AN EXPLORATORY STUDY ON THE INTENTION TO ADOPT …

AN EXPLORATORY STUDY ON THE INTENTION

TO ADOPT INTERNET OF THINGS IN WEATHER

FORECASTING BY THE KENYA

METEOROLOGICAL DEPARTMENT

BY

DAISY SHAGA NDANYI

UNITED STATES INTERNATIONAL UNIVERSITY

SPRING 2018

ii

AN EXPLORATORY STUDY ON THE INTENTION

TO ADOPT INTERNET OF THINGS IN WEATHER

FORECASTING BY THE KENYA

METEOROLOGICAL DEPARTMENT

BY

DAISY SHAGA NDANYI

A Project Report Submitted to the School of Science

and Technology in Partial Fulfillment of the

Requirement for the Degree of Master of Science in

Information Systems and Technology

UNITED STATES INTERNATIONAL UNIVERSITY

SPRING 2018

iii

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:

Daisy Shaga Ndanyi (ID No 649179)

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

supervisor.

Signed: Date:

Dr. Silvester A. Namuye

Signed: Date:

Dean, School of Science and Technology

iv

COPYRIGHT

All Rights Reserved. No part of this project may be photocopied, recorded or otherwise

reproduced, stored in retrieval system or transmitted in any electronic or mechanical means

without prior permission of USIU-A or the author.

Copyright © 2018 Daisy S. Ndanyi.

v

ABSTRACT

Internet of Things has been described as a system of related computing devices, machines,

animals or people that are provided with unique identifiers to capture and transfer data over

a network without requiring human intervention or human-to-computer interaction. It has

been used in several fields such as surveillance, tracking and weather forecasting. Weather

forecasting is the process by which the state of the atmosphere and the weather conditions

are predicted for some future period. Weather forecasting is important for individuals and

organizations. Accuracy of weather forecasts can tell a resident in a coastal area of the

impending danger when a hurricane might strike, an airport tower controller of what

information should be sent to planes that are landing or taking off and a farmer of the best

time to plant.

The purpose of this study was to investigate the challenges of the current weather

forecasting practices in Kenya with a view to address them by creating a framework that

would enable the adoption of Internet of Things (IoT) technology. This study used

descriptive research design methodology to meet its objectives; it focused on developing a

framework for the adoption of IoT by the Kenya Meteorological Department. The study

found that the current weather forecasting practices in Kenya were not satisfactory. The

challenges identified in weather forecasting were: few weather stations especially at the

county level, lack of funding to carry out projects that will enhance weather forecasting

practices, lack of adequate staff some of whom work 24 hours thus affecting their

efficiency, the current systems that cannot measure cloud cover accurately and insecurity

of their automated weather stations. The study revealed that the various benefits that would

be derived from the use of IoT were: sensing accuracy, large area coverage, minimal human

interaction, sensor nodes can be deployed in harsh environments that make the sensor

networks more effective, fault tolerance, transmission of real-time data and dynamic sensor

scheduling. This research study therefore suggested a possible solution to alleviate the

technological challenges currently faced by the meteorological department of Kenya which

were the adoption of a framework that will enable successful adoption of Internet of Things

for weather forecasting, purchase of new equipment for weather forecasting and training of

the users on how to use IoT.

Keywords: Factor Analysis, IoT, WSN, Weather Forecasting, Framework

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ACKNOWLEDGEMENT

Foremost, my utmost gratitude goes to the Lord Almighty for blessing me with the

capability to undertake this task and accomplish it well.

I would like to thank the Kenya Meteorological Department for their support this far in

enabling me to conduct the research in their organization.

I would wish to express my extreme gratitude to my supervisor Dr S. Namuye for his

professional support and guidance throughout the study.

I would also like to thank my classmates as well as my Msc. IT lecturers, who have given

me guidance in various capacities.

I am indebted to so many individuals, institutions and organization for their contribution

and support towards the successful completion of this research project. It may not be

possible to mention all by names. Please accept my sincere appreciation and gratitude.

vii

DEDICATION

This research project is dedicated to my beloved mother, Nivah Mulaya.

viii

TABLE OF CONTENTS

STUDENT’S DECLARATION ....................................................................................... iii

COPYRIGHT .................................................................................................................... iv

ABSTRACT ........................................................................................................................ v

ACKNOWLEDGEMENT ................................................................................................ vi

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

TABLE OF CONTENTS ............................................................................................... viii

LIST OF TABLES ........................................................................................................... xii

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

LIST OF ABBREVIATIONS ........................................................................................ xiv

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

1. INTRODUCTION ...................................................................................................... 1

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

1.2. Statement of the Problem ...................................................................................... 2

1.3. Purpose of the Study ............................................................................................. 3

1.3.1. General Objective .......................................................................................... 3

1.3.2. Specific Objectives ........................................................................................ 3

1.4. Significance of the Study ...................................................................................... 4

1.5. Scope of the Study................................................................................................. 4

1.6. Definition of Terms ............................................................................................... 4

1.7. Chapter Summary .................................................................................................. 5

CHAPTER TWO ............................................................................................................... 6

2. LITERATURE REVIEW .......................................................................................... 6

2.1. Introduction ........................................................................................................... 6

2.2. Theoretical Foundation ......................................................................................... 6

2.2.1. Expectation Confirmation Theory ................................................................. 6

2.2.2. Delone and McLean IS Success Model ......................................................... 7

ix

2.2.3. Diffusion of Innovations Theory ................................................................... 8

2.2.4. Extended Technology Acceptance Model (TAM) Theory ............................ 8

2.3. Current Weather Forecasting Practice in Kenya ................................................... 9

2.4. Internet of Things ................................................................................................ 11

2.4.1. Overview ...................................................................................................... 11

2.4.2. Architecture of IoT Technology .................................................................. 13

2.4.3. Security Concerns of Internet of Things Technology .................................. 14

2.5. Evaluation of Application Areas of Internet of Things Technology ................... 16

2.5.1. Application Areas in Africa ......................................................................... 16

2.5.2. Events Reporting .......................................................................................... 16

2.5.3. Environmental Applications Worldwide ..................................................... 17

2.5.4. Factors that Affect Implementation of Technologies in Organizations ....... 18

2.6. Conceptual Framework ....................................................................................... 19

2.7. Chapter Summary ................................................................................................ 22

CHAPTER THREE ......................................................................................................... 23

3. METHODOLOGY ................................................................................................... 23

3.1. Introduction ......................................................................................................... 23

3.2. Research Design .................................................................................................. 23

3.3. Population and Sampling Design ........................................................................ 23

3.3.1. Target Population ......................................................................................... 23

3.3.2. Sampling Design and Sample Size .............................................................. 23

3.4. Data Collection Methods ..................................................................................... 24

3.4.2. Reliability Analysis ...................................................................................... 25

3.5. Research Procedures ........................................................................................... 25

3.6. Data Analysis Methods ....................................................................................... 26

3.7. Chapter Summary ................................................................................................ 27

CHAPTER FOUR ............................................................................................................ 28

x

4. MODEL ..................................................................................................................... 28

4.1. Introduction ......................................................................................................... 28

4.2. Analysis ............................................................................................................... 28

4.3. Modelling and Design ......................................................................................... 28

4.4. Proof of Concept ................................................................................................. 30

4.5. Chapter Summary ................................................................................................ 33

CHAPTER FIVE ............................................................................................................. 34

5. RESULTS AND FINDINGS .................................................................................... 34

5.1. Introduction ......................................................................................................... 34

5.2. Demographic Data ............................................................................................... 34

5.3. Technological Challenges Faced by KMD on the Current Weather Forecasting

Practices ......................................................................................................................... 36

5.4. Framework for the Adoption of IoT in Weather Forecasting Practices .............. 37

5.5. Evaluation of the Framework for the Adoption of IoT in Weather Forecasting

Practices by Kenya Meteorological Department ............................................................ 43

5.5.1. Introduction .................................................................................................. 43

5.5.2. Factor Analysis ............................................................................................ 43

5.6. Chapter Summary ................................................................................................ 48

CHAPTER SIX ................................................................................................................ 49

6. DISCUSSION, CONCLUSION AND RECOMMENDATIONS ......................... 49

6.1. Introduction ......................................................................................................... 49

6.2. Summary ............................................................................................................. 49

6.3. Discussion ........................................................................................................... 49

6.3.1. Technological Challenges Faced by KMD on the Current Weather

Forecasting Practices .................................................................................................. 50

6.3.2. Framework for the Adoption of IoT in Weather Forecasting Practices ....... 50

6.3.3. Evaluation of the Framework for the Adoption of IoT in Weather

Forecasting Practices by Kenya Meteorological Department .................................... 52

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6.4. Conclusion ........................................................................................................... 53


Forecasting Practices .................................................................................................. 53

6.4.2. Framework for the Adoption of IoT in Weather Forecasting Practices ....... 53

6.4.3. Evaluation of the Framework In Relation To the Adoption of IoT in

Weather Forecasting Practices by Kenya Meteorological Department ...................... 53

6.5. Recommendations and Future Work ................................................................... 54

6.5.1. General Recommendations .......................................................................... 54

6.5.2. Recommendations for Further Work ........................................................... 54

REFERENCES ................................................................................................................. 55

APPENDICES .................................................................................................................. 61

APPENDIX I: QUESTIONNAIRE ................................................................................ 61

APPENDIX II: WEATHER FORECASTING INSTRUMENTS ............................... 68

xii

LIST OF TABLES

Table 3.1: Operationalization of Variables………………………………........…......…..26

Table 4.1: Proposed Framework Variables ........................................................................ 29

Table 5.1: Opinion on Perceived Ease of Use of Internet of Things…………...………...37

Table 5.2: Opinion on Perceived usefulness of Internet of Things…………..…………..38

Table 5.3: Opinion on Behavioral Intention of Internet of Things…………………....…39

Table 5.4: Opinion on Observability of Internet of Things…………………..…...……...40

Table 5.5: Opinion on Relevance of Internet of Things…………………..………...……41

Table 5.6: Opinion on System Quality of Internet of Things…………………..…...……42

Table 5.7: Opinion on Compatibility of Internet of Things…………………..……..…...42

Table 5.8: Communalities…………………………………………..………………..…..44

Table 5.9: Component Matrix…………………………………..………………..………45

Table 5.10: Total Variances……………………………………..……………………….47

xiii

LIST OF FIGURES

Figure 2.1: Expectation Confirmation Theory…………………………………………....7

Figure 2.2: Delone and McLean IS success model……………………………..………...7

Figure 2.3: Architecture of IoT……………………………………………......…......…..13

Figure 2.4: Taipei Weather Science Learning Network Architecture……....…...............18

Figure 2.5: The Proposed Conceptual Framework for the Adoption of IoT.........…........20

Figure 4.1: Model for the Adoption of IoT………………………………….........……..30

Figure 4.2: Validated framework for Adoption of Internet of Things…………...…........31

Figure 5.1: Various Divisions at KMD………………………………………...……......34

Figure 5.2: Length of Time In the Organization………………………………………...35

Figure 5.3: Distribution of Respondent by Gender…………………………………...…35

Figure 5.4: Age Bracket of the Respondents…………………………………………....36

Figure 5.5: Level of Education……………………..…………………….……………..36

Figure 5.6: Opinion on Perceived Ease of Use of Internet of Things …………………..38

Figure 5.7: Opinion on Perceived usefulness of Internet of Things …………………....39

Figure 5.8: Opinion on Behavioral Intention of Internet of Things ………………........40

Figure 5.9: Opinion on Observability of Internet of Things………...…….…………....41

Figure 5.10: Opinion on Relevance of Internet of Things .………………………….....41

Figure 5.11: Opinion on System Quality of Internet of Things…………………...........42

Figure 5.12: Opinion on Compatibility of Internet of Things.…………………………43

xiv

LIST OF ABBREVIATIONS

ABREVIATION DESCRIPTION

AWS Automatic Weather Station

EAC East African Community

ID Identification Number

IoT Internet of Things

IT Information Technology

KMD Kenya Meteorological Department

PEOU Perceived Ease of Use

PCA Principal Component Analysis

RFID Radio Frequency Identification Device

SPSS Statistical Package for Social Scientists

TAM Technology Acceptance Model

TAM2 Extended Technology Acceptance Model

UK United Kingdom

Varimax Variance Maximization

WSN Wireless Sensor Network

1

CHAPTER ONE

1. INTRODUCTION

1.1. Background of the Problem

Recent advances in microelectronics and wireless networks have seen the rise of Internet

of Things (IoT). The term Internet of Things (IoT) refers to a network of physical and

virtual objects attached with electronics, software, sensors and connectivity to enable

objects to achieve greater value and service by exchanging data with other connected

objects via the Internet (Accenture, 2014). IoT devices have found application in a wide

range of everyday life applications such as environmental monitoring, battlefield and harsh

areas surveillance, healthcare and agriculture applications. In agriculture for example,

monitoring drought and providing timely seasonal forecasts and advice to farmers are

essential for managing drought risk especially in a developing country like Kenya, where

livelihoods are closely intertwined with climate variability (Amissah-Arthur, 2003;

Hansen, Defries, Townshend, & Sohlberg, 2000; Hayes, Wilhelmi, & Knutson, 2004; Pozzi

et al., 2013). Knowledge of long-term rainfall variability and weather is essential for water-

resource and land-use management in arid and semi-arid regions of Kenya. However, the

data relevant to this variability is scarce due to lack of long instrumental climate records.

