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Article

Analyzing the Coupling Coordination and Forecast Trends of Digital Transformation and Operational Efficiency in Logistics Enterprises

School of Economics, Management, and Law, Shandong Institute of Petroleum and Chemical Technology, Dongying 257061, China
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Author to whom correspondence should be addressed.
J. Theor. Appl. Electron. Commer. Res. 2025, 20(3), 211; https://doi.org/10.3390/jtaer20030211
Submission received: 4 June 2025 / Revised: 7 August 2025 / Accepted: 12 August 2025 / Published: 13 August 2025

Abstract

Understanding the coupling mechanism and coordinated development between digital transformation and operational efficiency in logistics enterprises is vital for optimizing resource allocation and promoting high-quality, sustainable growth in the logistics industry. This study analyzes panel data from 52 listed logistics enterprises in China from 2014 to 2023. It constructs evaluation index systems for digital transformation and operational efficiency and applies an integrated methodology comprising the super-efficiency SBM model, coupling coordination degree model, and random forest regression model to evaluate efficiency, assess coupling dynamics, and forecast future trends. The main findings are as follows: (1) Overall operational efficiency has shown a pattern of fluctuating growth, increasing from 0.520 to 0.585. Road transport consistently outperformed other sectors, water transport maintained steady growth, and air transport exhibited significant volatility, particularly during the COVID-19 pandemic. (2) The coupling coordination degree remains in the initial coordination stage (0.642–0.677), with road transport achieving intermediate-level coordination (0.718) by 2021. Water transport showed gradual but stable improvement, and air transport remained unstable due to external shocks. (3) Road transport leads in overall industry performance, while water transport exhibits stable progress, and air transport is hindered by international supply chain disruptions and technological adoption challenges. (4) Projections for 2024–2026 suggest an average annual growth rate of 0.31% in coupling coordination across all subsectors, although inter-sectoral synergistic mechanisms require further enhancement. Based on these findings, this study proposes targeted recommendations: increasing comprehensive investments in digital technologies across the entire supply chain, cultivating interdisciplinary talent, optimizing risk management frameworks, and refining policy support. These measures aim to strengthen the integration of digital transformation and operational efficiency, contributing to the sustainable development of the logistics industry.

1. Introduction

The logistics industry is a fundamental and strategic pillar of the national economy, serving as the core nexus connecting production, distribution, and consumption. It plays a pivotal role in facilitating economic circulation and enhancing the efficiency of societal resource allocation. The logistics industry is not only the supporting foundation of e-commerce but also a crucial component of its core competitiveness. The operational efficiency of logistics enterprises directly impacts consumers’ logistics experience and the brand image of seller platforms, thereby influencing the high-quality development of e-commerce. The 20th National Congress of the Communist Party of China emphasized the need to “accelerate the development of a modernized economic system, prioritize the improvement of total factor productivity, and strengthen the resilience and security of industrial and supply chains,” thereby setting higher standards for the sector’s high-quality development. Amid global supply chain restructuring and China’s economic structural transformation, logistics enterprises face multifaceted challenges, including rising operational costs, suboptimal efficiency, and service homogeneity. Among them, service homogeneity is particularly obvious in the road transportation industry. When users need to transport a batch of goods to a specified place, most companies will provide the same vehicle configuration, quote according to the average time limit of the industry, and provide similar service plans—transportation and loading and unloading goods. But in the digital economy era, the growing demand for personalized and real-time services has further exposed the inadequacies of traditional operational models, making digital transformation essential for restoring and strengthening competitive advantage.
Emerging digital technologies, such as the Internet of Things (IoT), big data analytics, and artificial intelligence (AI), are accelerating the transformation of logistics enterprises. These technologies enable the integration of key operational processes, including warehouse management, route optimization, and intelligent delivery, thereby improving both operational speed and response precision. While logistics enterprises have increasingly adopted digital tools to enhance efficiency, quantitative research on the relationship between digital transformation and operational performance remains limited, particularly regarding their coupling coordination mechanisms and evolutionary trends across subsectors (e.g., road, water, and air transportation). This is mainly because most logistics enterprises pay more attention to the transformation of digital technology, and ignore the coordinated development of digital technology application and operational efficiency.
To address these gaps, this study selects the listed logistics enterprises in China as samples, and searches for relevant data after screening and searching for conditions such as listing time, complete data, and risk warning, employing empirical analysis and predictive modeling to systematically investigate the coupling coordination between digital transformation and operational efficiency in logistics enterprises. By forecasting future developmental trajectories based on observed trends, the study aims to provide actionable insights for overcoming barriers to high-quality development, optimizing digital transformation strategies, and fostering sustainable improvements in operational performance.

2. Literature Review

2.1. Research on the Pathways of Enterprise Digital Transformation

The research on the pathways of enterprise digital transformation has gradually shifted from the application of a single technology to a multi-dimensional, systematic evaluation [1].
The research on the digital transformation pathways of logistics enterprises focuses on exploring how traditional logistics enterprises can systematically apply new-generation information technologies such as cloud computing, the Internet of Things, big data, and artificial intelligence to reconstruct business processes, optimize operational models, enhance service efficiency, and innovate ways of value creation. For example, Wang et al. [2] argued that logistics companies can optimize transportation routes through intelligent scheduling systems. Constantin and Robert [3] demonstrated the use of IoT for predictive equipment maintenance and big data analytics for customer behavior analysis, enabling the delivery of personalized services through the deep integration of emerging technologies.
However, some studies on the pathways of enterprise digital transformation aim to conduct an in-depth analysis of the core driving forces behind the digital transformation of logistics enterprises, including but not limited to the evolution of customer demands, bottlenecks in operational efficiency, intensified market competition, and the reduction in technology costs. Logistics companies typically combine intelligent scheduling systems with real-time traffic data to optimize transportation routes and reduce idle rates. IoT sensors are used to monitor equipment performance for predictive maintenance, minimizing operational downtime. Anthony [4] noted that SF Technology improved equipment fault response efficiency by 40% through AioT-based solutions. Du et al. [5] analyzed customer behavior patterns using big data, while Chen [6] highlighted how JD Logistics implemented “Timed Delivery” services based on user profiles, increasing customer satisfaction by 25%.
In the process of enterprises’ digital transformation, they encounter key challenges, such as data silos, difficulties in technology selection, talent shortages, resistance to organizational culture change, challenges in measuring input and output, and imbalances in warehousing resources. Based on these, a clear transformation roadmap is formulated.
In the face of these challenges, Yang et al. [7] proposed that digital transformation requires the digitization of both static resources (e.g., storage facilities) and dynamic resources (e.g., transportation vehicles) while enhancing supply chain transparency through platform-based collaboration. Feng et al. [8] found that integrating upstream and downstream supply chain resources through a hybrid online–offline “freight network” model can improve collaborative efficiency. Qian et al. [9] further suggested differentiating between static resources (e.g., warehouses and fixed assets) and dynamic resources (e.g., vehicles and human capital) in planning digital transformation strategies. Liu et al. [10] found that Cainiao Network achieved a 30% increase in inventory turnover rate through the use of “electronic waybill + intelligent warehouse distribution” systems. Erly et al. [11] proposed a model for real-time connectivity across national warehouse data, leading to a 30% improvement in inventory turnover. Friesz [12] cited the example of Transfar Group’s “Freight Network” platform, which integrated resources from two million truck drivers, enabling accurate matching of transport supply and demand and improving vehicle utilization by 15%. Wu et al. [13] concluded that effective resource digitalization requires overcoming “data silos” and enhancing supply chain transparency through technologies such as API interfaces and blockchain.
In terms of organizational classification and transformation path, Ziwen et al. [14], using the fsQCA method, identified two primary digital transformation pathways for logistics enterprises: (1) an operations-driven path, which emphasizes process optimization, and (2) an asset-intensive path, which focuses on the digital upgrading of infrastructure. The core of research on enterprise digital transformation pathways lies in designing a phased, implementable implementation roadmap, which typically covers key components such as infrastructure cloudification and IoT deployment, business process digitalization and automation, intelligent operation management, precision customer service, ecological collaboration, full-process visualization, personalized solutions, and platform interconnection. The ultimate goal is to construct a digitally driven smart logistics operation system through the in-depth integration of technology and business, thereby significantly enhancing enterprises’ core competitiveness, service response speed, and sustainable development capabilities.
Building on this, Xia et al. [15] proposed a four-stage transformation model that covers aspects such as business process digitalization and service product digitalization. Advancing these frameworks, Mingyue [16] conducted an empirical fsQCA-based study that demonstrated the significant heterogeneity in the digital transformation paths of logistics enterprises. For example, operational-driven companies such as Deppon Express focus on process automation and digital customer service, while asset-heavy-driven companies prioritize the upgrading of smart warehousing systems and unmanned delivery networks.
In addition, both domestic and international scholars have proposed a broader four-stage evolutionary model. In the initial stage, the emphasis is on digitalizing business processes (e.g., replacing paper-based documents with electronic waybills). In the intermediate stages, the focus shifts to service product digitalization and fostering ecological collaboration through integrated digital platforms for cross-border logistics and customs clearance. In the final stage, digital transformation leads to data-driven business model innovations, as highlighted by Wang et al. [17].
Scholars both at home and abroad have achieved substantial progress in research on the digital transformation of logistics enterprises, offering valuable insights for the high-quality development of the logistics industry.

