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Drivers of Procurement Performance in the Motor

Industry: A Case of CMC Motors Group, Kenya

Juma Veronica Ayieko1 & Dr. Justus Kinoti

2

1M.Sc Scholar, Jomo Kenyatta University of Agriculture & Technology, Kenya

2Lecturer, Jomo Kenyatta University of Agriculture & Technology, Kenya

ABSTRACT

The procurement function is becoming more and more dynamic and complex. This study therefore

sought to examine the drivers of procurement performance in the motors industry with a focus on

CMC Motors Group,Kenya .The study adopted a descriptive research design with a focus on 158

respondents obtained through stratified random sampling of 528 employees from CMC Motors

Group operations in Kenya. The study analyzed the relationship between procurement perfomance in

the motor industry and procurement policy,electronic procurement,supplier relationship management

and outsourcing using regression analysis as was expressed through the multiple linear regression

model .The study results showed that the correlation coefficient was 0.799. This indicates a very

strong postive relationship between the independent variable and depedent variable. The data showed

that the high R square is 0.638. It shows that the independent variables in the study were able to

explain 63.80% variation in the procurement perfomance in the organization while the remaining

36.20% is explained by the variables or other aspects outside the model. This implies that these

variables are very significant and they therefore need to be considered in any effort to boost

procurement perfomance in the organization. The study therefore identifies variables as critical

drivers of procurement perfomance in the organization. The study recommend that organization

should come up with procurement policy and provide the assurance that the policy process (policy

formulation, implementation, monitoring and evaluation) is operating optimally with effectiveness,

efficiency, and economy, and is underpinned by broader principles such as rule of law, transparency,

accountability and integrity. The study also recommends that the distribution organization should

invest on electronic procurement like ERP which will help them coordinate the organization activities.

The study recommends for the improvement of supplier management to enhance procurement

performance in the motor industry.

Keywords: procurement performance, supplier management, motor industry

INTRODUCTION

Procurement performance has become a conventional standard in the contemporary world. Kakwezi

and Nyeko (2014), state that procurement performance is associated with effectiveness and efficiency

of procurement operations. Most organizations are incorporating good procurement practices in a bid

to retain competitive market leadership in the international corporate environment. There are

numerous enticements for organizations to engage procurement as a business success strategy.

Notably effective procurement performance is heightened by various drivers as presented in previous

scholarly literature. Vagadia (2012) established that organizations are now re-examining their

business models and structures and outsourcing is being a tool for business transformation. Hong and

Kwon (2012), postulates that in today‘s dynamic market environment, procurement is positioned as a

critical integrative business process and its focus has been extended from short term cost

minimization to long term value creation and delivery.

Procurement guidelines and procedures play an unexplainable role in the overall performance. Victor

(2012), noted that procurement expenditure could be minimized through implementation of effective

procurement practices. Burt et al. (2010), affirms that every organization develops procedures to

enable its personnel implement policies and designed to meet her objectives. The current Supply chain

trends shows that the global dynamic nature of procurement cannot escape technology as it is a

International Journal of Business & Law Research 6(1):8-26, Jan.-Mar., 2018

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leading driver in this field, (Hong & Kwon, 2012). Competence in procurement functions culminate to

key benefits including improved efficiency, reduced transaction costs, increased productivity and

visible controls. In reference to causal motives for procurement outsourcing, firms strive to search for

the best price or seek efficient ways of acquisition from outside sources. According to Epiq

Technologies (2010) report, IT enables a firm to organize its interactions with its most crucial

suppliers, a set of built-in monitoring tools to help control costs, assure maximum supplier

performance and keeping an open line of communication with potential suppliers during a business

process. According to Shaw (2010), every organization should put in place effective systems of

procurement to protect shareholder‘s funds while (Lewis & Roehrich ,2009) agitate that procurement

is a key activity in the supply chain.

Procurement pointedly impacts on the overall accomplishment of an emergency response depending

on how it is managed. Oyuke and Shale (2014) focused on role of strategic procurement practices on

organizational performance and resonate that organizations must maximize the use of procurement in

every aspect of the business, linking across all members of the supply chain, increasing the speed of

information transfer, and reducing non-value adding tasks. Procurement trends are changing

simultaneously across both the public and private sector platforms. (Prajogo et al., 2008) reported that

public procurement as a theme is consistent with research in the private sector that has shown how

value chain activities affect innovation. Vagadia (2012), examined the role of strategic sourcing in a

changing world, and established that organizations were now re-examining their business models and

structures and outsourcing is being a tool for business transformation. The restrictive literature

available authenticating causal relationship between procurement performance and drivers such as

procurement policy, electronic procurement, supplier relationship management and outsourcing in

essence is has given the researcher the determination to delve into the drivers of procurement

performance in the motor industry more specifically at CMC Motors Group, Kenya

Global Perspective on Procurement Performance

Globalization has given rise to a new era of international competition that is reshaping global

production and trade and altering the organization of industries (Gereffi, 2011). From its inception

during World War I & II, when the procurement function seemed important for the prime purpose of

obtaining raw materials, supplies, and services needed to keep the factories and mines operating, the

procurement function has grown in leaps and bounds over the decades. Mahmood (2010), posed an

estimation that 18.42% of the world‘s gross domestic product (GDP) was spent through procurement.

It was further estimated that procurement accounts for 9%–13% of the GDP of the economies of

developing countries. In the wake of the 2008–09 global economic crisis, the rapid growth of

productive capabilities in China, India and other large emerging economies has created a profound

shift in global demand for both finished goods and intermediates from north to south, with both

positive and negative implications for developing country exporters, (Kaplinsky & Farooki, 2011).

Regional Perspective of Procurement Performance

Procurement performance is fast becoming an economic driver in many developing countries in

Africa. According to Agaba and Shipman (2008) Sub-Sahara Africa channels between U$ 30-43

billion to the procurement market in the region. This demand for a well-functioning procurement

system particularly for the developing countries where procurement usually accounts for high

proportion of total expenditure at 40% against the global average of 12-20%. This perhaps is anchored

on the lack of financial muscle and technical expertise required to produce the substances on demand

locally in Africa.

