5/12/2015

Supply Chain Risk Management

Factors Affecting Logistic Performance: A Global Cross-Section Supply Chain Study

byMUHAMMAD ZAIN SIDDIQUIReg #: 8709Submitted to: Mr. Farhan Mehboob

A thesissubmitted in partial fulfillment of the requirementsfor the degree of Master of Business Administrationtothe Iqra University.

Karachi, Pakistan

Factors Affecting Logistic Performance: A Global Supply Chain Perspective 2

MAY, 2015

AbstractThe underlying objective and purpose of this thesis is to test a model that studies relationship between costs to export, cost to import, GDP, per capita income and IT on logistics performance. This research will assist the logistics industry for identifying the opportunities and challenges in terms of their trade logistics performance, what factors affect this benchmarking tool and what steps can the logistics industry take to improvise their performance. The data is selected for 41 countries worldwide on the basis of their land area from World Bank for the year of 2010.When the viability of the model was checked the results shown that all the independent variables contribute some exertions to affect the logistic performance of any country. The exports and imports of goods and services contribute to about 40% and 42% to the logistic performance to be precise. However, GDP, IT, and income per capita have an impact of about 16%, 8%, and 61% to the logistic performance respectively.However, for the countries having lower degree of logistic performance can improve their performance by focusing on their imports and exports of goods and services, and their per capita income which are the factors having enormous effect on the logistic performance of any country.

Table of ContentsAbstract2Chapter 1 Introduction11.0 Overview11.1 Background21.2 Problem Statement31.3 Purpose of Research41.4 Objectives of Research41.5 Research Questions41.6 Research Hypothesis51.7 Limitation of Study51.8 Scope6Chapter 2 Literature Review72.1 Theoretical Background82.2 Logistics82.3 Empirical Studies92.4 Logistics Framework102.5 GDP122.6 Cost to Export & Import122.7 Per Capita Income132.8 Information Technology132.9 Conceptual Framework13Chapter 3 Methodology153.1Research Purpose163.2Research Approach163.3 Research Design163.4 Secondary Data:173.5 Research Model183.6 Variables Description183.6.1 Dependent Variable:183.6.2 Independent Variable:19Chapter 4 Data Analysis204.1 Introduction214.2 Cross sectional Regression23Chapter 5 Conclusion and Recommendation305.1 Conclusion315.2 Managerial Recommendation325.3 Future Research32References33

Factors Affecting Logistic Performance 6Chapter 1 Introduction1.0 OverviewThe notion of logistics has travelled a long way in recent years. As previously logistics was seen as individual components of product flow, such as storing, handling or transport. However, now logistics has evolved into comprehensively managed and integrated supply chains. Logistics form a significant base for success of organizations and businesses around the world as the logistics processes of distribution, production and sourcing have become global, and so countries need to focus on improving their logistic performance to achieve long-term growth in international markets. In terms of global comparison, the importance of logistic services largely depends on the nations economic power. For instance, the prospects of logistics services have been quite strong in Europe, Japan, and United States for a long time. There are certain factors that affect a dynamics of logistics in a country.First of all foreign trade, especially export is quite important to increase a countrys economic growth rate (Johnson, 2013). Moreover, export plays a key role for the countries to receive a greater share of the global market. Satisfactory and sustainability levels of countries export depend on exporting high value-added products and increasing the diversity of products and markets. Meanwhile, foreign trade transactions exhibit a complex view and have enhanced the importance of logistics. Logistics is considered as an important constituent in the field of service, manufacturing and agriculture industry. Moreover logistics has to be smoothly managed so that distribution and production functions can operate effectively.According to a research by Hollweg and Wong (2009) cost to import & import of goods and services; cost to export & export of goods and services and GDP are indirectly proportional to logistics performance index. On the other hand IT expense is directly proportional to logistics performance. In this regard, countries that work on controlling their cost of import, cost of export, GDP, IT enhances the quality of logistics and ensures competitiveness and eventually reach the top positions in the Logistics Performance.According to Christopher (2012) efficiency of logistics can be measured through the application of logistics performance index (LPI). This index primarily depends on the quality and competence of customs and border management, trade and transport infrastructure and logistics services. In this paper, it is worked on the model that studies relationship between costs to export, cost to import, GDP, per capita income and IT on logistics performance.This study investigates the affect of GDP, export and import of goods and services, cost to export and import, IT, and income per capita on logistic performance. The introductory chapter of this study will provide background information relevant to research questions, its contextual framework, and problem identification, purpose of study, research question, justification and limitation of this research.

1.1 BackgroundThe prospect of logistics performance starts with its definition. According to World Bank, the logistic performance of countries at the same level of per capita income with the best logistics performance experience additional growth of 1% in gross domestic product (GDP) and 2% in trade. So its essential to improve a countries logistics performance as it has significant valuable effects on the statistics of a countries economy. Additionally no matter if there is successful logistics or not the trade cycle is always present and it eventually relies on the pace and extent of government strategy and measures that will liberalize logistics supply (Havenga, 2011).Furthermore World Bank denotes LPI as an index that captures mainly the main features of the existing logistics environment. LPI is deliberated by the efforts of BRIC countries (Brazil, Russia, India and China); World Bank; and various other sophisticated emerging economies. Efficient supply chain and logistics of any country can become its competitive advantage over its competitor, so focus should be on improving the Logistics Performance Index of a country. LPI, as implied by the acronym, places great emphasis on performance, expressed through the reliability and predictability factor, unlike the conventional performance metrics, such as average delays and direct freight costs, or more generically expressed in terms of time and costs. World Bank representatives, experts in the field, and academics, came to the conclusion that, currently important indicators such as reliability, predictability and quality of service, along with transparency of processes, cannot be comprehended solely from costs and time information. The predictability and reliability of shipments, while more difficult to measure, are more important for firms and may have a more dramatic impact on their ability to compete (Arvis, et al 2007:4).