2.2. Research on the Measurement of Enterprise Operational Efficiency

Enterprise operational efficiency is often assessed using quantitative models, with Data Envelopment Analysis (DEA) being the dominant method. As demonstrated in [18], DEA is applied to evaluate technical efficiency, pure technical efficiency, and scale efficiency of listed logistics enterprises. The findings suggest that most companies fail to achieve DEA efficiency, primarily due to inefficient resource allocation or insufficient technological investment.
Compared to domestic studies, foreign research on operational efficiency started earlier, resulting in a relatively mature theoretical framework and methodological system. For example, Lan [19] conducted in-depth studies using financial ratio analysis, such as the DuPont analysis system, to evaluate corporate operational performance. In addition to financial ratios, Holst-Jaeger et al. [20] employed trend analysis, comparative analysis, and cash flow analysis to provide a multidimensional assessment of operational efficiency. Boutyour and Idriss [21] focused on supply chain optimization and integration, viewing supply chain synergy as a critical pathway to improving operational efficiency.
Cost control is another crucial component of corporate operational efficiency. Xu et al. [22] demonstrated that cost–benefit analysis can help enterprises optimize their cost structures and enhance profitability. At the same time, Yuhang et al. [23] focused on enterprise risk management, aiming to strengthen operational resilience by identifying and addressing potential risks. Similarly, Lin et al. [24] contributed not only through theoretical research but also by emphasizing the practical application of findings in business operations. Moreover, the Balanced Scorecard (BSC) has been widely utilized as a performance management tool, and Guo et al. [25] applied it to analyze and improve corporate operational efficiency.
In recent years, domestic research on operational efficiency measurement has made significant progress. Although it started later than its international counterparts, domestic research has advanced rapidly alongside the continuous improvement of China’s market economy system and the growing demand from enterprises for performance evaluation tools. Han [26] made significant contributions by integrating international research results with the specific conditions of Chinese enterprises, conducting in-depth theoretical and practical explorations. Building on this, he localized the financial indicator system, while Hu et al. [27] proposed a financial ratio analysis framework suitable for the context of Chinese enterprises. Qiao [28] extended the analyses beyond financial indicators to include production management, enhancing the assessment of enterprise performance.
With the rise in emerging technologies such as big data and cloud computing, domestic scholars have increasingly explored technology-driven approaches to measuring operational efficiency. For instance, Zhao [29] applied big data analytics to predict market demand and optimize inventory management, thereby improving operational performance. Cheng et al. [30] focused on process optimization, using digital tools to improve overall enterprise efficiency.
Recent domestic studies have increasingly considered not only the economic performance of enterprises but also their social responsibility and sustainable development. Some studies have explored how corporate social responsibility affects operational efficiency and how firms can balance environmental protection and social responsibility while pursuing economic benefits. Zhou et al. [31] noted that both domestic and international studies address multiple aspects such as financial management, production management, and supply chain management. However, Xinman [32] pointed out that international research tends to place greater emphasis on analyzing operational efficiency across cultural backgrounds, exploring both differences and commonalities in business operations under different cultural contexts.
Local and international researchers have conducted extensive studies on the measurement of enterprise operational efficiency and have achieved substantial findings, which serve as an important reference for evaluating the operational efficiency of logistics enterprises in this paper.

2.3. Research on the Relationship Between Digital Transformation and Enterprise Operational Efficiency

The relationship between digital transformation and corporate efficiency has been a research hotspot in the fields of management and information economics in recent years. Early studies often focused on how technology applications optimize operational processes. For example, Chen et al. [33] pointed out that digital technologies significantly shorten business process cycles and reduce transaction costs through automation, data integration, and real-time decision-making. As digital transformation research progressed, Wang and Huang [34] identified the multidimensional impact of digital transformation on efficiency. At the technical level, Wang et al. [35] proposed that technologies such as cloud computing and the Internet of Things enhance asset utilization through resource virtualization. At the organizational level, Brovko et al. [36] suggested that flatter structures and agile work models reduce information transmission losses.
Empirical research by Liu et al. [37] showed that the effect of digital transformation on operational efficiency follows a threshold effect, with efficiency improvements accelerating substantially once a company reaches a critical point of digital maturity. More recent studies have explored the indirect mechanisms of digital transformation. For example, Nan et al. [38] argued that digitalization reshapes corporate knowledge management by promoting data-driven iterative innovation and accelerating organizational learning. Some scholars have noted the “double-edged sword” effect of digital transformation. For example, Liu et al. [39] found that initial technology investments may temporarily reduce efficiency due to insufficient employee adaptation, although, over time, enhanced organizational digital resilience leads to sustained performance gains.
Cross-industry studies by Liu et al. [40] indicate that manufacturing companies achieve significantly greater efficiency gains, averaging 18% to 25%, from industrial internet applications compared to service-oriented enterprises, suggesting that industry-specific characteristics moderate the effectiveness of digital transformation. Recent research has started focusing on the broader ecological impact of digital transformation. Fahimeh et al. [41] introduced the concept of “digital ecosystem efficiency,” which emphasizes network-based efficiency improvements driven by data sharing and collaborative innovation among enterprises. Notably, scholars such as Liao [42] have critiqued the narrow focus of efficiency metrics, advocating for the inclusion of non-traditional dimensions like customer value co-creation and organizational agility in efficiency assessments. Daniel has made fruitful achievements in the research on digital transformation [43].
Extensive research has been conducted both locally and internally on the interrelationship between digital transformation, enterprise management, and enterprise process optimization. However, there is relatively limited research on the relationship between digital transformation and enterprise operational efficiency, and even less on the operational efficiency of logistics enterprises in the context of digital transformation.