Kenyan Perspective on Procurement Performance

Effective procurement performance is desired by most firms primarily for efficiency and cost

reduction. This is vital for the survival and growth of firms because most customers view the

performance of firms based on the supply chain practices and habits adopted by the firm, and this

form the basis of their ultimate future buy and relationship decisions. Awino (2011), postulates that

measurement of performance of firms is based on both quantitative and qualitative performance

indicators. Juma (2010), observes that procurement performance is the backbone of an organization

success since it contributes to competitive purchase and acquisition of quality goods that puts the

organization products or services in the competitive edge in the market. Juma further holds that poor

procurement performance in the private sector has been a problem due to incompetent staff,

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traditional procurement procedures, and inability to embrace e-procurement, poor coordination of

procurement activities, lack of quality assurance policies and lack of proper regulations.

Adoption of best procurement practices has been a challenging journey to most Kenyan firms. Juma

(2010), affirms in his study that lack of quality systems in the private sector contributes to increase of

defect and wastes. The study further links the increase of loss due to lead time and inability of the

private sector in Kenya to compete with other players globally as based on poor investment in

information communication technology. This significantly reduces procurement performance in the

private sector in Kenya. Nasra (2014), notes that effective and efficient procurement systems and

collaborative relationships are essential to the achievement of organizational goals, cost reduction and

supply chain performance. Banda (2009), asserts that private sectors in Africa are grappling with

setting up the operational framework of procurement processes while developed countries as United

States and European countries main problem is how to make it efficient. Larson (2009), established

major fundamental differences between the public and private sector procurement as the reporting

structure, regulating bodies, funding sources and operating motives. The study further reports that the

public sector is governed by legislative bodies, laws, and untold numbers of state and federal

regulations. The private sector is guided by boards of directors, business plans and the organization‘s

purchasing policies. While the private sector has been extensively covered in study, there exists little

literature on the possible drivers of procurement performance in the motor industry in Kenya.

The Motor Industry in Kenya

Kenya‘s GDP per capita is growing very fast. Expenditure on the purchase of cars, motorcycles and

other vehicles accounted for 1.5% of total consumer expenditure in 2015 and is expected to remain

relatively stable to 2025 as incomes rise. The volume of imported cars and motorcycles has been on

the increase due to the availability of attractive credit from financial institutions and the rise of the

middle-class, (Schiller & Pillay, 2016). According to the Kenya National Bureau of Statistics (KNBS)

the volume of imported vehicles between 2003 and 2012 have grown at over 300% from 33 000 units

to 110,474 units (KNBS, 2015). Passenger vehicles were Kenya‘s fourth largest import overall in

2014, valued at US$420 million and making up 2.3% of total imports (by value) while commercial

vehicles ranked seventh, valued at US$370 million. To date, Kenya is still highly dependent on

imports to meet domestic demand, with imports making up 94% of bilateral automotive trade and

second-hand vehicles accounting for over 80% of those imports, (Delloitte, 2016).

Because of the regional gateway at the port of Mombasa, 99.9% of Kenya‘s automotive exports target

other African countries, with Uganda and Tanzania being the biggest markets. Kenya‘s automotive

market is largely focused on retail and distribution of vehicles, and after-sales support in servicing and

spare parts sales (Delloitte, 2016). The main motor vehicle dealers operating within the country are

Toyota (East Africa), Cooper Motor Corporation (CMC), General Motors (GM), Simba Colt and DT

Dobie, Honda Motors, and other small upcoming local dealerships. (KPMG, 2015). Small scale

assembly of motor vehicles is done at three assembly plants, the General Motors East Africa (GMEA)

plant in Nairobi, the Associated Vehicle Assemblers (AVA) plant in Mombasa and the Kenya Vehicle

Manufacturers (KVM) plant in Thika. All three of the plants are operating below their capacity.

However, the country‘s good infrastructure, relative to other countries in the region, as well as it is

physical and strong economic position within the East Africa Community (EAC), make it a potential

hub for automotive assembly and production in the region, (Schiller & Pillay, 2016).

Profile of CMC Motors Group

The CMC website reveals a detailed history of the Motor firm, (CMC, 2017). CMC Motors Group

was incorporated as a private limited in July 1948 to sell Land Rovers. In 1956 Cooper Motor

Corporation was converted from a private company to a public company which provided vehicle

sales, parts, service and administration under one roof. CMC Motors Group Ltd was a public

company until April 2014, when shareholders, unable to agree and in consistent boardroom wrangles,

opted to be paid off by a Dubai firm Alffutaim Group KSh7.5 billion in a buyout that has effectively

seen the auto dealer delist from the Nairobi Securities Exchange (CMC, 2017).

CMC Motors Group has seven branches countrywide namely Mombasa, Nakuru, Meru, Nanyuki,

Kisumu, Eldoret and Kitale, and six divisions at the headquarters in Nairobi. Other trading

subsidiaries owned by CMC Holdings Ltd include Kenya Vehicle Manufactures Ltd (KVM), Cooper

Motor Corporation (Uganda) Ltd, Hughes Motors (Tanzania) Ltd and Hughes Agriculture (Tanzania)

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Ltd. CMC Motors Group holds exclusive distribution of Ford, Mazda, Suzuki, Maruti, Nissan Diesel,

Eicher and MAN range of trucks (medium and heavy commercial) buses, New Holland, and an

extensive range of farming implements from ploughs to irrigation equipment. (CMC, 2017).

The Kenyan government identified the automotive and auto parts industry as a major economic driver

in the Kenya national industrialisation Policy Framework released in 2010, (Delloitte, 2016).In view

of the aggressiveness in growth of the automobile sector In Kenya, and having reviewed in detail the

organization layout and operations from the information as fore cited, the researcher feels that CMC is

the most suitable place for study because of the diverse brands and activities that the organization

deals with coupled with the regional coverage muscle flex that CMC enjoys in East Africa.

Statement of the Problem

The procurement function is evolving every day, the urge to keep up with the upcoming market trends

has given many organizations sleepless nights figuring out the best practices to adopt to improve

efficiency, effectiveness, to remain competitive and to achieve the overall organization‘s Objectives.

According to Agaba and Shipman (2008) Sub-Sahara Africa channels between U$ 30-43 billion to the

procurement market in the region. This demands for a well-functioning procurement system

particularly for the developing countries where procurement usually accounts for high proportion of

total expenditure at 40% against the global average of 18.42%. The motor industry in Kenya is

increasingly becoming dynamic and complex. According to the Kenya National Bureau of Statistics

(KNBS) the volume of imported vehicles between 2003 and 2012 grew over 300% from 33 000 units

to 110,474 units (KNBS, 2015). To date, Kenya is still highly dependent on imports to meet domestic

demand, with imports making up 94% of bilateral automotive trade and second-hand vehicles

accounting for over 80% of those imports, (Delloitte, 2016).

