• Tiada Hasil Ditemukan



Academic year: 2022


Tunjuk Lagi ( halaman)






A thesis submitted in fulfilment of the requirement for the degree of Doctor of Philosophy in Information Technology

Kulliyyah of Information and Communication Technology International Islamic University Malaysia

JUNE 2018




The innovation in Information and Communication Technology (ICT) has provided the future technology Internet of Things (IoT) with immense prospects and opportunities.

IoT is a network founded on the use of sensors, actuators, beams, Radio Frequency Identification (RFID) devices, and software in things that can transform the future healthcare into pervasive healthcare delivery. IoT-based healthcare system can improve the delivery of healthcare services efficiently and innovatively by growing huge volume of patients’ data (big data) that can invoke proactive, predictive decisions and insights in future healthcare of Pakistan. However, no prior research is conducted so far in the context of IoT-based healthcare in Pakistan. To fill the research gap, the study contributed to develop and validate proposed research model and investigated significant factors influencing the IoT-based healthcare adoption by the community, medical professionals (doctors, support staff, health administrators), and end-users specifically in underserved areas of Pakistan. The research study confirmed proposed research framework in the precision of UTAUT and HBM theories on a sample of 281 diverse medical professionals’ responses from five hospitals located in different cities of Pakistan. The research employed exploratory, descriptive, and causal approaches to test and validate the research hypotheses using Exploratory Factor Analysis (EFA), measurement model, Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) statistical techniques. The empirical findings statistically confirmed that performance expectancy (PE), effort expectancy (EE), facilitating condition (FC), doctor-patient relation (DPR) were significant predictors in technological context whereas, perceived severity(PS) of health risk behavior was also a significant factor of IoT-based healthcare adoption. The factors, which had no statistically significant impact, included perceived susceptibility (PSS) of health risk, social influence (SI), and trust (TR). These research findings are expected to improve prevailing healthcare in underserved areas and potentially benefit several stakeholders such as the healthcare service providers, health insurance companies, and remote healthcare providers, and designers of IoT-based health information systems in future.



ثحبلا ةصلاخ

قل ايجولونكتل ةريبك اصرف تلااصتلااو تامولعملا ايجولونكت يف راكتبلاا رفو د ا

يهو لبقتسمل

ميهافمو تاقيبطت راشتنا نا .)تنرتنلاا ةكبش ربع ةزهجلاا لكب مكحتلا متي ثيح( ءايشلاا تنرتنا يف ةيحصلا ةياعرلا نسحي نأ نكمي ءايشلاا تنرتنا لبقتسملا

ا ميدقت ثيح نم تامدخل


.اهب ةطبترملا تايلمعلا تاينقت ىلع ادامتعا اهسيسأت مت تامولعم ةكبش اهنأب ءايشلاا تنرتنا فرعت

لوحت حيتي امب رتويبمكلا جماربو ةيويدارلاا ةزهجلااو ةيكيناكيملا تلاغشملاو راعشتسلاا ةزهجأ .اراشتنا رثكأو ةروطتم ةيحص تامدخو ةياعر ىلإ ةيحصلا ةياعرلا عاطقلا هجاوي ،نهارلا تقولا ي ف

ددع ةدايزو ،ةيحصلا ةياعرلا فيلاكت عافترا كلذ يف امب تايدحتلا نم اددع ناتسكاب يف يحصلا نيينهملا صقنو ،نيينفلا نيفظوملا ةيافك مدعو ،ةيبطلا ءاطخلأا تلااح ديازتو ،ةنمزملا ضارملأا .تامدخلا ةصقانو ةيفيرلا قطانملا يف نييبطلا ه تتا دقو

مادختساو لوبق مييقتل ةيثحبلا ةساردلا هذ

يف ناتسكاب يف ىضرملاو ءابطلأاو نييبطلا نيينهملا لبق نم ةيحصلا ةياعرلا ماظن يف ةينقتلا ( دحوم يجولونكت جذومن مادختسا UTAUT