1.2 Problem StatementIn the year 2007, Singapore had the highest logistic performance index score of 4.19 with logistic competence of 4.21 which is the highest of all (World Bank). Whereas, when the data was extracted for the year 2010 Germany was the country where logistic performance index found to be 4.11 with logistic competence of 4.11 (World Bank). (Shown below in table 1.2.1)Table: 1.2.1CountryYearLPI RankLPI ScoreLogistics competence

Singapore200714.194.21

CountryYearLPI RankLPI ScoreLogistics competence

Germany201014.114.14

Korinek and Sourdin (2011) study based on low, middle and higher-income countries gives the idea that relationship between logistics and trade is directly proportional. Efficient logistics facilitates trade and play a crucial role of transporting goods over international border. On the other hand if logistics performance is inefficient, it will result in trade block up due to extra money and time needed (Korinek & Sourdin, 2011). As developed countries are shifting from traditional agriculture and manufacturing model to globalized trade they are increasingly interacting in international markets and need an efficient logistics services to gain competitive advantage. Therefore in this study we try to focus on logistics of developed and developing countries and finds out how quality and competency of logistics services is affected by country specific factors such as, GDP, export and import of goods and services, cost to export and import, IT, and income per capita.

1.3 Purpose of Research As mentioned above the quality logistics performance serves as a competitive advantage for countries. This research has tried to find factors which affect the Logistics dynamics and efficacy. For this research 41 countrys data will be assessed and influence of different variables will be examined on logistics. The independent variables which are selected for this research are also important and critical in todays world i.e. GDP, export and import of goods and services, cost to export and import, trade services, IT, and income per capita. This research can serve as a guideline for regulatory bodies to select strategic actions for improving their logistics. The fundamental idea of this research is to study the relationship between logistics performance and cost to export, cost to import, GDP, trade services, per capita income and IT (Arvis, et al 2007) on the basis of a model. This research will explore the relationship between dependent and independent variables on the basis of a model.

1.4 Objectives of Research The objective of this research is to assess the concept of logistics performance and various factors that affect its efficacy. The value of logistics performance is dependent on various factors and this paper explores the relationship among them.

1.5 Research Questions This study proposes to study the following questions:1. What is the impact of export and cost of goods and services on Logistics Performance?2. What is the impact of import and cost of goods and services on Logistics Performance?3. What is the impact of GDP on Logistics Performance?4. What is the impact of per capita income on Logistics Performance?5. What is the impact of Information Technology on Logistics Performance?

1.6 Research Hypothesis1. HO1: Export and cost of goods and services does not affect Logistics Performance?2. HO2: Import and cost of goods and services does not affect Logistics Performance?3. HO3: GDP does not affect Logistics Performance?4. HO4: Per capita income does not affect Logistics Performance?5. Ho5: Information Technology does not affect Logistics Performance? 1.7 Limitation of StudyThere are certain limitations in this research. 1. Limitation in terms of variables is that we have limited exposure of variables as we included the effect of only cost to export, cost to import, GDP, per capita income and IT on logistics performance, however dynamics of logistics are influenced by various other factors apart from these. Moreover, Researchers can include other factors to investigate logistics performance further.2. Another limitation is that this study is conducted on cross sectional data of 2010, so using panel or time series data can offer additional insights about the relationship of dependent and independent variables.3. The data gathered in this research is based on 41 countries that are selected on the basis of their size. So further research can include other countries as well.4. Quantitative model has been applied to study association between dependent and independent variables, so qualitative aspects can also be assessed to further gain insights into this topic.

1.8 Scope This research comprises on the data of 2010 for 41 countries. The data was taken from the website of World Bank. The countries were chosen on the basis of their size (area) and logistics data availability. Similarly, the research area can be more broaden by taking the data for more countries other than these 41 already selected on the base of their land area. Moreover, the research is based on the impacts of costs of exports and imports of goods and services, GDP, IT, and income per capita, where more other variables can be added to gauge the impact on logistic performance.

Chapter 2 Literature Review Factors Affecting Logistic Performance 39

2.1 Theoretical BackgroundThe theories of management that can be applied to the domain of logistics management are relationship orientation (Panayides & So, 2005), resource-based view (Rungtusanatham, Salvador, Forza & Choi, 2003), competitive advantage (Sandberg & Abrahamsson, 2011) transaction cost analysis (TCA) (Bowersox, Mentzer & Speh, 2008), and the principal-agent theory (PAT) (Fei & Yun-fei, 2009).According to Panayides and So (2005) the idea of relationship orientation denotes proactively creating, developing and maintaining strong relationships with stakeholders that would ultimately provide benefit in the form of mutual exchange and profitable opportunities. Similarly in the domain of logistics management there are varies parties or stakeholders acting hand in hand to support the intricate operations of logistics, this there is a dire need for efficient relationship orientation.Moreover, the link between resource based view and logistics management is that capabilities and resources can only be obtained from a particular market to certain extent and after that there is a need to outsource the resources from other markets (ldrsson & Skjtt-Larsen, 2004). In this regard the concept of logistics becomes very important because it shows that its important for a country to improve it logistics services to achieve long-term mutual commitment (Rungtusanatham, Salvador, Forza & Choi, 2003). Similarly if a country wants to attain the competitive advantage it need to improve its logistics performance (Sandberg & Abrahamsson, 2011).To add on, the connection among transaction cost analysis (TCA) and logistics management is that TCA sets the boundaries of firms in terms of entering into inter-organizational arrangements. The underlying idea is that a firm can decrease its overall transaction costs by partnering and cooperating with external parties. A firm should perform those activities internally or within their local geographical domain, in which it has a competitive edge and for others, the firm should outsource them from external or international parties, thus fostering International logistics (Bowersox, Mentzer & Speh, 2008). So all in all, the approach of TCA is used to decide on the make-or-buy decisions in supply chains, for example restructuring of supply chains, buyer supplier relationships and outsourcing of logistics activities.Lastly, since there is separation of economic activities and ownership among principle and agent, agency problems can incur, such as bounded rationality, behavior based on self-interest, outcome uncertainty, differences in risk aversion, conflicting objectives and asymmetric information among agent and the principal and the agent. In this regard, an important issue of logistics management is the alignment of incentives, as misalignment frequently comes from hidden information or hidden actions. Nevertheless, the prospect of misalignment can be controlled by creating contracts with supply chain partners balancing penalties and rewards.

