Table of Contents
....................................................................................................................................................2
CHAPTER ONE.............................................................................................................................2
INTRODUCTION..........................................................................................................................2
1.1 Background of the Study..................................................................................................2
1.1.1 Artificial Intelligence Capability in Procurement.......................................................4
1.1.2 Decision-Making Quality as a Mediator....................................................................5
1.1.3 Public Sector Organizations and Procurement in Nairobi County, Kenya.................7
1.2 Statement of the Problem................................................................................................8
1.3 General Objective of the Study........................................................................................9
1.3.1 Specific Objectives.....................................................................................................9
1.4 Research Questions........................................................................................................10
1.5 Significance of the Study................................................................................................10
1.5.1 Management of Manufacturing Firms....................................................................10
1.5.2 Policy Makers...........................................................................................................11
1.5.3 Procurement Professionals and Supply Chain Practitioners...................................11
1.5.4 Academic and Research Community.......................................................................11
1.5.5 Government of Kenya, and Development Partners................................................12
1.6 Scope of the Study..........................................................................................................12
CHAPTER TWO..........................................................................................................................14
LITERATURE REVIEW.................................................................................................................14
2.1 Introduction....................................................................................................................14
2.2 Theoretical Review.........................................................................................................15
2.2.1 Resource-Based View (RBV) Theory........................................................................15
2.2.2 Dynamic Capabilities Theory...................................................................................17
2.3 Empirical Review............................................................................................................18
2.3.1 Artificial Intelligence Capability and Procurement Performance............................18
2.3.2 Top Management Support and Procurement Performance....................................19
2.3.3 Employee AI Competence and Procurement Performance....................................20
2.3.4 Combined Effect of the Variables on Procurement Performance...........................22
2.4 Conceptual Framework..................................................................................................23
2.5 Knowledge Gap...............................................................................................................23
2.6 Operationalization of Variables......................................................................................24
,References................................................................................................................................26
CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
The manufacturing industry remains among the most crucial pillars of employment
creation, economic development and industrialization agenda in Kenya. In a period
categorized by drastically shifting customer demands, fluctuating prices of raw materials,
intense world competition, and supply chain interruptions, manufacturing organizations
constantly seeks means of optimizing their activities. Procurement, which entails the
acquisition of spare parts, raw materials, services and components, forms a large part of
overall production costs, mostly ranging 50-70% in numerous manufacturing firms.
Ineffective procurement practices often lead to quality inconsistencies, production delays,
elevated inventory holding costs, and decreased general competitiveness.
This research examines how artificial intelligence capability influences procurement
performance, where decision-making quality functions as a mediating variable. Artificial
Intelligence has risen as a critical and highly transformative technology which can
revolutionize procurement practices in the manufacturing sector. The study focuses on public
sector institutions within Nairobi County. Kenya. There is a positive link between AI
adoption in procurement in relation to tackling systemic issues like suboptimal resource
, allocation, inefficiency, delays and corruption which have been a major hindrance to public
expenditure in developing economies.
Public procurement is a critical component of public financial management.
Universally, public procurement contributes to 10% to 20% of GDP in majority of nations
and up to 40% of countries’ public budgets including Kenya (National Treasury, 2023; World
Bank, 2021; OECD, 2022). In Kenya, the Public Procurement and Asset Disposal Act of 2015
govern public procurement processes. The Act seeks to foster value for money, transparency,
competitiveness and accountability. Recurrent PPRA evaluations and reports by the Auditor
General continuously underpin challenges like weak contract management, integrity issues,
overstated procurement costs, supplier underperformances, and lengthy bidding processes
(PPRA, 2023; Office of the Auditor General, 2022–2024).
In the African setting, manufacturing companies in Morocco and South Africa are
increasingly adopting AI-enabled and digital procurement solutions to foster competitiveness
in the middle universal supply chain challenges (Anzolin & Andreoni, 2023). In the Kenyan
context, the manufacturing industry is renowned for its substantial contribution to the
Bottom-Up Economic Transformation Agenda and Vision 2030. The manufacturing sector
contributes significantly to GDP, employment creation, and export earnings, especially in
Nairobi, which functions as the nation’s commercial and industrial capital. Nonetheless,
numerous manufacturing companies continuously depend on traditional manual procurement
strategies, resulting in loss of opportunities, inefficiencies, and elevated costs. The Kenya
National Artificial Intelligence Strategy offers a national policy paradigm to bolster AI
adoption, but empirical evidence on its adoption in procurement in the manufacturing
industry within Nairobi. This research study seeks to bridge crucial knowledge gap.
