Search results

This study aimed to examine how knowledge sharing, knowledge management, supply chain efficiency and integration collectively impacted firm performance. Additionally, it investigated the moderating influence of reverse logistics on these relationships, seeking to enhance understanding of the complex dynamics within organizations.

A comprehensive method was used in the research design, combining a thorough evaluation of the body of literature with organized questionnaire data collection. Random sampling was used to collect data from Pakistani manufacturing companies, and PLS-SEM was used to analyze the collected data.

The findings demonstrated the strong positive relationships between knowledge management, integration, supply chain effectiveness, and information sharing and business performance. The study also showed that reverse logistics improved and moderated these correlations, highlighting the significance of managing reverse logistics well for the best possible company performance.

In terms of practical implications, the study offered organizations looking to improve performance useful information. Making informed strategic decisions was made possible by realizing the benefits of knowledge management, integration, supply chain efficiency, and sharing. The relevance of using successful tactics to maximize company outcomes was highlighted by highlighting the moderating effects of reverse logistics.

By thoroughly analyzing the connections between knowledge management, supply chain effectiveness, integration, and firm performance—while taking into account the moderating influence of reverse logistics—this study enhanced the body of existing literature. The discoveries significantly added value to this research topic by enhancing our understanding of how these elements collectively influence business performance, especially in the sometimes disregarded field of reverse logistics.

The 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).

A 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.

The 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.

This 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.

Significant unexplored research gaps exist in relation to assessing how governments influence innovations in the logistics and supply chains of SMEs to mitigate risks. This study emphasizes the impacts of regulatory coercion and government subsidization on logistics and supply chain innovations and the corresponding effect of logistics and supply chain innovations on financial performance, logistics and supply chain robustness, green competitiveness, social and environmental responsibilities.

Using a quantitative approach, partial least square structural equation modeling and a survey research design, data were collected and analyzed on 210 logistics and manufacturing firms.

The results support the fundamentals of the stakeholder theory and natural resources-based view (NRBV) regarding the positive impacts of regulatory coercion and government subsidization on logistics and supply chain innovations. Furthermore, logistics and supply chain innovations significantly influenced firm performance (financial performance, logistics and supply chain robustness and green competitiveness) and societal impact (social and environmental responsibilities). Particularly, while logistics and supply chain innovations had insignificant influence on social and environmental responsibilities, the effects of logistics and supply chain robustness were significant.

The study presents empirical findings on the impact of government influences on logistics and supply chain management and the corresponding implications for firms and society. Thus, this study contributes to corporate social responsibility (CSR) and logistics and supply chain literature and provides guidance for policymakers, industry players, scholars and practitioners.

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.

The purpose of the paper is to determine the importance of trade logistics in export intensification and competitiveness on the level of individual companies. Within the framework of logistics performance, the influences of customs efficiency and physical trade infrastructure quality were tested on export while the contribution to improving competitiveness was analyzed through logistics services and the Logistics 4.0 component. In addition, the influence of competitiveness on companies’ exports was examined.

The research was conducted based on a survey intended for the management of export-oriented companies which was distributed through the system of the Chamber of Commerce and Industry of Serbia. A total of 298 responses were collected and provided the basis for the application of structural equation modeling (SEM) approach.

The physical trade infrastructure was found to be a significant factor in stimulating exports, while the quality of logistics services has a statistically significant impact on the level of competitiveness. The results also indicate that improving the competitiveness of companies gives positive results in stimulating their exports.

Studies that analyze the impact of trade logistics using primary data from surveys are rare. In this paper, for the first time, the SEM methodology was applied in the assessment of logistics performance effects on export and competitiveness of individual companies. Also, the impact of the logistics 4.0 concept on selected companies’ performance was empirically tested for the first time.

Using the lens of the natural resource-based view (NRBV) theory, this study investigates the effect of green supply chain management (GSCM) practices such as green manufacturing (GM), eco-design (ED), green purchasing (GP) and investment recovery (IR) on the carbon-neutral supply chain (CNSC) performance of firms through the mediating influence of logistics eco-centricity (LE).

A conceptual framework that hypothesizes the relationship between GSCM practices, LE and the CNSC performance of firms is developed. Key GSCM practices are then identified using experts’ opinions. Furthermore, we collected responses from logistics companies to validate the conceptual framework using the partial least squares structural equation modeling (PLS-SEM) method.

Through this study, we found that GSCM practices significantly improve a firm's CNSC performance, and the relationships between GSCM practices and CNSC performance are positively mediated by LE.

The implications of the study suggest that logistics managers can benefit from the findings of this study to comprehend the impact of various GSCM techniques on LE and CNSC from the viewpoint of the NRBV paradigm.

This research provides valuable perspectives for managers and supply chain (SC) practitioners in their quest for sustainable and environmentally responsible SC operations through an extensive and novel analysis of the connection between GSCM practices, LE and CNSC performance.

The cyber-physical system (CPS) is a well proven technology for improving system performance, resilience, and interconnectivity. In emerging nations like India, the logistics industry lacks practices connecting logistical equipment with cyberspace. This paper aims to bridge this gap by identifying and evaluating the performance metrics of connectivity solutions. Its goal is to establish an appropriate infrastructure that enables seamless connectivity within the CPS-enabled logistics ecosystem.

A novel integrated decision method is employed to classify the optimal connectivity solution for CPS. It integrates Regret Theory (RT) and Preference Ranking for Organization Method for Enrichment Evaluation (PROMETHEE-1) method in a Hesitant Fuzzy (HF) environment. This method considers the psychological traits of decision-makers and effectively incorporates their hesitancy for the classification.

The findings highlight security (c10) as the foremost critical performance metric, followed by cost (c6), scalability (c9), traceability (c2) and trustworthiness (c1) to build connective infrastructure for CPS. For extensive coverage scenarios, like freight transportation, cellular connectivity (a2) emerges as the most suitable connectivity solution.

This study provides a roadmap to logistics managers for selecting a suitable connectivity infrastructure to enhance seamless connectivity in logistics operations and processes. Technology providers can utilize the findings to develop the CPS infrastructure for effective freight logistics management.

This research introduces a novel decision-making tool for making choices related to advanced technology assessment. It holds significant value in facilitating well-informed decisions in the digital transformation era.

This study aims to empirically connect green logistics performance, renewable energy, non-renewable energy, CO₂ emissions and gross domestic product in Vietnam from 2000 to 2022.

Within this study, the author uses innovative tools, specifically a R2 decomposed linkage method, to scrutinize the connections between green logistics, environmental issues and the use of green and dirty energy.

The results highlight the two-way relationship between green logistics and energy security in Vietnam. Green logistics plays various roles in diverse periods, from a net shock transmitter to a net shock receiver in the designed system. Using a dynamic and contemporaneous dynamic linkage method, this study emphasizes the change in the role and the dominance of green logistics and renewable energy consumption. Notably, the unexpected shocks also lead to changes in these variables’ roles.

This paper presents two significant contributions to the existing body of literature. Firstly, as previously emphasized, this research marks a pioneering effort to examine the connection between green logistics, environmental issues and the use of green and dirty energy when it comes to developing nations such as Vietnam. Secondly, this research introduces a novel approach to investigating the interconnectedness of volatility across diverse markets, offering a more suitable method for such analyses. Within this study, the author uses innovative tools, specifically an R2 decomposed linkage method, to scrutinize the connections between green logistics, environmental issues and the use of green and dirty energy. In this analysis, the author examined data from 2000 through 2022. A thorough analysis is presented using the data, exploring the connections between the volatilities resulting from various problems in Vietnam.