Unveiling the Drivers of Global Logistics Efficiency: Insights from Cross-Country Analysis

By inergency On Mar 25, 2024

1. Introduction

Globalization is naturally forcing countries to think beyond their regional competitiveness. International trade has become the key to economic welfare in today’s unprecedentedly well-connected global economy. As countries increasingly face global competition, they seek ways to operate more efficiently globally. More efficient logistics operations positively and significantly affect a country’s competitiveness and sustainability by ensuring resilient, convenient, and low-priced mobility of goods and services. Furthermore, the logistics sector not only substantially contributes to a country’s economic performance and advancement, but also plays a crucial role in the environment and society [1]. Hence, the performance of logistics has a significant impact on both economic prosperity and sustainability [2]. On the other side, logistics operations involve the management of the efficient flow of goods, services, and information from the source to the final consumer [3]. These operations have become more critical in ensuring customer satisfaction and improving profitability and sustainability. Organizations have been increasingly paying attention to their logistics operations to enhance their delivery speed and timeliness, optimize their inventory controls, improve their procurement practices and carbon footprints, and boost the information flow within their supply chains.

Market conditions shift the competition among supply chains rather than organizations [4]. Therefore, today, organizations look beyond improving their infrastructures and seeking ways to integrate themselves into important supply chains. Today’s supply chains span multiple countries. Logistics operations efficiency, as a result, has now become an issue at the supply chain level rather than at the organizational level. Increasing logistics efficiency depends on several factors, such as using the latest technologies (image recognition, IoT, sensors, RFIDs, etc.), improving transportation infrastructure, increasing the adaptive capability to laws and regulations, and enlarging road and port capacities. While some of these factors can be managed by organizations participating in the supply chains, the others can only be improved at the country level. Physical infrastructure, laws, regulations, and the capacity of roads and ports determine the quality of logistics operations administered by governments. Therefore, logistics investments impact not only a specific firm, a supply chain, but also the entire country. Public resources finance many logistics infrastructure projects, and customs are regulated for public well-being. In return, the countries expect to increase their gross domestic product, public welfare, and competitiveness. Thus, logistics efficiency is critical at the country level to set the priority areas to invest in improving logistics performance.

The World Bank has developed the Logistics Performance Index (LPI), which provides a metric to assess countries’ logistics performance quantitatively and makes it possible to compare their customs regulations, logistics costs, and transportation infrastructure. LPI uses a questionnaire-based survey method to evaluate six dimensions of logistics related to a specific country. The dimensions reported in the index are as follows: (i) customs, (ii) infrastructure, (iii) international shipments, (iv) logistics competence, (v) tracking and tracing, and (vi) timeliness. The customs dimension refers to the efficiency of customs regulations clearance. The infrastructure dimension measures the transportation infrastructure, such as road capacity and railroad networks. International shipments measure the convenience of planning competitively priced shipments. Logistics competence touches upon the competence and quality of logistics services. The tracking and tracing dimension is related to the ability to control the time and place of transportation. Finally, timeliness refers to the compatibility of scheduled and expected delivery [5].

LPI is a powerful guideline for comparing the logistics performances of individual countries on any of the six dimensions. These dimensions are not independent of each other. For example, infrastructure and customs efficiency may significantly affect timeliness. Therefore, some of these dimensions may be precursors to the others [6,7]. In this study, we first qualitatively review each LPI dimension individually as antecedents and consequents. Therefore, LPI is used to compare the logistics efficiencies of individual countries.

This study investigates the factors driving the countries’ logistics performances relying on the LPI indicators of the World Bank. To figure out the factors making countries logistically perform well, we clustered them into three groups based on their logistics performances in 2010. The efficiency change along the observed time frame was found by calculating the Malmquist index from the same dataset, and its relation to the logistics drivers was discussed. Using the DEA approach, each country was first benchmarked with its peers in the same group, and the improvement dimensions of logistics performance indicators were identified. After these performance discrepancies were compensated in each group, all the countries were pooled into the same DEA model. These discrepancies among the logistics performance groups were considered the drivers of logistics performance efficiency.