2.4. Research on the Relationship Between Logistics Enterprise Operational Efficiency and High-Quality Development of E-Commerce

The existing research widely emphasizes that enhancing the operational efficiency of logistics enterprises serves as the core engine and critical pathway for driving high-quality development in e-commerce. Singh Vibhav points out that the vigorous growth of e-commerce and continuous upgrades in user experience impose unprecedented demands on logistics services regarding timeliness, reliability, cost control, and flexibility [44]. Through empirical analysis, M V Kolesnikov demonstrates that efficient logistics operations can significantly reduce e-commerce transaction costs [45], shorten order fulfillment cycles, and enhance consumer satisfaction and loyalty, thereby directly strengthening the market competitiveness and growth foundation of e-commerce platforms [46]. Riffat Jabeen’s research reveals that logistics delays or high costs have become major bottlenecks constraining e-commerce development, particularly in lower-tier markets and cross-border sectors [47]. More importantly, Ali Yousaf contends that improved logistics efficiency not only enables “cost reduction and efficiency enhancement” in e-commerce operations but also acts as the core driver for its transition toward “high-quality” development. On one hand, precise and agile logistics networks support high-value-added models like personalized services (e.g., instant delivery, scheduled distribution, fresh-product e-commerce, and reverse logistics) [48]; on the other hand, as Pecherskaya E proposes, the deep mining and intelligent application of logistics data—such as smart route planning and demand forecasting—empowers e-commerce platforms to optimize inventory management, precision marketing, and supply chain collaboration, achieving end-to-end efficiency upgrades [49]. Notably, Osman Mary Catherine highlights that advancing green logistics practices (e.g., new-energy transportation and packaging reduction) has become a vital dimension for e-commerce to address sustainability demands and enhance comprehensive quality [50].
In the research on the coupling mechanism between digital transformation and operational efficiency of logistics enterprises, the integration of green supply chains and digital technologies is emerging as a cutting-edge exploration direction. Recently, the paper Integrated Green Supply Chain System Development with Digital Transformation published by Min-Ren Yan et al. in the International Journal of Logistics Research and Applications offers significant theoretical insights [51]. This study proposes an innovative “twin transition” framework and, through system dynamics (SD) modeling and empirical case analysis, demonstrates how digital technologies drive green supply chains to achieve zero-carbon goals through IoT and big data analytics while improving operational efficiency. Its core contribution lies in the transferability of the system dynamics model to the efficiency evaluation of logistics enterprises, particularly within complex, multi-agent supply chain networks. Furthermore, the authors emphasize that current research lacks long-term simulations of transformation pathways, which directly aligns with the “trend prediction” focus of this paper.
Previous studies have achieved substantial progress in areas such as digital transformation pathways, enterprise operational efficiency, and the effects of digital transformation on enterprise management, providing a strong foundation for the theoretical framework of this study. However, a review of the existing literature reveals relatively limited research on the specific relationship between digital transformation and the operational efficiency of logistics enterprises. To address this gap and support the high-quality development of e-commerce, this paper contributes to the theory of logistics enterprise transformation in the digital economy era by constructing a coupling coordination degree evaluation system and a trend prediction model, offering a theoretical basis for formulating coordinated development strategies across the industry.
Therefore, current research has reached a consensus that elevating the continuous optimization of logistics enterprise operational efficiency to a strategic priority—through technological innovation, business model innovation, and policy support—serves as the essential pathway and pivotal lever for systematically resolving logistics bottlenecks. This approach is critical to unleashing e-commerce growth potential and achieving its profound transformation from “scale expansion” to a “quality-and-efficiency orientation” [52]. This underscores that the existing domestic and international research places paramount importance on enhancing logistics efficiency to advance high-quality development in e-commerce.

3. Study Area, Methodology, and Variable Selection

3.1. Research Object

This study is based on the “Results of Industry Classification for Listed Companies in the First Half of 2023”, published by the China Listed Companies Association in February 2024. The research focuses on listed companies in the transportation, warehousing, and postal services sectors, with a specific emphasis on logistics companies listed between 2014 and 2023. To ensure data reliability and representativeness, the following screening criteria were applied: (1) companies with trading status ST (it says the listed company had been placed under “other risk warnings” by the exchange), ST* (it means that the stock has delisting risk, which is higher than ST risk), suspended listing, or delisting during the observation period were excluded; (2) firms with missing data or notable erroneous data were removed. Following these steps, 52 listed logistics companies were retained for empirical analysis, including 49 state-owned enterprises.

3.2. Research Method

3.2.1. Super-Efficiency SBM Model

Data Envelopment Analysis (DEA), as a non-parametric method for measuring efficiency, takes decision-making units (DMUs) as the research objects. Its basic principle is to determine the production frontier of each DMU using linear programming. Based on the degree of deviation of each DMU from the production frontier, it measures and quantifies relative efficiency, thereby obtaining the efficiency score. Among traditional DEA models, the most representative are the CCR model and the BCC model.
The traditional CCR model assumes constant returns to scale, believing that all the evaluated DMUs are at the stage of optimal production scale. The technical efficiency derived from this model includes the scale efficiency and is typically referred to as overall technical efficiency (TE). However, in actual production settings, many production units do not operate at an optimal scale. To address this, Banker et al. improved the CCR model and proposed the BCC model, which assumes variable returns to scale. The technical efficiency obtained from the BCC model excludes the impact of scale factors and is referred to as pure technical efficiency (PTE). Further derivation shows that S c a l e   E f f i c i e n c y   ( S E )   =   o v e r a l l   t e c h n i c a l   e f f i c i e n c y / p u r e   t e c h n i c a l   e f f i c i e n c y , i.e., S E   =   T E / P T E .
Traditional DEA models have been widely used to evaluate the relative efficiency of decision-making units; however, they exhibit certain limitations in addressing differences in input–output slacks. Compared with traditional DEA models, the Slack-Based Measure (SBM) model offers the advantage of accounting for input and output slacks, which allows for more precise efficiency assessments. However, the conventional SBM model limits efficiency scores to a maximum of 1, making it difficult to distinguish between efficient units. To overcome this limitation, this study adopts the super-efficiency SBM model.
The objective function ρ * = m i n 1 m x i x i o aims to optimize the efficiency of resource utilization by minimizing the ratio of input to relaxation variables and current inputs. In this paper, digital technologies are conceptualized as reducing redundant inputs. The use of digital technology can replace manual labor with digital robots and digital logistics sorting, and intelligent route planning can reduce transportation time input, improve work transportation efficiency, and reduce operating costs, with these efficiency gains reflected through the slack variables. Therefore, the super-efficiency SBM model is selected to evaluate the operational efficiency of logistics enterprises. The model’s mathematical expression is as follows:
ρ * = min 1 m i = 1 m x ¯ i x i k 1 s 1 q = 1 s 1 y ¯ q w y q k w
x ¯ i j = 1 , j k n λ j x i j ( i = 1 , , m ) y ¯ q w j = 1 , j k n λ j y q j w ( q = 1 , , s 1 ) j = 1 , j k n λ j = 1 x ¯ i x i k , 0 y ¯ q w y q k w , λ j 0
where ρ * is the target efficiency value; m is the number of input indicators; n is the number of decision units; s 1 is the expected output quantity; x i k and y q k w represent the inputs and expected outputs; x ¯ i and y ¯ q w represent the input vector and output vector in the new subset of production possibilities excluding ( x i k   , y q k w ); and λ j is the weight vector of cross-sectional data. In the super-efficiency SBM models, the efficiency score ρ * can be greater than or equal to 1.