( هيبطلا ةقثلا جذومن كلذكو ،) مت . ) HBM

نم ةنيع ىلع يميهافملا جذومنلا ديكأت 281

ءابطلأا( نيبيجملا بلاطو ،نييبطلا نيينهملاو ،

يف ةفلتخم ةنيدم تايفشتسم ةسمخ نم )تامولعملا ايجولونكت يف نيصصختملاو ،بطلا .ناتسكاب يفاشكتسلاا رادحنلاا ليلحت ءارجإ مت ،ةيثحبلا ةساردلا لماعل ةيفاشكتسلاا ةعيبطلل ارظنو

ةيلكيهلا تلاداعملا جذومنو تايضرفلا رابتخلا ددعتملاو ت ليلحت عم

ةينبلا تاقيبط (

امك ) SEM

راطلإا نأ نم جئاتنلا تققحت دقو .ةعقوتملا جئاتنلا ىلع لوصحلل قيبطتلا تايجمرب مادختسا مت ةيارد رثكلأا تايرظنلا ديكأت ىلإ ادانتساو .ةظوحلملا تانايبلا عم هذيفنت متيل بسانم حرتقملا ( UTAUT & HBM )

رطاخملا ةساردلا هذه رهظتو ،يحصلا راطلإا يف ةروصتملا ةيحصلا

:نيلقتسم نيلماع نم فلأتت يتلا ببسب ةليلق ربتعت ايجولونكتلا هاجت نيمدختسملاا ةيساسح نا

يف صاخشلاا كولس يف ايباجيا اريثأت يطعي امب ةيقيبطتلا جماربلاو ةيكذلا ةزهجلا مادختسا راشتنا . ثحبلا ةنيع أ ضيرملاو بيبطلا نيب ةقلاعلا نأف ،كلذ ىلع ةولاعو

ف يباجيإ ريثأت هل اضي ي


مادختسا هاجت ثحبلا ةنيع يف صاخشلاا نا تاينقت ىلع ةمئاقلا ةيكذلا ةيحصلا ةياعرلا تاينقت


ناتسكاب يف ءايشلاا





The thesis of Zulfiqar Ali Solangi has been approved by the following:


Madihah S.Abd. Aziz Supervisor


Mohd. Syarqawy Bin Hamzah Co-Supervisor


Akram Zeki MZM Khedher Internal Examiner


Marini Othman External Examiner


Khalil-Ur-Rahmen Khoumbati External Examiner


Asadullah Shah Chairman




I hereby declare that this thesis is the result of my own investigations, except where otherwise stated. I also declare that it has not been previously or concurrently submitted as a part of whole for any other degrees at IIUM or other institutions.

Zulfiqar Ali Solangi

Signature ... Date ...






I declare that the copyright holders of this thesis are jointly owned by the student and IIUM.

Copyright © 2018 Zulfiqar Ali Solangi and International Islamic University Malaysia. All rights reserved.

No part of this unpublished research may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise without prior written permission of the copyright holder except as provided below

1. Any material contained in or derived from this unpublished research may be used by others in their writing with due acknowledgement.

2. IIUM or its library will have the right to make and transmit copies (print or electronic) for institutional and academic purposes.

3. The IIUM library will have the right to make, store in a retrieved system and supply copies of this unpublished research if requested by other universities and research libraries.

By signing this form, I acknowledged that I have read and understand the IIUM Intellectual Property Right and Commercialization policy.

Affirmed by Zulfiqar Ali Solangi

……..……….. ………..

Signature Date



This thesis is dedicated to my beloved father who dedicated himself for the complete professional occupancy in the educational cause to enlighten humanity as a teacher.

More, I dedicate this work to all my teachers who laid the foundation of what I turned out to be in life.




Allah (S.W.T) has told us that His pleasure may be attained through gratitude. “If you are grateful, He is pleased with you..." (Az-Zumar 39:7). It is for this reason that the Prophet exhorted us to "Love Allah as we benefit from His grace" (Tirmidhi). So all magnificence is due to Allah (S.W.T), Whose Grace and Mercies have been on me throughout my life, His Mercies and Grace on me ease the challenging tasks of completing this study and thesis.

Further, I wish to express my appreciation to my family members and thanks to those who provided their time, effort and support for this research.