2.2 LogisticsLogistics by definition is considered a functional system that incorporates coordination and combination of operations of diverse transport modes as a primary pre-requisite for making sure that there is efficient service (Leal, 2012). In other words, logistics can be defined as a management framework for business planning for management of capital flows, information, service and material. Logistics function also incorporates the intricate control systems, IT and information that are needed in todays dynamic business environment. Furthermore logistics can also be defined as the replacement, distribution, maintenance and procurement of material and personnel. Logistics framework typically consists of physical distribution of services and goods, internal operations; physical distribution and internal operations; and physical supply of goods and services (Mentzer, Stank & Esper, 2008). Simultaneously, logistics framework can be seen as a structure that ensures that a country have the right type of service or product designated at the right order, place and time. However, our expectations for a firm or company are directly related to logistics.

2.3 Empirical Studies According to Arvis and colleagues (2014) improving logistics performance is at the core of the economic growth and competitiveness agenda. Considering this, the global policymakers must distinguish the sector of logistics as a key milestone for their growth and development. Sustainable and seamless logistics are seen as engine of integrating global marketplace and value chains by the trade powerhouses like the Netherlands or developing countries such as Indonesia. The authors further stress on the idea that is logistics is inefficient, and then it can reduce the prospects of global integration and increase the costs of trading. Considering this, one of the important goals of the global economy is to enhance the prospect of logistics framework (Waters & Rinsler, 2014). To add on, according to Waters and Rinsler (2014) the institution of Global Supply Chains has raised the issue of moving goods inexpensively, reliably and rapidly around the globe. As the importance of logistics has increased steadily, there is a mounting necessity of assessing the components of logistics and comparing different countries performance and data with regard to these components. The prospect of logistics performance starts with its definition. According to World Bank, the logistic performance of countries at the same level of per capita income with the best logistics performance experience additional growth of 1% in gross domestic product (GDP) and 2% in trade. So its essential to improve a countries logistics performance as it has significant valuable effects on the statistics of a countries economy. Additionally no matter if there is successful logistics or not the trade cycle is always present and it eventually relies on the pace and extent of government strategy and measures that will liberalize logistics supply (Havenga, 2011).The framework of Logistics Performance Index (LPI) is measured by World Bank a tool to survey on the operators in charge of trading and moving goods. A report taken from the World Bank gives the idea that LPI is produced to close the knowledge gap related to logistics and to facilitate nations in developing reforms of to improve their competitive circumstances. The results of LPI ranking introduce some interesting findings; first, the higher the score in terms of LPI, the greater the countries role in logistics industry, and vice versa. On a second note, scoring low in LPI terms can be interpreted as being trapped in a vicious circle of overregulation, poor quality services, and under- investment (Arvis, et al 2007).Furthermore World Bank denotes LPI as an index that captures mainly the main features of the existing logistics environment. LPI is deliberated by the efforts of BRIC countries (Brazil, Russia, India and China); World Bank; and various other sophisticated emerging economies. Efficient supply chain and logistics of any country can become its competitive advantage over its competitor, so focus should be on improving the Logistics Performance Index of a country. LPI, as implied by the acronym, places great emphasis on performance, expressed through the reliability and predictability factor, unlike the conventional performance metrics, such as average delays and direct freight costs, or more generically expressed in terms of time and costs. World Bank representatives, experts in the field, and academics, came to the conclusion that, currently important indicators such as reliability, predictability and quality of service, along with transparency of processes, cannot be comprehended solely from costs and time information. The predictability and reliability of shipments, while more difficult to measure, are more important for firms and may have a more dramatic impact on their ability to compete (Arvis, et al 2007:4).According to Christopher (2012) efficiency of logistics can be measured through the application of logistics performance index (LPI). This index primarily depends on the quality and competence of customs and border management, trade and transport infrastructure and logistics services. In a study performed by Mohan in 2013, it was showed that the logistics management has effect on global competitiveness. Furthermore, the paper also examined the salient features of Indian logistics systems (Mohan 2013). The prospect of logistic performance index is built upon previous literature (Arvis, Mustra, Panzer, Ojala & Naula, 2007). Its focus however, is primarily on supply chain performance, and its indicators have been developed in such a way that, they complement the existing competitive indicators in the two fore-mentioned studies.According to Islam (2014) LPI consists of two main parts, namely International and Domestic LPI. The former has encompassed a range of metrics, they estimate as crucial in the current international trading environment, and conditions:1. Shipments timeliness in reaching target location 2. Effectiveness of clearance process monitored by customs agencies 3. Quality of transport infrastructure that is needed for efficient logistics4. Affordability and easiness of arranging shipments5. Ability to trace and track shipments6. Proficiency in local logistics industry (for instance, customs brokers and transport operators)7. Costs of domestic logistics (for instance, warehousing, terminal handling and local transportation)The second constituent of logistics performance is domestic logistics performance indicator that provides quantitative and qualitative assessments of a countrys logistics by professionals working inside it. According to Solakivi, Tyli, Engblom and Ojala (2011) domestic logistics tend to include comprehensive information on the cost data, performance time, institutions, and core logistics processes and logistics environment.

