1b, and H1c are assumed to be valid.

4.3.2. Mediating Effect Test

The bootstrap method was adopted in this study; it tested the mediating effect of information interaction between PLSI and CBEC enterprise performance, and used the SPSS macro program Process 3.3 to achieve this. The principle of the Bootstrap method is to extract again and again, obtain the estimated value obtained by multiplying coefficients, and take 50% as the point for estimation to obtain the point estimate. The 95% confidence interval of the point estimate can be formed if the estimated value ranges from 2.5% to 97.5%. If 0 occurs within this range, it means that the mediating effect does not exist. If there is no 0, the mediating effect exists.

Based on Model 4 in the SPSS software, this paper tests the mediating effect of information interaction between PLSI and CBEC enterprise performance.

Table 14. Test of the mediating effect of information interaction (N =310).

Outcome Variable	Predictor Variable	R	R2	F (df)	β	t
CBEC Enterprise Performance (Y)	PLSI (X)	0.477	0.228	90.845	0.392 ***	9.531
Information Interaction (M)	PLSI (X)	0.639	0.408	212.156	0.605 ***	14.566
CBEC Enterprise Performance (Y)	PLSI (X)	0.540	0.292	63.207	0.220 ***	4.285
	Information Interaction (M)				0.285 ***	5.263

Note: *** indicates p < 0.001.

shows that PLSI has a significant impact on the performance of CBEC enterprises (β = 0.392, t = 9.531, and p < 0.001), and, when the mediating variable is added, PLSI still has a significant impact on the performance of CBEC enterprises (β = 0.220, t = 4.285, and p < 0.001). PLSI can positively predict information interaction and have a significant impact (β = 0.605, t = 14.566, and p < 0.001), and information interaction can positively predict the performance of CBEC enterprises and have a significant impact (β = 0.285, t = 5.263, and p < 0.001).

As shown in

Table 15. The breakdown of total effect, mediating effect, and direct effect.

	Effect Value	Boot Standard Error	Boot CI Lower Limit	Boot CI Superior Limit	Opposite Effect Value
Gross Effect	0.392	0.041	0.311	0.473
Direct Effect	0.220	0.051	0.119	0.320	56.122%
The Mediating Effect of Information Interaction	0.172	0.046	0.088	0.270	43.878%

, the Bootstrap 95% confidence interval value of the direct effect formed by the correlation between PLSI and CBEC enterprise performance ranges from 0.119 to 0.320, without 0, and the Bootstrap 95% confidence interval value of the information-interaction-mediating effect ranges from 0.088 to 0.270. There is no zero. PLSI can directly predict the performance of CBEC enterprises, on the one hand, and indirectly predict the performance of CBEC enterprises through the mediating role of information interaction, on the other hand. The direct effect (0.220) and the mediating effect (0.172) accounted for 56.122% and 43.878% of the total effect (0.392), respectively. Thus, hypothesis H2 is confirmed.

4.3.3. Moderating Effect Test

This study first standardized the variables of PLSI, information interaction, and environmental upgrade, and then multiplied the information interaction and environmental upgrade to obtain the interaction term of the two, to test the moderating effect. The results are shown in

Table 16. Test of the moderating effect of environmental upgrade (N =310).

Outcome Variable	Predictor Variable	R	R2	F (df)	β	t
CBEC Enterprise Performance (Y)		0.588	0.346	40.335
	PLSI (X)				0.145 **	2.822
	Information Interaction (M)				0.180 **	2.833
	Environment Upgrade (Z)				0.212 **	3.785
	Information Interaction × Environment Upgrade				0.139 **	2.944

Note: ** indicates p < 0.01.

: After adding environmental upgrade into the model, the product term of information interaction and environmental upgrade has a significant predictive effect on the performance of CBEC enterprises (β = 0.139, t = 2.944, p < 0.01), indicating that environmental upgrade positively moderates the predictive relationship between information interaction and the performance of CBEC enterprises.