Finally, I like pay special thanks to my supervisor Dr. Madihah S. Abd. Aziz, co-supervisor Dr. Mohammad Syarqawy Bin Hamzah and Professor Dr. Asadullah Shah for their continuous support, encouragement, and leadership, and for that I will ever be grateful and obliged to them.




Abstract ... ii

Abstract in Arabic ... iii

Approval Page ... iv

Declaration ... v

Dedication ... vii

Acknowledgements ... viii

Table of Contents ... ix

List of Tables ... xiii

List of Figures ... xxviii

List of Abbreviations ... xx

List of Formulae ... xxiii


1.1 Overview... 1

1.2 Background ... 7

1.3 Problem Statement ... 11

1.4 Research Questions ... 12

1.5 Aims and Objectives of Research ... 13

1.6 Research Scope ... 14

1.7 Significance of Research Study ... 14

1.8 Context of Research Study ... 15

1.9 Research Approach Adopted... 16

1.10Thesis Structure ... 16


2.1 Introduction... 20

2.2 Healthcare Crises in Pakistan ... 21

2.3 Public-Private Sector Efforts and Use of ICT in Healthcare ... 24

2.4 ICT in Health Sector ... 26

2.4.1 Electronic Health ... 27

2.4.2 Mobile Health (mHealth) ... 28

2.4.3 Driving Forces for ICT in Health ... 29

2.5 Internet of Things (IoT) ... 32

2.5.1 Diverse Applications of Internet of Things ... 33

2.5.2 Drivers for the Internet of Things ... 34

2.5.3 IoT Sensors and Actuators ... 35

2.5.4 IoT Network Connectivity ... 36

2.5.5 IoT People and Processes ... 40

2.5.6 IoT Storage Platform ... 41

2.6 Internet of (Health) Things (IoHT) ... 43

2.7 Smart Health and Medical IoT Development Platform ... 47

2.7.1 MySignals SW Complete Kit (eHMDP) ... 47

2.7.2 e-Health Sensor Shield V2.0 (eHSSV2) ... 49

2.7.3 Waspmote V15 ... 51

2.8 Smart Health Wearables ... 52



2.9 Information System Acceptance Theories and Models ... 54

2.9.1 Theories Related To Behavioral Intention ... 55

2.9.2 Theory of Reasoned Action (TRA) ... 56

2.9.3 Technology Acceptance Model ... 57

2.9.4 Theory of Planned Behavior ... 58

2.9.5 Unified Theory of Acceptance and Use of Technology ... 59

2.9.6 Health Belief Model (HBM) ... 61

2.10 Summary of Previous Researches in Health UTAUT AND HBM ... 63

2.11 Chapter Summary ... 80


3.1 Introduction... 81

3.2 Development Research Framework and Research Hypotheses... 81

3.3 Dependent Variable ... 86

3.3.1 Use Behavior ... 87

3.3.2 Cues to Action/Behavioral Intention ... 87

3.3.3 Perceived Health Risk ... 88

3.4 Independent Variables ... 88

3.4.1 Trust Factor ... 88

3.4.2 Doctor-Patient Relationship ... 89

3.4.3 Performance Expectancy:... 90

3.4.4 Effort Expectancy: ... 90

3.4.5 Social Influence:... 91

3.4.6 Facilitating Conditions: ... 91

3.4.7 Perceived Susceptibility: ... 92

3.4.8 Perceived Severity:... 92

3.5 Chapter Summary ... 93


4.1 Introduction... 95

4.2 Research Paradigm ... 97

4.2.1 Positivist Approach ... 98

4.2.2 Interpretivist Approach ... 99

4.3 Research Approach Adopted ... 100

4.4 Research Design ... 101

4.4.1 Unit of Analysis ... 105

4.4.2 Time Horizon: Cross-sectional ... 106

4.5 Sampling and Population Strategy ... 106

4.5.1 Population of Survey ... 106

4.5.2 Sampling of Survey ... 108

4.5.3 Sample Size ... 109

4.6 Instrument/Questionnaire Development ... 110

4.7 Data Collection Procedure ... 116

4.7.1 Response Formatting ... 118

4.7.2 Scale Development... 119