3.2.2. Coupling Coordination Degree Model

Coupling degree refers to a metric used to measure the intensity of interconnections and mutual influences between different elements and variables. Digital transformation serves as a driving force for improving the operational efficiency of logistics enterprises, while enhancements in operational efficiency, in turn, reinforce the process of digital transformation. Therefore, digital transformation and the operational efficiency of logistics enterprises exhibit a mutually reinforcing and supporting relationship.
To quantitatively measure the coupling coordination between digital transformation and logistics operational efficiency, this study introduces a coupling coordination degree model. Considering the n-element system model used in previous studies [53], U i denotes the value of each subsystem, and its range set is between 0 and 1. The coupling degree C indicates the degree of interaction between subsystems, where higher values indicate greater synergy and lower dispersion. The formula for the coupling degree is expressed as follows:
C = i = 1 n U i 1 n i = 1 n U i n 1 n
Therefore, when n = 2 , C = 2 × ( u 1 × u 2 ) u 1 + u 2 .
In this model, U i represents the degree of digital transformation and U 2 represents the operational efficiency of logistics enterprises, which is normalized before calculation. The coupling degree C ranges from 0 to 1, where values closer to 1 reflect stronger coupling, while those nearer to 0 suggest weaker systemic integration. Although the coupling degree reflects the strength of the coupling relationship, it does not capture the level of coordinated development between the two subsystems.
To more accurately capture the level of synergy, the coupling coordination degree model is introduced, given by the following expression:
T = α X 1 + β X 2
D = C × T
where T represents the comprehensive evaluation index, and D refers to the coupling coordination degree between digital transformation and operational efficiency. The coefficients α and β indicate the contribution of each subsystem, such that α + β = 1 . Assuming equal importance, both parameters are set to 0.5 in this study. The value of D also ranges from 0 to 1, with higher values indicating stronger and more balanced coupling coordination.

3.2.3. Random Forest Model

The sample size used in this paper is 52, which falls under the category of a small sample. The core challenge in small-sample prediction lies in the limited data availability, which introduces noise and uncertainty into the model learning process. This can result in issues such as overfitting, high variance, inaccurate predictions, unreliable evaluations, and extreme sensitivity to data defects.
Random forest [54], as an ensemble learning method, demonstrates strong nonlinear modeling capabilities and high generalization performance. It is particularly effective in handling small sample sizes and complex variable relationships. Due to the small number of samples selected in this paper [55], this study employs the random forest model to forecast trends in the coupling coordination degree between digital transformation and operational efficiency in logistics enterprises.
The model integrates multiple decision trees using Bootstrap sampling and random feature selection. Annual data from 2014 to 2023 are used as the training set, with “year” defined as the feature and various transportation mode indices serving as predictive labels. Key model parameters include the number of base learners, maximum tree depth, and automatic tuning procedures, all while maintaining a fixed random state to ensure reproducibility. Finally, the built-in OOB (Out-of-Bag) score and cross-validation (5-fold cross) are used to evaluate the prediction accuracy, and the comparison chart of the actual value and fitted value is drawn to verify the fitting effect of the model. Compared to traditional time series models, random forests bypass the constraints of data stationarity and enable variable importance analysis to support decision-making. Additionally, aggregating predictions from multiple decision trees reduces variance and improves both accuracy and stability, making the model well-suited for short, volatile time series.
The random forest model is constructed as follows: First, Bootstrap sampling is used to randomly draw N samples, with replacement, from the original data set of size N ; these are then used to train a single decision tree. This process is repeated m times to generate m new training sets, each used to train an independent tree. Each tree is constructed using the CART (Classification And Regression Tree) algorithm, which selects node-splitting features based on the Gini coefficient. For a given sample set T with n categories, the Gini coefficient is calculated as follows:
G i n i ( T ) = 1 i = 1 n P i 2
where P i denotes the proportion of class i samples at the current node.
To determine the optimal split at each node, the algorithm evaluates whether a certain feature A meets a specific segmentation threshold. Based on this criterion, the sample set T is divided into T 1 and T 2 . Let s 1 and s 2 denote the number of samples in T 1 and T 2 , respectively. The Gini index of the split, denoted as G i n i r o o t ( T , A ) , is computed as follows:
G i n i r o o t ( T , A ) = s 1 s 1 + s 2 G i n i ( T 1 ) + s 2 s 1 + s 2 G i n i ( T 2 ) ,
The feature and threshold yielding the lowest Gini index are selected, and a decision tree h ( X ) is generated accordingly.
A total of m decision trees are independently trained using the m Bootstrap samples, forming a sequence of classifiers { h 1 X , h 2 X , , h m ( X ) } .
For prediction, each tree votes on the output class. The final result is determined by majority voting across all decision trees:
H X = a r g m a x y ^ i = 1 m I h i X = y ^ ,
where y ^ denotes the predicted class label, and I ( · ) is an indicator function.

3.3. Variable Selection

The coupling and coordinated development of digital transformation and operational efficiency of logistics enterprises is a dynamic process involving the mutual influence and interaction of both systems. The construction of a comprehensive index system must reflect the distinct characteristics of each system and their interactions to ensure the validity and comprehensiveness of the evaluation.

3.3.1. Enterprise Digital Transformation Index System

The CSMAR (China Stock Market & Accounting Research) database is a comprehensive and authoritative academic resource widely recognized in the scholarly community. It contains high-quality, rigorously verified data on finance, economics, and accounting related to China’s capital market and macroeconomy. Among them, the China Listed Firm Digital Transformation Research Database, which is jointly developed by the CSMAR team and research groups from leading universities, provides detailed data on the strategies, technologies, organizational structures, and digital achievements of listed companies. It also includes indicators reflecting macro-environmental support and corporate-level digital transformation, thus providing robust data support for research on corporate digitalization.
This study utilizes digital transformation indicators from the China Listed Firm Digital Transformation Research Database, which consists of six first-level and thirty-one second-level indicators (see Table 1). These indicators are designed to capture multiple dimensions of digital transformation in logistics enterprises. The index structure and weights are derived from the database document titled “Enterprise Digital Transformation Database Specification”, and the detailed descriptions of the 31 s-level indicators are shown in Table 2.