Current approaches in drought monitoring and weather forecasting in developing countries

have been hampered by prohibitive internet costs, unreliable mobile networks and poor

access to technology that prevents the development of systems locally. In addition, there is

generally low institutional capacity and lack of national policy on drought mitigation.

Therefore, there is a need for a technological platform that can easily be used by the Kenya

Meteorological Department to disseminate weather-related information to people country-

wide.

The Kenya Meteorological Department was established to assist the public by providing

meteorological and climatological services to various users in different fields such as:

agriculture, forestry, water resources management, civil aviation, military aviation,

organization and administration of surface and upper air meteorological observations;

evolvement of suitable training programs in all fields of meteorology and other related

scientific subjects.

Current technological innovations focus largely on the efficient monitoring and control of

different activities. One of the activities that requires monitoring and forecasting is weather

2

monitoring. This is achieved when relevant objects in the environment are attached to

sensors that enable a self-monitoring and self-managing environment. This type of

environment is referred to as a smart environment (B.S. Rao, K.S. Rao & Ome, 2016).

Weather forecasting is a prime example of an area that would benefit from smart

environment technology (B.S. Rao, K.S. Rao & Ome, 2016). Internet of Things technology

is in the development of smart environments. Given the numerous advantages that this new

technology has brought to the new age of computing, in terms of monitoring and

forecasting, it is an opportunity to take advantage of the Internet of Things technology to

address the problem of poor dissemination of weather information. The IoT technology

used in formulating the suggested framework was the wireless sensor networks, which have

already been used and tested in different fields such as monitoring of fire, flooding, air

condition change or hazardous material leak, among others.

Wireless Sensor Networks are built from a number of small spatially dispersed sensor

nodes, each with limited processing capacity and memory, which transmit data in digital

form to a base station (Akyildiz, Su, Sankarasubramaniam, & Cayirci, 2002). The sensors

are mobile and can record and store data until they come again in range of the base station

and transmit the stored data. Wireless Sensor Networks systems can be equipped with

various types of sensors, to measure environmental parameters such as temperature,

humidity, and volatile compound detection to monitor different areas of the environment

(Callaway, 2004). The base station collects data from multiple sensors and sends it via a

mobile network such as GSM (Global System for Mobile communication) to a central

server. When a change occurs in the environment, an alert is sent automatically to the

intended systems.

This research study therefore focused on the design of a framework that would enable

efficient weather forecasting in Kenya. This would enable the required parameters to be

monitored remotely using internet and the data gathered from the sensors is then stored in

a server.

1.2. Statement of the Problem

The Kenya Meteorological Department (KMD) has been tasked with providing regular

weather forecasts for more than 50 years. This is because weather forecasts are very crucial,

especially in our day to day life; the output is used in decision making by decision makers

at organizational levels as well as by individuals. Currently the Government of Kenya uses

3

expensive weather stations, which are sparsely deployed in form of relatively small number

of fixed locations to provide climate maps for droughts and other natural disasters

prediction. KMD runs three main types of stations that are currently managed by the

Climatological Section of the Department which include 700 rainfall stations, 62

temperature stations and 27 synoptic stations (Kenya Meteorological Department, 2017).

The KMD has been faced with several challenges in the weather forecasting practices, these

weaknesses exist particularly in the application of meteorological information in the

decision-making processes of the climate-sensitive sectors. However, the department is still

facing challenges such as: lack of awareness of the vulnerable farmers on impacts of

climate change, lack of weather stations in some areas prone to natural disasters,

dissemination of meteorological information not sourced from Kenya Meteorological

Department (KMD) by some media houses and limited contact with the end users of the

climate information. One of the recommendations of the East African Community (EAC)

report (2008) was that KMD requires funds to acquire, install and maintain the relevant

observation and display instruments in areas prone to formation of fog that endangers road

users. Hence there is a need for KMD to adopt Internet of Things (IoT) in its weather

forecasting practices. This study sought to fill the existing research gap above by examining

the challenges of the current weather forecasting practices and subsequently developing a

framework that would enable the adoption of IoT technology by the Kenya Meteorological

Department. Evaluation of the benefits of the adoption of IoT in weather-forecasting

practices in other countries was also done.

1.3. Purpose of the Study

1.3.1. General Objective

The main objective of this research was to develop a framework that would enable the

adoption of the Internet of Things technology by the Kenya Meteorological Department

in order to carry out their weather forecasting practices efficiently.

1.3.2. Specific Objectives

i. To identify the IOT-related technological challenges of the current weather

forecasting practices currently faced by Kenya Meteorological Department.

ii. To develop a framework that enables the adoption of IoT in the weather forecasting

practices carried out by Kenya Meteorological Department.

4

iii. To evaluate the framework in relation to adoption of IoT in weather forecasting

practices by Kenya Meteorological Department.

1.4. Significance of the Study

Accuracy of weather monitoring and forecasting directly or indirectly influences various

sectors of economy including agricultural sector and transportation sector. This raises the

need for a system that facilitates higher accuracy of real time monitoring and future weather

prediction. This study looked at the technological challenges currently being faced by the

KMD in their current weather forecasting practices, with a view to address them through

the development of a framework that would enable the adoption of IoT. By leveraging on

the use of IoT, the Kenya Meteorological Department will be able to forecast and

disseminate information related to weather forecasting to various sectors such as the

agricultural sector more accurately.

1.5. Scope of the Study

This was an exploratory study that proposed the design of a framework that would enable

the adoption of IoT technology by the Kenya Meteorological Department. The adoption of

IoT would solve the technological weather forecasting challenges currently being faced. It

did not include the implementation of the Internet of Things technology.

1.6. Definition of Terms

Internet of Things (IoT)

This refers to a system of related computing devices and machines that may be embedded

in a nimals or people, that have unique identifiers to capture and transfer data over a

network without requiring human intervention or human-to-computer interaction

(Accenture, 2014).

Wireless Sensor Networks

A Wireless Sensor Network is a network that is made up of of many wireless sensors, which

collect, store, processenvironmental information, and communicate this information to the

neighboring environment (Mahalik, 2007).

5

Smart Environment

This is an environment that is richly and invisibly interwoven with sensors that are

networked to each other and embedded seamlessly in the everyday objects of our lives (B.S.

Rao, K.S. Rao & N. Ome, 2016).

Sensor

This is a device that detects or measures a physical property and uses this information to

record, indicate, or otherwise respond to the environment (Akyildiz, Su,

Sankarasubramaniam, & Cayirci, 2002).

Gateway

This is a network node that connects two or more networks that use different protocols

(Mahalik, 2007).

Radio-Frequency Identification (RFID)

This is an IoT technology that uses radio waves to read and capture information stored on

a tag that has been attached to an object (Powell& Shim, 2009).

1.7. Chapter Summary

This chapter has given the background of this study as well as an overview of the Internet

of Things technology. It has covered several topics such as the main purpose of this study,

identified the problem statement of the study as well as assessed the scope of study. The

following chapter will focus on the literature review of factors influencing the adoption of

the IoT technology by the Kenya Meteorological Department.

6

CHAPTER TWO

2. LITERATURE REVIEW

2.1. Introduction

Weather monitoring and forecasting is important to the citizens of Kenya due to many

disastrous climatic conditions that are frequently experienced such as drought and floods.

Drought is a recurrent climatic catastrophe across the world. It affects the public human

race in a number of ways such as causing loss of life, crop failures, food shortages which

may lead to famine in many regions. In addition to this, malnutrition, health issues and

mass migration may also be experienced. This therefore poses a big risk to citizens in Kenya

especially among farmers as their products’ success is influenced by weather patterns.

There is clearly a need for increased and integrated efforts in weather forecasting to reduce

the negative impacts of not having adequate weather information that would help in

reducing weather-related issues anticipated in the future.

Remote sensing technology has opened the gates for real time analysis of weather data and

has transformed the way that weather data is collected and analyzed (Mahendra et al.,

2017). This has resulted in reliable weather forecasts due to sensors being used to collect

accurate data and in real time. Internet of Things (IoT) technology has been proposed as

the ICT technology in this research project, to handle dissemination of information to

various people in Kenya. The main objective of this research was therefore to develop a

framework for a weather forecasting system based on IoT technology that would assist the

meteorological department in forecasting information. To meet this objective, this chapter

presents the literature review that was undertaken to assess different architectures of IoT-

based systems and the analysis that was carried out on the current available systems that

deliver weather updates to farmers, and to identify areas of improvements.

2.2. Theoretical Foundation

The adoption of new technologies has been explored through different theoretical

frameworks. These theories include Expectation Confirmation Theory, Delone and

McLean IS success model, Diffusion of Innovations theory and Extended TAM

(Technology Acceptance Model) theory.

2.2.1. Expectation Confirmation Theory

This theory explains that if a product meets expectations, then satisfaction after

implementation will be high. It explains that satisfaction after procuring a product is

7

directly related to the expectations, perceived performance, and disconfirmation of beliefs

as illustrated in Figure 2.1.

Figure 2.1: Expectation Confirmation Theory. Source: (Oliver, 1980)

The customer experience shared when using a service will become perceived performance

and this leads to either confirmation or disconfirmation of their presumed statements.

Whether their opinions are verifications of their beliefs or not, affects their satisfaction.

2.2.2. Delone and McLean IS Success Model

This theory explains that a system can be evaluated in terms of information, system, and

service quality as these constructs affect user’s satisfaction as well as their subsequent use.

The relation between these constructs is shown in Figure 2.2. System use is said to influence

user satisfaction which in turn influences the system benefits being realized.

Figure 2.2: Delone and McLean IS success model. Source: (Delone & McLean, 2003)

First Set of Variables Second Set of Variables Final Variable

8

2.2.3. Diffusion of Innovations Theory

This theory explains that for a new innovation, the factors that affect its successful

implementation are the technical compatibility, its ease of use and perceived need. Moore

and Benbasat (1991) explained that there are a number of constructs to be used that examine

individual technology acceptance such as relative advantage, ease of use, image,

compatibility and trialability. Some of the constructs used in this model are as follows:

Relative Advantage: This is the degree to which an innovation is seen as better than the

idea, program, or product it is replacing. The higher the relative advantage being seen by

the users, the higher the rate of adoption by the users, all other factors being equal. This

construct must also take into account what task is being undertaken.

