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The aim of this paper is to identify the impact of logistics performance on consumer satisfaction and store image in the retail context.

The authors conducted a quantitative study with a sample of 201 consumers. The questionnaire is the instrument that was chosen to collect the data. Data processing was carried out using the statistical package for the social science (SPSS). The data analysis was conducted in two phases. The first phase consisted of testing the reliability and validity of the measurement scales. While the second phase of data processing consisted of testing the research hypotheses on the basis of data collected in the field.

The results of this research are as follows: consumer satisfaction positively affects their loyalty to the store. The results also indicate that store image affects the satisfaction of consumers. Indeed, “service quality” is often evaluated as a source of differentiation affecting consumer satisfaction. Concerning the effect of logistics performance on consumer satisfaction, the factor “product availability” was found to be the major factor affecting consumer satisfaction. A lack of logistics performance, in the context of retailing, negatively affects consumer satisfaction. On the other hand, when the consumer gets the right quantities at the right time, this can positively affect his satisfaction.

All studies carried out on this subject have presented an evaluation of the performance measures used in supply chain models. However, the results of these works were different in terms of performance measurement. It is difficult to specify the impact of logistics performance with only two variants (checkout level, and shelf level) in the retail context. Moreover, research related to this field in Morocco remains unexplored. In this context, it is necessary to explore the links between logistics performance, store image and consumer behavioral intentions in the Moroccan retailing context while taking into account three variants of logistics performance, which are: checkout level, shelf level and product disponibility.

This study examines how compliance with government and firm's own policy and reverse logistics practices relate with firm environmental performance.

This study draws on insights from stakeholder theory, and follows a two-phase research approach. The first phase utilized an extended literature review that seeks to provide a qualitative and comprehensive understanding of the research problem. The 2001–2023 data was collected from the Web of Science and Scopus databases, complemented with Google Scholar. The second phase involved an empirical study—adopting a quantitative cross-sectional survey design with a self-administered questionnaire to validate the theoretical conceptualizations deriving from the literature review. The empirical data were collected from 203 food and beverages manufacturing firms in Uganda and analysed using the partial least squares structural equation modelling (PLS-SEM) approach.

The study findings suggest that compliance with government policy positively influences firm environmental performance, both directly, and indirectly through fostering reverse logistics practices, and that the relationship between compliance with government policy and reverse logistics practices is contingent upon compliance with the focal firm's own policy.

The study findings will enhance the theoretical and conceptual development of the ideas that underpin stakeholder theory and applications. The Ugandan government will come up with better mechanisms for enforcing compliance with policy regulating the application of reverse logistics practices. In addition, the study advances the use of multi-method approaches in investigating interesting research aspects requiring in-depth examination. However, considering the fact that the empirical study was conducted in a single country context, and focused on firms more or less from the same sub-sector, the findings of the study might not be generalizable globally.

This study provides useful insights to logistics and supply chain managers involved in reverse logistics activities in food and beverages manufacturing firms. These managers can know how to leverage reverse logistics practices to enhance environmental performance of firms amidst environmental policies in the industry where they operate.

This study contributes to the built body of knowledge in operations, logistics and supply chain management literature; understanding about reverse logistics practices as a mechanism through which compliance with government policy influences environmental performance of firms. The interaction between compliance with government policy and compliance with firm policy is essential in explaining the performance effects of reverse logistics practices. In addition, the study advances the use of multi-method approaches in investigating interesting research aspects requiring in-depth examination. Complementing extended literature review with and empirical research to investigate reverse logistics practices influences on firm environmental performance, and incorporating the role of policy in explaining this relationship should make considerable contribution. Besides, the study highlights important areas for future research.

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 study provides new insights into antecedents and outcomes of reverse logistics practices by examining green customer salience as the driver of reverse logistics practices and examining environmental performance and green firm reputation as the outcomes of reverse logistics practices.

A research model examining the proposed relationships was developed and tested using data from beverage manufacturers in Ghana. The model was analysed using partial least squares structural equation modelling.

This study confirmed that green customer salience drives reverse logistics practices. It was also revealed that reverse logistics directly enhances environmental performance, but not green firm reputation. Additionally, the effect of reverse logistics on green firm reputation was fully mediated through environmental performance.

To the best of the authors’ knowledge, no previous studies have empirically examined the relationship between green customer salience, reverse logistics, environmental performance and green firm reputation.

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.

