FACTORS AFFECTING DIFFUSION OF PRODUCT

INNOVATION IN MEDIUM SIZED MANUFACTURING

ENTERPRISES IN KENYA

BY

BETTY MBAYA

UNITED STATES INTERNATIONAL UNIVERSITY -

AFRICA

SUMMER 2017

FACTORS AFFECTING DIFFUSION OF PRODUCT

INNOVATION IN MEDIUM SIZED MANUFACTURING

ENTERPRISES IN KENYA

BY

BETTY MBAYA

A Research Project Report Submitted to the Chandaria School of

Business in Partial Fulfillment of the Requirement for the Degree of

Master in Business Administration (MBA)

UNITED STATES INTERNATIONAL UNIVERSITY -

AFRICA

SUMMER 2017

ii

STUDENT’S DECLARATION

I, the undersigned, declare that this is my original work and has not been submitted to any

other college, institution or university other than the United States International

University - Africa in Nairobi for academic credit.

Signed: ________________________ Date: _____________________

Betty Makena Mbaya (ID No. 625692)

This project has been presented for examination with my approval as the appointed

supervisor.

Signed: ________________________ Date: _____________________

Prof. George O. K’Aol

Signed: _______________________ Date: _____________________

Dean, Chandaria School of Business

iii

COPYRIGHT

© Betty Makena Mbaya (2017). All rights reserved. No part of this project may be copied

or reproduced in any form or by any means of electronic, photocopying or printing

without the prior written consent of the author.

iv

ABSTRACT

The purpose of this study was to identify factors affecting diffusion of product innovation

in medium sized manufacturing enterprises in Kenya. The study was guided by the

following research questions: How does relative advantage affect diffusion of product

innovation in medium sized manufacturing enterprises in Kenya? How does compatibility

affect diffusion of product innovation in medium sized manufacturing enterprises in

Kenya? How does complexity affect diffusion of product innovation in medium sized

manufacturing enterprises in Kenya?

A descriptive correlation research design was used to conduct the study. The population

comprised of 108 top 100 medium sized enterprises in the manufacturing sector between

year 2008 and 2016. Stratified random sampling was adopted to select a sample of 102

manufacturing enterprises from the 108 total population. A questionnaire was used to

collect data. Data was analyzed using descriptive statistical techniques such as mean and

standard deviation whereas inferential techniques used included Spearman’s Rank

Correlation, One way Analysis of Variance (ANOVA) and linear regression. Statistical

Package for the Social Sciences (SPSS) was used as a data analysis tool. Thereafter, the

data was presented in tables and figures.

Findings on the effect of relative advantage on diffusion of product innovation among

medium sized manufacturing enterprises revealed that male respondents who agreed that

relative advantage had an effect on the diffusion of product innovation accounted for 37%

while that of female respondents accounted for 30%. Findings from Spearman Rank

Correlation test indicated that there was a statistically significant positive correlation

between relative advantage and product innovations that are perceived to be more cost

effective diffuse faster among medium sized manufacturing enterprises, r(95) = .188, p <

.05. One Way ANOVA results revealed that there was a statistically significant difference

by gender F(1, 94) = 4.10, p < .05 and the years of enterprise existence F(1, 94) = 5.56, p

< .05. Linear regression analysis indicated that relative advantage explained 67.3% of the

variability on the spread of product innovation among medium sized manufacturing

enterprises, R2= .673 and statistically significantly predicted the spread of product

innovation, F(1, 94) =15.28, p < .05.

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Findings on the effect of compatibility on diffusion of product innovation among medium

sized manufacturing enterprises, revealed the proportion of female respondents who

strongly agreed that compatibility had an effect on the diffusion of product innovation

accounted for 47% while male respondents accounted for 29%. Spearman Rank

Correlation test showed that compatibility was strongly correlated to technological

innovation, r(95) = .235, p < .05 and lifestyles or cultures, r(95) = .213, p < .05 on the

diffusion of product innovation among medium sized manufacturing enterprises. One

Way ANOVA revealed that there was a statistically significant difference by gender F(1,

95) = 5.67, p < .05. The linear regression analysis indicated that compatibility explained

54.4% of the variability in diffusion of product innovation among medium sized

manufacturing enterprises, R2= 0.54 and statistically significantly predicted the spread of

product innovation among the medium sized manufacturing enterprises, F(1, 94) = 16.12,

p < .05.

Findings on the effect of complexity on diffusion of product innovation among medium

sized manufacturing enterprises revealed that the proportion of female respondents who

agreed that complexity had an effect on the diffusion of product innovation accounted for

37% while male respondents accounted for 30%. Spearman Rank Correlation test showed

that complexity was significantly correlated to clear communication, r(95) = .163, p < .05

on the diffusion of product innovation among medium sized manufacturing enterprises.

The results from One Way ANOVA test indicated that there was a statistically significant

difference by years of enterprise existence F(1, 95) = 4.56, p < .05. The linear regression

analysis indicated that complexity explained 52.2% of the variability in diffusion of

product innovation among medium sized manufacturing enterprises, R2= 0.52 and

statistically significantly predicted the spread of product innovation among medium sized

manufacturing enterprises, F(1, 94) = 13.69, p < .05.

The research concluded that relative advantage based on job effectiveness and

convenience affected diffusion of product innovation. The study recommends

manufacturing enterprises should establish unique and convenient product innovation

strategies to tap in the market as well as perceive ease of use of innovative products as

powerful in explaining satisfaction of customer needs and wants. The study suggests

further research should be conducted to investigate the impact of access to diffusion of

product innovation on the growth of medium sized manufacturing enterprises in Kenya.

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ACKNOWLEDGEMENTS

First and foremost, I would like to thank God for the gift of life and good health

throughout the study period. Secondly, I would like to thank my supervisor Prof. George

K’Aol for his guidance and mentorship when conducting this study. Thirdly, I would like

to thank the top 100 Medium Sized Manufacturing Enterprises in Kenya who took time to

respond to my questionnaires. Finally, I thank my family and friends who supported and

encouraged me throughout my studies.

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DEDICATION

To my family for their encouragement and support throughout my studies.

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TABLE OF CONTENT

STUDENT’S DECLARATION ........................................................................................ ii

COPYRIGHT ....................................................................................................................iii

ABSTRACT ....................................................................................................................... iv

ACKNOWLEDGEMENTS ............................................................................................. vi

DEDICATION.................................................................................................................. vii

LIST OF ABBREVIATIONS ........................................................................................... x

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

LIST OF FIGURES ........................................................................................................xiii

CHAPTER ONE ................................................................................................................ 1

1.0 INTRODUCTION ........................................................................................................ 1

1.1 Background of the Problem ........................................................................................... 1

1.2 Statement of the Problem ............................................................................................... 3

1.3 Purpose of the Study ...................................................................................................... 5

1.4 Research Questions ........................................................................................................ 5

1.5 Importance of the Study ................................................................................................. 5

1.6 Scope of the Study ......................................................................................................... 6

CHAPTER TWO ............................................................................................................... 9

2.0 LITERATURE REVIEW ........................................................................................... 9

2.1 Introduction .................................................................................................................... 9

2.2 Effects of Relative Advantage on Diffusion of Product Innovation .............................. 9

2.3 Effects of Compatibility on Diffusion of Innovation ................................................... 14

2.4 Effects of complexity on Diffusion of Innovation ....................................................... 19

2.5 Chapter Summary ........................................................................................................ 24

CHAPTER THREE ......................................................................................................... 25

3.0 RESEARCH METHODOLOGY ............................................................................. 25

3.1 Introduction .................................................................................................................. 25

3.2 Research Design........................................................................................................... 25

3.3 Population and Sampling Design ................................................................................. 26

3.4 Data Collection Methods ............................................................................................. 29

3.5 Research Procedures .................................................................................................... 29

3.6 Data Analysis ............................................................................................................... 30

3.7 Chapter Summary ........................................................................................................ 31

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CHAPTER FOUR ............................................................................................................ 32

4.0 RESULTS AND FINDINGS ..................................................................................... 32

4.1 Introduction .................................................................................................................. 32

4.2 Demographic Information ............................................................................................ 32

4.3 Effects of Relative Advantage on the Spread of Product Innovation among Medium

Sized Manufacturing Enterprises ....................................................................................... 35

4.4 Effects of Compatibility on the Spread of Product Innovation among Medium Sized

Manufacturing Enterprises ................................................................................................. 41

4.5 Effects of Complexity on the Spread of Product Innovation among Medium Sized

Manufacturing Enterprises ................................................................................................. 47

4.6 Chapter Summary ........................................................................................................ 53

CHAPTER FIVE ............................................................................................................. 55

5.0 SUMMARY, DISCUSSION, CONCLUSIONS AND RECOMMENDATION ... 55

5.1 Introduction .................................................................................................................. 55

5.2 Summary ...................................................................................................................... 55

5.3 Discussion .................................................................................................................... 57

5.4 Conclusions .................................................................................................................. 61

5.5 Recommendations ........................................................................................................ 62

REFERENCES ................................................................................................................. 65

APPENDICES .................................................................................................................. 71

APPENDIX 1: COVER LETTER .................................................................................. 71

APPENDIX 2: QUESTIONNAIRE................................................................................ 72

APPENDIX 3: TOP 100 MEDIUM SIZED MANUFACTURING ENTERPRISES.76

x

LIST OF ABBREVIATIONS

SME Small and Medium Sized Enterprise

SPSS Statistical Package for the Social Sciences

US United States

xi

LIST OF TABLES

Table 3.1: Population Distribution ..................................................................................... 26

Table 3.2: Sample Size Distribution…………………………………………………….. 29

Table 4.1: Descriptive Statistics for the Effects of Relative Advantage on Diffusion of

Product Innovation among Medium Sized Manufacturing Enterprises………………… 36

Table 4.2: Cross Tabulation of the Effects of Relative Advantage on the Spread of

Product Innovation among Medium Sized Manufacturing Enterprises by Gender…….. 37

Table 4.3: Correlation between Relative Advantage and Product Innovation among

Medium Sized Manufacturing Enterprises……………………………………………… 38

Table 4.4 ANOVA between Effect of Relative Advantage and Product Innovation among

Medium Sized Manufacturing Enterprises by Gender, Designation and Years of

Enterprise Existence........................................................................................................... 39

Table 4.5 (a): Model Summary .......................................................................................... 40

Table 4.5 (b): ANOVA ...................................................................................................... 40

Table 4.5 (c): Coefficient ................................................................................................... 41

Table 4.6: Descriptive Statistics for the Effects of Compatibility on the Spread of Product

Innovation among Medium Sized Manufacturing Enterprises ......................................... 42

Table 4.7: Cross Tabulation of the Effects of Compatibility on the Spread of Product

Innovation among Medium Sized Manufacturing Enterprises .......................................... 43

Table 4.8: Correlation between Compatibility and Product Innovation among Medium

Sized Manufacturing Enterprises ....................................................................................... 44

Table 4.9: ANOVA between Effect of Compatibility and Product Innovation among

Medium Sized Manufacturing Enterprises by Gender, Designation and Years of

Enterprise Existence........................................................................................................... 45

Table 4.10 (a): Model Summary ........................................................................................ 46

Table 4.10 (b): ANOVA .................................................................................................... 46

Table 4.10 (c): Coeffiecient ............................................................................................... 47

Table 4.11 Descriptive Statistics for the Effects of Complexity on the Spread of Product

Innovation among Medium Sized Manufacturing Enterprises .......................................... 48

Table 4.12 Cross Tabulation of the Effect of Complexity on the Spread of Product

Innovation among Medium Sized Manufacturing Enterprises by Gender ........................ 49

Table 4:13 Correlation between Complexity and Product Innovation among Medium

Sized Manufacturing Enterprises ....................................................................................... 50

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Table 4.14: ANOVA between Effects of Complexity and Product Innovation among

Medium Sized Manufacturing Enterprises by Gender, Designation and Years of

Enterprise Existence........................................................................................................... 51

Table 4.15 (a): Model Summary ........................................................................................ 52

Table 4.15 (b): ANOVA .................................................................................................... 52

Table 4.15 (c): Coefficient ................................................................................................. 53

xiii

LIST OF FIGURES

Figure 4.1: Distribution of Respondents by Gender .......................................................... 33

Figure 4.2: Distribution of Respondents by Age .............................................................. 33

Figure 4.3: Distribution of Respondents by Designation................................................... 34

Figure 4.4: Distribution of Respondents by Highest Education Level .............................. 34

Figure 4.5: Distribution of Respondents by Years of Enterprise Existence ...................... 35

Figure 4.6: Cross Tabulation for the Effects of Relative Advantage on the Spread of

Product Innovation among Medium Sized Manufacturing Enterprises by Gender ........... 37

Figure 4.7: Cross Tabulation for the Effect of Compatibility on the Spread of Product

Innovation among Medium Sized Manufacturing Enterprises by Gender ........................ 43

Figure 4.8: Cross Tabulation for the Effects of Complexity on the Spread of Product

Innovation among Medium Sized Manufacturing Enterprises by Gender ........................ 49

1

CHAPTER ONE

1.0 INTRODUCTION

1.1 Background of the Problem

The failure rate for a substantial number of new product innovations in the market place

has been on a high rise, causing a major concern among business owners, managers and

market researchers. Specifically, medium sized manufacturing enterprises have been the

most affected yet; the long term survival for these enterprises being dependent on their

new products success (Nejad, Sherrell & Babakus, 2014). According to Barczak, Griffin

and Kahn (2012) new products account to 28% of manufacturing enterprises sales and

profits yet about 41% of the all the new products fail in the market. Innovations play a

critical role in the development of manufacturing enterprises. However, despite, the

widespread recognition of the important roles that product innovations play in most

economies, relatively little research has been focused on the integrated aspects of

developing products in the context of medium sized enterprises and understanding why

innovations fail and succeed (Ansari, Reinecke & Span, 2014).

Medium sized enterprises have been indicated to be the most ambiguous influencing

factors in the diffusion literature (Bulte, 2012). Studies investigating the impact of

business size on the diffusion of product innovations have produced mixed results

influencing factors in the diffusion literature. For the past years, many governments have

developed policies incentives focusing on the important role they play in economic

development in order to enhance effective diffusion of their innovations in the market

place (Hilbert, 2013).

Literature on diffusion indicates there are significant differences in emphasis regarding

the impact of influential factors on the diffusion of innovations with some researchers

giving more attention to the characteristics of the innovation and those of the adopting

firms, including size, whereas other researchers have given relatively more emphasis to

the society, economy, and communication or information flow process (Ashtianiapouri &

Zandhessame, 2015). The type of innovations, interaction between the influencing

factors, importance and the influence of factors responsible for the diffusion of an

innovation keep changing giving added advantage to some innovations that might not

2

have been diffused, and reduce the importance of other innovations associated with a

specific social, economic, geographical and institutional situation within which successful

diffusion would have occurred (Tanev & Frederiksen, 2014).