Compatibility: This refers to how much the innovation fulfills the requirements at hand

with regards to the values, experiences, and needs of the potential adopters. Compatibility

is positively correlated with the rate of adoption. If a technology is compatible to an

organization and addresses the needs, the chances of adoption will be high.

Complexity: This refers to how easy or difficult the new product or innovation is, to use.

This attribute is negatively correlated to the rate of adoption. If a product is complex to use,

the users might not readily adopt the product.

Trialability: This involves how much an innovation can be tested without cost implications

before commitment to adopt is made. Niederman (1998) explains that

trialability/divisibility is the degree to which an innovation can be adopted in phases, with

each phase leading to a greater adoption of the technology being introduced. Innovations

that can be tried in phases are inherently easier to adopt than those for which the entire

technology has to be mastered before any use can be made.

Observability: This involves the extent to which the product satisfies the requirements and

shows results. In some innovations, it is easy for others to see the results of adoptions from

those who have already adopted the technology while for other innovations, it may be

difficult. Observability is positively correlated with the rate of adoption. If the results of an

innovation cannot be easily seen, users may be skeptical to adopt it.

2.2.4. Extended Technology Acceptance Model (TAM) Theory

This theory proposed an extension of TAM (TAM2) by adding more important

determinants of perceived usefulness that is, subjective norm, image, job relevant, output

9

quality, result demonstrability, and perceived ease of use. Two additional moderators:

experience and voluntariness were also added to the theory (Venkatesh and Davis, 2000).

As given by Venkatesh and Davis, (2000), TAM2 consists of social influence and cognitive

instrumental processes as the determinants of perceived usefulness. The social

determinants are subjective norm and image. The cognitive determinants are job relevance,

output quality and result demonstrability (Venkatesh and Davis, 2000). Experience and

voluntariness were included as moderating factors of subjective norm.

Subjective norm is the degree to which an individual perceives that most people who are

important to him think he should or should not use the system.

Image refers to the degree to which an individual perceives that use of an innovation will

enhance his or her status in his or her social system.

Job relevance refers to the degree to which an individual believes that the target system is

applicable to his or her job.

Output Quality refers to the degree to which an individual believes that the system performs

his or her job tasks well.

Result demonstrability refers to the perception by an individual that the results of using a

system are observable and can be measured by the end user.

2.3. Current Weather Forecasting Practice in Kenya

The Kenya Meteorological Department deals with monitoring of weather patterns in

Kenya. Kenya is one of the three meteorological hubs in Africa, with others being in Cairo

and Pretoria. As a hub, it is linked directly to satellites which relay weather information to

one or all of the three-world meteorological centers in Washington DC, Moscow in Russia

and Melbourne in Australia. It consists of 3 main stations that are managed by the

Climatological Section of the Department. For agricultural forecasts, the Agro

Meteorological Section manages 13 stations (Kenya Meteorological Department, 2017).

Observations by this section include: meteorological observations, soil temperature,

sunshine duration, radiation, pan evaporation and potential evapotranspiration. The

observed weather forecasting data is stored at the meteorological headquarters in Dagoretti,

Nairobi, Kenya. This data helps in forecasting of weather patterns to farmers in Kenya.

10

The systems in several sites are automated but at the county level, the weather forecasting

is still being done manually through the data collected from the weather forecasting

instruments. At the county level, two methods are used in relaying information to the

public. The first method is the use of the main meteorological center in Dagoretti that liaises

with the county director of meteorology in each county, who directly informs the public in

the counties on what has been forecasted. They also use a radio platform to communicate

the weather patterns. This is done through a radio station called Runnet. Despite having

these systems in place, the Kenya Meteorological Department still faces a number of

challenges that affect effective dissemination of weather forecasts and alerts to farmers.

These challenges are outlined as follows.

Minimal Coverage by Weather Stations - The Kenya Meteorological Department has

few weather stations that are concentrated in major towns. This leaves the rest of the remote

areas without any coverage and makes it difficult for effective weather forecasting in the

remote areas. There’s therefore lack of meteorological observation stations at the county

and sub-county levels (Nyakwada, 2004).

Cost - The cost of procuring and installing additional automatic weather stations is quite

high. Due to the constraints in funding, there has been a small growth rate in setting up

weather stations. This has therefore led to minimal number of stations in Kenya

(Nyakwada, 2004).

Technical Skills - Lack of technical skills required to enable the installation and

maintenance of weather stations has also posed as a big challenge. Technical knowledge

required for installation, operation and maintenance of otherwise complex AWS has

therefore slowed the impact of AWS (Nyakwada, 2004).

Ineffective Information Dissemination - The channels that the Kenya Meteorological

Department uses to disseminate the forecast information are ineffective; the farmers that

need it most do not get it and those that do, cannot comprehend the information (Nyakwada,

2004).

Lack of Useful Weather Forecast Information - The usefulness of forecast information

provided by the Kenya Meteorological Department to key stakeholders especially the

farmers and policy formulators is not very reliable to make agricultural decisions. The

actual implications of the weather observations made needs to be incorporated when

11

providing weather information to farmers and policy makers, in order for the reporting to

be more useful (Nyakwada, 2004).

Security Issues - The installation of Automatic Weather Stations (AWS) in remote areas

has proved difficult due to insecurity of the instruments. Many individuals in rural areas try

to steal these instruments thus, posing a security risk (Nyakwada, 2004).

Lack of Staff - With fewer staff at the county level, the staff have to work for 24 hours

some days. The result of this is that the staff end up not working diligently, resulting in

inaccuracy of weather forecasted information (Nyakwada, 2004).

Inability to measure cloud coverage - Cloud cover is measured in Oktas. However, in

Kenya the cloud coverage is currently not measured accurately as they are still using

manual methods to read the cloud coverage, due to lack of proper infrastructure in place

(Nyakwada, 2004).

2.4. Internet of Things

2.4.1. Overview

The Internet of Things (IoT) is a new paradigm which was mentioned for the first time by

Ashton in 1999 (Gao & Bai, 2014). IoT weather systems are designed to collect data from

various objects, using sensors. The ultimate goal is to create a better world for human

beings that is a smart environment, where all objects around humans act accordingly

without explicit instructions. The sensors, help in collecting weather data which is further

pooled remotely to servers where analysis of data can be done. Sensor devices are placed

at different locations to collect the data to predict the behavior of a particular area of interest

(B.S. Rao, K.S. Rao & N. Ome, 2016).

With the help of the communication technologies such as wireless sensor networks (WSN)

and Radio Frequency Identification (RFID), sharing of information takes place. RFID is an

IoT technology that enables storing and retrieving of data through electromagnetic

transmission to a radio frequency compatible integrated circuit (Powell& Shim, 2009). It

is usually used to label and track items in supermarkets and manufactories (Powell& Shim,

2009). A wireless sensor network is made up of wireless sensors which have the capability

of collecting, storing, processing environmental information, and communicating this

information to the neighboring nodes in the environment (Mahalik, 2007). Once this

information is collected by sensors, it is transmitted through the use of a WSN gateway.

After data has been received from wireless sensor network, the gateway analyzes and

12

extracts data and thereafter packages the data into a network format data and sends the data

to the server. This data can then be accessed by users via smart phones, internet browsers

or other web-enabled devices that are connected through LAN and made available for users

through the Internet.

There has been support on the benefits that Internet of Things Technology has on various

organizations. As Al-Sakib and Humayun (2006) noted, the reporting accuracy of a

wireless sensor network is greatly enhanced in gathering information compared to the

information obtained from one sensor node. Therefore, by using a wireless sensor network,

there’s greater potential in reporting accurately. The other advantage of using IoT for

weather monitoring and forecasting systems is automation and control. Without human

involvement, machines are automating and controlling vast amount of information, which

leads to faster and timely output (Shruti & Soumyalatha, 2015).

When using the IoT, a wireless sensor network would be fast and efficient in gathering

information and can span a greater geographical area without adverse impact on the overall

network cost. This will result in less equipment needed to report on different weather

patterns thus saving on cost. Time is also saved as gathering of information is done fast in

comparison to other weather monitoring and forecasting systems.

IoT enabled systems also promote better quality of life and smart environments through

prior alert of the weather conditions. e.g. if you are planning to visit a place and you want

to know the weather parameters over that place, all you have to do is access the weather

monitoring and forecasting system online. IoT also helps in creating a more green and

sustainable planet. Through accurate reporting, the environment is used to report on

weather patterns automatically (Shruti & Soumyalatha, 2015).

Sensor nodes can be deployed in harsh environments that make the sensor networks more

effective. This enables it to be used in all types of environments and access to weather

monitoring information is easy. The wireless sensor network is also fault tolerant as several

nodes are deployed in the network. Information redundancy as well as device redundancy

can be utilized to ensure a level of fault tolerance in individual sensors.

Multiple sensor networks may be connected through sink nodes, along with existing

networks (e.g. Internet). The clustering of networks enables each individual network to

focus on specific areas or events and share only relevant information.

13

2.4.2. Architecture of IoT Technology

The IoT technology consists of three levels that include the hardware in the first level,

followed by the infrastructure in the second level, and the application and services level on

the third level (Gu & Liu, 2013; Gomez et al., 2013). The IoT Technologies are classified

into the following four layers (Shruti & Soumyalatha, 2015) as shown in Figure 2.4.

Sensor Layer - This is the lowest layer of IoT Architecture, which consists of sensor

networks, embedded systems, RFID tags and readers. This layer enables identification,

information storage and information collection. Each of these scattered sensor nodes has

the capabilities to collect data and route data back to the small nearby embedded system.

Gateway and Network Layer - This layer is responsible for transferring the information

collected by sensors to the mainframe server in the next layer – management service layer.

This layer should have high performance and robust network. It should also support

multiple organizations to communicate independently (Shruti & Soumyalatha, 2015).

Management Service Layer - This layer acts as an interface between the gateway &

network layer and the application layer by communicating to these layers in both directions.

It is responsible for capturing large amounts of the raw data, storing this data and extracting

relevant information from the stored data (Shruti & Soumyalatha, 2015).

Application layer - This is the top most layer of the IoT. It provides a user interface to

access various applications by different users. All these layers are shown in Figure 2.3

below, representing the IoT architecture and the relationship between the layers from the

lowest to the highest layer.

Figure 2.3: Architecture of IoT. Source: Shruti & Soumyalatha (2015)

Application Layer

Sensor Layer

Management Service Layer

Gateway & Network Layer

14

2.4.3. Security Concerns of Internet of Things Technology

Although the IoT provides huge benefits, it is prone to various security threats in our daily

life. Most of the security threats revolve around leakage of information and loss of services.

Most of the devices connected to the internet are not equipped with efficient security

mechanisms and are vulnerable to various privacy and security issues such as

confidentiality, integrity, and authenticity. For IoT, some security requirements must be

fulfilled to prevent the network from malicious attacks (Weber, 2010). The IoT-related

devices consist of different devices with access credentials, where every system needs a

login to promote security requirements depending upon its functionality.

For IoT services, there are a lot of security issues on user privacy at the network layer

amongst other security issues that have been listed below in detail:

End to End Data life cycle protection: This is used to ensure that the security of data in

IoT environment is implemented. Data is collected from different devices connected to

each other and this information is then shared with other devices. Thus, it requires a

framework to protect the data, confidentiality of data and to manage information privacy

in full data life cycle (Abdur et al., 2017).

Secure thing planning: The communication and how deices are connected in the IoT-

related devices vary according to the situation. Therefore, the devices must be capable of

maintaining security level (Abdur et al., 2017).

Visible/usable security and privacy: Most of the security and privacy concerns are

invoked by misconfiguration of users. It is very difficult and unrealistic for users to execute

such privacy policies and complex security mechanism. It is needed to select security and

privacy policies that may apply automatically (Abdur et al., 2017).

