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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.

This paper aims to discuss the development and use of a green logistics performance index (GLPI) for easy comparison of performance among industries and countries. It uses the survey data collected from the home electronic appliance industry in China and Japan as an example to demonstrate the index development process and compare the performance of green logistics (GL) practices between the two countries using the proposed index.

Two‐sample t‐test and one‐way analysis of variance (ANOVA) were used to analyse the data collected from a questionnaire survey. Principal component analysis (PCA) was employed to derive the weights from the survey data for the GLPI.

The findings reveal that the GLPI derived using PCA is robust and gives similar results as obtained through two‐sample t‐test and ANOVA of the dataset in the comparison of performance among firms and between countries in the study.

This study lends insight into the use of an objectively derived composite index to measure and compare GL performance. To serve mainly as a proof of concept and to enhance response rate in the questionnaire survey, the scope of the study is limited to three major logistics functions in an industry in two countries.

Managers can use the GLPI to benchmark their performance in the respective logistics areas and revise their supply chain strategy accordingly. The proposed index may also assist governments in formulating policies on promoting their GL implementation.

A comprehensive composite index to benchmark GL performance can facilitate and encourage industries to invest in GL. This will help reduce negative impacts of logistics activities on the environment.

Research in GL to date has largely focused on theory and management approach. This paper fills the gap in the literature by empirically comparing GL performance among firms and countries through the use of a composite index. It also contributes to a better understanding of the association between GL performance and firm size as well as the driving factors behind it.

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.

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.

Companies with multiple roles, as both a profit generator and public service provider, have specific characteristics that differentiate them from single-role companies. However, literature suggests that no performance measurement framework exists to fully suit the characteristics of such companies. This study aims to develop a novel performance-measuring framework to manage logistics performance in multiple-role companies.

The framework is developed by combining a literature review and a case study approach as applied to a company with multiple roles. The case study is conducted to identify the characteristics of multiple-role companies, the factors that potentially affect such companies’ logistics performance and criteria for a new framework. The literature review identifies the concepts, performance attributes and indicators from existing frameworks with the potential for incorporation and synthesis in the new framework.

This study identified several characteristics, performance drivers and criteria for a logistics performance-measuring framework for multiple-role companies. Additionally, this study successfully developed a new logistics performance-measuring framework that consists of eight performance management stages and an aggregated index to measure overall logistics performance.

The proposed logistics performance-measuring methodology provides useful guidance for stakeholders of multiple-role companies to plan, organize, execute and evaluate logistics performance.

This research fills a theoretical gap by explaining the characteristics of logistics systems in multiple-role companies, as well as the factors that may affect such companies’ logistics performance. Further, the proposed framework contributes to academia by integrating performance attributes from several available frameworks to manage objective conflicts, dynamism issues and the inconsistent performance evaluations that exist in companies with multiple roles.

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 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.

The paper aims to incorporate the relationship of reverse logistics into the economic, environmental, and social sustainability, known as triple bottom line and developed a framework for reverse logistics performance evaluation.

The performance measures, based on triple bottom line approach, were selected, and fuzzy analytical hierarchy process and extent analysis approach was applied for estimating the weights, global weights of performance measures and hence, the reverse logistics performance index. Reverse logistics performance of three electronic companies were evaluated and compared for the demonstration of the methodology.

The results show that economic performance has highest performance index followed by environmental performance and social performance. “Recapturing value” and “return on investment” from economic, “minimum energy consumption” and “optimum use of raw material” from environmental and “community complaints” and “customer health and safety” from social perspective have higher performance indexes. Over all, “reduced packaging”, “use of recycled material” and “employee benefits” show very poor performance indexes.

The study will provide useful guidance to the academicians and practitioners for evaluating, improving and benchmarking the reverse logistics performance.

The analysis adds to the very few studies on triple bottom line aspects of reverse logistics and its performance evaluation. Also, fuzzy analytical hierarchy process and extent analysis is used first time being an efficient tool to tackle the fuzziness of the data involved in performance evaluation.

The purpose of this paper is to evaluate the logistics performance of intermodal freight transportation, a reasonably innovative methodology, to Thailand.

Fuzzy set techniques are applied to assess the meaning of logistics performance within the decision process of freight operators. The fuzzy‐analytical hierarchy process is applied to identify the relative significance among the attributes in the hierarchy framework levels. Consequently, the fuzzy‐multi‐criteria analysis is used to assess decision makers' perception of the logistics performance via proper assignment of numerical scores. The subjective judgments for hierarchical attributes are transformed into fuzzy degrees of score.

Results show a summary of performance indices for different levels in the hierarchy framework. The results demonstrate that the lack of coordination among modes limit the attractiveness of intermodal system. In addition, the methodology is proposed to examine changing variables regarding modal shift and challenge the truck dominance in Thailand.

The proposed framework is only a starting point for integrating the views of logistics operators in the analytical processes. This study did not include several external social costs such as accidents, congestion, and environmental costs.

The methodology provides an approach lead to the index values for assessment and comparative analysis. The strengths and limitations of each alternative were benchmarked relative to a set of hierarchical attributes. Sensitivity analyses of significant variables are performed to examine the impact on changed costs and services levels. The model can entail use of corresponding parameters to improve a logistics system.

The performance measurement is formulated from the modal choice perspective with a human decision problem. The use of logistics performance base indicators has been linked to the intermodal transport system. This study extends the newly developed methodological framework to evaluate the logistics performance of intermodal transportation.