The Relationship Between Urban Logistics and Village Logistics Performance Factor – An Application in Libya

1. Introduction

Logistics is a significant part of manufacturing and distribution, where there are many factors that are affecting the logistics and its infrastructure (Viera, Yoshizaki, & Ho, 2015). However, the differences between the logistics offered in different parts of a country are apparent, in terms of infrastructure and supporting services that are required for a seamless operation (Küçük, 2014). This research applies the indicators of logistics performance to Libya with evaluation depending on the location of the logistical operation. Due to the differences in infrastructure and services, it is expected to observe differences between the results, as well as a relationship between logistics performance in urban and village areas. The problem addressed in this study is the relationship between the logistics in the urban and suburban areas and the connection between the infrastructure provided in both regions (Rodrigue & Behrends, 2018). The aim of this research is to study the relationship between urban logistics and village logistics through the Libyan case.

2. Theoretical Framework

Logistics and its infrastructure are considered essential for the economic growth and the continuation of human activities around the globe (Kherbach & Mocan, 2016). Mobilizing products, equipment and materials is a daily activity that continues around the clock, which is needed for businesses to continue their operations and for the population to find their necessities in an accessible manner (Benjelloun & Crainic, 2009). The availability and continuity of logistical operations are essential for business growth and performance. Therefore, enhancements in logistics are always expected to improve performance of the organization (Graeml & Peinado, 2011).

Jia et al. (2022) study on coordinated development between urban-rural logistics systems and integrated urban-rural development in China. The authors construct a coupling coordination model to measure the synergy of the logistics system between regions using panel data and spatial econometrics; infrastructure investment, government policy support, and industrial integration are found to be the major determinants of coupling levels. The study is significant in demonstrating that the asymmetry in development between urban and rural logistics can be quantitatively measured and promoting the balance of logistical infrastructure as a key contributor to regional economic integration. For Libya, this equates to a methodological basis to measure the performance gap between urban and village logistics and to guide policy formulation to counteract spatial inequality.

The Indonesian case study by Farah et al. (2024) looks into the policy integration in urban-rural logistics by way of assessing the National Logistics System (Sislognas). Findings show fragmented policy implementation between the central and local governments that create bottlenecks for logistics performance between urban and peripheral areas, by way of qualitative document analysis and stakeholder interviews. They are recommending better inter-institutional coordination, regional adaptation of national policies, and investment in rural logistics nodes. This is relevant to Libya where there exists a degree of fragmentation in authority and infrastructural disparity as well. The study stresses institutional alignment and multilevel governance as central to urban-rural logistics integration, although not yet addressing the infrastructure itself.

He et al. (2022) study explores ways by which smart logistics and data-driven transport systems influence urban logistics efficiency from the perspective of business analytics. It employs big data techniques and predictive modeling to assist in optimizations that include fleet management, traffic congestion, and keeping the urban deliveries very precise. While the study focuses more on urban systems, it shows that digital transformation has the potential to reduce urban congestion and indirectly ease rural logistics through urban throughput management. Hence, for Libyan cities facing inefficiencies and informal logistics networks as a result, considering the implementation of smart logistics would be an apt suggestion for improving links between core and peripheral networks of logistics.

Henriquez Urena (2024) conducts a location analysis for urban logistics hubs in Barcelona through the application of geographic information systems (GIS) and multicriteria decision-making tools. The research identifies the optimum sites for logistics centers based on accessibility, compatibility with land use, and environmental impact. Though site-specific to Barcelona, the methods are highly transferable to other contexts. The relevance for Libya lies in offering guidance for evidence-based spatial planning of logistics infrastructure, mainly in the design of nodes that support the flow between urban cores and surrounding rural areas to the higher-level coordination of system performance.

Apostolopoulos & Kasselouris (2022) article studies how transport pooling and shared logistics infrastructure could improve urban logistics efficiency, applying the case of the Thriasio Logistics Centre in Greece. By means of empirical data and scenario analysis, the study concludes that transport pooling and collaborative logistics reduce considerably costs, emissions, and delivery times. The study presents a cooperative logistics concept that could be applicable to a Libyan setting, where both urban and rural logistics suffer from very high operational costs and lack of adequate infrastructure. Shared logistics centers would be an asset in pooling flows originating in urban and rural regions for their better performance.

Küçük (2017) defines urban logistics as the concept developed to evaluate the infrastructure and other facilities of the settlement, revealing its strengths, opportunities, weaknesses and threats in terms of logistics performance factors, such as loading, unloading, storage capacity and transportation facilities of cities. Moreover, the author defines suburban/ village logistics as the suburban regions hosting logistics around the major cities with transportation alternatives, loading, unloading, handling, financing, insurance services and transit operations. The urban and suburban logistics work together through logistic systems that can be designed through the settlement plan(Liberatore & Miller, 2016).

