THE EFFECTS OF THE ADOPTION OF NEW TECHNOLOGY BY CUSTOMS ON LOGISTICS PERFORMANCE IN MOMBASA COUNTY

THE EFFECTS OF THE ADOPTION OF NEW TECHNOLOGY BY CUSTOMS ON

LOGISTICS PERFORMANCE IN MOMBASA COUNTY

SCM 344 - Applied Logistics Management

JULY 2018

A RESEARCH PROJECT SUBMITTED TO ARIZONA STATE UNIVERSITY

DECLARATION

I certify that all the material in this research that is not my own work has been identified and that

it has not been presented to any other examination body for academic credit.

ABSTRACT

New technology such as a single-window system, cargo scanner management solution and

regional electronic cargo tracking systems are becoming ubiquitous in the customs department’s

logistics performance. From the selection of information, sorting of information, strengthening

custom’s protection of consumers from counterfeit products to the real-time tracking of cargo, it

is becoming a requirement to use these new technologies to meet customer service expectations

and raise the productivity of the workforce. The study's main objective was to examine the

effects of new technology by customs on logistics performance in Mombasa County. The study

adopted a descriptive cross-sectional research design which was exploratory in nature to obtain

qualitative information. The target population was 38 customs departments in Mombasa County.

A questionnaire was the preferred instrument for data collection. The researcher gave out 10

questionnaires and received 8 for analysis representing 80% response rate which was considered

adequate. Analysis of data was by the help of SPSS and it brought out the relationship between

the adoption of new technology and logistics performance. The findings revealed that new

technology variables considered in this study namely single window system, cargo scanner

management solution and regional electronic cargo tracking systems had a positive relationship

with logistics performance. The relationship was significant at 95% confidence (p<0.05) for all

the three independent variables. This shows that they are important factors affecting logistics

performance albeit at varying degrees. Furthermore, the study found out that new technology has

been adapted to a moderate extent. Also, new technology adoption affects logistics performance

to a large extent. More importantly, a single-window system was found to have a strong positive

correlation with logistics performance while cargo scanner management solution had a weak

relationship with logistics performance. The study, therefore, recommends that the firms should

increase new technology adoption and make use of various aspects of new technologies on offer

in order to gain competitive advantage.

iii

TABLE OF CONTENTS

DECLARATION ......................................................................................................................................................... I

ABSTRACT .............................................................................................................................................................. II

TABLE OF CONTENTS ............................................................................................................................................. III

LIST OF FIGURES ..................................................................................................................................................... V

LIST OF ABBREVIATIONS ........................................................................................................................................ V

CHAPTER ONE ........................................................................................................................................................ 1

INTRODUCTION ..................................................................................................................................................... 1

1.0 INTRODUCTION ........................................................................................................................................................ 1

1.2 BACKGROUND OF THE STUDY ...................................................................................................................................... 2

1.2.1 New Technology ........................................................................................................................................... 2

1.2.2 Logistics performance .................................................................................................................................. 4

1.2.3 Customs Department ................................................................................................................................... 5

1.3 RATIONALE, THEORETICAL BACKGROUND ...................................................................................................................... 6

1.4 STATEMENT OF THE PROBLEM ..................................................................................................................................... 7

1.5 THE PURPOSE OF THE STUDY ...................................................................................................................................... 9

1.6 GENERAL OBJECTIVE ............................................................................................................................................... 10

1.7 SPECIFIC OBJECTIVES ............................................................................................................................................... 10

1.8 RESEARCH QUESTIONS ............................................................................................................................................ 10

1.9 STATEMENT OF HYPOTHESIS ..................................................................................................................................... 10

1.11 ASSUMPTIONS OF THE STUDY .................................................................................................................................. 12

1.12 THE SCOPE OF THE STUDY ...................................................................................................................................... 12

1.13 LIMITATION ......................................................................................................................................................... 12

1.14 DELIMITATION ..................................................................................................................................................... 13

1.15 DEFINITION OF TERMS ........................................................................................................................................... 13

CHAPTER TWO ..................................................................................................................................................... 15

LITERATURE REVIEW ............................................................................................................................................ 15

2.0 INTRODUCTION ...................................................................................................................................................... 15

2.2 THEORETICAL REVIEW ............................................................................................................................................. 15

2.2.1 Resource Advantage Theory of Competition .............................................................................................. 15

2.2.2 Task Technology Fit theory. ....................................................................................................................... 16

2.2.3 Instrumental Theory of Technology ........................................................................................................... 17

2.2 EMPIRICAL REVIEW ................................................................................................................................................. 18

2.2.1 Regional Electronic Cargo Tracking System (RECTS) .................................................................................. 19

2.2.3 Cargo Scanner Management Solutions ...................................................................................................... 20

2.2.4 Singe-Window System. ............................................................................................................................... 21

2.3 CONCEPTUAL FRAMEWORK ...................................................................................................................................... 23

2.4 SUMMARY AND RESEARCH GAP ................................................................................................................................. 24

CHAPTER THREE ................................................................................................................................................... 26

RESEARCH METHODOLOGY ................................................................................................................................. 26

3.1 INTRODUCTION ...................................................................................................................................................... 26

3.2 RESEARCH DESIGN .................................................................................................................................................. 26

3.3 TARGET POPULATION. ............................................................................................................................................. 26

3.4 SAMPLING AND SAMPLE SIZE .................................................................................................................................... 26

3.5 DATA COLLECTION .................................................................................................................................................. 27

3.6 RELIABILITY AND VALIDITY ........................................................................................................................................ 27

3.8 DATA ANALYSIS ...................................................................................................................................................... 28

3.9. OPERATIONALIZATION OF STUDY VARIABLES ............................................................................................................... 29

CHAPTER FOUR .................................................................................................................................................... 31

DATA ANALYSIS AND PRESENTATION OF RESULTS ............................................................................................... 31

4.1 INTRODUCTION ...................................................................................................................................................... 31

4.2 RESPONSE RATE ..................................................................................................................................................... 31

4.3 DEMOGRAPHIC INFORMATION .................................................................................................................................. 31

4.3.1 Gender of the Respondents ........................................................................................................................ 31

Source: Research data (2019) ............................................................................................................................. 32

4.3.2 Age of the respondents .............................................................................................................................. 32

4.3.3 Period of Service with the Custom’s Logistics Department ........................................................................ 33

4.4 NEW TECHNOLOGY ADOPTION AND LOGISTICS PERFORMANCE ........................................................................................ 33

4.4.1The effects of the adoption of the Single Window System by customs logistics departments ................... 33

4.4.2 The adoption of Cargo scanner Management Solution by customs logistics departments ....................... 35

4.4.3 The effects of the adoption of Regional Cargo Electronic Tracking Systems (RECTS) on logistics

performance ........................................................................................................................................................ 36

4.4.4 Mean and Standard Deviation ................................................................................................................... 37

4.5 4 PEARSON CORRELATION ANALYSIS .......................................................................................................................... 38

4.6 RELIABILITY TEST .................................................................................................................................................... 40

4.7 MODEL SUMMARY AND ANOVA .............................................................................................................................. 40

4.8 DISTRIBUTION OF COEFFICIENTS AND HYPOTHESIS TESTING ............................................................................................ 41

CHAPTER FIVE ...................................................................................................................................................... 44

SUMMARY, CONCLUSION, AND RECOMMENDATION .......................................................................................... 44

5.1 INTRODUCTION ...................................................................................................................................................... 44

5.2 SUMMARY OF FINDINGS .......................................................................................................................................... 44

5.2.1 The effects of single window system on logistics performance ................................................................. 45

5.2.2 The effects cargo scanner management solution on logistics performance .............................................. 46

5.2.3 The effects of the adoption of Regional Cargo Electronic Tracking Systems (RECTS) on logistics

performance ........................................................................................................................................................ 46

5.3 CONCLUSIONS ........................................................................................................................................................ 47

5.4 RECOMMENDATIONS ............................................................................................................................................... 48

5.4 SUGGESTIONS FOR FURTHER RESEARCH ....................................................................................................................... 48

REFERENCES ......................................................................................................................................................... 50

APPENDIX I: RESEARCH QUESTIONNAIRE ............................................................................................................. 55

   
v 
 
 
                                                          LIST OF TABLES 
Table 1: Gender of the Respondents ............................................................................................. 32 
Table 2: Age of the Respondents .................................................................................................. 32 
Table 3: Period of Service at the Department ............................................................................... 33 
Table 4: Single Window System Uses in logistics departments ................................................... 34 
Table 5: Cargo Scanner Management Solution uses in logistics Departments ............................ 35 
Table 6: Regional Cargo Tracking Management Systems uses in Customs logistics departments
....................................................................................................................................................... 36 
Table 7: The mean and standard deviation Table ......................................................................... 37 
Table 8: Pearson Correlations ....................................................................................................... 39 
Table 9: Reliability Test................................................................................................................ 40 
Table 10: Model Summary ........................................................................................................... 40 
Table 11: ANOVA ........................................................................................................................ 41 
Table 12: Distribution of Coefficients .......................................................................................... 42 
 
LIST OF FIGURES 
Figure 1: Single Window System of Model of Trade ................................................................... 22 
Figure 2: The Conceptual Framework .......................................................................................... 24 
 
 
LIST OF ABBREVIATIONS 
RECTS- Regional Electronic Cargo Management Solution  
KPA- Kenya Ports Authority 
KRA- Kenya Revenue Authority  
LPI- Logistics Performance Index 
VAT- Value Added Tax 
IDF- Import Declaration Feed 
NGO- Non-Governmental Organization  

ECTS- Electronic Cargo Tracking System

AEO-Authorized Economic Operator

CHAPTER ONE

INTRODUCTION

1.0 Introduction

This chapter provides background information about the research at hand through analyses

validations of the problem in which the researcher is seeking to provide a solution. This is done

through the presentation and analysis of experiences, observations onions, and views on the

current situation in regard to the effects of the adoption of new technology by customs on

logistics performance in Mombasa County. Other aspects that this chapter will look into while

addressing the topic include background information such as social, cultural and historical

information on the research’s topic as well as the theoretical, rationale or the conceptual

background that gives knowledge on the topic.

The world of logistics is revolutionizing each day and every customs department has to cope

with these advancements in technology required in simplifying their logistics services. New

technology refers to the set of productive methods and techniques that provides significant

improvements over established technology for a specific process in a specified historical context

(Rotolo, Hicks & Martin, 2015). New technologies in the customs departments have helped in

making the work easier and better in the process of improving the logistics services. Nowadays,

the growth of international trade has gone high and customs departments play a very crucial link

in the international supply chains. Due to this role, logistics, as a key component of this

department plays a fundamental role in the supply chain success. According to Christopher

(2016) logistics involves the management of goods and carrying out information and services

from the point of origin to the consumption point. It aligns the complex patterns of traffic and

shipping, transportation and receiving, export and import operations. Due to the losses in time,

customs need to make changes in their logistics through the adoption of new technologies. Some

of the key forms of technology adopted in customs departments include cargo scanner

management solution, regional electronic cargo tracking system (RECTS) and the single window

system. These new technologies have revolutionized logistics performance in the industry.

The connectedness of the world today has led to an increased need for customer satisfaction and

expectations. These come with various logistics challenges facing most customs institutions

today. Luo Et al, (2017) assert that some of these challenges include cutting operation costs due

to increasing fuel prices. The other problem that Luo (2017) points out includes the improvement

of the business process which calls for the need for new technology that has been increasingly

challenging for the logistics industry especially in developing countries such as Kenya. The other

challenge is the improvement of customer service as they want full transparency into where their

deliveries are at all times during the transportation process. The other challenge that Tukuta and

Saruchera (2015) point out is the technology strategy and implementation that comes with

concerns such as its payments, as well as improvements, possess more difficulties. Therefore,

given these challenges, the research saw the need to address the issue of the effects of the

adoption of new technology by customs on logistics performance.

