IMPACT OF DIGITAL TECHNOLOGIES ON SUPPLY CHAIN PERFORMANCE OF
ONLINE RETAILERS IN PORTHARCOURT METROPOLIS, RIVERS STATE, NIGERIA.
BERNARD, Peniel
PhD Admissions Proposal Submitted to The Postgraduate School, Rivers State University.
Nkpolu-Oroworukwo, Port Harcourt.
June, 2022
Chapter One
Introduction
1.1
Background to the Study
Several Studies (Gurria, 2017, Laaper, 2017, and Dall’Omo, 2016) have shown that digital
technologies play a critical role in managing supply chain processes that generate performance
gains for the respective firms. The foundation of digital transformation requires a complete
understanding and holistic analysis of the internal and external capabilities (Uhl, et al., 2014).
Digital technologies improve capabilities by allowing companies to trim operating cost, improve
product quality while increasing sales revenue through expanding market shares, developing new
products that meet customer needs, and creating strategic advantage that improve all business
operations (Gurria, 2017).
This study focusses on two information variables: digital technologies and supply chain
performance. The essence of deploying digital technologies is to improve the supply chain
performance which can be measured qualitatively as customer satisfaction, quality customer
service and on-time delivery; and quantitatively as lower cost, higher sales volume, higher profit,
inventory investment and higher percentages in Return on Investment (ROI) (Beamon, 1998).
This study examines the functional role of digital technologies in supply chain performance and
reveals that the adoption of digital technology by a service provider (online retailer) can lead to
improved performance, customer satisfaction and in turn increase the profitability of the online
retail outlets in Port Harcourt metropolis.Digital technology capabilities and the managing of
information flow has an effect on dimensions of supply chain, such as cost, quality, flexibility
and timely delivery of goods and services and ultimately profit of organization (Droodchy &
NickMehr, 2008).
Supply chain consists of a network of partners and various channels operating throughout the
organization which affect the utility of supply chain control centre, and its management entails
integrating activities through improving chain relationship in order to access stable competitive
advantage (Gilaninia, ShChirani & Ramezani, 2011).
The specific function of supply chain technology (SCT) is to generate value by enabling
information transfer within and across firm boundaries (Rai, Patnayakuni & Seth, 2006; Sanders,
2008; Subramani, 2004). SCT refers to “the tools and/or techniques that may be implemented to
effectuate integrated supply chain management” (Autry et al., 2010, p. 523). Included in this
definition are such information technologies (IT) and information systems (IS) as barcoding,
warehouse management systems (WMS), and transportation. Therefore, firms implement SCT in
an effort to facilitate cross-functional collaboration and to coordinate business processes with
members of their supply chains. The use of SCT leads to improved financial performance of
firms in developed markets (Dehning, Richardson & Zmud, 2007). Likewise, in emerging
markets, SCT can be critical to building firms’ business values and thus to their competitive
successes (Prasad & Heales, 2010).
For example, in their study of supply chain management practices in India, Sahay and Mohan
(2003) recognized that SCT is a necessity to compete. Yet, SCT investment is very low for firms
in India (Sahay & Mohan, 2003). Firms in India are more likely to implement standalone
software than integrated SCT, adversely affecting integration both within and across the firm
(Sahay, Cavale & Mohan, 2003; Sahay, Gupta & Mohan, 2006). India’s institutional
environment presents challenges to SCT implementation that are distinct from, and considerably
more daunting than those faced by firms in developed markets (Kilgore, Joseph & Metersky,
2007; Shah & Suresh, 2009). The challenges of implementing SCT in India include the minimal
use of IT of any kind by the vast majority of firms, especially by small and medium-sized
enterprises (Singh, Narain & Yadav, 2010); consequent lack of external integration of SCT
among members of a supply chain (Sahay et al., 2006; Shah & Suresh, 2009); poor data quality
in relation to forecasts, shipments, replenishments, and inventories (Kilgore et al., 2007; Shah &
Suresh, 2009); and the shortfall of logistics talent, accentuated by minimal training opportunities
(Johnson, 2007; Venkatesh, Bala & Sykes, 2010).
Consequently, firms likely resort to experimentation (Khanna, 2014), in order to determine how
to best adapt implemented SCT to suit their specific contexts in an emerging market. Overall, our
discipline’s understanding of how a firm’s management must implement and then adapt SCT to
suit an emerging market context remains embryonic (Schoenherr, 2009). Thus, the few firms that
are early adopters of SCT in emerging markets (Sahay et al., 2006) know little about how the
specific challenges of adapting SCT (during implementation) relate to the institutional
environment of their country.
