Effects of Mobile Phone Use on Household Travel Behavior In

EFFECTS OF MOBILE PHONE USE ON HOUSEHOLD TRAVEL BEHAVIOR IN

KUMASI, GHANA

Dennis Kwadwo Okyere

APPROVED BY SUPERVISORY COMMITTEE:

Brian J. L. Berry, Chair

Bobby C. Alexander

Harold D. Clarke

Euel W. Elliot Copyright c 2018

Dennis Kwadwo Okyere

All rights reserved To my dear wife, Helina Sarkodie-Minkah, with heartfelt thanks for your unconditional love, support and understanding. To my beautiful kids, Michael and Michelle, for the inspiration. This achievement is yours! EFFECTS OF MOBILE PHONE USE ON HOUSEHOLD TRAVEL BEHAVIOR IN

KUMASI, GHANA

DENNIS KWADWO OKYERE, BSc, MPhil, MS

DISSERTATION

Presented to the Faculty of

The University of Texas at Dallas

in Partial Fulﬁllment

of the Requirements

for the Degree of

DOCTOR OF PHILOSOPHY IN

PUBLIC POLICY AND POLITICAL ECONOMY

THE UNIVERSITY OF TEXAS AT DALLAS

December 2018 ACKNOWLEDGMENTS

This dissertation would not have seen the light of day if not for the support and encouragement I received from several extraordinary people, a few of which are mentioned here. I owe much gratitude to my dissertation chair, Dr. Brian J. L. Berry, for his guidance, insight and mentorship, as well as spending time to review this work. He even provided substantial

ﬁnancial assistance in my data collection eﬀort. I wish to thank my mentor, Dr. Kwasi

Kwafo Adarkwa, of the Kwame Nkrumah University of Science and Technology, Ghana, for introducing me to the ﬁeld of transportation planning. Your inspiring guidance and constant encouragement is what has brought me this far in my young academic career.

I am also grateful to my committee members, Drs. Bobby Alexander, Harold Clarke and

Euel Elliot. I became interested in mixed methods research when Dr. Alexander encouraged me to explore how results from the quantitative research approach would be explained by the qualitative data. I wish to thank Dr. Clarke for introducing me to structural equation modeling, and he became a great guide as I undertook my quantitative analysis. I am very thankful to Dr. Elliot for his helpful suggestions. I am grateful to other faculty members, in particular Dr. Karl Ho, for his meticulous comments and suggestions.

Special thanks to Dr. Francis Bilson Darku who provided extraordinary support at various stages of the dissertation, including the questionnaire design, data analysis, editing earlier versions of the manuscript, among many other things. I am indeed grateful to you. I am also thankful to Messrs Eric Gaisie and Romeo Abraham for taking some time to read through and edit portions of this dissertation.

v Messrs Eric Adabor, Yaw Yeboah Kwarteng, Joy Aryan Kizito, and Prosper Tornyeviadzie were of tremendous assistance in the collection of primary data for this dissertation. Mr. Yaw Yeboah Kwarteng also helped in the cleaning and translation of the qualitative data for which I am indeed greatly indebted.

I would especially like to acknowledge my parents and siblings for their support, prayers and motivation. Finally, I acknowledge the unseen hand of the Almighty God which protected and directed me during my PhD training.

October 2018

vi EFFECTS OF MOBILE PHONE USE ON HOUSEHOLD TRAVEL BEHAVIOR IN

KUMASI, GHANA

Dennis Kwadwo Okyere, PhD The University of Texas at Dallas, 2018

Supervising Professor: Brian J. L. Berry, Chair

This research addressed how the use of mobile phone affects travel behavior in a developing country context – Kumasi, Ghana – in light of the growing interest in research that seek to understand the relationship between telecommunication and transportation, which until now has been confined to countries that have experienced gradual evolution of the use of technology as they developed. Using mixed methods research, two studies were conducted to examine the relationship between the variables of interest and to identify the mechanisms that underlie the relationship. In the first phase of the study, 661 adults completed a cross-sectional survey to investigate primarily the nature of the relationship between mobile phone use and travel (e.g., substitution, complementary, neutral). Using structural equation modeling, results from the survey showed a positive relationship between the main study variables, that is, participants who used the mobile phone more often and for more applications tended to travel more. This relationship was mirrored in an extension of the model where several demographic measures, including age, gender, educational level, family type, vehicle ownership, income, and location, were considered. In the follow-up interviews it was discovered that the participants believed the effect of mobile phone use on travel to be substitutionary, thus contradicting the conclusion from the survey analysis. The qualitative study was conducted as a sub-sample of the larger household survey, to dig deeper into