The Kenyan government identified the automotive and auto parts industry as a major economic driver

in the Kenya national industrialisation Policy Framework released in 2010, (Delloitte, 2016).Despite

the Motor industry having expanded the job market and enabled Kenya‘s GDP to grow, it still faces

stiff encounters arising from competing second-hand motor vehicle industry, general business

slowdown, tighter credit markets following the capping of interest rates and reduced government

leasing. Based on in-market research, (Deloitte, 2016) estimates that in three African countries,

Ethiopia, Kenya, Nigeria at least 8 out of 10 imported vehicles are used vehicles. According to KMI

(2017), the new vehicle market dropped 30.5% to 13,869 units in 2016.This open market and stiff

competition have led to most of the motor companies in Kenya trying to re strategize in a bid to

survive, with procurement process changes and massive layoffs taking effect in a bid by the firms to

protect their margins, with payroll expenses ranking among the biggest cost items.

Several studies have been carried out vastly on procurement performance but little attention has been

given to the vibrant motor industry. For example, Larson (2009) established major fundamental

differences between the public and private sector procurement as the reporting structure, regulating

bodies, funding sources and operating motives. Kakwezi and Nyeko (2010) studied efficiency and

effectiveness of the procurement function and concludes that procurement efficiency and procurement

effectiveness of the purchasing function are measures of procurement performance. According to

(Seroney and Mwangangi, 2014) contend that procurement policies and information flow does affect

the competitiveness of the motor industry in Kenya. In view of the detailed assessment of the motor

industry in Kenya, the researcher views procurement performance as one of the strategies that ought

to be intensely considered in this industry. CMC Motors Group Kenya is suitably placed for this study

because of the diverse activities that the organization deals with coupled with the regional coverage

muscle flex. Therefore, the study sought to imperatively determine the drivers of procurement

performance in the motor industry in Kenya, a case of CMC Motors Group Kenya.

Objectives of the Study

The general objective of the study was to examine the drivers of procurement performance in the

motor industry in Kenya.

The specific objectives of this study were:

i. To establish the influence of procurement policy on procurement performance in the motor

industry in Kenya.

ii. To determine the influence of electronic procurement on procurement performance in the

motor industry in Kenya.

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iii. To examine the effect of supplier relationship management on procurement performance in

the motor industry in Kenya.

iv. To assess the influence of outsourcing on procurement performance in the motor industry in

Kenya.

Research Questions

The following questions guided the study;

i. How does procurement policy influence procurement performance in the motor industry in

Kenya?

ii. What is the influence of electronic procurement on procurement performance in the motor

industry in Kenya?

iii. To what extent does supplier relationship management affect procurement performance in the

motor industry in Kenya?

iv. Does outsourcing influence procurement performance in the motor industry in Kenya?

LITERATURE REVIEW

Theoretical Review

In this study, theories will be used to provide a deeper understanding of the drivers of procurement

performance in CMC Motors Group,Kenya.

Resource Dependency Theory

Resource dependency theory was first experienced by (Pfeffer & Salanick, 1978). This theory states

that firms don‘t have all the resources they need, therefore, to some degree, they depend on their

external environment for some resources. Captivatingly, the initial drive of the resource dependence

was originally developed to provide an alternative perspective to economic theories of mergers and

board interlocks, and to understand precisely the type of IORs that had played such a large role in

market failures then, (Pfeiffer, 2003). This theory holds three core concepts namely; social context

matters; secondly that organizations have strategies to enhance their autonomy and pursue interests;

and thirdly power is important for understanding internal and external actions of organizations.

Social-Technical Systems Approach Theory

This theory will guide the study on the second variable. Passmore (1988), holds that the socio-

technical systems approach is based on the premise that every organization consists of the people, the

technical system and the environment. People (the social system) use tools, techniques and knowledge

(the technical system) to produce goods or services valued by consumers or users (who are part of the

organization‘s external environment). Passmore (1988) further emphasises that an equilibrium among

the social system, the technical system and the environment is necessary to make the organization

more effective. Embracing electronic procurement as a meaningful tool for use is equally important as

it propels better results.

Supply-chain operations reference (SCOR) model

This theory re-counts the third variable in the study namely Supplier relationship management.

Supply chain operations reference model (SCOR) was first developed by the management consulting

firm PRTM, now part of price water house coopers and was endorsed by supply chain council as a

supply chain management diagnostic tool as observed by (Achieng & Rotich, 2013). PRTM

management outlines SCOR as a tool that enable the users to address, improve and communicate the

activities within a supply chain and all the other parties involved. It spans from supplier to the

customer, (Simchi-levi, 2008). The SCOR model ―provides a unique framework that links business

processes, metrics, best practices and technology features into a unified structure to support

communication among supply chain partners and to improve the effectiveness of supply chain

management and related supply chain improvement activities, (Supply Chain Council, 2009). SCOR

is based on several distinct management processes which include planning, sourcing, make decisions,

deliver and return (Rolf, 2007).

Transaction Cost Theory

This theory underpins the fourth variable namely outsourcing which is directly reflected through the

transaction cost theory‘s pillars of what to buy since the process of buying is ultimately intertwined.

Firms need to consider both production costs and transactions costs for any outsourcing transaction.

This way the firm can justify the need and eventually realize the cost benefits achieved through the

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outsourcing process. Transaction Cost theory focuses on the reasons to firm‘s existence, most

effective strategies for maximizing profits, the core function of firms and what should be acquired by

firms in relation to the firm‘s goals. Simon (1957) carried out a study and postulates that the main

hypothetical argument of this theory is concerned with the conditions under which certain

characteristics of the transaction or the object of the transaction would lead to its internal hybrid, or

external governance. The two-important fundamental behavioral assumptions are bounded rationality

and opportunism. (Simon, 1957). Bounded rationality argues that human beings are limited.

Therefore, it is only possible for both parties in a transaction to sign an incomplete contract, while the

opportunism assumption holds that people cunningly behave opportunistically at the expense of

others. The danger of opportunism is assumed to be less likely within a firm than in market

coordination since it can be prevented within a firm by means of the authority principle namely,

hierarchy, (Simon, 1957).