3.3.2. Enterprise Operational Efficiency Evaluation Index System

The evaluation index system for enterprise operational efficiency (see Table 3) provides a structured approach to assessing business performance. It aims to scientifically and comprehensively evaluate how effectively enterprises utilize resources to generate output. In this study, the input indicators are selected to reflect the underlying resource conditions and consumption patterns of logistics enterprises.
Capital input is measured by the net value of fixed assets, reflecting the actual value of long-term tangible assets and forming the material foundation for production and operational activities. Labor input is measured by the number of employees, indicating the scale of human resources available to the enterprise. The variable plays a significant role in determining the company’s production capacity and operational effectiveness. Cost input is reflected by operating costs, which encompass a range of expenses incurred during business activities, including operational costs, financial expenses, administrative expenses, R&D expenses, and other related charges. These expenses reflect the degree of resource consumption required for maintaining routing operations.
For the output dimension, this study selects operating revenue as the expected output indicator. Revenue reflects the economic returns from business activities over a certain period and provides an intuitive measure of the enterprise’s market performance and profitability. This indicator is particularly relevant in evaluation logistics enterprises, where profitability and market responsiveness are integral to sustained operational efficiency.

3.3.3. Data Sources

The data used to evaluate enterprise operational efficiency are obtained from the WIND database, a widely recognized platform for financial and economic information in China. Specifically, the database provides information on net fixed assets, the number of employees, operating costs, and operating income. These variables form the basis for constructing the input and output indicators required for the empirical analysis. There are no missing values in the data source, and no filling method is adopted.

4. Empirical Analysis

4.1. Analysis of Operational Efficiency of Logistics Enterprises

This study employs Dearun Tools V3.2.0.5 to assess the operational efficiency of logistics enterprises using the super-efficiency SBM model. A total of 52 logistics enterprises are categorized into three main sectors: road transportation, water transportation, and air transportation. The operational efficiency of each enterprise is calculated separately, after which the geometric mean is used to determine industry-wide and sector-specific efficiency levels, and logistics enterprises may have extreme values due to accidental factors such as short-term policy preferences and industry shocks. Compared with the arithmetic mean method and weighted mean method, the geometric mean method has the advantage of less interference from extreme values, so it is used to calculate the operational efficiency of each enterprise, as shown in Table 4 and Figure 1.
From 2014 to 2023, the overall operational efficiency of logistics enterprises ranged between 0.520 and 0.585, indicating a generally fluctuating upward trend. Although this suggests some improvement, the increase in efficiency has been modest and remains insufficient. Among the three sectors, road transportation exhibited the highest operational efficiency, largely due to gradual improvements in multi-level transportation infrastructure, such as highways, provincial roads, and rural roads, increasing traffic capacity and transport effectiveness. The widespread adoption of technologies such as satellite positioning systems has also contributed to operational improvements. However, efficiency in the road transportation sector exhibited significant fluctuations. For instance, a 12.04% decline was recorded in 2020, attributed to the sector’s dependence on offline labor and road networks, which were severely disrupted during pandemic-related lockdowns. Its operational efficiency rebounded to 0.681 in 2023, highlighting the sector’s strong operational resilience.
In contrast, the operational efficiency of water transportation remained relatively stable, fluctuating between 0.486 and 0.549, with no significant upward trend. This stability is attributed to longer contractual cycles and the limited regional impact of epidemic outbreaks, which rendered the sector less sensitive to external shocks. Air transportation, however, exhibited considerable volatility. Efficiency dropped from 0.515 in 2019 to 0.362 in 2020, primarily due to its high dependence on international routes, which were significantly affected by international flight suspensions during the pandemic. After a further decline to 0.287 in 2022, operational efficiency rose to 0.452 in 2023, driven by restructuring in global supply chains. Moreover, fuel costs account for a significant portion of operating expenses in the aviation sector, and the underdevelopment of digital fuel-saving technologies has hindered cost control, thereby constraining improvements in overall efficiency.
The overall operational efficiency trend of the 52 logistics companies is closely aligned with that of the road transport sector, indicating its dominant influence within the logistics industry. The decline in average efficiency from 0.579 in 2019 to 0.520 in 2020 further confirms the industry-wide impact of the COVID-19 pandemic.

4.2. Analysis of the Coupling and Coordination Degree Between Digital Transformation and Operational Efficiency

To better reflect the level of coupling and coordinated development between digital transformation and operational efficiency, this study draws on the existing research to categorize the degree of coupling and coordination, as shown in Table 5. From 2014 to 2023, the overall coupling and coordination degree remained within the initial coordination range but exhibited a steady upward trend over time. Significant differences were observed across different logistics sectors. The highest coupling and coordination degree was 0.718 (intermediate coordination), while the lowest was 0.560 (basic coordination). These findings indicate notable disparities in digital operations integration among logistics subsectors and suggest that mechanisms supporting sustainable integration remain underdeveloped and require further investigation.
Over the ten-year period, road and water transportation sectors made the most significant contributions to the overall coupling and coordination level, while the air transportation sector exhibited substantial volatility that constrained its performance. As presented in Table 6 and Figure 2, logistics enterprises across all three transport sectors experienced slight declines in coupling and coordination between 2019 and 2020, largely attributable to the global pandemic. While the water transport sector showed a minor increase during this period, it was smaller compared to gains in previous years. The road transport sector showed consistent growth, rising from 0.673 in 2014 to 0.715 in 2023, peaking at 0.718 in 2021, indicating strong overall performance and sustained improvement.
The water transport sector also showed a steady upward trend, increasing from 0.615 in 2014 to 0.651 in 2023, suggesting gradual progress in digital–operation integration. In contrast, the air transport sector showed significant volatility. Its coupling and coordination index increased from 0.619 in 2014 to 0.642 in 2019 but declined sharply to 0.593 by 2022, reflecting the sector’s vulnerability to external disruptions. Although the index recovered slightly to 0.632 in 2023, the sector remained at the basic coordination level, underscoring persistent challenges in stable digital integration. These findings suggest that future policy and strategic interventions should focus on enhancing digital coordination in the aviation sector while sustaining improvements in road and water transport.
Despite gradual improvement, the logistics industry as a whole remained in the initial coordination stage throughout the study period. This reflects the early stage of digital transformation, where the implementation of digital technologies and the maturity of management systems remain limited. As investments in digital infrastructure increase and technology adoption expands, the degree of coordination is expected to improve. Among the sectors, road transportation demonstrated the highest level of integration, supported by a strong foundation in digital infrastructure. Government policies have accelerated this trend by promoting the use of electronic certificates and expanding the implementation of non-stop electronic toll collection (ETC) systems. The relative concentration of operational scenarios in road transport also facilitates the large-scale implementation of digital solutions.
The water transport sector is gradually introducing digital management systems; however, the complexity of its operations, such as port scheduling and route planning, presents considerable challenges. These processes are often constrained by geographical factors and infrastructural limitations, making technology application and data integration more difficult. As a result, improvements in coupling coordination are occurring slowly and steadily. In contrast, the air transport sector exhibited significant fluctuations in coordination levels, underscoring its sensitivity to external factors such as global disruptions and policy changes. Operational efficiency in aviation is closely linked to the performance of multiple interconnected processes, including flight scheduling, passenger screening, and baggage handling. The integration of digital technology in such a complex environment remains difficult. Any inefficiency in a single process can compromise the overall operational performance, highlighting the need for system-wide digital coordination across all functions.