This study is unique from several perspectives. Based on the best of our knowledge, it is one of the first studies using DEA to this extent to explore the drivers of logistics efficiency from the LPI dataset. The approach of extracting the logistics drivers from a DEA model and evaluating them at operational and structural levels separately according to three logistics performance levels of countries is quite novel in logistics. In terms of findings, this study also points out the difference between performance and efficiency. While performance may be defined as the degree of achievement of a country’s goal, efficiency refers to utilizing resources to achieve its goals [8]. From this perspective, the study is also one of the few research projects discussing both issues in the literature. Finally, many studies in the literature consider single-year LPI scores for logistics efficiency. However, this study captures the dynamically changing impact of logistics dimensions on performance using a Malmquist index (MI).

We organize the rest of the paper as follows. Section 2 provides the background and an extensive literature review on studies addressing countries’ logistics performance using LPI. Section 3 focuses on the methodology used, and the data collected, background information, and application of the proposed framework. The solution methodology and results are discussed in Section 4. Finally, Section 5 provides concluding remarks, managerial insights, and future research directions.

2. Background and Literature Review

The logistics networks are a crucial part of domestic and international trade with various operations, such as warehousing, brokerage, delivery, and terminal operations [6]. Martí et al. [7] analyzed the impact of LPI on developing countries’ trade performance. Their findings indicated that the components of LPI were critical for international trade in countries located in Africa, South America, and Eastern Europe. Uca et al. [9] showed that there were statistically significant relationships between countries’ economic performance and both “customs” and “logistics infrastructure”. Gungor et al. [10] examined the relationship between LPI data and the economies of Mediterranean countries and argued that countries can have higher levels of economic performance if they attach importance to “infrastructure” and “customs” dimensions. Widiyanto et al. [11] also indicated the importance of “logistics infrastructure” for foreign trade. Moreover, the extant papers showed that international shipments, logistics competence, and timeliness had a positive effect on the transportation sector [12,13]. In addition, the importance and positive effects of tracking and tracing for logistics operations are discussed in various research in the literature [14,15].

Efficient logistics operations also increase the competitiveness of countries. D’Aleo and Sergi [16] investigated the mediator role of LPI on the relationship between the Global Competitiveness Index (GCI) and gross domestic product (GDP). Their results showed that GCI pillars have an essential role in the economic growth of European countries through the mediation of logistics performance. Entrepreneurship and environmental awareness are furthermore important extents to competitiveness. Using LPI, Mesjasz-Lech [17] identified the difficulties faced by entrepreneurs in the transport and storage sector within the European Union. They performed a correlation analysis between entrepreneurship rate and logistics performance and found a significant link between LPI and entrepreneurial activity within the sector. Liu et al. [18] investigated the link between the logistics performance of Asian countries and environmental degradation. Their results showed that LPI indicators significantly impacted CO₂ emissions. Successful logistics operations help countries enhance trade and attract more foreign direct investment (FDI). Coto-Millán et al. [19] inspected the impact of countries’ logistics performance on global economic growth. They estimated that a 1% increase in LPI might enhance the world economic growth between 0.011% and 0.034%. Luttermann et al. [20] studied the relationship between logistics performance, trade, and FDI. Performing a panel data analysis on 20 Asian countries, they found a significant association between logistics performance and FDI.

On the contrary, poor logistics performance negatively influences the country’s competitiveness. Inadequate infrastructures, weak custom regulations, poor quality of logistics services, and faulty interconnections make the logistics service networks vulnerable to uncertainties, leading to unpredictable transaction delays. The COVID-19 pandemic, the accidents in the bottleneck waterways, such as in the Suez Canal, and recent conflicts in different regions of the world have had a prominent impact on the overall coordination, organization, and planning of the logistics networks. Therefore, evaluating and understanding insights behind countries’ logistics performance is highly important. A country’s logistics performance may be evaluated based on several indicators of its transportation system, such as the railway usage rate, the length of the railways, and the use of waterways quantitatively. The World Bank reports LPI that provides detailed information on the logistics performance of over 160 countries based on the logistics experts’ opinions on a five-point scale. LPI survey data provide quantitative evidence about the easiness of custom procedures, available infrastructure, the quality of logistics services, timeliness of logistics transactions, barriers in front of international shipments, and availability of tracking and tracing tools for countries. Arvis et al. [6] evaluate the dimensions of LPI in two categories: the policy arrangements pointing out the main inputs for the supply chains (customs, infrastructure, and logistics services) and the supply chain performance indicators (timeliness, international shipments, and tracking and tracing).

Although most of the literature comprises micro-level studies, relatively fewer works focus on the logistics performance at the macro-level [1,21,22]. Many of these macro-level studies use the World Bank’s LPI dataset, an important source to assess and compare the logistics performance of the countries [6]. Table 1 summarizes the current literature on logistics performance assessed by LPI. The table lists the studies concerning their scope, data, the method used, and major outcomes.

