INFLUENCE OF TECHNICAL SKILLS ON PERFORMANCE OF MANUFACTURING 

FIRMS IN NAIROBI CITY COUNTY IN KENYA 

 

 

 

 

DAN OWUOR ODONGO 

 

 

A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE 

REQUIREMENTS FOR THE AWARD OF MASTER OF BUSINESS ADMINSTRATION 

DEGREE IN FINANCE OF 

MOUNT KENYA UNIVERSIT 

 

 

 



 

ii 

 

JULY 2024 

DECLARATION AND APPROVAL 

 



 

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iv 

 

DEDICATION 

This study is devoted to the Almighty God. To my father, Mr. Odongo, in appreciation for the 

assistance that was provided over the time period. 

 

 

 

 

 

 

 

 

 

 

 

ACKNOWLEDGEMENT 



 

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I wish to thank God for good health when developing this research project. My special thanks to 

my supervisor Dr. Henry Yatich and the entire Mount Kenya University fraternity for the 

opportunity to undertake research proposal.  

  



 

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ABSTRACT 

The contribution to Gross Domestic Product (GDP) by the manufacturing sector in Kenya has been 

slow, falling from 13.8 percent in 2020 to 12.9 percent in 2021. The sector has potential to grow 

and is expected to perform better, post covid-19. This is an indicator that the manufacturing sector 

in Kenya is below its 20230 Vision envisaging a target of 20% contribution to the GDP. The sales 

turnover of manufacturing firms in Kenya as measure of performance has been decreasing in recent 

years. In 2022, the total sales turnover of manufacturing firms in Kenya was estimated to be Ksh 

3.5 trillion (approximately USD 30 billion). This represents a decrease of 10.2% from 2021. There 

have been some fluctuations in the net profit margin of manufacturing firms in Kenya over the 

past few years. For example, in 2022, the net profit margin declined to 9.3% due to cost of 

production including energy, currency fluctuation and competition from imported goods. The 

study aimed to examine how technical skills practices may affect manufacturing firms’ firm 

performance among selected manufacturing firms in Nairobi, Kenya. The specific objectives To 

analyze tthe tinfluence tof tinformation tcommunication ttechnology tskills, data science skills and 

innovative skills effect on performance tof tmanufacturing tfirms tin tNairobi tCity tCounty tin tKenya. 

The theories of the study included contingency theory, and human capital theory. The study 

adopted a quantitative approach and used descriptive survey research design. The study targeted 

253 senior managers drawn from finance managers, procurement managers, human resource 

managers, customer relations managers and operations managers. The study sampled 75, which is 

30% of the total population. Data was collected by use of structured questionnaire. Data analysis 

was done descriptively (mean, standard deviation, frequencies and percentages). Data presentation 

was presented by use of tables and charts. The results indicated that technical skills have significant 

positive effect on the performance tof tmanufacturing tfirms tin tNairobi tCity tCounty tin tKenya. The 

outcomes disclosed that Information communication skills had a substantial beneficial influence 

on the performance of manufacturing tfirms tin tNairobi tCity tCounty tin tKenya, with a B-value of.218 

and a p-value of.001. Similar result were reported for data science skills as well as innovative 

skills. The conclusion is that technical skills accounted for 60.7% of the substantial variation in 

outcomes for manufacturing companies in Nairobi City County in Kenya (R square =.607). The 

findings of this study suggest that manufacturing firms in Nairobi City County should invest in 

developing the ICT skills of their employees. This can be done through training programs, 

workshops, and on-the-job training. In addition to investing in data science skills, firms should 

also create a culture that is data-driven. This can be done by encouraging employees to use data to 

inform their decisions and by providing them with the tools and resources they need to do so. The 

study's findings suggest that manufacturing firms in Nairobi City County should focus on 

developing their innovative skills in order to improve their performance. This can be done through 

a variety of means, such as investing in research and development, training employees in 

innovation techniques, and creating a culture that is supportive of innovation. 

  



 

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TABLE OF CONTENTS 

DECLARATION AND APPROVAL .......................................................................................... ii 

DEDICATION.............................................................................................................................. iv 

ACKNOWLEDGEMENT ........................................................................................................... iv 

ABSTRACT .................................................................................................................................. vi 

TABLE OF CONTENTS ........................................................................................................... vii 

LIST OF TABLES ....................................................................................................................... xi 

LIST OF FIGURES .................................................................................................................... xii 

LIST OF ABBREVIATION AND ACRONYMS ................................................................... xiii 

CHAPTER ONE: INTRODUCTION ......................................................................................... 1 

1.0. Introduction .............................................................................................................................. 1 

1.1. Background of the Study ......................................................................................................... 1 

1.2 Statement of the Problem .......................................................................................................... 5 

1.3 Purpose of the Study ................................................................................................................. 6 

1.4 Objectives of the Study ............................................................................................................. 6 

1.5 Research Questions ................................................................................................................... 6 

1.6 Significance of the Study .......................................................................................................... 6 

1.7 Scope of the Study .................................................................................................................... 7 

1.8 Limitations of the study ............................................................................................................ 7 

1.9 Delimitations of the Study ........................................................................................................ 7 

1.10 Assumptions of the Study ....................................................................................................... 8 

1.11 Operational Definition of Key Terms ..................................................................................... 9 

CHAPTER TWO: LITERATURE REVIEW .......................................................................... 10 

2.0 Introduction ............................................................................................................................. 10 

2.1 Theoretical Literature Review ................................................................................................ 10 

2.1.1 Contingency Theory....................................................................................................... 10 

2.1.2 Human Capital Theory ................................................................................................... 11 

2.2 Review of literature on variables ............................................................................................ 11 



 

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2.2.1 Information Technology Skills on Manufacturing Firms Performance ......................... 11 

2.2.2 Data Science Skills on Manufacturing Firms Performance ........................................... 15 

2.2.3 Innovative Skills on Manufacturing Firms Performance ............................................... 16 

2.3 Empirical Review..................................................................... Error! Bookmark not defined. 

2.4 Conceptual Framework ........................................................................................................... 19 

2.5 Recap of Literature Review .................................................................................................... 20 

2.6 Research Gap .......................................................................................................................... 22 

CHAPTER THREE: RESEARCH METHODOLOGY ......................................................... 23 

3.0 Introduction ............................................................................................................................. 23 

3.1 Research Design...................................................................................................................... 23 

3.2 Population ............................................................................................................................... 25 

3.3 Sampling Frame ...................................................................................................................... 25 

3.4 Sample and Sampling Technique............................................................................................ 25 

3.4.1 Sampling Technique ...................................................................................................... 25 

3.4.2 Sample Size .................................................................................................................... 26 

3.5 Data Collection Instrument ..................................................................................................... 26 

3.6 Data Collection Procedure ...................................................................................................... 26 

3.7 Pilot Testing ............................................................................................................................ 27 

3.8 Data Processing and Analysis ................................................................................................. 29 

3.8.1 Simple Linear Regression Analysis ............................................................................. 30 

3.8.2 Multiple Regression Analysis ...................................................................................... 30 

3.8.3 Diagnostic Tests ........................................................................................................... 31 

3.9 Ethical Consideration .............................................................................................................. 32 

CHAPTER FOUR ....................................................................................................................... 34 

RESEARCH FINDINGS AND DISCUSSIONS ........................... Error! Bookmark not defined. 

4.1 Introduction ............................................................................................................................. 34 

4.2 Response Rate ......................................................................................................................... 34 

4.3 Demographic Characteristics .................................................................................................. 34 



 

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4.4 Influence of Information Communication Technology Skills on Performance of Manufacturing 

Firms ............................................................................................................................................. 38 

4.4.1 Descriptive Statistics .................................................................................................... 38 

4.4.2 Simple Linear Linear Regression Analysis .................................................................. 41 

4.5 Influence of Data Science Skills on Performance of Manufacturing Firms ........................... 44 

4.5.1 Descriptive Statistics ...................................................................................................... 44 

4.5.2 Linear Regression Analysis ........................................................................................... 46 

4.6 Influence of Innovative skills on Performance of Manufacturing Firms ................................ 49 

4.6.1 Descriptive Statistics ...................................................................................................... 49 

4.5.2 Linear Regression Analysis ........................................................................................... 51 

4.7 Performance of manufacturing firms in Nairobi City County in Kenya ................................ 54 

4.7.1 Descriptive Statisics ....................................................................................................... 54 

4.6.1 Assumption of Linear Regression .................................................................................. 54 

4.7.2 Correlation Analysis ...................................................................................................... 56 

4.7.3 Multiple Regression Analysis ........................................................................................ 58 

4.7.3.1 Model Summary.......................................................................................................... 58 

4.7.3.2 Model’s Goodness of Fit ............................................................................................. 59 

CHAPTER FIVE ........................................................................................................................ 66 

SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ............................................ 66 

5.1 Introduction ............................................................................................................................. 66 

5.2 Summary of the Findings ........................................................................................................ 66 

5.3 Conclusions ............................................................................................................................. 68 

5.4 Recommendations ................................................................................................................... 69 

5.5 Areas for Further Research ..................................................................................................... 70 

REFERENCES ............................................................................................................................ 71 

APPENDICES ............................................................................................................................. 79 

Appendix I: CONSENT FOR PARTICIPATION ........................................................... 79 

Appendix II: QUESTIONNAIRE ...................................................................................... 80 



 

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Appendix III: INTRODUCTION LETTER FROM POST GRADUATE..................... 83 

Appendix IV: ERC PERMIT ............................................................................................. 85 

Appendix V: NACOSTI PERMIT ..................................................................................... 86 

Appendix VI: LIST OF MANUFACTURING FIRMS IN NAIROBI CITY COUNTY

 ............................................................................................................................................... 86 