4.3. Trend Forecasting

To accurately predict the coupling and coordination degree between digital transformation and operational efficiency across various transportation sectors (overall, road transport, water transport, and air transport), this study employed the random forest model. Annual data from 2014 to 2023 for each transport mode were used as training samples, with “year” serving as the independent variable and the corresponding coupling and coordination degree as the dependent variable. Four separate RFR models were constructed, one for each transport category. Given the relatively stable year-to-year changes in the data, normalization was not applied; instead, original values were used directly for modeling purposes.
After selecting suitable model parameters, the RFR models were trained using the data from 2014 to 2023. Once fitted, the models were used to predict the coupling coordination degrees for 2024 to 2026. Model performance was assessed using the built-in Out-of-Bag (OOB) score and 5-fold cross-validation to ensure robustness and predictive accuracy. In addition, comparison charts between the actual and fitted values were generated to visually evaluate the quality of the model fit. Finally, the results were verified. In the bag-out error (OOB Error) test, the OOB score was above 0.92, and the average R2 score (determination coefficient) of 5-fold cross-validation (Cross-validation) was between 0.889 and 0.934, indicating that the model had a high explanatory power for the training data. The trained models were then used to forecast future coupling and coordination degrees for the overall logistics industry, as well as for the road, water, and air transportation sectors. The forecasted results are presented in Table 7 and Figure 3.
When predicting the coupling and coordination degree of digital transformation and operational efficiency in the logistics transportation industry, the predictions may exhibit uncertainty. Despite an overall trend of steady growth, the industry’s development is influenced by multiple factors, including the pace of technological iteration, policy adjustments, and changes in market conditions, which can cause the actual coupling and coordination degree to deviate from the predicted values.
The data in Table 7 indicate that all transportation sectors are expected to experience steady growth in their coupling and coordination levels. The predicted overall values for 2024, 2025, and 2026 are 0.6789, 0.6821, and 0.6852, respectively, reflecting an average annual growth rate of about 0.31%. This suggests that integration between overall digital transformation and operational efficiency in the transportation industry is gradually improving. Although the rate of increase is moderate, the stable trend aligns with the broader trajectory of global digitalization, where long-term investments in technology are beginning to yield operational benefits.
For the road transport sector, the forecasted coupling coordination values rise from 0.7195 in 2024 to 0.7269 in 2026, corresponding to an average annual growth rate of approximately 0.34%. While the trend remains upward, the year-over-year growth rate appears to slow slightly from 2025 to 2026. In the water transport sector, the predicted values increase from 0.6532 to 0.6595, with an average annual growth rate of 0.32%, indicating a stable and consistent development trajectory. The air transport sector is forecasted to grow from 0.6350 to 0.6420, showing a steady upward trend with an average annual growth rate of 0.37%.
Among these three sectors, road transport continues to lead in coupling coordination, possibly due to its more mature infrastructure and the relatively easier application of digital technologies. The air transport sector, while still expected to trail in absolute value, is maintaining a stable growth trajectory, although it remains more susceptible to volatility due to its complex operational environment. The water transport sector falls in the middle, showing consistent development without notable fluctuations. Overall, all the sectors demonstrate positive trends, indicating that digital transformation efforts are gradually improving the operational efficiency of China’s logistics industry.

5. Conclusions and Recommendations

5.1. Conclusions

  • The overall operational efficiency of logistics enterprises remains low but has shown gradual improvements with fluctuations. From 2014 to 2023, the efficiency values ranged from 0.520 to 0.585, reflecting modest but upward progress. The road transport sector consistently recorded the highest efficiency but was notably impacted by the pandemic. The air transport sector experienced severe volatility, with efficiency plunging in 2020, highlighting its relatively weak risk resilience. In contrast, the water transport sector exhibited relatively stable but slower growth. This pattern of “low-level growth” reflects the incomplete integration of digital technology investments with traditional operational models. Technical applications are mostly concentrated in isolated segments of the logistics chain, and full-chain collaborative efficiency has not been fully realized. As a result, achieving substantial improvements in overall operational efficiency remains a challenge.
  • The coupling and coordination between digital transformation and operational efficiency remain at the primary coordination stage, though pronounced sectoral differences are observed. From 2014 to 2023, the overall coupling coordination degree ranged from 0.642 to 0.677. Road transport performed best, reaching 0.718 in 2021, entering intermediate coordination with a generally upward trend. Water transport improved steadily from 0.615 in 2014 to 0.651 in 2023, while air transport, affected by external environmental shocks, fell below 0.6 in 2020, indicating instability. These findings suggest that the synergy between digital transformation and operational efficiency is still in a developmental phase, and a stronger alignment between digital initiatives and performance outcomes is needed. Moreover, the significant fluctuations observed in the air transport industry indicate that the external environment exerts considerable influence on the coupling and coordination between digital transformation and operational efficiency. Emergencies such as the COVID-19 pandemic and international supply chain disruptions have had a significant impact on the transport sector. In contrast, the road transportation sector is likely to recover more rapidly due to its mature infrastructure. This highlights the need to integrate digital transformation with risk prevention mechanisms to enhance the resilience of logistics enterprises.
  • Forecasts indicate steady growth in coupling coordination, although the projected values vary across sectors. From 2024 to 2026, coupling coordination is expected to grow at an average annual rate of 0.31%. Road transport is projected to maintain steady gains, air transport is expected to grow from a lower base but with stable momentum, and water transport is anticipated to exhibit gradual improvements. Technological progress and increasingly mature digital applications are facilitating the smooth digital transformation of enterprises, thereby enhancing operational efficiency. In turn, improvements in operational efficiency reinforce the degree of digital transformation, encouraging enterprises to further invest in digital advancements. This mutual reinforcement is expected to drive the continued growth of coupling and coordination between digital transformation and operational efficiency in logistics enterprises. Moving forward, greater emphasis should be placed on improving the resilience of air transport and advancing technological innovation in road transport.