Among the studies employing LPI in national or global contexts, it is common to investigate the factors affecting logistics performance. Önsel Ekici et al. [25] addressed how national resources could improve the logistics competitiveness of countries. They evaluated the relationship between logistics performance and competitiveness at the national level using Artificial Neural Network (ANN) and Cumulative Belief Degrees (CBDs). They found that the fixed broadband internet infrastructure was the most crucial factor in Turkey’s logistics performance. Using a similar approach, Kabak et al. [27] investigated the relationship between LPI and the competitiveness of a country. They found that the GCI pillars of the World Economic Forum (namely, “Business Sophistication”, “Financial Market Development”, “Infrastructure”, “Good Market Efficiency”, and “Higher Education and Training”) had a significant impact on improving a country’s logistics performance. Using GCI and LPI, Önsel Ekici et al. [22] proposed a framework for policymakers to improve countries’ logistics performance. Integrating tree-augmented naive Bayesian network (BN-TAN), partial least square (PLS), and importance-performance map analysis (IPMA) techniques, they found that countries should focus on digitalization and supply chain analytics to improve their logistics performances. Stojanović and Ivetić [28] investigated the relationship between the Incoterms score (IS) and LPI. Their results showed that LPI significantly impacted the national IS. In one of the recent studies, Göçer et al. [29] proposed a methodological framework using LPI and secondary data that include various online sources, news, and academic reports. They implemented it in Turkey to determine the optimum combination of strategies for improving the country’s overall logistics performance to enhance its competitiveness in world trade.

Efficiency evaluation has been one of the prominent topics in logistics literature. There is an abundance of studies focusing on the measurement of efficiency in different modes of transportation, such as air [30,31,32,33], freight [34,35,36,37], rail [38,39], and intermodal [40,41,42]. DEA has been frequently used to measure the logistics efficiency in the literature [13,14]. Although there are various studies on LPI in the literature, many of them have investigated logistics efficiencies for a specific country and period. In cross-country efficiency assessment using LPI, researchers applied different DEA models to measure the relative efficiency of each decision-making unit representing an individual country, as it allowed multiple inputs and outputs. Depleting this approach, the authors of [23] developed a hybrid logistics and environmental sustainability index.

Yu and Hsiao [24] proposed a meta-frontier DEA model with assurance regions (Meta-DEA–AR) to evaluate the LPI scores. They suggested possible directions for countries to adjust their resources to improve logistics efficiencies. To evaluate and compare the overall performance of green transportation and logistics practices in 112 selected countries, Lu et al. [26] developed an environmental logistics performance index (ELPI) using the DEA approach. Their findings depicted that ELPI and LPI were strongly correlated. Martí et al. [21] proposed a DEA-based approach to benchmark the countries’ logistics performance using LPI and some additional variables, such as income and geographical area. Their findings showed that high-income countries, mainly from the EU, performed the best. Rashidi and Cullinane [1] evaluated the sustainability of OECD nations’ operational logistics performance (SOLP). Their analysis revealed that the relationship between LPI and SOLP is statistically insignificant.

3. Research Methodology

In this study, a DEA model is developed to compare countries’ logistics efficiency and to investigate the factors affecting it. DEA is a prominent mathematical programming-based method initially proposed by Charnes et al. [43] to evaluate the relative efficiency of organizational units. The units, the countries in our case, are usually denoted as the decision-making units (DMUs) in the DEA terminology [44]. DEA has wide-ranging applications due to its advantages over traditional methods. First, DEA enables a peer group comparison among the many similar units by producing a comparable score for each unit and determining efficient and inefficient units. It reveals the sources and the levels of inefficiency for each of its inputs and outputs [45]. It can also handle multiple incomparable inputs and outputs expressed in different units of measurement. Another advantage of DEA over other traditional methods is that there is no need for any assumption about the form of the production function.

3.1. The BCC Model

Different DEA models serve diverse needs under different scenarios. CCR [43] and BCC [46] are commonly referenced models. In this study, we employ the CCR model to calculate the overall technical efficiency for MI and the BCC model to consider the pure technical efficiency assuming variable returns to scale.