In Kenya for instance, the logistics performance needs improvement which means that the

customs departments have a challenge of enhancing efficiency and customer experiences. In

their study of the Kenya Ports Authority (KPA), Ruto and Datche (2015), found that logistics

technology problems often bring businesses to a halt. They state that technology issues happen

almost everywhere from a few times, to even every day of the week. They reported that the

frequency of the technologic problems is increasing every day. However, they noted that despite

these problems, new technology has revolutionized the logistics operations in the country since

the past decade. With this said, it is, therefore, clear that in Kenya, there are challenges that come

with the adoption of new technology as much as it improves performance and customer

satisfaction.

1.2 Background of the Study

1.2.1 New Technology

In the past, the lack of technology was a major problem in the logistics sector across the globe. In

the last 20th century, things were different in the logistics industry. In the 1990s, things were

different in the logistics sector as management systems did not have recent forms of technology

to revolutionize them. Pugliese et al, (2017) note that during this period to early 2000,

paperwork, data entry packing task interleaving among other tasks in logistics was done

manually by workers as automation was not yet in place to improve efficiency. However, in the

late 2000s to date, things have changed in the customs departments across the world.

Technological advances have allowed these departments to accomplish a lot in a short time and

effectively with high levels of accuracy and with effective financial sustainability (Kalinicheva et

al, 2016). In the past, for instance, customers placed orders, booked shipments and received the

estimated time of delivery and then they were left in the dark without knowing the state of their

cargo on shipment. Telephone calls were the only ways in which they could track the status of

their shipments. Today, however, things have changed as customer experiences have changed as

software and the internet advances have allowed them to access their shipping and tracking

systems anytime and anywhere they want. This clearly, proves that technology has made changes

to the industry.

The economic advances in most countries across the world have improved trade between

countries. The evolution of technology is pushing the boundaries and changing how companies

do business (Goldsby & Zinn, 2016). Today, companies are accustomed to almost everything

being done by computers and right at their fingertips for immediate access. Through the various

technologies in the customs departments, there has been intensive adoption of new technology in

service delivery that has made it possible to receive cargo and packages in a short period of time

and with effective control and monitoring as well as management. Sindi and Roe (2017) state

that improved technology has led to increased productivity in the logistics sector of the customs

departments as well as in the entire supply chain minimizing errors and costs.

There are numerous types of new technologies in place today used by customs departments

across the world in improving their logistics performance. In Kenya for instance, one of them is

the regional electronic cargo tracking systems (RECTS) developed by Kenya Revenue Authority

in conjunction with revenue authorities from Rwanda and Uganda. According to Mugambi

(2017), it enables real tracking of cargo in transit from one region to the other for the

improvement of tax as well as security. The other one is the Single Window System which is

a trade facilitation tool that enables international traders to submit regulatory documents such as

customs declarations and applications for import/export permits at a single location (Nizeyimana

& De Wulf, 2015). This system allows parties involved in trade and transport to lodge

standardized information and documents with a single entry point to fulfill all import, export, and

transit-related regulatory requirements. “If information is electronic, then individual data

elements should only be submitted once. Independently from its description as a platform,

environment, or facility, an SW can best be understood by the service that it aims to provide to

traders and government authorities. The third one is the Cargo Scanner Management Solution.

Paresi (2018) indicates that this solution plays a key role in ensuring that there is a non-intrusive

inspection of export, import and security controls. This solution has helped the customs

authorities in ensuring that their cargo is effectively screen booth on sea and land.

1.2.2 Logistics performance

The growth and evolution in international trade have been quite intensive in the past two

decades. According to Arvis et al, (2016) many economies across the world have recognized that

trade plays a crucial role in economic growth. International trade involves the movements of

goods across borders involving numerous procedures. The competitiveness of a country’s

logistics depends on its customs clearance efficiency and effectiveness. Therefore, the

effectiveness of customs clearance in regards to logistics performance of a country leads to trade

facilitation which is quite pivotal to its development. Good logistics performance fosters a

country’s competitiveness as it allows it to trade services and goods on a timely basis and on

lower costs of transaction (Martí, Martín & Puertas, 2017). On the other hand, inefficient

logistics pose a significant obstacle to trade in the country since it makes it difficult for it to

develop or tap new markets and to improve its overall competitiveness in the trading system.

This means that the customs department has a crucial role to play in improving the logistics

performance of a country to facilitate trade and to improve the economy of that country.

Logistics comprises a network of services that supports the physical movement of goods across

and within borders. It is estimated to be a $4.3 trillion industry (Gani, 2017). The logistics

performance index (LPI) scores countries on how efficiently they move goods across and within

borders. According to Mwangangi (2016), the developing country’s capacity to move goods

from one country or region to the other efficiently and connecting consumers and manufacturers

with international markets is considered to have improved in the past few years –albeit slowly.

However, there more that needs to be done in order to close the existing logistics “performance

gap” between the low and high performers. Ojala and Celebi (2015) agree that supply chains are

deemed only as good as their weakest link, and sustainable improvements need complex changes

in a range of policy dimensions in areas that include trade facilitation, services, and

infrastructure. These efforts need persistence and focus, a combination of few countries have

achieved according to the survey conducted by World Bank on trade logistics.

In Kenya, logistics performance is quite impressive. According to the World Bank, Kenya’s

logistics performance is termed as the best in East Africa due to the continued removal of

administrative controls as well as the continued improvement of infrastructure. Ojala and Celebi

(2015) indicate that Kenya’s logistics performance index (LPI) ranks Kenya at position 42

globally with a score of 3.33 points in 2019. By comparison, the surveys conducted in 2019

placed Uganda and Tanzania at positions 58 and 61 with a score of 3.04 and 2.99 respectively.

Mwangangi (2016) asserts that Kenya’s logistics performance is second in the African continent

after South Africa which is in the 20th position on the global survey with a score of 3.78. This

LPI indicates that Kenya has greatly reduced the costs of doing business and it has improved its

trade flow for exporters and importers. According to the World Bank report (2019), efficient

logistics connect forms to international and domestic trade through reliable supply chain

networks which is a major characteristic of the Kenyan trade. Also, the adoption of new

technology is attributed as a key aspect of the good performance of the customs departments in

Kenya, making it a viable topic for research.

1.2.3 Customs Department

The customs Service Departments in Kenya under the Kenya Revenue Authority has a

fundamental role in collecting and accounting for import VAT and duty on imports. The other

taxes that this department include petroleum development levy, sugar levy, road maintenance

levy, import declaration feed (IDF) roped transit toll, directorate of civil aviation fees, air

passenger services charge, Kenya airport authority concession fees, and various fees associated

with motor vehicles (Odhiambo, 2018). It performs various logistics services that may expound

to shipping but mainly its services may include creating an invoice for international shipping and

making arrangements for the shipment pickup and cargo delivery reports. Further, Ouma (2019)

adds that it makes arrangements and it coordinates customs for attaching warehousing,

thoroughly completing all the documentation work required for shipments and finally confirming

the delivery of your shipment.

The Customs Service Department implements bilateral regional and international trade

arrangements. Nandagopal (2018) asserts that the department also provides support to global

enforcement efforts against smuggling and other vices that negatively affect trade, illegal

importation, regularly abused drugs and exportation and importation of arms, as mandated

through the various international legal instruments. As an agency of the government, customs

departments are entrusted with a responsibility to control and monitor imports as well as exports,

it implements trade and customs clauses of regional trade agreements (Nandagopal, 2018).

In regard to logistics, every country publishes every country annually publishes its policy for

Foreign Trade, which stipulates the conditions under which goods and services are eligible to be

exported or imported. According to Truel, (2017), customs departments implement the

provisions of the policy under customs rules, regulations and tariffs. Imports in many countries

may be allowed freely, or some categories may be permitted with due licenses. Many items are

also published as banned for import and not allowed entry into the country. All of the items

imported into the country have to be custom cleared. Truel (2017) adds that this applies to the

items brought in as personal effects and also imported by trade and business establishments

including governmental and defense agencies. Necessary stipulated duties would have to be paid

before the goods are released by Customs. Cargo imported into the country from any point of

entry is warehoused at Customs bonded area under customs jurisdiction until it is released after

clearance. Therefore, customs departments have a greater mandate in international and local

trade in many countries and favorable logistics performance is vital to their functions.

1.3 Rationale, Theoretical Background

Technology has become a key competent of every aspect of society today as organizations,

governments, individuals among other groups, in general, strive to adopt new technology for

various reasons. In conducting research on the effects of the adoption of new technology on

customs logistics performance in Mombasa County, the research wants to determine how

technology and its key components are bringing change to the customs logistics. Technology

adoption is meant to ensure that efficiency and accuracy achieved in whoever endeavors one is

perusing and this is no exception to the customs departments. This study hopes to find out how

technology has enhanced the logistics performance of the customs departments in Mombasa

County including components such as the RECTS, cargo scanners, and single window systems.

This topic is important because it will provide an insight into how new technology can enhance

logistics performance providing recommendations on what could be done to improve its adoption

as well as the logistics performance. The researcher expects to conclude that technology has

brought about major improvements in logistics performance among customs departments in

Mombasa County.

This study is also anchored on various theories that provide a better understanding of the topic

and the various aspects surrounding it. One of the theories is the Resource Advantage Theory of

Competition which was coined by Shelby Hunt and Robert Morgan in 1995 (Hunt, 1995). This

theory offers a challenge to practitioners and managers to look at the competition based on what

develops as `resource-advantage.' Resources in an organization should be seen as unique,

imperfectly mobile and heterogeneous. Resource Advantage theory is the foundation upon

which organizational performance and strategic competitive advantage can be predicted and in

this study, the use of new technology (Hunt, 1995). The other theory that anchors this study is

the Takes Technology Fit Theory. This theory states that new technology has a likelihood of

impacting positively one employee's performance and be used if the capabilities of the

technology match the tasks that a user must perform (Goodhue & Thompson, 1995). This theory

is crucial in the understanding of how new technology could fit into the customs departments in

increasing logistics performance. The third Theory anchoring the study is the Instrumental

Theory. This theory was developed by Andrew Feenberg at Oxford University in 2002. The

theory looks into how people use technology to their benefit in an organization rather than the

Technology itself. It helps in understanding how technology affects how people use it in an

organization (Feenberg, 2002). This could be useful in understanding how technology used by

customs departments impacts its users and consequently the logistics performance.

1.4 Statement of the Problem

New technology in the management of logistics is relatively a new thing and it allows customs

departments to have a good flow of goods from one region to the other effectively and

efficiently. Technology is an important tool in ensuring that the activities of an organization

become realistic and cost-effective. According to Bowersox and Daughterty (2015), most

scholars, practitioners, and policymakers agree that new technology is of value to managing

logistics; however, the numerous effects and applications are not understood. It is important to

understand that information technology has been made accessible for firms in transport,

manufacturing, and transport logistics organizations. Olah et al., (2018) asserts that the ability to

relay information and to automate tasks in custom departments effectively among all trade

partners in the distribution chain through automation enhances performance. New technological

development and application on logistics in the custom operation already had greater effects on

many sectors specifically in the transportation industry. According to Bolata, et al. (2016) in a

study on the Northern Corridor efficiency showed that the transport costs in East and Central

Africa are almost 42% of the imports compared to 22% of what is targeted in the industry. This

makes the region to have the highest cost of transportation around the globe. Olah, et al. (2018)

adds that besides the direct cost of transportation, there are various, time consuming and complex

transactions at border stations and ports that contribute to the high cost of logistics costs in East

Africa region

In Mombasa County, some of the challenges include the efficiency of the procedures due to the

lack of effective utilization of new technologies. In the customs departments in the County, most

of the systems such as the Regional Cargo tracking System are not effectively utilized. Other

new technologies in use in this department are the Single Window System and the Cargo

Scanner Management Solution. All these tools are useful but the challenge is its management,

lack of scanner standardization, system instabilities, improper software updates, lack of basic

security and technological underinvestment. Mwangangi (2016) asserts that these are some of the

key problems that prompted the researcher to undertake this study. If these technologies are

properly managed and applied in logistics of the customs departments in Mombasa Count they

will act as good tools for trade facilitation as it will enhance tax collection, enforcement of cargo

handling regulations and it will allow the customs department to make the country a preferred

route for trade in the region. Mwangi and Moronge (2016) suggest that focus should be put on

the performance of inland, customs; trade-related infrastructure and transit logistics service

provision, air and seaport efficiency, and the utilization of technology for timely trade in goods

at low costs.