5.2. Recommendations

Based on the findings, the following practical and targeted recommendations are proposed to overcome bottlenecks in coordinated development and promote the high-quality, sustainable growth of the logistics industry:
  • To accelerate digital transformation, it is essential to sustain investment in digital logistics technologies and build a fully integrated digital chain. Logistics companies should invest in research and development and prioritize eliminating information silos between warehousing, transportation, and distribution. This includes the deep integration of artificial intelligence, big data, blockchain, and other digital technologies into core operations. For instance, road transport enterprises can optimize vehicle routing using intelligent logistics scheduling systems to reduce operating costs and energy waste, while blockchain technology can improve cargo tracking transparency in the air transport sector.
  • Companies should strengthen talent development to improve resource allocation efficiency. Cultivating hybrid professionals skilled in both digital technology and logistics management is essential. Strategic partnerships with research institutes and technology centers should be established to accelerate the supply of digital talent. Enterprises should also monitor the input–output ratio, adjust digital capital structures dynamically, and prioritize investments in high-yield digital application areas.
  • Risk management capabilities must be enhanced to improve shock resilience. Establishing digital risk early-warning systems is essential, particularly for the air transport sector, which is more vulnerable to external disruptions. Road transport companies can also employ real-time traffic data to anticipate extreme weather impacts and adjust fleet routes accordingly. In addition, companies should use big data analytics and digital technology to build decision simulation systems, enabling the evaluation of multiple response strategies and reducing operational delays and losses.
  • The government should promote enterprise-level innovation and competition while ensuring the accurate allocation of digital transformation subsidies. Incentives such as tax incentives, dedicated funds for digital transformation, and fiscal subsidies can motivate logistics companies to achieve digital upgrading and enhance efficiency. Additionally, an improved evaluation system is needed to promote and guide the coordinated development of digital transformation and operational efficiency. Regular assessments and policy oversight will help ensure effectiveness and prevent the misallocation of public resources. For industries with persistently low or unstable coordination degrees, such as air transport, government support should focus on improving foundational infrastructure, ensuring its practical application, extending subsidy benefits, and encouraging sector-specific digital transformation to improve operational efficiency.
  • Building a synergistic system between logistics infrastructure and high-quality e-commerce development to form a self-reinforcing “Logistics–E-commerce Cycle”. Leveraging the highly symbiotic, interdependent, and mutually reinforcing relationship between the logistics industry and e-commerce, this ultimately achieves a dynamic where the pursuit of high-quality e-commerce development compels logistics operational efficiency improvements, and enhanced enterprise operational efficiency propels high-quality e-commerce development in turn.
Small- and medium-sized enterprises can learn from this suggestion, combine their own characteristics, and make use of external policy dividends, enterprise cooperation, and other methods to make up for their deficiencies, so as to realize digital transformation and improve operational efficiency.

5.3. Discussion

This study systematically explores the coupling relationship between digital transformation and operational efficiency in logistics enterprises by employing a super-efficiency SBM model, a coupling coordination degree model, and a random forest regression model. It also forecasts the coupling coordination trend over the next three years, offering multidimensional theoretical support for the high-quality development of the logistics industry. Notably, this is the first study to quantify digital–efficiency synergy across different transportation modes using a coupling coordination framework. The results reveal that the efficiency trajectory of road transport is aligned with the overall industry trend, while air transport remains highly susceptible to external shocks. The random forest predictions indicate that the coupling coordination degree of the industry is expected to grow at an average annual rate of 0.31% between 2024 and 2026, supporting the view that digital transformation is a gradual and long-term process.
However, this study faces certain limitations:
  • The issue of model assumptions: The coupling coordination degree model assumes equal weighting between digital transformation and operational efficiency. In practice, however, logistics enterprises at different stages of development may emphasize one dimension more than the other. This suggests a need to explore dynamic weighting mechanisms to better reflect actual enterprise conditions.
  • The issue of the construction of the evaluation index system: The output indicators in the operational efficiency evaluation system only consider operating income, which may not fully capture the distinctive dimensions of digital transformation across different logistics enterprises. Due to data limitations and reference to previous studies, non-financial indicators such as customer satisfaction and environmental benefits were excluded, potentially leading to an incomplete assessment of operational efficiency. In future research, incorporating output indicators such as delivery time, customer satisfaction, and environmental benefits could enable a more comprehensive evaluation.
  • The issue of data timeliness: The data set used in this study extends only to 2023 and does not account for the potential influence of emerging technologies by logistics enterprises in 2024 and beyond. Rapid technological iteration and sudden policy changes may significantly affect the coupling and coordination relationship between digital transformation and operational efficiency, but these are not reflected in the current data. As a result, there may be deviations between the model’s projected values and actual future developments.
  • The issue of regional heterogeneity: The article focuses on the registered addresses of the selected sample logistics companies, which operate both domestically and internationally. It does not conduct a regional heterogeneity analysis. Further analysis will be conducted using provincial panel data from the logistics industry.
In the future, a multi-dimensional random forest model will be considered, incorporating additional features such as policy support and external environmental impacts to enhance the robustness of the predictions. Additionally, given that logistics serves as the pivotal element for e-commerce development, the effectiveness of logistics enterprises’ digital transformation will profoundly shape their overall growth trajectory. Consequently, the next phase of this research will focus on examining the impact of digital transformation on high-quality e-commerce development, formulating actionable recommendations to ultimately facilitate synergistic closed-loop advancement between logistics and e-commerce.

Author Contributions

Conceptualization, Y.F. and P.Z.; methodology, P.Z.; software, P.Z.; validation, Y.F.; formal analysis, Y.F.; investigation, Y.F. and P.Z.; resources, Y.F. and P.Z.; data curation, Y.F. and P.Z.; writing—original draft preparation, Y.F. and B.L.; writing—review and editing, P.Z.; visualization, P.Z.; supervision, P.Z.; project administration, P.Z.; funding acquisition, P.Z. All authors have read and agreed to the published version of the manuscript.

Funding

Shandong Provincial Social Science Planning Research Project: 23CSDJ57.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data supporting the plots within this paper and other study findings are available from the corresponding author upon reasonable request.