4.7.3 Mapping of RQs, ROs and RHs ... 120

4.8 Pre-Testing and Pilot Study ... 123

4.8.1 Pre-testing ... 123

4.8.2 Pilot Study ... 124



4.8.3 Demographic Sample Profile of Pilot Study ... 124

4.8.4 Background Information about IoT, Health Sensors ... 126

4.8.5 Reliability of the Instrument (Pilot Study) ... 128

4.8.6 Conclusion of Pilot Study ... 130

4.9 Data Analysis ... 131

4.9.1 Preliminary Data Analysis ... 131

4.9.2 Factor Analysis ... 134

4.9.3 Structural Equation Modeling ... 137

4.9.4 Measurement Model... 138

4.9.5 Estimation and Model Fit ... 138

4.9.6 Structural Model Evaluation and Hypothesis Testing ... 143

4.10 Ethical Considerations ... 144

4.11 Chapter Summary ... 145


5.1 Introduction... 147

5.2 Sample Size and Response Rate ... 148

5.2.1 Data Examination and Screening ... 149

5.2.2 Identification of Missing Values ... 149

5.2.3 Outliers Examination ... 150

5.3 Classification of the Sample ... 152

5.4 Demographic Characteristics of Respondents ... 153

5.4.1 Background Information ... 155

5.5 Descriptive Statistics of Construct Items ... 158

5.5.1 Cues-to-Usage (CTU) ... 158

5.5.2 Performance Expectancy (PE) ... 158

5.5.3 Effort Expectancy (EE) ... 159

5.5.4 Social Influence (SI) ... 160

5.5.5 Facilitating Conditions (FC) ... 161

5.5.6 Trust Construct ... 161

5.5.7 Doctor-patient relation (DPR) ... 162

5.5.8 Perceived Susceptibility (PSS) ... 163

5.5.9 Perceived Severity (PS) ... 163

5.6 Factor Analysis ... 164

5.6.1 Exploratory Factor Analysis (EFA) ... 165

5.6.2 Communalities ... 167

5.6.3 Exploratory Factors Extraction Model ... 168

5.7 Reliability Scale ... 172

5.7.1 Cues-to-Usage ... 172

5.7.2 Performance Expectancy ... 173

5.7.3 Effort Expectancy ... 174

5.7.4 Social Influence ... 175

5.7.5 Facilitating Conditions ... 176

5.7.7 Doctor-Patient Relation ... 179

5.7.8 Perceived Susceptibility (Perceived Health Threat)... 180

5.7.9 Perceived Severity (Perceived Health Threat) ... 181

5.8 Bivariate Correlations Between Latent Factors ... 182

5.9 Normality of Data for Latent Factors ... 183

5.10 Descriptive Statistics of Latent Variables ... 184



5.11 Structural Equation Model (SEM) ... 184

5.11.1 Measurement Model and Confirmatory Factor Analysis Results 185 5.11.2 Goodness-of-Fit (GOF) Indices ... 186

5.12 Assessment of Reliability and Validity of Constructs ... 192

5.12.1 Reliability of Constructs ... 192

5.12.2 Average Variance Extracted (AVE) ... 194

5.13 Structural Model Evaluation and Hypotheses Testing ... 200

5.14 Modifying Structural Model by Removing Non-Significant Paths ... 210

5.15 Chapter Summary ... 215


6.1 Introduction... 217

6.2 Discussion of Survey Response ... 219

6.2.1 Response Rate ... 219

6.2.2 Demographical Characteristics of Respondents ... 220

6.2.3 Constructs and Items ... 222

6.2.4 Hypotheses Testing ... 229

6.3 Chapter Summary ... 237


7.1 Introduction... 239

7.2 Implications of Research Findings ... 239

7.3 Practical Implications ... 242

7.4 Summary of the Research Contribution ... 243

7.5 Limitations of the Study ... 245

7.6 Future Researches ... 247

7.7 Conclusion ... 249







Table 2.1 Key Health Issues in Pakistan 23

Table 2.2 Health Category Targets for Pakistan Vision 2030 25 Table 2.3 Examples of the Use of ICT in Health Systems and Services 30