A number of innovation researchers have maintained that for diffusion to take place

particular factors such as innovation attributes are best predictors of the rate of diffusion

(Rogers 2003; Hassan, Mourad & Tolba, 2013). Research conducted by Straub (2014) to

evaluate the time taken for a product to be adopted in the market, concluded that the rate

of diffusion was influenced by macroeconomics, microeconomics and demographic

factors relating to the product attributes of the innovation. Several other researchers have

studied the diffusion theory and tested it across various countries and industries

concluding innovation attributes are a major predictor of diffusion (Bulte, 2012). A

number of models have been developed to evaluate diffusion and one of the possible

reasons for the failure rates has been inappropriate application of the diffusion model

(Hassan et al, 2013). Clearly, these researches indicate innovation attributes are an

important consideration for diffusion and adoption of product innovations.

This study utilized Rogers (2003) diffusion of innovation theory which focuses on the

process of diffusion after an observation that potential adopters assess innovation

attributes of the products; these attributes include relative advantage, compatibility,

complexity, trial ability, and observe ability. Relative advantage focuses on innovation

additional benefits; compatibility focuses on innovation compliance with customers

existing values; complexity focuses on the ability to understand and use the innovation

whereas trial-ability focuses on the extent of product to be experimented and lastly,

observe-ability focuses on visibility of the innovation results to others (Straub, 2014).

This research focused on relative advantage and complexity which represent the

functional aspect of a product innovation and, compatibility which represent the social

dimension of the product innovation.

There are profound implications that product innovations attributes give competitive

advantage to manufacturing firms in developing countries (Wanyoike, Waititu & Mukulu,

2012). For instance, in the US, the economics of automobile assembly have been

transformed by exploiting diffusion of innovation systems in product development,

engineering, flexible manufacturing, distribution and marketing focusing on relative

advantages such as cost, quality and lead time advantages to increase their diffusion rate.

3

New product innovation advantages over the previous innovation are becoming very

important that industrialized country manufacturers are being forced to adopt these

practices by ensuring the new product benefits surpass the previous innovations to survive

in the market (Flight, Allaway, Kim & D’Souza, 2013).

According to Leoni (2013) mass production which has been a dominant manufacturing

concept in the industrialization of the twentieth century is being replaced by “lean

production” methods, which are highly advantageous, information and communication

intensive, with the future concept being viewed as mass customization. These methods

are spreading among industries and, unevenly, across countries. Regionally, industries

and countries which have adopted these methods, their factor productivity is believed to

have increased as a result of working capital, inventory reductions and increased asset

utilization through flexible and integrated diffusion of innovation hence developing more

innovative products.

It is argued that diffusion of product innovation is influencing the flow of foreign

investments and trade, as it changes both the production and management functions.

Locally, it tends to encourage decentralization and specialization of the production

function, by lowering the minimum efficient size of production units (Wang, 2011).

Simultaneously, it tends to enhance integration and economies of scope, and reduce the

size of the management structure, by reducing the costs of coordination and control.

1.2 Statement of the Problem

Over the years diffusion of innovation researches have produced a number of findings,

which have been summarized by many writers. However, these research summaries have

been programmatic rather than theoretical in nature and lack of theoretical work in these

studies has recognized there exists a real need for more general research related to the

field to be conducted (Rogers, 2003).

Innovation is recognized as a critical component of industrialization in developing

countries and more so in Medium Sized Enterprises (Brunswicker & Vanhaverbeke,

2014). Product Innovation, one of the most important aspects of innovation, is a critical

and vital key to the economic development and ignoring this aspect has led to the failure

of many companies and organizations (Ashtianipouri & Zandhessame, 2015). Globally,

4

according to Zhou and Wu (2014) investigation on diffusion of innovations in various

industries concluded, diffusion of product innovations in manufacturing enterprises was

slow and particularly in developing countries where these enterprises were considered to

be very important in developing the economy by immensely contributing to the country’s

gross domestic product (GDP) and offering employment. From the study, they

recommended more investigation to be carried out in identifying factors that influenced

diffusion and adoption of product innovations in manufacturing enterprises in developing

countries.

Barczak et al. (2012) study on the drivers of new product development accounted the

failure rate of new products in the markets place to be 41%, which was considered high

begging the question why the failure rate is so high. As a result, the research findings

noted a connection between the product characteristics and the new product failure rate.

This research recommended future researches to be conducted on the various factors that

affected new products diffusion in the market and more so what characteristics consumers

considered before purchasing any products.

Regionally, a number of researches have theoretically and emphatically investigated the

influence of lead users on the innovation process towards modifying an existing product

and enhancing its spread (Hassan et al, 2013). It is argued that new product modifications

help enterprises reduce risk of failure in the market. However, there is need to find out the

specific product modifications consumers look out for in enhancing acceptance and

reducing market failure. Additionally, Kalliny and Hausman (2014) study on influence of

lead users on diffusion of innovations noted there are other factors such as

communication, product attributes and cultural factors that influenced diffusion which

future researches should focus on to enhance new products innovation market success.

Mourad and Tolba (2014), study on how culture affected diffusion of innovation in

Egyptian medium enterprises, concluded culture had a major impact on diffusion of

product innovation as users were more likely to adopt innovations that were compatible

with their lifestyles and practices. Additionally, they noted different consumers had

different attribute preferences they were interested in when it came to choosing products

innovation which affected diffusion of innovations. From this study, the researchers

recommended further studies to be carried out on the impact individual cultures had on

5

the product innovation features in order to enhance diffusion and adoption of innovation

by consumers.

Based on the concerns outlined, product innovation attributes are a consideration for

diffusion of innovation Rogers (2003), and there is a need to conduct more studies

focusing on innovation attributes effect on diffusion of product innovation among

medium sized manufacturing enterprises in Kenya. Therefore, this study focused on

establishing what ignited diffusion of product innovation in manufacturing enterprises.

This is because the result of diffusion of their innovations is the overall knowledge of

their capabilities and skills to ensure they remain as the top 100 manufacturing enterprises

in Kenya by producing products that easily spread in the market (Wanyoike et al, 2012).

1.3 Purpose of the Study

The purpose of the study was to determine factors affecting diffusion of product

innovation in medium sized manufacturing enterprises in Kenya.

1.4 Research Questions

The study was guided by the following research questions:

1.4.1 How does relative advantage affect the diffusion of product innovation in medium

sized manufacturing enterprises?

1.4.2 How does compatibility affect the diffusion of product innovation in medium

sized manufacturing enterprises?

1.4.3 How does complexity affect the diffusion of product innovation in medium sized

manufacturing enterprises?

1.5 Importance of the Study

The findings of a research are not only useful to the researcher but also to other group of

stakeholders. This study will be important to the following stakeholders:

1.5.1 Medium Sized Enterprises

The results of the study would provide an incentive for the medium sized enterprises and

shareholders to develop appropriate interventions with the potential to enhance uptake of

innovation in a cost effective manner. It is important for medium sized enterprises to

6

develop products that are going to be successful in the market by ensuring they spread at

a higher rate and be adopted.

1.5.2 Policy Makers and Government

An understanding of the determinants of diffusion of innovation would be critical in

designing policies and interventions that would help providers to deliver appropriate

innovations that would be more attractive to potential consumers. Product innovations are

increasingly being recognized as a key component of growth in the medium sector; hence

policy formulation arising from the results of this study would guide the government,

especially the Ministry of Trade and Ministry of Information, Communication and

Technology in instituting reforms that would make investment in the innovation more

attractive.

1.5.3 Researchers and Academicians

This study finding would provide a theoretical and empirical framework for research in

the area of product diffusion within this key sector of the Kenyan economy. Academics

and students alike will find the study methodology and subsequent results rich enough to

guide future research. Further, the study will act as an impetus to reignite interest in this

critical area of study.

1.6 Scope of the Study

The study focused on medium sized manufacturing enterprises in Kenya. The total

population of the study was 108 Top 100 medium sized enterprises in Kenya. The study

was conducted in 2 weeks’ time in the month of July.

1.7 Definition of the Terms

1.7.1 Diffusion

Diffusion is a type of communication that is concerned with the spread of new ideas. It is

the process by which an innovation is communicated among the members of a social

system through certain channels over time (Rogers, 2003).

7

1.7.2 Adoption

Rogers (2003) defines adoption as the use of an innovation at its best for any actions

available.

1.7.3 Innovation

Schilling (2013) defines innovation as the implementation of an idea into a new process

or device. Innovation can also be defined as an idea or object individuals or other units of

adoption perceived as new (Rogers, 2003).

1.7.4 Technology

It is a branch of knowledge that focuses on development and utilization of technical

means and their interaction with the society, environment and life by drawing upon

subjects such as engineering, applied science, industrial arts and pure science (Smith,

2012).

1.7.5 Medium Sized Enterprise

According to the Government of Kenya Medium Sized Enterprise Act (2015) a Medium

Sized Enterprise is a firm, trade, service, industry or business activity whose annual

turnover is over 150 million and employs between 50 to 99 people.

1.7.6 Relative Advantage

Rogers (2003) defines relative advantage as the extent to which an innovation is

perceived to be better than the ideas it takes over from or the degree to which an

innovation is perceived to be more cost effective, convenient, and efficient or improves

existing applications and practices.

1.7.7 Compatibility

Compatibility is the extent to which an innovation is perceived to be consistent with

potential adopters past experiences, needs and values (Rogers, 2003).

8

1.7.8 Complexity

According to Rogers (2003), complexity is the extent to which an innovation is perceived

as complicated to understand and use.

1.8 Chapter Summary

This chapter introduced the background of the study and the problem statement.

Additionally, it described the scope under which the study was undertaken and stated the

research questions that were used to carry the study on; factors affecting diffusion of

product innovations in medium sized manufacturing enterprises in Kenya. Finally, the

various terminologies used were defined. Chapter two provided a detailed literature on

effects of innovation attributes in relation to diffusion of product innovation in

manufacturing enterprises in Kenya whereas Chapter three presented the detailed

methodology that was utilized in this study. Chapter four presented findings of the

research and interpreted date whereas chapter five analyses and discusses findings

drawing conclusions and recommendations for future studies.

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CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 Introduction

This chapter presents literature review on the factors that affect diffusion of product

innovation in medium sized manufacturing enterprises in Kenya. The factors are

discussed on the basis of the research questions. Firstly, it discusses effects of relative

advantage on diffusion of product innovation secondly, effects of compatibility on

diffusion of product innovations and thirdly, effects of complexity on diffusion of product

innovations.

2.2 Effects of Relative Advantage on Diffusion of Product Innovation

This section addresses the effects of relative advantage on the diffusion of product

innovation in medium sized manufacturing enterprises in Kenya. The elements discussed

include job effectiveness, economic profitability, time, cost, effort, convenience and

efficiency.

The relative advantage of a product innovation has a direct positive relation with how the

innovation spreads in the market. Zhai (2011) notes that the greater the perceived relative

advantage to the consumers the faster the product innovation spreads in the market. Wang

(2011) adds product innovations with additional relative advantages are easily adopted.

Rogers (2003) suggests innovations that are clearly unambiguous and advantages surpass

previous innovations easily spread in the market and are easily adopted. Wanyoike et al.

(2012) argues if no relative advantage is perceived by a potential consumer in using the

product, it will not diffuse in the market appropriately.

Relative advantage can be measured in economic terms, social prestige, convenience,

satisfaction, saving time and effort, economic profitability, cost reduction, and increased

production. Based on the above advantages it is evident, the most important determinant

of a new product spreading in the market place is its additional benefits over the previous

product. To a great extent this happens where substitute older than the innovation exist

(Matlay & Weathead, 2013).

10

A new product relative advantage is one of the critical factors evaluated by consumers

when acquiring new products and, products that are of better quality and offer better

services have a high rate of spreading in the market (Tanev & Frederiksen, 2014).

According to Hung et al. (2010), manufacturing enterprises can build customers trust and

satisfaction on new products by ensuring all new products have a relative advantage in

order to enhance success in the market.

2.2.1 Job Effectiveness

Manufacturing enterprises can distinguish themselves from competitors by ensuring their

new products have the latest innovations which are considered to positively affect a firm

(Damanpour et al, 2014). A research conducted by Ismail and Mamat (2012) found out

that there was a need for companies to clearly define their product adoption strategies

while developing new products to enhance job effectiveness and ensure successful

diffusion. Sawada, Matsuda and Kimura (2012) add that productivity paradox caused by

poor management of resources, measurement errors and profit redistribution can be

avoided with proper implementation of production strategies in manufacturing

enterprises. Additionally, other scholars provide empirical evidence that product

innovations in the manufacturing industry provide opportunities for companies to increase

job efficiency hence gaining competitive advantage (Leoni, 2013).

According to Paunov (2013) medium sized enterprises are the backbone of modern

market economies and play a key role in driving sustainable economy in developing

countries. Kozak (2011) adds product innovations that are deemed to enhance job

effectiveness are easily diffused in the market place, which ensures medium sized

enterprises grow three times faster and products reach the targeted market at a higher rate.

Hung, Tsai and Jiang (2010) add product innovations that provide better quality solutions

enhancing consumers’ job effectiveness have a higher chance to diffuse in the market

and, trust, commitment and customers’ satisfaction of these quality products can be

maintained by continuous production of competitive products that provide total solutions

to customers and partners.

2.2.2 Economic Profitability

Innovations that are deemed to profit the consumers enhance productivity and economic

growth appear to spread easily (Oliveira & Martins, 2011). Evidence on diffusion by Hall

11

(2011), suggests that products innovation have a significant effect on revenue and

productivity of a firm. Lu, Quan and Cao (2013) add that innovations act as a catalyst for

economic power status because if new innovations are perceived to have additional

benefits to enhance the lives of consumers they spread beyond business environment

expectations at a very high rate.

According to Tanev and Frederiksen (2014) there are many factors that influence success

or failure of manufacturing firms in their attempt to be competitive. Amongst them

product innovations are deemed to have an effective notion playing a significant role in

enhancing the competitiveness of the company. Undoubtedly, the ultimate goal of any

business is to achieve greater profits which can only be possible when they are on the

road to success in competitive situations by offering competitive products (Bulte, 2012).

Identification of the different aspects of competitiveness, on the one hand, and familiarity

with concepts such as product innovation and its many dimensions including spread, on

the other hand can help managers to be more effective in leading the manufacturing

processes thus enhancing firms’ profitability (Ashtianipouri & Zandhessame, 2015).

Globalization has made medium sized enterprises to manufacture products that have

greater relative advantage in order to survive in the competitive world. Relative

advantage has exceeded its role as being just a support tool in helping medium enterprises

gain competitive advantage and being more profitable over their large counterparts

(Ahmed, Shahzad, Umar & Khilji, 2013). With time product innovation diffusions are

increasing as manufacturing enterprises are realizing their value in the market. Migiro

(2016) adds that adoption of medium sized enterprises innovations enable them to

compete in the global market, improve their efficiency and close the relationship gap

between customers and suppliers.

Nejad et al. (2014) argues that an important factor in explaining the slowness of product

innovation spread is the fact that relative advantage of the new products is frequently

rather small and does not profit the consumer in any way when first introduced.