Appendix VII: PLAGIARISM REPORT ......................................................................... 89 

 

  



 

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LIST OF TABLES 

Table 1: Age of the respondents ................................................................................................... 35 

Table 2: Highest Level of Academic ............................................................................................ 36 

Table 3: Number of Years Respondents have worked for their current employer ....................... 37 

Table 4: Performance of manufacturing firms in Nairobi City County in Kenya ........................ 54 

Table 5: Pearson Correlation Analysis ......................................................................................... 57 

Table 6: Model Summary ............................................................................................................. 59 

Table 7: ANOVA .......................................................................................................................... 60 

Table 8: Regression Coefficient .................................................................................................... 61 

 

 

 

 

 

 

 

 



 

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LIST OF FIGURES 

Figure 1: Conceptual Framework ................................................................................................. 20 

 

  

 

 

 

 

 

 

 

 

 

 

 

file:///C:/Users/Dell/Downloads/Research%20Proposals%20.docx%23_Toc129821786


 

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LIST OF ABBREVIATION AND ACRONYMS 

CDF   - Constituency Development Fund  

COVID-19 - Coronavirus Disease 

CRM   - Customer Relationship Management 

ERM   - Enterprise Risk Management 

GDP   - Gross tDomestic tProduct 

KAM   - Kenya Association tof tManufacturers tMTP t- tMedium tTerm tPlan 

RBV   - Resource Based View 

 

 

 

 

 

 

 



 

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CHAPTER ONE: INTRODUCTION 

1.0. Introduction 

This tsection tdiscusses tthe tstudy tbackground, tstatement tof tthe tproblem, tpurpose tof tstudy, tstudy 

tobjectives, tresearch tquestions, timportance tof tthe tstudy, tscope tof tthe tstudy, tstudy tlimitations, 

tdelimitations, tand tassumptions tof tthe tstudy tand tlastly toperational tdefinition tof tterm. 

1.1. Background of the Study 

The primary role of manufacturing firms is to increase turnover. Increase in revenue to firms 

implies an improvement of productivity. According to (Issack & Muathe, 2017) firms should take 

informed actions to achieve an organization's short and long-term production is taken into account. 

As a result, there is need to plan an action so as to protect firms from the unreliability that come 

with unpredictably changing conditions. One of the reasons why some firms flourish and others 

diminish is addressed to some extent by organizational reaction and informed decisions (Okoth & 

Njeru, 2019). 

The primary goal of enterprises is to maximize profit, thus the concept of organizational 

performance is essential. According to (Gure & Karugu, 2018) successful companies have to 

generate higher profits and analyze execution drivers from the strategic level to the operational 

level. Not only have the surroundings in which companies operate become more uncertain, but 

they have also become more interconnected and disruptive (Teece, 2018). Technical skills are 

considered to be specialized knowledge and expertise that enable employees to perform specific 

tasks, use specific tools and programs to drive increased firm performance. From information 

technology and corporate management to healthcare and academia, technical skills are essential in 

every sector of the economy. Technical skills not only contribute to overall organizational success 

but also assures the employees of of better job opportunities, better salaries, and increased career 

stability (Bontis, 2018). 

With such focus in enhancing technical skills, companies can maximum production by aiding in 



 

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building competitive benefit (Wang et al., 2018). Technical skills is an aspect of strategy that gives 

companies long-term direction to deal with variation, and allows it to concentrate on 

improvements. Productivity becomes key when enhancing technical skills depending on the 

industry in which a company operates, because there is no single accepted metric for gauging 

success, firms must develop their own metrics and strategies (Nosratpour et al., 2018). 

At the global level, technical skills are consistently identified as key drivers of manufacturing 

productivity and innovation. A recent study by Deloitte and The Manufacturing Institute estimated 

that the skills gap in US manufacturing could leave up to 2.1 million jobs unfilled by 2030, 

resulting in $1 trillion in lost economic output (Deloitte and The Manufacturing Institute, 2021). 

Meanwhile, a meta-analysis of 59 studies found that investments in employee training had a 

moderate to large effect size on manufacturing productivity (Arditi et al., 2016). 

In developed economies like Germany, Japan, and South Korea, where manufacturing plays a 

prominent role in national income and employment, technical skills are seen as critical enablers of 

competitiveness. For instance, Germany's dual system of vocational education and training (VET) 

is often cited as a model for other countries seeking to strengthen their manufacturing sectors 

(European Centre for the Development of Vocational Training, 2017). Similarly, Japanese 

companies invest heavily in continuous improvement practices such as kaizen, which rely on 

frontline workers' abilities to identify and resolve production problems (Takeuchi and Nonaka, 

2014). 

However, the value of technical skills may vary across regions and industries. For example, in 

low-income countries with predominantly agrarian economies, basic literacy and numeracy skills 

may take precedence over specialized technical skills in manufacturing (Psacharopoulos and 

Patrinos, 2018). By contrast, middle-income countries such as Brazil and China, where 

manufacturing accounts for a larger share of GDP, have made substantial investments in VET 

programs to develop technologically sophisticated workforces (de Oliveira et al., 2019; Li et al., 

2019). 



 

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Within Africa, there is considerable variation in the extent to which technical skills shape 

manufacturing performance. While some countries such as Egypt, Morocco, and Tunisia boast 

well-established engineering schools and research institutes, others struggle with weak educational 

systems and skill mismatches (World Bank, 2021). Nevertheless, several African nations have 

implemented reforms aimed at strengthening their VET systems, recognizing the potential benefits 

of a technically proficient workforce for attracting foreign direct investment (FDIs) and fostering 

domestic entrepreneurship (World Economic Forum, 2017).  

It's common knowledge that Africa's manufacturing sector, especially in Sub-Saharan Africa, is 

failing. A 2019 World Bank research found that Africa's industrial firms were underperforming. 

Africa's manufacturing industry contributes just 3.8%-11% to GDP (compared to 30%-40% in 

industrialized countries), corresponding to the African Development Bank (AFDB, 2016). 

According to an AFDB report from 2016, manufacturing enterprises in Kenya have been doing 

poorly as a source of total GDP growth. In a similar vein, the Kenya Strategic Policies for Vision 

2030 study (2018) stated that manufacturing performance began to deteriorate in the middle of the 

1980s (Agwu, 2018). 

Turning to Kenya, the manufacturing sector faces numerous challenges despite accounting for 

approximately 10% of GDP and employing roughly 8% of the workforce (National Bureau of 

Statistics, 2020). One major constraint is the lack of adequately trained personnel capable of 

operating complex machinery and adopting innovative production processes (World Bank, 2021). 

In response, the government has launched initiatives such as the Manufacturing Skills Training 

Centres (MOSTCs) program, which aims to provide industry-relevant training to thousands of 

youth and women (Ministry of Industry, Trade and Cooperatives, n.d.). Early evaluations suggest 

that MOSTC graduates enjoy higher wage premiums and better employment prospects than their 

peers without technical qualifications (USAID, 2019). 

Performance, is a method for evaluating an organization's performance based on specific criteria 

like total revenue, sales, and effectiveness of processes. For there is a lot of strategic thinking 

which focuses on introducing and weighing performance, management of performance and 



 

4 

 

development is the central focus of plan of action management (Gartenberg et al., 2019). 

According to (Korir et al., 2020) organization's performance is influenced by three ideologies: the 

goal approach, which says that an organization works toward specific, measurable objectives, in 

which overall increase in revenues is key. Manufacturing enterprises, in accordance to the World 

Bank (2019) are the major source of employment in both growing and developed economies, 

accounting above 90% of African company activities and contributing to over 50% of Africa 

employment ratio and GDP. Because of Africa's resources, which human capital is key, the 

encouragement of manufacturing enterprises, particularly those in metropolitan areas to manage 

human resources, is seen as a potential path to sustainable development (Brandt, 2019). 80% of 

Kenya’s manufacturing firms are centered in Nairobi, the country's capital, which is well-

connected and well-equipped with training institutions and better infrastructure (Samuel et al., 

2021).  

The focus on technical skills has experienced rapid expansion over the past two decades (Malykhin 

et al., 2021), and this expansion continues on a daily basis. Acquisition of technical skills is 

frequently a crucial aspect of the strategic management process, as it incorporates all divisions of 

an enterprise company. This is targeted at employee competency, and companies should 

demonstrate such desire in strategy execution because the outcomes of skills enhancement can 

affect a company's success or failure in its industry. Because no company has infinite resources, 

strategists must determine the other strategies, which will benefit the company the most 

(McGuinness, 2023).  

Manufacturing makes up 70% of Kenya’s industrial sector's GDP, with the remaining 30% coming 

from building, construction, mining, and quarrying (Kenya Association of Manufacturers, 2015). 

According to Kenya Vision 2030, manufacturing will play a major role in achieving a 10-percent 

annual growth rate in GDP. In terms of job creation and GDP expansion, manufacturing has a lot 

of untapped potential. For instance, the manufacturing sector, in contrast to agriculture, which is 

severely constrained by land size, holds a lot of promise for job creation and poverty reduction 

(Barasa, 2018). The nation's capital, Nairobi, is home to eighty percent of manufacturing 



 

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companies, and it is well-connected and well-equipped (World Bank's report, 2015). Although its 

contribution to wage employment has been decreasing, the manufacturing sector's contribution to 

GDP has remained stable at around 10%. The first Medium Term Plan (MTP) goals for 

implementing Vision 2030 from 2008 to 2012 were mostly not met in terms of the sector's 

contribution to GDP and the completion of major projects. Vision 2030 envisions a manufacturing 

sector that is dynamic, diverse, and competitive, able to drive employment. 