2.4 Logistics FrameworkThe significance of having an efficient logistics framework is currently acknowledged by decision makers worldwide. Private operators move commerce and trade are moved and within borders. Logistics performance actually measures the competence of these supply chain -logistics performance. The value of logistics performance depends on government policy that is formulated by regional economic groups and individual countries in development and regulation of services, infrastructure provision or trade facilitation all the way with the help of friendly border procedures that substantially facilitate in efficient performance of logistics (WTO 2014). According to Puertas, Mart and Garca (2013) the provision of International LPI is based on assessment of foreign operators and that consider the average of six components, namely, tracking and tracing; services quality; infrastructure; customs; timeliness; and international shipments.Fig 2.4 Input and Outcome LPI Indicators

Retrieved From: (WTO 2012)The components of supply chain delivery and logistics are selected on the basis of empirical and recent theoretical research and moreover on practical understanding of logistics professionals that are concerned with international freight forwarding (as shown in Fig 2.4). There have been four logistics performance surveys made so far accordingly in 2007, 2010, 2012 and 2014. On the basis of the worldwide survey of express carriers and global freight forwarders, the LPI is regarded as a benchmarking tool that evaluates performance of a country in terms of the efficacy of its logistics supply chain. This index allows comparisons of 160 countries, and therefore helps the countries in identifying opportunities and challenges in improving their logistics performance (WTO 2014). The value of logistics LPI ranges from 1 to 5, in which higher the number of index, the better comparative performance of the country (The World Bank 2014).First of all foreign trade, especially export is quite important to increase a countrys economic growth rate (Johnson, 2013). Moreover, export plays a key role for the countries to receive a greater share of the global market. Satisfactory and sustainability levels of countries export depend on exporting high value-added products and increasing the diversity of products and markets. Meanwhile, foreign trade transactions exhibit a complex view and have enhanced the importance of logistics. Logistics is considered as an important constituent in the field of service, manufacturing and agriculture industry. Moreover logistics has to be smoothly managed so that distribution and production functions can operate effectively.According to a research by Hollweg and Wong (2009) cost to import & import of goods and services; cost to export & export of goods and services and GDP are indirectly proportional to logistics performance index. On the other hand IT expense is directly proportional to logistics performance. In this regard, countries that work on controlling their cost of import, cost of export, GDP, IT enhances the quality of logistics and ensures competitiveness and eventually reach the top positions in the Logistics Performance (Hollweg & Wong, 2009).Gogoneata (2008) stated that nations that are leading the International markets are characterized by major logistics hubs and global transport, which obtained huge advantage of globalizations consequences. In terms of LPI, the lowest developed countries are placed at the base of ranking, as they are going through constant resources shortage and experiencing conflicts. The LPI is quite important for comparing between nations and for cross-sectional statistical investigations (Gogoneata, 2008).To add on, a research concluded that least developed countries in terms of logistics are making a significant effort to improve their situation, which is boosting international trade and their own economic growth (Puertas, Mart & Garca, 2013).

2.5 GDP According to Korinek and Sourdin (2011) trade logistics serves a very important job in facilitating trade services as it supports the transportation of international goods and services. On the other hand, if the logistics services are inefficient then they tend to impede trade services as there is imposition of extra money and time cost. Now a days developed nations are shifting from traditional agriculture and manufacturing and are progressing towards international vertical specialization, so due to this transition there is greater need for competent logistics services. Furthermore when quality of logistics services is enhanced, it improves a countrys competitive position by decreasing the overall costs that are involved in transporting good. So cost of imports and exports are closely link of logistics performance index (Korinek & Sourdin, 2011).Moreover, according to a research study by Gogoneata (2008) the findings portray that logistics performance improves as the share of value added services and exports increase in GDP. Therefore, the findings point to an elevated elasticity of logistics performance to the expansion of services sector, as compared to the relative importance of exports. To add on, the research findings suggest that lower descriptive influence of exports for logistics performance can be defended on the note that a higher portion of exports in GDP are observed from middle-income nations, that indulge in international trade on one hand, but do not possess adequate resources to build better institutions and infrastructure (Gogoneata, 2008).

2.6 Cost to Export & ImportAccording to Arvis, Saslavsky, Ojala, Shepherd, Busch and Raj (2014) there is a strong association between logistics performance, consistency of supply chains and the service delivery certainty. So this denotes the idea that efficient logistics of a country directly supports its export function. Moreover, according to Puertas, Mart and Garca (2013) in terms of assessing a countrys export competitiveness; logistics performance has evolved as a decisive factor. According to World Economic Forum (2013) there is massive potential for enhancing global trade and export by reducing barriers of logistics and supply chain. Furthermore it is not easy for an exporter to export their goods and services at competitive prices if the logistics and transport sector is dysfunctional or inefficient. This means that if there is lack of certainty in logistics and transport, poor service and high prices, then it will translate in the form of isolating the country from world markets (Arvis et al., 2013).

2.7 Per Capita IncomeYildiz (2014) states that as domestic production per capita increases, so does the range of human activities, both for society as a whole and for individual lifestyles. Thus, each development proposal must be examined not only for its economic, environmental and social impacts, but also for its implications for transport. This denotes that as per capita income increases, it directly influences the increasing need for efficient transportation, which is turn leads to improvisation in logistics performance. 2.8 Information Technology According to OECD (2002) the use of ICT has improved the exchange of supply chain information, leading to the development of integrated production and logistics management systems and has thereby improved supply chain performance in many ways. This portray that due to the provision of improving Information technologies in any country, the logistics performance also improves significantly. This relationship in directly influenced by provision of Electronic Data Interchange (EDI) and ICT-supported information exchange systems. Moreover, the effects of technological innovation and advanced information technologies on logistics is logistics performance improvement as information exchange in supply chain is improved; development of logistics management and integrated production systems improved management of logistics; and the provision of Electronic Data Interchange (EDI) radically altered the manner in which commercial and international transactions were being managed, thereby promoting the prospects for improved logistics performance (OECD, 2002). To add on provision of IT enables creation of new supply chain structures. Direct trade is enhanced due to easy access to information between users and suppliers leading to provision of easily available information to all partners of supply chain. These initiatives have also resulted in creation of a particular service, known as virtual logistics chain (Lusch, 2011). Other avenues created by the prospect of leading IT that foster logistics performance are Value-Added Network (VAN), PARIS (Planning And Routing Intermodal System) and Information Clearing House (ICH) (OECD, 2002).Furthermore, Information technology promotes contributing to intermodal transport and modal shift. The idea is that is transport and logistci8s technology will develop; it will increase intermodal transports competiveness. Well-organized system of information technology has condensed cost and processing time and formed seamless links, by this means easing out intermodal transport (OECD, 2002).