Security Attacks and System Vulnerabilities: There has been a lot of work done on IoT

security. The related work can be divided into system security, application security, and

network security (Ning, Liu, & Yang, 2013). These types of security have been explained

in detail below.

System Security: This mainly focuses on the different security challenges of the overall

system and allows for different security solutions to be designed and proper security

guidelines to be provided to maintain the security of a network.

15

Application security: This works for IoT application to handle security issues according to

scenario requirements.

Network security: This deals with securing the IoT communication network for

communication of different IoT devices.

The IoT technology faces various types of attacks such as the active attacks and passive

attacks that may easily disrupt the functionality and affect the benefits of the services

provided. In a passive attack, the attacker may sense the node or may steal the information,

but he/she never attacks physically. However, active attacks disturb the performance

physically. Such vulnerable attacks can prevent the devices to communicate smartly.

Therefore, it’s important that the security constraints must be applied to prevent devices

from being subjected to malicious attacks.

Different levels of attacks are categorized into four types according to their behavior and

propose possible solutions to threats/attacks (Abdur et al., 2017).

Low-level attack: If an attacker tries to attack a network and his attack is not

successful.

Medium-level attack: If an attacker listens into the medium but doesn’t alter the

integrity of data.

High-level attack: If an attack is carried on a network and it alters the integrity of

data or modifies the data.

Extremely High-level attack: This type pf attack occurs when an intruder attacks

a network by gaining unauthorized access and performing an illegal operation. This

results in the network being unavailable, sending bulk messages, or jamming

network.

It is therefore important to install a security mechanism in IoT devices and communication

networks. Moreover, to protect from any intruders or security threat, it is also recommended

not to use default passwords for the devices and to read the security requirements for the

devices before using it. By disabling the features that are not used, the users may protect

themselves as the chances of security attacks are decreased. Moreover, it is important to

study different security protocols used in IoT devices and networks (Abdur et al., 2017).

16

2.5. Evaluation of Application Areas of Internet of Things Technology

The application of IoT typically involves monitoring, tracking and controlling data

especially in habit monitoring, object tracking, nuclear reactor control, fire detection, flood

detection and traffic monitoring. This has been seen through various application areas all

over the world some of which are listed below.

2.5.1. Application Areas in Africa

Airtel Congo has partnered with a local vehicle tracking company to offer fleet

location services to its customers (Ndubuaku & Okereafor, 2015).

MTN Rwanda recently reported that the fastest growth in connections was in the area of

point-of- sale (PoS) terminals. The market has seen rapid growth over recent years. The

market is being driven by the focus of financial institutions in the country on growing the

number of payment cards in use. In South Africa. MTN implemented its first smart

metering project for the City of Johannesburg. This project aimed to install 50,000 meters

by June 2014 as part of the first phase of the project (Ndubuaku & Okereafor, 2015).

In South Africa, a company called Sequoia Technology provides a HIV diagnosis

communications system using GPRS printers and a dedicated GSM gateway. The solution

is used by the health sector and allows for test results from far away laboratories to reach

the clinics much faster, savings lives in the process (Ndubuaku & Okereafor, 2015).

2.5.2. Events Reporting

Events reporting can be defined as reporting on an exceptional change in the environment

parameters such as temperature, light, humidity, etc. Internet of Things technology has been

used to report on events all over the world.

Bouabdallah and Bouabdallah (2008) carried out an analysis on the impact of the number

of reporting nodes on WSNs performance (energy consumption and reporting time). They

proved that using only a small number of sensor nodes to report the event occurrence rather

than all the nodes in the event area reduces considerably the energy consumption and

improves the network lifetime. They also showed that when only one reporting node is

activated, the maximal network lifetime is achieved.

Shih, et al. (2008) focused on both event detection and tracking. They tackled the event

boundary determination issue in critical scenarios such as fire or pollution by a hazardous

gas. For this purpose, they proposed a dynamic role assignment to sensor nodes so that the

17

event can be tracked. Nevertheless, their approach is evaluated from one perspective only,

the accuracy of the edge of the event of interest. However, they failed to analyze some

important performance metrics such as energy consumption or delay.

2.5.3. Environmental Applications Worldwide

IoT has been applied all over the world in different areas. The examples below show how

IoT is being used in different areas of the world to promote a smart environment.

Fire Detection in South Korea - According to Son, et al. (2006) Forest-Fires Surveillance

System (FFSS) was developed to prevent forest fires in the South Korean Mountains and

to have an early fire-alarm in real time. The system senses environment state such as

temperature, humidity, smoke and determines forest-fires risk-level. This allows for people

to be alerted in real time when the forest-fire occurs, enabling people to extinguish forest-

fires before it grows.

Flood Detection in the USA - An alert system for flood detection and prevention was

deployed in the US, rainfalls, water level and weather sensors were used in that system to

detect, predict and hence prevent floods. The sensors would supply information to a

centralized database system in a pre-defined way (Coulson, 2006).

City-Wide Wireless Weather Sensor Network in Taipei - Chang, et al. (2010) developed

the wireless sensor network to analyze its effectiveness in facilitating elementary and junior

high students’ study of weather science. The city-wide wireless sender network provided a

distributed wireless weather sensor network throughout Taipei and promoted science

learning activities related to weather, for students. The network composed of sixty school-

based weather sensor nodes that were connected by a centralized archive server. The

weather data from the Taipei environment were collected every five minutes and wirelessly

transferred to the wireless sensor network’s server. This provided students with current

weather data at specific locations in the city.

18

Figure 2.4: Taipei Weather Science Learning Network Architecture. Source: Chang, et al.

(2010)

The Taipei Weather Science Learning network has made its website open to the public

users who are interested in using the data for Taipei City weather science learning. Users

can freely access the database as the website does not only provide the current weather

predictions of a particular area, but also provides the past data for elapsed-time periods.

Commercial Applications - Some of the commercial applications of WSNs include:

burglary detection and monitoring, vehicle tracking and detection, interactive museums,

environmental control in the buildings, robot control and guidance in automatic

manufacturing environments, factory process control and automation and sensor nodes

embedded in smart structures (Akyildiz et al., 2002).

Military Applications - Dense deployment of low cost disposable sensor nodes make

WSNs concept beneficial for battle fields. Some of the areas where IoT has been

implemented in this field include; monitoring friendly forces, equipment and ammunition;

battlefield surveillance; exploration of opposing forces and terrain, targeting, battle damage

assessment and nuclear, biological and chemical attack detection.

In order to ensure that the IoT technology has been implemented accordingly in different

application areas, it is key that the key measures required to achieve success of technology

adoption are considered. These have been explained in detail in section 2.3.4.3.

2.5.4. Factors that Affect Implementation of Technologies in Organizations

Culture - A culture is a system put in place within an organization that determines largely

how employees act. Shared values, norms and organizational practices do shape the culture

that assist organizations to adopt the changes. Slowinkowski and Jarratt (1997) noted that

the effect of cultural factors, specifically traditions, religion and fatality have greater impact

on adoption of technology and must be considered with great care in adoption process.

19

Khalil and Elkordy (1999) pointed out that the cultural sensitivities of host environments

are often ignored in technology adoption decision. This is especially true in the work place

since the adoption decision is often negotiated by upper-level managers who either work

for international companies or who have spent time in the industrialized countries. Yet it is

the lower level managers and workers who, without the diverse cultural experiences, have

the responsibility of the daily use of the new technology, and ultimately accept or don’t

accept it.

Human Factor - Szewczak and Snodgrass (2003) said that individuals play an effective

and important role in technology adoption process. A technology is not successful if its

user does not accept it. Avergou (1996) said that user participation could be considered as

“taking part” in some activity. Such participation may be covering varying scopes of

activities during systems development and implementation.

Organizational Structure Factor - Robbins and Coulter (2002) described the

organizational structure as a framework, which is expressed by its degree of complexity,

formalization, and centralization. An organization can be sub-divided into different

divisions, departments, and teams, to enable a smooth working environment and each

member in the organization is given certain responsibility and authority to his/her position.

Economic Factor - Lind (1999) identified that the barriers for the adoption of technology

include lack of awareness of available technologies and its uses, capabilities, and return on

investment. Additionally, lack of knowledge about technology selection, adoption, and

implementation as well as lack of knowledge in organizational development and strategic

planning, restricts the use of new technology in organization.

Social Factor - The social change works into ways: it become the reason for technological

change and also, plays a role of a great barrier in any technology adoption decision. Godwin

and Guimaraes, (1994) said that there are three factors to be considered to see social

involvement in technology advances; Social need-to feel strong desire of something, Social

resources-the capital, material, and skilled personnel vital for innovation and adoption of

new thing, Sympathetic social ethos-an environment in which the dominant groups are

prepared to consider innovation seriously and are receptive to new idea.

2.6. Conceptual Framework

The suggested framework for the adoption of IoT by the Kenya Meteorological Department

was derived from the survey carried out at KMD as well as through analysis of all the

20

theories presented in section 2.2. The constructs required for the successful adoption of IoT

were identified and this resulted in the development of the conceptual framework shown in

Figure 2.5.

Perceived

Usefulness

Perceived Ease of

Use

ObservabilityBehavioral

Intention to Use

Compatibility

Trialability

System

Quality

Relevance

Adoption of IOT

Privacy and

Security

Figure 2.5: The Proposed Conceptual Framework for the Adoption of IoT

From Figure 2.5, a number of constructs have been identified that form the proposed

conceptual framework. These are explained below.

System Quality - This refers to the overall quality of a system. System quality impacts the

extent to which the system can provide certain benefits by relating to the user satisfaction

variable.

Compatibility – This is the degree to which an innovation or certain technology is perceived

to be consistent with an organization’s needs, social cultural values and the past experiences

of potential adopters. In this research study, this construct was used to mean the degree to

which the IoT technology was understood to be consistent with existing needs and past

experiences of potential adopters at KMD.

Trialability – This is the degree to which a technology is experimented several times before

it is fully adopted without undue cost. Innovations that can be tried several times within a

period of time are proven to be easily adopted than those for which the entire technology

21

has to be mastered before any use can be made. In this research study, trialability was the

degree to which IoT technology was experimented for a limited time before adoption,

without undue cost.

Perceived Usefulness - Davis (1989) defines perceived usefulness (PU) as “the degree to

which a person or a user believes in using a particular technology, as well believes that the

technology would enhance his/her job performance”. The relevance of the technology

being adopted by KMD staff as well as the quality of the system being developed, directly

influence the perceived usefulness of the technology being adopted. Perceived usefulness

is therefore the degree to which the staff of KMD believes in using IoT technology and

how IoT will enhance their job performance.

Perceived Ease of Use (PEOU) - Davis (1989) explains the meaning of this construct as

"the degree to which a person believes that using a particular system would be free from

effort". This depends on the compatibility and trialability of an innovation, and therefore

these two variables are linked to perceived ease of use of a technology. Perceived ease of

use was used in this research study to show the ease of which IoT technology would be

adopted by KMD.

Observability - This is the degree to which the results of an innovation are visible to others.

Observability moves in tandem with the rate of adoption. Therefore, when an innovation

provides tangible results, the user satisfaction is realized which leads to actual adoption of

the technology. Observability was measured by the degree to which the adoption of IoT

will provide results to the KMD staff and lead to their intention to adopt the technology.

Privacy and Security - Security is the extent to which a person believes that using a

particular application will be risk free while privacy is the potential loss of control over

personal information. In this research study, privacy and security were used to mean the

users’ need to feel safe when interacting with such systems.

The seven independent variables are expected to affect the behavioral intention to adopt the

technology which in turn is expected to affect the use behavior of the IoT services.

As observed in the theories in section 2.2, every theory focuses on particular constructs.

The theory of extended TAM only focuses on why people accept and adopt new technology

but doesn’t explain the psychological aspect that leads to its adoption. The theory of

expectation confirmation theory focuses on what is required for a product to meet its users’

22

expectations. The proposed framework will incorporate the major aspect of the other entire

model.


