Revealing patterns of technology and commerce: Evidence from digital competitiveness and logistics performance

Countries will be able to determine the degree of logistics performance input components to provide the logistics output components, and countries will be able to reveal which logistics input components should be carried out to provide output components. This situation enables countries to make sense of their contributions to the global economy and global trade. In this context, the relationship structure between logistics performance input components and logistics performance output components over the values of Logistic Performance Index (LPI) components of the countries in 2018, 2016, 2014, 2012 and 2010 LPI reports has been predicted with Artificial Neural Networks. According to the findings, the predictive values of contribution to the affective structure of the input components were listed as infrastructure, customs and service quality, and the predictive values of contribution to the influence structure of the output components as timeliness, international shipments and tracking and trace. As a result of the findings, it was found that the independent variables included in the analysis have quite high predictive power for dependent variables. Finally, the significance and normalized significance estimation significance values of the independent variables are listed as infrastructure, service quality and customs. In the study, it was observed that the contribution predictions of customs and service quality logistic input components to the structure of affecting the logistic output components were less than the infrastructure component. Therefore, it has been concluded that countries should carry out activities that will provide logistics performance output components of service quality and customs components in order to increase their own logistics performance and to contribute more to the global economy and trade.

Analyzing two-way interaction between the competitiveness and logistics performance of countries

The World Bank Logistics Performance Index (LPI) assesses supply chain efficiency and trade competitiveness globally. This study examined India's logistics performance (2007-2023) and its association with exports. Additionally, this research assessed India's progress against similar economies - Vietnam, Indonesia, and Brazil. Results were estimated using secondary data obtained from the World Bank LPI database and export data by analysing six components: Customs, Infrastructure, International Shipments, Logistics Competence, Tracking and Tracing, and Timeliness. Graphical comparisons and Pearson correlation analysis were used to discuss trends and relationships in logistics performance and exports. The findings suggested that India's LPI improved slightly in recent years. Most of the improvements are attributed to the gains in international shipments and customs performance; however, performance in Infrastructure remains poor. Similarly, Brazil and Vietnam depict modest LPI improvements, while Indonesia shows a minimal change in trends. Among the peer economies, Vietnam leads in logistics performance with the highest ranking LPI and the strongest correlation (0.9) between LPI and exports. While Brazil (0.55) and India (0.43) exhibit a moderate correlation between LPI and exports, Indonesia presents a weak association at 0.16, indicating distinctly different structural characteristics in their logistics environments. The findings indicate that India, Brazil, and Indonesia should evaluate the alignment between their logistics reforms and their export strategy. They must prioritize logistics policy initiatives, such as investment in infrastructure and identifying policy-driven approaches across its peers, like Vietnam's port improvements, upgrades, and improvements in digital tracking and tracing systems.

PurposeThe Logistics Performance Index (LPI), published by the World Bank, is a key measure of national-level logistics performance. It comprises six indicators: customs, infrastructure, international shipments, service quality, timeliness, and tracking and tracing. The objective of this study is to explore temporal dependencies among the six LPI indicators while operationalizing the World Bank’s LPI framework in terms of mapping the input indicators (customs, infrastructure, and service quality) to the outcome indicators (international shipments representing cost, timeliness, and tracking and tracing representing reliability).Design/methodology/approachA Bayesian Belief Network (BBN)-based methodology was adopted to effectively map temporal dependencies among variables in a probabilistic network setting. Using forward and backward propagation features of BBN inferencing, critical variables were also identified. A BBN model was developed using the World Bank’s LPI datasets for 2010, 2012, 2014, 2016, 2018, and 2023, covering the six LPI indicators for 118 countries.FindingsThe prediction accuracy of the model is 88.1%. Strong dependencies are found across the six LPI indicators over time. The forward propagation analysis of the model reveals that “logistics competence and quality” is the most critical input indicator that can influence all three outcome indicators over time. The backward propagation analysis indicates that “customs” is the most critical indicator for improving the performance on the “international shipments” indicator, whereas “logistics competence and quality” can significantly improve the performance on the “timeliness” and “tracking and tracing” indicators. The sensitivity analysis of the model reveals that “logistics competence and quality” and “infrastructure” are the key indicators that can influence the results across the three outcome indicators. These findings provide useful insights to researchers regarding the importance of exploring the temporal modeling of dependencies among the LPI indicators. Moreover, policymakers can use these findings to help their countries target specific input indicators to improve country-level logistics performance.Originality/valueThis paper contributes to the literature on logistics management by exploring the temporal dependencies among the six LPI indicators for 118 countries over the last 14 years. Moreover, this paper proposes and operationalizes a data-driven BBN modeling approach in this unique context.