2.9 Conceptual Framework The research is based on a conceptual framework that seeks to analyze the effect of cost to export (Arvis, Saslavsky, Ojala, Shepherd, Busch, & 2014; Hausman, Lee & Subramanian, 2005; Naud & Matthee, 2012; Turkson, 2011); cost to import (Arvis, Saslavsky, Ojala, Shepherd, Busch, & 2014; Hausman, Lee & Subramanian, 2005; Naud & Matthee, 2012; Turkson, 2011); GDP and Trade services (Diop, 2010; Havenga, 2011); and per capita income (Portugal-Perez & Wilson, 2012) on logistics performance.

Chapter 3 Methodology

3.1Research PurposeThe underlying objective of this thesis is to analyze the effect of cost to export, cost to import, GDP, per capita income and IT on logistics performance. This research will assist the logistics industry for identifying the opportunities and challenges in terms of their trade logistics performance, what factors affect this benchmarking tool and what steps can the logistics industry take to improvise their performance. Logistics industry is growing at an increasing pace but on the other hand, according to Business Recorder estimate, Pakistan is losing $2.6 billion annually because of inefficiencies in its logistics despite the significant growth in the efficiency of the road transport system (Mirza, 2013). So if logistics industry focuses on leveraging the key logistics performance indicators (LPI) then they can gain competitive advantage in long-run.

3.2Research ApproachThe approach of this research is based on multiple regressions that are focused on learning more about the relationship between several independent or predictor variables and a dependent or criterion variable. This thesis utilizes multivariate regression analysis that describes and evaluates the relationships between a given dependent variable and one or more independent variables.

3.3 Research DesignA general perception of quantitative research is to assess the association between dependent and independent research variables. In this cross-sectional research focus has been on statistical and numeric data collected from World Bank to study the effect of dependent variables on independent variables with the help of a proposed mode. Historical statistics has been used in this research. The research design connects the whole idea of research project together. The main approach is to conduct a quantitative analysis by gathering data from authentic sources. The criteria for selecting particular countries in data selection have been the size of the countries. The research data for this research was obtained from World Bank on the basis of size of country and the list of countries that are included in the data set for quantitative research is attached as Appendix 1.

3.4 Secondary Data:Data is collected from the World Banks website. In this, sample size of the study Top 41 countries on the basis of country sizes. In this study (OLS) ordinary least is used. This is a procedure to determine the relationship between the dependent and independent variable. In this study sample size top 41 countries based on year 2010. Ordinary least (OLS) is used in this study. It is a procedure to work out the relationship between the predictor (dependent) variable and predicted (independent) variables.

3.5 Research ModelFigure 3.7 Factors Affecting Logistic Performance

LPI = + 1 (ImpCost) + 2 (ImpG&S) + (Equation 1)LPI = + 1 (ExpCost) + 2 (ExpG&S) + (Equation 2)LPI = + 1 (GDP) + (Equation 3)LPI = + 1 (Income per capita) + (Equation 4)LPI = + 1 (IT) + (Equation 5)

3.6 Variables Description3.6.1 Dependent Variable:LPI: Logistic Performance Index:Data relevant to the logistics performance is collected from World Bank. In logistics performance Index survey of 2009 more than 5,000 countries were included in evaluation that was carried out by almost 1,000 international freight forwarders. The research respondents evaluated eight markets on a scale of 1 to 5, on the basis of six dimensions. The dimensions are neighboring countries connecting country with global markets, landlocked countries, random selection, significant import and export markets of respondent's country.

3.6.2 Independent Variable:GDPAccording to Korinek and Sourdin (2011) if there is efficient trade logistics in any country then it will facilitate trade services of the country and thereby improve the outlooks for GDP growth. Moreover the quality of logistics services serves an integral role in terms of supporting transportation of goods in international trade. In this research, GDP is measured in US Dollars.

Income per CapitaYildiz (2014) states that as domestic production per capita increases, so does the range of human activities, both for society as a whole and for individual lifestyles. Thus, each development proposal must be examined not only for its economic, environmental and social impacts, but also for its implications for transport. This denotes that as per capita income increases, it directly influences the increasing need for efficient transportation, which is turn leads to improvisation in logistics performance. In this research, income per capita is measured in US Dollars.

Cost to Export & ImportAccording to Puertas, Mart and Garca (2013) the notion of logistics performance has become a decisive factor in export competitiveness. At the same time, and as a result of the continuous enlargement processes it has undergone, the European Union is a very interesting case to study how the reforms that enhance logistics performance have affected export. In this research, cost to export & import is measured in US Dollars.

ITWhen the Information technology aspect of a country improves, it leads to significant improvement in dynamics of logistics because of the provision of new technologies and advancements, making the prospect of logistics more affective (OECD, 2002). This portrays that due to the provision of improving Information technologies in any country, the logistics performance also improves significantly. This relationship in directly influenced by provision of Electronic Data Interchange (EDI) and ICT-supported information exchange systems.

Chapter 4 Data Analysis

4.1 IntroductionThe main purpose of this research is to analyze the effect of Cost to Export, cost to import, GDP, per capita income and IT on logistics performance. Data analysis is considered as one of the most crucial phase of a quantitative research (Mangan, Lalwani & Gardner, 2004). The main purpose of data analysis is to identify the general theme of the collected data. The data analysis techniques being applied in this quantitative research are T-stats, Adjusted R-Squared, F-Statistics and Prob (F-Statistics). First of all in linear regression, the F-statistic is the test statistic for the analysis of variance (ANOVA) approach to test the significance of the model or the components in the model. The F-statistic in the linear model output display is the test statistic for testing the statistical significance of the model. The F-statistic values in the display are for assessing the significance of the terms or components in the model.Coefficient of determination (R-squared) indicates the proportionate amount of variation in the response variable y explained by the independent variables X in the linear regression model. The larger the R-squared is the more variability is explained by the linear regression model. The analysis has been conducted for the data of the year 2010.