1.2 Statement of the Problem 

Manufacturing firms in Nairobi City County would possess a workforce equipped with robust 

technical skills. These skills would encompass a broad spectrum, ranging from proficiency in 

operating modern machinery to advanced knowledge in industrial automation and digital 

technologies. Employees would be adept at troubleshooting technical issues, optimizing 

production processes, and implementing innovative solutions to enhance productivity and quality 

standards. This vibrant manufacturing sector would significantly boost Kenya's Gross Domestic 

Product (GDP), stimulating economic growth, and providing ample job opportunities for the 

burgeoning population. Following farming and gardening, manufacture in Kenya is the third most 

important contribution to GDP.  

Despite this, the manufacturing sector growth dropped down in 2020 to 4.4%, from 5.8% in 2019, 

and has continued to have mixed (KAM, 2019). The contribution of the sector to total pay 

employment has deteriorated, falling from 13.8% in 2020 to 12.9% in 2021. (KIPPRA, 2021). This 

suggests that the manufacturing sector in Kenya is still a long way from contributing 20% of GDP 

as envisaged in Vision 2030. The sales turnover of manufacturing firms in Kenya as measure of 

performance has been decreasing in recent years. In 2022, the total sales turnover of manufacturing 

firms in Kenya was estimated to be Ksh 3.5 trillion (approximately USD 30 billion). This 

represents a decrease of 10.2% from 2021 (KAM, 2022). There have been some fluctuations in the 

net profit margin of manufacturing firms in Kenya over the past few years. For example, in 2022, 

the net profit margin declined to 9.3% due to cost of production including energy, currency 

fluctuation and competition from imported goods (KAM, 2022). As a result, the sector's 



 

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contribution to GDP has remained relatively stagnant at around 5%. Thus the study examined the 

influence of technical skills in tperformance of manufacturing firms in Nairobi City County in 

Kenya. 

1.3 Purpose of the Study 

The tpurpose tof tthe tstudy twas tto tinvestigate tthe tinfluence tof ttechnical tskills ton tperformance of 

manufacturing firms in Nairobi City County in Kenya. 

1.4 Objectives of the Study 

i. To analyze tthe tinfluence tof tinformation tcommunication ttechnology tskills ton tperformance 

tof tmanufacturing tfirms tin tNairobi tCity tCounty tin tKenya. 

ii. To assess the effects of data science skills on tperformance tof tmanufacturing tfirms tin 

tNairobi tCity tCounty tin tKenya. 

iii. To evaluate the extent of innovative skills effect on performance tof tmanufacturing tfirms tin 

tNairobi tCity tCounty tin tKenya. 

1.5 Research Questions 

i. What tis tthe tinfluence tof information communication technology skills on performance tof 

tmanufacturing tfirms tin tNairobi tCity tCounty tin tKenya. 

ii. What are the effects of data science skills on performance tof tmanufacturing tfirms tin tNairobi 

tCity tCounty tin tKenya. 

iii. To what extent does innovative skills affect performance tof tmanufacturing tfirms tin tNairobi 

tCity tCounty tin tKenya. 

1.6 Significance of the Study 

The tfindings tof tthis tstudy tmay be of the utmost importance to policymakers responsible for 

strategic management in manufacturing companies. The failure of one or couple of the many parts 

of strategic management techniques is frequently linked to the issues of organizational 



 

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performance. The outcome, this research may also be critical in developing key policies on 

strategic management practices, which may eventually aid in the improvement of inherent 

practices in manufacturing organizations. 

1.7 Scope of the Study 

The research explored key technical skills required of employees that should be employed by the 

Kenyan Manufacturing Firms in Kenya. Data collected from employees at three organizational 

management levels were gathered for the study, which was carried out in manufacturing firms in 

Nairobi City County. (Operational managers, finance managers, customer relations managers and 

human resource managers) with the assumption that these managers will have information relevant 

to the researcher's research on technical skills and performance. The study took place for 9 months 

between March and December 2023. The content scope was information communication 

technology (ICT), data science and innovative skills required in sustaining firm performance in 

the manufacturing sector. 

1.8 Limitations of the study 

Limitations in research refer to the factors or constraints that influence the extent to which a study's 

findings can be interpreted, generalized, or applied to the broader population or real-world 

situations. They arise from various sources, including the research design, data collection methods, 

resources, and external factors. The participants chosen for this study may not be fully 

representative of the larger population or may have unique characteristics that influence their 

performance. This can limit the generalizability of the findings to broader groups. This limitation 

was overcame by choosing a sample that is as representative as possible within the constraints of 

the study. Researcher may not have sufficient time to conduct extensive research, leading to 

limitations in the scope and depth of the study. To overcome this limitation, the study adopted drop 

and pick technique during data collection with constant reminders. 

1.9 Delimitations of the Study 



 

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The study delimited itself to selected manufacturing companies in Nairobi County. This implies 

that manufacturing companies from other counties in Kenya were not be included. Data used in 

this study was delimited to quantitative data collected using structured questionnaire, this suggests 

that qualitative data was not used in the study. In regards to technical skills, the study delimited 

itself to information technology skills, data analysis skills and innovative skills. 

1.10 Assumptions of the Study 

The study considered a number of assumptions. 

1. Researchers made the assumption that their sample reflected the whole population. 

2. Through validation and reliability, the study assumed that findings from the sample can be 

generalized to a larger population.  

3. Participants' availability was also expected for the research. 

4. Lastly, the study assumed that respondents were able to differentiate technical skills from 

other types of skills. 

  



 

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1.11 Operational Definition of Key Terms 

Technical Skills: In manufacturing companies, they are the skills and knowledge necessary to 

carry out various activities.  

Performance: This is improvement of revenues, total units produced and sales volumes 

Manufacturing Firms: A commercial enterprise that transforms raw materials or components into 

finished goods is known as a manufacturing company. 

Data science Skills: are the skills necessary to gather, clean, analyze, and comprehend data 

utilizing scientific techniques, procedures, algorithms, and systems to derive meaning from data 

in both organized and unorganized formats. 

 Innovation Skills: are a set of competencies and abilities that enable individuals, teams, and 

organizations to create, develop, and implement new and valuable ideas, products, processes, or 

solutions. 

Firm performance: means that the business has been successful in reaching its objectives. 

Innovation, firm performance (such as profitability, revenue growth, and return on investment), 

and non-firm performance (such as customer happiness and market share) are some of the ways it 

may be assessed. 



 

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CHAPTER TWO: LITERATURE REVIEW 

2.0 Introduction 

This tchapter's tfocus tis ta treview tof ttheoretical tand tempirical tliterature. This overview covers a 

variety of pertinent theories, including the resource-based perspective, the contingency theory, and 

the dynamic capability theory. Furthermore, it centers around earlier examination that is 

appropriate to the review's variables. The chapter also contains a conceptual framework, a 

summary of the evaluated literature, and research gaps. 

2.1 Theoretical Literature Review 

2.1.1 Contingency Theory 

Coulter, (2018) says that this theory proposed that a structured governance design in which an 

organization's structure matches its circumstances is the most suitable and effective. As a result, 

the theory states that the time managers come up with operational choices in their businesses, they 

must analyze all parts of the present circumstance and work on those that are important to the 

scenario at hand (Robbins, 2014). What is needed for management issues and concerns are based 

on the specific settings that exist in the organization (Tsolka, 2020). As a result, strategy execution 

should be based on the current situation or context. This indicates there is changing conditions 

with different business environments would necessitate different methods to addressing the 

difficulties of the organization, implying a link between environmental discernment.  

An organization’s performance in a given situation which describes unique remedies for 

challenges across the board throughout the organization and in turn recommends that strategic 

management techniques that would be used to lead the firm to proper collaborative market 

environment. According to (Robbins, 2014) an organization’s primary objective is sustainable 

long-term growth. The vision and strategic goals of business enterprise management have actual 

implications on the way, decision execution, and assessment of implementation of actions that are 

adopted.  



 

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As a result, it is critical for an organization to build a match between competencies and resources 

in order to capitalize on market possibilities and acquire a competitive edge. According to (Nakano 

& Wechsler, 2018), skills, and innovation, are essential for a firm's long-term viability. 

Organizations that want to sustain performance should look for ways to innovate and re-skill in 

order to find a strategic match for their expansion plans (Park and Hong, 2019). As a result, 

technical skills are considered to provide adaptation technique with the goal of achieving optimal 

performance outcomes hence customer satisfaction and ensure the long-term survival and success 

of business enterprises. 

2.1.2 Human Capital Theory 

According to Schultz’s (1972) Human Capital Theory, value creation in a firm may lead to 

improved business production. It suggests that a company's employees are assets rather than costs. 

Bontis (2008) says that an organization's human aspect, or the combination of intelligence, 

expertise, and skills that gives it personality, is called human capital. The HCT places an emphasis 

on the value that people can add to a company. This position is referred to as a "human capital 

advantage”. Businesses can and do benefit from the high levels of training and expertise that their 

employees possess despite the fact that they do not legally own human capital. They can do this 

by employing strategies like creating corporate cultures or learning vocabulary terms to foster 

unity and impart technical skills to employees. The theory's premise is based on the immeasurable 

nature of human capital's many manifestations.  

Human capital, on the other hand, has an intrinsic value that cannot always be quantified. Second, 

regular research and observation are challenging due to the fact that human capital can be stored 

but not always utilized fully (Boxall, 2021). It is therefore essential to have technical skills. The 

total amount of human capital is determined by Nafukho (2004) as the sum of all types of human 

capital. 

