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Asia has been transformed into a well-regulated dynamic platform for trade and is today world’s fastest-developing economic region. However, the increasing cross-border economic activities create new opportunities for corruption. The purpose of this paper is to assess the impact of corruption on trade facilitation using logistics performance index (LPI). This paper also examines the moderating effect of governance or government effectiveness (GE) on the relationship between corruption and LPI within Asian countries.

A panel of time-series data from year 2007 to 2014 of 26 Asian countries was collected for analysis. Static linear panel models which comprised of pooled ordinary least squares, fixed-effect model and random-effect model were utilised to analyse the panel data.

The findings show that corruption significantly affects LPI and each of the six dimensions in LPI. The results also show that governance or GE has a moderating effect on the relationship between corruption and LPI.

This study benefits Asian governments to gain a better understanding on influences of corruption on trade facilitation and triggering suggestions of a government role in the relationship. Practically, the results could be used as a guideline in improving national LPI. Besides, the findings could be used to support policy decision to modify corruption regulations at the national and regional levels.

This study reveals that the optimistic view of sands in the wheel overcomes the dark side of the grease in the wheel practices. To be corrupt free or less corrupt is a rare and inimitable resource capability that makes nations logistically competitive.

The authors extend the World Bank's Logistics Performance Index (LPI) for 30 additional countries and 13 additional years. The authors develop an inexpensive method for extending survey data when frequent, universal surveys are unavailable. The authors identify groups of country characteristics that influence LPI scores.

Using data from the World Development Indicators—the broadest global dataset of country socioeconomic features—the authors test machine learning algorithms for their ability to predict the LPI. The authors examine importance scores to identify factors that influence LPI scores.

The best performing algorithm produces predictions on unseen data that account for nearly 90% of observed variation, and are accurate to within 6%. It performs twice as well as an OLS model with per capita income as the only predictor. Explanatory factors are business environment, economic structure, finance, environment, human development, and institutional quality.

Machine learning offers a simple, inexpensive way of extending the coverage of survey data. This dataset provides a richer picture of logistics performance around the world. The factors the authors identify as predicting higher LPI scores can help policymakers and practitioners target interventions.

This paper is one of the first applications of machine learning to extend coverage of an index based on an international survey. The authors use the new data to provide the most wide-ranging analysis of logistics performance across countries and over time. The output is an important resource for policymakers tracking performance, and researchers particularly in smaller and lower income countries. The authors also examine a wider range of explanatory factors for LPI scores than previous work.

This paper aims to assess the impact of different drivers of country risk, including business environment, corruption, economic, environmental, financial, health and safety and political risks, on the country-level logistics performance.

This study utilizes three datasets published by reputed international organizations, including the World Bank Group, AM Best and Global Risk Profile, to explore interactions among country risk drivers and the Logistics Performance Index (LPI) in a network setting. The LPI, published by the World Bank Group, is a composite measure of the country-level logistics performance. Using the three datasets, a Bayesian Belief Network (BBN) model is developed to investigate the relative importance of country risk drivers that influence logistics performance.

The results indicate a moderate to a strong correlation among individual risks and between individual risks and the LPI score. The financial risk significantly varies relative to the extreme states of the LPI score, whereas corruption risk and political risk are the most critical factors influencing the LPI score relative to their resilience and vulnerability potential, respectively.

This study has made two unique contributions to the literature on logistics performance assessment. First, to the best of the authors’ knowledge, this is the first study to establish associations between country risk drivers and country-level logistics performance in a probabilistic network setting. Second, a new BBN-based process has been proposed for logistics performance assessment and operationalized to help researchers and practitioners establish the relative importance of risk drivers influencing logistics performance. The key feature of the proposed process is adapting the BBN methodology to logistics performance assessment through the lens of risk analysis.

This paper aims to examine whether some countries achieve logistics efficiency at the cost of undermining environmental quality. In so doing, a hybrid index, the green logistics performance index (GLPI) combining both the LPI and the environmental performance index (EPI), is constructed.

Being a macro analysis measuring the green supply chain efficiency of a country, this paper utilizes the secondary data compiled by the World Bank and the World Economic Forum. A series of simple regression analyses were conducted in order to find out the varying degrees of association between the LPI, the EPI, the GLPI and the national income level.

As active logistics activities can have an impact on carbon footprints such as greenhouse gas emissions, it was found that some countries chose to increase their income level at the expense of the environment degradation. Consequently, the GLPI is suggested as a good indicator of a country's green logistics efficiency, showing what impact the country's logistics competitiveness has on its environment.