Conceptual Framework

The conceptual framework is a plane of interlinked concepts that together provide a comprehensive

understanding of a phenomenon or phenomena. The concepts that constitute a conceptual framework

support one another, articulate their respective phenomena, and establish a framework-specific

philosophy. (Jabareen, 2009). The concepts on focus in this study as tabulated below are procurement

policy, electronic procurement, supplier relationship management and outsourcing.

Independent Variables

Figure 2.1: Conceptual Framework

Independent Variables Dependent Variable

Figure 2.1: Conceptual Framework

Procurement Policy

Procurement structure

Communication

Distribution layout

Rules and regulations

Supplier Relationship Management

Supplier commitment

Supplier development

Supplier selection

Supplier negotiation

Outsourcing

Capital

Focus on core competencies

Technical capacity

Risk transfer

Procurement Performance

Cost reduction

Achieved lead-times

Quality of goods purchased

Electronic Procurement

Enterprise resource planning

Training

Electronic data integration

Internet connectivity

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RESEARCH METHODOLOGY

Research Design

Descriptive research design was used in the study. Descriptive research seeks to establish factors

associated with certain occurrences, outcomes, conditions or types of behavior. Descriptive research

seeks to obtain information that describes existing phenomena (Kothari, 2012). The design is

considered appropriate as it permits an exhaustive study of the problem under investigation.

Target Population

The target population is the total number of subjects targeted by the study (Oso & Onen, 2011).

Mugenda and Mugenda, (2009) postulates that target population should have some observable

characteristics, to which the research intends to generalize the results of the study. Data by the

consulting company Price water house coopers (PWC, 2011) lists the players in the Kenyan motor

industry as Toyota East Africa, Cooper Motor Corporation (CMC), General motors, DT Dobie, Simba

colt, Subaru, Foton, Tata, Nissan, Mahindra and most recent entrants south Korean car manufacturer

Hyundai, Honda. For purpose of this study, the study population contained a total of 528 members of

staff from CMC Motors Group Kenya drawn from procurement and related departments who were

engaged or benefited from procurement activities as tabulated below;

Table 3. 1 Target Population

Category Population Percentage (%)

Top Level Management 45 8.52

Middle Level Management 148 28.03

Operation Level

Total

335

528

63.4

100

Source: CMC Motor Group (2017)

Sampling Frame

A sample is a small proportion of targeted population selected using some systematic form. Kothari

(2012) describes a sample frame as the list that includes all members of the population from which a

sample is to be taken; it is the complete list containing all the sampling units of the population. For

purpose of this study, the study population contained a total of 528 members of staff from CMC

Motors Group Kenya distributed across top level management, middle level managers and operational

level staff as obtained from the list of employees as per CMC Motors Group Kenya register (CMC,

2017).

Sampling Size and Sampling Technique

Sample size is the number of items involved in the study as the respondents in the study. Sampling

technique on the other hand is the scientific process through which the sample elements are selected

(Mugenda & Mugenda, 2009). The study applied the stratified random sampling technique to select a

sample size of 158 respondents obtained from the subgroups using simple random sampling. This

ensured that the sampling units have an equal chance in the study. The study sample population was

30% thus constituting 158 respondents who formed the sample size. Respondents from within each

group in the % proportions bearing to the indicated population as whole were taken using

proportionate sampling. The use of stratification focuses on reducing standard error by providing

some control over the possible variance. Table 3.2 shows the sampling frame that was 30% of the

target population that was taken as the sample.

The formula that mainly applies in most baseline stratified samples is also given as:

n= Z2PQ

m2

P = Likely value of the parameter

m= Permissible margin of error

Z = Value of the standard normal deviate corresponding to a level of significance

n= desired sample size

Q = 1-P

P = 0.2

m = 0.05

Z = 1.96

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n= {1.962 x 0.2 (1-0.2)} ÷ 0.05

2

n = 157.8 thus 158 respondents

To determine the sample size of each category of employees working in procurement and operations

according to the levels of management, proportionate stratified sampling was used as follows;

For Top Level Management

TLM = 45×158 = 15 employees

528

For Middle Level Management

MLM = 148×158 = 45 employees

528

For Operation Level Management

OLM = 335×158 = 100 employees

528

The respondent from every subgroup was then selected for inclusion in the sample size using simple

random sampling. This ensured that the sampling units had equal chance in the study. The sample

distribution is given as shown in Table 3.2;

Table 3.2: Sample Size Distribution

Category Target population(N) Percentage Sample size(n)

Top Level

Management

45 8.52 13

Middle Level

Management

148 28.3 45

Operation Level

Total

335

528

63.44

100

100

158

3.6 Data Collection Instruments Sukaran (2010), notes that for any research to reach its objective, the identification of an appropriate

means of data collection is obligatory. This study used primary data for statistical analysis. Kothari

(2012) outlines Primary data as data which is collected afresh and for the first time, and thus happens

to be original in character. This study used questionnaires as data collection tools. According to

(Mugenda & Mugenda, 2009), questionnaires are commonly used to obtain important information

about the population and each item in a questionnaire addresses a specific objective and research

question of the study. This tool was used because it enables the researcher to collect data from many

respondents within a short period of time, are easier to administer and analyze, are economical in

terms of time and money, there is low cost, it is free from the bias of the interviewer answers are in

respondent‘s own words, respondents have adequate time to give well thought answers, respondents

who are easily approachable can also be reached conveniently and large sample can be made use of

and thus the results can be made more dependable and reliable (Kothari , 2012). The questionnaires

consisted of mostly closed-ended and a few open-ended items.

Data Collection Procedure

The researcher adopted the drop and pick method to deliver the questionnaire to the selected

employees of CMC Motors Group Kenya and sent personalized emails with a return email address to

employees not within the regional coverage, Nairobi. The researcher monitored closely to ensure all

questionnaires issued to the respondents are received by maintaining a register for the questionnaires

issued and returned, (Mugenda & Mugenda, 2009). A total of 158 questionnaires were distributed to

the targeted respondents of the identified firms. Out of the population covered, 126 were responsive

representing a response rate of 79.75%. This was above the 50% which is considered adequate in

descriptive statistics according to Mugenda & Mugenda (2012)

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Pilot Testing

Questionnaires were first administered to 10% of the respondents from the sample populated prior to

the main study to test validity and reliability of the research instrument. Mugenda & Mugenda (2009),

contends that 1 to 10% of the sample is sufficient for pilot test; and further points out that, the

accuracy of data to be collected is largely dependent on the data collection instruments in terms of

validity and reliability. The Cronbach‘s alpha was used to test reliability of the data collection

instrument and an alpha of between 0.7 and 0.8 was considered enough to confirm and reflect the

internal consistency of the instrument, as guided by a study carried out by (Kothari, 2012). The

Cronbach‘s alpha results were Procurement Policy (.873), Electronic Procurement (.791), Supplier

Relationship Management (.723) and Outsourcing (.822), ranging between 0.723 and 0.873 and

therefore the construct was acceptable as shown by Table 4.2.