This literature review presented in this chapter has shown how multiple sensors will be

installed in the fields, which will collect real-time information regarding weather,

temperature, humidity, rainfall and any other environmental parameters. This predictive

statistical data will provide information to the KMD in order to make smarter decisions.

An evaluation has been carried out on the advantages of the IoT technology. As much as

there are disadvantages, the benefits outweigh the disadvantages by far. The chapter has

also highlighted the relevant theories that were used to develop the conceptual framework

and the constructs that were used to test the adoption of IoT by the Kenya Meteorological

Department.

23

CHAPTER THREE

3. METHODOLOGY

3.1. Introduction

The type of methodology chosen for a particular research project depends on the technical

and organizational requirements of the software/system one is developing. This chapter

describes the research methodology that was used by indicating the research design, target

population, data collection method, and data analysis that was utilized to investigate the

framework for adoption of IoT technology by the KMD.

3.2. Research Design

This study used the descriptive research design to meet its objectives as it focused on

developing a framework for the adoption of IoT by the KMD. A descriptive research design

is an in-depth investigation of an individual or a group or an institution with a primary

motive to determine factors and relationships that have resulted in the behavior of the study

(Stephen P. Robins, 2012). This research design enabled the researcher to undertake an in-

depth investigation of the framework for adoption of IoT by the KMD.

3.3. Population and Sampling Design

3.3.1. Target Population

The physical area of study was Kenya Meteorological Department as this was the only

organization in Kenya that dealt with acquisition and dissemination of weather information.

On the basis of the research conducted, the researcher defined the target group as the

officers of Kenya Meteorological Department.

3.3.2. Sampling Design and Sample Size

According to Mugenda and Mugenda (2003) a good sample population should be 10

percent to 30 percent of the entire population. The researcher therefore ensured that the

sample unit was within this threshold. Calculation of the sample size was done using the

formula below:

𝑛 = 𝑧2 𝑋 𝑝(1 − 𝑝)

𝑚2

Where: 𝒏 = required sample size

𝒛 = confidence level at 95 percent (standard value of 1.96)

𝒑 = proportion in the target market estimated to have a particular characteristic

𝒎 = margin of error at 5 percent (standard value of 0.05)

24

This was therefore equivalent to 400.

To get the true sample size, the actual population was used. The following

calculation was done with a population of 108 people:

True Sample = (𝑆𝑎𝑚𝑝𝑙𝑒 𝑆𝑖𝑧𝑒 𝑋 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛) / (𝑆𝑎𝑚𝑝𝑙𝑒 𝑆𝑖𝑧𝑒 + 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 – 1)

This was therefore approximately 100 people, for ease of use.

The True Sample Size was rounded up to the nearest whole person, and this became 84

people.

The simple random sampling technique was used to observe the sample unit on the impact

of the hypotheses. The sample unit measurement was their level of satisfaction with regards

to the solution provided.

3.4. Data Collection Methods

An already prepared questionnaire is extremely helpful for the researcher, to guide the flow

of how data is collected. An already prepared questionnaire provides help, to keep the flow

of data gathering on the right track. It also ensures that the researcher does not miss any

important questions due to complexity of topic, number of variables involved, pressure of

time, or simply because of human forgetfulness.

The questionnaire consisted of closed-ended and open-ended questions which were used to

collect primary and secondary data. The primary data focused on current practices done by

the Kenya Meteorological Department. The secondary data was collected from the

literature review collected from organization reports, publications and other literature

relating framework for adoption of IoT in the KMD.

25

The questions in the questionnaire were as a result of the problem definition and theoretical

framework.

3.4.1. Pilot Test Report

A pilot test was carried out on 10 employees of KMD from the forecasting division. These

10 employees were not involved in the final survey. The pilot study enabled the researcher

to be familiar with the research area and its administration policies and procedures as well

as helped in identifying items that required modification. The researcher was able to modify

the questionnaire based on the responses that were received from the employees of KMD

during the pilot study. The result helped the researcher to correct inconsistencies arising

from the questionnaire, which ensured that they measured what was intended. The clarity

of the questionnaire to the respondents was also established so as to enhance the

instrument‘s reliability.

3.4.2. Reliability Analysis

Reliability of the final questionnaire was tested using Cronbach’s alpha which measured

its internal consistency. Nunnally (1999) established the Alpha value threshold at 0.7 which

the study benchmarked against. Cronbach Alpha was used on every objective in order to

determine if each scale would produce consistent results should the research be done later

on. The study found that the instrument had reliability (α=0.885). This illustrates that all

the four scales used; Strongly agree, Agree, Disagree and Strongly disagree, were reliable

as their reliability values exceeded the prescribed threshold.

3.5. Research Procedures

The researcher handed out 10 questionnaires to the employees of the KMD as part of the

pilot study. The pilot study enabled the researcher to understand what areas of the

questionnaire needed to be improved. Based on the feedback received, the questionnaire

was modified. The final survey was then carried out after the questionnaire had been

modified. The questionnaire developed to collect data was structured into two main parts:

the introduction section and a section that involved the demographic questions.

The researcher handed out the questionnaires to the KMD employees again and gave them

two weeks to populate the questionnaires. The data was then collected after the

questionnaires had been populated.

26

3.6. Data Analysis Methods

The primary data collected was analyzed using the IBM SPSS 23 tool, to generate

appropriate tables, bar graphs and pie charts. Frequencies and percentages were used to

show how the variables identified in the framework developed, would influence the

adoption of IoT by the Kenya Meteorological Department. The open-ended questions were

analyzed using thematic analysis.

Factor analysis was applied to determine the relative importance of each of the constructs

identified in the proposed framework with respect to adoption of IoT by the KMD.

The operationalization of variables as shown in Table 3.1 explains how the variables were

analyzed and conclusions drawn thereafter.

Table 3.1: Operationalization of Variables

Variable of

Conceptual

framework

Indicator

Measurement

Scale

Study Design

Tools Of

Analysis

System Quality High output due to

IoT adoption

Ordinal Descriptive Likert scale

Compatibility IoT is compatible

with the structure of

KMD


Trial ability Trials with minimal

errors


Perceived

Usefulness

IoT is perceived to

be useful in KMD


Perceived Ease

of Use (PEOU)

IoT is easy to use Ordinal Descriptive Likert scale

Observability IoT is trusted to

produce accurate

information


27

Behavioral

Intention

KMD employees

have intention to

use IoT



Based on the pilot study results, the final questionnaire was modified accordingly. The data

was collected, analyzed and presented in tables, charts and frequencies. A descriptive

research study was undertaken, focusing on the staff of KMD. Based on the data collected

and analyzed, the various constructs were analyzed, and conclusions drawn from them.

28

CHAPTER FOUR

4. MODEL

4.1. Introduction

This chapter explains how the researcher developed a model to help in solving the studied

problem. The model was developed by analyzing the various theoretical frameworks

identified in section 2.2. The theoretical frameworks used were: Expectation Confirmation

Theory, Delone and McLean IS Success Model, Diffusion of Innovation Theory and

Extended Technology Acceptance Model.

4.2. Analysis

Researchers have also attempted to identify the factors that affect the acceptance of IoT by

customers. For example, Acquity Group, (2014) investigated the concerns of customers to

adopt the IoT. Total of 2000 customers in US have been surveyed. The findings showed

that awareness of the technology, usefulness, price (cost), security, privacy are the main

concerns of the customers.

The researchers attempted to conduct qualitative studies to identify the factors that affect

the intention to use the new technology. For example, Kowatsch and Maass (2012)

investigated the intention to use IoT service in Spain. The study interviewed several experts

in the field of IoT to validate a conceptual model that included constructs such as perceived

IoT privacy, trust in IoT services, personal interest in IoT and expected usefulness. The

findings showed that the intentions to adopt IoT-related services were influenced by

variables such as privacy risks and personal interest, legislation, data security, and

transparency of information use. In a similar approach, Brown et al. (2013) conducted an

exploratory study on the adoption of IoT. The study collected data using the mix approach.

Quantitative and qualitative data were collected from 35 respondents. The findings showed

that the most important factors are usefulness, ease of use, privacy, knowledge and

awareness of the technology.

4.3. Modelling and Design

The proposed framework incorporated the following major constructs borrowed from all

the theories above as well as the literature reviewed with regards to the research problem.

The major constructs included: perceived ease of use, perceived usefulness, technical

compatibility, privacy and security, observability, trialability and actual system use. Table

29

4.1 shows how these constructs were derived from the different theories identified in

section 4.1.

Table 4.1: Proposed Framework Variables

Expectation

confirmation

theory

Delone and

McLean IS

success

model

Extended

TAM

Theory

Diffusion of

innovations

theory

Literature

review

Proposed

framework

Perceived need System

Quality

Perceived

Usefulness

Relative

advantage

Privacy

concern

Perceived

usefulness

User Satisfaction User

Satisfaction

Perceived

ease of use

Complexity/Ease

of use

Security Ease of use

Perceived

Performance

Subsequent

Use

Technical

compatibility

Technical

compatibility

Disconfirmation

of beliefs

Information

Quality

Observability Observability

Service

Quality

Trialability Trialability

Privacy &

Security

Actual

Use/Adoption

The proposed framework in Table 4.1 can be explained further using the TOE

(Technological, Organizational and Environmental) Framework. The TOE Framework

considers three features of an organization that influence the adoption of an innovation

environment (Tornatzky & Fleischer, 1990). These include: technology, organization and

environment context. The technology context refers to the internal and external

technology relevant to the organization, and the relevant technologies that are available

for possible adoption. The organization context refers to the descriptive characteristics of

a firm (i.e., organizational structure, firm size, managerial structure, degree of

centralization), resources (human resources and slack resources), and process of

communication (formal and informal) among employees. The environment context

30

consists of the market elements, competitors, and the regulatory environment (Tornatzky

& Fleischer, 1990). Figure 4.1 gives a detailed explanation of the variables identified

from the different theories to create the proposed framework and show its relation to the

TOE Framework.

Figure 4.1: Model for the Adoption of IoT. Source: Rogers (2003)

From the above literature, it can be well informed that the TOE framework is widely used

on the adoption of different innovative technologies and proven to be validated (Ramdani

& Kawalek, 2007). To be able to understand the research model, a combination of theories

was used to measure the constructs and test their relation to the research carried out.

4.4. Proof of Concept

The constructs required for the successful adoption of IoT were identified and this resulted

in the development of the proposed conceptual framework shown in Figure 4.2.

31

Perceived

Usefulness

Perceived Ease of

Use

ObservabilityBehavioral

Intention to Use

Compatibility

Trialability

System

Quality

Relevance

Adoption of IOT

Privacy and

Security

Subjective NormCulture

Social Ethos

Human

Factors

Figure 4.2: Validated framework for Adoption of Internet of Things

The validated framework consisted of the following main constructs of the study that were

tested when the pilot study was carried out:

Perceived Ease of Use – The researcher validated that the adoption of IoT would be

easy for the users of KMD as it involved the automation of the weather forecasting

practice.

Perceived usefulness – The researcher validated that IoT would be relevant to the

activities performed at KMD with regards to weather forecasting. The IOT

technology would enable the weather forecasting practices to be conducted

efficiently.

Observability - The researcher validated that the results of IoT could easily be seen

by the KMD staff and lead to their intention to adopt the technology. This is because

32

the IOT technology would use the wireless sensor network to transmit the data

collected to a main server.

Relevance – The researcher validated that IoT would be relevant to the weather

forecasting practices carried out at KMD. IoT would be clear and understandable

and would not require a lot of mental effort since the process would be automated.

System Quality – Use of IoT would increase the job performance of employees at

KMD. It would also increase the efficiency of weather forecasting due to the

accuracy of weather forecasting information provided.

Compatibility – The researcher validated that IoT was directly compatible with the

need for weather forecasting. The researcher also validated that IoT was technically

compatible with KMD’s current IT platform. For equipment that was not available,

this would easily be acquired from the different IT suppliers.