The input-oriented DEA models aim to determine how much input is needed to achieve a designated output level for an inefficient organization to become DEA-efficient. In contrast, an output-oriented model seeks to determine the potential output for the given inputs of an inefficient firm to become DEA-efficient. In this study, we employ the output-oriented DEA model to determine how to increase countries’ logistics performance with their current inputs. The study explores which output variables need to be improved for the given inputs to increase logistics performance. An output-oriented DEA model, referred to as BCC in the literature, can be expressed as below for m outputs, n inputs, and k number of countries:

$M a x φ + ε (\sum_{i = 1}^{m}| s_{i}^{-}) + \sum_{j = 1}^{m} s_{j}^{+}$

(1)

$\sum_{r = 1}^{k} x_{i r} λ_{r} + s_{i}^{-} = x_{i o} f o r i = 1,2, \dots, n$

(2)

$\sum_{r = 1}^{k} y_{j r} λ_{r} - s_{j}^{+} = {φ y}_{j o} f o r j = 1,2, \dots, m$

(3)

$λ_{r}, {s_{j}}^{+}, {s_{i}}^{-} \geq 0 f o r a l l i, j, r$

(5)

where $φ$ is efficiency score for country o under investigation; x_io and y_jo are observed values of input i consumed and output j yielded by firm o, respectively; $s_{j}^{+}$ and $s_{i}^{-}$ are the amounts of excess input i and deficit output j for country o; ε > 0 is a predefined non-Archimedean element; λ_r’ s are the dual variables utilized to construct an ideal composite country to dominate country r.

Equation (1) is the objective function that assesses the organization’s efficiency score ( $φ$ ) being evaluated. Equation (2) indicates that the input level for i is a linear combination of the inputs consumed by benchmarked countries and the excess input of i. Equation (3) ensures that the optimal output of j is a linear combination of the outputs generated by benchmarked countries minus its slacks. The organization o is called efficient if $φ$ =1 and $s_{j}^{+}$ = $s_{i}^{-}$ = 0 for all i and j in the optimal solution of the model given in (1)–(5). The efficient country forms the efficiency frontier, a reference set, for country o.

3.2. Malmquist Index

Classical DEA methods measure the efficiency of DMUs for only a single time unit. However, in this research, we utilize the DEA-based Malmquist productivity index proposed by Färe et al. [47] to evaluate the efficiency change in a DMU between two time periods. The Malmquist index is calculated as the product of “Catch-up” and “Frontier-shift” terms. The catch-up term is the ratio of observed DMU efficiency score in period

p_{2}

to the one on period

p_{1}

, regarding period

p_{2}

and

p_{1}

frontiers, respectively. It measures the change in efficiency between periods

p_{1}

and

p_{2}

. On the other side, the frontier-shift term represents the change in the efficient frontiers for the DMU evaluated between periods

{p_{1}}_{}

and

p_{2}

and calculated as the geometric mean of frontier-shift effects for periods

{p_{1}}_{}

and

p_{2}

. The frontier-shift effect in each period is computed by the ratio of observed DMU efficiency score with respect to period

{p_{1}}_{}

and

p_{2}

frontiers, respectively. The resulting output-oriented Malmquist index is given by Färe et al. [47] between periods

{p_{1}}_{}

and

p_{2}

as follows for DMU k:

$M I_{} = {\begin{matrix} \frac{d^{p_{2}} (x_{k}^{p_{2}}, y_{k}^{p_{2}})}{d^{p_{1}} (x_{k}^{p_{1}}, y_{k}^{p_{1}})} \times \end{matrix} {[\frac{d^{p_{1}} (x_{k}^{p_{2}}, y_{k}^{p_{2}})}{d^{p_{1}} (x_{k}^{p_{1}}, y_{k}^{p_{1}})} \times \frac{d^{p_{2}} (x_{k}^{p_{2}}, y_{k}^{p_{2}})}{d^{p_{2}} (x_{k}^{p_{1}}, y_{k}^{p_{1}})}]}^{\frac{1}{2}}}^{}$

(6)

where $d^{p_{1}} (x_{k}^{p_{1}}, y_{k}^{p_{1}})$ and $d^{p_{2}} (x_{k}^{p_{1}}, y_{k}^{p_{1}})$ represent the distance functions of the inputs and outputs in period ${p_{1}}_{}$ to the frontier ${p_{1}}_{} a n d p_{2}$ , respectively. The first component of Equation (6) represents the catch-up index and evaluates the technical efficiency change over two time periods. The second component of Equation (6) represents the frontier-shift index that evaluates the technology change over two time periods. The catch-up index specifies whether a DMU is getting closer to its frontier, and the frontier-shift index shows the frontier change between the two periods. MI > 1 indicates the increase in total product productivity of observed DMU from the period $t_{}$ to $t_{} + 1$ . Similarly, MI = 1 and MI 48].