To further verify the interaction between information interaction and environment upgrade, the values obtained by adding or subtracting 1 standard deviation from the mean value of environment upgrade were divided into the “high environment upgrade group” and “low environment upgrade group”, and then a simple slope analysis was performed. The moderating effect is shown in Figure 8.

It can be seen from Figure 8 that, under different environmental upgrade levels, information interaction has different slopes on the performance of CBEC enterprises: when the level of environmental upgrade is high (M + 1 SD), the slope of the straight line (simple slope = 0.290) is larger than that (simple slope = 0.071) when the level of environmental upgrade is low (M − 1 SD), which indicates that information interaction has a stronger positive impact on the performance of CBEC enterprises. This further verifies that the improvement of the level of environmental upgrade strengthens the promoting effect of information interaction on the performance of CBEC enterprises. Hypothesis H3 is verified.

4.3.4. Moderated Mediating Effect Test

To test whether there is a moderated mediating effect in this study, the Process plug-in was used to integrate relevant variables into a Model for verification and analysis. A Model 14 consistent with this study was selected, and the sample size of Bootstrap was set to 5000 with a confidence interval of 95%. The specific operation results are shown in

Table 17. The mediating effect of information interaction under different environment upgrade levels.

Independent Variable	Moderator Variable	Indirect Effect	Standard Error	95% Confidence Interval
Information Interaction (M)	Low Environmental Upgrade (Z)	0.043	0.060	−0.060	0.181
	High Environmental Upgrade (Z)	0.175 ***	0.066	0.047	0.304
	The Moderated Mediated Index	0.084 **	0.048	0.034	0.156

Note: High environment upgrade and low environment upgrade, respectively, mean plus and minus one standard deviation; *** means p < 0.001, ** means p < 0.01.

.

It can be seen from Table 17 that environmental upgrade has a significant moderating effect on the mediating effect of information interaction between PLSI and CBEC enterprise performance (moderating index is 0.084, p < 0.01, 95% confidence interval [0.034, 0.156], excluding 0), indicating that there is a moderated mediating effect.

When the environment upgrade level is low (M − 1 SD), the 95% confidence interval [−0.060, 0.181] contains 0, indicating that the mediating effect of information interaction is not significant at the low environment upgrade (M − 1 SD). When the level of environment upgrade is high (M + 1 SD), the 95% confidence interval [0.047, 0.304] does not contain 0, indicating that the mediating effect of information interaction is significant. Moreover, it can be seen from Table 17 that the indirect effect value (0.175) of the high environment upgrade (M + 1 SD) is larger than that of the low environment upgrade (M−1 SD) (0.043), which further indicates the moderating effect of environment upgrade on the mediating effect of information interaction. In other words, the mediating effect of information interaction between PLSI and CBEC enterprise performance is moderated by environmental upgrades. The higher the level of environmental upgrade, the stronger the mediating effect of information interaction, and H4 has been verified.

5. Conclusions and Future Prospects

5.1. Research Conclusions

This study focuses on port logistics service, focuses on the intermediary role of information interaction and the regulating role of the environmental upgrade, demonstrates the function mechanism of PLSI on the performance of CBEC enterprises, and obtains the following research conclusions:

First, PLSI has a significant and positive impact on the performance of CBEC enterprises. As cross-border e-commerce has gradually become a new growth pole of China’s import and export, its customer needs are becoming more and more personalized, and the demand for customized services in the industry is increasing. As a transportation hub on the cross-border logistics chain, port logistics enterprises will respond in a timely manner to the diversified service needs of CBEC enterprises by providing new service modes, service technologies, and equipment, which will directly promote the performance improvement of CBEC enterprises.

Second, PLSI has a significant positive impact on information interaction. Customer participation has an important impact on enterprise innovation. In the process of innovating and reforming logistics services, port logistics enterprises cannot leave the customer demand orientation of CBEC enterprises, which will promote the improvement of the frequency and expansion of the scope of information exchange between the two. Port logistics enterprises draw from the reform of reform to make the PLSI more accurate and efficient.