Over the years, there has been a debate on whether or not adopting new technology enhances

logistics performance. Customs departments in Mombasa County and the country at large must

continuously establish ways to add value to customers by minimizing costs. Locally, studies

have been done on logistics performance but focus on new technology as a whole. For example,

Olah, et al. (2018) in a study on the influence of new technology on logistics performance found

that it influences the performance of logistics in cargo transportation. Also, Mwangangi (2016)

found out that logistics performance is impacted positively and significantly by the adoption of

new technology. On the other hand, Mwangi and Moronge (2016) did a study on the effects of

new technology on the performance of logistics firms in Nairobi County. Data collected from ten

firms revealed that 50% of the companies are applying new technology in their operation

division and service delivering. However, they indicated a low level of new technology usage

among logistic companies in Nairobi County. A study by Gabuubi (2015) revealed that e-

logistics has a positive effect on the performance of the organization and logistics firms. This,

therefore, means that customs departments in Kenya should be encouraged to invest and adopt it

in order to be more competitive in the East African Market.

1.5 The Purpose of the Study

The intention of the researcher carrying out this study was to determine the effects of the

adoption of new technology by customs on logistics performance in Mombasa County. By

carrying out this research, the researcher hopes to link the use of new technologies in the County

such as the use of Cargo Scanner Management Solutions, Singe Window System and Regional

Cargo Tracking System (RECTS) to the good performance that the customs department has

elicited in the recent years. Also, the researcher hopes to achieve the objectives such as

determining effects of the Single Window System on logistics performance, finding out the

effects of the adoption of cargo scanner management solution on logistics performance as well as

finding out the effects regional cargo electronic tracking systems (RECTS) on logistics

performance in the County. At the end of the research, some of the questions to which answers

can be expected include: What are the effects of the adoption of Single Window System on

logistics performance?, What are the effects of the effects of the adoption of cargo scanner

management solution on logistics performance and What are the effects of the adoption of

regional cargo electronic tracking systems (RECTS) in Mombasa County?

1.6 General Objective

The general objective of this research is to examine the effects of new technology by customs on

logistics performance in Mombasa County.

1.7 Specific Objectives

1. To determine the effects of the adoption of the Single Window System on logistics

performance in Mombasa County.

2. To find out the effects of the adoption of Cargo scanner Management Solution on logistics

performance in Mombasa County.

3. To examine the effects of the adoption of Regional Cargo Electronic Tracking Systems

(RECTS) on logistics performance in the County.

1.8 Research Questions

In order for the researcher to address the research objectives, the answers to the following

research questions were crucial.

1. What are the effects of the adoption of the Single Window System on logistics performance

in Mombasa County?

2. How does the adoption of Regional Cargo Electronic Tracking Systems (RECTS) affect

logistics performance in Mombasa County?

3. What are the effects of the adoption of the Cargo scanner Management Solution on logistics

performance in Mombasa County?

1.9 Statement of Hypothesis

The following hypotheses are some of the researcher’s means of seeking solutions to the

problem.

1. It is hypothesized that the adoption of new technology by customs will improve logistics

performance in Mombasa County through tracking of cargo, scanning of cargo and single entry

point standardization.

1.10 Significance of the Study

This research will be of benefit to a number of players in the field of logistics in Mombasa

County and the country as a whole. The first one is the Scholars. This study will be of concern to

the education sector in the county because it will enrich the existing body of research and

knowledge. This is because it will be essential material for carrying out further research on the

topic of logistics performance and technology, and it will also be a useful resource for future

reference among scholars.

The customs Service Department under the Kenya Revenue Authority will also benefit from this

research. It will be a useful research material for the customs departments since their consultants

will find it as a helpful reference material in advising their investors as well as the government

on some of the practical application of the new technologies such as the Regional Cargo

Electronic Tracking Systems (RECTS), Single Window System and outreach programs in

Mombasa County and Cargo scanner Management Solution in other counties in the country.

Moreover, those businesses that will have benefitted from the customs services will be able to

understand their importance of the adoption of new technology in enhancing service delivery to

them.

The other crucial beneficiary of this research is the Kenya Revenue Authority and the Kenyan

Government. The regulator such as the government and the Kenya Revenue Authority will

benefit from this research because it will get a glimpse of some of the vital factors that customs

departments consider before adopting new technologies and some of the effects of new

technology on logistics performance when undergoing expansion processes. This research will

also provide basic blueprints to the regulator in drafting its policies that will fit all the customs

departments’ logistics. The government will also use the findings of this research in developing

a sustainable logistics performance and the integration of customs and logistics into the broader

financial sector.

1.11 Assumptions of the Study

This study will be conducted with the assumptions below.

1. When the new technology will be fully implemented in the County, all the employees and

clients will embrace its significance.

2. That the implementation of the new technologies will serve the purposes they are

intended to serve.

3. That the data needed for this study will have been provided with utmost faith by the

respondents for higher accuracy.

1.12 The Scope of the Study

This study will focus only on 10 custom agencies in Mombasa County which is convenient to the

researcher. The researcher, confined by time constraints and resources assumes that the data

from the one department will be reflective of the customs department experiences in the county.

The reason behind the adoption of new technology by customs departments is to foster better

logistics performance. The data collected covered a period of five years starting 2015 to 2019

which provides a clear picture of the situation in regard to the adoption of new technology. Most

of the data used are from both primary and secondary sources about the adoption of new

technology. The study will cover the effects of new technology by customs on logistics

performance in Mombasa County.

1.13 Limitation

There are a number of limitations to this study. One of them is that this study depends on

primary data collected from respondents. One problem that accompanies this is that some

respondents may not provide information as accurately as possible to due biases depending on

their views on the adoption of new technology. Secondly, the other limitation is that the

researcher confined to the research data from secondary sources from within a range of the past 8

years which makes its scope a little narrower and it may not give a completely clear picture of

the situation before the emergence of key technologies and after their emergence. The third

limitation is that the study is its generalizability. The data and responses from the customs

department in Mombasa County may not be applicable outside of Kenya among other lager

customs departments due to geographical, demographic and economic differences.

1.14 Delimitation

The issue of the dependence on primary data that could be open to bias from the respondents is

addressed through requesting the respondents to provide information with utmost faith and that

their responses and names will be treated with confidentiality and the results of the study will be

provided anonymously. In regard to the scope of data from secondary sources, the researcher

ensures that the data came from reliable sources. According to Munthe-Kaas, et al., (2019)

reliable sources are sources such as scholarly journals, government websites, trusted

organizations such as NGO’s websites, and textbooks among other credible sources. The data

was also scrutinized in regard to their accuracy in providing information concerning the effects

of new technology on logistics performance both locally and internationally. In regard to the

generalizability, the researcher framed the research questionnaire in a way that captured the

aspects of new technology that is universally applicable to almost every customs department

logistics.

1.15 Definition of Terms

Generalizability- This refers to the extension of research findings and conclusions from a study

conducted on a sample population to a larger population.

Accuracy- a term used in survey research to refer to the match between the target population and

the sample.

Anonymity- This is a research condition in which no one, including the researcher, knows the

identities of the research participants.

Bias - a loss of balance and accuracy in the use of research methods. It can appear in research via

the sampling frame, random sampling, or non-response. It can also occur at other stages in

research, such as while interviewing, in the design of questions, or in the way data are analyzed

and presented. Bias means that the research findings will not be representative of, or

generalizable to, a wider population.

Confidentiality - a research condition in which no one except the researcher(s) knows the

identities of the participants in a study. It refers to the treatment of information that a participant

has disclosed to the researcher in a relationship of trust and with the expectation that it will not

be revealed to others in ways that violate the original consent agreement unless permission is

granted by the participant.

Credibility - a researcher's ability to demonstrate that the object of a study is accurately

identified and described based on the way in which the study was conducted.

Data - factual information [as measurements or statistics] used as a basis for reasoning,

discussion, or calculation.

Hypothesis - a tentative explanation based on theory to predict a causal relationship between

variables.

Population - the target group under investigation. The population is the entire set under

consideration. Samples are drawn from populations.

Sample - the population researched in a particular study. Usually, attempts are made to select a

"sample population" that is considered representative of groups of people to whom results will be

generalized or transferred. In studies that use inferential statistics to analyze results or which are

designed to be generalizable, the sample size is critical, generally the larger the number in the

sample, the higher the likelihood of a representative distribution of the population.

Theory - a general explanation about a specific behavior or set of events that are based on

known principles and serves to organize related events in a meaningful way. A theory is not as

specific as a hypothesis.

CHAPTER TWO

LITERATURE REVIEW

2.0 Introduction

This chapter addresses the various theories, concepts, empirical works and reviews on the

literature on Customs adoption of new technology and the effects it has on logistics performance.

Extensive literature reviews covering, among others, relevant national-levels studies and reports,

conference proceedings, working papers and other sources of information will be explored. It

presents and critically analyzes the empirical experiences of other scholars on the topic that

contributes towards the research. The theoretical and conceptual framework is also presented.

2.2 Theoretical Review

In the past years, there have been few studies focusing on the effects of new technology adoption

by customs on logistics performance. As most customs department’s logistics continue to

blossom and spread trade across the globe, researchers have developed an interest in the various

aspects of new technology and how it has contributed to better logistics performance. As a result,

multiple scholars have come up with theories that provide a systematic understanding of new

technology and its effects on logistics performance. The theory discussed in this section is the

Resource Advantage Theory of Competition, Takes Technology Fit Theory and the Instrumental

Theory.

2.2.1 Resource Advantage Theory of Competition

This theory provides crucial information regarding the utilization of new technology in

advancing logistics performance. It offers a challenge to practitioners and managers to look at

the competition based on what develops as `resource-advantage.' According to Hitt, Xu and

Carnes (2016) resources in an organization should be seen as unique, imperfectly mobile and

heterogeneous. According to the Resource Advantage theory, a company should develop the

required competencies or dynamic capabilities so as to address customer marketing needs and

quality (Hunty 1995; Hit, Xu & Carnes 2016). On the other hand, resource Advantage theory is

the foundation upon which organizational performance and strategic competitive advantage can

be predicted and in this study the use of new technology (Gakuubi, 2018). Thus, customs

departments can enhance logistics performance through the use of new technology in order to

gain a competitive advantage over competitors.

The adoption of new technology is one of the ways in which the customs departments in

Mombasa County can develop the required competencies or dynamic capabilities so as to

address customer marketing needs and quality. For instance, the use of new technologies such as

the Sing Window System will provide various advantages to the department because it is a

practical application of trade facilitation concept that reduces non-tariff trade barriers and

delivers immediate benefits to all, members of the trading community (Noe, et al., (2017).

Therefore, the Resources Advantage Theory of Completion urges firms such as the customs

departments to adopt new technologies that could give it the much-needed competencies that

could give it a competitive advantage. Also, it is important to note that new technology helps in

the utilization of the crucial resources to the advantage of the organization due to its efficiency,

economy, and effectiveness.

2.2.2 Task Technology Fit theory.

The Task Technology Fit Theory can explain about new technology and how it can fit into tasks

to foster efficiency and improve performance. Goodhue and Thompson (1995) assert that new

technology has a likelihood of impacting positively on employee performance and could be used

if the capabilities of the technology match the tasks that a user must perform. Goodhue and

Thompson (1995) came up with the task-technology fit measure composed of eight (8) factors:

capability, quality, the compatibility, authorization, ease of use and training, timeliness in

production, system reliability and relationship with users. Each of the factors is measured using

questions rated on a seven-point scale which ranges from strongly agree to strongly disagree.

Goodhue & Thompson (1995) found the task-technology fit measure in conjunction with

utilization, to be an important predictor of user reports of improved job performance and

effectiveness that was attributable to their use of the system under investigation.