According to Nelson (2012) many authors have emphasized the need for companies to

manufacture products with additional relative advantage because as diffusion proceeds,

learning about benefits of the product takes place, innovation is advanced and adapted in

various enterprises hence making it attractive to various consumers. The implication of

manufacturing products with greater relative advantage is that benefits to adopters

12

increase over time; which will lead to decrease in cost and occurrence of faster diffusion

(Leoni, 2013).

2.2.3 Saving time, Cost and Effort

Development of product innovations with great capabilities using efficient technological

advancements can help organizations to keep prices of their products down hence creating

a competitive leverage and ensuring more consumers are able to save cost (Ashtianipouri

& Zandhessame, 2015). Study by Dixon, Parkin and Collins (2012) found out innovations

that enable the consumer to save more cost get to spread easily in the market. In

concurrence Egbetokun, Adeniyi, Siyanloba and Olamade (2012) adds that reduction of

taxes is likely to achieve the same results.

Adoption of new innovations is considered higher in developing countries and fast

delivery, technical capability, organization flexibility and, convenient after sale services

that save customers’ time and money can improve customers’ perception about the

product which end up improving their market diffusion (Gardner, 2013). The indirect

impact of the above factors can rise the expectations’ of the customers significantly

affecting the culture of manufacturing firms, causing them to innovate more in order to

produce more innovative products.

Relative advantage is an important determinant of product innovation adoption among

various consumers groups (Lu et al, 2013). It is expected that if various consumers

believe the product innovations will improve their job performance and save them cost

they would have a higher intention of adopting the new innovations (Gardner, 2013). The

extent of perception of the characteristics of the product as measures of saving time and

effort has a significant influence on innovation acceptance (Wang, 2011). Cost of the

product innovation is an important influencing factor in the diffusion and adoption of a

product. Additionally, gender plays a key role in diffusion of product innovation as

women tend to adopt products with higher relative advantage and lower cost as compared

to their male counterparts who cost saving is not a major consideration when buying

certain products (Frimpong & Nwankwo, 2012).

A study by Matlay and Weathead (2013), found that the cost of a product is an important

influencing factor in the diffusion and adoption of new innovation in many medium

enterprises. The authors elude that these enterprises innovations are less likely to spread

13

in the market if the initial set up cost is perceived to be high. Medium sized

manufacturing enterprises in Africa often have a great challenge to source financial

support and innovations considered expensive may not diffuse effectively. According to

Paul and Pascale (2013), many medium sized enterprises in Africa face limited financial

resources which are considered a big problem in the formulation and implementation of

new product innovations. Therefore, an innovation that promises to save them cost in

implementation is likely to spread easily. New innovations provide a drive for

manufacturing enterprises to improve the life of consumers by offering product

innovations that get to save consumers time and money and in return gain profits. Apulu

and Lathan (2011), adds there is an assurance for the product innovations to spread if they

are considered to save cost and time. For instance, in Ghana customers access a wide

range of products at lower costs which leads to a higher adoption rate hence improving

manufacturing business process (Frimpong & Nwankwo, 2012).

2.2.4 Convenience and Efficiency

Product innovations that provide greater benefits to consumers are highly considered

convenient and efficient to consumers (Passerini, El Tarabishy & Patten, 2012).

Additionally, innovations that prove to be more convenient are likely to be accepted

faster; for instance in the financial sector – customers are able to perform transactions

whenever they wish using the multipurpose simcards (Gardner, 2013). The introductions

of these multipurpose simcards in the banking sector have led to shorter queues in banks

and improved customers’ convenience of banking. They have also helped banks to reduce

inefficiencies hence improving customer service. A study by Chataway, Hanlin &

Kaplinsky (2013) notes that perceived convenience and efficiency of an innovation has a

positive significant effect on its ease to be accepted.

Egbetokun et al. (2012) study describes convenience as when an innovation contributes to

the business process simplification. For instance, in e-trade, companies recognize

perceived convenience will affect their business processes and consumers. Perceived

convenience attributes of a product lead to faster diffusion and adoption of products. The

greater perceived convenience of an innovation, the greater the drive to try it and

thereafter, undertake continuous adoption. According to Tanev and Frederiksen (2014)

innovations that facilitate the transformation of the healthcare sector through enhanced

service quality and improved operational efficiency, such as radio frequency

14

identification products which offer improved capabilities compared to the traditional

products, motivate the spread and adoption of these new innovations compared to the

previous ones.

2.3 Effects of Compatibility on Diffusion of Innovation

This section addresses the effects of compatibility on the diffusion of product innovation

in medium sized manufacturing enterprises in Kenya. The elements discussed include

technological, environmental, organizational and individual factors.

Innovations that are compatible with the intended adopters' values, norms, and perceived

needs are more likely to diffuse in the market (Rogers, 2003). Lack of innovation

compatibility with potential adopters’ lifestyles makes them lose confidence in the

product which leads to failure of diffusion (Zhai, 2011).

2.3.1 Technological Factors

Cupolas (2013), argues the decision to adopt a product is dependent on what is available

in the market and how new technologies complement those consumers who already

possess them. The major factors that characterize such technologies include compatibility

which is both technical and organizational. According to a study by Huy (2012)

compatibility has been found to be a significant determinant of diffusion and adoption of

new innovations as it deals with perception of the importance of innovation in performing

various tasks both presently and in the future. For example, if products are perceived to

be compatible with the traditional way of performing various tasks, with existing values

and different communication involving day to day operations and future developments, a

higher diffusion rate is likely to occur.

Fu and Gong (2011) study reveals appropriate institutions and policies that are compatible

with the consumers lifestyles are required to guide incentives and facilitate the process of

diffusion. In addition local technological compatibility is required to ensure appropriate

transfer and absorption of new innovations according to the technical and environmental

conditions. The nature of product innovations influence whether the innovation will

spread and rate of diffusion. Low- technical innovations do not require demanding pre-

conditions capacity in terms of skills and capital and therefore, have the potential to

15

diffuse faster as compared to high- technological products that require high tech pre-

conditional capacity to be operated (Zanello, Fu, Mohnen & Ventresca, 2015).

2.3.2 Environmental Factors

There is a positive correction between innovation adoption and the business owners’

perception of the intensity of the competition, industry pressure support, supplier and the

sector in which the business operates (Huy, 2012). Additionally, the industry in which a

firm operates majorly influences how innovations diffuse in the marketplace with

manufacturing industry being one of the key industries in which poor institutional

environments demonstrate a significant relationship through reduction of diffusion rate

and lower return for innovations (Nguyen& Jaramillo, 2014).

Allard, Martinez and Williams (2012) study found out innovations that were compatible

with the national systems in political stable countries were likely to spread easily and less

likely to spread in unstable countries. Arguably, firms that serve broader markets with

greater market scope in politically stable countries are more likely to adopt innovations

because innovations are greatly affected by political environment which direct affect

competition and consumers. Srholec (2011) analysis of 14,000 enterprises in 32

developing countries survey by the World Bank, argued that the way a political system is

organized has indirect powerful effects on development and spread of innovations;

democratic governments are more likely to offer incentives to enhance innovativeness.

Additionally, De Waldemar (2012) analysis of Indian firms by the Enterprise Survey

found out that corruption was a major influence of how innovation diffused in developing

countries. He noted corrupt environments slowed the spread of innovations.

Porter (2013) suggests diffusion of product innovations in a politically stable environment

change the competitive environment in three ways which include; change of the industry

structure, competition rules and businesses new method which help businesses to gain

competitive advantage by ensuring their new products spread in the market easily.

Ensuring fair competition and entry opportunities for market players, particularly in

medium sized enterprises there must be an on-going policy to enhance diffusion of

innovation. Al-Qirim (2013), study found out innovations that are likely to fit in

consumers environments will have a greater impact diffusing, and service innovations are

16

likely to easily diffuse in developing countries compared to product innovations due the

environmental conditions created by these countries.

Nguyen& Jaramillo (2014) adds that potential or existing customers prefer a suitable

environment that allows some sort of interaction when consulting with the supplier

because information intensive products are more complicated and require more

accompanying information which is critical in their spread. Additionally, external

pressure from competitors or customers already used to the products may encourage the

manufacturing firms to consider the environment in which they interact with their

customers in order to enhance diffusion.

2.3.3 Organizational Factors

The culture of an organization can be a powerful driving force for the spread of

innovations. Organizations that support innovativeness and have innovative practices

already in place are likely to manufacture products that diffuse easily in the market (Fu &

Gong, 2011). However, a culture can also prevent a company from meeting competitive

threats or adapting to changing economic and social environment that an innovation is

designed to overcome. Allard et al. (2012) notes other than organizational culture there

are other features of the organization such as structures and systems that affect how

innovations are developed in the organizations and if not taken into consideration can be

very impactful in the failure of innovations spread. Hall (2011) adds that there is need for

organizations to develop capacity for change and allocate operational capabilities that are

in line with product innovativeness in order to sustain long term performance.

A study by Huy (2012), on the significance of organizational determinants as factors of

diffusion and adoption found out that employees knowledge of various innovation trends,

enterprise size and attitudes of the managers and business owners towards innovation

were significantly positive. Another finding by Al – Qirim (2013), confirms that the

positive relationship between managers and employees attitude towards innovation

knowledge had a significant impact on the spread of organizations innovation.

The organization size is an indicator of the level of operational resources of the company

which is positively and significantly related to diffusion of innovation (Lu et al, 2013).

For instance, small organizations may face challenges with manufacturing products that

17

appeal to the market due to the limited human resources they may experience. For

products to spread in the market there are a number of factors considered such as

resources which should be in line with the organization capability. Study by Kaplan

(2012) found out, employees’ experiences at work are a critical factor that influences the

success of product adoption due to the significant relationship between organization

governance, characteristics of the products and the number of products. When employees’

internal systems are innovative and they work in an environment that supports innovation

they are prompted to develop products that will as well appeal to the consumers lifestyles.

Past studies show, organization compatibility has a significant impact on the willingness

of innovation diffusing and consumers adopting the products. Cupolas (2013) found out

there is a need for managements to upgrade their way of operation and how employees

think as institutions that are bureaucratic are believed to actively inhibit diffusion of

innovation. For instance, if an innovation is compatible with the organization culture and

governance, uncertainty is likely to decrease and awareness of the innovation to increase

among employees who are considered great ambassadors of the company products

(Porter, 2013). Employees can only be good ambassadors and consumers of their

company products if their beliefs are in line with their organization values.

Development and diffusion of product innovations is strongly affected by values

difference between organizations and their target audience. For instance there are a

number of issues linked to lack of diffusion for industries in the North West Frontier

province in Pakistan related to values indifference (Bashir, Khan & Malik, 2010). The

target audiences feel that some of the products are manufactured by industries which

values fail to complement theirs. Meagher (2012) adds that industrial developments are

better facilitated if the mechanisms driving them are embedded in the community and

social institutions found in the region.

Organization factors deal mostly with adoption of innovation by organizations and

diffusion of innovation within organizations. Secondly, organizational compatibility in

relation to interactivity of new innovations needs to be consistent with the needs of

existing company practices. Thirdly, perceived benefits of the innovation are critical in

diffusion of innovation that business owners and managers need to put into consideration

Cupolas (2013).

18

2.3.4 Individual Factors

Diffusion is supported by social networks of influences, where the influence process is

carried out by direct contact between individuals in personal networks and social system

trends at micro level (individual), based on the exposure level, the individual

characteristics evolution and on the factors that influence decision making (Mourad &

Tolba, 2014). Issues surrounding individuals are an important aspect of the diffusion of

product innovations and individual factors based on demographics such as age and

genders have an effect on the spread of product innovations. According to Udin (2016) on

the usage and acceptance of wearable products, younger age are more likely to accept

product innovations compared to the older age, in contrast female at older also more

likely to adopt emerging innovations compared to male.

Individual misunderstanding and cultural differences on how negotiations are carried out

can also affect how innovations diffuse in the market. Zanello et al. (2015) argues that

sometimes developing countries innovations do not spread because they do not meet

individual local needs and are not in line with individual lifestyles and, preferences. Fu

and Gong (2011) adds many researchers believe that products that are recently launched

in the market diffuse faster in comparison with the situation several years ago. This fact is

a result of the emergence of different players whose negotiation values are believed to be

in line with the majority.

One of the main players that accelerate the diffusion process is the opinion leader, Rogers

(2003), who proved that opinion leaders have major roles in activating diffusion

networks. Opinion leaders are identified as having greater access to mass media as well as

interpersonal networks in comparison with their followers. In addition, they are perceived

as having higher socio-economic status and tendency to adopt new innovative ideas

before their followers who share same values. It has been empirically proved if the

product is compatible with the opinion leader lifestyles they influence the consumer

decision-making process through spreading positive word of mouth of the product which

later leads to a high rate of diffusion and adoption. In addition, opinion leaders act as role

models to be imitated by various individuals. This relationship was supported by the

diffusion research, which highlighted that opinion leaders influence the evaluation of new

innovation, majority followers and hence affect the rate of diffusion (Dearing, 2012).

19

Finally, in medium sized enterprises, the position of individuals in relation to the new

innovation diffusion matters a lot as they are supposed to be generalist in performing their

functions (Allard et al, 2012). Most enterprises personnel are either in key managerial

positions or are classified in a unit or function that performs a certain task or numerous

tasks. Most are also run by managers who own the organizations (Matlay & Weathead,

2013). It is important that there is a higher appreciation of innovation because higher

level of appreciation of innovations by the owner or key manager will directly influence

considerations for further innovation uptake by consumers who are believed to only adopt

a product if it meets their individual needs and complement their lifestyles.

2.4 Effects of Complexity on Diffusion of Innovation

This section addresses the effects of complexity on diffusion of product innovation in

medium sized manufacturing enterprises in Kenya. The elements discussed include level

of understanding, communication, technical and social skills.

Complexity of product innovation is critical to its acceptance. According to Rogers

(2003) complexity focuses on the degree to which the innovation is perceived as

complicated to understand generally, the more complex the innovation is in terms of

operating, the less rapid its acceptance will be. Studies by Al-Qirim (2013) found out

existence of an inverse relationship between the likelihood of diffusion and perception of

innovation complexity. Furthermore, Rogers (2003) suggests that key players easily take

up innovations that are perceived simple to use.

In manufacturing intensive industries, the pace and complexity of product innovations

changes create many uncertainties for organizations which in turn, force organizations to

innovate continuously to be competitive. Some of these innovations are in the

manufacturing of communication equipment, computers and other electronic equipment.

However, lack of attention on the comparison among different types of equipment in

manufacturing industries may lose the benchmark function, and restrain learning and

communicating between each other which is important in the evaluation of different types

of equipment manufacturing industries (Nakata & Weidner, 2012).

20

2.4.1 Level of Understanding

Innovations that are perceived by key players as simple to use are more easily adopted. If

the innovation can be broken down into more manageable parts, it will be assimilated

more easily (Rogers, 2003). The more complex an innovation is the slower its diffusion.

For instance, the modern day consumers find using electrical products such as computers

as easy to use because they tend to be educated and have sufficient understanding of

computer (Mohd, 2010). The act of adoption of innovations and the process of diffusion

is shaped and influenced by actors’ interaction within a social context and system. Hence

it is critical to understand why and how things happen the way they do, in the course of

innovation and diffusion of systems and electrical products (Cohen, 2012).