Table 2.4 Diverse Applications of IoT 33

Table 2.5 MySignals SW Complete Kit Sensors 48

Table 2.6 e-Health Sensor Shield V2.0 50

Table 2.7 Waspmote Radio Interface 52

Table 2.8 Summary of Past Research 68

Table 3.1 Constructs Hypothetical Relationship 86

Table 4.1 Research Paradigms 97

Table 4.3 Overall Research Approach 105

Table 4.4 Internet Users and Internet Penetration Rate in Pakistan 107

Table 4.5 Total Teledensity (Fixed + WLL + Mobile) 112

Table 4.6 Questionnaire Distribution and Respnse Rate 117

Table 4.7 Mapping of RQs with ROs and Research Hypotheses 120 Table 4.8 Demographics Details of Respondents (Pilot Study N =40) 125 Table 4.9 Background Information about IoT Health Sensors, Mobile Apps 127

Table 4.10 Overall Cronbach’s α Score of Pilot Study 127

Table 4.11 Factor Loading, Cronbach’s Alpha, CR, AVE 129

Table 4.12 Standardized Estimates of Measurement Model 139

Table 4.13 Cutoff Criteria for Goodness of Fit Measures 140

Table 5.1 Identification of Missing Values 149

Table 5.2 Multivariate Outlier Detection 151



Table 5.3 Final Quantitative Study: Occupation of Respondents 152

Table 5.4 Final Quantitative Study: Workplace 152

Table 5.5 Final Quantitative Study: Geographic Location 153 Table 5.6 Demographics Details of Respondents (Final Study) 154 Table 5.7 Participant Background Information about Internet of Things 155

Table 5.8 Health Sensors Frequencies 157

Table 5.9 Descriptive Statistics of Measured Items CTU Construct 158 Table 5.10 Descriptive Statistics of Measured Items of PE Construct 159 Table 5.11 Descriptive Statistics of Measured Items of EE Construct 160 Table 5.12 Descriptive statistics of Measured Items of SI Construct 160 Table 5.13 Descriptive Statistics of Measured Items of FC Construct 161 Table 5.14 Descriptive Statistics of Measured Items of Trust construct 162 Table 5.15 Descriptive Statistics of Measured Items of DPR Construct 162 Table 5.16 Descriptive Statistics of Measured Items of PSS Construct 163 Table 5.17 Descriptive Statistics of Measured Items of PS Construct 164 Table 5.18 KMO Statistics and Bartlett’s Test of Sphericity 166

Table 5.19 Communalities 167

Table 5.20 Factors Extracted and Total Variance Explained in EFA Model 169

Table 5.21 Loading of Measured Items on Latent Factors 170

Table 5.22 (1) Reliability Statistics (Cues-to-Usage) 172

Table 5.22 (2) Summary Item Statistics (Cues-to-Usage) 172

Table 5.22 (3) Item Total Statistics (Cues-to-Usage) 173

Table 5.23 (1) Reliability Statistics (Performance Expectancy) 173 Table 5.23 (2) Summary Item Statistics (Performance Expectancy) 174 Table 5.23 (3) Item Total Statistics (Performance Expectancy) 174



Table 5.24 (1) Reliability Statistics (Effort Expectancy) 175 Table 5.24 (2) Summary Item Statistics (Effort Expectancy) 175

Table 5.24 (3) ItemTotal Statistics (Effort Expectancy) 175

Table 5.25 (1) Reliability Statistics (Social Influence) 176 Table 5.25 (2) Summary Item Statistics (Social Influence) 176

Table 5.25 (3) Item-Total Statistics (Social Influence) 176

Table 5.26 (1) Reliability Statistics (Facilitating Condition) 177 Table 5.26 (2) Summary Item Statistics (Facilitating Condition) 177 Table 5.26 (3) Item-Total Statistics (Facilitating Condition) 177