Linking to compete: Logistics and global competitiveness interaction

Logistics which can be considered as the science of planning, implementing and controlling the physical flow of materials and goods from point of origin to point of use to meet customer`s need at a profit has become very important in recent times that it now plays a vital role in companies’ performance as well as the economies of nations. One of the attempts to measure logistics performance at national level is the Logistics Performance Index (LPI) published by the World Bank Group. In fact this study dubbed ”Benchmark of countries’ performance in Logistics – an approach based on World Bank Open Data” argues that there is a close relationship between global competitiveness level of a country and its logistics performance level. It discusses some types of Logistics and also aims to analyze the logistics competitiveness of some countries from a national competitiveness perspective using World Bank Open Data. This work sought to compare Logistics Performance Index (LPI) of Ghana and its region as well as Hungary and its region. Finally, the work made use of international score card where six key dimensions (thus Customs, Infrastructure, International shipments, logistics competitiveness, Tracking & tracing and Timeliness) are applied to benchmark countries' performance and also display their derived overall LPI index to discuss Logistic Performance of Hungary together with that of Ghana.

Logistics, beyond being a strategically important function for global supply chains, is a sector of considerable size in terms of the global economy. Thus, economy-wide logistics capabilities have a strategic impact at the national level, supporting countries to achieve a global competitive advantage. For this reason, the logistics performances of the countries not only show the success of using their existing logistics capabilities but also provide significant indications about their competitiveness at the global level. Due to this strategic impact, this study aims to deal with the logistics performances of OECD countries from the perspective of competitiveness and to determine the competitiveness-based logistics performance index (CB-LPI). For this purpose, data envelopment analysis has been applied with two different techniques. The input variables are the Global Competitiveness Index, and the output variables are the Logistic Performance Index. In this empirical study, 7 inputs (infrastructure, skills, product market, financial system, information and communication technology adoption, business dynamism, innovation capability) and 6 output variables (Customs, Infrastructure, International shipments, Logistics quality and competence, Tracking and tracing, Timeliness) have been used. This study is handled in a total of 5 periods. These periods are 2010, 2012, 2014, 2016 and 2018. As a result, it was determined that the competitive logistics performances of 8 countries (Australia, Germany, Italy, Mexico, Poland, Portugal, Spain, and Turkey) were at the level of full efficiency in each period in the application of both techniques. In addition, the CB-LPI covering all OECD countries has been established. Based on the scores obtained, country-based suggestions for countries have been developed.

The Logistics Performance Index (LPI) developed by the World Bank is important in evaluating countries’ logistics activities. Moreover, the LPI creates a competitive environment among various countries. It also provides an opportunity for countries to develop their logistics performances based on their geographic positions. This study uses the World Bank’s recently published LPI data of 2018. Based on this data, the logistics performances of 38 OECD member countries are determined by integrating the ENTROPY and WASPAS methods. The result of the calculations conducted using the ENTROPY method shows that in order of importance the logistics performance criteria are infrastructure, customs, logistics quality and competence, tracking and tracing, international shipments, and timeliness, according to their significance. Therefore, the infrastructure criterion is the most important in comparing the logistics performances of the OECD member countries. As a result of the analyzes carried out by the WASPAS method using the criterion weights determined by the ENTROPY method, the logistics performance rankings of the OECD member countries are obtained. According to the ranking results, the first five countries are Germany, Sweden, Denmark, Netherland, and Austria. In addition, the analysis results obtained by the integrated ENTROPY and WASPAS method were compared with the LPI rankings of the sample taken from the World Bank report results. Key Words: logistics, logistics performance index, ENTROPY, WASPAS JEL Classification: C44, L91, R40