2.2 Empirical Literature Review 

2.2.1 Information Technology Skills on Manufacturing Firms Performance 



 

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Technical skills contributes to effective management, enhanced throughput and financial 

attainment (Garcia et al., 2018). Information Communication Technology skills is key to retaining 

employees who will support greater value proposition for the organization and the customer. It is 

becoming more popular as a measure of performance around the world, and it is secondly 

characterized as the most significant tool of management. According to Endende et al. (2016), ICT 

skills promotes longer, more advantageous firm productivity and customer satisfaction. 

A survey by federation of Kenya (FKE) employers in 2018 revealed that, most organizations in 

Kenya is still lack behind when it comes to technical skills. The study proposed that firms 

continuously liaise with training institutions to offer her employees new training on dynamic 

requirements of the modern job (FKE, 2018). Hence, there is need for modern firms to assess and 

ensure that their employees are continuously trained so as to acquire relevant skills for better firm 

performance and employee job satisfaction. 

Technical skills, according to McGuinness (2018), enhances the company’s relationship with its 

customers who requires constant bilateral interaction and communication. According to (Anderson 

2018), there is need for novel approaches of updating skills and recognizing the latent skills of 

people currently required by the employee. New skills must be used by the employee in the 

informal economy in order to improve their productivity. However, the reorganization takes time 

and is rarely smooth. Employees and businesses both suffer in different ways. The skills of some 

people are in short supply, while those of others are out of date (Wambugu 2015). 

The investigation executed by Hysong (2018) was to investigate the possible added value of 

technical competence in managing performance for first-tier managers, in comparison to 

managerial skill alone. Additionally, the investigation aimed to examine potential mediators that 

may influence this connection. The study included a sample of 107 first-tier supervisors employed 

in local petrochemical and engineering industries. These supervisors were invited to participate in 

an online survey, which aimed to gather information on their professional experience and 

management abilities. Additionally, the study collected ratings from subordinates about the 

supervisors' technical expertise, power, and influence tactic habits.  



 

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Managerial effectiveness was evaluated based on three metrics: employee output, employee 

contentment, and assessments from those under the manager's supervision. The incremental 

increase in technical expertise was shown to be a significant predictor of subordinate judgments 

of management performance, surpassing the influence of managerial talent. The association 

between technical competence and subordinate evaluations, as well as work contentment, was 

shown to be mediated by referent power. However, expert power was only found to mediate 

connections with job satisfaction. The link among expert power and subordinate assessments of 

management effectiveness was shown to be mediated by rational persuasion. 

The Technical Ability centered Job readiness Forecasting Model (TSBEPM) is developed by 

Manjushree, Varsha, Arvind, and Laxman (2021) using ML methods. Students' grades in different 

computer science classes reflect their technological competence. A Support Vector Machine, 

Naive Bayesian Logic Regression, Probabilistic Forest, Decision Tree, AdaBoost is and Artificial 

Neural Network all contributed to the experimental effort by providing forecasts. All models were 

validated by conducting experiments with data acquired from University of. Various models are 

developed to forecast where a student will be allocated based on performance-measuring 

characteristics. An F1-Score of 0.85 is the best that can be achieved using Random Forest, and its 

accuracy may reach up to 70%. The model is built to be utilized in making placement decisions. 

The purpose of the research by Mutheu and Perris (2021) was to determine how hiring technical 

experts affected the success of building houses in Kajiado County. Resource dependence theory 

was used for the research. In addition, a descriptive methodology was used for this study. The 

focus was on residential building projects in Kajiado County that would be finished by 2020. 

Specifically, we looked at a sample of 124 construction projects in Kajiado County that are at least 

95% complete. Thirty-seven projects were chosen as a representative sample. The sampling 

methods used were not based on chance. Clients, vendors, and consultants all had a role in the 

research, since they were the ones implementing the project. Data was gathered via the use of 

questionnaires. The investigator gave them a hand and then came back to collect them. The 

investigation found that using technical competence led to better results for residential building 



 

14 

 

projects. The research concluded that M&E procedures did affect the success of residential 

building projects. 

The purpose of the study by Yahya, Iskandar, and Sunardi (2017) was to assess the role of scientific 

techniques in the practical training of students at a high school for trades. The study used a 

quantitative, non-experimental design using an ex-post facto poll. There are a total of 523 students 

enrolled in the mechanical engineering skills package; 172 men and 49 females make up the 

sample set for this study. A test, questionnaire, and notes were utilized to compile the data. Both 

descriptive and structural equation modeling (SEM) analyses were performed on the data. The 

findings revealed that the scientific method's incorporation into vocational education significantly 

contributes to students' acquisition of technical expertise and has a positive effect on their 

employability. Technical and employability skills may be honed with the use of a data-driven 

strategy, as seen above. 

According to research by Van Minh, Badir, Quang, and Afsar (2017), leadership endorsement is a 

key factor in fostering staff development and creativity. Few studies have examined how leaders' 

technical expertise affects minions' ability to learn and innovate, in contrast to the abundance of 

literature on methods of leadership and management abilities. Information was gathered from 52 

managers and 127 employees at 68 different Vietnamese telecom firms. The findings indicate that 

followers' creative and instructive actions are positively related to their leaders' technical 

proficiency. In addition, the link between leaders' technical expertise and their employees' creative 

work behavior is partially mediated by employees' openness to acquiring new skills on the job. 

Sylvester and Okorie's (2019) study attempted to determine the influence that workers' technical 

abilities had on the productivity of manufacturing companies in South-East Nigeria. Eighty-six 

people, representing a wide range of functions across the manufacturing companies, took part in 

the study by completing surveys and other measures meant to assess their ICT skills and 

achievements on the job. The data was subjected to a linear regression analysis. There was a highly 

significant association among ICT competence and work performance, the study found. results, 

ramifications, and judgments are spoken about 



 

15 

 

Rizk and Daniel (2016) examine the influence of ICT skills on individual job performance in the 

context of a multinational corporation. The study utilizes a mixed-methods approach, combining 

surveys to assess ICT skills levels and performance evaluations from supervisors. Data analysis 

includes regression analysis to determine the relationship between ICT skills and job performance. 

The study found a positive and significant relationship between employees' ICT skills levels and 

their job performance. Employees with higher ICT skills demonstrated higher levels of 

performance in their roles within the organization. 

 

2.2.2 Data Science Skills on Manufacturing Firms Performance 

According to Bandanaraike (2018), skill development has the potential to increase enterprise and 

national production. According to Mitchell & Flin (2018), one of the most significant implications 

among organizations is that skill development in data analytics must be integrated into larger 

development initiatives if it is to realize its significant potential to increase employment growth 

and productivity. Essential data skills are needed for firm’s' structural adjustment. The transition 

of all production factors from lower to higher value-added activities is slowed by the inability to 

learn new skills due to a lack of basic education or opportunities. Employees and businesses both 

suffer in different ways. The skills of some people are in short supply, while those of others are 

out of date (Schumpeter, 2017). Bandanaraike, (2018) say that new ways of keeping skills up to 

date and recognizing people's hidden skills are what the employee needs right now. In the informal 

economy, employees must utilize new skills to increase productivity. 

"Innovative Capacity and Performance: An Empirical Analysis" by Massimo Colombo and 

Gianmarco Iannuzzi (2017). This study investigated the link between innovative capacity and firm 

performance in Italy. Using panel data from more than 2,000 manufacturing firms, they found that 

innovation capability has a positive influence on firm performance, as measured by sales growth 

and labor productivity. 

"Innovation Capabilities and Firm Performance: Does Environmental Uncertainty Matter?" by 



 

16 

 

Cheng Luo, Qiugen Wang, and Xiaohui Liu (2017). This study analyzed the role of innovation 

capabilities in enhancing firm performance under varying degrees of environmental uncertainty. 

Based on a survey of 260 Chinese manufacturing firms, results showed that innovation capabilities 

have a stronger influence on firm performance when environmental uncertainty is high. 

"How Do Innovation Capabilities Improve Operational Performance? Exploring the Mediation 

Role of Process Improvement" by Jun Wu and Wenbin Zhang (2017). This study focused on 

exploring the mediating mechanism underlying the relationship between innovation capabilities 

and operational performance. Drawing on a survey of 201 manufacturing firms in China, they 

found that process improvement plays a critical role in transmitting the benefits of innovation 

capabilities into better operational performance. 

"The Relationship Between Entrepreneurial Orientation and Firm Performance: The Moderating 

Roles of Industry Characteristics and Knowledge Sharing" by Soheila Kolahchi and Mahdi 

Esfandiar (2017). This study examined the moderating roles of industry characteristics and 

knowledge sharing on the relationship between entrepreneurial orientation and firm performance. 

Based on a survey of 216 Iranian manufacturers, their findings suggested that knowledge sharing 

strengthened the positive association between entrepreneurial orientation and firm performance, 

particularly in dynamic environments. 

"Exploring the Linkage Between Intellectual Capital and Innovation Capabilities: Implications for 

Firm Performance" by Muhammad Amir Adam, Ali Ahmed Shah, and Tariq Mahmood (2016). 

This study explored the relationship between intellectual capital and innovation capabilities, and 

how these factors contribute to firm performance. Based on a survey of 200 Pakistani 

manufacturing firms, the authors found that intellectual capital has a direct positive influence on 

innovation capabilities, which in turn leads to enhanced firm performance. Additionally, they 

discovered that market-oriented culture played a moderating role in this relationship. 

 

2.2.3 Innovative Skills on Manufacturing Firms Performance 



 

17 

 

Innovation is considered the key driver of most successful organizations in modern firms. 

Inadequate innovative skills, leads to organizations incurring additional costs, due to the need to 

engage external resource persons (Moon, 2019). In manufacturing sector, one of the challenges 

that led to the poor performance of leading brands in the world such as KODAK, and Eveready in 

Kenya is slow pace of acknowledging the need to innovate (Poret, 2019).   Innovative skills such 

as training employees on design thinking has the potential to increase employee’s contribution to 

the firms in form of knowledge and ideas that can improve overall organizational products and 

services .  