Nakata and Weidner (2012) study found out that complexity of product innovations can

be considered a key factor affecting diffusion of innovation in medium sized enterprises.

This is because new ideas that are simpler to understand by members of a social system

are adopted more rapidly than innovations that require the adopter to develop new skills

and understanding. Rogers (2003) theory indicates that the lower the complexity of an

innovation the higher the diffusion rate.

Bahaddad, Chang and Lai (2013) notes that medium sized manufacturing enterprises

differ from large companies as they tend to focus on simple methods to manage their

operations which require less advanced technical expertise. These enterprises follow

simple management form and avoid complex governance and complex innovations that

are difficult to understand. A study by Bulte (2012) adds if product innovations are easy

to understand they have a higher intention of spreading among consumers. The perceived

ease of use of innovative product is powerful in explaining satisfaction of consumer needs

and wants.

2.4.2 Communication

Communication channels are essential vectors of innovations diffusion: potential adopters

will embrace an innovation only if they come across it or hear about it. The channels may

involve transmission of information (Zanello et al, 2015). Issues related to

communication address interactivity of a new product innovation to a certain degree.

Communication issues enclose many different aspects of product interaction, online

communities and user's individual motivation towards a certain innovation and successful

21

market acceptance (Zhou & Wu, 2014). The efficiency of communication depends on the

level of development of infrastructures and on the geographical and cultural distances

between the actors involved in the communication. Developed countries have efficient

transport systems that facilitate the diffusion of knowledge and goods. In many

developing countries the quality and efficiency of infrastructures limit the transport of

goods both from other countries and internally thereby hampering the spread of

innovation (Amendolagine, Boly, Coniglio, Prota & Seric, 2013). Additionally, in order

to access effectively new markets, companies may need to re-think the production and

delivery of goods, often re-engineering products in order to reduce the complexity and

cost of production (Bhatti & Ventresca, 2012).

Diffusion is a specific kind of communication, which can be said to be a social process

that involves interpersonal communication relationships. Thus, interpersonal channels are

more powerful to create or change strong attitudes held by an individual (Rogers, 2003).

For each innovation that is not spread, it is essential to assess whether this is due to a

design fault, missing channels, or lack of local capacities.

Zanello et al. (2015) argues it is important for medium enterprises to communicate

effectively about their new innovations to its audience to enhance new innovations

update. According to Yung and Shin (2013) the two ways in which new information is

communicated to consumers is through epidemic and game-theoric. In epidemic models,

consumers obtain new information and adopt new products when they have contact with

others who already had the new product while in game-theoretic models, a user adopts a

new product only if it maximizes her utility, which increases with the number of

neighbors who adopt the same product.

Further studies by Mutoko (2013) found out that where diffusion of an innovation is

desirable at the beginning of campaigns, innovations penetrate larger geographical

regions with supportive communication policies and sustainable multi-stakeholder

partnerships. This is because cooperation between interested and affected groups is

necessary for better understanding of opportunities and challenges involved in achieving

conversation goals at multiple scales. Seker (2012) adds developing countries weak

national- level institutions can enhance such communication collaborations if they have

access to restricted training skills in marketing and production of their new product thus

encouraging product innovation diffusion. Additionally, Porter (2013) emphasizes the

22

need to communicate effectively about new product as effective campaigns have a

significant effect on diffusion of innovation.

Finally, new innovations and consumer awareness is really critical in the diffusion

process. For instance in Malaysia, in order to create awareness of new products, most

manufacturing enterprises launch online campaigns initially which start providing

information on important characteristic of the product in order to enhance acceptance

(Barczak et al, 2012).

2.4.3 Technical and Social Skills

Product innovations that require a new skill in order to adopt or complicated, will have a

slower rate of adoption than an innovation that is easy to understand and implement by

most members of the social system (Rogers, 2003). Lack of advanced and specific skills,

are factors that hamper both diffusion and adoption of innovation in developed as well as

developing countries (Zanello et al, 2015).

Mahazir and Mohd (2012) found that perceived complexity is a vital factor influencing

the decision to adopt innovation and that the likelihood of diffusion of the innovation is

inversely related to the perceived complexity variable especially amongst the medium

sized enterprises and its consumers. For instance, according to Bhatti and Ventresca

(2012), in Kenya, these enterprises struggle with qualified personnel who can manage

their firms and forget that both resources and capabilities also affect their consumers

because innovation requires creative and innovative re-combination of resources and

skills. Hall (2011) notes that, for every job created there is a lot of unskilled personnel

handling the work. However, work in the medium enterprises is not so attractive because

of low salaries compared to large companies, less attractive work conditions, lack of

career paths, and employment security. As a result, most suffer from high staff turnover

and limited-access to qualified and skilled staff which indirectly affects the quality of

products released to the market.

The introduction of new product innovations might require the employees to develop new

skills in order to use the product as well as the consumers. Rogers (2003) contends that

the new innovation can be intimidating, particularly if it requires change in the existing

businesses practices or acquisition of new skills. The measurement of perceived

complexity or ease of use can be in the context of how innovation can be easily

23

controlled, the degree of flawlessness, reasonableness, and adaptability to changes, user

friendliness, and how easy it is for one to become skilful in using the product.

Altuwaijri (2011) studies found out the major barriers to diffusion of innovated products

include complicated infrastructure, ability to attract expertise, funding resources, and

weak implementation strategies. Mahazir and Mohd (2012) add companies should

provide funds, develop implementation strategies, and address challenges of complicated

product innovations infrastructure, and expertise if they are to experience any market

success with their products. For instance in healthcare, the shortage of skilled health

professionals to promote new innovations affect improvement of Health services. For

these innovations to be adopted health providers need to accommodate elements such as

staffing constraints, system operator skills, training time available and cost limitations

which directly affect how the particular innovation spreads in the market (Sawada et al,

2012).

Technical skills are expensive, and firms cannot afford to implement product innovations

effectively if their employees lack these skills. For instance, in a paper-manufacturing

company in Northern Vietnam, lack of technical skill to develop innovative products was

a major obstacle for the company to overcome compared to financial constraints (Kimura,

2011). Diffusion of innovations in developing countries is a driver for firms’ growth

whereas technical skills are influencers for development of innovations that are easily

accepted in the market (Goedhuys, Janz & Mohnen, 2014).

24

2.5 Chapter Summary

This chapter has reviewed the literature review on studies carried out on factors affecting

diffusion of product innovation in medium sized manufacturing enterprises in Kenya. It

has focused on the three research questions firstly, discussing effects of relative

advantage of diffusion focusing on convenience, cost effectiveness, time saved,

application effectiveness and improvement of the consumers’ activities. Secondly, it has

discussed the effects of compatibility on diffusion of product innovation, focusing on

technological innovation, consumers’ environment, business and government policies,

consumers’ lifestyles and cultures and, industry acceptance. Finally, it has discussed

effects of complexity on diffusion of innovation focusing on the ability to easily

understand the innovation, ease of usage, innovation’s clear communication, and

provision of support resources and technical expertise of the consumers’. The next

chapter discusses the research methodology.

25

CHAPTER THREE

3.0 RESEARCH METHODOLOGY

3.1 Introduction

This chapter discusses the research methodology that was used for the study. It describes

the research design, population and sampling design, data collection methods, research

procedures and data analysis methods.

3.2 Research Design

Copper and Schindler (2014) define research design as the plan used by a researcher to

carry out a research. It provides a framework for generation of evidence suitable for a

certain criteria and the research question a researcher is interested in. There are three

types of research designs namely; exploratory, descriptive and causal. Exploratory

research design is undertaken when a research problem has few or no earlier studies to

refer to; descriptive research design is undertaken to determine and describe variable

characteristics in a situation whereas, causal research design is undertaken to determine

the nature of relationship between cause and effect variables. This study adopted a

descriptive research design. According to Saunders, Lewis and Thornhill (2016) there are

four types of descriptive research design which include; correlation, survey, evaluative

and Meta analysis. Precisely, descriptive correlation research design was used; this

research design is utilized if the data collected is used to describe a person, organizations,

settings and phenomenon (Creswell, 2012). Descriptive correlational design was suitable

for this study because it covers a wide range of variables and enhances understanding of

the relationship between independent and dependent variables of a study (Tavakoli,

2015).

The independent variables for the study were relative advantage, compatibility and

complexity whereas the dependent variable was diffusion of product innovation. Relative

advantage was measured by innovation’s convenience, cost effectiveness, time saved,

application effectiveness and improvement of the consumers’ activities (Passerini, et al,

2012). Compatibility was measured by technological innovation, consumers’

environment, business and government policies, consumers’ lifestyles and cultures, and

industry acceptance (Zanello et al, 2015). Complexity was measured by the ability to

26

easily understand the innovation, ease of usage, innovation’s clear communication,

provision of support resources and technical expertise of the consumers’ (Al- Qirim,

2013).

3.3 Population and Sampling Design

Population and sampling designs describe what the target population comprises of and

how individual samples are selected from the total target population.

3.3.1 Population

A population consists of all the subjects about whom the researcher requires to draw some

inferences (Saunders et al, 2016). The population for this study consisted of 108 top 100

medium sized manufacturing enterprises recognized for their growth in profits and

revenue, shareholders return and liquidity in Kenya between year 2008 and 2016 (KPMG,

2015). The researcher obtained the list from the top 100 Medium Sized Enterprises

website developed by KPMG Kenya and Nation Media Group.

Table 3.1: Population Distribution

Year

Manufacturing

Enterprises in Top

100

Repeated

Enterprises in Top

100

Actual

Manufacturing

Enterprises in Top 100

2008 21 0 21

2009 23 10 13

2010 34 19 15

2011 32 18 14

2012 31 23 8

2013 33 21 12

2014 18 11 7

2015 23 13 10

2016 26 18 8

Total 241 108

Source: KPMG (2016)

From table 3.1, some enterprises were recognized severally between year 2008 and 2016

hence appearing as repeated. For instance, in 2009 there were 23 manufacturing

enterprises on the top 100 list however, 10 of them had already been recognized in 2008

leading to 13 actual enterprises. In 2012, 31 manufacturing enterprises were on the top

27

100 list with 23 enterprises having been recognized in the previous years’ hence 8 actual

enterprises. Finally, in 2016 there were 26 manufacturing enterprises on the top 100 list

with 18 having been recognized in the previous years, accounting to only 8 actual

enterprises in the year.

3.3.2 Sampling Design

The sampling design consists of sampling frame, sampling technique and the sample size

adopted for the study.

3.3.2.1 Sampling Frame

A sampling frame is a list of elements representing the population from which the sample

is derived (Cooper & Schindler, 2014). The sampling frame for this study consisted of

108 Manufacturing Enterprises drawn from the top 100 Medium Sized Enterprises

website, updated annually by KPMG Kenya and Nation Media Group.

3.3.2.2 Sampling Technique

A sampling technique describes the method used to select a sample (Cooper & Schindler,

2014). The selection of the sampling method depends on various theoretical and practical

issues (Hair, 2015). There are two types of sampling techniques; probability sampling and

non-probability sampling. Probability sampling technique is used in quantitative studies

where subjects of the sample are chosen from known probabilities; this technique

includes simple random, stratified random, cluster and systematic random sampling

whereas non-probability sampling is used in qualitative studies where subjects are not

based on random sampling techniques; this technique includes convenience, judgmental,

quota and snowball (Sekaran & Bougie, 2015). This study adopted stratified random

sampling because it takes into consideration different subgroups of individuals in the

population guaranteeing fair representation of the sample specific characteristics

(Saunders et al, 2016). In this study, the sampling procedure involved classifying the top

100 enterprises into 9 strata according to the year in which they were recognized between

2008 and 2016; then, simple random sampling was used to select individual enterprises

from each stratum. This technique ensured that a representative sample from different

years was used in the study.

28

3.3.2.3 Sample Size

A sample size is defined as subjects selected from the population to constitute a sample

(Tavakoli, 2015). According to Saunders et al. (2016) a sample size is determined by

selecting the number of observations to be part of the statistical sample which is the

actual number of intended respondents of a represented population under study.

Appropriate sample size consists of various factors that need to be taken into account

which include elements in target population, type of sample required, time available,

budget and whether findings are to be generalized and at what confidence degree

(Saunders et al, 2016). Yamane (1973) statistical formula was used to determine the

sample size. This formula was suitable for this study because the population was known;

in this case the top 100 manufacturing enterprises. This formula is normally applied when

determining a sample size for a finite population; this is whereby the specific number of

items in the target population is well defined.

n = N

(1 + Ne2)

Where;

n = size of the sample required

N = size of the population

e = maximum percentage of error required. For this study the error allowed was 5%, in

order to give a 95% confidence interval which is equal to 1.96

After substituting the formula, {n = 108 / [1+108 (0.05 * 0.05)] =108 /1.0588 = 102

n = 102

The sample size for this study was 102 medium sized manufacturing enterprises,

distributed based on the population size of individual strata as shown in table 3.2.

29

Table 3.2 Sample Size Distribution

Year Actual Population Sample Size Percentage Sample Size

2008 21 21/ 108 * 100% = 19 20

2009 13 13/ 108 * 100 % = 12 12

2010 15 15/ 108 * 100% = 14 14

2011 14 14 / 108* 100 % = 13 13

2012 8 8 / 108 * 100% = 8 8

2013 12 12 /108 * 100% = 11 11

2014 7 7 / 108 * 100% = 7 7

2015 10 10 / 108*100% = 9 9

2016 8 8 / 108 *100% = 7 8

Total 108 100 102

Source: Author (2017)

3.4 Data Collection Methods

The method used for collecting data should be clearly described. A structured

questionnaire developed by the researcher was used to collect primary data in this study;

this allowed the researcher to learn the intentions, expectations and opinions of the

respondents (Cooper & Schindler, 2014). A five likert - type scale ranking raging from

strongly agree to strongly disagree was adopted. A likert scale was suitable for this study

as it is widely used in most business studies and other related courses in social science

literature (Zikmund, 2012). The questionnaire was divided into 4 sections; section one

focused on demographics such as gender, designation, education and years’ of the

organization existence. Section two focused on relative advantage effects on diffusion of

product innovation. Section three focused on compatibility effects on diffusion of product

innovation and section four focused on complexity effects on diffusion of product

innovation.

3.5 Research Procedures

The research instrument was developed by the researcher and pilot tested among 10

respondents, 10% of the sample to detect weaknesses in design and instrumentation

(Cooper & Schindler, 2014). After the pilot test internal consistency and reliability of the

questions was determined by computing Cronbach’s alpha where anything above 0.7 was

30

considered acceptable. From Cronbach’s alpha results unclear questions were amended.

After amendment of the instrument, an introduction letter was obtained from Chandaria

School of Business, indicating the researcher was a student at the school. Thereafter, the

final instrument was administered to respondents within Nairobi by the Researcher and

Research Assistant, and via email to respondents outside Nairobi. Follow ups were made

weekly through telephone calls and emails. Data was collected in the month of July 2017.