1.2
Statement of the problem
There have been limited academic research that investigate how and why digital technologies
can create performance gains by improving and transforming supply chain capabilities. Digital
technologies improve capabilities by allowing companies to trim operating cost, improve product
quality while increasing sales revenue through expanding market shares, developing new
products that meet customer needs, and creating strategic advantage that improve all business
operations (Gurria, 2017).
The retail industry globally has become more competitive and dynamic than ever before, due to
the adoption of digital technologies in its operations. The rapid adoption and evolution of digital
technology across countries of the world has spurred many industries around the world to engage
in online retailing which massively integrates activities of vendors, carriers, third party
companies, and information systems providers. However, online retailers now develop strategies
that allow effective use of information systems in order to improve their supply chain activities
such as sourcing, procurement, conversion, and logistics management activities, coordination
and collaboration with channel partners (suppliers, intermediaries, third-party service providers,
and customers). Nigeria is not exempted from this growing phenomenon, as a good number of
similar online retailers such as Jumia, Konga, Coco Fortes, Kangro Mart, Computer Port Biz,
Genesis Centre, New Market; etc, have now emerged and some are still up coming. Despite the
associated benefits of information technology in the retail business globally, many Nigerian
firms including large scale retailers do not appreciate digital devices in their operations in order
to achieve greater supply chain performance. Often, they neglect the effectiveness of digital tools
for transaction execution, collaboration and coordination of supply chain networks, order
tracking and delivery coordination in their overall supply chain performance. Thus, the problem
statement for this work is “what is the relationship between digital technologies and supply chain
performance? Therefore, this work was set out to examine the impact of digital technologies
such as automatic identification platforms, communication & integrative platforms/systems on
supply chain performance of online retailers in Port Harcourt Metropolis, Rivers State.
1.3
Conceptual Framework
The Supply Chain Operations Reference (SCOR) model was developed by the Supply Chain
Council with the help of the top leading manufacturing companies. The SCOR model is a
diagnostic tool for supply chain (Ntabe, et al., 2015) and a major framework that features supply
chain management practices and business process reengineering (Lockamy III and McCormack,
2004; Wang, et al, 2010; Zhou, et al., 2011). It is used to address, improve, and communicate
supply chain management decisions within a company, its suppliers and customers (SC Council,
2004). It provides a methodology for managing supply chain activities and processes, which can
be used as a set of practical guidelines for analyzing supply chain management practices (Li, et
al., 2011). In this study, we offer an approach to operationalize the SCOR concept under the
guides of digital technologies (DT) and seek to determine how DT can transform supply chain
practices. Several Studies (Gurria, 2017, Laaper, 2017, and Dall’Omo, 2016) have shown that
digital technologies play a critical role in managing supply chain processes that generate
performance gains for the respective firms. The foundation of digital transformation requires a
complete understanding and holistic analysis of the internal and external capabilities (Uhl, et al.,
2014). However, there have been limited academic research that investigate how and why digital
technologies can create performance gains by improving and transforming supply chain
capabilities. The digital technologies improve capabilities by allowing companies to trim
operating cost, improve product quality while increasing sales revenue through expanding
market shares, developing new products that meet customer needs, and creating strategic
advantage that improve all business operations (Gurria, 2017).
In this paper we present a three-stage model that describes the relationship amongst supply chain
digitalization, supply chain capabilities and operational performance. We draw on the SCOR
framework to investigate the role of supply chain digitalization on improving supply chain
capabilities and its overall effect on operational performance (see Figure 1). In assessing the
supply chain digitalization, we investigate the level of investment of digital technologies, current
level of use and future directions of implementation of digital technologies (see Figure 2). Based
on current use and future direction of digital technologies implementation, our goal is to unveil
the transformational effect of supply chain digitalization on supply chain capabilities which will
impact the overall operational performance.
The research framework is presented in Figure 1.
Fig. 1 Conceptual framework
Source: Ike Ehie and Luis Miguel D F Ferreira
Fig. 2. Adoption of digital technology.
Source: Ike Ehie and Luis Miguel D F Ferreira
The SCOR model explains the processes along the entire supply chain and provides a basis for
how to improve those processes. It has been described as a promising model for strategic
decision making in supply chains (Turhan et al., 2011). The model has been widely adopted in
many companies and anecdotal evidence and trade journals have reported significant
improvements after firms adopt the score model (Zhou, et al., 2011). The basic building blocks
of supply chain management processes in the SCOR Model are: plan, source, make and deliver.