Table 4.1Highest and Lowest Countries (three) with rest to dependent variable (LPI) (2010)VariablesExportsImportsGDPIncome per capitaLPI

UnitUS$ (Mn)US$ (Mn)US$ (Mn)US$0-5

Top 3 LPI Countries

Germany1,558,7601,373,9133,284,47440,1644.11

Sweden223,444196,440462,90349,3604.08

Japan871,533796,6745,488,41643,0633.97

Bottom 3 LPI Countries

Iraq54,59947,19781,1122,5322.11

Angola51,45235,42182,4714,3222.25

Algeria60,65650,792161,9794,5672.36

According to the results of table 4.1, the highest ranking country in terms of respective LPI is Germany with LPI of 4.11. However, Germany has less per capita income and GP in comparison to Sweden and Japan. The fact that Germany ranks highest in LPI comes from Germanys lead in net exports and imports market. Secondly, Sweden takes the 2n highest place in terms of LPI, due to its highest per capita income. On the other hand, Swedens LPI is lower than that of Germany due to lesser GDP, exports and imports. The third highest place is taken by Japan, with an LPI of 3.97. Japan is leading due to its highest GDP and relatively better import and export outlook in comparison to Sweden.Moreover, the table 4.1 shows that highest and lowest three countries in terms of their respective logistics performance for 2010. According to this table, Iraq has the lowest LPI of 2.11, while on the other hand Iraqs income per capita and GDP is also lowest at $2,532 million and 81,112 million respectively. In terms of exports and imports, Iraq ranks better than Angola with net exports and imports of $54,599 million and $47,197 accordingly. On the other hand Angolas LPI is 2nd lowest at around 2.25, having better per capita income and GDP than Iraq at $4,322 million and 82,471 million respectively; net exports of 51,452 an imports worth of 35,421. All values of Angolas variables are less than those of Iraq other than per capita income and GDP which shows that the logistic infrastructure of Angola not fully utilized at its full potential however it has a relatively good GDP an economic outlook. To add on, Algeria has the 3rd lowest LPI of 2.36 as it has relatively better export and import outlook; more profound GDP and per capita income, in comparison with Iraq and Algeria.

4.2 Cross sectional RegressionTable 4.2.1 Ordering Least SquareVariablesCoefficientt-statisticProb.VIF

COSTEXP0.000-2.96330.005

EXPGS0.0003.7840.000

C3.36421.6210.000NA

Adjusted R-squared0.401

F-statistic14.363

Prob (F-statistic)0.000

Dependent VariableLPI5

Included Obs.41

Source: Authors observationTable 4.2 shows that fifty nine units increase in cost to export will increase logistics performance by one unit, on the other hand a while a twenty seven units increase of export of goods and services will reduce logistics performance by one unit. There is no strong relationship between cost to export and cost of goods and services. The P-value of cost to export is 0.005 and P-value of export of goods and services 0.000 showing a relatively stronger significance level. Moreover the Adjusted R square demonstrates the accuracy in the modification of logistic performance as elucidated by the COSTEXP and EXPGS. In the above model, the value of adjusted R square is 0.401 showing that COSTEXP and EXPGS can be estimated by 40.1 percent of the variance in Logistic Performance, meaning that our model is describing around 40.1 percent variance in observed countrys logistic performance. The value of probability of F-Statistic shows the significance of the model and our model is at 0.000 illustrating that the variables COSTEXP and EXPGS are outstanding and accurate in order to forecast varying importance of logistic performance. The value of F-Statistics, in above model is 14.363, while its probability is 0, showing that independent variable is considerably excellent to predict the alterations in dependent variable.

Table 4.2.2 Ordering Least SquareVariablesCoefficientt-statisticProb.VIF

COSTIMP0-3.2950.002

IMPGS5.48E-133.8500.000

C3.40922.6450.000NA

Adjusted R-squared0.448

F-statistic15.423

Prob (F-statistic)0.000

Dependent VariableLPI

Included Obs.41

Source: Authors observationThe results from Table 4.2.2 show a positive relationship among cost to import and import of goods and services. The table reveals that one thousand forty four units increase in cost to import will result in logistics performance increasing by a unit. Similarly if import of goods and services will increase by fourteen thousand two hundred forty five units then logistics performance will increase by a unit.To add on, the P-value of COSTIMP 0.002 and IMPGS 0.000 is showing a relatively stronger significance level. Moreover the Adjusted R square demonstrates the accuracy in the modification of logistic performance as elucidated by the COSTIMP and IMPGS. In the above model, the value of adjusted R square is 0.448 showing that COSTIMP and IMPGS can be estimated by 44.8 percent of the variance in Logistic Performance, meaning that our model is describing around 44.8 percent variance in observed countrys logistic performance. The value of probability of F-Statistic shows the significance of the model and our model is at 0.000 illustrating that the variables COSTIMP and IMPGS are outstanding and accurate in order to forecast varying importance of logistic performance. The value of F-Statistics, in above model is 15.423, while its probability is 0, showing that independent variable is considerably excellent to predict the alterations in dependent variable.

Table 4.2.3 Ordering Least SquareVariablesCoefficientt-statisticProb.VIF

GDP$9.47E-142.9700.005

C3.06134.4980.000NA

Adjusted R-squared0.164

F-statistic8.822

Prob (F-statistic)0.005

Dependent VariableLPI

Included Obs.41

Source: Authors observationThe results from Table 4.2.3 shows that one thousand forty four units increase in Gross Domestic Product will result in logistics performance increasing by a unit. To add on, the P-value of GDP$ is 0.005, showing a relatively stronger significance level. Moreover the Adjusted R square demonstrates the accuracy in the modification of logistic performance as elucidated by the GDP$. In the above model, the value of adjusted R square is 0.164 showing that GDP$ can be estimated by 16.4 percent of the variance in Logistic Performance, meaning that our model is describing around 16.4 percent variance in observed countrys logistic performance. The value of probability of F-Statistic shows the significance of the model and our model is at 0.005 illustrating that the variables GDP$ is outstanding and accurate in order to forecast varying importance of logistic performance. The value of F-Statistics, in above model is 8.822, showing that independent variable is considerably excellent to predict the alterations in dependent variable.