3.6 Data Analysis

Data analysis involves developing summaries, looking for patterns in the collected data

and applying statistical techniques to analyse the data. Descriptive and Inferential

statistical techniques were used to analyze the data. Descriptive statistics involves display

of characteristics of the location, spread and shape of a data array whereas inferential

statistics includes the estimation of population values and testing of statistical hypotheses

(Cooper & Schindler, 2014). Descriptive statistics techniques included the mean and

standard deviation whereas inferential statistics techniques included Spearman’s Rank

correlation analysis, One way Analysis of Variance (ANOVA) and linear regression.

Spearman’s Correlation Coefficient technique was applied because the variables were

monotonically related and they assumed an interval. Spearman’s Correlation Coefficient

is a statistical measure of the strength of a monotonic relationship between paired data.

The index of the association strength between variables, range from zero denoting no

association to +1 denoting perfect association. A high index denotes a strong correlation

between the study variables whereas a low index denotes a weak correlation (Healey,

2011). ANOVA was used to establish the significant differences between the mean

scores. Linear regression technique was used to test the statistical significance on the

relationship between relative advantage, compatibility and complexity on diffusion of

product innovation. Finally, the data was analyzed using the Statistical Package for the

Social Sciences (SPSS) tool and results presented in tables and figures.

31

3.7 Chapter Summary

This chapter described the methodology that was used to carry out the study on factors

affecting diffusion of product innovation in medium sized manufacturing enterprises in

Kenya. It discussed the research design, population, sampling design, data collection,

research procedures and data analysis method. Chapter 4 presents results and findings of

the study.

32

CHAPTER FOUR

4.0 RESULTS AND FINDINGS

4.1 Introduction

The purpose of the study was to determine factors affecting diffusion of product

innovation in medium sized manufacturing enterprises. This chapter presents the results

and findings of the study based on three research questions. Section one presents the

descriptive analysis of demographic information; Section two presents findings on the

effects of relative advantage on diffusion of product innovation among medium sized

manufacturing enterprises; section three presents the findings on the effects of

compatibility on diffusion of product innovation among medium sized manufacturing

enterprises and lastly, section four presents the findings on effects of complexity on the

diffusion of product innovation among medium sized manufacturing enterprises. A

summary of the findings will be presented at the end of the chapter. A total of 102

questionnaires were administered to owners and managers of medium sized

manufacturing enterprises in Kenya, of which 91 were filled and returned, giving a

response rate of 80% which was considered adequate for the analysis.

4.2 Demographic Information

This section, presents results of demographic information which included respondents

gender, age, designation, level of education and the years of enterprise existence.

4.2.1 Distribution of Respondents Gender

The gender distribution of respondents is shown in Figure 4.1. The figure shows that 67%

of the respondents were male and 33% of the respondents were female.

33

Figure 4.1: Distribution of Respondents by Gender

4.2.2 Distribution of Respondents Age

The distribution of respondents’ age is shown in Figure 4.2. Out of 91 respondents, 49%

were within the age bracket of 30 – 39 years, followed by 28% of respondents above 40

years and 18% of the respondents aged between 20 – 29 years.

Figure 4.2: Distribution of Respondents by Age

4.2.3 Distribution of Respondents by Designation

The distribution of the respondents by designation is shown in Figure 4.3. The figure

shows that the majority of respondents were managers who accounted for 67%, followed

by other staff who accounted for 17% and owners who accounted for 16%.

34

Figure 4.3: Distribution of Respondents by Designation

4.2.4 Distribution of Respondents by Highest Education Level

Figure 4.4 shows distribution of the respondents by highest level of education. The figure

shows that 52% of the respondents had graduated with bachelor’s degrees, followed by

21% of the respondents who had master’s degree and 8% who had acquired secondary

education.

Figure 4.4: Distribution of Respondents by Highest Education Level

4.2.5 Distribution of Respondents by Years of Enterprise Existence

Figure 4.5 shows distribution of the respondents by the years of enterprise existence. The

figure shows 43% of the respondents had operated their business between 16 to 25 years

followed by 24% of the respondents had operated their business between 11 to 15 years

35

and 13% who had operated their business for a period of 26 years and above. The

respondents who had operated their business between 6-10 years were 16% while 4% had

operated their businesses below 5 years.

Figure 4.5: Distribution of Respondents by Years of Enterprise Existence

4.3 Effects of Relative Advantage on the Spread of Product Innovation among

Medium Sized Manufacturing Enterprises

This section sought to establish the relationship between relative advantage and the

spread of product innovation among medium sized manufacturing enterprises. Relative

advantage on the spread of product innovation was measured by innovation convenience,

cost effectiveness, time saved, application effectiveness and improvement of the

consumers’ activities. These characteristics measured how relative advantage affected the

diffusion of product innovation in the respondents’ businesses.

4.3.1 Descriptive Statistics for the Effects of Relative Advantage on the Spread of

Product Innovation among Medium Sized Manufacturing Enterprises

The frequencies and percentages were computed and the mean scores showed

respondents level of agreement or disagreement on effects of relative advantage on

diffusion of product innovation. The respondents were required to answer the questions

by indicating their opinion on statements given by using a 4-point Likert scale of 1-4,

where 1 = Strongly Disagree, 2 = Disagree, 3 = Agree and 4 = Strongly Agree.

Effectiveness of the score was denoted by mean scores of above 3.5.

36

Table 4.1 indicates that 49% of the respondents agreed that product innovation perceived

to lead to convenient application by consumers or customers diffused faster while 11% of

the respondents strongly disagreed with the statement (M= 3.81, SD= 0.65). The

respondents who agreed that product innovations perceived to be more time saving

diffused faster accounted for 65% while 5% of respondents disagreed with the statement

(M= 3.65, SD= 0.85).

Table 4.1: Descriptive Statistics for the Effects of Relative Advantage on Diffusion

of Product Innovation among Medium Sized Manufacturing Enterprises

Effects of Relative Advantage on the

Diffusion of Product Innovation in

Medium Sized Enterprises

%

1 2 3 4 Total M SD

Product innovations that are perceived to

lead to convenient application by

consumers or customers diffuse faster

% 11% 13% 49% 27% 100%

3.81 .653 f 10 12 47 26 95

Product innovations that are perceived to

be more cost effective diffuse faster

% 7% 3% 58% 33% 100% 3.84 .746

f 7 3 55 31 95

Product innovations that are perceived to

be more time saving diffuse faster

% 6% 5% 65% 23% 100% 3.65 .851

f 6 5 62 22 95

Product innovations that are perceived to

be more effective in their application

diffuse faster

% 1% 2% 75% 22% 100%

3.72 1.07 f 1 2 71 21 95

Product innovations that are perceived to

lead to improvement of consumers current

activities diffuse faster

% 5% 5% 59% 31% 100%

3.64 .665 f 5 5 56 29 95

4.3.2 Cross Tabulation of the Effects of Relative Advantage on the Spread of

Product Innovation among Medium Sized Manufacturing Enterprises by Gender

Cross tabulation was used to evaluate the views of the male and female on the effects of

relative advantage on the spread of product innovation among medium sized

manufacturing enterprises. Findings presented in Table 4.2 and Figure 4.6 indicate that

the proportion of male respondents who agreed and strongly agreed that relative

37

advantage on the spread of product innovation had an effect on medium sized

manufacturing enterprises accounted for 37% and 25% respectively compared to that of

female respondents which accounted for 30% and 50% respectively.

Table 4.2: Cross Tabulation of the Effects of Relative Advantage on the Spread of

Product Innovation among Medium Sized Manufacturing Enterprises by Gender

Strongly

Disagree Disagree Agree Strongly Agree Total

Male f 14 11 24 16 65

% 22% 17% 37% 25% 100%

Female f 3 3 9 15 30

% 10% 10% 30% 50% 100%

Total f 17 14 33 31 95

% 18% 15% 35% 33% 100%

Figure 4.6: Cross Tabulation for the Effects of Relative Advantage on the Spread of

Product Innovation among Medium Sized Manufacturing Enterprises by Gender

4.3.3 Correlation between Relative Advantage and Product Innovation among

Medium Sized Manufacturing Enterprises

Spearman’s Rank Correlation test was used to test the relationship between relative

advantage and the spread of product innovation among medium sized manufacturing

enterprises. The results in Table 4.3 indicates that there was a statistically significant

strong positive correlation between relative advantage and product innovations that are

38

perceived to be more cost effective diffuse faster among medium sized manufacturing

enterprises, r(95) = .188, p < .05.

Table 4.3: Correlation between Relative Advantage and Product Innovation among

Medium Sized Manufacturing Enterprises

Effects of Relative Advantage on the Diffusion of Product Innovation in

Medium Sized Enterprises

1 Product innovations that are

perceived to lead to convenient

application by consumers or

customers diffuse faster

Correlation Coefficient 1.000

Sig. (2-tailed) -

N 95

2 Product innovations that are

perceived to be more cost

effective diffuse faster

Correlation Coefficient .098

Sig. (2-tailed) .182

N 95

3 Product innovations that are

perceived to be more time

saving diffuse faster

Correlation Coefficient .188*

Sig. (2-tailed) .023

N 95

4 Product innovations that are

perceived to be more effective

in their application diffuse

faster

Correlation Coefficient .095

Sig. (2-tailed) .210

N 95

5 Product innovations that are

perceived to lead to

improvement of consumers

current activities diffuse faster

Correlation Coefficient .013

Sig. (2-tailed) .854

N 95

*.Correlation is significant at the 0.05 level (2-tailed) ( *p < .05)

4.3.4 A One Way Analysis of Variance (ANOVA) between Relative Advantage and

Product Innovation among Medium Sized Manufacturing Enterprises

One Way Analysis of Variance (ANOVA) test was carried out to establish if there were

significant differences between means in respondents’ perception on the effect of relative

advantage on the spread of product innovation among medium sized manufacturing

enterprises by gender, designation and years of enterprise existence. Table 4.4 presents

ANOVA findings which indicate that there was a statistically significant effect by gender

F(1, 94) = 4.10, p < .05 and the years of enterprise existence F(1, 94) = 5.56, p < .05.

39

However, there was no statistically significant effect for the designation F(2, 93) = 0.45, p

> .05.

Table 4.4: ANOVA between Effect of Relative Advantage and Product Innovation

among Medium Sized Manufacturing Enterprises by Gender, Designation and

Years of Enterprise Existence

Effect of Relative Advantage Sum of

Squares

df

Mean

Square

F

Sig.

Gender Between Groups 2.872 1 2.872 4.104 .031*

Within Groups 139.762 94 .655

Total 142.634 95

Designation Between Groups 1.278 2 1.278 .453 .461

Within Groups 123.112 93 .611

Total 124.39 95 Years of enterprise

existence

Between Groups 4.354 1 4.354 5.562 .022*

Within Groups 161.144 94 .783

Total 165.498 95 *p < .05 (*Correlation is significant at the 0.05 level (2-tailed)

4.3.5 Regression analysis between Relative Advantage and Product Innovation

among Medium Sized Manufacturing Enterprises

Linear regression was conducted to establish the extent to which relative advantage had

an effect on the spread of product innovation among medium sized manufacturing

enterprises.

4.3.5.1 Model Summary

The findings of the model summary presented in Table 4.5 (a) indicates that relative

advantage explained about 67.3% of the variability on the spread of product innovation

among medium sized manufacturing enterprises (R2= .673, F(1, 94) =15.28, p < .05) and

the strength of the relationship (r = .782).

40

Table 4.5 (a): Model Summary

Model R R Square Adjusted R Square Std. Error of the

Estimate

1 .782a 0.673 0.063 1.72684

a. Predictors: (Constant), Relative Advantage

*p < .05

4.3.5.2 Regression ANOVA

The linear regression test results presented in Table 4.5 (b) indicates that relative

advantage statistically predicted the spread of product innovation among medium sized

manufacturing enterprises F(1, 94) = 15.28, p < .05.

Table 4.5 (b): ANOVA

ANOVAa

Model

Sum of

Squares

df Mean Square

F

Sig.

1

Regression 14.114 1 19.544 15.281 0.013b*

Residual 256.561 94 1.004

Total 270.561 95

a. Dependent Variable: Product Innovation among medium sized manufacturing enterprises

b. Predictors: (Constant), Relative Advantage

*p < .05

4.3.5.3 Regression Coefficients

Regression coefficient findings presented in Table 4.5 (c) indicates that relative advantage

predicted the spread of product innovation among medium sized manufacturing

enterprises (B= .271, p < .05) which means that one unit of increase in relative advantage

would lead to an increase in the spread of product innovation among medium sized

manufacturing enterprises by a unit of 0.271. The general form of the linear regression

model equation that was established from the coefficients, as follows; product innovation

among medium sized manufacturing enterprises = 1.631 + 0.271 Relative Advantage.

41

Table 4.5 (c): Coefficient

Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients T Sig.

B Std. Error Beta

1

(Constant) 1.631 1.145 1.178 .000

Relative Advantage .271 .067 .145 2.687 .043*

a. Dependent Variable: Product Innovation among Medium Sized Manufacturing Enterprises

*p < .05

4.4 Effects of Compatibility on the Spread of Product Innovation among Medium

Sized Manufacturing Enterprises

This section sought to establish the relationship between compatibility and the spread of

product innovation among medium sized manufacturing enterprises from the respondents.

Compatibility on the spread of product innovation was measured by technological

innovation, consumers’ environment, business and government policies, consumers’

lifestyles and cultures and, industry acceptance. These measurements characterized how

compatibility had an effect on the spread of product innovation in the respondents’

businesses.

4.4.1 Descriptive Statistics for the Effects of Compatibility on the Spread of Product

Innovation among Medium Sized Manufacturing Enterprises

The frequencies and percentages were computed and the mean scores showed the

respondents’ level of agreement or disagreement on the effects of compatibility on the

spread of product innovation in their business. The respondents were required to answer

the questions by indicating their opinion on given statements using a 4-point Likert

scale of 1-4, where 1 = Strongly Disagree, 2 = Disagree, 3 = Agree and 4 = Strongly

Agree. Effectiveness of the score was denoted by mean scores of above 3.5.

Table 4.6 indicates that 64% of the respondents strongly agreed that product innovation

perceived by the consumers to be beneficial and compatible with their lifestyles or

cultures diffuse faster while 5% of the respondents strongly disagreed with the statement

(M= 3.66, SD= 1.09). The respondents who agreed that product innovations already

42

accepted in the business industry diffuse faster in the market accounted for 58% while 7%

of the respondents disagreed (M= 3.70, SD= 0.77).