Wang and Luo (2018) investigated the influence of ICT skills on academic performance among 

university students. The study employs a quantitative survey methodology, collecting data through 

questionnaires distributed to undergraduate students. Statistical analysis, including correlation and 

regression analysis, is used to examine the relationship between students' ICT skills and their 

academic performance. The findings indicate a positive correlation between students' ICT skills 

levels and their academic performance. Higher ICT skills are associated with better academic 

outcomes, including higher grades and academic achievement. 

In their 2020 study, Alzahrani and Houghton investigated how SMEs' IT proficiency affected the 

way they perform. The research uses a mixed-methods strategy, gathering data from surveys and 

lengthy conversations with business owners and executives. Quantitative data analysis includes 

regression analysis to assess the relationship between employees' ICT skills levels and 

organizational performance indicators. Qualitative analysis is used to provide deeper insights into 

the mechanisms through which ICT skills influence performance. The study found that higher 

levels of ICT skills among employees were associated with improved organizational performance 

in SMEs. Specifically, SMEs with employees possessing advanced ICT skills demonstrated higher 

levels of productivity, innovation, and competitiveness. 

Sun and Zhang (2017) examined the influence of ICT skills on healthcare professionals' job 

performance in a hospital setting. The study utilizes a quantitative survey methodology, collecting 

data through questionnaires distributed to healthcare professionals, including doctors, nurses, and 



 

18 

 

administrative staff. Statistical analysis, such as regression analysis, is used to investigate the 

relationship between healthcare professionals' ICT skills levels and their job performance. The 

findings reveal a positive relationship between healthcare professionals' ICT skills levels and their 

job performance. Healthcare professionals with higher ICT skills demonstrated greater efficiency, 

accuracy, and effectiveness in their roles, contributing to improved patient care and organizational 

outcomes. 

Wang and Li (2019) examined the influence of data science skills on business performance in the 

context of manufacturing firms. The study employs a quantitative survey methodology, collecting 

data through questionnaires distributed to manufacturing firms. Regression analysis is used to 

assess the relationship between employees' data science skills and business performance 

indicators, such as productivity, innovation, and profitability. The findings suggest a positive 

association between employees' data science skills and business performance. Firms with 

employees possessing advanced data science skills demonstrate higher levels of productivity, 

innovation, and financial performance. 

Chen and Zhang (2020) investigated the influence of data science skills on financial performance 

among companies in the financial services sector. Combining quantifiable evaluation of data with 

conversations with financial experts, the research employs an approach that combines both 

methods. Examining the correlation between data science competencies and financial performance 

indicators like ROI and ebitda requires statistical methods like regression analysis. The study finds 

a positive correlation between firms' data science capabilities and financial performance. 

Companies with stronger data science capabilities demonstrate higher profitability and competitive 

advantage in the financial services industry. 

Liu and Wang (2018) explored the influence of data science skills on academic performance 

among university students in STEM fields. The study employs a quantitative survey methodology, 

collecting data through questionnaires administered to undergraduate and graduate students. 

Regression analysis is used to assess the relationship between students' data science skills and 

academic performance indicators, such as grades and research output. The findings indicate a 



 

19 

 

positive correlation between students' data science skills levels and their academic performance. 

Higher data science skills are associated with better academic outcomes, including higher grades 

and research productivity. 

Park and Lee (2017) examined the influence of data science skills on organizational performance 

in the healthcare sector The study utilizes a quantitative survey methodology, collecting data 

through questionnaires distributed to healthcare organizations. Statistical analysis, such as 

regression analysis, is used to investigate the relationship between organizations' data science 

capabilities and performance metrics, such as patient outcomes and operational efficiency. The 

findings reveal a positive relationship between organizations' data science capabilities and 

performance. Healthcare organizations with advanced data science skills demonstrate improved 

patient care, operational efficiency, and overall organizational effectiveness. 

Kim and Lee (2019) aimed to assess the influence of data science skills on marketing performance 

in the retail industry. The study employs a mixed-methods approach, combining surveys with 

analysis of marketing metrics and sales data. Statistical analysis, including regression analysis, is 

used to examine the relationship between employees' data science skills and marketing 

performance indicators, such as customer engagement and sales revenue. The study finds a positive 

association between employees' data science skills and marketing performance. Retail firms with 

employees possessing advanced data science skills demonstrate higher levels of customer 

engagement, sales conversion rates, and revenue growth. 

2.3 Conceptual Framework 

This is an introduction technique in which the researcher visualizes the relationships between 

elements and the interrelationships in the research graphically or diagrammatically (Orodho, 

2008). A variable, according to Kothari (2009), is a notion capable of taking distinct quantitative 

aspects. On the other hand, Mugenda (2008) defines a variable as a quantifiable hallmark that 

predicts distinct traits among units of a population. The autonomous variable and ward variable 

are the two most important components in this investigation. According to Mugenda (2008), the 



 

20 

 

free factors are called indicator factors since they predict variation in another variable. A needed 

variable, also known as a model variable, is one that is influenced by another variable. The reliant 

variable is the one that needs to be clarified by the researcher. Following that, this investigation 

attempted to determine how strategic customer relationship management, risk management, 

strategic technical skills, and financial management practices influence manufacturing firm 

performance in Kenya's Nairobi City County. 

Independent variable                                                                  Dependent variable  

        

                                    

                                                                 

                                                                                      

                                               

                                                                 

 

                                                        

                                                                                                                                  

                                                                     

Source: Researcher, 2023 

2.4 Recap of Literature Review 

Hermenegildo et al. (2020) carried out a study on sustainable business model innovation, digital 

Information Communication 

Technology 

 Computer Packages Skills 

 Social Media Skills 

 Communication Skills 

 

 
Performance of 

Manufacturing Firms 

 Firm Revenue 

 Market Share 

 Gross profit 

Data Science 

 Data collection  

 Data analysis 

 Data presentation 

 

Innovation 

 Ideation 

 Implementation  

 Value addition 

 

 

 

Figure 1: Conceptual Framework 



 

21 

 

transformation, and customer relationship management. The researcher placed a greater emphasis 

on customer relationship management and neglected to mention other management techniques 

such as strategic technical skills, risk management, and financial management practices, all of 

which are critical to the success of manufacturing businesses. A study by (Khedker et aI.,2015) on 

looking at how customer relationship management affects customer satisfaction and loyalty 

emphasized marketing tactics rather than technical skills strategies.  (Osman, 2019) used strategic 

management methods to investigate Kenya Commercial Bank's performance in Nairobi City 

County, Kenya.  

The researcher discussed about strategic control, strategy creation, data science skills, and intent. 

However, the researcher discussed the process of developing a strategic plan. The health 

department of Nairobi City County, Kenya (Nzoka, 2017) conducted a study on strategic 

management techniques adoption and service delivery. Without identifying any of them, the 

researcher simply focused on the adoption of strategic management approaches. (Mitchell & Flin, 

2018) conducted research on human capital as a technique for increasing organizational 

productivity. Cheluget (2017) conducted research in Uasin Gishu County to determine the 

importance of FMP in achieving project objectives. Ngugi (2015) conducted study in Machakos 

County on the relationship between budget control and CDF accomplishments. (Pimchangthong 

and coworkers,2017) looked into how IT project success was affected by risk management 

strategies (Osman, 2019). 

The influence of risk management practices on Ghana's banking industry was investigated by 

(Niah et aI,2014). (Aduma et al, 2018) conducted research in Nairobi, Kenya, to learn about risk 

management procedures at the National Hospital Insurance Fund. An investigation was done on 

the use of strategic planning approaches and execution of services by the health division of the 

county of Nairobi, Kenya (Nzoka, 2017). In the context of customer relationship management, a 

study was carried out on customer satisfaction and loyalty (Khedker et al., 2015). (Hermenegildo 

et al., 2020) carried out a study on management of existing client relationships, digital innovation, 

and the creation of new ones. 



 

22 

 

2.5 Research Gap 

Creating a Competitive Advantage technical skills acquisition was the subject of research (Nagwan 

et al., 2020). The study however, did not capture any other aspect of management practices other 

than customer relationship management. The researcher recommended the more study of the 

dimensions of other management practices of strategy, hence the need for the current study to 

focus on technical skills. (Hermenegildo et al., 2020) carried out a study on Management of 

existing client relationships, digital innovation, and the creation of new ones. The researcher 

placed a greater emphasis on customer relationship management and neglected to mention other 

management techniques such as strategic technical skills, risk management, and financial 

management practices, all of which are critical to the success of manufacturing businesses.  

The expert also suggested that more research be done in the manufacturing sector. (Khedker et al 

2015) performed a study on relationships of consumer’s management and its influence on 

consumer happiness and loyalty, focusing mostly on marketing techniques and ignoring strategic 

management practices such as financial management and risk management. (Osman, 2019) used 

strategic management methods to investigate Kenya Commercial Bank's performance in Nairobi 

City County, Kenya. The researcher spoke about strategic control, strategy creation, data science 

skills, and intent. The researcher highlighted strategic plan processes but placed no emphasis on 

the need for technical skills. 

 

 

 

  



 

23 

 

CHAPTER THREE: RESEARCH METHODOLOGY 

3.0 Introduction 

This chapter shows study design, data gathering techniques and methods, study area, target 

populace, piloting, data tanalysis tand tdata tpresentation. 

3.1 Research Methodology 

The study adopted a quantitative approach method, where quantitative data was collected to enable 

the study answer the research objectives. This approach helps researchers to gain more complete 

examination of the aspects of the study (Mugenda and Mugenda, 2013). The approach also, enables 

generalization of research findings, in addition to providing credible results as compared to the use 

of singular method of approach (Khotari and Garg, 2015). 