Ay and Yeşilyurt (2017) have researched the relationship between logistics performance and disaster intervention performance. The findings of the research showed that the two variables are positively and strongly correlated with a coefficient of 0.776, significant at the 0.05 level. Bozma and Başar (2017) studied the logistics performance and economic growth in ten countries, including Germany, Luxemburg, Sweden, Netherlands, Singapore, Belgium, Australia, England, China and the United States. The aim of the research is to study the impact of logistic performance on economic growth in these countries through a mathematical model. The results show that there is an evident positive impact of logistic performance index (LPI) on the economic growth of the country.

Yeşilyurt (2019) carried out research on companies operating in Kastamonu industrial zone in order to study the relationship between urban logistics, distribution logistics and firm performance. The study adopted the LPI scale developed by Küçük (2017) with 19 indicators and all indicators scored above 0.4 in the factor analysis. The correlational analysis shows that there is a positive correlation between the three variables. Medium to strong correlations were found between them.

There are different factors that have been identified to affect the logistic performance. Hwang, Hong and Lee (2017) identified five critical factors that impact the logistic performance based on a comparison between Japan, China and South Korea, which are:

•        Industrial policy priorities

•        Strategic infrastructure development

•        Public-private logistics market growth

•        Communication network configurations

•        Logistics performance of the firms

Green Jr, Whitten and Inman (2008) studied the impact of logistics performance on the performance of the organization with the supply chain management system through more than 1400 companies in the United States. The study model hypothesized the impact of supply chain management system (SCMS) on logistics performance, marketing performance and financial performance. Moreover, the research studies the impact of logistics performance on marketing performance and financial performance. The results of the research showed correlations with weak nature between all the variables and a strong correlation between financial performance and marketing performance. The conclusion of the research emphasized that there is a positive relationship between logistics performance and organizational performance in manufacturing companies.

Helmy, et al. (2018) studied the effect of logistics performance on three aspects of competitive advantage; cost, differentiation and focus, within the Egyptian market by collecting 460 questionnaires from customers. Six aspects of logistics performance were tested, which are customs, infrastructure, shipment, competence, tracking and timeliness. The correlational analysis showed that cost is negatively correlated to customs, while positively correlated to infrastructure, shipment, tracking and timeliness. Differentiation was found to be positively correlated to infrastructure, shipment and timeliness, while focus was found to be positively and strongly correlated to shipment. Through the regression analysis, R square values of 0.412, 0.18 and 0.517 were found for logistics performance impact on cost, differentiation and focus, respectively.

3. The Methodology

A subjective method is used in this research through a questionnaire for people who work in different manufacturing and logistical sectors in Libya. The sampling methods that are used to collect the data are both random and non-random, as the participants were required to be working in a sector that benefits from the logistic infrastructure in Libya. As far as the previous condition was satisfied, the participants were chosen randomly from the population pool. Furthermore, it is important to mention the tools and scales that were used to collect the data. The tools that are used for data collection need to be reliable and tested through many studies (Küçük, 2016, pp. 68-81). The scale used in the study is based on research developed by Küçük (2017).

Each of the indicators of the two concepts was tested on a 5-point Likert scale. The scale ranged from strongly agree (5) to strongly disagree (1). The sampling technique used in collecting the data was random from the users of the logistics infrastructure in Libya. Furthermore, the sample size has to reach a minimum of 382 participants in order to achieve a reliability of 95% (Küçük, 2016, pp. 93-98). Therefore, the sample quota is determined to be 200 for this study (Küçük, 2014), where physical questionnaires were distributed and collected from each participant. Such a sample is expected to achieve the required reliability level from the research. After the questionnaire implementation, a total of 250 questionnaires were distributed and a total of 192 questionnaires were completed and received back.

3.1 Research Model

The geographical dimension of logistics is crucial in the efficiency of logistic performance, where it mainly depends on the transportation network at disposal within a certain geographic region. The concept of logistics revolutionized in the 1990s in order to optimize the systems that deal with the assets, services and infrastructure that affect logistics and subsequently the costs and organizational performance (Hesse & Rodrigue, 2004). Another concept emerged in the recent years, which is the study of the freight and logistics landscape. The concept addresses the estimation of the logistical capacity according to population density in a specific region (Rodrigue, Dablanc & Giuliano, 2017). Küçük (2017) distinguished between the logistics performances of the urban and suburban regions through key indicators addressing the several aspects of logistics. Through nineteen indicators, it is possible to assess the logistic performance based on the loading, unloading and storage capacities available in a region. Moreover, the logistics performance is assessed through the available infrastructure for operations, as well as the supporting services, such as insurance. As shown in FIGURE 1, two concepts are included in the evaluation of this research; Urban logistics and village logistics, based on the classification of Küçük (2017). The statistical analysis tests the relationship between the two variables.

Figure 1: Research Model

3.2 Hypothesis

The scale to measure logistic performance was developed by Küçük (2017) aiming to include the majority of the infrastructure and supporting services within the scale. The scale consisted of 19 indicators that address the different needs for an efficient logistical operation. The difference between the logistics of the urban and suburban areas is hinted at in the literature. However, Rodrigue and Behrends (2018) strongly suggest that there is a relationship between the logistics in the urban and the suburban areas, as the cities have gotten more crowded, which caused traffic restricts and regulations. The authors show that a relationship is emerging between the logistics in both regions in order to find solutions to many issues suffered in densely populated areas.