Trialability – The researcher validated that IoT could be adopted in phases, with

each phase leading to a greater acceptance by the users of KMD. IoT would enable

one type of sensor to be deployed and tested before all the others could be added

on.

Privacy and Security – Adoption of IoT will promote privacy and security of the

data transmitted. Privacy violation is a major issue and using IoT will take care of

this concern.

When validating the framework, additional constructs were identified that influence the

behavioral intention of users to adopt IoT. These included the following:

Subjective Norm – The researcher validated that for IoT to be adopted efficiently,

the technology needs to get buy-in from the senior management as well as

individuals who would influence KMD in their adoption.

Social Ethos – IoT could be trusted to provide data accurately and would therefore

be trustworthy.

Culture – The researcher saw that once an organization buys into IoT and makes it

part of the way they perform their job, the technology would be easily adopted

successfully.

Human Factors – The researcher validated that the participation of users in the

design and implementation of projects promotes greater user acceptance and would

be key.

33


From this chapter, we were able to see how the researcher was able to validate the proposed

framework model through the pilot study that was carried out. An analysis of the

frameworks was carried out and the relevant constructs identified that would be best

adopted for the successful implementation of IoT. Based on this, the proposed framework

was modeled and both the dependent and independent variables were identified.

34

CHAPTER FIVE

5. RESULTS AND FINDINGS

5.1. Introduction

This chapter presents results and findings of the research. From the study population target

of 84 respondents, 72 respondents filled and returned their questionnaires, constituting 85%

response rate. Data analysis was done through Statistical Package for Social Scientists

(SPSS) version 23 tool. Descriptive statistics as well as factor analysis was used to analyze

the data. In the descriptive statistics, relative frequencies were used in some questions and

others were analyzed using mean scores with the help of Likert scale ratings in the analysis.

5.2. Demographic Data

From the findings, the study revealed that the respondents were working in various

divisions which included: ICT, forecasting, public weather dissemination, instruments,

telecommunications, disaster prevention and mitigation, international relation and data

processing. This was an indication that the divisions in the KMD were well represented,

with the forecasting department having the majority staff as shown in Figure 5.1.

Figure 5.1: Various Divisions at KMD

From the findings on how long the respondents had served in the KMD, the study found

that most of the respondents as shown by 49.17% indicated that they had served the KMD

for more than 10 years, 25% of the respondents indicated that they had served in their KMD

for 5 to 10 years, 20.83% of the respondents indicated that they had served in the KMD for

35

2 to 5 years whereas 5% had served in the KMD for less than a year. This was an indication

that a majority of the respondents had served in the KMD long enough to give credible

information to the study on the research. This is shown in Figure 5.2.

Figure 5.2: Length of Time in the Organization

From the findings of the study on the gender of the respondents, the study found that 64.2%

were males whereas 35.8% of the respondents were females as shown in Figure 5.3. This

was an indication that both male and females were working at the KMD, though there were

more males than females.

Figure 5.3: Distribution of respondents by gender

On the age bracket of the respondents the study found that 40.27% were aged between 45

to 55 years, 36.4% of the respondents were aged between 25 to 35 years whereas 23.33%

were aged between 35 to 45 years. This was an indication that the respondents were well

36

distributed in terms of their age and the researcher was therefore able to collect data from

a well distributed population as shown in Figure 5.4.

Figure 5.4: Age Bracket of the Respondents

From the findings on the respondents highest level of education, the study found that 50.7%

of the respondents indicated that they were in possession of bachelor‘s degree, 33.3% of

the respondents had attained master level of education, 12.8% of the respondents indicated

that they had attained diploma level of education whereas 3.7% of the respondents were in

possession of a PhD. This was an indication that a majority of the respondents were well

educated and were in a position to understand and give credible information to the study as

shown in Figure 5.5.

Figure 5.5: Respondents’ Level of Education

5.3. Technological Challenges Faced by KMD on the Current Weather Forecasting

Practices

From the findings on whether the current weather forecasting practices in Kenya were

satisfactory, 72% of the employees indicated that the current weather forecasting practices

were not satisfactory while 28% indicated that the current practices were satisfactory. The

37

results indicated that there was need for adoption of IoT in weather forecasting in Kenya

as most of the employees were in agreement that they were not satisfied with the current

weather forecasting practices.

The study found that KMD faced various external challenges which affected their weather

forecasting. This was shown by 100% of the respondents who answered yes on the

questionnaire to the question ‘Are there any external challenges, which you think affect

weather forecasting in KMD?’ The challenges listed by the respondents were: poor

coverage by weather stations, high cost of procuring, installation and maintenance of AWS,

lack of technical knowledge required for installation, operation and maintenance of

otherwise complex AWS has slowed the impact of AWS, insecurity of the instruments,

ineffective information dissemination and non-user centered weather forecast information.

The study further revealed that there was need to adopt IoT in the weather forecasting

practices in Kenya as shown by 100% of the respondents who indicated yes when asked if

there was a need to adopt IoT in the weather forecasting practices in Kenya. The respondent

who answered this question were well aware of what IoT was. This was because a

conditional statement as put on the questionnaire that guided users to only answer questions

regarding IoT, if they were aware of the technology being discussed.

5.4. Framework for the Adoption of IoT in Weather Forecasting Practices

In section 5.4, the study presents the research findings on the descriptive statistics in the

data collected. Means and standard deviations were used to analyze the responses.

Table 5.1: Opinion on Perceived Ease of Use of Internet of Things

Mean Std.

Deviation

Use of IoT would be easy for me to adopt in carrying out my job 1.690 .793

Interacting with IoT would not require a lot of my mental effort 1.500 .632

My interaction with IoT would be clear and understandable 1.810 .750

From the findings on the respondents’ level of agreement on various aspects of perceived

ease of use of IoT, the study found that majority of the respondents agreed that adoption of

IoT is easy for them as shown by a mean of 1.69 and they found it easy to adopt IoT as

38

shown by a mean of 1.500. All this information was supported by low standard deviation,

an indication that respondents had similar opinions. An example of the responses to the

first question in Table 5.1 has been shown in Figure 5.6.

Figure 5.6: Opinion on Perceived Ease of Use of Internet of Things

Table 5.2: Opinion on Perceived usefulness of Internet of Things

Mean Std.

Deviation

Adoption of IoT would improve my performance in my job 1.940 .772

Adoption of IoT is more convenient than other technologies 1.637 .584

Adoption of IoT in my job would increase my productivity 1.726 .545

On the perceived usefulness of IoT, the study found that majority of the respondents agreed

that adoption of IoT is more convenient than AWS as shown by a mean of 1.637,

productivity was a major problem for adoption of IoT as shown by a mean of 1.726 and

adoption of IoT would improve job performance as shown by a mean of 1.940. This

information was supported by low standard deviation which was an indication that

respondent had similar opinions. An example of the responses to the first question in Table

5.2 has been shown in Figure 5.7.

39

Figure 5.7: Opinion on Perceived Usefulness of Internet of Things

Table 5.3: Opinion on Behavioral Intention of Internet of Things

Mean Std. Deviation

Assuming I have access to Wireless Sensor Networks, I predict

that I would use it.

2.060 .680

Assuming that KMD has access to Wireless Sensor Networks,

the organization’s resistance to the technology would be high.

1.510 .614

From the findings on the respondents’ opinion on the behavioral intention to use IoT, the

study revealed that majority of the respondents agreed that given that they had access to

IoT, they predicted that they would use it with minimal resistance as shown by a mean of

1.510 and assuming they had access to a Wireless Sensor Network, they intended to use it

as shown by a mean of 2.060. The study further found that the above information was

supported by low standard deviation, an indication that the respondents had similar

opinions. An example of the responses to the first question in Table 5.3 has been shown in

Figure 5.8.

40

Figure 5.8: Opinion on Behavioral Intention of Internet of Things

Table 5.4: Opinion on Observability of Internet of Things

Mean Std.

Deviation

The quality of the output I would get from using IoT would

be high

1.690 .704

IoT could be trusted to provide accurate and timely weather

data information

2.259 .815

On the observability of IoT, the study found that majority of the respondents agreed that

the quality of the output they would get from using IoT would be high as shown by a mean

of 2.027 and that the information provided would be accurate and timely as shown by a

mean of 2.259. This information was supported by low standard deviation which was an

indication that respondents had similar opinions. An example of the responses to the first

question in Table 5.4 has been shown in Figure 5.9.

41

Figure 5.9: Opinion on Observability of Internet of Things

Table 5.5: Opinion on Relevance of Internet of Things

Mean Std.

Deviation

In my job, usage of IoT would be important 1.750 .716

In my job, usage of IoT would be relevant 2.055 .769

From the findings on the respondents’ opinion on job relevance of IoT, the study

established that in their jobs, usage of IoT would be important as shown by mean of 1.750

and in their jobs, usage of IoT would be relevant as shown by mean of 2.055.This

information was supported by low standard deviation, an indication that respondents had

similar opinion on job relevance. Figure 5.11 shows the response to the first question in

Table 5.5. An example of the responses to the first question has been shown in Figure 5.10.

Figure 5.10: Opinion on Relevance of Internet of Things

42

Table 5.6: Opinion on System Quality of Internet of Things

Mean Std.

Deviation

The quality of the output I get from IoT would be high 2.027 .820

I would have no problem with the quality of IoT systems'

output

2.259 .815

From the findings on the respondents‘ opinion on output quality of IoT, the study found

that the respondents agreed that the quality of the output they got from IoT would be high

as shown by mean of 2.027 and they would have no problem with the quality of IoT

systems' output as shown by mean of 2.259. An example of the responses to the first

question in Table 5.6 has been shown in Figure 5.11.

Figure 5.11: Opinion on System Quality of Internet of Things

Table 5.7: Opinion on Compatibility of Internet of Things

Mean Std.

Deviation

I think using IoT would fit well with the way that I like to gather

information from other organizations

1.628 .550

I think using IoT would fit well with the way that I like to interact with

other organizations

1.741 .656

Using IoT to interact with other organizations would fit into my lifestyle 1.460 .597

Using IoT to interact with other organizations would be compatible with

how I like to do things.

1.485 .539

43

On the compatibility of IoT, the study found that using IoT to interact with other

organization would fit into their lifestyle, as shown by a mean of 1.460 and using IoT to

interact with other organization would be compatible with how they liked to do things as

shown by a mean of 1.485. The respondents further agreed that they thought using IoT

would fit well with the way that they liked to gather information from other organizations

as shown by a mean of 1.628 and that they thought using IoT would fit well with the way

that they liked to interact with other organizations as shown by a mean of 1.741. An

example of the responses to the first question in Table 5.7 has been shown in Figure 5.12.

Figure 5.12: Opinion on Compatibility of Internet of Things

5.5. Evaluation of the Framework for the Adoption of IoT in Weather Forecasting

Practices by Kenya Meteorological Department

5.5.1. Introduction

An analysis was carried out on the results of the research study survey. This was done using

factor analysis. The results were based on the validated framework shown in Figure 4.2.

5.5.2. Factor Analysis

Factor analysis is an analysis method in statistics that is used to describe the relation among

identified variables that relate in terms of a potentially lower number of unobserved

variables called factors. Factor analysis groups together survey questions that vary and ends

up filtering the large number of questions into a smaller set of factors. This technique

extracts the maximum possible variance from all variables and puts them into a common

value. This study deduced the factors that related to the independent variables identified,

from the framework developed. The identified factors were subjected to factor analysis

44

using SPSS tool version 23. Principle component analysis (PCA) was used for the

extraction process. Factor weights were computed to extract the maximum possible

variance, and this continued until there was no further meaningful variance left, as shown

in Table 5.8.

The communality measures the percentage of variance a variable has compared to all the

factors listed and may be explained as the reliability of the variable in the research study.