Table 4.6: Descriptive Statistics for the Effects of Compatibility on the Spread of

Product Innovation among Medium Sized Manufacturing Enterprises

Effects of Compatibility on the

Diffusion of Product Innovation in

Medium Sized Enterprises

%f 1 2 3 4 Total M SD

Product innovations that are compatible

with the technological innovation

activities undertaken by consumers

diffuse faster

% 9% 14% 54% 23% 100%

3.62 .522

f 9 13 51 22 95

Product innovations that complement

consumers environment greatly affect

how fast innovations will diffuse in the

market

% 8% 5% 46% 40% 100%

3.71 .655

f 8 5 44 38 95

Product innovations that already have

business and government supportive

policies in place diffuse faster in the

market

% 4% 8% 60% 27% 100%

3.82 .764

f 4 8 57 26 95

Product innovations that are perceived by

the consumers to be beneficial and in line

with their lifestyles or cultures diffuse

faster

% 5% 5% 25% 64% 100%

3.66 1.09

f 5 5 24 61 95

Product innovations that are already

accepted in the business industry diffuse

faster in the market

% 5% 7% 58% 29% 100%

3.70 .773 f 5 7 55 28 95

4.4.2 Cross Tabulation of the Effects of Compatibility on the Spread of Product

Innovation among Medium Sized Manufacturing Enterprises by Gender

Cross tabulation was used to evaluate the views of the male and female on the effects of

compatibility on the spread of product innovation among medium sized manufacturing

43

enterprises. The findings presented in Table 4.7 and Figure 4.7 indicate that the

proportion of male respondents who agreed and strongly agreed that compatibility on the

spread of product innovation had an effect on medium sized manufacturing enterprises

accounted for 34% and 29% respectively compared to that of female respondents which

accounted for 33% and 47% respectively.

Table 4.7: Cross Tabulation of the Effects of Compatibility on the Spread of Product

Innovation among Medium Sized Manufacturing Enterprises by Gender

Strongly

Disagree Disagree Agree Strongly Agree Total

Male f 14 9 22 19 65

% 22% 14% 34% 29% 100%

Female f 2 4 10 14 30

% 7% 13% 33% 47% 100%

Total f 16 32 33 13 95

% 17% 14% 34% 35% 100%

Figure 4.7: Cross Tabulation for the Effects of Compatibility on the Spread of

Product Innovation among Medium Sized Manufacturing Enterprises by Gender

44

4.4.3 Correlation between Compatibility and Product Innovation among Medium

Sized Manufacturing Enterprises

Spearman’s Rank Correlation test was used to test the relationship between compatibility

and the spread of product innovation among medium sized manufacturing enterprises.

Table 4.8 results indicate that there was a significant strong positive correlation between

compatibility and product innovation, r (95) = .235, p < .05 and lifestyles or cultures,

r(95) = .213, p < .05 on the diffusion of product innovation among medium sized

manufacturing enterprises.

Table 4.8: Correlation between Compatibility and Production Innovation among

Medium Sized Manufacturing Enterprises

Effects of Compatibility on the Diffusion of Product Innovation in Medium

Sized Enterprises

1 Product innovations that are

compatible with the technological

innovation activities undertaken by

consumers diffuse faster

Correlation

Coefficient 1.000

Sig. (2-tailed) -

N 95

2 Product innovations that

complement consumers environment

greatly affect how fast innovations

will diffuse in the market

Correlation

Coefficient .235*

Sig. (2-tailed) .000

N 95

3 Product innovations that already

have business and government

supportive policies in place diffuse

faster in the market

Correlation

Coefficient .055

Sig. (2-tailed) .121

N 95

4 Product innovations that are

perceived by the consumers to be

beneficial and in line with their

lifestyles or cultures diffuse faster

Correlation

Coefficient .067

policies

Sig. (2-tailed) .220

N 95

5 Product innovations that are already

accepted in the business industry

diffuse faster in the market

Correlation

Coefficient .213*

Sig. (2-tailed) .002

N 95

*.Correlation is significant at the 0.05 level (2-tailed) ( p < .05)

45

4.4.4 A One Way Analysis of Variance (ANOVA) between Compatibility and

Product Innovation among Medium Sized Manufacturing Enterprises

One Way Analysis of Variance (ANOVA) test was carried out to establish if there were

significant differences between means in respondents’ perception on the effect of

compatibility on the spread of product innovation among medium sized manufacturing

enterprises by gender, designation and years of enterprise existence. Table 4.9 presents

ANOVA findings which indicate that there was a statistically significant difference by

gender F(1, 95) = 5.67, p < .05. However, there was no statistically significant effect by

the years of enterprise existence F(1, 95) = 0.35, p > .05 and the designation F(1, 95) =

0.46, p > .05.

Table 4.9: ANOVA between Effect of Compatibility and Product Innovation among

Medium Sized Manufacturing Enterprises by Gender, Designation and Years of

Enterprise Existence

Effect of Compatibility Sum of

Squares

df

Mean

Square

F

Sig.

Gender

Between Groups 3.116 1 3.116 5.672 .045*

Within Groups 123.889 94 .665

Total 127.005 95

Designation

Between Groups 2.776 1 2.776 1.303 .461

Within Groups 133.909 94 .521

Total 136.685 95

Years of enterprise

existence

Between Groups 3.283 1 3.807 5.562 .352

Within Groups 132.961 94 .976

Total 136.244 95 *p < .05 (Correlation is significant at the 0.05 level (2-tailed).

4.4.5 Regression analysis between Compatibility and Product Innovation among

Medium Sized Manufacturing Enterprises

Linear regression was conducted to establish the extent to which compatibility had an

effect on the spread of product innovation among medium sized manufacturing

enterprises.

4.4.5.1 Model Summary

The findings of the model summary presented in Table 4.10 (a) indicate that compatibility

explained about 54.4% of the variability on the spread of product innovation among

46

medium sized manufacturing enterprises (R2= .544, F(1, 94) =16.12, p < .05) and the

strength of the relationship (r = .594).

Table 4.10 (a): Model Summary

Model R R Square Adjusted R Square Std. Error of the

Estimate

1 .594a 0.544 0.045 1.52340

a. Predictors: (Constant), Compatibility

*p < .05

4.4.5.2 Regression ANOVA

Linear regression ANOVA results presented in Table 4.10 (b) indicates that compatibility

statistically predicted the spread of product innovation among medium sized

manufacturing enterprises F(1, 94) = 16.12, p < .05.

Table 4.10 (b): ANOVA

ANOVAa

Model

Sum of

Squares

df

Mean Square

F

Sig.

1

Regression 13.104 1 16.899 16.124 0.021b*

Residual 247.167 94 1.132

Total 260.271 95

a. Dependent Variable: Product Innovation among medium sized manufacturing enterprises

b. Predictors: (Constant), Compatibility

*p < .05

4.4.5.3. Regression Coefficient

Regressions coefficient findings presented in Table 4.10 (c) indicates that compatibility

predicted the spread of product innovation among medium sized manufacturing

enterprises (B= .354, p < .05) which means that one unit of increase in compatibility

would lead to an increase in the spread of product innovation among medium sized

manufacturing enterprises by a unit of 0.354. The general form from the coefficients of

the linear regression model equation that was established was as follows; product

47

innovation among medium sized manufacturing enterprises = 1.641 + 0.354

Compatibility.

Table 4.10 (c): Coefficient

Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients T Sig.

B Std. Error Beta

1

(Constant) 1.641 1.331 1.108 .000

Compatibility .354 .055 .231 2.117 .033* a. Dependent Variable: Product Innovation among medium sized manufacturing enterprises

*p < .05

4.5 Effects of Complexity on the Spread of Product Innovation among Medium

Sized Manufacturing Enterprises

This section sought to establish the relationship between complexity and the spread of

product innovation among medium sized manufacturing enterprises from the respondents.

Complexity on the spread of product innovation was measured by the ability to easily

understand the innovation, ease of usage, innovation’s clear communication, provision of

support resources and technical expertise of the consumers’. These characteristics

measured how complexity had an effect on the spread of product innovation in the

respondents’ businesses.

4.5.1 Descriptive Statistics for the Effects of Complexity on the Spread of Product

Innovation among Medium Sized Manufacturing Enterprises

The frequencies and percentages were computed and the mean scores showed the

respondents’ level of agreement or disagreement on the effects of complexity on the

spread of product innovation in their business. The respondents were asked to answer

the questions by indicating their opinion on given statements using a 4-point Likert

scale of 1-4, where 1 = Strongly Disagree, 2 = Disagree, 3 = Agree and 4 = Strongly

Agree. Effectiveness of the scores was denoted by mean scores of above 3.5.

48

Table 4.11 results indicate that 51% of the respondents strongly agreed that product

innovations that are perceived easy to understand by consumers diffuse faster while 6% of

the respondents strongly disagreed (M = 3.76, SD = 0.56). The respondents who agreed

that product innovations that have been communicated clearly to the consumers diffuse

faster accounted for 56% while 6% of the respondents disagreed (M = 3.61, SD = 1.12).

Table 4.11: Descriptive Statistics for the Effects of Complexity on the Spread of

Product Innovation among Medium Sized Manufacturing Enterprises

Effects of Complexity on the Diffusion

of Product Innovation in Medium Sized

Enterprises

%f 1 2 3 4 Total M SD

Product innovations that are perceived

easy to understand by consumers diffuse

faster

% 6% 15% 28% 51% 100%

3.76 .561

f 6 14 27 48 95

Product innovations that are simple and

easy to use diffuse faster in the market

% 7% 15% 43% 35% 100% 3.80 .675

f 7 14 41 33 95

Product innovations that have been

communicated clearly to the consumers

diffuse faster

% 5% 6% 56% 33% 100%

3.61 .809

f 5 6 53 31 95

Product innovations that provide

consumers with support resources on

usage diffuse faster

% 4% 12% 63% 21% 100%

3.73 1.12

f 4 11 60 20 95

Product innovations that consumers have

the technical expertise to handle diffuse

faster in the market

% 2% 9% 61% 27% 100%

3.56 .609 f 2 9 58 26 95

4.5.2 Cross Tabulation of the Effects of Complexity on the Spread of Product

Innovation among Medium Sized Manufacturing Enterprises by Gender

Cross tabulation was used to evaluate the views of the male and female on the effects of

complexity on the spread of product innovation among medium sized manufacturing

enterprises. Table 4.12 and Figure 4.8 present findings which indicate that the proportion

49

of female respondents who agreed and strongly agreed that complexity on the spread of

product innovation had an effect on medium sized manufacturing enterprises accounted

for 37% and 25% respectively compared to that of female respondents which accounted

for 30% and 50% respectively.

Table 4.12: Cross Tabulation of the Effects of Complexity on the Spread of Product

Innovation among Medium Sized Manufacturing Enterprises by Gender

Strongly

Disagree Disagree Agree Strongly Agree Total

Male f 6 7 23 29 65

% 9% 11% 35% 45% 100%

Female f 3 3 7 17 30

% 10% 10% 23% 57% 100%

Total f 9 10 30 46 95

% 9% 11% 32% 48% 100%

Figure 4.8: Cross Tabulation for the Effects of Complexity on the Spread of Product

Innovation among Medium Sized Manufacturing Enterprises by Gender

4.5.3 Correlation between Complexity and Product Innovation among Medium

Sized Manufacturing Enterprises

Spearman’s Rank Correlation test was used to test the relationship between complexity

and the spread of product innovation among medium sized manufacturing enterprises.

Table 4.13 results indicates that there was a significant strong positive correlation

50

between complexity and clear communication, r(95) = .163, p < .05 on the diffusion of

product innovation among medium sized manufacturing enterprises.

Table 4.13: Correlation between Complexity and Production Innovation among

Medium Sized Manufacturing Enterprises

Effects of Complexity on the Diffusion of Product Innovation in Medium

Sized Enterprises

1 Product innovations that are

perceived easy to understand

by consumers diffuse faster

Correlation Coefficient 1.000

Sig. (2-tailed) -

N 95

2 Product innovations that are

simple and easy to use diffuse

faster in the market

Correlation Coefficient .081

Sig. (2-tailed) .461

N 95

3 Product innovations that have

been communicated clearly to

the consumers diffuse faster

Correlation Coefficient .018

Sig. (2-tailed) .123

N 95

4 Product innovations that

provide consumers with

support resources on usage

diffuse faster

Correlation Coefficient .163*

Sig. (2-tailed) .001

N 95

5 Product innovations that

consumers have the technical

expertise to handle diffuse

faster in the market

Correlation Coefficient .013

Sig. (2-tailed) .854

N 95

*.Correlation is significant at the 0.05 level (2-tailed). (p < .05)

4.4.4 A One Way Analysis of Variance (ANOVA) between Complexity and Product

Innovation among Medium Sized Manufacturing Enterprises

One Way Analysis of Variance (ANOVA) test was carried out to establish if there were

significant differences between means in respondents’ perception on the effect of

complexity on the spread of product innovation among medium sized manufacturing

enterprises by gender, designation and years of enterprise existence. Table 4.14 ANOVA

findings presented indicate that there were statistically significant difference by years of

51

enterprise existence F(1, 95) = 4.56, p < .05. However, there was no statistically

significant effect by gender F(1, 95) = 0.28, p > .05 and designation F(1, 95) = 0.46, p >

.05.

Table 4.14: ANOVA between Effect of Complexity and Product Innovation among

Medium Sized Manufacturing Enterprises by Gender, Designation and Years of

Enterprise Existence

Effect of Compatibility Sum of

Squares

df

Mean

Square

F

Sig.

Gender Between Groups 3.223 1 3.223 3.118 .283

Within Groups 129.228 94 .544

Total 132.451 95

Designation Between Groups 2.309 1 2.309 .390 .461

Within Groups 133.197 94 .590

Total 135.506 95 Years of enterprise

existence

Between Groups 2.445 1 2.445 4.562 .042*

Within Groups 163.146 94 .652

Total 165.591 95 *p < .05 (Correlation is significant at the 0.05 level (2-tailed).

4.4.5 Regression analysis between Complexity and Product Innovation among

Medium Sized Manufacturing Enterprises

Linear regression was conducted to establish the extent to which complexity had an effect

on the diffusion of product innovation among medium sized manufacturing enterprises.

4.4.5.1 Model Summary

The findings of the model summary presented in Table 4.15 (a) indicates that complexity

explained about 52.2% of the variability on the spread of product innovation among

medium sized manufacturing enterprises (R2= .522, F(1, 94) =15.28, p < .05) and the

strength of the relationship (r = .704).

52

Table 4.15 (a): Model Summary

Model R R Square Adjusted R Square Std. Error of the

Estimate

1 .704a 0.522 0.063 1.40061

a. Predictors: (Constant), Complexity

*p < .05

4.4.5.2 Regression ANOVA

The linear regression ANOVA results presented in Table 4.5 (b) indicates that complexity

statistically predicted the diffusion of product innovation among medium sized

manufacturing enterprises F(1, 94) = 13.69, p < .05.

Table 4.15 (b): ANOVA

ANOVAa

Model

Sum of

Squares

df Mean Square

F

Sig.

1

Regression 18.109 1 14.303 13.691 0.046b*

Residual 257.502 94 1.223

Total 270.561 95

a. Dependent Variable: Product Innovation among medium sized manufacturing

enterprises b. Predictors: (Constant), Complexity

*p < .05

4.4.5.3 Regression Coefficient

Regressions coefficient findings presented in Table 4.5 (c) indicates that complexity

predicted the diffusion of product innovation among medium sized manufacturing

enterprises (B= .311, p < .05). This means that one unit of increase in complexity would

lead to an increase in diffusion of product innovation among medium sized manufacturing

enterprises by a unit of 0.311. From the coefficients, the general form of the linear

regression model equation that was established was as follows; product innovation among

medium sized manufacturing enterprises = 1.428 + 0.311 Complexity.