Data Analysis

To enable the researcher, understand the data collected and assign meaning to the resulting statistics,

an analysis of data was done to summarize the essential features and relationships of data to

generalize and determine patterns of behavior and outcomes. The completed questionnaires were

reviewed for completeness and consistency before responses can be processed. Qualitative and

quantitative techniques were used in the data analysis. Content analysis was done, while descriptive

analysis such as mean, frequencies and percentages were used to analyze the data. Data representation

was done through tables, graphs, figures and charts. Regression analysis using Multiple Linear

Regression model was employed to establish the significance of the independent variables on the

dependent variable. Data was organized and interpreted on account of concurrence to objectives using

assistance of the computer package, statistical package for social scientists (SPSS) version 22, to

communicate research findings. Tables and charts were used for data presentation. After analysis and

interpretation of data, a final report was presented summarizing the findings and conclusions as well

as the research recommendations. Regression Analysis was run to examine the relationship among the

independent and the dependent study variables which are set out in the objectives of the study. The

regression model is as below;

Y=βo+β1X1+β2X2+β3X3+β4X4+ε

Where;

Y= Procurement performance of CMC Motors Group Kenya

βo =Constant

X1= Procurement Policy

X2=Electronic Procurement

X3= Supplier Relationship Management

X4= Outsourcing

β1, β2, β3, β4 = Regression co-efficient

ε =Error

β0 represents the constant

β1,2,3,4 are regression coefficients

RESULTS AND DISCUSSION

The study sought to establish the drivers of performance in the motor industry in Kenya with specific

reference to CMC Motors Kenya. The study presents the research findings for all the study objectives

which include Procurement policy, Electronic procurement, Supplier relationship management,

Information management and procurement performance in motor industry in Kenya.

Procurement Policy and Procurement Performance

The first objective of the study was to determine the effects of procurement policy on procurement

performance in motor industry in Kenya. This study was interested in evaluating the impact of

procurement policy on procurement performance in CMC Motors Group, Kenya. This section

presents findings to statements posed in this regard with responses given on a five-point Likert scale

(where 5 = Strongly Agree; 4 = Agree; 3 = Neutral; 2 = Disagree; 1= Strongly Disagree). Table 4.1

presents the findings. The scores of ‗strongly disagree‘ and ‗disagree‘ have been taken to represent a

statement not agreed upon, equivalent to mean score of 0 to 2.5. The score of ‗Neutral‘ has been taken

to represent a statement agreed upon moderately, equivalent to a mean score of 2.6 to 3.4. The score

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of ‗agree‘ and ‗strongly agree‘ have been taken to represent a statement highly agreed upon equivalent

to a mean score of 3.5 to 5.0.

Table 4.1 below presents the findings. With a grand mean of 3.250, a majority of respondents were

neutral with most statements posed as regards influence of procurement policy on procurement

performance in the motor industry in Kenya. The respondents were neutral that the staff has a good

knowledge and procurement ethics all through the procurement structure. (M=3.36578); The

implemented communication policy can be a hindrance to achieving efficient procurement

performance. (M=3.3572); Distribution layout require a centralized procurement which maximizes on

the overall costs saved but can affect efficiency. (M=3.5290); The distribution layout policy impacts

on the overall procurement performance (M=3.7862); Procurement structure is contributory to the

overall procurement performance. (M=3.4456); rules and regulations provide guidance to the

procurement process(M=3.2567). The rules and regulations have helped to reduce corruption and

enhanced transparency (M=3.2358). The study results imply that procurement policy does play a very

important role on the procurement performance in the motor industry in Kenya. The study findings

are in line with literature review by Wisegeek (2013) who stated that procurement policies entail a set

of rules and regulations put in place to govern the process of acquiring goods and services needed by

an organization to function efficiently procurement policies determine the overall progress and

outcome regarding the organization set goals. Victor (2012) also established that procurement

expenditure could be minimized through implementation of effective procurement practices.

Procurement functions in a centralized procurement setup must be carried out with efficiency and

cascaded through the procurement cycle for satisfaction to be felt in the entire organization. Bandiera

et al. (2009) established that a central agency can produce considerable cost savings. Any deviation

from the norm will be felt in the procurement operations efficiency.

The study agrees with study by OECD (2014) who observed that good governance provides the

assurance that the policy process (policy formulation, implementation, monitoring and evaluation) is

operating optimally with effectiveness, efficiency, and economy, and is underpinned by broader

principles such as rule of law, transparency, accountability and integrity. Similarly, (Bandiera et al.,

2009) provide an important finding regarding the comparison between centralized and decentralized

procurement, holding that a central agency can produce considerable cost savings. Policies set

regarding channels of communication in the procurement function should be sensible, controllable but

fair to the functioning of the department. Table 4.1: Influence of Procurement Policy on Procurement Performance

Procurement Policy Mean Std. Dev

The staff has a good knowledge and procurement ethics all through the

procurement structure.

3.6578 .4321

The implemented communication policy can be a hindrance to achieving efficient

procurement performance.

3.3572 .5672

Distribution layout requires a centralized procurement which maximizes on the

overall costs saved but can affect efficiency.