Third, information interaction has a significant positive impact on the performance of CBEC enterprises. Information exchange and communication with port logistics enterprises enable CBEC enterprises to deeply participate in the development process of new modes and processes of port logistics services, create “tailor-made” logistics service technologies and scenarios, further compress logistics time, optimize logistics nodes, and greatly improve the efficiency and business performance of cross-border logistics.

Fourth, information interaction plays an intermediary role between PLSI and the performance of CBEC enterprises. CBEC enterprises are the core of port logistics enterprises. It is the fundamental goal to fully tap their potential customer needs and continuously innovate to provide a higher-level cross-border logistics service experience. It is an important way to generate logistics service innovation by strengthening interpersonal interaction with CBEC enterprises to promote information sharing, and to obtain more information about customer preferences, process evaluation, and service feedback.

Fifth, whether the real business performance improvement of the latter can be realized through the information interaction between port logistics enterprises and CBEC enterprises is also regulated by the environmental upgrade. The external environment upgrade, the faster uncertainty of the enterprise management, the two sides for information sharing, awareness of valuable information, and the effectiveness of the information identification use are higher and will master the dominant and recessive information into PLSI and increase the chance of CBEC performance being higher.

5.2. Theoretical Significance

First, this study breaks through the existing literature, which mainly focuses on the port logistics service itself’s internal innovation mechanism, relatively ignoring the service innovation to the service object of the external spillover mechanism of limitation. This study takes port logistics as the breakthrough point, not only enriching the service’s leading logic theory and value theory, but also broadening the service innovation research boundary, and deepening the port logistics service which affects the CBEC “black box” theory, from the perspective of logistics service innovation.

Second, this study brings in the key process variable information interaction. Specifically, this study analyzes the information interaction in the PLSI and CBEC enterprise performance between intermediary mechanisms, and expounds the information resource sharing for the important role of PLSI, to improve the cross-border logistics chain port logistics enterprises and CBEC business effect between the causal chain theory explanation, and clear the PLSI of CBEC enterprise performance of the micro-mechanism.

Third, this study integrates the key external factor environment upgrade. That is, this study builds a regulated mediation model, to explore the environment upgrade as an important boundary condition, and how to regulate the mediation mechanism of information interaction, to refine the PLSI through information interaction on how CBEC enterprise performance influences the role of a path, further broadening the main effect of extension boundaries, complementing and enriching the existing theory.

5.3. Empirical Significance for Management Enlightenment

First, we should increase the investment of innovation resources and pay attention to the innovation of logistics services. With the advent of the service economy era, the role of the customer is in continuous evolution, from past passive consumers to customers who are now focused on the experience of feeling. The personalized, customized demands of CBEC enterprises constantly emerge. Port logistics enterprises need to increase innovation resources and speed up the innovation logistics service for a timely response to the new needs of customers. First, with regard to upgrading professional hardware and software resources, the efficiency of port logistics service should be improved by updating the storage, transportation, loading, and unloading equipment, optimizing the operation process, and using advanced information management technology. Secondly, with regard to introducing professional talent resources, it is essential that we accelerate the gathering of more port compound talents, optimize the structure of professional talents, improve the team innovation ability, and break through the frontier service innovation bottleneck of port logistics. Thirdly, strengthening the industry–university–research cooperation mechanism. We will promote communication with universities and scientific research institutions, vigorously carry out the construction of various joint construction platforms and research on related projects, actively promote the transformation of innovation achievements, and create a good atmosphere for innovation. Fourthly, with regard to strengthening the innovation consciousness and innovation ability, as the main body of enterprise logistics service innovation, whether the employees have innovation consciousness and innovation motivation determines whether they will produce innovative behavior. Port logistics enterprises should not only strengthen the employees’ understanding of innovation through training, but also improve the innovation incentive system of the employees, actively seek the thrust and pull that influence the logistics service innovation, and maintain the employees’ innovation passion to the greatest extent.