53

Table 4.15 (c): Coefficient

Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients T Sig.

B Std. Error Beta

1

(Constant) 1.428 1.301 1.190 .000

Complexity .311 .081 .301 2.611 .034*

a. Dependent Variable: Product Innovation among medium sized manufacturing enterprises

*p < .05

4.6 Chapter Summary

This chapter presents the summary of the major findings of the research questions. The

finding on the first research question indicates that in terms of the effects of relative

advantage on the diffusion of product innovation among medium sized manufacturing

enterprises, the proportion of male respondents who agreed that relative advantage had an

effect on diffusion of product innovation accounted for 37% while female respondents

accounted for 30%. The results of the Spearman Rank Correlation test indicated that there

was a significant positive correlation between relative advantage and product innovations

that are perceived to be more cost effective diffuse faster among medium sized

manufacturing enterprises, r(95) = .188, p < .05. One Way ANOVA test findings

indicated that there was a significant difference by gender F(1, 94) = 4.10, p < .05 and the

years of enterprise existence F(1, 94) = 5.56, p < .05. Linear regression analysis indicated

that relative advantage explained 67.3% of the variability on the spread of product

innovation among medium sized manufacturing enterprises, R2= .673 and statistically

significantly predicted the spread of product innovation, F(1, 94) =15.28, p < .05.

Relative advantage had a statistically significant effect on diffusion of product innovation

among medium sized manufacturing enterprises; B= 0.271, p < .05.

Concerning effect of compatibility on diffusion of product innovation among medium

sized manufacturing enterprises the findings indicated the proportion of female

respondents who strongly agreed that compatibility had an effect on the diffusion of

product innovation among medium sized manufacturing enterprises accounted for 47%

54

while male respondents were 29%. Spearman Rank Correlation test results showed that

compatibility was strongly correlated to product innovation, r(95) = .235, p < .05 and

lifestyles or cultures, r(95) = .213, p < .05 on the diffusion of product innovation among

medium sized manufacturing enterprises. Results from One Way ANOVA test showed

that there was a statistically significant difference by gender F(1, 95) = 5.67, p < .05. The

linear regression analysis indicated that compatibility explained 54.4% of the variability

in diffusion of product innovation among medium sized manufacturing enterprises, R2=

0.54 and statistically significantly had an effect on diffusion of product innovation among

medium sized manufacturing enterprise F(1, 94) = 16.12, p < .05.

Finally, findings from the third research question on the effects of complexity on

diffusion of product innovation among medium sized manufacturing enterprises

indicated, the proportion of female respondents who agreed that complexity had an effect

on diffusion of product innovation accounted for 37% while that of male respondents was

30%. Spearman Rank Correlation test indicated that complexity was significantly

correlated to clear communication, r(95) = .163, p < .05 on the diffusion of product

innovation among medium sized manufacturing enterprises. One Way ANOVA results

tests indicated that there was a statistically significant difference by years of enterprise

existence F(1, 95) = 4.56, p < .05. The linear regression analysis indicated that

complexity statistically significantly predicted the diffusion of product innovation among

medium sized manufacturing enterprises; R2= 0.52, F(1, 94) = 13.69, p < .05. Finding of

this study are discussed in the next chapter.

55

CHAPTER FIVE

5.0 SUMMARY, DISCUSSION, CONCLUSIONS AND RECOMMENDATION

5.1 Introduction

This chapter presents the summary of the study along, a detailed discussion of the results,

conclusions and the recommendations of the study. The discussion of the results,

conclusions, and recommendations for improvements are presented as per the study

research questions.

5.2 Summary

The purpose of this study was to determine the factors affecting diffusion of product

innovation in medium sized manufacturing enterprises in Kenya. The study was guided

by the following research questions; How does relative advantage affect the diffusion of

product innovation in medium sized manufacturing enterprises in Kenya? How does

compatibility affect the diffusion of product innovation in medium sized manufacturing

enterprises in Kenya? And lastly, how does complexity affect the diffusion of product

innovation in medium sized manufacturing enterprises in Kenya

Descriptive correlational research design was utilized by this study. The population

comprised one hundred and eight (108) medium sized manufacturing enterprises that had

been operating in the last nine years in Kenya at the time of the study. A stratified random

sampling technique was used to select a sample of one hundred and two (102) medium

sized manufacturing enterprises from the total population. A questionnaire was utilized to

collect data in this study. Descriptive and inferential statistics techniques were used to

analyze the data. The descriptive statistical analysis included frequencies and percentage

distributions, mean and standard deviation while the inferential statistical analysis

included Spearman’s Rank Correlation, One Way Aanalysis of Variance (ANOVA) and

Regression analysis. Finally, the data was analyzed through the Statistical Package for

the Social Sciences (SPSS) tool and results presented in tables and figures.

Findings on the first research question concerning the effect of relative advantage on

diffusion of innovation among medium sized manufacturing enterprises revealed that

56

male respondents who agreed that relative advantage had an effect on the diffusion of

product innovation accounted for 37% while that of female respondents accounted for

30%. Findings from Spearman Rank Correlation test indicated that there was a

statistically significant positive correlation between relative advantage and product

innovations that are perceived to be more cost effective diffuse faster among medium

sized manufacturing enterprises, r(95) = .188, p < .05. One Way ANOVA results revealed

that there was a statistically significant difference by gender F(1, 94) = 4.10, p < .05 and

the years of enterprise existence F(1, 94) = 5.56, p < .05. Linear regression analysis

indicated that relative advantage explained 67.3% of the variability on the spread of

product innovation among medium sized manufacturing enterprises, R2= .673 and

statistically significantly predicted the spread of product innovation, F(1, 94) =15.28, p <

.05.

Findings on the second research question concerning the effect of compatibility on

diffusion of product innovation among medium sized manufacturing enterprises, revealed

the proportion of female respondents who strongly agreed that compatibility had an effect

on the diffusion of product innovation accounted for 47% while male respondents were

29%. Spearman Rank Correlation test showed that compatibility was strongly correlated

to technological innovation, r(95) = .235, p < .05 and lifestyles or cultures, r(95) = .213,

p < .05 on the diffusion of product innovation among medium sized manufacturing

enterprises. One Way ANOVA revealed that there was a statistically significant

difference by gender F(1, 95) = 5.67, p < .05. The linear regression analysis indicated that

compatibility explained 54.4% of the variability in diffusion of product innovation among

medium sized manufacturing enterprises, R2= 0.54 and statistically significantly predicted

the spread of product innovation among the medium sized manufacturing enterprises,

F(1, 94) = 16.12, p < .05.

Lastly, findings of the third research question concerning the effect of complexity on

diffusion of product innovation among medium sized manufacturing enterprises, revealed

the proportion of female respondents who agreed that complexity had an effect on the

diffusion of product innovation accounted for 37% while that of male respondents was

30%. Spearman Rank Correlation test showed that complexity was significantly

correlated to clear communication, r(95) = .163, p < .05 on the diffusion of product

innovation among medium sized manufacturing enterprises. The results from One Way

57

ANOVA test indicated that there was a statistically significant difference by years of

enterprise existence F(1, 95) = 4.56, p < .05. The linear regression analysis indicated that

complexity explained 52.2% of the variability in diffusion of product innovation among

medium sized manufacturing enterprises, R2= 0.52 and statistically significantly predicted

the spread of product innovation among medium sized manufacturing enterprises, F(1,

94) = 13.69, p < .05.

5.3 Discussion

This section discusses the results of the study based on the study’s research questions.

The findings of the research are discussed in relation with the literature review.

5.3.1 Effects of Relative Advantage on the Spread of Product Innovation among

Medium Sized Manufacturing Enterprises

The results showed that 65% and 23% of the respondents agreed and strongly agreed

respectively that product innovations that are perceived to be more effective in their

application diffuse faster. This finding agrees with a study by Lu et al, (2013) who

acknowledged relative advantage as important determinant of product innovation

adoption among consumers. It is expected that if medium sized manufacturing enterprises

believe that product characteristics affect how products diffuse in the market, they will be

keen to manufacture products with great and additional benefits.

Spearman Rank Correlation test indicated that there was a statistically significant positive

correlation between relative advantage and product innovations that are perceived to be

more cost effective diffuse faster among medium sized manufacturing enterprises, r(95) =

.188, p < .05. The findings confirmed the findings of a study by Dixon et al. (2012) who

acknowledged that cost of product innovation is an important influencing factor in

diffusion of product innovation among enterprises. Innovations are highly likely to be

adopted if they are considered to save cost.

One Way ANOVA test established that there was a significant effect by gender F(1, 94) =

4.10, p < .05 and the years of enterprise existence F(1, 94) = 5.56, p < .05. The finding

contradicts the results of a study by Frimpong & Nwankwo (2012) which concluded that

gender differences do not play a role in the effect of relative advantage in diffusion of

58

product innovation among medium sized manufacturing enterprises. This variable puts

emphasis on relative advantage by gender on products offered and does not indicate much

difference in the spread of product innovation by gender. The contradiction between the

studies may be explained by potential differences in business situations facing business

owners targeting product innovation based on marketing activities and programs that are

geared towards determining the target market’s perceptions, behavior and buying

processes (Gardner, 2013).

Linear regression analysis showed that relative advantage explained 67.3% of the

variability on the spread of product innovation among medium sized manufacturing

enterprises, R2= .673 and significantly predicted the spread of product innovation among

medium sized manufacturing enterprises (B= 0.271, p < .05. These findings agree with a

study by Matlay and Weathead (2013) which found that the most important determinant

of the benefit derived from adopting a new product is the amount of improvement which

the product innovation offers over any previous product. This is to a great extent

determined by the extent to which there exist substitute older innovations that are fairly

close. New product innovations that provide better quality and solutions than similar

others also have better chance to diffuse in the market. Relative advantage of a new

product is one important factor for a new product to be recognized and ensure the

diffusion in the market and adoption by customers. This can be reinforced by raising

partners’ satisfaction, trust, and commitment, in addition to the effort in creating

competitive products which provide total solutions for partners leading to success of new

products spread (Hung et al, 2010).

5.3.2 Effects of Compatibility on the Spread of Product Innovation among Medium

Sized Manufacturing Enterprises

The findings showed that 54% and 23% of the respondents agreed and strongly agreed

respectively felt that product innovations that are compatible with the technological

innovation activities undertaken by consumers diffuse faster. This finding agrees with the

study by Huy (2012) that the key factor characterizing product innovation includes both

technical and organizational compatibility. Compatibility has been found to be a

significant determinant of diffusion and adoption because it deals with perception of the

importance of the innovation in performing various tasks presently and in future. For

instance, if new products are compatible with the traditional way of performing various

59

activities, with the existing values and mentality of the professionals, and with different

communication parts involving day-today operations and their future development, then a

higher rate of diffusion will occur (Cupolas, 2013).

Spearman Rank Correlation test showed that compatibility was strongly correlated to

lifestyles or cultures, r(95) = .213, p < .05 on the diffusion of product innovation among

medium sized manufacturing enterprises. These results confirm study findings by

Oliveira and Martins (2011) that an organization’s culture can be a major strength when it

is consistent with the succession and thus can be a powerful driving force in

implementation and spread of product innovations. Diffusion is supported, on one hand,

by social networks of influences where the influence process is carried out by direct

contact between individuals in personal networks. On the other hand, it is supported in the

social system trends at micro level (individual), based on the exposure level, the

individual characteristics evolution and on the factors that influence decision making.

Issues surrounding individual persons are an important aspect of the diffusion of product

innovations. Individual factors based on demographics such as age and genders have an

effect on the spread of innovation (Mourad & Tolba, 2014).

The One Way ANOVA test revealed that there was a statistically significant difference by

gender F(1, 95) = 5.67, p < .05. The findings are similar to the study by Al-Qirim (2013)

which established that innovations developed by female managers that are compatible

with the intended adopters' values, norms, and perceived needs are more readily diffuse.

Lack of compatibility not only makes potential adopters lose their confidence to the

product innovation but also leads to the failure of innovation diffusion.

Linear regression analysis showed that compatibility explained 54.4% of the variability in

diffusion of product innovation among medium sized manufacturing enterprises R2= 0.54

and statistically significantly predicted the spread of product innovation among medium

sized manufacturing enterprises. The findings are in support of the research by Rogers

(2003) who emphasized that products that are recently launched in the market diffuse

faster in comparison with the situation several years ago. This fact is a result of the

emergence of different players in the market. One of the main players that accelerate the

diffusion process is the opinion leader. Opinion leaders are identified as having greater

access to mass media as well as interpersonal networks in comparison with their

followers. In addition, they are perceived as having higher socio-economic status and

60

tendency to adopt new innovative ideas before their followers. It has been empirically

proved that opinion leaders influence the consumer decision-making process through

spreading positive word of mouth. This relationship was supported by the diffusion

research, which highlight that opinion leaders influence the evaluation of new innovation

and hence they affect the rate of diffusion (Dearing, 2012).

5.3.3 Effects of Complexity on the Spread of Product Innovation among Medium

Sized Manufacturing Enterprises

The findings showed that 51% and 28% of the respondents strongly agreed and agreed

that product innovations that are perceived easy to understand by consumers diffuse

faster. The findings are in line with a study by Rogers (2003) who found out that new idea

that are simpler to understand by members of a social system diffuse more rapidly than

innovations that require the adopter to develop new skills and understanding. A low level

of complexity leads to higher diffusion rate or complexity increases the rate of rejection.

Medium sized enterprises differ from large companies’; Bahaddad et al. (2013) argued

that they focus on simple methods to manage their operations that require less advanced

technical expertise and a smaller number of professionals who depend on the owner or

executive director for solutions. These enterprises follow simple management form and

avoid complex governance and complex innovations that are difficult to understand.

Spearman Rank Correlation test showed that complexity was significantly correlated to

clear communication, r(95) = .163, p < .05 on the diffusion of product innovation among

medium sized manufacturing enterprises. This is consistent with the findings by Yung

and Shin (2013) that people obtain new information and adopt new technology when they

have just a contact with others who already had the new product while in game-theoretic

models, a user adopts a new product innovation only if the product (behavior) maximizes

her utility, which increases with the number of neighbors who adopt the same innovation.

The One Way ANOVA test revealed that there was a statistically significant difference by

years of enterprise existence F(1, 95) = 4.56, p < .05. The findings are similar to the study

by Oliviera and Martins (2011) that clearly indicated that relationships and coordination

comes about from vast enterprise operational existence between workers in medium sized

enterprises supported the diffusion process. Therefore, diffusion of innovation mandates

joint efforts integrated with years of business existence and expertise from both the

61

organization and its consumers in the market place. Creating platforms for innovation

diffusion and adoption involves funding and commitment.

Linear regression analysis indicated that complexity explained 52.2% of the variability in

diffusion of product innovation among medium sized manufacturing enterprises, R2= 0.52

and statistically significantly predicted the spread of product innovation among medium

sized manufacturing enterprises, F(1, 94) = 13.69, p < .05. These findings are in line with

a study by Mahazir and Mohd (2012) who acknowledged that perceived complexity is a

vital factor influencing the diffusion of innovation and that the likelihood of diffusion of

innovation is inversely related to the perceived complexity variable especially amongst

the medium sized enterprises. The introduction of new product might require the

consumers to develop new skills in order to use the new innovation. The measurement of

perceived complexity or ease of use can be in the context of how innovation can be easily

controlled, the degree of flawlessness, reasonableness, and adaptability to changes, user

friendliness, and how easy it is for one to become skilful in using the product. Al-Qirim

(2013) found an inverse relationship between the likelihood of diffusion and perception of

innovation complexity.