3.5290 .9812

The distribution layout policy impacts on the overall procurement performance. 3.7862 .6218

Procurement structure is contributory to the overall procurement performance. 3.4456 .3843

Rules and regulations provide guidance to the procurement process 3.2567 1.5324

The rules and regulations have helped to reduce corruption and enhanced

transparency with the public sector

3.2358 .6543

Composite Mean 3.2451

Electronic Procurement and Procurement Performance

The second objective of the study was to determine the effects of electronic procurement on

procurement performance in motor industry in Kenya. This study was interested in evaluating the

impact of electronic procurement on procurement performance in CMC Motors Group, Kenya. This

section presents findings to statements posed in this regard with responses given on a five-point Likert

scale (where 5 = Strongly Agree; 4 = Agree; 3 = Neutral; 2 = Disagree; 1= Strongly Disagree). Table

4.2 presents the findings. The scores of ‗strongly disagree‘ and ‗disagree‘ have been taken to represent

a statement not agreed upon, equivalent to mean score of 0 to 2.5. The score of ‗Neutral‘ has been

taken to represent a statement agreed upon moderately, equivalent to a mean score of 2.6 to 3.4. The

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score of ‗agree‘ and ‗strongly agree‘ have been taken to represent a statement highly agreed upon

equivalent to a mean score of 3.5 to 5.0. Table 4.5 below presents the findings. With a grand mean of

3.1126, a majority of respondents were neutral with most statements posed as regards influence of

electronic procurement on procurement performance in the motor industry in Kenya. The respondents

were neutral that the use of the enterprise resource planning software is a major contributor to

procurement efficiency at CMC Motors Group (M=3.3335); Consistent training of stakeholders

improves the level of electronic procurement performance at CMC Motors Group (M=3.7654);

Electronic procurement has led to the electronic data integration leads to cost reduction (M=3.3245);

Electronic data integration enables the organization to streamline processes (M=3.5643); E-

procurement Facilitates real time response to customers. (M=3.2223); Enterprise resource planning

facilitates real time response to customers (M=3.2210). Internet connectivity influence the day to day

procurement activities (M=3.5424).The study results imply that electronic procurement is very

important on the procurement performance in the motor industry in Kenya.

The study findings agree with the study by Singh & Sanders (2007) who implied that it is imperative

to appreciate that internet connectivity is the driving force behind electronic procurement. The use of

e-purchasing tools to integrate systems within and between organizations is a potentially powerful

source of supply chain improvement. These e-procurement technologies can be clustered into three

main groups – e-sourcing tools, e-process tools, and e-transaction tools. According to (Khanapuri,

Nayak, Sharma &Soni, 2011), E-procurement results to profitability, control and simplicity in the

process of corporate procurement. Further, e-procurement leads to reduction in lead time & cost of

procurement and enhanced transparency (Bof & Previtali, 2010). Technology is advancing every day

and therefore employees require continuous effective training that will automate to efficiency. In a

related study, Iqbal et al (2014) maintains that an employee will become more efficient and

productive if he trained well. Table 4.2: Influence of Electronic Procurement on Procurement Performance

Electronic Procurement Mean Std. Dev

The use of the enterprise resource planning software is a major contributor to

procurement efficiency at CMC Motors Group

3.3335 .4321

Consistent training of stakeholders improves the level of electronic procurement

performance at CMC Motors Group

3.7654 1.6890

Electronic procurement has led to the electronic data integration leads to cost

reduction.

3.3245 .5480

Electronic data integration enables the organization to streamline processes. 3.5643 1.5320

Enterprise resource planning facilitates real time response to customers. 3.2223 .5623

Electronic data integration improves transparency in the procurement process. 3.2210 1.2609

Internet connectivity influence the day to day procurement activities 3.5424

Composite Mean 3.1126

Supplier Relationship Management and Procurement Performance

The third objective of the study was to determine the effects of supplier relationship management on

procurement performance in motor industry in Kenya. This study was interested in evaluating the

impact of supplier relationship management on procurement performance in CMC Motors Group,

Kenya. This section presents findings to statements posed in this regard with responses given on a

five-point likert scale (where 5 = Strongly Agree; 4 = Agree; 3 = Neutral; 2 = Disagree; 1= Strongly

Disagree). Table 4.6 presents the findings. The scores of ‗strongly disagree‘ and ‗disagree‘ have been

taken to represent a statement not agreed upon, equivalent to mean score of 0 to 2.5. The score of

‗Neutral‘ has been taken to represent a statement agreed upon moderately, equivalent to a mean score

of 2.6 to 3.4. The score of ‗agree‘ and ‗strongly agree‘ have been taken to represent a statement highly

agreed upon equivalent to a mean score of 3.5 to 5.0. Table 4.3 presents the findings.

Table 4.3 below presents the findings. With a grand mean of 3.2252, a majority of respondents were

neutral with most statements posed as regards influence of supplier relationship management on

procurement performance in the motor industry in Kenya. The respondents were neutral that supplier

commitment is of great essence to the overall procurement cycle at CMC Motors Group (M=3.2235);

Supplier development impacts on the concurrent procurement activities at CMC Motors Group

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(M=3.5690); The organization does the supplier selection for honouring the agreed vendor terms of

payment develops trust and propels future transactions smoothly (M=3.5214); Sharing information in

advance of changing needs is vital to facilitate fore planning by both the vendors and CMC

(M=3.6320); Supplier negotiations enhance reliable problem-solving mechanisms which are in place

between CMC and suppliers (M=3.4521).The study findings indicate that supplier relationship

management influence procurement performance in the motor industry. For successful partnership,

both vendors and customers must build their trust and always strive to perform better than agreed, as

this form the basic part of the relational investments that arise which are vital for future prosperity.

Trust is a combination of performance, relationship and commitment capability. Yang et al. (2009)

resonates that failing to establish supplier commitment, a buyer firm can suffer from shortage of

continuous and reliable inputs ranging from office supplies and equipment, to facility maintenance

and repair services in support of their operations. As an organizational resource, a committed supplier

is willing to resist short-term alternatives, make investments, and allocate resources to satisfy the

requirements of buyer firms.

Table 4.3: Influence of Supplier Relationship Management on Procurement Performance

Supplier Relationship Management Mean Std. Dev

Supplier commitment is of great essence to the overall procurement cycle at CMC

Motors Group.

3.2235 .5690

Supplier development impacts on the concurrent procurement activities at CMC

Motors Group.

3.5690 .2368

The organization does the supplier selection for honouring the agreed vendor terms

of payment develops trust and propels future transactions smoothly.

3.5214 .5690

Sharing information helps in negotiations in advance of changing needs is vital to

facilitate fore planning by both the vendors and CMC.

3.6320 .2236

Supplier negotiations enhance reliable problem-solving mechanisms which are in

place between CMC and suppliers.