Table 5.27 (1) Reliability Statistics (Trust) 178

Table 5.27 (2) Summary Item Statistics (Trust) 178

Table 5.27 (3) Item-Total Statistics (Trust) 178

Table 5.28 (1) Reliability Statistics (Doctor-patient Relation) 179 Table 5.28 (2) Summary Item Statistics (Doctor-patient Relation) 179 Table 5.28 (3) Item-Total Statistics (Doctor-patient Relation) 179 Table 5.29 (1) Reliability Statistics (Perceived Susceptibility) 180 Table 5.29 (2) Summary Item Statistics (Perceived Susceptibility) 180 Table 5.29 (3) Item-Total Statistics (Perceived Susceptibility) 181 Table 5.30 (1) Reliability Statistics Perceived Severity (Perceived Health Threat) 181 Table 5.30 (2) Summary Item Statistics Perceived Severity 182

Table 5.30 (3) Item-Total Statistics Perceived Severity 182

Table 5.31 Pearson’s Correlation between Latent Factors/Constructs 182

Table 5.32 Tests of Normality 183

Table 5.33 Descriptive Statistics of Latent Variables 184

Table 5.34 Details of Constructs, Number of Items and Item Codes 188



Table 5.35 Model Goodness-of-Fit for Initial CFA 189

Table 5.36 Model Fit Indices of Final CFA 192

Table 5.37 Reliability Coefficient of the Observed Variables 193

Table 5.38 Average Variance Extracted 194

Table 5.39 Convergent Validity 196

Table 5.40 Discriminant Validity 197

Table 5.41 Inter-Construct Correlation 198

Table 5.42 AMOS Output Covariance: (CFA model) 198

Table 5.43 AMOS Output Correlations: (Group number 1 - Default model) 199

Table 5.44 List of Hypotheses Status 201

Table 5.45 Structural Model Fit Indices 202

Table 5.46 Regression Weights of Latent Constructs in Structural Model 203

Table 5.47 Hypotheses Testing Results 204

Table 5.48 Regression Weights of latent Constructs in Modified SM 210

Table 5.49 Revised Hypotheses Testing Results 212

Table 5.50 Goodness-of-Fit Indices Revised Structural Model 213

Table 6.1 Results of Proposed Hypotheses 222

Table 6.2 Results of Proposed Hypotheses 230




Figure 1.1 The confluence brought about by the IoT (Vujovic, 2015) 3

Figure 1.2 IoT Healthcare Model 4

Figure 1.3 Potential Value of the Internet of Things (McKinsey, 2014 page7) 10

Figure 1.4 Thesis structure 17

Figure 2.1 Literature Review Flow 20

Figure 2.2 Connected Devices in the Market 33

Figure 2.3 IoT Infrastructure 34

Figure 2.4 IoT Sensors 35

Figure 2.5 IoT Connectivity 36

Figure 2.6 ZigBee IEEE 802.15.4 37

Figure 2.7 Sigfox Wi-Fi Network Interface Card 37

Figure 2.8 LoRAWAN Connectivity 38

Figure 2.9 IoT People and Processes 40

Figure 2.10 IoT Devices Connectivity 41

Figure 2.11 MySignals SW Complete Kit (eHealth MDP) 48

Figure 2.12 e-Health Sensor Shield V2.0 50

Figure 2.13 Waspmote V15 51

Figure 2.14 Connected Wearable by 2021 53

Figure 2.15 Theory of Reasoned Action (TRA) Model 56

Figure 2.16 Davis Technology Acceptance Model 58

Figure 2.17 Theory of Planned Behavior 59

Figure 2.18 UTAUT Model 60

Figure 2.19 Health Belief Model (HBM) 62



Figure 2.20 HBM Components 62

Figure 3.1 Development of Main Constrcuts in Proposed Research Framework 82 Figure 3.2 Development of Main Constructs From Health Context 83