New technology is designed for asset organizations or users to perform tasks in a more effective

and efficient manner. Companies spend their fortunes on new technologies to improve their

performances and this is no exception to the customs departments. Lai (2017) states that within

customs departments, new technology has been put in place to ensure that individual and

organization performance is improved. However, the key challenge emergency when the

technology deployed does not fit into the task or to the skills of the employees. Therefore,

customs departments should match tasks performed by various people in order to enhance

performance. This theory looks at the individual level contributions as opposed to group

contribution. As such, if individuals perform poorly in their tasks it will affect the entire

organization (Lai, 2017). The capabilities of new technology must tally with the tasks a user

must perform or be compatible in order to improve logistics performance.

2.2.3 Instrumental Theory of Technology

This theory was developed by Andrew Feenberg at Oxford University in 2002. The theory looks

into how people use technology to their benefit in an organization rather than the Technology

itself. The instrumental theory offers the most widely accepted view of technology. It is based on

the common-sense idea that technologies are "tools" standing ready to serve the purposes of their

users. Technology is deemed "neutral," without evaluative content of its own. However, what

does the notion of the "neutrality" of technology actually mean? The concept usually implies at

least four points. The first technology, as a pure instrumentality, is indifferent to the variety of

ends it can be employed to achieve (Feenberg, 2002). Thus, the neutrality of technology is

merely a special case of the neutrality of instrumental means, which are only contingently related

to the substantive values they serve (Røvik, 2016). Secondly, technology also appears to be

indifferent with respect to politics, at least in the modern world, and especially with respect to

the capitalist and socialist societies (Feenberg, 2002). Thirdly, the socio-political neutrality of

technology is usually attributed to its "rational" character and the universality of the truth it

embodies. Technology, in other words, is based on verifiable causal propositions (Feeberg,

2002). Lastly, the universality of technology also means that the same standards of measurement

can be applied in different settings (Røvik, 2016). Thus, technology is routinely said to increase

the productivity of labor in different countries, different eras, and different civilizations.

Technologies are neutral because they stand essentially under the very same norm of efficiency

in any and every context.

Instrumental Theory, therefore, helps in understanding how technology affects how people use it

in an organization (Feenberg, 2002). This could be useful in understanding how technology used

by customs departments impacts its users and consequently the logistics performance. In the

customs departments, there have been issues between people and the use of technology. This

theory is useful to this study because it explains some of the causes of resistance to technology to

be an issue with individuals and not the technology itself. If this is understood and employees are

given the required education, then it becomes easier for them to adopt the new technologies.

2.2 Empirical Review

Technology has brought about a major paradigm shift in the customs departments across the

world. Studies conducted by Bhandari (2014) found that the new business processes and the

great innovations have changed the flow of goods from the manufacturers, retailers or

wholesalers until it gets to the customers. On the other hand, Saidi and Hammami (2011) add

that the face of logistics has been hugely impacted by innovations in communication technology,

information technology and the transformation of identification technology from manual to an

automatic one. The improvement in efficiency, reduction of costs, increased competitiveness and

changes in strategies have changed in today’s business. Another study by Mathauer and

Hofmann (2019) found out that technology has greatly enhanced logistics through innovations in

new technology. The adoption and development of new technology have led to the adoption of

emerging technologies such as the Cargo Scanner Management Solutions, Singe Window

System and Regional Cargo Tracking System (RECTS) (Olah, et al., 2018).

These technologies offer useful capabilities to the customs departments seeking to improve their

performance. Bolatan, et al., (2016) conducted a study on the impacts of new technology on

logistics performance and according to their study, new technology Improves customer relations

creating entry barriers, speeds transactions and it reduces the order cycle. Also, Gunasekaran,

Subramanian and Papadopoulos (2017) seconds that new technology brings about real-time

interactions such that customers and trucking companies can instantly locate the position of the

delivery. New technology also eliminates some human warehouse operations that reduce costs

and it Improves efficiency and reliability of logistics systems (Gunasekaran, Subramanian &

Papadopoulos (2017); Mathauer & Hofmann, 2019). Further, Aziz, et al., 2016) says it has

enhanced supply chain management through improved accuracy, increased efficiency and

reliability of the logistic system.

2.2.1 Regional Electronic Cargo Tracking System (RECTS)

Regional Cargo Tracking System (RECTS), according to Kenya Revenue Authority is an

initiative created by KRA and the revenue administrations of Rwanda, Uganda, Kenya, and

Tanzania. According to Kabiru, et al., (2016), this system enables real-time tracking of transit

cargo from the port of Mombasa to its final destination through an online digital platform.

Mugambi (2017) who conducted a study on the effects of the Regional Electronic Cargo

Tracking System found out that the reason for the development of this system was to implement

cargo security and tracking system as a response to the interest of the governments of the four

countries to improve tax collection. Additionally, Miler (2015) indicates that the system was also

aimed at enhancing enforcement of cargo handling regulations and maintaining Kenya as a

preferred trade route for cargo in East Africa. This initiative is critical in supporting the national

programs that are aimed at promoting trade among the countries in East Africa. Mugambi (2017)

study also found that the other reason why this system was created was to replace the Electronic

Cargo Tracking System (ECTS) that was being managed by the private sector that failed to

achieve the objects it was created to deliver to the customs departments.

The Regional Electronic Cargo Tracking System encompasses the use of satellites, special

electronic seals fitted into cargo containers and a monitoring center (Kenya Revenue Authority,

2016). Mugambi (2017) points out that RECTS covers trade routes that extend from the port of

Mombasa to the Free Zones within Kenya, and from the port via the country's main

transportation trade routes to the neighboring landlocked countries including Uganda and

Rwanda. Ross (2017), on the other hand, asserts that the introduction of RECTS is timely and is

in the backdrop of rampant illegal dumping. Dumping takes place when goods consigned to a

destination in another customs jurisdiction are unprocedurally offloaded while in transit

(Mugambi, 2017). He adds that not only does such action result in unfair trade practice, but also

in major duty and tax losses. This system has brought major results to the customs departments

in the region. Kithiia (2015) found out that the implementation of RECTS has led to the closing

of prevalent loopholes of tax loss and it has increased the flow of cargo through the e-monitoring

system. Juma (2016) also found out that RECTS has eliminated cargo diversions into the local

market as well as creating alert and responses during the trailer stop-overs that take more than

the allowed time. Therefore, it is clear that its implementation has brought major trade boosts in

Kenya and the entire East African region.

2.2.3 Cargo Scanner Management Solutions

In a global marketplace where illicit cross border trade is still thriving, supply chain security has

never been more crucial. Customs need to prevent the illegal transport of drugs, arms, other

illegal goods, and migrants. Nwankwo, Olayinka, and Benson (2019) conducted a study on the

cargo scanner management solutions and they state that Cargo Scanning plays a critical part in

the non-intrusive inspection of import, export and security controls. Föcker, et al., (2015) added

that when combined with effective profiling methods, non-intrusive scanners can greatly

improve the Customs and Security functions by screening cargo flows at both sea and land

borders. Nwankwo, Olayinka, and Benson (2019) also articulated that the cargo scanner

management solutions do not only scan containers but it provides container loading inspections

services that guarantee that customs departments staff members monitor the entire loading

process.

Cargo Scanner Management Solution is a Cargo scanning or non-intrusive inspection (NII) is a

method of inspecting and identifying goods in transportation systems without a time-intensive

unloading process (Bendahan, 2015). It is often used for scanning of intermodal freight

containers. Cutmore, Liu, and Tickner (2013) found out that the inspection of a container’s

contents through scanning is conducted in order to provide Customs officials with the ability to

verify the accuracy of information provided by shippers on a container’s contents and the

effectiveness of container integrity measures. Moreover, Föcker, et al., (2015) indicated that

scanning is important because it can help identify dangerous cargo when the originating shipper,

or the party responsible for stuffing and sealing the container, appears to be legitimate (AEO) but

has actually been infiltrated by a criminal group. In these cases, other layers of security may

provide a false sense of security because the shipments appear to be outwardly “legitimate” when

in fact it is illegal (Cutmore, Liu & Tickner, 2013).

2.2.4 Singe-Window System.

International and local trade has been a vital economic development for ages among nations,

individuals and groups. For governments to reap more taxes from the various types of trade,

various interventions have been put in place. One of these systems is the single window system.

Ndonga (2013) asserts that a single-window system is a complex government implementation of

a computer to facilitate international trade by enables the submission of regulatory documents to

a single entity or a single location. Further, Ahn and Han (2017) define it that it is a facility that

allows the shareholders in international transport and trade to submit homogeneous official

papers and information to a single point of entry which then fulfills all the exports, imports and

transit requirements. Ndonga (2013) adds that’s If the documents are submitted through the

internet, in most cases, the person performing the transaction has to submit the documents only

once. Some of the common documentations that go through this system include certificates of

origin, commercial invoices, customs manifest declaration, and i8mprots/export trade

declarations.

Single Window System of trade is helping all offer the world as mammy countries across the

globe have adopted it in facilitating trade. In their study, McMaster and Nowak (2016) indicated

that in countries such USA single window system in the form of the Automated Commercial

Environment (ACE) aims to facilitate the import and export of goods. This is because a single

point of entry for the exchange of electronic information between regulatory agencies and

trading participants is simpler, faster and more efficient. For example, carriers, brokers, and

shippers only need to enter information once instead of multiple times, significantly reducing the

risk of errors and duplication. Studies conducted by Aman, et al., (2017) showed that in countries

such as South Africa and Nigeria, Single Window Systems have been adapted to improve

customer satisfaction in logistics since cutting through customs procedures can take a long time,

leading to delayed shipments and unhappy customers. Finally, Ndonga (2013) concludes that in a

market characterized by low margins and high costs, using SW like ACE helps speed up cargo

clearance so that shippers and carriers can deliver merchandise to customers quickly.

The figure below illustrates how a single-window system of trade works.

Figure 1: Single Window System of Model of Trade

Source: Pugliatti (2011).

Kenya is no exception when it comes to the utilization of the single window system of trade. In a

study conducted by Ndonga (2013), he found out that the customs department in Kenya has

implemented the use of a single-window system as a way of maximizing revenue as well as

keeping track of both local and international trade in the country. Pugliatti (2011) points out that

the reasons why governments set up a single-window system is because businesses suffer both

direct border-related costs, such as expenses linked to supplying information and documents to

the relevant authority, and indirect costs, such as those arising from procedural delays, lost

business opportunities and a lack of predictability in the regulations. Tosevska-Trpcevska (2014)

clarifies this claim by stating that surveys aimed at calculating these costs suggest that they may

range from two percent to 15 percent of the value of traded goods in developed countries and up

to 30-42 percent in production costs in developing countries such as Kenya. Therefore, setting up

a single-window system, also called the Kenya TradeNet Systems would help in facilitating

international trade in Kenya by reducing delays and lowering costs associated with clearance of

goods at the borders, while maintaining the requisite controls and collection of levies, fees,

duties and taxes, where applicable, on imports or exports.

2.3 Conceptual Framework

In this study, there are three types of variables which include the independent variable, the

dependent variable, and the intervening variables. The independent variable is the variable that

affects the dependent variable. Seuring and Müller (2018) state that the values of an independent

variable do not represent a problem that needs explanation in analysis and its variation doe not

depend on the other. The independent variables in this study will be new technology which

encompasses the Cargo Scanner Management Solutions, Singe Window System and Regional

Cargo Tracking System (RECTS). In regard to the dependent variable, Seuring and Müller

(2018) define it as a variable measured in the experiment and what is affected during the

experiment as it responds to the independent variable and in this research, it is the logistics

performance. The efficiency of the new technologies represents the moderating variables and it

affects the relationship between new technology and logistics performance.

The diagram below shows the relations between the independent variable and the dependent

variables.

New Technology:

1. Single Window

System

2. Regional

Electronic Cargo

Tracking System

3. Cargo Scanner

Management

Solution

Logistics

Performance

Figure 2: The Conceptual Framework

Source: Researcher’s Compilation (2019).