3.2 Research Design 

The study adopted a quantitative approach method, where quantitative data was collected to enable 

the study answer the research objectives. This approach helps researchers to gain more complete 

examination of the aspects of the study (Mugenda and Mugenda, 2013). The approach also, enables 

generalization of research findings, in addition to providing credible results as compared to the use 

of singular method of approach (Khotari and Garg, 2015). 

A descriptive research strategy was employed for this investigation. Descriptive design allows the 

researcher to examine the background of research problem, before embarking on the actual 

research. In doing so, the researcher was able to determine the characteristics of the manufacturing 



 

24 

 

firms’ respondents such as their opinions, traits, and performance behaviors, in relation to 

employee retention. According to (Khotari and Garg, 2015), descriptive design accurately 

describes the study’s population and its characteristics. It enables researchers to answer questions 

such as what, where, when and how but not the why questions. In addition, it assists researchers 

investigate more than one variable while not being able to tcontrol tor tmanipulate tany tof tthe tvariables 

tbeing tstudied, hence can only observe and measure them. (Mugenda and Mugenda, 2013). 

3.3 Study Location 

Manufacturing firms in Nairobi City County, Kenya, constitute a vital component of the nation's 

economy, contributing significantly to employment, production output, and GDP. Nairobi, as the 

capital city, serves as a hub for industrial activities, hosting a diverse array of manufacturing 

industries. These include food processing, textiles, chemicals, pharmaceuticals, electronics, and 

construction materials, reflecting the city's role as a center for industrial diversity and economic 

activity. 

According to the Kenya National Bureau of Statistics (KNBS), the manufacturing sector in Kenya 

contributed approximately 9.2% to the country's GDP in recent years. This underscores its 

substantial economic importance and contribution to national development. Manufacturing firms 

in Nairobi play a crucial role in job creation, absorbing a significant portion of the urban workforce. 

This aspect is pivotal for urban development and poverty alleviation, providing employment 

opportunities and contributing to household incomes. 

However, despite its significance, manufacturing in Nairobi faces several challenges. These 



 

25 

 

include inadequate infrastructure, high production costs, regulatory hurdles, and intense market 

competition. These factors can hamper the sector's growth and competitiveness both domestically 

and internationally. The World Bank has highlighted these challenges in its reports, emphasizing 

the need for improvements in infrastructure and the business environment to support industrial 

development effectively. 

3.3 Population  

The study population was business development/Strategic/Innovation managers among selected 

manufacturing companies in Nairobi County who are viewed in this study to have a broad range 

of capabilities and are contributors to and leaders of the growth agenda. There are 253 

manufactured firms in Nairobi City,  hence, this was considered as the target population for the 

study. 

3.4 Sampling Frame 

Sampling frame is the source used by researchers to define the population of interest. It is the list 

containing elements tfrom twhich tthe tresearcher tcan tselect tthe tsample tfor tthe tstudy (Kothari, 2004; 

Mugenda & Mugenda, 1999). As such, the sampling frame was the list of manufacturing firms 

listed on KAM directory. 

3.5 Sample and Sampling Technique 

3.5.1 Sampling Technique 

The study utilized simple random sampling to identify respondents for the study. This method 



 

26 

 

allowed the researcher to partitioned the population into relatively smaller homogeneous groups 

called strata and thereafter, a basic method of randomization is used to choose a representative 

sample from each stratum. (Kothari, 2004).  

3.5.2 Sample Size 

A sample is a selection of data points from a larger population tfor tthe tpurpose of illustration. The 

right sample size is important in finding a statistically significant result (Mugenda & Mugenda, 

1999). A sample of 76 was obtained using 30% of the total population. 

3.6 Data Collection Instrument  

The data collection was done using structured questionnaire. This encompassed closed questions. 

Questionnaires are preferred model of collecting data because they are able to collect large amount 

of data in a short time (Khotari and Garg, 2015). The closed-ended utilized likert scale type 

questions to facilitate quantitative data analysis and interpretation. The questionnaire were 

structured into 5 likert scale, from Strongly agree (5), Agree (4), Not Sure (3), Disagree (2) and 

Strongly Disagree (1). Closed-ended questions are considered the best for research surveys, since 

it yields a higher response. In addition, they can easily be analysed statistically especially with 

survey data (Mugenda & Mugenda, 1999). 

3.7 Data Collection Procedure  

The first step of data collection procedure was to get permission to conduct the research from 

Mount Kenya University. The next step in gathering data was to apply for NACOSTI permit, 



 

27 

 

which was obtained after two weeks. Thereafter, the researcher accessed the sampled 

manufacturing via an official letter to Human Resource Manager. The tquestionnaires twere thand 

tdelivered and issued to respondents of 76 manufacturing tfirms tin tNairobi tCity tCounty with the aid 

of four research assistants who collated the questionnaires once filled for onward submission to 

the researcher. The two assistants were briefed beforehand on the ethical consideration to be 

observed when carrying out the data collection exercise, in addition to adhering to the two 

institutional work ethic and procedures. A token of appreciation was offered to the two assistants 

once the exercise is completed. 

3.8 Pilot Testing  

A pilot study enables the researcher to assess and improve the data protocol by testing it on a 

smaller-sized sample so as to enable planning and modification of the instrument for the main 

study (Kothari, 2004). As such, the questionnaire was piloted in Kiambu County which Neighbors 

Nairobi City. In the pilot phase, we only asked for feedback from a small subset of participants (8 

people).  Pilot investigations are conducted to determine the reliability and veracity of research 

tools. 

3.8.1 Test of validity 

The quality of the proposals or measures is ascribed to the extent to which they correspond to 

existing knowledge or truth according to Neuman (2005). For example, an attitude scale is deemed 

legitimate to the degree to which the findings correspond to other attitude ownership measures. 

Content and construct techniques of validity are thus used for validity assessment, using 10 copies 



 

28 

 

of the instrument during pilot tests for validation. This is to check that questions are properly 

worded and relevant, and that they include sufficient information to guarantee content and validity. 

3.7.2 Test of reliability 

According to Mugenda & Mugenda (2008), consistency is the evaluation of how well an 

investigation tool maintains its credibility and dependability across time. A Cronbach alpha 

coefficient criterion of at least 0.70 is considered trustworthy. Pilot tests were done and a Cronbach 

alpha is calculated using version 26 of SPSS. Tabulated below are the results of our reliability tests 

  



 

29 

 

Table 1: Reliability of Research Instruments 

Variable No of 

Items 

Items deleted Cronbach Alpha Reliable 

Information Technology Skills  3 0 0.836 Yes 

Data Science Skills 3 0 0.761 Yes 

Innovative Skills 3 0 0.816 Yes 

Firm Performance  4 0 0.844 Yes 

Total 13 Average 0.814 Yes 

Source: Field Data (2022) 

In accordance with the data presented in Table 1, the Cronbach alpha coefficient varied within 

0.761 for Data Science Skills and 0.844 for Firm Performance. The questionnaire demonstrated a 

high level of consistency, with a consistency coefficient above 0.7. This indicates a strong internal 

consistency among the questions inside the questionnaire instrument. Consequently, the tool was 

preserved in its original state without undergoing any further modifications. 

3.9 Data Processing and Analysis  

Descriptive statistics were applied to the numerical responses to the ranked Likert scale questions. 

Data presentation was done using tables, charts and frequencies. Cross-tabulations was also carried 

out to enable further interpretation and in answering research objectives. Kothari (2004) asserts 

that the fundamental statistical metrics often used to succinctly summarize data consist of measures 

of central tendency, namely the mean, median, and mode, as well as indicators of dispersion, such 



 

30 

 

as the deviation from the mean. 

3.9.1 Simple Linear Regression Analysis 

The simple linear equation for regression was tused tto trepresent tthe trelationship tbetween tthe 

tindividual independent tfactors tand tthe tdependent tvariable; 

Y=α t+ tβ1X1 +ε 

Y=α t+ tβ2X2+ε 

Y=α  +β3X3+ε 

Where; 

γ= tDependent tvariable t[Performance tof tManufacturing tFirms] 

α=Constant; tthe ty tintercept tor tthe taverage tresponse twhen tpredictor tvariables tare t0 

X1= tIndependent tvariable t1 t[Information tTechnology tSkills] 

X2= tIndependent tvariable t2 t[Data tScience tSkills] 

X3= tIndependent tvariable t3 t[Innovative tSkills] 

ε= terror tterm 

β1…. tΒ3 t= tBeta tCoefficients 

3.9.2 Multiple Regression Analysis 

The multiple equation for regression was tused tto trepresent tthe trelationship tbetween tthe tindependent 

tfactors tand tthe tdependent tvariable; 



 

31 

 

Y=α t+ tβ1X1 t+ tβ2X2+β3X3+ε 

Where; 

γ= tDependent tvariable t[Performance tof tManufacturing tFirms] 

α=Constant; tthe ty tintercept tor tthe taverage tresponse twhen tpredictor tvariables tare t0 

X1= tIndependent tvariable t1 t[Information tTechnology tSkills] 

X2= tIndependent tvariable t2 t[Data tScience tSkills] 

X3= tIndependent tvariable t3 t[Innovative tSkills] 

ε= terror tterm 

β1…. tΒ3 t= tBeta tCoefficients 

3.9.3 Diagnostic Tests 

Before proceeding with inferential statistics, the researchers conducted diagnostic analyses to 

assess the assumptions underlying Pearson correlation and multiple regression analyses. By 

conducting diagnostic analyses, researchers can identify any potential issues or violations of 

assumptions that may affect the accuracy and interpretation of the results. This approach enhances 

the robustness of the statistical analyses and strengthens the overall validity of the research 

findings. 