From the analysis, variables with high values are said to be well represented in the research

study, while variables with low values are not well represented.

Table 5.8: Communalities

Table 5.8 helped the researcher to estimate the communalities for each variable. This is the

proportion of variance that each item has in common with other factors. For example, the

analysis showed that ‘Adoption of IoT is more convenient than other technologies’ had

96.9% communality or shared relationship with other factors. This value had the greatest

communality with others it was relating to, while ‘I think using IoT would fit well with the

45

way that I like to gather information from other organizations‘ had the least communality

with others of about 81.1%.

According to the variance extraction rule, the extraction variance should be more than 0.7.

If the variance is less than 0.7, then we should not consider that a factor. As shown in Table

4.1, all the factors identified had an extraction variance that was above 0.7. This showed

that all the factors used were reliable and could therefore be utilized in the study as the least

variance was 0.81 which is more than the threshold of 0.7. The best 8 factors were then

extracted from the 19 factors analysed.

In order to analyze the above information, the factor model was rotated. The component

matrix shown in Table 5.9 was then rotated using Varimax (Variance Maximization) with

Kaiser Normalization.

Table 5.9: Component Matrix

Component Matrixa

Component

1 2 3 4 5 6 7 8

Use of IOT would be easy for me to adopt in

carrying out my job .440 .310 .704 -.414 -.013 -.090 .113 .026

Interacting with IOT would not require a lot of

my mental effort. .285 .692 .222 -.430 -.063 -.223 .148 -.080

My interaction with IOT would be clear and

understandable .155 .396 -.036 -.069 .580 .419 -.102 .326

Adoption of IOT would improve my

performance in my job -.218 .423 .205 -.027 -.028 .598 -.383 -.331

Adoption of IOT is more convenient than other

technologies -.145 -.239 .055 .565 .419 -.346 -.051 -.527

Adoption of Internet of Things in my job

would increase my productivity .450 -.570 .453 .041 .129 .218 .077 .286

Assuming I have access to Wireless Sensor

Networks, I would use it. .359 -.381 -.624 -.196 -.275 .420 .122 -.063

Assuming that KMD has access to Wireless

Sensor Networks, the organization’s

resistance to the technology would be high.

-.748 .100 -.115 -.108 -.264 -.022 -.379 .041

I think using IOT would fit well with the way

that I like to gather information for the

organization

-.722 .061 .101 .130 -.340 -.070 .251 .313

46

I think using IOT would result in many users

being satisfied with the results of its

implementation

.170 -.336 .802 -.067 -.019 .004 -.338 -.129

The quality of the output I would get from

using IOT would be high .162 .074 .207 .642 -.037 .532 -.123 .270

IOT could be trusted to provide accurate and

timely weather data information .262 .811 -.077 -.006 -.112 -.279 .016 .171

In my job, usage of IOT would be important .289 .668 -.088 .471 .201 .210 .361 -.063

In my job, usage of IOT would be relevant .780 -.169 .008 .324 -.149 -.083 -.056 -.104

The quality of the output I get from IOT would

be high .693 -.177 -.194 -.184 .483 -.295 -.195 .109

I would have no problem with the quality of

IOT systems' output .048 -.241 -.796 -.300 .298 .091 .051 -.009


that I like to gather information from other

organizations

-.499 .514 -.140 .181 .506 -.172 -.218 .077


that I like to gather information from other

organizations

-.426 -.473 .214 .145 .265 -.296 .026 .418

Using IOT to interact with other organizations

would fit into my lifestyle -.473 -.168 .231 -.591 .431 .310 .045 -.098

Using IOT to interact with other organizations

would be compatible with how I like to do

things.

-.408 -.134 .330 .024 .238 .180 .683 -.224

The results in Table 5.9 allowed the researcher to identify what variables fall under each of

the 8 major extracted factors. The component columns displays these 8 major extracted

factors. Each of the 19 variables was looked at and placed to one of the 8 factors. The 19

variables were displayed on the row level of Table 5.9. A variable is said to belong to a

factor to which it explains more variation than any other factor. From Table 5.9 the

individual variables constituting the eight factors extracted were summarized and identified

in Table 5.10.

47

Table 5.10: Total Variances

From the Table 5.10, the initial eigenvalues as well as the extraction sums of squared

loadings are displayed for each of the 19 questions analysed. Eigenvalues are the variances

of the factors. As the researcher conducted the factor analysis, each variable had a variance

of 1. The total variance was equal to the number of variables used in the analysis, in this

case, 19. The eigenvalue for a given factor measures the variance of the factor in relation

to all the variables. The ratio of eigenvalues is the ratio of importance of the factors with

respect to the total variables. If a factor has a low eigenvalue, then it is contributing little to

the area of study and may be ignored as redundant with more important factors. Eigenvalues

measure the amount of variation in the total sample accounted for by each factor. According

to the Kaiser Criterion, Eigenvalues is a good criterion for determining a factor. If

Eigenvalues have a value greater than one, we should consider that a factor and if

Eigenvalues has a value less than one, then we should not consider that a factor. Under the

Initial Eigenvalues, several items were looked into as explained below.

Component: The component column represented the 19 variables (questionnaire

questions), that were looked at. The initial number of factors was 19 which was the same

48

as the number of variables used in the factor analysis. However, not all 19 factors were

retained. In Table 4.4, only the first 8 factors were retained.

Total: This column contains the eigenvalues. The first factor will always account for the

most variance and therefore will always have the highest eigenvalue. The next factor will

account for what has remained, and so on. Therefore, each successive factor will account

for less and less variance.

Percentage of Variance: This column contains the percentage of total variance accounted

for by each factor. E.g. For the first factor, (3.460/19) ∗ 100 = 𝟏𝟖. 𝟐𝟏𝟏%

Cumulative Percentage: This column displays the cumulative percentage of variances of

the current factor as well as all the preceding factors.

Under the Extraction Sums of Squared Loadings section, the number of rows in the table

correspond to the number of factors retained. In this example, we requested that 8 factors

be retained, so there are 8 rows, one for each retained factor. The values in this side of the

table are calculated in the same way as the values in the left side of the table. The values in

both sides of the table will be the same, because they were rotated using Varimax.

The cumulative value of 90.152, means that the various factors considered by the study as

influencing adoption of IoT are up to 90.152%, which was an indication that they were the

major factors that explained the adoption of IoT. Extraction sums of squared loadings,

initial eigenvalues and eigenvalues after extraction were the same for Principal Component

Analysis (PCA) extraction, but if other extraction methods would be used, eigenvalues after

extraction would be lower than their initial counterparts.


From the study, it was seen that: KMD would find it easy to adopt IoT as a new technology

being introduced to assist with weather forecasting, interaction with IoT would be

understandable for the employees of KMD, interacting with IoT would not require a lot of

the users’ mental effort and KMD would therefore be willing to adopt this technology. The

study established that usage of IoT would be important and relevant to the jobs carried out

at KMD, especially effective and accurate weather forecasting. The results of the data

analysed therefore showed that IoT was a good choice of the technology that would

alleviate their current challenges with regards to weather forecasting.

49

CHAPTER SIX

6. DISCUSSION, CONCLUSION AND RECOMMENDATIONS

6.1. Introduction

This chapter finalizes the analysis and findings identified in order to provide the answers

to the research objectives. In this chapter, the researcher reflects upon the results and states

what needs to be done better, by the KMD in the adoption of the IoT technology.

Furthermore, the researcher brings up some thoughts concerning further research for

unfollowed areas.

6.2. Summary

The research was intended to examine the technological challenges faced by KMD in their

current weather practices, to develop a framework that will enable the successful adoption

of IoT by KMD and to evaluate the framework developed on whether it could be used to

adopt IoT in the weather forecasting practices of the KMD. The study found out that there

were several challenges faced by KMD in the current weather forecasting practices. The

researcher then opted to use descriptive research design as the methodology of the study.

This is because it provided an in-depth investigation of the KMD with a primary motive to

determine factors and relationships that have resulted in their current weather forecasting

practices.

The study established that the possible solutions to improve current challenges of weather

forecast by KMD were: the adoption of IoT to enable the weather forecasting practices in

Kenya to be conducted properly as well as improve the efficiency of the dissemination of

weather information, purchase of new equipment for weather forecasting and training of

the staff on the use of IoT in weather forecasting. This research study has therefore

successfully developed and evaluated a framework for adoption IoT in weather forecasting,

in order to solve the current technological challenges faced by KMD.

6.3. Discussion

This section will look at the discussion that was carried out in relation to the three objectives

of the study.

50


Forecasting Practices

The first objective of the study was “To identify the technological challenges of the current

weather forecasting practices currently faced by Kenya Meteorological Department”. In

order to determine the technological challenges faced, the researcher carried out a literature

review study as well as visited the Kenya Meteorological Department to identify the current

technological challenges with regards to weather forecasting that they were facing. The

study found that KMD faced various technological challenges which affected weather

forecasting in KMD as shown by the respondents who indicated yes to the question. From

the findings on the opinion on whether the current weather forecasting practices in Kenya

were satisfactory, the study found that the majority of the respondents indicated that the

current weather forecasting practices in Kenya were not satisfactory. The study further

revealed there was need to adopt IoT in the weather forecasting practices in Kenya as shown

by majority of the respondents who indicated yes to the study.

The challenges faced by KMD in the current weather forecasting practices include: poor

coverage by weather stations, high cost of procuring, installation and maintenance of AWS,

lack of technical knowledge required for installation, operation and maintenance of

otherwise complex AWS has slowed the impact of AWS, insecurity of the instruments,

ineffective information dissemination and non-user centered weather forecast information.

This indicated that there was need for adoption of IoT in weather forecasting in Kenya as

this would improve the methods of forecasting weather information.

6.3.2. Framework for the Adoption of IoT in Weather Forecasting Practices

The second objective of the study was “To develop a framework that enables the adoption

of IoT in weather forecasting practices by Kenya Meteorological Department”. A

framework was developed based on the literature review carried out as well as an analysis

of the theoretical frameworks identified for the study. The framework consisted of factors

influencing the adoption of IoT by the KMD which included: perceived ease of use,

perceived usefulness, technical compatibility, observability, trialability and actual system

use.

The study found out that there was a strong correlation of perceived usefulness to the rate

of adoption of IoT. The employees predicted that if they had access to IoT, they would use

it or would intend to use it. Similarly, Acquity Group (2014) found that one of the most

51

important factors for the adoption of IoT services in US is the usefulness of the technology.

Similarly, Brown et al. (2012) suggested that perceived usefulness is a significant predictor

of the intention to use IoT services in the UK.

On observability of IoT, the study found that majority of the respondents agreed that the

quality of the output they would get from using IoT would be high. When the output from

a system is high, the users’ confidence in the system is increased. This directly affects the

rate of adoption of the technology in the organization as it’s easily embraced.

The study revealed that there is a significant positive effect of security and privacy on the

behavioral intention to use IoT. Privacy and security are major concerns of any organization

when adopting a new technology, and it has significant influence on the adoption of

technology. In order to increase the adoption and usage level of information systems and

applications, users need to feel safe when interacting with such systems. The findings were

concurrent with Coughlan et al. (2012) in UK, who found that privacy and security are

important factors for the adoption of IoT in the country.

The study revealed that compatibility is positively correlated with the rate of adoption. IoT

is directly compatible with the need for weather forecasting practices at KMD. Trialability

is linked to divisibility of an innovation. IoT can be adopted in phases, with each phase

potentially leading to a greater adoption. From the study, it was clear that the adoption of

IoT would be easy for KMD and they would find it easy to adopt IoT. Perceived ease of

use is therefore positively correlated to the rate of adoption of IoT. Similarly, Gao and Bai

(2014) pointed out that perceived ease of use has significant effect on the behavioral

intention to use IoT services in China. Thus, based on the above discussion, it is expected

in this study that the effect of perceived ease of use on behavioral intention is significant.