Second, we should strengthen the construction of information collection and acquisition and co-creation and utilization mechanisms, and pay attention to filling the information blind area. In the era of big data where information, data, and other intangible assets such as information and data generally become key factors of production and operation, port logistics enterprises and CBEC enterprises must pay more attention to smooth information communication and feedback. Adequate and high-quality information is an important source of enterprise innovation and creativity. Firstly, we should build a convenient and friendly communication environment. Enterprises should actively create a good atmosphere of mutual trust for information exchange, and open mutual emotional links, and ensure that information sharing is frank and sufficient. Secondly, we should build a perfect new media information exchange platform. Based on the characteristics of fast communication speed, wide coverage, strong interactivity, traceability, and diversification, we actively improve communication carriers with customers such as we-media, social media, and mobile media, to realize knowledge sharing and continuously generate core competitiveness. In addition, we should vigorously advocate for the timely tracking experience of enterprises and active dialogue and reflection between enterprises and consumers, to promote the deep participation of various partners in the cross-border logistics chain and realize the co-creation value in a real sense.

Third, we should speed up the dynamic assessment of the external environment and the optimization of the rapid response capacity, and pay attention to the cultivation of business resilience. Port logistics enterprises must have strong operational resilience to seize the market opportunity in the uncertain environment and achieve sustainable development. Business resilience is a kind of internal invisible ability for enterprises to effectively respond to the impact of emergencies in a dynamic environment. Enterprises need to identify and respond to the opportunities and challenges brought by the dynamic environment. It is the key business ability that enterprises need to attach great importance to when the external environment tends to be dynamic and uncertain. First, the ability to identify sensitive environment upgrades should be established. To observe and interpret technology equipment upgrades, customer demand changes, the evolution of market structure, policy guidance adjustment of external events to specific departments, and personnel, as an important daily work content continues to advance, for major changes, should be established by the daily recognition of individual evaluation monitoring mechanisms. Second, build up the ability to effectively respond to environmental shocks. The external environment upgrade is uncontrollable. Port logistics enterprises should predict the future, do a good job of “scenario planning”, prepare for a rainy day, formulate targeted response measures in advance, and invite experts and scholars to study and plan jointly when necessary. In addition, we will actively seek the support of the government. Under the background of the national advocacy for the construction of digital ports and smart logistics, port logistics enterprises should take advantage of the situation to strive for policy support, financial subsidies, tax incentives, etc., rely on government support to accelerate the innovation of port logistics services, enhance operational resilience, and effectively resist the negative effects brought by environmental impact.

5.4. Research Limitations and Outlook

This study significantly contributes to enriching relevant theoretical frameworks and broadening the research horizons within its domain; it is imperative to acknowledge certain inherent limitations. Firstly, the sample enterprise diversity is constrained. Although the study distinguishes port logistics enterprises into the warehousing, transportation, and comprehensive categories, CBEC enterprises remain unclassified in a comparable manner, resulting in an under-representation of the latter. To address this, future endeavors should meticulously categorize CBEC enterprises based on their unique characteristics and varied transaction modalities, thereby investigating whether PLSI exerts differential impacts across these diverse enterprises. At the same time, the sample size can be expanded, and the conclusion of this study can be verified repeatedly by investigating the top management of enterprises to understand the enterprises, so as to enhance the reliability of the research conclusion.

Secondly, the geographical scope of the investigated enterprises is limited. Rooted in the analysis of logistics and CBEC enterprises operating within Ningbo-Zhoushan port, the study fails to capture the intricate interplay between port logistics service innovation and the multifaceted factors that encompass natural port conditions, technological advancements, local government policies, and other regional nuances, all of which can significantly influence innovation efficiency and outcomes. To mitigate this, future research should expand its geographical reach, ensuring a balanced representation of samples across regions, and subsequently differentiate and compare the findings based on regional specificities.

Lastly, the study’s examination of endogeneity constraints leaves room for improvement. Specifically, there remains a need to delve into the potential reverse causality between PLSI and CBEC enterprise performance, neglecting the intricate endogenous relationships among variables. To strengthen this aspect, future investigations could incorporate lagged terms into regression models, and employ linear feedback tests or leverage structural equation modeling to not only extend the research frontier but also deepen our understanding of the intricate dynamics at play.