Figure 3.3 Proposed Research Framework 85

Figure 4.1 Research Design 103

Figure 5.1 Scree Plot of Extracted Factors 170

Figure 5.2 Initial CFA (Measurement Model) Hypothesized from EFA 187

Figure 5.3 Final CFA 191

Figure 5.4 Structural Model 205

Figure 5.5 Final Structural Model 206

Figure 5.6 Revised Structural Model Without Non-significant Hypotheses 214




3G 3rd Generation 4G 4th Generation

AGFI Adjusted Goodness –of-Fit index

AMOS Analysis Moment of Structures Software ANOVA Analysis Of Variance

AVE Average Variance Extracted BI Behavioral Intention

BLE Bluetooth Low Energy CMO Chief Medical Office

COPD Chronic Obstructive Pulmonary Disease CTA Cues to Action

CTU Cues-To-Usage

CFA Confirmatory Factor Analysis CFI Comparative Fit Index

CR Critical Ratio

DF Degree of Freedom

DPR Doctor-Patient Relation ECG Electrocardiogram Sensor

EE Effort Expectancy

eHealth Electronic Health

eHMDP eHealth Medical Development Platform EFA Exploratory Factor Analysis

eHSSV2 e-Health Sensor Shield V2.0 EMG Electromyography Sensor EMR Electronic Medical Record

FA Factor Analysis

FC Facilitating Condition GDP Gross Domestic Product GFI Goodness-of-Fit Index GNP Gross National Product GOF Goodness-of-Fit

GSR Galvanic Skin Response HBM Health Belief Model

HDI Human Development Index

HIS Health Information System HPI Human Poverty Index

HR Health Risk

HRS Human Resource for Health IBM International Business Machines



ICT Information and Communication Technology IEEE International Electrical and Electronics Engineers IMR Infant Mortality Rate

IoE Internet of EveryThing IoHT Internet of Health Things IoIT Internet of Industrial Things IoT Internet of Things

IDT Innovation Diffusion Theory

IS Information Systems

KMO Kaiser-Meyer-Olkin LBS Location Based Services

LoRaWAN Low Power Wide Area Network LV Latent Variables

MAC Media Access Control MI Modification Indices

ML Maximum Likelihood

mHealth Mobile Health

MMR Maternal Mortality Ratio MPCU Model for PC Utilization NFC Near Field Communication

NFI Normed Fit Index

PCA Principle Component Analysis PDA Personal Digital Assistant

PE Performance Expectancy

PHR Perceived Health Risk PHR Personal Health Record PS Perceived Severity PSS Perceived Susceptibility

RFId Radio Frequency Indemnification

RMSEA Root Mean Square Error of Approximation

SE Standard Error

SEM Structural Equation Model SI Social Influence

SIC Squared Inter-construct Correlation

SM Structural Model

SPSS Statistical Package for Social Sciences TAM Technology Acceptance Model

TPB Theory of Planned Behavior

TR Trust

TRA Theory of Reasoned Action

UTAUT Unified Theory of Acceptance and Use of Technology Wi-Fi Wireless Fidelity

WLS Weighted Least Square



WWW World Wide Web




Formula 5.1 Measurement of Composite Reliability 193

Formula 5.2 Measurement of Average Variance Extracted 194





This research studies the new smart innovation of Information and Communication Technology (ICT) available today in healthcare, IoT-based or smart healthcare, focusing on innovation adoption, specifically the factors determining usage intention. This study is to recognize and measure the motivational factors that would expedite the introduction and pervasive use of IoT-based healthcare services and improve prevailing conditions of healthcare in under-served areas of Pakistan. Developing countries like Pakistan have the main goals to provide access to medical services for the population living in rural areas and to use proficiently limited healthcare resources (Babar Tasneem Shaikh Arslan Mazhar and Assad Hafeez, 2013).

This marvel has been depicted as the versatile wonder, and has driven wide societal and monetary changes. Without a doubt, smart devices utilization has changed the way individuals impact, as well as the way they plan their everyday lives, arrange themselves socially, and get to informative, business, employment and healthcare managing opportunities. Modern Information Systems (IS), more specifically the new trend announced these days: the Internet of Things (IoT) or Internet of EveryThing (IoE), has immense prospectus and opportunities in supporting and managing healthcare cost, and improving quality of care (Daim, Behkami, Basoglu, Kök &