Acknowledgments

We would like to express our gratitude to the Dongying Regional High-Quality Economic Development Research Base at Shandong Institute of Petroleum and Chemical Technology for their related support.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Evaluation of logistics enterprise operational efficiency.
Figure 1. Evaluation of logistics enterprise operational efficiency.
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Figure 2. Coupling and coordination of digital transformation and operational efficiency.
Figure 2. Coupling and coordination of digital transformation and operational efficiency.
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Figure 3. Prediction value of coupling and coordination degree between digital transformation and operational efficiency.
Figure 3. Prediction value of coupling and coordination degree between digital transformation and operational efficiency.
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Table 1. Detailed indicators and weights of the digital transformation index system.
Table 1. Detailed indicators and weights of the digital transformation index system.
Primary IndicatorsWeight of the First-Level IndexSecondary IndicatorsSecondary Indicator Weights
Strategic guidance34.72%Establish digital positions for management23.82%
The management is oriented towards digital innovation and forward-looking27.88%
The management layer is oriented towards digital innovation and sustainability18.79%
The number of management layers is oriented towards the breadth of innovation12.83%
Management layer digital innovation orientation intensity16.68%
Technology-driven16.20%artificial intelligence technology55.04%
Blockchain technology12.98%
Cloud computing technology18.32%
Big data technology13.66%
Organizational empowerment9.69%Digital capital investment program50.22%
Digital human input plan25.53%
Digital infrastructure construction12.06%
Construction of scientific and technological innovation bases12.19%
Environmental support3.42%Number of invention patents in the industry19.23%
R&D activities in the industry17.79%
New product development and sales in the industry14.98%
The intensity of digital technology in the industry11.57%
Digital capital input intensity in the industry11.4%
The intensity of human capital input in the same industry7.89%
Cable density in the city4.77%
Capacity of mobile switches in the city4.03%
The number of Internet broadband access users in the city4.00%
The number of mobile Internet users in the city4.34%
Digital achievements27.13%Digital innovation standards36.68%
Digital innovation paper11.74%
Digital invention patent23.54%
Digital innovation credentials14.73%
Digital national awards13.31%
Digital applications8.84%technical innovation63.42%
Process innovation23.78%
Business innovation12.80%
Table 2. Detailed indicators of the digital transformation index system.
Table 2. Detailed indicators of the digital transformation index system.
Secondary IndicatorsInterpretation
Establish digital positions for managementPositions specifically set up within the enterprise management layer to be responsible for digital-related work
The management is oriented towards digital innovation and forward-lookingThe management’s awareness of predicting and making advanced arrangements for future trends, technology applications, and industry changes in digital innovation
The management layer is oriented towards digital innovation and sustainabilityThe management’s determination and actions to maintain long-term investment and promotion in digital innovation, and to continuously integrate digital concepts into enterprise operations
The number of management layers is oriented towards the breadth of innovationThe scope of the management layer’s promotion of digital innovation across enterprise business and management, covering digital exploration in links like production, logistics, sales, and management
Management layer digital innovation orientation intensityThe level of resources the management layer invests in digital innovation and the resolve to drive innovation implementation
Artificial intelligence technologyTechnology that uses computers to simulate, extend, and expand human intelligence
Blockchain technologyA type of distributed ledger technology
Cloud computing technologyTechnology that provides computing resources via network “clouds” (remote server clusters)
Big data technologyTechnology for collecting, storing, analyzing, and mining vast and diverse data
Digital capital investment programEnterprises’ capital investment plans for digital transformation, including digital technology equipment procurement, digital platform building, and digital R&D
Digital human input planEnterprises’ personnel allocation plans for digital talent recruitment, cultivation, and introduction
Digital infrastructure constructionDigital hardware, network facilities, and digital platform construction by enterprises as basic support for digital operations
Construction of scientific and technological innovation basesPhysical platforms in logistics and related industries for digital and other sci-tech innovation activities
Number of invention patents in the industryThe count of invention patents related to digitalization and logistics technology obtained by enterprises or institutions in logistics and related industries, reflecting industry tech innovation achievements
R&D activities in the industryThe frequency, investment, and scope of Research and Development (R&D) activities by industry enterprises
New product development and sales in the industryThe launch and market sales of new logistics service products and new transportation tools developed by industry enterprises based on digital and other innovations
The intensity of digital technology in the industryThe proportion and significance of digital technology in overall technology application and business operations within the industry
Digital capital input intensity in the industryThe proportion of industry enterprises’ digital capital investment to total investment
The intensity of human capital input in the same industryThe proportion of industry enterprises’ investment in digital talent and other human capital to total human capital investment
Cable density in the cityThe laying density of optical cables within the city, i.e., the length of optical cables per unit area
Capacity of mobile switches in the cityThe maximum number of mobile calls, data services, etc., that mobile switches in the city can handle
The number of Internet broadband access users in the cityThe number of users accessing Internet broadband services in the city
The number of mobile Internet users in the cityThe number of users accessing mobile Internet in the city
Digital innovation standardsSpecifications and criteria for technology, processes, quality, etc., formulated in the digital innovation field
Digital innovation paperThe number of academic papers published in digital innovation-related research
Digital invention patentThe number of invention patents related to digital innovation
Digital innovation credentialsQualifications obtained by enterprises or institutions in digital innovation
Digital national awardsAwards recognized at the national level for enterprises or individuals in the digital innovation field
Technical innovationAdopting new digital and logistics technologies in logistics and other businesses
Process innovationDigitally transforming and optimizing logistics business processes, reshaping process links via digital technology for efficiency
Business innovationDeveloping new logistics business models and service forms based on digital technology
Table 3. Evaluation index system of enterprise operational efficiency.
Table 3. Evaluation index system of enterprise operational efficiency.
Indicator CategoriesName of IndexMeasures of Achievement
Input indicatorscapital inputNet value of fixed assets/10,000 yuan
Labor inputNumber of employees/10,000
CostsOperating cost/10,000 yuan
Output indicatorsExpected outputsOperating income/10,000 yuan
Table 4. Evaluation of logistics enterprise operational efficiency.
Table 4. Evaluation of logistics enterprise operational efficiency.
Sector2014201520162017201820192020202120222023
Road transport0.6530.6410.6610.6980.6770.6810.5990.6750.6230.681
Water transport0.5140.4980.4860.4950.5020.5000.5070.5350.5490.541
Air transport0.5040.5050.4980.5090.5040.5150.3620.3550.2870.452
Logistics industry0.5720.5610.5620.5830.5770.5790.5200.5590.5270.585
Table 5. Coupling coordination standard.
Table 5. Coupling coordination standard.
Scale DivisionDegree of CoordinationScale DivisionDegree of Coordination
0 ≤ D < 0.1Extreme dislocation0.5 ≤ D < 0.6Basic coordination
0.1 ≤ D < 0.2major maladjustment0.6 ≤ D < 0.7Primary coordination
0.2 ≤ D < 0.3Moderate disorientation0.7 ≤ D < 0.8Intermediate coordination
0.3 ≤ D < 0.4Mild dislocation0.8 ≤ D < 0.9Good coordination
0.4 ≤ D < 0.5Near to dislocation0.9 ≤ D ≤ 1Quality coordination
Table 6. Coupling and coordination degree of digital transformation and operational efficiency.
Table 6. Coupling and coordination degree of digital transformation and operational efficiency.
Iterms2014201520162017201820192020202120222023
The road transport industry0.6730.6890.6880.7110.7100.7160.6990.7180.7000.715
The water transport industry0.6150.6220.6100.6190.6270.6330.6390.6560.6510.651
The aviation transport industry0.6190.6370.6230.6370.6370.6420.5930.5960.5600.632
Logistics industry0.6420.6540.6470.6630.6660.6710.6580.6740.6580.677
Table 7. Prediction values of coupling and coordination degree between digital transformation and operational efficiency.
Table 7. Prediction values of coupling and coordination degree between digital transformation and operational efficiency.
YearLogistics Industry ForecastThe Road Transport Industry Prediction ValuesThe Water Transport Industry Forecast ValuesThe Aviation Transport Industry Forecast
20240.67890.71950.65320.6350
20250.68210.72380.65670.6389
20260.68520.72690.65950.6420
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MDPI and ACS Style

Zhang, P.; Fu, Y.; Lu, B. Analyzing the Coupling Coordination and Forecast Trends of Digital Transformation and Operational Efficiency in Logistics Enterprises. J. Theor. Appl. Electron. Commer. Res. 2025, 20, 211. https://doi.org/10.3390/jtaer20030211

AMA Style

Zhang P, Fu Y, Lu B. Analyzing the Coupling Coordination and Forecast Trends of Digital Transformation and Operational Efficiency in Logistics Enterprises. Journal of Theoretical and Applied Electronic Commerce Research. 2025; 20(3):211. https://doi.org/10.3390/jtaer20030211

Chicago/Turabian Style

Zhang, Pengcheng, Yaoyao Fu, and Boliang Lu. 2025. "Analyzing the Coupling Coordination and Forecast Trends of Digital Transformation and Operational Efficiency in Logistics Enterprises" Journal of Theoretical and Applied Electronic Commerce Research 20, no. 3: 211. https://doi.org/10.3390/jtaer20030211

APA Style

Zhang, P., Fu, Y., & Lu, B. (2025). Analyzing the Coupling Coordination and Forecast Trends of Digital Transformation and Operational Efficiency in Logistics Enterprises. Journal of Theoretical and Applied Electronic Commerce Research, 20(3), 211. https://doi.org/10.3390/jtaer20030211

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