The ultimate dependent variable of this study is the use behavior and it is defined as the

individual's positive or negative feeling about performing the target behavior. Venkatesh et

al. (2000) pointed out that behavioral intention and user behavior are variables that predict

the adoption of a new technology. If a customer perceives a new technology service to be

useful, his behavioral intention is affected toward using the technology. This intention is

translated into actual usage of the technology which becomes a pattern. The study revealed

that there is a significant positive effect of behavioral intention on the use behavior of IoT

services.

52

6.3.3. Evaluation of the Framework for the Adoption of IoT in Weather

Forecasting Practices by Kenya Meteorological Department

The third objective of the study was “To evaluate the framework in relation to adoption of

IoT in weather forecasting practices by Kenya Meteorological Department”. The

evaluation of the framework was done using statistical analysis of the model. Factor

analysis was used to show whether the data collected was fit for the study. The analysis

showed that the constructs had high variances which was a good sign of the model used.

The researcher saw that the adoption of IoT would be easily adopted as the users required

a technology that was able to address the current technological challenges that the KMD

were facing with regards to weather forecasting practices. The study revealed that the

advantages of IoT identified were: automation of weather forecasting practices, greater area

coverage, greater accuracy, saving of both time and cost, sensor nodes can be deployed in

harsh environments that make the sensor networks more effective, fault tolerance,

connectivity and dynamic sensor scheduling. This was through the literature review that

was carried out on the IoT technology. In addition to this, a majority of the population

agreed that they would adopt IoT if it were implemented in the organization. This was

shown by the majority of the respondents who agreed that given that they had access to

IoT, they predicted that they would use it.


forecasting by KMD were adoption of IoT in the weather forecasting practices in Kenya

and dissemination of the information, purchase of new equipment for weather forecasting

and training of the staff on use of IoT in weather forecasting. Additionally, the study found

out that there were other influences of technology adoption that would easily affect how

IoT was received by KMD as an organization.

The study found that one of the factors affecting the adoption of IoT in weather forecasting

practices was culture where culture is a system of shared meaning within an organization

that determines to large degree how employees act. The shared values, norms and the

organizational practices do shape the culture that assist organizations to adopt the changes.

Human factor was another factor affecting the adoption of IoT where human factor explains

the way in which individuals play an effective and important role in the technology

adoption process. Technology is not successful if its users do not accept it. It is argued that

the participation of users in the design and implementation of projects promote greater user

53

acceptance. Other factors affecting the adoption of IoT included the social need to feel a

strong desire of something. Social resources which involves the capital, material, and

skilled personnel vital for innovation and adoption of a new thing was also another factor

affecting the adoption of IoT. Sympathetic social ethos which involves an environment in

which the dominant groups are prepared to consider innovation seriously and are receptive

to new idea, was also a major factor. Additional factors included organizational structure,

governmental and political factors and the cost of adopting IoT.

6.4. Conclusion

The study found that the current weather forecasting practices in Kenya were not

satisfactory, thus the need for adoption of IoT in weather forecasting practices in Kenya.

The study found that KMD faced various external challenges which affected weather

forecasting in KMD, which necessitate the need to adopt IoT in the weather forecasting

practices in Kenya.


Forecasting Practices

The study concluded that the various challenges facing the KMD in weather forecasting

were: poor coverage by weather stations, high cost of procuring, installation and

maintenance of AWS, lack of technical knowledge required for installation, operation and

maintenance of otherwise complex AWS has slowed the impact of AWS, insecurity of the

instruments, ineffective information dissemination and non-user centered weather forecast

information.

6.4.2. Framework for the Adoption of IoT in Weather Forecasting Practices

On the benefits of IoT, the study revealed they were: sensing accuracy, large area coverage,

minimal human interaction, sensor nodes that can be deployed in harsh environments

making the sensor networks more effective, fault tolerance, connectivity and dynamic

sensor scheduling. It was therefore clear that IoT would help the KMD in their weather

forecasting practices.

6.4.3. Evaluation of the Framework In Relation To the Adoption of IoT in

Weather Forecasting Practices by Kenya Meteorological Department

The expansion of the IoT technology for weather forecasting will deliver vital weather

prediction information by the Kenya Meteorological Department to the public at large, to

enable them in taking essential steps to diversify weather hazards. IoT enabled weather

54

systems should therefore address these issues that current systems running on different

technologies have not been able to address with regards to weather forecasting.


forecasting by KMD were the adoption of IoT in the weather forecasting practices in Kenya

and dissemination of the information, purchase of new pieces of equipment for weather

forecasting and training of the staff on IoT in weather forecasting.

6.5. Recommendations and Future Work

6.5.1. General Recommendations

The study recommends that the possible solutions for KMD are: creating awareness of the

new technologies in the weather forecasting practices, improving staff training on new

technologies in weather forecasting, installation of more weather stations, more research to

be done to explore new and efficient methods of weather forecasting, use of automated

wireless sensor weather stations, employment of qualified personnel, government financial

inputs and proper use of effective drought index. The study contributes to the literature by

providing a new conceptual model and filling the gap of incorporating trust and IT

knowledge as well as security and privacy into a framework.

In the process of conducting this study, the researcher encountered several limitations some

of which offer opportunities for future research. Many of the respondents were managers

in the department who may not have the final authority in making the decision to adopt IoT

for weather forecasting practices. Since the study was solely conducted on the

meteorological department head office in Nairobi, the results may suffer from regional

biases. Therefore, the results need to be interpreted carefully and replicated in other

meteorological departments of other countries to improve their relevance.

6.5.2. Recommendations for Further Work

With regards to future work, the results of this study suggest new directions for future

research. Researchers in the field of information system ought to put more emphasis on

adoption and assimilation of IoT as a technological innovation rather than administrative

innovation that people hear about.

55

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APPENDICES

APPENDIX I: QUESTIONNAIRE

INTRODUCTION

Internet of Things (IoT) is a system of related computing devices, machines, animals or

people that are provided with unique identifiers to capture and transfer data over a

network without requiring human intervention or human-to-computer interaction. For

example, IoT-enabled weather systems are designed to collect data from various objects,

by the use of sensors. The ultimate goal is to create a better world for human beings that

is a smart environment, where all objects around humans know what it is humans like,

want and need, and act accordingly without explicit instructions. The sensors in

integration with IoT help in collecting weather data which is further pooled in the cloud

for analysis. Sensor devices are placed at different locations to collect the data to predict

the weather patterns of an area.

This questionnaire will be used for a research project, to investigate on the adoption of

Internet of Things (IoT) by the Kenya Meteorological Department (KMD). The results of

the report will be used solely for academic purposes and a copy of the same will be

availed to KMD on request, with the permission of United States International University,

of which the research work is to be undertaken with partial fulfillment for Masters Degree

in Information Systems and Technology.

DEMOGRAPHICS

Please fill in each question, on the spaces provided where applicable.

PART I:

1. Name of the Division you are working for……………………………

2. How long have you worked in the company?

Less than 1year [ ]

2 to 5 years [ ]

5 to 10 years [ ]

More than 10 years [ ]

3. What is your gender?

Male [ ] Female [ ]

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4. What is your age bracket?

Below 25 years [ ]

25 to 35 years [ ]

35 to 45 years [ ]

45 to 55 years [ ]

Above 55 years [ ]

5. What is your level of education? (Tick where appropriate)

PhD [ ]

Masters [ ]

Bachelors [ ]

Diploma or equivalent [ ]

PART II: Perspective of Weather Forecasting in Kenya

6. Please indicate the level which you agree/disagree with the following statements based

on the following rankings by ticking 1,2,3,4 as per ranking: 1(Strongly agree), 2(Agree) 3

(Disagree), 4(Strongly disagree)?

Strongly

agree

Agree Disagree Strongly

disagree

The current weather forecasting practices

in Kenya are satisfactory


in Kenya could be improved


in Kenya satisfy the end users

7. Are there any external challenges, which you think affect weather forecasting in KMD?

Yes [ ]

No [ ]

If Yes, please list briefly

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………………………………………………………………………………………

………………………………………………………………………………………

………………………………………………………………………………………

………………………………………………

8. Is there need to adopt IoT in the weather forecasting practices in Kenya?

Yes [ ]

No [ ]

If Yes, please list briefly

………………………………………………………………………………………

………………………………………………………………………………………

………………………………………………………………………………………

………………………………………………

9. What are the various benefits of Wireless Sensor Networks in IoT that you are aware

of?

………………………………………………………………………………………………

………………………………………………………………………………………………

………………………………………………………………………………………………

………………………………………………………………………………………………

10. What would you consider are the challenges faced by the Kenya Meteorological

Department in weather forecasting? List them below.

………………………………………………………………………………………………

………………………………………………………………………………………………

………………………………………………………………………………………………

………………………………………………………………………………………………

………………………………………………………..……

11. What are the different solutions that the KMD has implemented in order to improve

the current practices of weather forecasting? List them below.

………………………………………………………………………………………………

………………………………………………………………………………………………

………………………………………………………………………………………………

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………………………………………………………………………………………………

……………………………………………………..……

12. Are you aware of the Internet of Things technology?

Yes [ ]

No [ ]

If you answered Yes to Question 12, please answer the questions in Part III below,

otherwise stop at Question 12.

Part III: Framework for Adoption of Internet of Things

(Please indicate the level which you agree/disagree with the following statements based

on the following rankings by ticking 1,2,3,4 as per ranking:1( Strongly agree), 2(Agree)3

(Disagree), 4(Strongly disagree).

13. Perceived ease of use (Your opinion on perceived ease of use Internet of Things)

Strongly

agree


disagree

Use of IoT would be easy for me to adopt

in carrying out my job

Interacting with IoT would not require a lot

of my mental effort.

My interaction with IoT would be clear

and understandable

14. Perceived usefulness (Your opinion on perceived usefulness of Internet of Things)

Strongly

agree


disagree

Adoption of IoT would improve my

performance in my job

Adoption of IoT is more convenient than

other technologies

Adoption of Internet of Things

in my job would increase my productivity

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15. Behavioral Intention (Your opinion on behavioral intention of Internet of Things)

Strongly

agree


disagree

Assuming I have access to Wireless Sensor

Networks, I would use it.

Assuming that KMD has access to

Wireless Sensor Networks, the

organization’s resistance to the technology

would be high.

16. User Satisfaction (Your opinion on user satisfaction of Internet of Things)

Strongly

agree


disagree

I think using IoT would fit well with the

way that I like to gather information for

the organization

I think using IoT would result in many

users being satisfied with the results of

its implementation

17. Observability (Your opinion on observability of Internet of Things)

Strongly

agree


disagree

The quality of the output I would get

from using IoT would be high

IoT could be trusted to provide accurate

and timely weather data information

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18. Relevance (Your opinion on relevance of Internet of Things)

Strongly

agree


disagree

In my job, usage of IoT would be

important

In my job, usage of IoT would be

relevant

19. System Quality (Your opinion on System Quality of Internet of Things)

Strongly

agree


disagree

The quality of the output I get from IoT

would be high

I would have no problem with the quality

of IoT systems' output

20. Compatibility (Your opinion on Compatibility of Internet of Things)

Strongly

agree


disagree


way that I like to gather information from

other organizations


way that I like to interact with other

organizations

Using IoT to interact with other

organizations would fit into my lifestyle

Using IoT to interact with other

organizations would be compatible with

how I like to do things.

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THE END

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APPENDIX II: WEATHER FORECASTING INSTRUMENTS

Some of the instruments used to collect weather information at KMD include the following:

Figure II.1: Rain Gauge – To measure rainfall

Figure II.2: Sunshine Recorder – To measure the duration of sunshine

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Figure II.3: Tensiometers – To measure soil moisture intensity and temperature

Figure II.4: Stevenson Screen – To measure humidity and air temperature

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Figure II.5: Automatic Weather Station

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