There are several ways to calculate MI. Färe et al. [47] propose an input and output-oriented DEA model to calculate MI. In order to overcome some inadequacies of radial DEA models, non-radial and slack-based DEA models are proposed [49,50,51]. We utilize the output-oriented DEA model to calculate MI, as Tone (2004) [48] suggested. For each DMU, two LP models are solved to compute the efficiency scores in

{p_{1}}_{}

and

p_{2}

, while two others calculate the inter-temporal efficiencies between periods

{p_{1}}_{}

and

p_{2}

3.3. Measurement of the Input and Output Variables

The Logistics Performance Index (LPI), created by the World Bank, measures the trade logistics performance of countries, pointing out the challenges and opportunities. It also gives insight to governments on how they can improve logistics performance. LPI relies on six indicators to assess the logistics performance, namely, “customs”, “infrastructure”, “logistics competence”, “international shipments”, “tracking and tracing”, and “timeliness”. The World Bank LPI report categorizes international shipments, tracking/tracing, and timeliness as logistics performance outcomes, and customs, infrastructure, and logistics competence as supply chain inputs [6]. Similar to Martí et al. [21], we also consider “customs”, “infrastructure”, and “logistics competence” as input variables and “international shipments”, “tracking/tracing”, and “timeliness” as output variables in DEA analysis. The first indicator in LPI, customs, evaluates the efficiency and simplicity of customs procedures. Efficient customs procedures enhance the trade through reduced delays and costs, as well as streamlined processes. The second indicator, infrastructure, assesses not only the quality of transport and trade infrastructure, but also IT and telecommunications. Robust infrastructure reduces transit times and lowers logistics costs. Logistics competence refers to the quality of logistics services provided by the local providers, such as freight forwarders. International shipments indicator reflects the ease of arranging competitively priced international shipments, indicating the competitiveness and connectivity of the shipping market. Tracking and tracing indicator measures the capability to monitor goods in transit. Higher scores signal advanced tracking technologies that can contribute to supply chain performance and risk mitigation. Lastly, timeliness scores indicate the competence of the domestic logistics industry in terms of reaching a destination without delay [6]. The index provides a single score for each indicator.

3.4. Data and Sample

LPI benchmarks the logistics activities of 160 countries based on a survey with international logistics experts and operators on the ground (freight forwarders and carriers). The index includes qualitative and quantitative questions to analyze the logistics performance of a country [52]. The theoretical background of the index is available from LPI report of the World Bank (Arvis et al. [53]). The inputs and outputs in our models represent the critical elements in achieving a successful logistics operation. LPI assesses these inputs and output in a five-scale metric. The higher score represents a favorable condition for better logistics performance.

While the World Bank published LPI ratings six times between 2007 and 2018, only those published after 2010 use a consistent evaluation methodology that examines all six logistics performance dimensions. Therefore, this study selected 2010 and 2018 to evaluate the drivers for better logistics performance in 148 countries reported in both years.

3.5. Research Framework

The research framework of the study is displayed in Figure 1. Using LPI dataset from 2010 and 2018, the study initially identifies the inputs and outputs from the list of LPI indicators to assess the logistics performance. In fact, LPI input indicators are the variables to augment for higher logistics performance. Therefore, to use LPI input indicators in our DEA models, a monotone decreasing transformation (subtracting the original values from the maximum score of five) is applied to each of the inputs, as suggested by Marti et al. [21]. Under this setting, DEA models aim to minimize the potential improvement possibilities of each modified input variable (equivalently, maximize original input variables). Hence, modified inputs in the DEA model represent the potential improvements, and their slacks point out possible shortcoming areas for enhancement.

The first step of the analysis is the calculation of the output-oriented MI using each DMU’s LPI indicators and investigates its efficiency progress from 2010 to 2018. Along with MI, catch-up and frontier-shift effects are assessed to evaluate the technical efficiency change and technology change, respectively.