In order to compare the countries' logistic operations, the World Bank developed the logistic performance index (LPI). Data for this index are published every two years. This study aims to compare Turkey’s logistic performance with the European Union states by using the LPI of the year 2018 with ENTROPY weighted EDAS method. In this respect, firstly, the importance degree (weights)of the criteria to be used when comparing countries was determined with ENTROPY method. Afterwards, the ranking of the countries according to their logistic performance was made with EDAS method by using criteria weights as determined Furthermore, the rankings of the countries were compared with the logistics performance index. In this study, as a result of the analysis made by ENTROPI method, the ranking of the criteria according to the importance degree is customs, infrastructure, logistics competence, freight tracking and traceability, timing and international shipments. Therefore, the most important criteria for comparing the logistic performance of the countries was found to be customs. As a result of the analyzes performed by EDAS method using criteria weights determined by ENTROPY method, the logistics performance rankings of the countries respectively are Germany, Sweden, Denmark, Netherlands, Austria, Belgium, United Kingdom, Finland, France, Spain, Italy, Norway, Czechia, Luxembourg, Portugal, Poland, Ireland, Hungary, Slovenia, Estonia, Greece, Turkey, Cyprus, Croatia, Lithuania, Bulgaria, Slovakia, Malta and Latvia. When the sequence obtained from the results of the analysis carried out with EDAS method compared logistics performance index rank in the sample, five countries (Netherlands, Denmark, Luxembourg, Lithuania and Turkey) increased their rank, six countries (Belgium, Austria, Portugal, Cyprus, Bulgaria and Slovakia) lower their rank and 19 countries (Germany, Sweden, United Kingdom, Finland, France, Spain, Italy, Norway, Czechia, Poland, Ireland, Hungary, Slovenia, Estonia, Greece, Croatia, Malta and Latvia) maintained their order. While Turkey’s rank was 23 According to the logistics performance index in the sample, as a result of the analysis has increased to 22nd place.

This study investigates the impact of Logistics Performance Index (LPI) indicators on the overall LPI score through comprehensive statistical analyses, including regression and factor analysis, using data from the World Bank. The study covers 105 countries from 2007 to 2023, focusing on six key indicators: customs clearance efficiency, quality of trade transport-related infrastructure, ease of arranging competitively priced shipments, logistics service competence, shipment tracking and tracing ability, and timeliness. These indicators serve as independent variables, while the overall LPI score is the dependent variable. The findings reveal that the most influential factor is logistics service competence, followed by infrastructure quality and tracking and tracing capabilities. Customs clearance efficiency, ease of arranging competitively priced shipments, and timeliness also play crucial roles, though to a lesser extent. The analysis highlights that logistics competence significantly drives improvements in global logistics performance. Moreover, the study reveals regional variations in indicator impacts. Europe and Asia exhibit higher sensitivity to logistics competence and infrastructure, while Africa and South America demonstrate relatively lower but still significant responses. Factor analysis identifies three core dimensions influencing logistics performance: logistics efficiency, infrastructure and customs, and international shipments and tracking. This study underscores the necessity for tailored strategies to enhance logistics performance, emphasizing the prioritization of logistics competence and infrastructure development. Policymakers and businesses are encouraged to adopt targeted investments, streamline customs procedures, enhance tracking technologies, and improve international shipment processes to boost their global LPI rankings and strengthen economic competitiveness. These findings provide valuable insights for developing actionable logistics improvement frameworks on a national and international scale.

Background: This article critically examines the role of trade logistics in the exports of African countries. The performance of the trade logistics of African countries was analysed using the World Bank logistics performance index (LPI) and its components. The study was conducted based on the performance statistics of countries around the world in 2016. Objectives: The aim of this study was to identify African countries’ inefficient trade logistics areas based on LPI components for future improvement; and to investigate the relevance of trade logistics performance of African countries on export values in order to boost the region’s merchandise export share in the global market. Method: The methodological approach employed in this study is a combination of both descriptive and inferential data analysis. The African countries’ logistics performance in international trade was summarised using LPI median values. The effect of the performance of trade logistics on exports was explored based on a gravity model of international trade. For the estimation, the Heckman selection approach was applied to incorporate zero bilateral trades. Results: On average, African countries experience the lowest LPI score, particularly in terms of quality of trade and transport-related infrastructure, and customs and border clearance. A successful improvement in these areas would enhance African countries’ supply chain deliveries including on-time delivery, tracking and tracing and international shipments in cross-border transactions. Conclusion: The evidence from the gravity model estimates of this study suggests that an improvement in any of the LPI components can lead to significant growth in the export of African countries. This could then increase the African merchandise export share in global trade. The gravity model results also show that landlocked countries have lower trade flows than their coastal neighbours.