Within the context of this model, we investigate the extent to which the use of the novel digital
technologies in the plan, source, make and deliver decision areas of the SCOR model influence
supply chain capabilities which in effect enhance operational performance of the firm.
1.4
Purpose of the Study
The main purpose of this study is to examine the impact of digital technologies on supply chain
performance of online retailers in Port Harcourt Metropolis, Rivers State. Specifically, the study
objectives are:
i)
To examine and determine which digital technology relates to supply chain
performance of online retailers in Port Harcourt Metropolis, Rivers State.
ii)
To determine the extent to which information service delivery relates with supply chain
performance of online retailers in Port Harcourt Metropolis, Rivers State.
iii)
To ascertain the extent to which information service integration relates with supply
chain performance of online retailers in Port Harcourt Metropolis, Rivers State.
iv)
To determine the extent to which innovation moderates the relationship between digital
technology adoption and supply chain performance of online retailers in Port Harcourt
Metropolis, Rivers State.
1.5
Research Questions
The study will attempt to answer the following research questions:
i) What impact does the digital technologies’ information of a supply chain have on the
supply chain’s performance relating to online retailers in Port Harcourt Metropolis, Rivers
State?
ii) How does the information generated by these digital technologies in a supply chain affect
the performance of partnering firms?
iii) To what extent does information service integrations relate with supply chain performance
of online retailers in Port Harcourt Metropolis, Rivers State?
iv) To what extent does Innovation significantly moderate the relationship between
information technology adoption and supply chain performance of online retailers in Port
Harcourt Metropolis, Rivers State?
v) How does supply chain digital technologies facilitate or inhibit the supply chain’s
performance of online retailers in Port Harcourt Metropolis, Rivers State?
1.6
Research Hypotheses
Hypothesis 1. A supply chain’s information management capability will positively impact the
supply chain’s performance.
H01: There is no significant relationship between information management capability and supply
chain’s performance of online retailers in Port Harcourt Metropolis, Rivers State.
Hypothesis 2. A supply chain’s IT infrastructure capability will positively impact the supply
chain’s information management capability.
H02: There is no significant relationship between supply chain’s IT infrastructure capability and
the supply chain’s information management capability.
Hypothesis 3. A supply chain’s IT infrastructure capability will positively impact the supply
chain’s relational capability.
H03: There is no significant relationship between supply chain’s IT infrastructure capability and
the supply chain’s relational capability.
Hypothesis 4. A supply chain’s relational capability will positively impact the supply chain’s
information management capability.
H04: There is no significant relationship between a supply chain’s relational capability with the
supply chain’s information management capability.
Hypothesis 5. Dependence will moderate the relationship between the supply chain’s IT
infrastructure capability and the supply chain’s information management capability. The
relationship between IT infrastructure capability and information management capability
will be the strongest when dependence between firms is both high and symmetric.
H05: There is no significant relationship between the supply chain’s IT infrastructure capability
and the supply chain’s information management capability arising from dependence between
firms.
Hypothesis 6. Dependence will moderate the relationship between the supply chain’s relational
capability and the supply chain’s information management capability. The relationship between
relational capability and information management capability will be the strongest when
dependence between firms is both high and symmetric.
H06: There is no significant relationship between the supply chain’s relational capability and the
supply chain’s information management capability arising from dependence between
firms.
1.7 Significance of the Study
This study will yield significant result in solving the myriad of problems confronting online
retailers in the supply chain industry. The study will be beneficial to supply chain industry owners,
online retailing companies, customers, managers and staff within Port Harcourt Metropolis, Rivers
State, to understand the relationship between firms’ digital information and supply chain
performance. The rationale for this empirical research is to provide logical information. This
investigation is a novel endeavor at knowledge building essentially to the following:
Online Retail Management: This examination will convey to the fore novel information and
methodologies on how the management of online retail companies can sufficiently adjust work
structure in their organization activity deliberately to achieve greater success. The investigation
will likewise give understanding about the significance of operational structure on the execution
of their organization, in this way empowering them to utilize their situation to settle on
fundamental key choice on the use of occupation plan in their organizations.
Government: Basically, work configuration is a valuable apparatus in enhancing operational
adequacy and proficiency. With this impact, it makes noteworthy to the administration by
uncovering the increases of general operational effectiveness in settling on key choice concerning
methods for improving quality administration conveyance.