This paper is the first attempt to measure the efficiency of the supply chain of a country from a green perspective by proposing the GLPI combining the LPI and the EPI. It is also the first literature in the supply chain management academia to utilize both the LPI and the EPI.

The purpose of this paper is to analyze the impact of logistics performance on the carbon (CO2) emissions of Balkan countries.

Fixed-effects panel regression analysis is used to estimate the causal relationship between CO2 emissions and logistic performances of Balkan countries. Logistics performance is measured by logistics performance index (LPI) which was published by the World Bank in 2007, 2010, 2012, 2014 and 2016 and used for ranking countries by means of their logistics performance. LPI is based on six main indicators: customs procedures, logistics costs and the quality of the infrastructure for overland and maritime transport. As a measure of carbon emissions of sampled countries, the natural logarithm of carbon dioxide emission per capita is used in this study.

The results obtained reveal that there is a positive and significant relationship between logistics performance and CO2 performances of the sampled Balkan countries.

This study is based on only 11 Balkan countries. In this sense, the data used in the analysis is limited.

Considering the important geostrategic position of the Balkan region, logistics sector has an important role for the development of the countries in that region. In this sense, the findings of this study may provide useful insights for policymakers to achieve sustainable economic development. Furthermore, as far as the authors know, this is the first study that focuses on the relationship between logistics performance and carbon emissions of Balkan countries.

The purpose of this paper is to develop and test theory-driven hypotheses on the influence of corruption and gender inequality on logistics performance.

This paper develops hypotheses based on a review of the literature and theory linking corruption, gender inequality and logistics performance. Testing the hypotheses draws on the following secondary data sources: the World Bank Logistics Performance Index, Transparency International’s Corruption Perceptions Index and the United Nations Development Programme Gender Inequality Index. Regression analysis is used to test the hypotheses.

A significant direct effect is evident between corruption perceptions and perceived logistics performance. Corruption is detrimental to logistics. Further, there is evidence of an indirect effect, via gender inequality. Gender inequality is also linked directly to lower logistics performance. Gross domestic product/capita enters the analysis as a control variable.

While the analysis uses secondary data, sources are credible and their methods – while not perfect – are logical and appear to be reasonable. It is possible that excluded variables could further explain the relationships under study. This implies future research opportunities, perhaps involving case studies of specific nations.

The results should inspire businesses, non-governmental organizations and governments to invest in, aid, advocate for and legislate toward greater gender equality – and against corruption. Logistics educators have an important role in disseminating this message.

Gender inequality and corruption are current, global social issues. Moving forward toward equality and away from corruption are the right moves. Such moves appear to also yield better logistics.

This paper is among the first linking corruption and gender inequality to logistics performance. It shows how social issues impact logistics performance at a national level.

Logistics sector is recognized as one of the core enablers of the economic development of a nation. However, inefficiency in logistics operations impedes the achievement of intended targets by increasing the cost of doing business. Also, it is difficult to improve the efficiency of a country’s logistics operations without a metric for evaluating and understanding logistics capabilities and efficiency. Therefore, the present study has developed In-country Logistics Performance Index (ILP Index) to propose a benchmarking tool to measure the in-country logistics competitiveness, particularly in the setting of emerging economies, i.e. India.

This study has developed a unified index using principal component analysis and quintile approach. In addition, the proposed index relies on several dimensions that are developed and illustrated using quantitative secondary panel data.

The findings of this study reveal that the quality of infrastructure, economy, and telecommunications are the three most important dimensions that may significantly support the growth of the transportation and logistics sector. The results reveal that Gujarat, Tamil Nadu, and Maharashtra are the top performers whereas, Bihar, Jharkhand, and Jammu and Kashmir scores the least due to the insufficient logistics infrastructure as compared to other Indian states.

Given the extensive focus on international-level logistics index (like World Bank’s LPI) in the existing literature, this study intends to develop in-country logistics index to evaluate the logistics capabilities at the regional and state level. In addition, unlike prior studies, this study utilizes quantitative secondary data to eliminate cognitive and opinion bias. Moreover, this benchmarking tool would assist decision-makers in idealizing standard practices toward sustainable logistics operations. Additionally, the ILP index could serve the international investors in crucial decision-making, as it provides valuable insights into a country’s logistics readiness, influencing their investment choices and trade preferences. Finally, the proposed approach is adaptable to measuring the overall performance of any other industry/economy.