Table 4.2.4a Ordering Least SquareVariablesCoefficientt-statisticProb.VIF

COMMUNICATIONSEXP0.0092.1450.038

C2.85016.1990.000NA

Adjusted R-squared0.083

F-statistic4.602

Prob (F-statistic)0.038

Dependent VariableLPI

Included Obs.41

Source: Authors observationThe test of Hetroskedaticity reveals that there is a chance of Hetroskedaticity in our model. In order to remove Hetroskedaticity, the technique of weighted least square (WLS) is used.

Table 4.2.4b Hetroskedaticity CorrectedVariablesCoefficientt-statisticProb.VIF

COMMUNICATIONSEXP0.0102.4310.020

C2.81320.0430.0000NA

Adjusted R-squared0.109

F-statistic5.908

Prob (F-statistic)0.020

Dependent VariableLPI

Included Obs.41

Source: Authors observationThe results from Table 4.2.4b shows that one thousand forty four units increase Communication Expense will result in logistics performance increasing by a unit. To add on, the P-value of COMMUNICATIONSEXP is 0.020, showing a relatively stronger significance level. Moreover the Adjusted R square demonstrates the accuracy in the modification of logistic performance as elucidated by the COMMUNICATIONSEXP In the above model, the value of adjusted R square is 0.109 showing that COMMUNICATIONSEXP can be estimated by 10.9 percent of the variance in Logistic Performance, meaning that our model is describing around 10.9 percent variance in observed countrys logistic performance. The value of probability of F-Statistic shows the significance of the model and our model is at 0.020 illustrating that the variables COMMUNICATIONSEXP is outstanding and accurate in order to forecast varying importance of logistic performance. The value of F-Statistics, in above model is 5.988, showing that independent variable is considerably excellent to predict the alterations in dependent variable.

Table 4.2.5a Ordering Least SquareVariablesCoefficientt-statisticProb.VIF

GDPPERCAPITA2.28E-058.0140.000

C2.77637.6400.000NA

Adjusted R-squared0.613

F-statistic64.230

Prob (F-statistic)0.000

Dependent VariableLPI

Included Obs.41

Source: Authors observationThe test of Hetroskedaticity reveals that there is a chance of Hetroskedaticity in our model. In order to remove Hetroskedaticity, the technique of weighted least square (WLS) is used.Table 4.2.5b Hetroskedaticity CorrectedVariablesCoefficientt-statisticProb.VIF

GDPPERCAPITA2.77018e-511.0970.000

C2.72630.5080.0000NA

Adjusted R-squared0.613

F-statistic64.230

Prob (F-statistic)0.000

Dependent VariableLPI

Included Obs.41

Source: Authors observationThe results from Table 4.2.5b shows that one thousand forty four units increase GDP per Capita will result in logistics performance increasing by a unit. To add on, the P-value of GDPPERCAPITA is 0.000, showing a relatively stronger significance level. Moreover the Adjusted R square demonstrates the accuracy in the modification of logistic performance as elucidated by the GDPPERCAPITA. In the above model, the value of adjusted R square is 0.613 showing that GDPPERCAPITA can be estimated by 61.3 percent of the variance in Logistic Performance, meaning that our model is describing around 61.3 percent variance in observed countrys logistic performance. The value of probability of F-Statistic shows the significance of the model and our model is at 0.000 illustrating that the variables GDPPERCAPITA is outstanding and accurate in order to forecast varying importance of logistic performance. The value of F-Statistics, in above model is 64.230, showing that independent variable is considerably excellent to predict the alterations in dependent variable.

Chapter 5 Conclusion and Recommendation

5.1 ConclusionAll in all Logistics Performance Index is a tool for benchmarking countries logistics performance, as created by World Bank. This framework is based on a global survey of worldwide express carriers and freight forwarders, which assess a countrys performance along the logistics supply chain. This framework allow its users to conduct comparison of 160 nations, facilitate countries recognize opportunities and challenges and progress their logistics performance. The idea is that if a country has reliable logistics, it is essential to incorporate global value chains and obtain the advantages of trade opportunities for poverty reduction and growth. A countrys ability to connect to global logistics is highly dependent on its trade processes, service markets and countrys infrastructure.Today, in the arena of global trade where price competition alone does not make any sense and production costs converges together; the importance of logistics services and strategies has gained equal importance. Countries that move from transportation to logistics are getting a greater share of the international market. It is understood more clearly by the countries that take the lead over competitors with regulations made on logistics activities and comprehensive logistics strategies. When countries increase their investment on logistics research and give more importance to their logistics, economic development will also be anticipated. Indeed, in the face of the increase in logistics performance of a country, economic growth, employment level and productivity will increase, poverty will decrease. Moreover, as logistics supports the movement and flow of many economic transactions, all economic activities throughout the supply chain will be influenced by these effects. After all, due to the logistics activities, country's comparative advantage will increase since efficient logistics will reduce costs of transport, and decrease the cost of production. The efficiency of logistics activities are what the Logistics Performance Index (LPI) and its components measure. This efficiency primarily depends on the quality and competence of customs and border management, trade and transport infrastructure and logistics services. The results of this study reveal that Cost to Import & Import of Goods and Services; Cost to Export & Export of Goods and Services and GDP are indirectly proportional to Logistics Performance Index. On the other hand IT expense is directly proportional to Logistics Performance Index. In this regard, countries that work on controlling their cost of import, cost of export, GDP, IT enhances the quality of logistics and ensures competitiveness and eventually reach the top positions in the Logistics Performance.When the viability of the model was checked the results show that all the independent variables contribute some exertions to affect the logistic performance of any country. The exports and imports of goods and services contribute to about 40% and 42% to the logistic performance to be precise. However, GDP, IT, and income per capita have an impact of about 16%, 8%, and 61% to the logistic performance respectively. However, for the countries having lower degree of logistic performance can improve their performance by focusing on their imports and exports of goods and services, and their per capita income which are the factors having enormous effect on the logistic performance of any country.