Normality tests: To ensure the validity of the statistical analyses, the study employed the Shapiro-

Wilk Test to assess the normality of the data. This test is widely used to determine if a dataset 

follows a normal distribution, helping researchers identify any deviations that may influence the 



 

32 

 

reliability of parametric tests. By examining normality, the study aimed to enhance the accuracy 

and validity of its statistical findings. 

Multicollinearity: When there is a correlation between a number of distinct variables this is called 

multi-collinearity. When the correlation between the variables that are autonomous is strong (r=0.9 

or above), we say that there is multiple collinearity This is a highly serious issue for several 

regressions. According to Tabachnick and Fidell (2001), it is advised to use caution when 

combining variables that have a bivariate correlation of 0.7 or higher in a single study. We will 

utilize the Variance Inflation Factor and the Tolerance threshold to check for a multi-col It is 

considered acceptable to have a VIF lower than 10 or a margin of error higher than 0.1. 

A linearity test determines how well the dependent variable tracks the change in the independent 

variable. Meaning, for regression to work, there can be no non-linear correlations between the 

independent and dependent variables. All of the study's predictor variables were analyzed using 

pearson correlation to see whether they were significantly correlated with the dependent variable. 

Independence: The absence or presence of autocorrelation is a prerequisite for linear regression 

analysis. When there is a lack of independence amongst the residuals, autocorrelation occurs. The 

Durbin-Watson test was used to determine independence. It verifies the independence of residuals 

by running them through a series of linear or multiple regression tests. There is no issue with 

autocorrelation when the Durbin-Watson factors are between 1.5 and 2.5 (Malau, 2018). 

3.10 Ethical Consideration 

Given that the study includes the participation of human individuals, it was carried out in full 



 

33 

 

adherence to the relevant ethical protocols. The preservation of professionalism in research was 

maintained by the implementation of these ethical principles. The research guaranteed the 

preservation of confidentiality and respect for each participant. The privacy of individuals from 

whose personal data was gathered shall be preserved with utmost respect and discretion. The 

individuals who participated in the study provided their informed permission prior to the 

commencement of any research activities. Prior to being recruited as a study participant, 

individuals must first get an informed consent form. The study participants were not subjected to 

coercion or bribery in any form to ensure their participation. The consultation process included 

relevant stakeholders, including persons, bodies, and committees, and NACOSTI provided 

approval for the study. Prior to their agreement to participate, participants were provided with 

information on the objectives, methodologies, and possible results of the research. 

Each research study was adhered to the ethical criteria set by the respondents, and proper 

attribution was given to all authors and sources involved. The researcher used utmost caution to 

avoid any occurrences of scientific misconduct, including inadequate data collecting 

methodologies or fraudulent assertions of authorship. The research was conducted with a high 

level of expertise, according to the principles of impartiality and scientific rigor. It ensured that 

the approach, analysis, and interpretation of the data are free from any kind of bias. 

  



 

34 

 

CHAPTER FOUR  

DATA ANALYSIS, PRESENTATION AND DISCUSSION 

4.1 Introduction 

The purpose of this research was to examine tthe tinvestigate tthe tinfluence tof ttechnical tskills ton 

tperformance of manufacturing firms in Nairobi City County in Kenya. This chapter offers the 

study's analysis, conclusions, and comments. In particular, the research looked at how proficiency 

in ICT, data science, and creativity all play a role. The results of empirical investigations are 

presented in this chapter via the use of descriptive statistics, Pearson correlation, and regression 

analysis. Questionnaires were used to gather data, which was subsequently processed, analyzed, 

and shown in tables and models according to each variable that was independent using the 

Statistical Package for the Social Sciences (SPSS).  

4.2 Response Rate 

Based on the given sample size, a set of 76 surveys were sent to an equivalent number of 

responders. A total of 59 surveys were filled out correctly and returned, yielding a response rate 

of 77.6 percent. The pick and drop method had the greatest percentage of responses, with a return 

rate of 77.6 percent. Cooper and Schindler (2008) posit that response rates over 50% are deemed 

satisfactory for the purposes of data analysis and publishing. A response rate of 60% is considered 

commendable, while a rate of 70% is classified as very favorable. Furthermore, a response rate of 

80% is regarded as extraordinary. 

4.3 Demographic Characteristics  

The demographic characteristics of the respondents comprised of age, education level and duration 

worked in the company. The results are as follows. 



 

35 

 

Table 2: Age of the respondents 

 Age Frequency Percentage 

Under t20 tyears tof tage 0 - 

20-30 tyears tof tage 12 20.30 

31-40 tyears tof tage 29 49.20 

41-50 tyears tof tage 14 23.70 

Over t50 tyears tof tage 4 6.80 

 Total 59 100 

Table 1 shows that none of the respondents was found to be under 20 years old. However, majority 

of the sampled respondents were between 31 and 40 as shown by 49.20%, between 41 and 50 years 

were 23.7% while between 20 and 30 years were 20.3%. Over 50 respondents were 6.8% of the 

sampled respondents. It's safe to assume that most responses are under the age of 50. This means 

they were able to provide the requested data on the efficiency of factories in Kenya's Nairobi City 

County. 

 

Figure 2: Age of the Respondents 

0

10

20

30

40

50

60

Under 20 years

of age

20-30 years of

age

31-40 years of

age

41-50 years of

age

Over 50 years of

age



 

36 

 

Table 2 exhibits the outcome of the study's attempt to determine the average educational level of 

those who took part. The education level of respondents is an important factor to consider when 

designing and conducting data collection activities. By taking into account the potential influence 

of education level, researchers can help to ensure that their data is as accurate and representative 

as possible. 

Table 3: Highest Level of Academic 

 Highest Level of Academic Frequency Percentage 

College Diploma 7        11.90  

University Graduate 33        55.90  

Postgraduate 19        32.20  

 Total 59 100 

 

It is evident that more than half of the respondents were university graduates as shown by 55.9%, 

32.20% were having post graduate as highest level of academic while 11.9% were having college 

diplomas. The education level of respondents can play a significant role in data collection. The 

results imply that most of the respondents were knowledgeable and this aided in the collection of 

data. It's clear that the responders have the knowledge and abilities to do the jobs they've been 

given. 



 

37 

 

 

Figure 3: Highest Academic Level 

The researcher also sought to establish work duration of the respondents to their current employer. 

the work duration of respondents is a valuable variable to consider during data collection as it 

provides insights into their experience, engagement, and overall contribution to the organization. 

 

Table 4: Number of Years Respondents have worked for their current employer 

Years Frequency Percentage 

Less tthan t1 tyear 2          3.40  

1-5 tyears 11        18.60  

5-10 tyears 34        57.60  

Above t10 tyears 12        20.30  

 Total 59 100 

 

According to the findings shown in Table 4, it was observed that a proportion of 3.4% of the 

participants reported having worked for their current company for a period of less than one year. 

11.9 55.9 32.2
0

10

20

30

40

50

60

College Diploma University Graduate Postgraduate



 

38 

 

Additionally, 18.6% of the respondents showed a tenure ranging from 1 to 5 years, while a majority 

of 57.6% reported a duration of employment between 5 and 10 years. Furthermore, a percentage 

of 20.3% revealed having been employed by their current organization for over 10 years. 

 

Figure 4: Duration working with Current employer 

4.4 Influence of Information Communication Technology Skills on Performance of 

Manufacturing Firms 

Information communication technology skills variable was used in the first objective which sought 

to analyze the influence of information communication technology skills on performance of 

manufacturing firms in Nairobi City County in Kenya.  The findings are presented descriptively 

and inferentially as follows. 

4.4.1 Descriptive Statistics 

The distribution of statistics that are descriptive in academic research often involves reporting the 

frequencies, percentages, means, and standard deviations of the variables under consideration. The 

3.4 18.6 57.6 20.3
0

10

20

30

40

50

60

70

Less than 1 year 1-5 years 5-10 years Above 10 years



 

39 

 

independent factors in this study were information communication technology skills, data science 

skills, and inventive abilities. The variable that was dependent was the performance of 

manufacturing enterprises in Nairobi City County, Kenya. The respondents were asked to rate their 

extent from 1 not at all to 5- very great extent. The results are presented in Figure 5. 

 

Figure 5: Information communication technology skills 

From Figure 5, 30.5% of the respondents indicated that they possess a proficient skill set in using 

Microsoft Excel, Word, and PowerPoint applications at great extent while 23.7% confirmed at 

very great extent. On the other hand, 8.5% indicated 8.5% of the respondents that they do not 

possess a proficient skill set in using Microsoft Excel, Word, and PowerPoint applications while 

18.6% of the respondents at small extent. These findings align with the increasing importance of 

digital literacy skills in the modern workplace. Proficiency in Microsoft Office applications is 

often considered essential for various professional tasks, including data analysis, document 

creation, and presentation development (Levin et al., 2016). Organizations often prioritize training 

and development initiatives to enhance employees' proficiency in these applications, recognizing 

8.5

3.4
5.1

18.6

5.1
3.4

18.6

23.7

16.9

30.5

39.0 39.0

23.7

28.8

35.6

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

I possess a proficient skill set in

using Microsoft Excel, Word,

and PowerPoint applications.

I possess a comprehensive

understanding of using various

social media sites.

I possess a comprehensive

understanding and proficiency

in using email communication.

Not at all Small extent Moderate extent Great extent Very great extent



 

40 

 

their significance in improving productivity and facilitating effective communication and 

collaboration (Schroeder, 2018). 