Logistics plays a vital role in ensuring the competitiveness of countries in the international arena. The Logistics Performance Index, which reflects countries' international competitiveness, shows a country's efficiency in trade. However, the Logistics Performance Index determines the countries' on-the-ground efficiency of trade supply chains or logistics services from the view of worldwide logistics firms' managers. As a result, the robustness of the underlying data is related to individual logistics firms' factors; moreover, in the common literature, the effects of the Logistics Performance Index on countries' individual logistics firms' logistics performance have yet to be researched. This paper aims to analyze the effects of Logistics Performance Index and its subdimensions on the logistics firms' logistics performance. The data is gathered from the World Bank database between 2007-2018 and the International Association of Forwarding and Logistics Service Providers sectoral databank. Variance-based structural equation modeling with regression analyses is used for measurements. Contrary to the joint logistics and supply chain literature, the results show that infrastructure and tracking sub-dimensions of Logistics Performance Index negatively relate to firms' logistics performance in Turkey.

This study analyses the World Bank’s Logistics Performance Index (LPI) to quantify changes in logistics performance before and after COVID-19, using a slack-based measurement (SBM) model of data envelopment analysis (DEA) and the Malmquist Index (MI), which indicates efficiency changes over time. The LPI analysis shows performance gains, but challenges in timeliness and international shipments. The SBM-DEA model indicates efficiency improvements mainly through technological advancements. After the pandemic, the decline in efficiency in customs operations and international shipments signals a need for streamlined procedures and cost reductions. The analysis also shows that the post-pandemic recovery varied by region. This study contributes to the theory by showing how dynamic capabilities and institutional resilience influence logistics adaptability and widen global performance gaps. Informed by the resource-based view and dynamic capabilities theory, it advises policymakers to enhance logistics infrastructure, modernise customs, and reduce costs to tackle post-pandemic challenges in underperforming regions.

Determining criteria weights with genetic algorithms for multi-criteria decision making methods: The case of logistics performance index rankings of European Union countries

<p>The World Bank firstly issued the Logistic Performance Index (LPI) in 2007, which analyzes six different aspects that influence the logistics Performance of different countries. These six indicators are based on two main important Categories. They are: Policy Regulation Areas, indicating key inputs to the supply chain, containing three Indicators: Customs Performance (the efficiency of custom clearance), National Infrastructure Quality and Internal Logistics Service Quality Delivery Performance Results, being the other three indicators: Punctuality of operations, Competitiveness of Prices for International Shipments, and Ease of Tracking and Tracing. At the macro level logistics performance could have a major impact in the country economic performance. In the Logistics performance index 2018,the UAE ranked first in the Arab world and 11th globally, followed by Oman, Saudi Arabia, Bahrain and Kuwait in the 2018 LPI report . while Egypt ranked seventh in the Arab standings, followed by Lebanon, Jordan and Djibouti, with 67, 79, 84 and 90 respectively. Tunisia, the Comoros, Morocco, Algeria, Sudan and Mauritania followed in the rank, while Syria, Yemen, Somalia, Iraq and Libya were placed in last regionally. In 1996 most of the Arabian countries signed a regional trade agreement GAFTA ( Greater Arab Free Trade Area ) to eliminate trade barriers between the Arab countries however the bilateral trade volume between the Arabian countries have been far behind the saturation level , At the macro level its generally accepted that the countries logistics performance has a major impact on the economic performance of the country economic, Few researchers have addressed the effect of the Arabian countries logistics performance in their economic development . The aim of this paper is to analyze the Arabian countries logistics performance to suggest solution that might asses" policy makers in developing the logistics performance of their countries , Also the paper examine the relationship between Arab countries economic performances and how it affect by theirlogistics performance</p>