5.2 Managerial Recommendation With reference to above findings and results, this research suggests that policy-makers must pay attention on controlling its cost of import and export to enhance its logistics performance index in comparison to other countries. Moreover, particular focus should be on increasing GDP and per capita income as it helps to reduce income inequality and foster development. According to the research findings, economic indicators such as per capita income and GDP, allow countries to improve their logistics performance and experience multi-dimensional growth. The prospect of advancing Information Technology promotes growth in logistics, so countries should try to adopt the leading logistics technology in their integrated supply chain networks, to improve their global logistics standing.

5.3 Future Research The future researches must incorporate variables other than those discussed in this research (cost to export, cost to import, GDP, per capita income and IT on logistics performance), to assess more significant factors that will help a country to improvise its logistics performance index. The sample time period used for this research is 2010, so future researchers can alter same time period to come to varying conclusion. Future researchers can also use panel or time series data as it can offer additional insights about the relationship of dependent and independent variables. Since the data gathered in this research is based on 41 countries that are selected on the basis of their size, so future researchers can conduct research using other countries as well. Quantitative model has been applied to study association between dependent and independent variables, so qualitative aspects can also be assessed to further gain insights into this topic.

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Appendix 1: Sample CountriesCountries taken from World Bank Site, on the basis on country size in ascending order.Countries/ Variables Land area (sq. km)Trade in services (% of GDP)Imports of goods and services (BoP, current US$)Communications, computer, etc. (% of service exports, BoP)Exports of goods and services (BoP, current US$)Cost to export (US$ per container)Cost to import (US$ per container)Logistics performance index: Overall (1=low to 5=high)GDP (current US$)GDP per capita (current US$)

Russian Federation163768707.9772573.22E+1143.849164.46E+11258526852.611.49E+1210481.37

China 93274805.9941731.52E+1248.766691.74E+125005453.495.93E+124432.964

USA91474206.563282.34E+1247.374011.84E+12105013153.861.44E+1346611.98

Canada909351010.172274.93E+1149.294144.62E+11161016603.871.58E+1246212.03

Brazil84594204.405582.44E+1158.081652.34E+11179019753.22.14E+1210992.94

India297319014.284984.4E+1171.564123.49E+11105511053.121.68E+121375.384

Argentina27366907.3966046.81E+1046.521138.12E+10148018103.13.69E+119123.714

Kazakhstan269970010.526894.42E+1020.432756.58E+10300530552.831.48E+119070.01

Algeria23817409.5518295.08E+1064.974766.07E+10124813182.361.62E+114566.891

Saudia Arabia214969019.400391.74E+116.5453712.62E+117659363.224.51E+1116423.44

Mexico19439503.896813.27E+111.8724993.14E+11142018803.051.04E+129127.541

Indonesia18115706.0527531.54E+1140.487671.75E+116446602.767.08E+112951.699

Peru12800006.3345413.49E+1016.403993.93E+108608802.81.54E+115283.225

Angola124670023.779593.54E+1011.098775.15E+10185028402.258.25E+104321.941

South Africa12144708.9292441E+1115.731479.97E+10153118073.463.64E+117271.729

Colombia 11095004.3703784.67E+1024.977174.53E+10177017002.772.86E+116186.025

Egypt, Arab Rep.99545017.599685.99E+1012.962994.88E+106137552.612.19E+112698.365

Nigeria91077010.717136.78E+1017.101587.98E+10126314402.592.29E+111443.21

Venezuela, RB8820503.2769824.97E+1029.348416.76E+10259028682.683.94E+1113657.75

Pakistan7708807.7618954E+1072.364632.81E+106116802.531.76E+111016.614

Turkey7696307.456121.97E+119.7188891.56E+1199010633.227.31E+1110049.77

Chile74353010.454426.7E+1021.902698.18E+107457453.092.16E+1112639.52

Ukrain57932021.788817.32E+1029.090486.93E+10156015802.571.36E+112973.982

France54766014.232937.61E+1150.252067.11E+11107812483.842.55E+1239170.26

Thailand 51089024.817782.07E+1123.441372.28E+116257953.293.19E+114613.681

Spain49880015.369364.08E+1136.350593.81E+11122113503.631.38E+1229956.16

Iraq43432015.661974.72E+1027.062395.46E+10355036502.118.11E+102532.324

Sweden41034023.691491.96E+1163.924522.23E+116977354.084.63E+1149359.87

Paraguay39730012.152521.07E+1067.747989.99E+09144017502.751.83E+102840.35

Japan3645005.4483387.97E+1159.666528.72E+118809703.975.49E+1243063.14

Germany34861015.566051.37E+1254.827741.56E+128729374.113.28E+1240163.82

Malaysia 32855026.325311.89E+1129.772872.32E+114504503.442.47E+118690.57

Oman 30950014.157262.42E+1024.657533.85E+107256602.845.78E+1020790.84

Norway30547020.308811.19E+1138.88211.71E+119559293.934.18E+1185443.06

Poland30420013.269562.07E+1141.793511.98E+118848843.444.7E+1112303.21

Finland30390022.726419.27E+1075.241339.64E+105406203.892.35E+1143863.97

Philippines29817012.753687.31E+1070.940056.48E+106307303.142E+112140.122

Italy29414010.169145.86E+1140.282445.45E+11124512453.642.04E+1233786.64

New Zealand 26331012.982623.89E+1024.470014.09E+108558253.651.42E+1132407.07

Ecuador2483607.7320782.27E+1022.47171.96E+10145514022.775.8E+104008.238

UK24193018.845057.32E+1151.744026.68E+1195010453.952.26E+1236256.01