2.4 Summary and research Gap

The concept of new technology and its effect on logistics performance has been discussed in

detail both in the literature as well as from the empirical studies done on the subject area. It was

evident that Cargo Scanner Management Solutions, Singe Window System and Regional Cargo

Tracking System (RECTS) have had a positive effect on a customs logistics performance. These

new technologies have been used both as a financial and non-financial measure and in the

present day competitive business environment as a competitive tool. Based on published studies

over the last 8 years about new technology applied to logistics showed that research done to

assess the use of new technology to support logistics performance are very few. This makes this

study relatively important, due to its contribution to the literature review and also to businesses

done because it addresses the practical experiences by customs departments. This is also

important when customs departments in Kenya are concerned. Grawe (2019) indicates that

successful innovation creates a unique competitive position which gives a business competitive

advantage and good performance.

The innovation in software and microelectronics has brought about universal technologies which

have come to form a persistent cluster of information and communication technologies (Grawe,

2019). Whereas the link between technology use and logistics performance is theoretically

justified, no empirical evidence related to the link has been identified in Kenya especially among

customs departments. Thus, this study seeks to show that logistics performance relies on new

technology. The end result of this seamless connection is enhanced customs logistics

performance.

CHAPTER THREE

RESEARCH METHODOLOGY

3.1 Introduction

The objective of this part of the paper was to determine, through primary and secondary sources

on the effects of the adoption of new technology by customs on logistics performance in

Mombasa County. The chapter is organized in this manner: First, the research design is

discussed, followed by the target population. Then, the sample design and the data collection are

discussed and finally the reliability and validity as well as the data processing, presentation, and

analysis.

3.2 Research Design

A descriptive cross-sectional research design will be used for this study. This is deemed

appropriate for this study since it often answers the why, how, what and when of a phenomenon.

Omair (2015) says it also enables a clear presentation of the variables under investigation.

Descriptive statistics describe the basic features of the data in a study and also provide simple

summaries about the sample and the measures. It will be describing what is or what the data

shows and also will be used to present both the quantitative and the qualitative descriptions in a

form that is manageable. Mugenda and Mugenda (2003) also state that using the simple graphics

analysis, they form the basis of virtually every quantitative analysis of data because this research

is both quantitative and qualitative, it uses descriptive design.

3.3 Target Population.

The target population of this study will be the licensed and registered customs clearing agents in

Kenya. According to (KIFWA Newsletter May- Aug 201), in Mombasa County, there are 38

registered custom agencies.

3.4 Sampling and Sample size

From the target population, a sample will be drawn. The study will use a simple random

sampling technique.

The formula is as follows:

n = N

1 + N (e) 2

Where: n is the sample size

N is the population

e is the level of precision assuming 5% or 0.05 for rates.

Sample size

n = 38 = 10.8312 =10

1 + 38(0.05)2

Therefore the total sample size is 10 customs agencies and this sample size is considered

adequate. Mugenda & Mugenda (2003) observe that a sample size of 30% is adequate.

3.5 Data Collection

The study will involve the collection of primary data from the firms. Primary data will be

collected in this study because the study seeks to obtain views from one officer in charge of

logistics in the firms. This will be collected through a structured questionnaire administered to

the manager and or experienced officer with over three years of experience in the industry. The

collection of data will be by the use of questionnaires which will generate both the quantitative

and qualitative data. The measurement of the questionnaire will be on a Likert scale of 1-5. The

questionnaire structure will be organization profile, questions on new technology use and

Logistics Performance. Prior to the main study, a pilot study was carried out to determine the

instrument's content validity and reliability. The questionnaire was divided into two parts. Part

one is the respondent profile, part two was the effects of new technology on logistics

performance.

3.6 Reliability and Validity

Any test will be reliable if it will measure what it intended to measure consistently. Precision

consistency and accuracy of the research instrument will make the study reliable. It will measure

the degree to which a research instrument yields consistent results of data after several repeated

trials (Meyers, Gamst & Guarino, 2006). When doing the pilot study the researcher will adopt

the test-retest technique to test the reliability of the questionnaires. The questionnaires will be

administered to the 10 respondents. After administration of the questionnaires, Cronbach’s test

will be used to check the reliability of the data collection tool. Cronbach's alpha ranges from r =

0 to 1, with r = 0.7 or greater considered as sufficiently reliable (Nunnally & Bernstein, 2014).

Prior to the main study, a pilot study was carried out to determine the instrument’s content

validity.

3.8 Data Analysis

The processing of data involved the cleaning of the raw data to ensure that it is consistent with

the requirements for estimation and evaluation of accrual quality. The data collected using the

questionnaires was checked, edited and later computer coded to simplify the hugeness of data

obtained into a form suitable for size for analysis. Statistical Package for Social Sciences (SPSS)

20.0 was used to analyze data. Sprinthall and Fisk (2013) state that SPSS helps in generating

tables which can help in making ease interpretation and assisting in making conclusion and

recommendations. Analyzed data were then summarized using frequencies and percentages and

presented in tables. The percentages and frequencies used to explain, discuss and interpret

research findings obtained conclusion and recommendations. The analysis done is as per the

research objectives. To contain the data to manageable size descriptive statistics are used as well

as to provide insights into the pattern of the trend of the data (Sprinthall & Fisk, 2013). The

descriptive statistics techniques utilized the sum, mean and standard deviations. Correlation

analysis will be carried out on the data.

The relationships between the variables of this study are statistically treated using multivariate

regression analysis. According to this study statistical technique is employed given that the

study’s model has more than one variable and also the relationship existing on these variables is

assumed to be a linear relationship.

From the regression model the following regression equation is derived:

LP= β0 + β1X1+ β2X2+ β3X3+ε

Where;

LP= Logistics Performance

ε = Error term

β0 is the intercept of the model.

X1=Single Window System, X2=Cargo Scanner Management Solution X3= Regional Electronic

Cargo Tracking System

β1, β2, β3, β4, β5, β6 are the coefficients of the model.

The ε = error term or variable represents all the factors or variables that affect the dependent

variable but were not included in the model either because they were difficult to measure or not

known.

T-tests were used to test the significance of the relationship between the dependent and

independent variables. A key statistic is R2 which is a measure of goodness of fit that will show

the percentage variance in the dependent variable (logistics performance) that can be explained

by the independent variables (new technology). Also, the F-Statistic (ANOVA table) was used to

show how independent variables significantly explain the variance in logistics performance. The

F critical at 5% level of significance will be compared with F calculated to showed if the model

is significant or not. The significance should be less than 0.05 in order to indicate if predictor

variables strongly explain the variation in the dependent variable.

3.9. Operationalization of Study Variables

The table presented below shows how the variables have been operationalized. Table 3.1 shows

the variables, components and how they will be measured.

Table 3.1: Operationalization of Study Variables.

Variable

Nature Of

Variable

Components and

Indicators

Measurement

Scale

New

Technology

Independen

t Variable

Perceived

ease of use

(usability)

Perceived

usefulness

(technical

reasons)

New

technology

applications

(accessibility,

functionality

convenience)

Awareness

level

Trust and

security

Perceived

risks

Ordinal

1-5 where

1=Strongly

disagree)

2=Disagree)

3=Moderate

4=agree

5= Strongly

agree

Logistics

performanc

Dependent

variable

Flexibility

Effectiveness

Quality

Transit time

Customer

Relationship

Management

Ordinal

1-5 where

5= Greater

extent

4= great extent

3= Moderate

extent

2= Low extent

1=Very Low

extent

CHAPTER FOUR

DATA ANALYSIS AND PRESENTATION OF RESULTS

4.1 Introduction

This study sought to determine the effects of new technology by customs on logistics

performance in Mombasa County. The specific objectives of this study included: To determine

the effects of the adoption of the Single Window System on logistics performance in Mombasa

County; to find out the effects of the adoption of Cargo scanner Management Solution on

logistics performance in Mombasa County and to examine the effects of the adoption of

Regional Cargo Electronic Tracking Systems (RECTS) on logistics performance in the County.

This chapter presents findings from the data analysis in line with the research objectives. The

analysis is divided into three parts. Part 4.2 shows the response rate, 4.3 presents the

demographic information such as education level, gender, and age. In part 4.4 the analysis as per

the research objectives is presented and 4.5 present results from multiple regression analysis.

4.2 Response Rate

The study was carried out on 10 customs agencies based in Mombasa County.

In order to collect data, 10 questionnaires were issued out to one manager in charge of logistics

in the departments. Out of the 10 questionnaires, 8 questionnaires were received and analyzed

representing an 80% response rate which was considered adequate.

4.3 Demographic Information

4.3.1 Gender of the Respondents

As shown in Table 1below, most respondents were male at 75% and female at 25%. This could

imply that there are more male employees in custom department agencies based in Mombasa

County although it may entirely be attributed to the researcher’s selection bias.

Table 1: Gender of the Respondents

Gender

Frequency

Percentage

Male

Female

Total

100.00

Source: Research data (2019)

4.3.2 Age of the respondents

The researcher assumed that age and experience in the company and industry go hand in hand.

Experience comes with age and a better understanding of how new technology impact on

logistics performance. The results are shown in Table 2 below.

Table 2: Age of the Respondents

Frequency

Percentage

Less than 20 years

21-30 years

31-40 years

12.5

41-50 years

37.5

More than 50 years

Total

100.00

Source: Research Data (2019

Table 2 above shows that most of the respondents with 37.5% were aged above 41-50 years,

followed by 25% aged 21-30years and 25% aged more than 50 years and finally respondents

aged 21-30 years at 12.5%. The results show that none of the respondents was aged less than 20

years and this shows that the respondents were mature to respond to questions related to the

effects of new technology on logistics performance.

4.3.3 Period of Service with the Custom’s Logistics Department

The researcher believed that the length of service in the department and industry measured in

terms of years worked can be equated to a better understanding of new technology effects on

logistics performance. The results are tabulated in Table 3 below.

Table 3: Period of Service at the Department

Frequency

Percentage

Less than 2 years

12.5

2-5 years

6-10 years

37.5

More than 10 years

Total

100.00

Source: Research Data (2019)

As shown in Table 3 above, most of the respondents in the study 37.5% have worked between 10

years followed by those who have worked for more than 10 years at 25% then 2-5 years at 25%

and finally less than 2 years at 12.5%. This shows that the respondents had enough experience to

respond to questions relating to the effects of new technology on logistics performance.

4.4 New Technology Adoption and Logistics Performance

This section carries out an analysis of the effects of new technology adoption on logistics

performance in line with the research objectives.

4.4.1The effects of the adoption of the Single Window System by customs logistics

departments

To begin with, the respondents were asked to give an indication if their custom logistics

departments have adopted new technology that includes the Single Window Systems, Cargo

scanner Management Solution and Regional Cargo Tracking Systems in managing logistics and

operations. All the respondents 100% said yes and none said no implying customs’ logistics

departments in Mombasa County make use of these new technologies to perform various

functions. The respondents were told to indicate the extent to which the adoption of the single

window system on logistics performance has been implemented in their firms. The responses

were measured on a scale of 1-5 where 5) Greater extent; 4) Great extent; 3) Moderate extent; 2)

Low extent; 1) Very low extent and results are shown in Table 4 below.

Table 4: Single Window System Uses in logistics departments

Single Window System Uses in logistics

departments

Selection of Information

37..5%

25%

12.5%

Sorting of information

62.5%

12.5%

25%

Routing of information to target recipients

12.5%

37.5%

25%

Goods release

62.5%

25%

12.5%

Submission of regulatory documents

25%

37.5%

25%

12.5%

Survey Processes

75%

12.5%

Gate departure procedures

62.5%

25%

12.5%

Cargo manifest

25%

37.5%

25%

12.5%

B/L manifest

75%

12.5%

Source: Research data (2019)

The table 4 above shows the responses on the prominent uses of single window system in the

custom’s logistics departments in Mombasa County. It appears from these results that most of

the customs departments in the county have adopted the use of this system to a greater extent.

The respondents were asked to write the extent to which a single-window system has been

adopted in their department. The responses were measured on a scale of 1-5 where 5) Greater

extent; 4) Great extent; 3) Moderate extent; 2) Low extent; 1) very low extent. However, the

extent has not reached level 5 according to the respondents’ responses. The use of this system is

quite intensive in Survey Processes and B/L manifest with 75% intensity respectively. It is

followed by the sorting of information at 62.5% and gate departure procedures at 62.5%. 37.5%

of the respondents indicated that the use of a single-window system in the selection of

information at level 4 of intensity while 25% indicated a level 4 of extent in cargo manifest. The

submission of regulatory documents received a level 4 percentage of at a lower extent compared

to the other uses. The use of a single-window system in routing of information to target

recipients revived a low extent at 12.5% on level 4.