In regards to possessing a comprehensive understanding of using various social media sites, 39.0% 

of the respondents indicated they possess a comprehensive understanding and proficiency in using 

email communication at great extent and 35.6% at very great extent. This contradicted 5.1% who 

did not possess a comprehensive understanding and proficiency in using email communication at 

all and 3.4% at small extent. Email communication remains a fundamental tool for business 

communication, enabling efficient information exchange, collaboration, and coordination among 

employees (Kim & Shin, 2019). Proficiency in email usage is essential for maintaining 

professional relationships and managing work-related tasks effectively. Organizations often 

provide training and resources to enhance employees' email etiquette and communication skills, 

emphasizing clarity, professionalism, and responsiveness in email correspondence (Robinson & 

Shaver, 2018). 

Lastly, 39.0% of the respondents revealed that they possess a comprehensive understanding of 

using various social media sites at great extent as compared to 28.8% who indicate at very great 

extent. On the other hand, 3.4% revealed that they do not possess a comprehensive understanding 

of using various social media sites while 5.1% indicated at small extent. Social media platforms 

play a vital role in modern communication and networking, offering opportunities for professional 

networking, marketing, and knowledge sharing (Kaplan & Haenlein, 2017). Proficiency in social 

media usage enables individuals to leverage these platforms effectively for both personal and 

professional purposes. Organizations increasingly recognize the value of social media literacy 

among employees and may provide training and guidelines to promote responsible and effective 

use of social media in the workplace (Qualman, 2019). 

These findings align with previous research suggesting that many workers today need to be skilled 

in using digital tools like office software and email for work tasks (Blikstad-Balas et al., 2020; 

Kim et al., 2021). Moreover, recent studies have highlighted the increasing importance of social 

media literacy for professional purposes, including networking, brand management, and customer 



 

41 

 

engagement (Kaplan & Haenlein, 2020; Van Noort et al., 2018). Therefore, organizations may 

benefit from investing in training and development programs to enhance their employees' digital 

literacy, especially given the growing trend towards remote work and virtual collaboration (Chen 

et al., 2020). 

4.4.2 Simple Linear Linear Regression Analysis 

In the study, the researchers utilized a statistical technique known as simple linear regression to 

analyze the relationship between two variables: Information Communication Technology (ICT) 

skills and the performance of manufacturing firms. Simple linear regression helps in understanding 

how changes in one variable (ICT skills) relate to changes in another variable (firm performance). 

By employing this method, the researchers aimed to quantify the extent to which ICT skills 

influence the performance outcomes of manufacturing firms. This analysis helps in identifying the 

significance and direction of the relationship between the variables, providing valuable insights 

into the influence of ICT skills on firm performance.  The results are indicated in Table 5. 



 

42 

 

Table 5: Linear Regression Results of Information communication technology skills on the 

Performance of manufacturing firms 

Model tSummary 

Model R r-square Adjusted tr-square Std. tError tof tthe tEstimate 

1 .572a .327 .315 .33199 

ANOVAa 

Model Sum tof tSquares df Mean tSquare F Sig. 

1 Regression 3.055 1 3.055 27.714 .000b 

Residual 6.283 57 .110   

Total 9.337 58    

Coefficients’ 

Model 

Unstandardized 

tCoefficients 

Standardized 

tCoefficients 

t Sig.  

Std. 

tError Beta 

 (Constant) 2.933 .263  11.173 .000 

Information communication 

skills 
.363 .069 .572 5.264 .000 

 t t ta. tDependent tVariable: tFirm tperformance 
 

 

The findings from Table 5 indicate that the F-Statistic is statistically significant, with a F(1, 57) 

value of 27.714 and a p-value of 0.000, which is less than the predetermined significance level of 

0.05. These results suggest that the model successfully captures a linear connection among 

Information communication skills and the performance of manufacturing firms in Nairobi City 

County, Kenya. The statistical model, specifically focused on Information communication 

abilities, accounted for 32.7% of the variability seen in the results of manufacturing enterprises 

located in Nairobi City County, Kenya. This effect is represented by an r-square value of 0.327, as 

presented in Table 5 

The findings of the regression Coefficient shows that the unstandardized beta coefficient for the 

Information communication skills variable is significant as shown in Table 6 above; = 0.363, t = 

5.264, p=0.000 <0.05; therefore, Information communication skills had a statistically significant 

influence on the performance of manufacturing firms in Nairobi City County in Kenya. 



 

43 

 

Information communication skills had a positive standardized beta coefficient value of 0.572 as 

shown in the coefficients results of Table 6; these findings indicate that a unit improvement in the 

information communication skills is likely to improve performance of manufacturing firms in 

Nairobi City County in Kenya by 57.2%.  

The constant in the model was found to be statistically significant; =2.933, t = 11.173, p=0.000 

<0.05 (see table 6); This finding suggests that, in addition to the Information communication skills 

in the model, 5here are other variables that are not accounted for in the model, however they have 

a substantial influence on the operational outcomes of industrial enterprises within Nairobi City 

County, Kenya. The subsequent model was used to forecast the success of manufacturing 

enterprises in Nairobi City County, Kenya, under conditions of elevated Information 

Communication Technology (ICT) abilities; 

Firm performance= 2.933 + 0.363 Information communication skills 

Empirical evidence supports the positive relationship between ICT skills and manufacturing firm 

performance. A study by the World Bank found that firms with higher levels of ICT adoption tend 

to be more productive and profitable. Similarly, a study by the European Commission found that 

firms that invest in ICT training are more likely to achieve innovation success. The relationship 

between ICT skills and manufacturing firm performance is symbiotic (Radicic, Pugh, Hollanders, 

Wintjes & Fairburn, 2016). By investing in ICT skills development, manufacturing firms can reap 

a multitude of benefits, including enhanced productivity, improved quality, and increased 

innovation. In a world where technological change is accelerating, ICT skills are essential for 

manufacturing firms seeking to thrive in the competitive global marketplace (Moore & Manring, 

2019). 

The findings are corroborated by Sylvester and Okorie (2019), who conducted a study with the 

objective of determining the technical competencies of workers in manufacturing companies 

located in the South-East region of Nigeria, and examining the influence of these competencies on 

performance outcomes. The findings revealed a statistically significant connection between 



 

44 

 

information and communication technology (ICT) abilities and the work performance of 

employees. The discussion encompasses the findings, consequences, and conclusions. In their 

study, Mutheu and Perris (2021) aimed to investigate the influence of technical knowledge 

involvement on the performance of residential building projects in Kajiado County. The research 

conducted shown a significant association between the level of technical knowledge involvement 

and the overall success of residential building projects. 

4.5 Influence of Data Science Skills on Performance of Manufacturing Firms 

Data science skills variable was used in the second objective which sought to analyze the influence 

of data science skills on performance of manufacturing firms in Nairobi City County in Kenya.  

The findings are presented descriptively and inferentially as follows. 

4.5.1 Descriptive Statistics 

The respondents were asked to rate their extent from 1 not at all to 5- very great extent. The results 

are presented in Figure 6 

 
Figure 6: Descriptive Statistics for Data Science 

6.8 8.5
3.4

8.5
3.4

8.510.2

22.0

13.6

40.7

33.9

50.8

33.9 32.2

23.7

0.0

10.0

20.0

30.0

40.0

50.0

60.0

I am quite skilled in the

processes involved in the

collection of data.

I have received comprehensive

training in the use of

fundamental data analysis

approaches.

I possess exceptional skills in

the creation and delivery of

comprehensive reports.

Not at All Small Extent Moderate Extent Great Extent Very Great Extent



 

45 

 

As indicated in Figure 6, 40.7% of the respondents confirmed that they  are quite skilled in the 

processes involved in the collection of data and additional 33.9% indicated they are quite skilled 

at very great extent. On the other hand, 6.8% indicated that they are not skilled at all while 8.5% 

indicated they are skilled at small extent in the processes involved in the collection of data. 

Proficiency in data collection processes is essential for gathering accurate and reliable data, which 

forms the foundation for informed decision-making and analysis within organizations (Hair et al., 

2019). Effective data collection requires attention to detail, adherence to established 

methodologies, and the ability to identify and address potential biases or errors in the data 

collection process (Bryman, 2016). 

The results further revealed that 33.9% indicated that they have received comprehensive training 

in the use of fundamental data analysis approaches at great extent while 32.2% at very great extent. 

These contradicted 8.5% who indicated that they have not received comprehensive training in the 

use of fundamental data analysis approaches at all while 3.4% indicated at small extent. 

Comprehensive training in data analysis approaches equips individuals with the necessary skills 

and techniques to analyze and interpret data effectively, enabling evidence-based decision-making 

and insights generation (Field, 2018). Training in fundamental data analysis approaches may 

encompass various statistical methods, data visualization techniques, and software tools 

commonly used for data analysis, such as SPSS, R, or Python (Kabacoff, 2015). 

Lastly, 50.8% of the respondents indicated they possess exceptional skills in the creation and 

delivery of comprehensive reports at great extent and 23.7% at very great extent. However, 3.4% 

indicated they do not possess exceptional skills in the creation and delivery of comprehensive 

reports at all while 8.5% at small extent. Proficiency in report creation and delivery is crucial for 

effectively communicating insights and findings derived from data analysis to stakeholders within 

and outside the organization (Kaplan & Norton, 2016). Exceptional skills in report creation involve 

not only the ability to present data in a clear and concise manner but also the capacity to tailor 

reports to the specific needs and preferences of the audience (Few, 2013). 

Overall, these findings highlight the significance of proper training and education in developing 



 

46 

 

essential analytical skills, including data collection, analysis, and presentation, as noted in prior 

literature (e.g., Galvis & Rodríguez, 2018; Tseng et al., 2018). By equipping individuals with 

robust data literacy, employers stand to gain valuable insights