Hogaboam, 2016). IoT is a network of networks, in which typically a massive number of smart objects (smart phones, smart watches, smart glasses, smart TV etc.), things, sensors or devices are connected through the information and communication



technology to provide value-added services. IoT and its potential can provide new solutions to almost every aspect of daily activity. The IoT is the perfect storm of advanced sensors, real time networks, and massive data centers (Jwilliams, 2015). It is internationally recognized that Internet of Things (IoT) will support future healthcare systems in order to target the upcoming societal health challenges. Therefore, in future IoT-based healthcare is going to equip the traditional healthcare with advanced smart solutions in a new way to assess, assist, and treat the patients remotely or wherever the patient may be. By using Internet enabled devices like smart health sensors, actuators, beams, Sensor Shield V2.0 (SSV2), and smartphone apps etc. In future, there is change in tendency of healthcare with invention of medical wearable devices shift towards patient centric healthcare from hospitals to patient wherever he/she may be at home, workplace or on travel (Islam, Kwak & Kabir, 2015). In Internet of Things, the network is founded on the use of sensors, actuators, beams, RFID devices and software in things that can transform the future healthcare into pervasive healthcare. The use of sensors, actuators, beams, radio frequency identification (RFID) devices, and software in smart things can transform healthcare into pervasive healthcare, so it may improve future healthcare, its related issues to delivery, management, and development. IoT in the healthcare, for example, Physician may embed biological sensors in the body of the patient to measure blood pressure (BP), electrocardiogram (ECG), heartbeat, and oxygen saturation in order to better determine cardiac patient condition remotely and react before some vulnerabilities appear. It is clear that their purpose is to enable better healthcare. One can then easily extend IoT technology to a patient monitoring system (Agilent, 2015). “Things are potentially autonomous, semi-autonomous or not autonomous” (O’Leary, 2013). However, the sensor instruction, actuators, beams, and software may breed artificial intelligence into Internet of Things to act completely



However, other factors such as perceived cost, perceived quality, health consciousness, trust and social value are deemed as less significant in predicting intention to consume

The research has stated perceived ease of use, relative advantage, perceived usefulness, perceived risk and trust were used as factors to impact the behavioral intention

This research aims to examine the factors influencing the adoption of e-payment in Kuala Lumpur, Malaysia in which the independent variables include security, trust, perceived ease

Pi and Sangruang (2011) stated that perceived risk factors including convenience, physical, performance and social risk, have negative influence on online

The hypothesis of the research is made based on the factors such as destination attractions, attitude and self-sufficiency through social media which influence the travel

Perceived personal impact, perceived outcome organizations, trust in management and change communication are four factors hypothesized to have significant relationship

The study examines the factors influencing the adoption of e-banking in Somalia using these selected independent variables namely internet speed, trust, perceived ease

the impact of asset quality, income structure and macroeconomic factors on insolvency risk for both conventional and Islamic banks of Pakistan, while the second model

Perceived ease of use, usefulness and social influence are among the factors that influence people in using social media which also can be predicted by

(a) Factors that Influence on Purchase Intention among Youth in Melaka on Social Media Platforms Based on the findings of the previous section, there are four factors which are

Technology Acceptance Model (TAM) framework was used as variable factors which were perceived usefulness, perceived ease of use, perceived risk and trust, to measure factors

Exclusive QS survey data reveals how prospective international students and higher education institutions are responding to this global health

This research paper examines the impacts of company specific factors on the adoption of risk based auditing by the auditors as well as to establish whether these factors

This framework posits that six factors: Trust (TR), Perceived Usefulness (PU), Recommendations and Referrals (RR), Website Quality (WQ), Perceived Risk (PR), and

(2002b) stated that trust in online store sites enhances the possibility that the consumers will be willingly to deal with e-commerce transactions. This means that

In this research the author will examine which factors from the variables of privacy calculus (perceived benefits, perceived privacy control, perceived risk, perceived fairness,


This article reviews the potential of oil palm trunk (OPT) for SA production, from bioconversion aspects such as biomass pretreatment, enzymatic saccharification, and fermentation,

H1: There is a significant relationship between social influence and Malaysian entrepreneur’s behavioral intention to adopt social media marketing... Page 57 of

In this research, the researchers will examine the relationship between the fluctuation of housing price in the United States and the macroeconomic variables, which are

In this study, the variables used were perceived risk, perceived trust, social influence, behavioral intention and facilitating condition as they were

Thus, this study may create awareness among healthcare provider in HUSM to consider health belief especially perceived barriers as one of the factors that

With purpose to fill the gap caused by the lack of literature and research focusing on social media and cloud computing in supply chain, this study