3.4521 .3452

Composite Mean 3.2252

Outsourcing and Procurement Performance

The fourth objective of the study was to determine the influence of outsourcing on procurement

performance in motor industry in Kenya. This study was interested in evaluating the impact of

outsourcing on procurement performance in CMC Motors Group, Kenya. This section presents

findings to statements posed in this regard with responses given on a five-point likert scale (where 5 =

Strongly Agree; 4 = Agree; 3 = Neutral; 2 = Disagree; 1= Strongly Disagree). Table 4.4 presents the

findings. The scores of ‗strongly disagree‘ and ‗disagree‘ have been taken to represent a statement not

agreed upon, equivalent to mean score of 0 to 2.5. The score of ‗Neutral‘ has been taken to represent a

statement agreed upon moderately, equivalent to a mean score of 2.6 to 3.4. The score of ‗agree‘ and

‗strongly agree‘ have been taken to represent a statement highly agreed upon equivalent to a mean

score of 3.5 to 5.0.

With a grand mean of 3.2252, a majority of respondents were neutral with most statements posed as

regards influence of outsourcing on procurement performance in the motor industry in Kenya. The

respondents were neutral that outsourcing of services at CMC Motors Group relieves the company

from worrying about the massive capital that would have invested (M=3.6583); The outsourced

personnel at CMC Motors Group have a remarkable technical savvy (focus on core competencies)

because it is their line of specialty (M=3.8850); The technical capacity is embraced in outsourcing at

CMC Motors Group results to staff reduction resulting to reduced spend (M=3.4560); Technical

capacity is important because of outsourcing, CMC Motors has developed business relationships and

become competitive (M=3.5632); CMC Motors Group serves as a bench mark to other players in the

market through risk transfer (M=3.8803). Outsourced services transfer risks at CMC Motors

Group(M=3.5680). The study findings indicate that outsourcing influence procurement performance

in the motor industry. Gupta et al., (2009) state that in creating competitive advantage, a firm needs

the ability to make good use of resources. The concept of core competence has been developed to

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support more efficient identification and utilization of an organization's strength. A related study by

(Dorasamy et al., 2010) shows a significant relationship between outsourcing of functions and risks.

Risk has been well-defined as a measure of the likelihood and severity of adverse effects (Haimes,

2009). Mwikali and Kavale (2012) reveal that cost factors, technical capability, quality assessment,

organizational profile, service levels and risk factors, are key factors affecting supplier selection in

procurement management. Pamela (2013) echoes by citing supplier financial capacity expertise as one

of the key factors which determine the ultimate performance of both the supplier and procurement

performance, depicting a high correlation between the financial capacity of supplier and ability of

supplier to deliver. Table 4.4: Influence of Outsourcing on Procurement Performance

Outsourcing Mean Std. Dev

Outsourcing of services at CMC Motors Group relieves the company from

worrying about the massive capital that would have invested.

3.6583 .4320

The outsourced personnel at CMC Motors Group have a remarkable technical

savvy (focus on core competencies) because it is their line of specialty

3.8850 1.4680

The technical capacity is embraced in outsourcing at CMC Motors Group results to

staff reduction resulting to reduced spend.

3.4560 .5358

Technical capacity is important because of outsourcing, CMC Motors has

developed business relationships and become competitive

3.5632 .5908

CMC Motors Group serves as a bench mark to other players in the market through

risk transfer

3.8803 .3265

Outsourced services transfer risks at CMC Motors Group 3.5680 .3256

Composite Mean 3.4568

Procurement Performance

The study sought to examine the drivers of procurement performance in the motor industry in Kenya, attributed

to the influence of procurement policy, electronic procurement, supplier relationship management and

outsourcing. The study was particularly interested in three key indicators, namely order fulfillment, cost

reduction and lead time reduction, with all the three studied over a 5 year period, running from 2012 to 2016.

Findings in Table 4.5 reveal improved procurement performance across the 5 year period running from the year

2012 to 2016. Order fulfillment recorded positive growth with a majority affirming to less than 10% in 2012

(42.3%) and 2013 (37.7%), to 10% in 2014 (36.1%) then more than 10% in 2015 (41.1%) and 2016 (37.5%).

A similar trend was recorded in cost reduction, growing from less than 10% (44.1%) in 2012, to more than 10%

in 2013 (36.4%), 2014 (40.4%) and 2015 (37.3%). Lead time reduction further recorded positive growth with a

majority affirming to less than 10% in 2012 (37.9%) and 2013 (35.9%), to 10% in 2014 (35.9%) and 2015

(35.3%) then by more than 10% in 2016 (36.2%). It can be deduced from the findings that key procurement

performance indicators have considerably improved as influenced by among other procurement management

attributes, the influence of procurement policy, electronic procurement, supplier relationship management and

outsourcing. Order fulfillment, cost reduction and lead time reduction have particularly improved by at least 10

percent in the organization pointing to the significance of procurement policy, electronic procurement, supplier

relationship management and outsourcing in the supply chain process.

Table 4.5: Procurement Performance

Quality of goods purchased 2012 2013 2014 2015 2016

Improved by less than 10% 42.3 37.7 31.6 30.7 29.5

Improved by 10% 31.8 32.9 36.1 28.2 33

Improved by more than 10% 25.9 29.4 32.3 41.1 37.5

Cost reduction 2012 2013 2014 2015 2016

Improved by less than 10% 44.1 35.2 33.4 25.7 27.1

Improved by 10% 31.7 32.6 30.2 33.9 35.6

Improved by more than 10% 23.5 32.2 36.4 40.4 37.3

Timely Purchases-Stock out Reduction

(Achieved lead times)

2012 2013 2014 2015 2016

Improved by less than 10% 37.9 35.9 31.2 25.7 33.1

Improved by 10% 36.2 31.3 35.9 35.3 30.7

Improved by more than 10% 25.9 32.8 32.9 39 36.2

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Multiple Regression Analysis

The study adopted a multiple regression analysis so as to establish the relationship of independent

variables and dependent variables. The study applied SPSS to compute the measurements of the

multiple regression analysis. According to the model summary Table 4.6, the coefficient of

determination (R2) is used to measure how far the regression model‘s ability to explain the variation

of the independent variabies. The correlation coefficient was 0.799. This indicates a very strong

postive relationship between the independent variable and depedent variable. The data showed that

the high R square is 0.638. It shows that the independent variables in the study were able to explain

63.80% variation in the procurement perfomance in the organization while the remaining 36.20% is

explained by the variables or other aspects outside the model. This implies that these variables are

very significant and they therefore need to be considered in any effort to boost procurement

perfomance in the organization. The study therefore identifies variables as critical drivers of

procurement perfomance in the organization.