Reverse logistics services are designed to move goods from their point of consumption to an endpoint to capture value or properly dispose of products and materials. Artificial intelligence (AI)-based reverse logistics will help Micro, Small, and medium Enterprises (MSMEs) adequately recycle and reuse the materials in the firms. This research aims to measure the adoption of AI-based reverse logistics to improve circular economy (CE) performance.

In this study, we proposed ten hypotheses using the theory of natural resource-based view and technology, organizational and environmental framework. Data are collected from 363 Indian MSMEs as they are the backbone of the Indian economy, and there is a need for digital transformation in MSMEs. A structural equation modeling approach is applied to analyze and test the hypothesis.

Nine of the ten proposed hypotheses were accepted, and one was rejected. The results revealed that the relative advantage (RA), trust (TR), top management support (TMS), environmental regulations, industry dynamism (ID), compatibility, technology readiness and government support (GS) positively relate to AI-based reverse logistics adoption. AI-based reverse logistics indicated a positive relationship with CE performance. For mediation analysis, the results revealed that RA, TR, TMS and technological readiness are complementary mediation. Still, GS, ID, organizational flexibility, environmental uncertainty and technical capability have no mediation.

The study contributed to the CE performance and AI-based reverse logistics literature. The study will help managers understand the importance of AI-based reverse logistics for improving the performance of the CE in MSMEs. This study will help firms reduce their carbon footprint and achieve sustainable development goals.

Few studies focused on CE performance, but none measured the adoption of AI-based reverse logistics to enhance MSMEs’ CE performance.

This study aims to empirically analyze how the quality of alternatives influences the commitment level in the relationships between companies in supply chains. It also studies how this ultimately influences the associated satisfaction level and logistics performance.

For this purpose, the authors adopted an extended investment model based on interdependence theory. The authors explored the relationships between logistics outsourcing partners based on the components of the investment model. To examine the correlations between variables, the authors surveyed workers in domestic supply chain–related departments and used a total of 300 valid questionnaires for statistical analysis. The hypotheses were verified through structural equation modeling using SPSS 18.0 and AMOS 18.0.

The quality of alternatives had a positive influence on the commitment level. Additionally, the commitment level had a positive influence on the satisfaction level but did not have a significant influence on logistics performance. Finally, the satisfaction level exhibited a proportional relationship with logistics performance.

First, this study examined the effects of the relationships between supply chain parties and psychological factors on logistics outsourcing. Second, this study applied the investment model to a logistics outsourcing-related topic. Finally, under the COVID-19 and the trade war, this study is significant as it shows that the existing commonly accepted hypotheses can be overturned.

This study differs from other studies in that it applied the investment model to a logistics outsourcing-related topic.

Despite the wide use of quantitative assessment to identify the relationship between green logistics (GL) practices and the sustainability performance (SP) of firms, results of these studies are inconsistent. A lack of theoretical foundation has been cited as a potential reason for these contradictory findings. This study aims to explore the relationship between GL practices and SP qualitatively and to provide a theoretical foundation for this link.

Following a multi-methodology approach, the authors used the grounded theory method (GTM) to investigate perceived relationships through qualitative analysis and adopted the system thinking (ST) approach to identify causal relationships using causal loop diagrams (CLDs).

The authors identified different sustainability practices under three major categories: logistics capabilities, resource-related practices and people-related practices. This analysis showed the relationships among these practices are non-linear. Based on the results, the authors developed three propositions and introduced a theoretical foundation for the relationship between GL practices and SP.

Managerial personnel can use the theoretical foundation provided by this study when making decisions on GL practices adoption. This theoretical foundation suggests applying a holistic approach that can help optimize SP by selecting suitable practices. On the other hand, researchers can use a multi-methodology approach suggested by this study to explore complex social issues.

This study contributes to the knowledge from a methodology perspective as no previous studies have been conducted to identifying the relationship between GL practices and SP by combining GTM and ST approaches. This combination can be extended to build system dynamics models for sustainable logistics impacts bringing novelty to the research field of sustainable logistics.

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 study examines the effect of human and technological resource bundling on the financial and non-financial performance of third-party logistics (3PL) firms in India.

For achieving the research aim, 248 practitioners from India based 3PL firms were surveyed. The relationships between human and technology resources and firm performance were examined using structural equation modelling (SEM).

The results of empirical tests revealed that human and technological resources significantly enhance the performance of the 3PL firm. However, the firm's logistic capabilities related to track and trace, order management and final assembly do not mediate this relationship.

This study contributes by examining resource bundling in India's 3PL industry using empirical data and providing knowledge of the relationship between resources and business performance. It guides managers to consciously develop resource capabilities that influence firm performance.