In the second step, to split the countries into similar performance groups, the agglomerative clustering method is applied to the LPI dataset. As a linkage to measure distances across clusters, the complete-link criterion is selected to form similarly-sized clusters [54]. The criterion computes cluster proximities based on the distance between the farthest neighbors of each cluster, but it is susceptible to noise and outliers [55]. Therefore, the outliers should be screened using Tukey’s [56] outlier detection method. As a result, the countries are split up into three groups (high-, medium-, and low-performing) for the analysis. To avoid any discrimination problem involving DEA models, the number of countries in each group is selected at least as many as the maximum of m × n and 3(m + n) [57]. This study employs DEA models with three inputs (n) and three outputs (m); hence, the number of countries selected for each group satisfies the recommendation above in each subcategory.

The third step allows us to perform group specific analysis to identify the key improvement areas among the countries having similar logistics performance. For each logistics performance group, an output-oriented BCC-DEA model is developed and run to assess the country’s logistics efficiencies. These efficiency scores are the pure technical efficiencies indicating the conversion performance of inputs into outputs relative to best practices. Lack of knowledge and skill sets, mismanagement and other operational problems may lead to pure technical inefficiencies. The slacks in each logistics performance group indicate the deficiencies of the countries to reach their peer counterparts.

A fair benchmarking of the countries is only possible among those with similar logistics structures as assumed by DEA methodology. Country-level inefficiencies generally originate from managerial shortcomings or structural deficiencies. For each logistics performance group, common managerial shortcomings are analyzed and discussed in the third step. The fourth step investigates the logistics performances of the countries, verifying their structural differences according to group-based characteristics. For DEA, Brockett and Golany [58] and later Sueyoshi and Aoki [59] suggested two slightly different approaches to test efficiency differences for two and many groups of categories, respectively. Both approaches suggest eliminating the managerial/administrative shortcomings from the logistics performance groups. This is only possible by projecting the inefficient countries in each group into their efficiency frontier as if they are fully efficient. After updating the input and output values of each country, a new output-oriented BCC-DEA model is constructed and populated by the participation of all the countries belonging to all performance groups. This newly constructed DEA model consists only of the structural differences among the countries representing the frontiers of their performance groups. After solving the latest model, group-based average efficiency scores of the countries are calculated. These scores are tested using the Kruskal–Wallis rank test to verify whether there is any significant difference among the group efficiency scores [59]. If the test results indicate a significant difference among the performance groups, it is believed that statistically significant differences in the slacks between the logistics performance groups are the drivers of the logistics performance.

5. Conclusions

International trade and efficient logistics operations are important to be competitive in the global markets. Logistics operations have a significant impact on economic welfare and growth, as well. However, necessary logistics infrastructure in terms of main highways, railroads, airways, and ports requires costly investment plans. Therefore, the logistics efficiency of a country is an important issue. This study introduces a new systematic approach to analyzing countries’ relative logistics performance. Relying on the LPI panel data, an output-oriented DEA model is developed to assess the logistics efficiency of the countries. Firstly, the countries are clustered according to their logistics performance and grouped into low-, medium-, and high-performing countries. Then, each country is benchmarked with the countries within the group to understand its strengths and weaknesses. Recognizing the structural differences among the groups, a new DEA model with the participation of all countries is constructed, removing the operational and managerial weaknesses of each one. The results of this new model are used to analyze what drives the logistics efficiency in each group of countries. In this vein, the study is unique in the logistics literature in analyzing the countries to explore their logistics drivers and performance, and assess their efficiency in converting logistics factors into performance. It is also one of the few studies capturing the dynamic nature of logistics using panel data.

The descriptive analysis of LPI reveals that timeliness is the highest performance indicator for all performance groups in both 2010 and 2018. It is noteworthy that the best input indicator for high-performing countries is infrastructure, and the quality of logistics services is the highest for medium- and low-performing countries. However, the quality of logistics services score for high-performing countries is much higher than the rest.

The DEA-based Malmquist productivity index evaluating the efficiency change between 2010 and 2018 confirms a slight increase in logistics efficiencies. Notably, the only exception is the medium-performing country group with a low MI score (0.979), indicating a decrease in its efficiency. However, the MI differences among the country groups are not statistically different. During the same period, the most notable finding is that overall productivity is motivated mainly by the global frontier-shift effect. The catch-up effect indicates lower operational and managerial efficiency, with no difference among the country groups. Only significant differences were observed on the frontier-shift effect, where high-, medium-, and low-performing countries were distinguished according to their adoption of technological changes. Therefore, the technological advancements in the sector drove logistics efficiency between 2010 and 2018, while operational and managerial practices deteriorated. Many recent advancements made newer technologies affordable and accessible for all countries, but each group benefited differently according to their capabilities. On the other side, high-performing countries found that the technological and structural improvements in logistics operations enhance their competitiveness, increase their share of global trade, and reach better economic welfare and growth.