Table 4.6: Model Summary

Model R R2 Adjusted R

2 Std. Error of the Estimate

.799 .638 .599 .001

ANOVA Results

F-test is done to test the effect of independent variables on the dependent variable simultaneously.

The F-statistic test basically shows whether all the independent variables included in the model

jointly influence on the dependent variable. Based on the study results of the ANOVA Test or F-test

in Table 4.7, obtained F-calculated was 6.7960 greater the F-Table (4.8095) with significance of

0.000. Since the significance level of 0.000<0.05 we conclude that the set of independent variables

affect the procurement performance in the motor industry (Y-dependent variable) and this shows that

the overall model was significant.

Table 4.7: ANOVA

Model Sum of

Squares

d.f Mean Square F Sig.

Regression 12.888 4 3.222 6.7960 .000a

Residual 56.890 120 .4741

Total 69.778 125

NB: F-critical Value = 4.8095;

Regression Coefficients

The results of multiple regression analysis obtained regression coefficients t value and significance

level as indicated in Table 4.9. The study conducted a multiple regression analysis so as to determine

the relationship between the dependent variable and independent variables. The general form of the

equation was to predict procurement perfomance mpanies from procurement policy, electronic

procurement, supplier relationship management and outsourcing is: (Y = β0 + β1X1 + β2X2 + β3X3 +

β4X4 +ε) becomes: Y= 12.344+ 0.766X1+ 0.569X2+ 0.710X3 + 0.621X4. This indicates that

procurement perfomance = 12.344 + 0.766* Procurement Policy + 0.569*Electronic Procurement +

0.710*Supplier Relationship Management + 0.621*Outsourcing + 2.902. From the study findings on

the regression equation established, taking all factors into account (independent variables) constant at

zero procurement perfomance was 12.344. The data findings analyzed also shows that taking all

other independent variables at zero, a unit increase in procurement policy would lead to a 0.766

increase in procurement perfomance; a unit increase in electronic procurement would lead to a 0.569

increase in procurement perfomance, a unit increase in supplier relationship management would lead

to 0.710 increase in procurement perfomance and a unit increase in outsourcing would lead to 0.621

increase in procurement perfomance. This infers that procurement policy contributed most to

procurement perfomance. Based at 5% level of significance, procurement policy had a t-value

(5.665>1.96) with a .000 level of significance; electronic procurement had a t-value (3.654 > 1.96)

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with a .011 level of significance, supplier relationship management had a t-value (4.876>1.96) with a

.004 level of significance and ICT Integration had a t-value (2.771>1.96) with a .035 level of

significance hence the most significant factor was procurement policy.

Table 4.9: Coefficient Results

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

β Std. Error β

(Constant) 12.344 2.902 7.615 .000

X1_Procurement Policy .766 .110 .765 5.665 .000

X2_Electronic Procurement .569 .130 .654 3.654 .011

X3_Supplier Relationship

Management

.710 .145 .555 4.876 .004

X4_Outsourcing .621 .174 .532 2.771 .035

CONCLUSIONS

From the study findings, the study settles that procurement performance in motor industry is affected

by procurement policy, electronic procurement; supplier relationship management and outsourcing as

the major factors that mostly affect procurement performance in motor industry in Kenya.

The study findings similarly conclude that procurement policy is the first important factor that affects

procurement performance in the motor industry in Kenya. The regression coefficients of the study

show that procurement policy has a significant influence on procurement performance in the motor

industry in Kenya. This implies that increasing levels of procurement policy would increase the levels

of procurement performance in the motor industry in Kenya. This shows that procurement policy has

a positive influence on procurement performance in the motor industry in Kenya.

Additionally, the study concludes that electronic procurement is the second important factor that

affects procurement performance in the motor industry in Kenya. The regression coefficients of the

study show that electronic procurement has a significant influence on procurement performance in the

motor industry in Kenya. This implies that increasing levels of electronic procurement would increase

the levels of procurement performance in the motor industry in Kenya. This shows that electronic

procurement has a positive influence on procurement performance in the motor industry in Kenya.

Further, the study concludes that supplier relationship management is the third important factor that

affects procurement performance in the motor industry in Kenya. The regression coefficients of the

study show that supplier relationship management has a significant influence on procurement

performance in the motor industry in Kenya. This implies that increasing levels of supplier

relationship management would increase the levels of procurement performance in the motor industry

in Kenya. This shows that supplier relationship management has a positive influence on procurement

performance in the motor industry in Kenya.

Finally, the study concludes that outsourcing is the fourth important factor that affects procurement

performance in the motor industry in Kenya. The regression coefficients of the study show that

outsourcing has a significant influence on procurement performance in the motor industry in Kenya.

This implies that increasing levels of outsourcing would increase the levels of procurement

performance in the motor industry in Kenya. This shows that outsourcing has a positive influence on

procurement performance in the motor industry in Kenya.

RECOMMENDATIONS

The organization should provide the assurance that the procurement policy and subsequent policy

process (policy formulation, implementation, monitoring and evaluation) is operating optimally with

effectiveness, efficiency, and economy, and is underpinned by broader principles such as rule of law,

transparency, accountability and integrity. The procurement Policies set regarding channels of

communication in the procurement function should be sensible, controllable but fair to the functioning

of the department.

The study also recommends that the organization should invest on electronic procurement like ERP as

full automation of the procurement process will help them coordinate the organization activities.

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Correspondingly, Electronic procurement will help on transit, improved billing systems hence

reducing paper work on supply chain activities.

The study recommends for the improvement of supplier management to enhance procurement

performance in the motor industry. The companies require to be recognizant with the importance of

supplier reliability. The low inventory levels would mean that the supplier would begin their own

procurement process which prolong the lead time. Long lead time has the impression that the specific

supplier is less efficient, or he just has more customers than he can serve thus delaying deliveries.

Further, e-procurement leads to reduction in lead time & cost of procurement and enhanced

transparency. Technology is advancing every day and therefore employees require continuous

effective training that will automate to efficiency and to maintain it, the employee can only become

more efficient and productive if they are well trained. The study also recommends that the firms

should become more and more dependent on their suppliers and the suppliers should be seen as a as

key resources in the development of the buyer's own capabilities and performance.

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