The purpose of this paper is to develop and test theory-driven hypothesis on trade costs’ effect of logistics performance (LP) and bureaucratic efficiency, primarily from SAARC (South Asian Association for Regional Cooperation) perspective.

The paper develops hypothesis based on the review of the literature and theory linking LP, trade costs and institutions. The authors test the hypothesis using secondary data sources: World Bank-UNESCAP trade costs database, World Bank Logistics Performance Index (LPI) and Political Risk Service's Political Risk Rating. Fixed-effect approach is used to test the hypothesis.

The influential role of bureaucratic quality on relationship between LPI and South Asian trade costs (inter-SAARC and intra-SAARC) is evident. The results also point out that bureaucratic quality also conditions the effect of different dimensions of LPI on South Asian trade costs. Further, it is found that bureaucratic inefficiency mitigates the effects of LPI on South Asia's trade costs with its proximate trading partners APEC (Asia–Pacific Economic Cooperation) and ASEAN (Association of Southeast Asia Nations).

The analysis is conducted using short span of data. With the availability of long span of data, the understanding of the relationship studies in this paper will improve.

The results suggests policymakers to improve bureaucratic efficiency for utilizing the full potential effect of LPI in deceasing trade costs. The study inspires businesses to act and advocate in favor of reforms in governance system.

This paper is among the first, which investigates the possibility that the relationship between LPI and trade costs depends on the bureaucratic efficiency. It provides a more detailed description of the LPI-trade costs relationship.

Oil exporters have been identified as having problems in the friendliness and performance of their logistics sector, particularly in general cargo group. The purpose of this research is to identify, through proposed data envelopment analysis (DEA) models, those oil exporters with most potential to develop (who have resources for this through their economic prosperity). Benchmarks of improvement are not only identified among top oil exporters, but also from larger group of emerging countries.

The research uses two different DEA models, and quantitative data available from economic prosperity and general cargo logistics performance. Models are input oriented, and use the most recent data from year 2009.

Major oil exporters are not homogeneous group in their performance turning logistics competence on prosperity. Actually, there could be one group of very low performance identified in general cargo logistics performance as compared to the DEA models, and other group with similar or slightly above performance with general cargo handling. Those performing at the lower end, and with most development potential, include such countries as Russia, Venezuela, Algeria, Qatar, Azerbaijan and Turkmenistan (possibly also including Iran, Kuwait and UAE). From the group of major oil exporters, these countries should learn exceptional logistics competence from Malaysia and Canada. Similarly, this research shows that the emerging economies, particularly China, but also India as well as Philippines, Thailand and South Africa are useful benchmarks to develop general cargo logistics performance further (to some extent Korea (South), Taiwan, Czech Republic, Poland, Turkey and Lebanon also could be included).

The research is not based on longitudinal data, and should be enlarged to take into account earlier logistics performance index ratings from the World Bank studies (year 2007); this not only to verify the results, but also to highlight possible progress in the development of the logistics sector. Also, other global logistics performance ratings (e.g. from infrastructure) should be taken into account, and DEA models be developed further (often infrastructure drives other layers of performance).

Research work is seminal to study in respect to identifying which countries “lower performing oil producers” include, and which have potential in better developing their logistics sector (general cargo). Among this, research also proposes benchmarks for these countries, not only among oil exporters, but also from other emerging economies. The research findings give a unique and fresh perspective on the logistics performance of oil exporters and emerging economies in general.

This paper aims to examine the relationship between environmental factors, risk perception and decision-making in risk management. Specifically, using attribution theory, the authors study the influence of macro-level logistical capabilities of a host country on a firm’s actual and perceived supply chain risk, and examine if this country-level factor and the firm level perception of risk affect a firm’s decision-making in risk management.

This study uses a combination of primary data from 932 manufacturing firms from 22 countries and secondary data from the logistics performance index (LPI), and empirically tests the conceptual framework using partial least squares structural equation modeling.

Key results reveal that a country’s logistical capabilities, measured using LPI, have a significant impact on managers’ risk perception. Firms located in countries with high LPI perceive lower risk in their supply chain both in the upstream and downstream, and therefore do not invest much in external integration, compared to firms in low LPI countries, and hence are exposed to high risk.

This is one of the first empirical studies linking a country’s logistical capabilities with supply chain risk perceptions, objective supply chain risk and supply chain risk management efforts of a firm using the International Manufacturing Strategy Survey database.