Academicians: It will be vital in assisting study on the significance of digital technologies in
supply chain operation which will increase the interest of its efficiency in enhancing hierarchical
execution and administrative abilities. It will add to the collection of existing learning the
development of information on the identification of factors that affects supply chain performance
in Nigeria and other developing nations
1.8 Scope of the study
Content Scope: The scope of the study focuses on the relationship between digital technologies
adoption and supply chain performance.
Geographical Scope: The geographical scope is Port Harcourt Metropolis, Rivers State.
Unit of Analysis: The unit of analysis will be organizational level. Thus, employees of all the
online retailing companies will form the participants of the study.
1.9 Definition of Terms
Online retail efficiency: it refers to the process of improving the operations or activities in the
company by reducing costs and enhancing customer’s satisfaction
Information Sharing: this refers to the process of disseminating information from source to other
people in the same organization in a bid to help attain goals.
Information Technology Service Delivery: refers to the ability of individuals to use information
technology to offer effective services for clients.
Information Technology Service Integration:
refers to the ability to combine several
components of information technology for use in organizations in its bid to enhance performance.
Information Technology: is the use of techniques, skills and computer processes in developing,
storing and managing information in the organization.
Innovation: The introduction of new ideas.
Chapter Two
Literature Review
2.1 Theoretical Foundation
This study will be underpinned by three baseline theories – Theory of Data Envelopment Analysis,
DEA Window Analysis Theory and Task-technology Fit Theory. Both conceptual and empirical
literature will be reviewed with respect to digital technologies’ information (the independent
variable) has to do with the adoption of interrelated organizational procedures that are adopted to
enhance information sharing and processing across organizational domains. Again, literature will
be reviewed on the proxies of digital information adoption which are information sharing,
information service delivery, and information service integration. More so, literature will be
effectively reviewed on the concept of supply chain performance (the dependent variable).
Chapter Three
Research Methodology
3.1 Philosophical Foundation
A research philosophy is a belief about the way in which data about a phenomenon should be
gathered, analyzed and used. The term epistemology (what is known to be true) as opposed to
doxology (what is believed to be true) encompasses the various philosophies of research approach.
Three major research philosophies have been identified in the Western tradition of science, namely
positivist (sometimes called scientific) and interpretivist (also known as antipositivist) and
pragmatic philosophy (Žukauskas, Vveinhardt & Andriukaitienė, 2018). This study will adopt the
Positivist research philosophy
3.2
Research Design
This study, will adopt the cross-sectional research design method, this entails a situation where
data that is needed for research analysis is collected just once from a sample selected to represent
a larger population (Ahiauzu & Asawo, 2016). This method will be chosen because it allows
researchers to carry out studies in natural, real life setting, using the probability samples, thus
increasing the external validity of the research and also will permit the use of questionnaire for
generating data.
3.3
Population of the Study
Baridam (2008) opined that the population of a study identifies the total items within which a
researcher wishes to study. The author went further to assert that the target population is the entire
population to which the findings are applicable or can be generalized. Furthermore, Ahiauzu and
Asawo (2016), put forward that the population of a research refers to the entire people, groups of
person, organizations, or things of importance that the researcher intend to investigate. The
population of this study will be the two (2) online retail companies (Jumia and Konga) in Rivers
State.
3.4 Sampling and Sampling Design
This study sample will be the same as the population. Thus, all the elements in the population will
constitute the population of the study. However, the researcher will administer five (5) copies of
structured questionnaire to senior managers of these online retailing companies, making a total of
ten (10) respondents for the study. The respondents include all the managers from all the
departments.
3.5
Data Collection Methods.
The data to be utilized in this study is both primary and secondary data. The primary data or
information is those that the researchers obtains or gathers from the field during the course of the
research work. This means that such data are fresh, new, and raw, they are mostly not been used
previously by any other researcher. Primary data is usually obtained from field work observation
or through the use of research instrument such as questionnaire. The secondary data are
information obtained from earlier works of other scholars. It is always very useful in developing
the background, statement of problems, purpose of the study, literature review, methodology and
discussions of research findings. The secondary data basically is very essential in building the
foundation of every research exercise. The secondary data are obtained from thesis and
dissertations in libraries, scholarly works in school repository, journals, text books, magazines,
newspapers and notebooks.
3.6
Instrumentation
A questionnaire is a data collection mechanism used to collate data over a large sample or number
of respondents (Kombo & Tromp, 2006). We opted to employ the use of structured questionnaire
following the reference / guidelines of authors such as Kothari (2005), Saunders, Lewis,
&Thornhill, (2009) and Sekaran and Bougie (2010).