....................................................................................................................................................2
CHAPTER ONE.............................................................................................................................2
INTRODUCTION..........................................................................................................................2
1.1 Background of the Study..................................................................................................2
1.1.1 Artificial Intelligence Capability in Procurement.......................................................4
1.1.2 Decision-Making Quality as a Mediator....................................................................5
1.1.3 Public Sector Organizations and Procurement in Nairobi County, Kenya.................7
1.2 Statement of the Problem................................................................................................8
1.3 General Objective of the Study........................................................................................9
1.3.1 Specific Objectives.....................................................................................................9
1.4 Research Questions........................................................................................................10
1.5 Significance of the Study................................................................................................10
1.5.1 Management of Manufacturing Firms....................................................................10
1.5.2 Policy Makers...........................................................................................................11
1.5.3 Procurement Professionals and Supply Chain Practitioners...................................11
1.5.4 Academic and Research Community.......................................................................11
1.5.5 Government of Kenya, and Development Partners................................................12
1.6 Scope of the Study..........................................................................................................12
CHAPTER TWO..........................................................................................................................14
LITERATURE REVIEW.................................................................................................................14
2.1 Introduction....................................................................................................................14
2.2 Theoretical Review.........................................................................................................15
2.2.1 Resource-Based View (RBV) Theory........................................................................15
2.2.2 Dynamic Capabilities Theory...................................................................................17
2.3 Empirical Review............................................................................................................18
2.3.1 Artificial Intelligence Capability and Procurement Performance............................18
2.3.2 Top Management Support and Procurement Performance....................................19
2.3.3 Employee AI Competence and Procurement Performance....................................20
2.3.4 Combined Effect of the Variables on Procurement Performance...........................22
2.4 Conceptual Framework..................................................................................................23
2.5 Knowledge Gap...............................................................................................................23
2.6 Operationalization of Variables......................................................................................24
,References................................................................................................................................26
CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
The manufacturing industry remains among the most crucial pillars of employment
creation, economic development and industrialization agenda in Kenya. In a period
categorized by drastically shifting customer demands, fluctuating prices of raw materials,
intense world competition, and supply chain interruptions, manufacturing organizations
constantly seeks means of optimizing their activities. Procurement, which entails the
acquisition of spare parts, raw materials, services and components, forms a large part of
overall production costs, mostly ranging 50-70% in numerous manufacturing firms.
Ineffective procurement practices often lead to quality inconsistencies, production delays,
elevated inventory holding costs, and decreased general competitiveness.
This research examines how artificial intelligence capability influences procurement
performance, where decision-making quality functions as a mediating variable. Artificial
Intelligence has risen as a critical and highly transformative technology which can
revolutionize procurement practices in the manufacturing sector. The study focuses on public
sector institutions within Nairobi County. Kenya. There is a positive link between AI
adoption in procurement in relation to tackling systemic issues like suboptimal resource
, allocation, inefficiency, delays and corruption which have been a major hindrance to public
expenditure in developing economies.
Public procurement is a critical component of public financial management.
Universally, public procurement contributes to 10% to 20% of GDP in majority of nations
and up to 40% of countries’ public budgets including Kenya (National Treasury, 2023; World
Bank, 2021; OECD, 2022). In Kenya, the Public Procurement and Asset Disposal Act of 2015
govern public procurement processes. The Act seeks to foster value for money, transparency,
competitiveness and accountability. Recurrent PPRA evaluations and reports by the Auditor
General continuously underpin challenges like weak contract management, integrity issues,
overstated procurement costs, supplier underperformances, and lengthy bidding processes
(PPRA, 2023; Office of the Auditor General, 2022–2024).
In the African setting, manufacturing companies in Morocco and South Africa are
increasingly adopting AI-enabled and digital procurement solutions to foster competitiveness
in the middle universal supply chain challenges (Anzolin & Andreoni, 2023). In the Kenyan
context, the manufacturing industry is renowned for its substantial contribution to the
Bottom-Up Economic Transformation Agenda and Vision 2030. The manufacturing sector
contributes significantly to GDP, employment creation, and export earnings, especially in
Nairobi, which functions as the nation’s commercial and industrial capital. Nonetheless,
numerous manufacturing companies continuously depend on traditional manual procurement
strategies, resulting in loss of opportunities, inefficiencies, and elevated costs. The Kenya
National Artificial Intelligence Strategy offers a national policy paradigm to bolster AI
adoption, but empirical evidence on its adoption in procurement in the manufacturing
industry within Nairobi. This research study seeks to bridge crucial knowledge gap.