4.4.2 The adoption of Cargo scanner Management Solution by customs logistics

departments

Table 5: Cargo Scanner Management Solution uses in logistics Departments

Cargo Scanner Management Solution uses in

custom’s logistics Departments

Strengthening of Customs protection of

consumers from harmful goods

75%

12.5%

Detection of contraband

25%

37.5%

25%

12.5%

Increased revenue collection

12.5%

37.5%

25%

Reduction of physical examination of good

75%

12.5%

Utilization of resources

12.5%

37.5%

25%

Source: Research data (2019)

Table 5 indicates the extent to which the use of a cargo scanner management solution has been

adopted in the logistics departments of customs. This solution offers a number of services and

the respondents were asked questions in regard to the extent to which it has been used in their

departments. The respondents were asked to write the extent on which cargo scanner

management solution has been adopted in their department. The responses were measured on a

scale of 1-5 where 5) Greater extent; 4) Great extent; 3) Moderate extent; 2) Low extent; 1) very

low extent. The results indicated that this solution has been employed by the departments to a

greater extent; however, the extent has not reached level 5 according to the respondents’

responses. The results indicated that this solution has been used to a greater extent, 75% at level

4 in strengthening of customs protection of consumers from harmful goods. It has also been used

to a greater extent at 75% on level 4 in the reduction of physical examination of goods. The use

of this solution in the detection of contraband received a 25% response on level 4 while its use in

increasing revenue collection and utilization of resources received a lower extent with a 12.5%

on level 4 respectively.

4.4.3 The effects of the adoption of Regional Cargo Electronic Tracking Systems (RECTS)

on logistics performance

Table 6: Regional Cargo Tracking Management Systems uses in Customs logistics

departments

Regional Cargo Tracking Management

Systems uses in Customs logistics

departments

Real time tracking of transit cargo

75%

12.5%

Improvement of security

62.5%

25%

12.5%

Improving tax collection

75%

12.5%

Enhancing enforcement of cargo handling

regulations

12.5%

37.5%

25%

Reduction of rampant illegal dumping of goods

25%

37.5%

25%

12.5%

Improvement of Transit Time

75%

12.5%

Real Time response to clients

62.5%

25%

12.5%

Source: Research data (2019)

The other aspect that the researcher wanted to test was the extent of the adoption of Regional

Cargo Electronic Tracking Systems (RECTS) in logistics by customs departments in Mombasa

County. The respondents were asked to write the extent on which RECTS has been adopted in

their department. The responses were measured on a scale of 1-5 where 5) Greater extent; 4)

Great extent; 3) Moderate extent; 2) Low extent; 1) Very low extent. The results in Table 6 show

that this system has been utilized in the department to a greater extent than the other systems. It

is the only system, unlike the other two, that revived a level 5 response. RECTS has been used to

a greater extent at 62.5% on level 5 in the improvement of security. 25% of the respondents

indicated a level 5 of adoption extent in the utilization of RECTS in the reduction of rampant

illegal dumping of goods. The other uses of RECTS revived a level extent of use with real-time

tracking of transit cargo, its use in improving tax collection and its use in the improvement of

transit time receiving a greater extent at level 4 at 75% response from the respondents. Real-time

response to clients indicated a greater extent of adoption at 62.5% response at level 4 while its

use in enhancing enforcement of cargo handling regulations revived the lowest extent with only

12.5% response on level 4 of the extent of adoption.

4.4.4 Mean and Standard Deviation

Table 7 below shows the mean and standard deviation for single-window system, cargo scanner

management solution, and regional electronic cargo tracking system. The Single Window

System Uses in logistics departments had an overall mean of 4.33 which shows that it has been

adapted to a greater extent. On the other hand, the regional electronic cargo tracking system had

an overall mean of 3.82 while the cargo scanner management solution had an overall mean of

3.32. These results imply that a single-window system has been adopted more than regional

electronic cargo tracking system while RECTS has been adopted more than the cargo scanner

management solution. These overall results show a moderate adoption of new technology by

custom departments in Mombasa County on logistics.

The scores of low extent and very low extent were taken to represent a component that had been

adopted to a small extent (S.E) equivalent to a mean score of 0 to 3.0 on a continuous Likert

scale; (0≤ S.E≤ 3.0). Scores of moderate extent (M.E) were taken to represent a component that

had been adopted to a moderate extent (M.E) equivalent to a mean score of 2.1 to 4.0 on the

continuous Likert scale: (2.1≤M.E≤ 4.0). The scores for both great extent and very great extent

were taken to represent a component which had been adopted to a large extent (L.E) equivalent

to a mean score of 4.1 to 5 on a continuous Likert scale; (4.1≤ L.E≤ 5.0). Additionally, when the

individual factors under each new technology adoption element were considered a single-

window system’s use in routing of information to target recipients with a mean of 4.76 each

ranks high while the use of RECTS in improving tax collection with (mean: 2.21) at a lower

rank.

Table 7: The mean and standard deviation Table

Single Window System Uses in

logistics departments

Mean

S.Dev

Selection of Information

37..5%

25%

12.5%

4.12

.559

Sorting of information

62.5%

12.5%

25%

4.32

.630

Routing of information to target

recipients

12.5%

37.5%

25%

4.76

.715

Goods release

62.5%

25%

12.5%

4.13

.557

Submission of regulatory documents

25%

37.5%

25%

12.5%

4.34

.447

Survey Processes

75%

12.5%

4.21

.671

Gate departure procedures

62.5%

25%

12.5%

4.56

.567

Cargo manifest

25%

37.5%

25%

12.5%

4.33

.633

B/L manifest

75%

12.5%

4.22

.712

Overall Mean

4.33

Cargo Scanner Management

Solution uses in custom’s logistics

Departments

Mean

S.Dev

Strengthening of Customs protection

of consumers from harmful goods

75%

12.5%

3.78

.377

Detection of contraband

25%

37.5%

25%

12.5%

3.17

.694

Increased revenue collection

12.5%

37.5%

25%

3.58

.647

Reduction of physical examination of

good

75%

12.5%

3.44

.612

Utilization of resources

12.5%

37.5%

25%

2.63

.557

Overall Mean

3.32

Regional Cargo Tracking

Management Systems uses in

Customs logistics departments

Mean

S.Dev

Real time tracking of transit cargo

75%

12.5%

4.17

.565

Improvement of security

62.5%

25%

12.5%

4.67

.477

Improving tax collection

75%

12.5%

2.21

.594

Enhancing enforcement of cargo

handling regulations

12.5%

37.5%

25%

4.11

.347

Reduction of rampant illegal dumping

of goods

25%

37.5%

25%

12.5%

3.47

.421

Improvement of Transit Time

75%

12.5%

4.11

.561

Real Time response to clients

62.5%

25%

12.5%

3.99

.478

Overall mean

3.82

4.5 4 Pearson Correlation Analysis

In this research, the researcher expected that there would be a positive and linear relationship

between new technology adoption and logistics performance. The task for this study was to

establish the strength of the relationship between new technology (Single window system, cargo

scanner management solution and regional electronic cargo tracking system) and logistics

performance. Pearson correlation coefficient tests the strength of the relationship between

variables. The correlation coefficient ranges from -1 (perfect negative correlation) to +1 (perfect

positive correlation) and 0 (no correlation at all). Table 4.8 shows the Pearson correlation

coefficients

Table 8: Pearson Correlations

Source: Research Data (2019)

Correlation is significant at the 0.05 level.

Table 8 shows that there is a positive relationship between new technology (Single window

system, cargo scanner management solution and regional electronic cargo tracking system) and

logistics performance. None of the variables had a zero (0) correlation with logistics

performance. Single window system and warehouse had a strong positive and significant

correlation with logistics performance at r=.767 and P<0.05). Cargo Scanner Management

System has a weak correlation with logistics performance at r=.488 and P<0.05. Regional

Electronic Cargo Tracking System has a moderate correlation with logistics performance at

r=.576 and P<0.05. Overall, the relationship between the independent variables under new

Logistics Performance

Person Correlation

Sig. (2-tailed)

Single Window System

Person Correlation

.767

Sig.(2-tailed)

.016

Cargo Scanner Management System

Person Correlation

.488

Sig.(2-tailed)

.037

Regional Cargo Tracking System

(RECTS)

Person Correlation

.576

Sig.(2-tailed)

.013

technology and logistics performance is significant. The implication is that the Single window

system, cargo scanner management solution and regional electronic cargo tracking system have

significant effects on logistics performance.

4.6 Reliability Test

Table 4.9 shows the Cronbach's alpha which is used to find out the internal consistency of the

research components and how closely are they related to the set of components as a group. A

reliability coefficient of 0.70 and above is considered “acceptable" in most social science

research situations (Mosadeghrad & Yarmohammadian, 2006; Cronbach, 1951).

Table 9: Reliability Test

Variable

Cronbach’s Alpha

Single Window System

.834

Cargo Scanner Management Solution

.746

Regional Electronic Cargo Tracking System

.711

Source: Research Data (2019)

The findings in table 9 reveal that most of the elements have relatively high internal consistency

since they had Cronbach’s Alpha’s higher than 0.70 recommended by Cronbach (1951).

4.7 Model Summary and ANOVA

Table 10: Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .640a2 .410 .314 3.4373

Source: Research Data (2019)

Predictors: (Constant), single window system, cargo scanner management solution, and regional

electronic cargo tracking system. The R2 (coefficient of determination) tells us how a single-

window system, cargo scanner management solution and regional electronic cargo tracking

system affects logistics performance. With R2 .410 for the model, this means that the

independent variables (predictors) in the model (single window system, cargo scanner

management solution and regional electronic cargo tracking system) could offer about 41%

explanation of the variation in the dependent variable (logistics performance). The 59%

remaining is explained by other variables or factors not included in the model and represented by

the error term. Thus, The R-value (0.410) shows that the new technology adoption by customs

affects the logistics performance of customs departments in Mombasa County. Moreover, table

11 shows the ANOVA results which were done to test the model fit. The F statistic and its

significance (p-value) are presented and interpreted.

Table 11: ANOVA

Model

Sum of Squares

Mean Square

Sig.

Regression

3043.466

1.646

.047b

Residual

2114.951

179

11.815

Total

5158.417

180

a. Dependent Variable: Logistics Performance

b. Predictors: (Constant), single window system, cargo scanner management solution and

regional electronic cargo tracking system

Source: Research Data (2019)

The results in table 11 show that the F statistic was 1.646 and was significant at 5% level of

confidence (p = 0.047) i.e. P<0.05. This means that the model was fit to explain the relationship

between the effects of the adoption of new technology (single window system, cargo scanner

management solution and regional electronic cargo tracking system) and logistics performance).

4.8 Distribution of Coefficients and Hypothesis Testing

Table 4.12 shows the distribution of coefficients that gives an indication of how each variable

affects logistics performance.

Table 12: Distribution of Coefficients

b. Predictors: (Constant), single window system, cargo scanner management solution and

regional electronic cargo tracking system) and logistics performance

Model

Unstandardized

Coefficients

Standardized

Coefficients

Sig.

Std. Error

Beta

(Constant)

1.523

.072

1.601

.021

Sing Window System

.878

.364

.094

2.412

.025

Cargo Scanner Management

Solution

.479

.229

.276

2.092

.022

Regional Electronic Cargo

Tracking System

.532

.199

.041

2.673

.043

a. Dependent Variable: Logistics Performance

Source: Research Data (2016)

Note Sig. (Testing hypothesis accept p<0.05), p-value

The model shows a statistically significant positive relationship between Singe Window System

(β = 0.878, t= 2.412, p<0.05) and Logistics Performance. There is also statistically significant

positive relationship between Regional Electronic Cargo Tracking System (β = 0.532, t= 2.673,

p<0.05) and Cargo Scanner Management Solution (β = .479, t= 2.092, p<0.05) also have a weak

and positive relationship with logistics performance. Overall, all the independent variables had a

positive impact on logistics performance since the t-values were positive. The positive

relationship and impact were also significant at 95% confidence level, p<0.05 for all the

variables.