The questionnaire is a form of data
compilation instrument that applies a common set of questions to obtain information about a
particular area of research. It has the advantage of providing more valid data that can easily be
quantified.
The questionnaire to be used for this study will be structured and in two different sections. Section
one is structured to provide demographic information about the respondents, while section two
will extract data on the study variables. This method will be used to elicit answers that will help to
elaborate pertinent issues not covered exhaustively in the structured questionnaire. The primary
source of data collection involved the distribution and retrieval of questionnaires from respondents
(i.e. strategic managers under study).
3.7
Validity of the Research Instrument
According to Vogt (2007), validity is used to determine the truth or exactness of a research work.
On the other hand, Saunders et al (2009) posits that validity is the extent to which the data
collection instrument measures appropriateness of the measures before reaching accurate
conclusions. Validity tests will be conducted for content, criterion and construct validity to test
how well the instrument is representative, and confines the relationships between the variables as
well as measure the concepts (Vogt, 2007;
Saunders et al., 2009; Sekaran & Bougie, 2010). Validity of the survey instruments will be
achieved through assessment by my supervisor, lecturers and other knowledgeable professionals
on the subject in line with my supervisors’ approval.
3.8
Reliability of the Research Instrument
Reliability as defined by Vogt (2007) is the regularity of either measurement or design to give the
same conclusions if used at different times or by different scholars. The first step in ensuring
reliability was to make available clear operational description of the variables under study.
However, internal regularity was considered through internal consistency reliability (Sekaran &
Bougie, 2010) as well as split-half reliability using Cronbach ‘s alpha. If R2 (Alpha) value, equals
0.7 and above, then the instrument will be considered satisfactory (Cronbach, 1951; Nunnally,
1978; Sekaran & Bougie, 2010)
3.8
Methods of Data Analysis
At the primary level, this study will employ the use univariate descriptive statistical tool such as
mean, standard deviation, frequency tables, simple percentages, bar charts and histograms to
present the data that will be generated while bivariate inferential statistic of Pearson’s Product
Moment Correlation will be used at the secondary level of analysis, to test the proposed
hypotheses. At the tertiary level of analysis, the study will use Partial Correlation to test the impact
of the moderating variable (level of influence) on the relationship between the dependent and
independent variables. Also, the study will adopt the Multiple Regression Analysis in testing the
combine influence of all the dimensions of the study on each of the measures.
Analysis of Variance: The method is often referred to by its acronym ANOVA. ANOVA is often
a statistical method for determining the existence of difference among several population means.
While the aim of ANOVA is to detect difference among several population means, the technique
requires the analysis of different forms under study- hence the name Analysis of Variance (Aczel
and Sounderpandian, 2009).
The hypothesis test of ANOVA: The hypothesis test of Analysis of Variance is and as follows:
H0: μ1= μ2= μ3=…= μr
Ha: not all μi (i=1…r) are equal ……………………………. Equ (3.1)
This implies that there are ‘‘r’’ populations, or treatments, under study. The researcher will draw
an independent random sample from each of the ‘‘r’’ populations.
The size of the sample from population I (i=1… r) is ni and the total sample size is given as n=n1
+n2+…+nr
The researcher further presents the assumption that he is satisfied in using the Analysis of Variance
procedure in testing of the slated statistical hypotheses. They include:
i. Assume independent random sample from each of the ‘‘r’’ population of the online retail
trends.
ii. Assume that the ‘‘r’’ population under research are normally distributed, with means μ i
that may or may not be equal, but with equal variances σ 2.
Multiple regression analysis: Multiple regression analysis is the study of how many a dependent
variable ‘‘y’’ is related to two or more independent variable “x’’.
It is given by:
Y=βo+β1x1+β2x2+...+βpXp……………………………………Equ (3.2)
In the general case, as shown the researcher uses ‘‘x’’ to denote the number of independent
variables (digital technologies information). The equation that describes how the dependent
variable ‘’y’’ is related to the independent variables (supply chain performance) x1 x2 ... Xp and an
error term ‘‘E’’ is called the Multiple Regression Model, as shown below:
Y= β0+β1x1+β2x2+...+ βpXp + E ……………………………………… Equ(3.3)
In the multiple regression model, = β0, β1, β2 ...+ βp are the parameters and E’ (the Greek letter
epsilon) is a random variable. A close examination of this model reveals that ‘‘y’’ is a linear
function of x1, x2. . . xp (the β0+β1x1+β2x2+...+ βpXp part) plus an error term (Anderson, 2004).
