Drivers of Procurement Performance in the Motor Industry: a Case of CMC Motors Group, Kenya

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

Drivers of Procurement Performance in the Motor Industry: A Case of CMC Motors Group, Kenya

Juma Veronica Ayieko1 & Dr. Justus Kinoti2

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

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leading driver in this field, (Hong & Kwon, 2012).Ok 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, 9

Ayieko & Kinoti ….. ..Int. J. Business & Law Research 6(1):8-26, 2018 traditional procurement procedures, and inability to embrace e-procurement, poor coordination of Ok 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) 10

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Ltd. CMC Motors Group holds exclusive distributionOk 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. 11

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iii. To examine the effect of supplier relationshipOk 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 12

Ayieko & Kinoti ….. ..Int. J. Business & Law Research 6(1):8-26, 2018 outsourcing process. Transaction Cost theory focuses on the reasons to firm‘s existence, most effective strategies for maximizing profits, the coreOk 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.

Procurement Policy  Procurement structure  Communication  Distribution layout  Rules and regulations

Electronic Procurement  Enterprise resource planning  Training  Electronic data integration Procurement Performance  Internet connectivity  Cost reduction

 Achieved lead-times  Quality of goods purchased

Supplier Relationship Management  Supplier commitment  Supplier development  Supplier selection  Supplier negotiation

Outsourcing  Capital  Focus on core competencies Independent Technical capacityVariables  Risk transfer Figure 2.1: Conceptual Framework

Independent Variables Dependent Variable Figure 2.1: Conceptual Framework

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RESEARCH METHODOLOGY Ok 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 335 63.4 Total 528 100 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

Ayieko & Kinoti ….. ..Int. J. Business & Law Research 6(1):8-26, 2018 n= {1.962 x 0.2 (1-0.2)} ÷ 0.052 Ok 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 45 8.52 13 Management Middle Level 148 28.3 45 Management Operation Level 335 63.44 100 Total 528 100 158 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) 15

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Pilot Testing Ok 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 16

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of ‗agree‘ and ‗strongly agree‘ have been taken to representOk 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 3.6578 .4321 procurement structure. The implemented communication policy can be a hindrance to achieving efficient 3.3572 .5672 procurement performance. Distribution layout requires a centralized procurement which maximizes on the 3.5290 .9812 overall costs saved but can affect efficiency. 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 3.2358 .6543 transparency with the public sector 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 17

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score of ‗agree‘ and ‗strongly agree‘ have beenOk 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 3.3335 .4321 procurement efficiency at CMC Motors Group Consistent training of stakeholders improves the level of electronic procurement 3.7654 1.6890 performance at CMC Motors Group Electronic procurement has led to the electronic data integration leads to cost 3.3245 .5480 reduction. 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 18

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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 transactionsOk 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 3.2235 .5690 Motors Group. Supplier development impacts on the concurrent procurement activities at CMC 3.5690 .2368 Motors Group. 3.5214 .5690 The organization does the supplier selection for honouring the agreed vendor terms of payment develops trust and propels future transactions smoothly. Sharing information helps in negotiations in advance of changing needs is vital to 3.6320 .2236 facilitate fore planning by both the vendors and CMC. Supplier negotiations enhance reliable problem-solving mechanisms which are in 3.4521 .3452 place between CMC and suppliers. 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 19

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support more efficient identification and utilizationOk 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 3.6583 .4320 worrying about the massive capital that would have invested. The outsourced personnel at CMC Motors Group have a remarkable technical 3.8850 1.4680 savvy (focus on core competencies) because it is their line of specialty The technical capacity is embraced in outsourcing at CMC Motors Group results to 3.4560 .5358 staff reduction resulting to reduced spend. Technical capacity is important because of outsourcing, CMC Motors has 3.5632 .5908 developed business relationships and become competitive CMC Motors Group serves as a bench mark to other players in the market through 3.8803 .3265 risk transfer 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 2012 2013 2014 2015 2016 (Achieved lead times) 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 Ok 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 R2 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 d.f Mean Square F Sig. Squares 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)

Ayieko & Kinoti ….. ..Int. J. Business & Law Research 6(1):8-26, 2018 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 hadOk 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 Standardized t Sig. Coefficients Coefficients 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 .710 .145 .555 4.876 .004 Management 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. 22

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Correspondingly, Electronic procurement will Ok 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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