From the multivariate regression model the following regression equation was derived:

LP= β0 + .878X1+.479X2+.532X3+ε

Constant = 1.523, shows that if new technology factors are rated as zero or held constant;

logistics performance would be a factor of 1.523.

X1 = 0.878, shows that one unit increase in single window system results in an increase in

logistics performance by a factor of 0.878

X2 = 0.479, shows that one unit increase in cargo scanner management solution results in an

increase in logistics performance by a factor of 0.479

X3 = 0.532, shows that one unit increase in regional electronic cargo tracking systems results in

an increase in logistics performance by a factor of 0.532

From the above regression model, holding a single-window system, cargo scanner management

solution and regional electronic cargo tracking system constant, logistics performance would be

1.523.

CHAPTER FIVE

SUMMARY, CONCLUSION, AND RECOMMENDATION

5.1 Introduction

This study sought to determine the effects of new technology by customs on logistics

performance in Mombasa County. The specific objectives of this study included: To determine

the effects of the adoption of the Single Window System on logistics performance in Mombasa

County; to find out the effects of the adoption of Cargo scanner Management Solution on

logistics performance in Mombasa County and to examine the effects of the adoption of

Regional Cargo Electronic Tracking Systems (RECTS) on logistics performance in the County.

This chapter gives out a summary of findings in line with the specific objectives of the study.

These findings are based on descriptive statistics, factor analysis, and Pearson’s correlation.

5.2 Summary of Findings

The research in this section is the discussion of findings that has been structured around each

research objective and findings made from the analysis. Ideally, it was expected that the

relationship between the adoption of new technology and logistics performance would be

positive and significant. The study used a multivariate linear regression model of the form LP=

β0 + β1X1+ β2X2+ β3X3 +ε where LP=logistics performance, X1=Single Window System,

X2=Cargo Scanner Management Solution X3= Regional Electronic Cargo Tracking System and ε

= error term.

The study analyzed the relationship between the adoption of new technology and logistics

performance. The correlation results and descriptive statistics showed that the relationship was

significant at 95% confidence level. The single window system and cargo scanner management

solution had a strong positive and significant correlation with logistics performance. Regional

electronic cargo tracking system had a weak correlation with logistics performance. In addition,

all the independent variables (X1=Single Window System, X2=Cargo Scanner Management

Solution X3= Regional Electronic Cargo Tracking System) had a positive impact on logistics

performance. Thus, it can be seen that although all the independent variables have a positive

influence on the dependent variable, a single-window system and warehouse and cargo scanner

management solution have a larger effect on logistics performance compared to the regional

electronic cargo tracking system. Overall, the respondents felt that there was a strong influence

on the adoption of new technology on logistics performance in the county.

5.2.1 The effects of single window system on logistics performance

The study sought to find out the extent to which new technology is being used in the operations

of logistics in custom departments in Mombasa County. The findings revealed that a single-

window system with a mean of 4.33 had been adapted to a greater extent in comparison with the

other variables. Pearson correlation results also showed that a single-window system at r=.767

had a strong positive correlation with logistics performance. Additionally, the overall mean for

new technology adoption was 3.82 showing that it has been adapted to a moderate extent by

custom departments in Mombasa County. Furthermore, the findings reveal that customs in

Mombasa County use single window system in the selection of information, sorting of

information, routing of information to target recipients and in the release of goods (Ahn & Han,

2017) Although Ahn and Han, (2017) stated that routing of information to target recipients is

among the most prominent uses of single window system which allows ongoing operational

optimization, this study found out that that routing of information ranks low among new

technology uses by customs departments in Mombasa County.

This shows that the customs recognize that single window systems impacts on logistics

performance to a large extent. The findings that the firms have adopted single window systems

are in line with resource advantage theory of competition which challenges executives in this

industry to engage in the talks of competition based on what develops as resource-advantage

(Carnes, 2016). Firms should optimize available resources in order to gain competitive

advantage over competitors and one way of achieving this is by making use of a single-window

system. Technology Acceptance Model (TAM) by Davis Gakuubi, 2018) also shows that

technology will only be accepted due to perceived usefulness. In essence customs departments

based in Mombasa County have realized that a single-window system has usefulness in

performing some functions relating to key logistics performance.

5.2.2 The effects cargo scanner management solution on logistics performance

The study established the effects of cargo scanner management solution on the logistics

performance. As indicated earlier in this study, it was assumed that the relationship between new

technology and logistics performance would be positive which was proven by the Pearson

correlation. Pearson correlation results also showed that a single-window system at r=.488 had a

strong positive correlation with logistics performance. The findings of the study revealed that the

cargo scanner management solution impacts logistics performance to a lower extent with an

overall mean score of 3.32. In addition, one of the main impacts of cargo scanner management

solution on logistics performance was strengthening of customs protection of consumers from

harmful goods with a mean of 3.78. Other effects were that it helps in the detection of

contraband goods, it helps in increasing customs’ revenue collection, reduction of physical

examination of goods and utilization of resources (Bhandari, 2014). Thus, cargo scanner

management solution functions have a huge impact on logistics performance which in turn

affects overall business performance. Goodhue and Thompson (1995) while explaining Task Fit

Technology fit theory stated that the more the technology is used; it has positive effects on the

individual performance especially when the capabilities of the technology used matches the task

that a user must perform. The findings concur with past studies such as Lai (2017) that the

perceived impact of new technology adoption on logistics performance comprised of improved

performance in the reduction of physical examination of good.

5.2.3 The effects of the adoption of Regional Cargo Electronic Tracking Systems (RECTS)

on logistics performance

The study sought to find out the extent to which regional electronic cargo tracking system affects

the operations of logistics in custom departments in Mombasa County. The findings revealed that

regional electronic cargo tracking system with a mean of 3.82 had been adapted to a great extent.

Pearson correlation results also showed that a single-window system at r=.576 had a strong

positive correlation with logistics performance. Additionally, the overall mean for regional

electronic cargo tracking system was 3.82 showing that it has been adapted to a great extent by

custom departments in Mombasa County. Furthermore, the findings reveal that customs in

Mombasa County use regional electronic cargo tracking system in real-time tracking of transit

goods, in the improvement of security and in the improvement of tax collection (Cutmore, Liu &

Tickner, 2013). Further, Cutmore, Liu, and Tickner (2013) indicated that it is useful in the

enhancement of enormous if cargo handling regulation, reduces rampant illegal dumping of

goods, it improves transit time of goods and it allows for real-time responses to clients on transit

progress. However, even though Cutmore, Liu, and Tickner (2013) stated that utilization of

resources is among the most prominent uses of RECTS in logistics, this study found out that it

ranks low among new technology uses of RECTS by customs departments in Mombasa County.

This shows that the customs recognize that RECTS impacts on logistics performance. The

findings show that the firms have adopted RECTS which is agreement within line with resource

advantage theory of competition which challenges executives in this industry to engage in the

talks of competition based on what develops as resource-advantage (Carnes, 2016). Customs

departments should optimize available resources in order to gain competitive advantage over

competitors and one way of achieving this is by making use of a single-window system

(Gakuubi, 2018). In simple terms, customs departments in Mombasa County have realized that

RECTS s useful in logistics performance.

5.3 Conclusions

The theorized logistics performance model fits the data moderately well-providing support for

the three objectives. As a focal point, the performance of customs logistics departments is

positively affected by the adoption of new phone technology. The study found out that the

relationship between the three new technology variables and logistics performance was positive

and significant p<0.05. The single window system had a strong correlation with logistics

performance while cargo scanner management solution had a weak correlation with logistics

performance. Additionally, the analysis revealed that the new technology attributes identified in

this study namely single window system, cargo scanner management solution and regional

electronic cargo tracking system could offer 41% explanation of total variation in logistics

performance with 59% explained by other factors not considered in the study. The new

technology variables are strong predictors of logistics performance among customs departments

in Mombasa County.

This study successfully found answers to the three research objectives: first, the single window

system affected logistics performance to a greater extent although some uses such as routing of

information to target recipients and in the release of goods had not been greatly adopted. Second,

cargo scanner man agent solution had lower effects logistics performance. Third, the adoption of

a regional electronic cargo system affects logistics perforce to a moderate extent.

5.4 Recommendations

This study will be very resourceful for the academicians and the practitioners. The practitioners

are provided with some insights regarding the uses of new technology and its impacts on

logistics performance. Thus, the study results have important implications for executives in

customs departments in the country and elsewhere in the world. The sustained long-term success

of the firms will depend upon developing a competitive advantage as a member of one or more

supply chains. While the executives in the manufacturing sector have embraced various

technologies and strategies to enhance logistics performance, they should continue searching for

better tactical approaches to implement new technology strategies and achieving organizational

goals.

The logistics processes linking the firms, customers and suppliers play a crucial role in

supporting a logistics management strategy. As the firms work to improve the logistics

procedures, they support their company supply chain strategy resulting in better performance for

the general supply chain and ultimately overall performance. Continuous adoption of new

technologies such as single window systems, cargo scanner management solution and regional

electronic cargo tracking systems will go a long way in increasing logistics performance and in

turn financial performance of customs departments.

5.4 Suggestions for Further research

This study was done with a small sample size of 10 respondents in 10 customs departments in

Mombasa County, a small section of the country. This aspect brings concern when it comes to

replicating the results of the study to other parts of the country because the results may not be a

replica of what can be perceived of other parts of the country and other contexts in the world.

Therefore, this study was limited in regard to generalizability and representativeness. Therefore,

the researcher recommends that a wider researcher that reflects the issues of the effects of the

adoption of new technology by customs departments on logistics performance in the whole

country could help in resolving the issue of generalizability. Further research done on a bigger

scale with large sample size could shed more light on how new technology activities impact on

logistics performance in Kenya (Aziz, et al., 2016)

Secondly, in this study, the researcher confined to the research data from primary and secondary

sources from within a range of the past five years which makes its scope a little narrower and it

may not give a completely clear picture of the situation of the effects of new technology on

logistics performance in Mombasa County. A recordation on this is that further research could be

done with a wider scope in regard to data collection with the guidance provided by this research.

Covering a wider period of about ten years and increasing the number of custom department’s

understudy will help in providing an accurate picture of the situation. Further, Mugambi (2017)

asserts that reliable sources such as government websites, trusted organizations such as NGO’s

websites, are credible in conducting secondary research and this could help future studies on the

effects of outreach and financial sustainability of MFIs on poverty alleviation in Mombasa

County in future.

REFERENCES

Ahn, B. S., & Han, M. C. (2017). A comparative study on the single window between Korea and

Singapore. Journal of Korea Trade, 11(3), 279-303.

Aman, A., Al-Shabil, T., Mohamed, Z., & Auzair, S. (2017). E-Single window implementation:

a case of Jordan customs department. International Journal of West Asian Studies, 8(1).

Arvis, J. F., Saslavsky, D., Ojala, L., Shepherd, B., Busch, C., Raj, A., & Naula, T. (2016).

Connecting to Compete 2016: Trade Logistics in the Global Economy--The Logistics

Performance Index and Its Indicators. World Bank.

Aziz, Z. A., Razak, R. C., Yaacob, M. R., Hussin, N. S. N., & Razmin, N. H. M. (2016). Does

technological and organizational innovation have significant influences on logistics

performance?. International Journal of Business and Management Invention, 5(11), 55-

62.

Bendahan, J. (2015). U.S. Patent No. 9,086,497. Washington, DC: U.S. Patent and Trademark

Office.

Bhandari, R. (2014). Impact of technology on logistics and supply chain management. IOSR

Journal of Business and Management, 2, 17.

Bolatan, G. I. S., Gozlu, S., Alpkan, L., & Zaim, S. (2016). The impact of technology transfer

performance on total quality management and quality performance. Procedia-Social and

Behavioral Sciences, 235, 746-755.