In terms of logistics efficiencies indicating the utilization of the resources to yield a logistics performance, the high-performing country group still has the highest average score. Interestingly, the low-performing country group has a better efficiency score than the medium one. Based on recent developments, the easiness of accessing the newer technologies, the advantage of using the already tested approaches in the market, and taking into consideration the late-mover advantage may result in this conclusion for low-performing countries. On the other side, medium-performing countries seem to face stuck-in-the-middle syndrome. Some of them may invest in newer technologies as high-performing countries but need to be able to yield enough performance out of them in the presence of the other binding factors. The others may not undergo new investments, but their technologies and practices may be old and outdated. Groupwise inefficiencies point out that the most critical improvement suggestion for logistically high-performing countries was to enhance the quality of logistics services in their inputs to obtain better performance on international shipments in their outputs in 2010 and improve their infrastructure and tracking/tracing as the most critical input and output factors in 2018. For logistically medium-performing countries, the customs in their inputs were the most crucial factor in improving the timeliness performance in 2010 and the quality of logistics services and international shipments in 2018. Furthermore, for logistically low-performing countries, the essential improvement area to practice in their inputs was the quality of logistics services to boost their tracking and tracing performance in 2010 and international shipments for outputs in 2018.

Our analysis indicates significant structural differences among the logistics performance-based country groups, even eliminating operational and managerial inefficiencies belonging to each group. As expected, these differences are the leading drivers of logistics efficiencies. For example, high-performing countries have no statistically significant inefficiencies in their inputs and outputs. Thus, they are the leading logistics countries. In contrast, low-performing countries have significant slacks in all dimensions of logistics performance indicators. A groupwise comparison of the differences for each slack is considered to create a difference leading to competitiveness in the global arena. Since there is no statistically significant difference among the country groups, International shipment practices in both 2010 and 2018 and tracking and tracing in 2018 no longer contribute to the competitiveness of the countries, even though they have a potential for improvement. Low-performing countries should improve their structural problems related to customs, infrastructure, quality of logistics services, and timeliness in 2010 and 2018. However, these dimensions are the main drivers for logistics performance in medium- and high-performing countries. Even though the countries in the medium-performing group had some issues in 2010, they recovered them in 2018 and became compatible with the high-performing ones.

LPI published by the World Bank is a valuable dataset for countries’ self-evaluation. It allows the countries to benchmark their logistics performances and efficiencies with their peers. A descriptive analysis of the LPI scores clearly states that timeliness is the most critical performance indicator for all countries, regardless of their logistics performance groups. The decision-makers of each country should do something to improve timeliness continuously. The other two distinctive characteristics of high-performing countries are their infrastructure and the quality of logistics services. Though other countries are doing their best on these factors, they still need to catch up with the countries in the high-performing group. The administrators of these countries should find effective means to enhance their capabilities in these two dimensions. Low-performing group countries have good efficiency scores in their own group. However, their overall logistics performance could be better. As an opportunity, the decision-makers of these countries may follow the tested pathways of high-performing countries to boost their performance. On the other side, there is little difference among the countries in handling international shipment practices. Therefore, any effort to enhance this logistics dimension may be considered an opportunity to create an advantage for any country.

While our study has substantial implications, it also has certain limitations. First, LPI assessed by the World Bank is only available until 2018. In fact, there are many recent global issues, such as the COVID-19 pandemic, regional catastrophes, and concerns about climate change. When the newer LPI index scores are available, evaluating their impact on the logistics performance of country groups may be interesting to explore in a further study. Second, input and output variables admitted to the DEA analysis may be extended with additional technical and social data to enhance the scope of the DEA. This may help clarify the differences among the logistics performance groups in terms of dynamics, competitiveness, or geographical properties of countries and explore the information behind the country’s logistics development direction. DEA models associated with the regression analysis may be developed as a further research direction to incorporate these interactions. Third, other than logistics performance, the countries may be classified according to their income categories and geographical locations to identify the factors impacting the logistics performance. Fourth, logistics performance may be investigated according to the logistics-specific tools and techniques focusing on the country’s economic and continental characteristics. This will provide a guideline for countries to improve their logistics performance.