H1: There is a relationship between village logistics and urban logistics based on the case of Libya.

4. Data Analysis and Result

The demographics of the collected data, Table 1, shows that 50% of the participants work in the electronics supplies sector, followed by the construction sector 18.8% and fabric and clothing sector at 15.6%. The participants were also asked to indicate the number of employees in their organization, as well as their education level. The reliability of the sample was tested through Cronbach’s Alpha. As shown in Table 2, the reliability analysis was run on SPSS statistics, where the overall alpha was calculated as 0.968, with 0.947 alpha for village logistics and 0.952 alpha for urban logistics.

Table 1: Demographics (n=192)

Table 2: Cronbach’s Alpha for reliability (n=192)

In order to test the impact of the sample demographics on the results of the research, an ANOVA testing was conducted for sector, company size and education level impacts, as shown in Table 3, Table 4 and Table 5. Both sector and education level had no influence on the results to the p<0.05 level. However, there is a significant difference in urban logistics based on company size.

Table 3: Impact of sector on analysis using One-way ANOVA testing

Table 4: Impact of company size on analysis using One-way ANOVA testing

Table 5: Impact of education level on analysis using One-way ANOVA testing

The researcher conducted a factor analysis for the indicators of village logistics, where the KMO factor is 0.727 and an eigen value greater than 1, which is considered reliable. Indicators with factor loadings above 0.4 are included in the analysis (Küçük, 2016, pp. 227-232). This included all indicators, as shown in Table 6. Factor analysis is validated through the total variance explained to be above 60% (Küçük, 2014). In this case, the total variance explained is 70.159%, which satisfy this condition.

Table 6: Factor analysis for village logistics (n=192)

The researcher conducted a factor analysis for the indicators of urban logistics, where the KMO factor is 0.735 and an eigen value greater than 1, which is considered reliable. Indicators with factor loadings above 0.4 are included in the analysis (Küçük, 2016, pp. 227-232). This included all 19 factors, as shown in Table 7. Factor analysis is validated through the total variance explained to be above 60% (Küçük, 2014). In this case, the total variance explained is 70.693%, which satisfy this condition.

Table 7: Factor analysis for urban logistics (n=192)

Table 8 shows the correlational analysis between village logistics and urban logistics. The results indicate a positive correlation between village logistics and urban logistics with a strong coefficient of 0.752. The results are significant to 0.000 level, which is less than 0.05(Küçük, 2016, p. 250). Based on the results of the analysis the first hypothesis stating “H1: There is a relationship between village logistics and urban logistics based on the case of Libya.” is accepted.

 Table 8: Correlation between service quality and unlimited improvement

5. Discussion

The findings show that there is a strong relationship between urban and village logistic performance. The correlation is positive between the two variables and supported by a strong coefficient of 0.752 significant at the 0.01 level.

This main result support the suggestions made by Küçük (2017) and Rodrigue and Behrends (2018) that the new trend and challenges in logistics are pushing for an integrated role by the two regions. The only characteristic that was found influential on logistic performance is the company size. Such a result confirms the findings of Yeşilyurt (2019), which have proven the same result through statistical evidence. The scale developed by Küçük (2017) was tested through a factor analysis. The 19 indicators were found positively correlated with each other in both urban and suburban cases. The evaluation of urban and suburban logistic performances yielded close results, which indicates that through technological advances and provided facilities, the logistics operations are becoming more integral rather than differentiated.

6. Conclusion and Recommendations

The logistic performance is a key indicator of the economic activity efficiency and capability of the country. Thus, it is necessary to understand the factors and indicators that can facilitate better development, implementation and evaluation. The logistic performance is proven to enhance economic growth on the macro level, as well as the organizational performance on the micro level. Through the results of the Libyan case, it is observed that both urban and suburban logistic performances had mean values of 2.4, which is considered below the average of a 5-point Likert scale. Therefore, the researcher provides the following recommendations:

•    Focusing on developing the rail infrastructure in Libya.

•    Reinforcing the economic infrastructure through political stability and security as first step.

•    Enhancing other infrastructures, such as roads, seaports and airports in terms of capacity and efficiency.

•    Supporting a governmental entity that study the logistical issues of Libya and provides recommendations and development projects.

Through implementing the logistic performance scale provided by Küçük (2017), it is shown in the research the reliability of the scale and its ability to reflect the key issues and enhancement opportunities for logistic operations and performance. It is recommended for future research to apply this scale on other cases for comparison and enhancement. The final finding of the research is the string positive relationship between urban and village logistic performances and the effect of company size on logistic performance.

Author Contributions: Conceptualization, Z.S.; Methodology, Z.S.; Software, Z.S.; Formal Analysis, Z.S.; Resources, Z.S.; Writing – Original Draft Preparation, Z.S.; Writing – Review & Editing, Z.S.”

Funding: This research received no external funding

Conflicts of Interest: The authors declare no conflict of interest.

Informed Consent Statement/Ethics approval: All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee."

Declaration of Generative AI and AI-assisted Technologies: This study has not used any generative AI tools or technologies in the preparation of this manuscript.