Bowersox, D. J., & Daugherty, P. J. (2015). Logistics paradigms: the impact of information

technology. Journal of Business Logistics, 16(1), 65.

Christopher, M. (2016). Logistics & supply chain management. Pearson UK.

Cutmore, N. G., Liu, Y., & Tickner, J. R. (2013, November). Development and

commercialization of a fast-neutron/x-ray Cargo Scanner. In 2010 IEEE International

Conference on Technologies for Homeland Security (HST) (pp. 330-336). IEEE.

Feenberg, A. (2002). Transforming technology: A critical theory revisited. Oxford University

Press.

Föcker, F., Neubauer, A., Metzger, A., Gröner, G., & Pohl, K. (2015, April). Real-time cargo

volume recognition using internet-connected 3D scanners. In 2015 International

Conference on Evaluation of Novel Approaches to Software Engineering (ENASE) (pp.

323-330). IEEE.

Gakuubi, D. K. (2018). The Impact Of Information Communication Technology On

Organizational Performance: A Case of Nairobi Bottlers Logistics Operation’s

(Doctoral dissertation, United States International University-Africa).

Gani, A. (2017). The logistics performance effect in international trade. The Asian Journal of

Shipping and Logistics, 33(4), 279-288.

Goldsby, T. J., & Zinn, W. (2016). Technology innovation and new business models: can

logistics and supply chain research accelerate evolution?. Journal of Business Logistics,

37(2), 80-81.

Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance.

MIS quarterly, 213-236.

Grawe, S. J. (2009). Logistics innovation: a literature-based conceptual framework. The

International Journal of Logistics Management, 20(3), 360-377.

Gunasekaran, A., Subramanian, N., & Papadopoulos, T. (2017). Information technology for

competitive advantage within logistics and supply chains: A review. Transportation

Research Part E: Logistics and Transportation Review, 99, 14-33.

Hitt, M. A., Xu, K., & Carnes, C. M. (2016). Resource-based theory in operations management

research. Journal of Operations Management, 41, 77-94.

Hunt, S. D. (1995). The resource-advantage theory of competition: Toward explaining

productivity and economic growth. Journal of Management Inquiry, 4(4), 317-332.

Kabiru, V. N., Gachuhi, E. K., Gitau, J. K., Chepkemoi, B. C., Muathe, H. M., Muthuyia, A. K.,

& Juma, T. N. (2016). Electronic cargo tracking system and operational performance at

Kenya revenue authority and on transporters. Unpublished MBA Project, University of

Nairobi.

Kalinicheva, V., Dmitriev, S., Drigo, M., Kazimirova, N., Nikitina, A., & Igolnikova, I. (2016).

Features logistics customs warehouses in the integration of the world economy and the

globalization of business. International Review of Management and Marketing, 6(1S),

148-152.

Kithiia, A. K. (2015). Effects of Electronic Logistics on the Logistical Performance of Logistics

Firms in Kenya: A Case Study of Maersk Kenya Limited. The International Journal of

Business & Management, 3(12), 68.

Lai, P. C. (2017). The literature review of technology adoption models and theories for the

novelty technology. JISTEM-Journal of Information Systems and Technology

Management, 14(1), 21-38.

Luo, H., Wang, K., Kong, X. T., Lu, S., & Qu, T. (2017). Synchronized production and logistics

via ubiquitous computing technology. Robotics and Computer-Integrated

Manufacturing, 45, 99-115.

Martí, L., Martín, J. C., & Puertas, R. (2017). A DEA-logistics performance index. Journal of

applied economics, 20(1), 169-192.

Mathauer, M., & Hofmann, E. (2019). Technology adoption by logistics service providers.

International Journal of Physical Distribution & Logistics Management.

McMaster, J., & Nowak, J. (2016). The Evolution of Electronic Trade Facilitation: Towards a

Global Single Window Trade Portal. The Electronic Journal of Information, 1-19.

Miler, R. K. (2015). Electronic container tracking system as a cost-effective tool in intermodal

and maritime transport management. Economic Alternatives, 1(1), 40-52.

MUGAMBI, N. (2017). Effect of Cargo Tracking System On Cross-Border Trade Between

Kenya and Uganda. Unpublished MBA Project, University of Nairobi.

Mugambi, N. (2017). Effect of Cargo Tracking System On Cross-Border Trade Between Kenya

and Uganda. Unpublished MBA Project, University of Nairobi.

Mugenda, O., & Mugenda, A. (2003). Research methods: Quantitative and Qualitative Methods.

Revised in Nairobi.

Munthe-Kaas, H. M., Glenton, C., Booth, A., Noyes, J., & Lewin, S. (2019). Systematic mapping

of existing tools to appraise methodological strengths and limitations of qualitative

research: the first stage in the development of the CAMELOT tool. BMC medical

research methodology, 19(1), 113.

Mwangangi, P. W. (2016). Influence of logistics management on the performance of

manufacturing firms in Kenya (Doctoral dissertation, COHred, supply chain

management, JKuat).

Mwangi, P. N., & MORONGE, D. M. (2016). Influence of procurement practices on

performance of logistics firms in Kenya: A case of Nairobi County. Strategic Journal of

Business & Change Management, 3(2), 897-919.

Nandagopal, S. (2018). Customs Studies at the Cross Roads. Available at SSRN 3156221.

Ndonga, D. (2013). Managing the risk of corruption in Customs through single window systems.

World Customs Journal, 7(2), 23-38.

Nizeyimana, C., & De Wulf, L. (2015). Rwanda's electronic single window supports trade

facilitation. World Customs Journal, 73.

Noe, R. A., Hollenbeck, J. R., Gerhart, B., & Wright, P. M. (2017). Human resource

management: Gaining a competitive advantage. New York, NY: McGraw-Hill

Education.

Nwankwo, W., Olayinka, A. S., & Benson, B. U. (2019). X-ray Cargo Scanning and Risk

Management in Trade Facilitation: Analysis & Model of an Online Imaging and

Documentation Management System.

Odhiambo, M. E. (2018). STRATEGIC CHANGE MANAGEMENT AT KENYA REVENUE

AUTHORITY: A CASE OF CUSTOMS AND BORDER CONTROL (Doctoral

dissertation, School of Business, University of Nairobi).

Ojala, L., & Celebi, D. (2015). The World Bank’s Logistics Performance Index (LPI) and drivers

of logistics performance. Proceeding of MAC-EMM, OECD.

Oláh, J., Karmazin, G., Pető, K., & Popp, J. (2018). Information technology developments of

logistics service providers in Hungary. International Journal of Logistics Research and

Applications, 21(3), 332-344.

Omair, A. (2015). Selecting the appropriate study design for your research: Descriptive study

designs. Journal of Health Specialties, 3(3), 153.

Ouma, D. (2019). Revenue Effects of Tax Reforms, Economic Growth and Political

Environment in Kenya. African Journal of Economic Review, 7(1), 136-153.

Paresi, J. S. (2018). U.S. Patent Application No. 15/875,641.

Pruksasri, P., vd Berg, J., & Keretho, S. (2011). Accountability in Single Window systems using

an Internal Certificate Authority: A case study on Thailand’s National Single Window

system. In Proceedings of the IADIS International Conference, E-commerce 2011 (pp.

129-136).

Pugliatti, L. (2011). Cloud single window: legal implications of a new model of cross-border

single window. World Customs Journal, 5(2), 3-20.

Pugliese, E., Cimini, G., Patelli, A., Zaccaria, A., Pietronero, L., & Gabrielli, A. (2017).

Unfolding the innovation system for the development of countries: co-evolution of

Science, Technology, and Production. arXiv preprint arXiv:1707.05146.

Ross, H. (2017). Tracking and tracing tobacco products in Kenya. Preventive medicine, 105,

S15-S18.

Rotolo, D., Hicks, D., & Martin, B. R. (2015). What is an emerging technology?. Research

Policy, 44(10), 1827-1843.

Røvik, K. A. (2016). Knowledge transfer as translation: Review and elements of an instrumental

theory. International Journal of Management Reviews, 18(3), 290-310.

Rutherford, M. (2011). Clarence Ayres and the instrumental theory of value. Journal of

Economic Issues, 15(3), 657-673.

Ruto, W. K., & Datche, E. (2015). Logistical factors influencing port performance a case of

Kenya ports authority (KPA). International Journal of Current Research and Review,

7(12), 52.

Saidi, S., & Hammami, S. (2011, May). The role of transport and logistics to attract foreign

direct investment in the developing countries. In 2011 4th International Conference on

Logistics (pp. 484-489). IEEE.

Seuring, S., & Müller, M. (2018). From a literature review to a conceptual framework for

sustainable supply chain management. Journal of cleaner production, 16(15), 1699-

1710.

Sindi, S., & Roe, M. (2017). The evolution of supply chains and logistics. Strategic supply chain

management (pp. 7-25). Palgrave Macmillan, Cham.

Sprinthall, R. C., & Fisk, S. T. (2013). Basic statistical analysis. Englewood Cliffs, NJ: Prentice-

Hall.

Tosevska-Trpcevska, K. (2014). Effects of the implementation of a single window and simplified

customs procedures in the Republic of Macedonia. World Customs Journal, 8(1), 51-62.

Truel, C. (2017). A Short Guide to Customs Risk. Routledge.

Tukuta, M., & Saruchera, F. (2015). Challenges facing procurement professionals in developing

economies: Unlocking value through professional international purchasing. Journal of

Transport and Supply Chain Management, 9(1), 1-9.

APPENDIX I: RESEARCH QUESTIONNAIRE

Dear respondent, am conducting a research study on the effects of the adoption of new

technology on the logistics performance of customs departments in Mombasa County. The

questionnaire items are about the study and you are kindly requested to participate in responding

to the questions below. The information given will be treated as confidential and the results of

the study will be used for academic research purposes only.

PART A: Demographic and Respondents Profile

1. Name of the respondent (optional) ……………………………………….………

Name of your organization

(Optional)……………………………….……………………….

Gender: Male [ ] Female [ ]

2. What is your age bracket? (Tick as applicable).

Under 20 years

[ ]

21 – 30 years

[ ]

31 – 40 years

[ ]

41- 50 years

[ ]

Over 50 years

[ ]

3. Length of continuous service with the company?

Less than two years [ ]

2-5 years [ ]

6- 10 years [ ]

Over 10 years [ ]

Part B: Mobile Phone Technology Adoption

5. Has your company adopted New Technology in managing logistics?

Yes ( ) No ( )

6. To what extent have the following uses of single window system been implemented and

improved logistics performance in your department? Use 1-5 where 5) Greater extent; 4)

Great extent; 3) Moderate extent; 2)

Low extent; 1) Very low extent

Single Window System Uses in logistics

departments

Selection of Information

Sorting of information

Routing of information to target recipients

Goods release

Submission of regulatory documents

Survey Processes

Gate departure procedures

Cargo manifest

B/L manifest

7. To what extent have the following uses of cargo scanner management solution been

implemented and improved logistics performance in your department? Use 1-5 where 5)

Greater extent; 4) Great extent; 3) Moderate extent; 2) Low extent; 1) Very low extent

Regional Cargo Tracking Management

Systems uses in Customs logistics

departments

Real-time tracking of transit cargo

Improvement of security

Improving tax collection

Enhancing enforcement of cargo handling

regulations

Reduction of rampant illegal dumping of goods

Improvement of Transit Time

Real-Time response to clients

8. To what extent has the following uses of regional electronic cargo tracking system been

implemented and improved logistics performance in your department? Use 1-5 where 5)

Greater extent; 4) Great extent; 3) Moderate extent; 2)

Low extent; 1) Very low extent

Regional Cargo Tracking Management Systems

uses in Customs logistics departments

Real-time tracking of transit cargo

Improvement of security

Improving tax collection

Enhancing enforcement of cargo handling regulations

Reduction of rampant illegal dumping of goods

Improvement of Transit Time

Real-Time response to clients

END

THE EFFECTS OF THE ADOPTION OF NEW TECHNOLOGY BY CUSTOMS ON LOGISTICS PERFORMANCE IN MOMBASA COUNTY

Students also viewed