5.4 Conclusions

The following conclusions were made from the major findings discussion:

5.4.1 Effects of Relative Advantage on the Spread of Product Innovation among

Medium Sized Manufacturing Enterprises

Regression analysis revealed that there was a significant positive relationship between

relative advantages on the spread of product innovation among medium sized

manufacturing enterprises. The findings indicated that relative advantage predicted about

67.3% of the variability on the spread of product innovation among medium sized

manufacturing enterprises (R2= .673, F(1, 94) =15.28, p < .05). Based on these findings,

the study concludes that relative advantage based on job effectiveness, economic

profitability, time, cost, effort, convenience and efficiency results in faster diffusion of

product innovation among medium sized manufacturing enterprises.

62

5.4.2 Effects of Compatibility on the Spread of Product Innovation among Medium

Sized Manufacturing Enterprises

Findings from the regression analysis revealed that there was a significant positive

relationship between compatibility on the spread of product innovation among medium

sized manufacturing enterprises. The findings indicated that compatibility predicted about

54.4% of the variability on the spread of product innovation among medium sized

manufacturing enterprises (R2= .544, F(1, 94) =16.12, p < .05). Based on these findings,

the study concludes that technological, environmental, organizational and individual

factors compatible with the intended adopters' values, norms, and perceived needs diffuse

in the market.

5.4.3 Effects of Complexity on the Spread of Product Innovation among Medium

Sized Manufacturing Enterprises

Findings from the regression analysis revealed that there was a significant positive

relationship between complexity on the spread of product innovation among medium

sized manufacturing enterprises. The findings indicated that complexity predicted about

52.2% of the variability on the spread of product innovation among medium sized

manufacturing enterprises (R2= .522, F(1, 94) =15.28, p < .05). In technology-intensive

industries, the pace and complexity of product innovations changes create many

uncertainties for organizations which in turn, force organizations to innovate continuously

to be competitive. Some of these innovations are in the manufacturing of communication

equipment, computers and other electronic equipment. Therefore, the study concludes

that the level of understanding, communication, technical and social skills plays a critical

role towards faster diffusion of product innovation among medium sized manufacturing

enterprises.

5.5 Recommendations

The following are recommendations for the factors affecting diffusion of product

innovation among medium sized manufacturing enterprises in Kenya.

63

5.5.1 Recommendations for Improvement

5.5.1.1 Effects of Relative Advantage on the Spread of Product Innovation among

Medium Sized Manufacturing Enterprises

From the study, it is clear that medium sized manufacturing enterprises have relative

advantage of innovation and its positive relation with diffusion speed hence, the greater

the perceived relative advantage of an innovation the faster its diffusion. The study

recommends that the medium sized manufacturing enterprises should establish a unique

and convenient product innovation strategy to tap into the market as well as identify

alternative methods of promoting innovative products that are easy to use in order to grow

and gain competitive advantage in the long run. This is because the number of potential

adopters will increase over time, expanding the size of the adopting population to a

particular product innovation.

5.5.1.2 Effects of Compatibility on the Spread of Product Innovation among Medium

Sized Manufacturing Enterprises

The study established that innovations that are compatible with the intended adopters'

values, norms, and perceived needs are diffuse easily. Enterprises that serve broader

markets with greater market scope are more likely to have their innovations diffuse

rapidly; the reasonable argument for this observation is that if the innovation directly

affects the competition and consumers, then the adopter will have an incentive to take up

the innovation. Thus, the study recommends that owners and managers of medium sized

manufacturing enterprises should focus on participating in enterprise development

programs that will improve technological, environmental, organizational and individual

factors skills and abilities in order for product innovations to diffuse faster.

5.5.1.3 Effects of Complexity on the Spread of Product Innovation among Medium

Sized Manufacturing Enterprises

The study findings revealed that in order for medium sized manufacturing enterprises

product innovations to diffuse faster, new ideas that are simpler to understand by

members of a social system need to be implemented. Therefore, the study recommends

that owners and managers of medium sized manufacturing enterprises should perceive

64

ease of use of innovative products as powerful in explaining satisfaction of consumer

needs and wants.

5.5.2 Recommendations for Further Studies

This study suggests that further research should be conducted to investigate the impact of

access to diffusion of product innovation on the growth of medium sized manufacturing

enterprises in Kenya due to the study’s limitation on diffusion of product innovation

effects on enterprises development.

65

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APPENDICES

APPENDIX 1: COVER LETTER

Betty Mbaya,

United States International University-Africa,

P.O. Box 14634 - 00800,

Nairobi, Kenya

Dear Respondent,

RE: REQUEST FOR YOUR PARTICIPATION IN MY RESEARCH PROPOSAL

My name is Betty Mbaya, currently pursuing a course towards completion of Master of

Business Administration from United States International University – Africa. I am

conducting a study on Factors Affecting the Diffusion of Product Innovation in Medium

Sized Manufacturing Enterprises in Kenya which is in partial fulfillment of the

requirements of the award of the degree.

You have been selected to participate in this study by filling in the attached questionnaire.

The findings of the study will provide an incentive for the Medium Sized Enterprises and

stakeholders to develop appropriate interventions with the potential to enhance the uptake

of innovation in a cost effective manner. The findings will also help policy makers in

understanding determinants of diffusion which will be critical in designing policies that

will help providers to deliver appropriate innovations that will be more attractive to

potential consumers.

The information you provide will be treated as confidential, and will only be used for

academic purpose of this research. Kindly spare a few minutes of your time to fill in the

blanks of the attached list of questions to the best of your knowledge. For further

questions you are free to contact me on +254 (0) 722 558 742.

Thank you for your time

Yours faithfully,

Betty Mbaya

72

APPENDIX 2: QUESTIONNAIRE

Introduction

The purpose of this study is to determine the factors that affect the diffusion of Product

Innovation in Medium Sized Manufacturing Enterprises in Kenya. Kindly fill the

questions as accurately as you possible by ticking boxes or writing in the spaces provided.

Your response will be appreciated.

SECTION I: GENERAL INFORMATION

1. Gender: Male Female

2. Age Below 20 years 20- 29 years

30- 39 years Above 40 years

3. What is your designation?

Owner Manager

Others

(specify………………………………………………..)

4. Your highest level of education?

None Primary Secondary

BSc degree Master’s degree PhD

5. How many years has the enterprise been in existence?

Below 5 years 6 – 10 years

11 – 15 years 15 – 25 years

Above 26 years

73

SECTION 2: EFFECTS OF RELATIVE ADVANTAGE ON THE SPREAD OF

PRODUCT INNOVATION AMONG MEDIUM SIZED MANUFACTURING

ENTERPRISES

Diffusion is a type of communication that is concerned with the spread of new ideas. It is

the process by which an innovation is communicated among the members of a social

system through certain channels over time.

Relative advantage is the extent to which an innovation is perceived to be better than the

ideas it takes over from or the degree to which an innovation is perceived to be more cost

effective, convenient, efficient or improves existing applications and practices.

Indicate by ticking (√) the cell which closely reflects the extent to which relative

advantage has influenced diffusion of product innovations in your organization.

Use a scale of 1- 4 where: 1 = Strongly Disagree (SD), 2 = Disagree (D), 3 = Agree (A), 4

= Strongly Agree (SA)

Effects of Relative Advantage on the Diffusion

of Product Innovation in Medium Sized

Enterprises

Str

on

gly

Dis

agre

e

Dis

agre

e

Agre

e

Str

on

gly

Agre

e

(1) (2) (3) (4)

6. Product innovations that are perceived to lead to

convenient application by consumers or

customers diffuse faster

7. Product innovations that are perceived to be more

cost effective diffuse faster

8. Product innovations that are perceived to be more

time saving diffuse faster

9. Product innovations that are perceived to be more

effective in their application diffuse faster

10. Product innovations that are perceived to lead to

improvement of consumers current activities

diffuse faster

74

SECTION 3: EFFECTS OF COMPATIBILITY ON THE SPREAD OF PRODUCT

INNOVATION AMONG MEDIUM SIZED MANUFACTURING ENTERPRISES

Diffusion is a type of communication that is concerned with the spread of new ideas. It is

the process by which an innovation is communicated among the members of a social

system through certain channels over time.

Compatibility is the extent to which an innovation is perceived to be consistent with

potential adopters past experiences, needs and values.

Indicate by ticking (√) the cell which closely reflects the extent to which compatibility

has influenced diffusion of product innovations in your organization.

Use a scale of 1- 4 where: 1 = Strongly Disagree (SD), 2 = Disagree (D), 3 = Agree (A), 4

= Strongly Agree (SA)

Effects of Compatibility on the Diffusion of

Product innovation in Medium Sized

Manufacturing Enterprises S

tron

gly

Dis

agre

e

Dis

agre

e

Agre

e

Str

on

gly

Agre

e

(1) (2) (3) (4)

11. Product innovations that are compatible with the

technological innovation activities undertaken by

consumers diffuse faster

12. Product innovations that complement consumers

environment greatly affect how fast innovations

will diffuse in the market

13. Product innovations that already have business and

government supportive policies in place diffuse

faster in the market

14. Product innovations that are perceived by the

consumers to be beneficial and in line with their

lifestyles or cultures diffuse faster

15. Product innovations that are already accepted in

the business industry diffuse faster in the market

75

SECTION 4: EFFECTS OF COMPLEXITY ON THE SPREAD OF PRODUCT

INNOVATION AMONG MEDIUM SIZED MANUFACTURING ENTERPRISES

Diffusion is a type of communication that is concerned with the spread of new ideas. It is

the process by which an innovation is communicated among the members of a social

system through certain channels over time.

Complexity is the extent to which an innovation is perceived as complicated to

understand and use.

Indicate by ticking (√) the cell which closely reflects the extent to which complexity has

influenced diffusion of product innovations in your organization.

Use a scale of 1- 4 where: 1 = Strongly Disagree (SD), 2 = Disagree (D), 3 = Agree (A), 4

= Strongly Agree (SA)

Effects of Complexity on the Diffusion of

Product Innovation among Medium Sized

Manufacturing Enterprises S

tron

gly

Dis

agre

e

Dis

agre

e

Agre

e

Str

on

gly

Agre

e

(1) (2) (3) (4)

16. Product innovations that are perceived easy to

understand by consumers diffuse faster

17. Product innovations that are simple and easy to

use diffuse faster in the market

18. Product innovations that have been

communicated clearly to the consumers diffuse

faster

19. Product innovations that provide consumers with

support resources on usage diffuse faster

20. Product innovations that consumers have the

technical expertise to handle diffuse faster in the

market

‘THANK YOU’

76

APPENDIX 3: TOP 100 MEDIUM SIZED MANUFACTURING ENTERPRISES

No Organization

1 Alpha Dairy Products Ltd

2 Alpha Fine Foods Ltd

3 Alpha Medical Manufactures Ltd

4 Alpine Coolers Ltd

5 Astral Industries Ltd

6 Axel Engineering and Manufacturing Ltd

7 Bio Food Products Ltd

8 Biodeal Laboratories Ltd

9 Blowplast Ltd

10 Brollo Kenya Ltd

11 Canon Aluminium Fabricators Ltd

12 Canon Chemicals Ltd

13 Chuma Fabricators Limited

14 Complast Industries Ltd

15 Coninx Industries Ltd.

16 Continental Products

17 Crown Foods Ltd

18 Dawa Ltd

19 Deepa Industries Limited

20 Desbro Engineering Ltd

21 Dune Packaging Limited

22 East African Canvas co. Ltd

23 Economic Industries Limited

24 Edcor Kenya Ltd

25 Emmerdale Ltd

26 Eurocon Tiles Products Limited

27 Fayaz Bakers Limited

28 Furniture Rama Ltd

29 General Aluminium Fab Ltd

30 Glacier Products Ltd

31 Hebatullah Brothers Limited

32 Henkel Chemicals E.A

33 Heritage Foods Kenya Ltd

34 Ideal Manufacturing Co. Ltd

35 Impala Glass Industries Ltd

36 Interconsumer Products Ltd

77

37 Iron Art Limited

38 Izmir Enterprises Limited

39 Jungle Macs Epz Ltd

40 Kamili Packers Limited

41 Lema (E.A) Ltd

42 Kenapen Industries Ltd

43 Kenbro Industries Limited

44 Kenwest Cables Ltd

45 Kenya Builders & Concrete Co Ltd

46 Kenya Highland Seed Co Ltd

47 Kenya Suitcase Mfg Ltd

48 Kenya Sweets Ltd

49 Kevian Kenya Ltd

50 Kinpash Enterprises Limited

51 Madhupaper Kenya Ltd

52 Manji Food Industries Ltd

53 maroo polymers limited

54 Master Fabricators Ltd

55 Melvin Marsh International Ltd

56 Millbrook Garment

57 Mombasa Canvas Ltd

58 Mukurweini-Wakulima Dairy Ltd

59 Muringa Holdings Ltd

60 Napro Indutries Ltd

61 Nationwide Electrical Industries Ltd

62 Ndugu Transport Co. Ltd

63 Norda Industries Limited

64 Oasis Ltd

65 Orange Pharma Ltd

66 Orbit Engineering Limited

67 Packaging Manufacturers (1976) Ltd

68 Palmhouse Dairies Ltd

69 Panesars Kenya Limited

70 Pelican Signs Ltd

71 Pharmaken Limited

72 Polytanks Limited

78

73 Premier Industries Ltd

74 Print Fast (K) Ltd

75 Propack Kenya Ltd

76 R & R Plastics Limited

77 Ramco Printing Works Ltd

78 Reliable Concrete Works

79 Shade Systems E.A Ltd

80 Sheffield Steel Systems Limited

81 Sigma Feeds Ltd

82 Sigma Suppliers Ltd

83 Sollatek Electronics (K) Ltd

84 Specialized Aluminum Renovators Limited

85 Spice World Limited

86 Spenomatic Kenya Limited

87 Spry Engineering Co. Ltd

88 Stitch Masters Ltd

89 Sunpower Products Ltd

90 Superfoam Ltd

91 The Breakfast Cereal Company (K) Ltd

92 Thika Cloth Mills Ltd

93 Thika Wax Works Ltd

94 Tiger Brands Kenya Ltd

95 Tissue Kenya Ltd

96 Tononoka Rolling Mills Ltd

97 Tropikal Brands A Ltd

98 Trufoods Ltd

99 Tyremasters Ltd

100 Vajas Manufacturers Ltd

101 Viro Locks Ltd

102 Vitafoam Products Ltd

103 Vivek Investments Ltd

104 Warren Concrete Ltd

105 Warren Enterprise Ltd

106 Wartsila East Africa Ltd

107 Wines of The World Ltd

108 Zaverchand Punja Limited

79