Testing for significance: In this section, the researcher will show how he would conduct
significance test for a multiple regression relationship. He starts by defining the purposes of f test
and t test to be used. The F test will be used to determine whether a significant relationship exist
between the dependent variable and the set of all the independent variable in the model, the
researcher will refer to the f test as the test for over all significance. If the f test shows an overall
significance the test is used to determine whether each of the individual independent variables is
significant. A separate t test is conducted for each of the independent variable in the model; the
researcher refers to each of this t test as a test for individual significances.
All the statistical analyses will be performed using the Statistical Package for Social Sciences
(SPSS) version 23.0. This version has the ability to transform scaled data into discrete or
continuous data and vice versa.
References
Ahiauzu, A.I. & Asawo, S.P. (2016). Advanced social research methods. Port Harcourt, CIMRAT,
Publications
Ally, N., & Mbamba, U. (2009). The effect of ICT on service quality perception and customer
satisfaction: the case of ICTenabled Services in Selected Banks. African Journal of
Finance Management, 28(2), 78–92.
Arvis J.F, Mustra, M.A., Panzer, J., Ojala L., & Naula T (2007). Connecting to compete: trade
logistics in the global economy. Washington DC: The World Bank,
Baridam, D. M. (2008). Research methods in administrative. PortHarcourt: Sherbrooke
Associates.
Hesse, M. (2006). Global chain, local pain: regional implications of global distribution networks
in the German north range, Growth Change, 37(4), 570–596.
Ikediashi, D.I., Ikediashi, D.I., Ogwueleka, A.C., & Ogwueleka, A.C. (2016). Assessing the use
of ICT systems and their impact on construction project performance in the Nigerian
construction industry, Journal of Engineering Design and Technology, 14(2), 252–276.
Kombo, D. K., & Tromp, D.L. (2006). Proposal and thesis writing: An introduction. Nairobi:
Pauline’s Publications Africa.
Kunaka, C, Hartmann, O, Raballand, G, & Shamte, R. (2016). Trade, logistics infrastructure, and
regional integration. In: Tanzania: The Path to Prosperity, 105-115.
Liaoa, L., Patterson, D.J., Foxa, D., & Kautz, H. (2007). Learning and inferring transportation
routines. Artificial Intelligence, 171(5-6), 311–331.
Nair, P. R., Raju, V. and Anbudayahankar, S. P., 2012. Overview of information technology tools
for supply chain management. International Journal of Physical Distribution and Logistics
Management. 35(2)82-92.
Nickels, W. G. and Wood, M. B., 1997. Marketing: Relationship, quality, value. New York: Worth
Publishers.
Nigerian Ports Authority (2022). Sea Ports in Nigeria. Retrieved March 28, 2022, from
https://nigerianports.gov.ng/
Nowduri, S. (2011). Management information systems and business decision making: review,
analysis, and recommendations, Journal of Management and Market Research, 7(1),1–7.
Nwaze, C., 2009. Quality and internal control challenges: Internal Surveillance. Zenith Economic
Quarterly, 4 (1) January.
Oladejo, M. and Akanbi, T., 2012. Bankers perception of electronic banking in Nigeria: A review
of post consolidation experience. Research Journal of Financial Accounting. 3(2), 1-11.
Orodho, J.A. (2000). Techniques of writing, Nairobi: Masola Publishers
Osang, F.B. (2017). Repositioning the Logistic Industry for Effective Service Delivery in Nigeria:
A Case Study. International Journal of Computer (IJC), 24 (1), 130-145.
Salin, V. (2000). Information technology and cattle-beef supply chains, American Journal of
Agricultural Economics, 82(5),1105–1111.
Wang, W., Chan, H. and Pauleen, D. (2010). Aligning business process reengineering in
implementing global supply chain systems by the SCOR model. International Journal of
Production Research, 48 (19), 5647-5669.
Wilson, D.M., Iravo, M.A., Tirimba, O.I., & Ombui, K. (2015). Effects of information technology
on performance of logistics firms in Nairobi County, International Journal of Science
Research Publication, 5(4),1–26.
Wu, F., Yeniyurt, S., Kim, D. and Tamer, C.S. (2006). The impact of information technology on
supply chain capabilities and firm performance: a resource-based view. Industrial. Marketing
Management, 35(4), 493– 504.
Xue, L., Zhang, C., Ling, L. and Zhao, Z. (2013). RiskMitigation in Supply Chain Digitization:
A Study of System Modularity and IT Governance, Journal of Management Information
Systems, 30(1), 325-352.