vii the causal mechanisms by which mobile phone affected travel behavior. Semi-structured interviews were conducted with 24 participants who had participated in the quantitative study and selected using a criterion sampling. The evidence from the qualitative interviews pointed to the fact that, although few participants felt their travel had enhanced, generally, mobile phone use had led to a reduction in the amount of travel participants made in their day-to-day activities, therefore serving as a substitute. These results showed that, clearly, there was a conflict between what people did as in the quantitative survey and what they believed was happening. Possible explanations of this occurrence and its implications to mixed methods research are discussed in the conclusion chapter of the dissertation. Also, from the interviews, barriers including the nature of ones business, poor infrastructure and service delivery, insecurity and mistrust, as well as poor network connection and reliability, were found to inhibit the full utilization of the more advanced applications of mobile phone by the participants. These barriers also provided some explanation into the results from the quantitative phase. Although the divergence in the results from both methods do not provide the basis for clear conclusions, the preponderance of evidence from the study tends to support the complementary thesis rather than the substitution thesis. To the extent that findings from the qualitative data have provided a behavioral understanding of the results from the quantitative data, the study has provided a comprehensive view of the relationship between telecommunication and transportation, thus addressing the limitations of the vast majority of previous studies. To leverage on the complementarity relationship in Ghana, several policy recommendations are addressed in the conclusion chapter of the dissertation.

viii TABLE OF CONTENTS

ACKNOWLEDGMENTS ...... v ABSTRACT ...... vii LIST OF FIGURES ...... xiii LIST OF TABLES ...... xiv CHAPTER 1 PURPOSE STATEMENT AND RATIONALE OF THE STUDY . . 1 1.1 Purpose Statement ...... 1 1.2 Rationale of the Study ...... 2 1.3 Organization of the Study ...... 5 CHAPTER 2 STUDIES OF TELECOMMUNICATION – TRANSPORT NEXUS . 7 2.1 Introduction ...... 7 2.2 Historical Highlights and Growth in Communication Technologies ...... 8 2.3 Conceptual Considerations of Telecommunication – Transportation Relationship 13 2.3.1 The Impact of Transportation on the Demand of Telecommunication 13 2.3.2 The Impact of Transportation on the Supply of Telecommunication . 14 2.3.3 The Impact of Telecommunication on the Supply of Transportation . 15 2.3.4 The Impact of Telecommunication on the Demand of Transportation 15 2.4 Empirical Studies of Telecommunication – Transportation Relationship . . . 17 2.4.1 The Aggregate Level ...... 18 2.4.2 Disaggregate Level ...... 20 2.5 Theories on Technology Adoption and Use ...... 23 2.5.1 Innovation Diﬀusion Theory ...... 23 2.5.2 Technology Acceptance Model (TAM) ...... 25 2.6 Summary of Chapter and Limitations of the Existing Literature ...... 26 CHAPTER 3 CONTEXT OF THE STUDY ...... 29 3.1 Introduction ...... 29 3.2 Locational and Demographic Characteristics ...... 29 3.3 Development of Telecommunication and Transportation in Ghana ...... 31 3.3.1 Pre-Colonial and Colonial Era ...... 31

ix 3.3.2 Post Colonial (Independence) Era ...... 35 3.3.3 The Constitutional and Present Era ...... 38 CHAPTER 4 RESEARCH DESIGN AND METHODOLOGY ...... 46 4.1 Introduction ...... 46 4.2 Chapter Organization ...... 47 4.3 Research Design Strategy ...... 48 4.4 Target Population and Unit of Analysis ...... 53 4.5 Study Variables and Data Types ...... 54 4.6 Quantitative Phase ...... 57 4.6.1 Questionnaire Development ...... 57 4.6.2 Sample Selection and Data Collection ...... 60 4.6.3 Data Processing and Analysis ...... 67 4.7 Qualitative Phase ...... 77 4.7.1 Participants ...... 77 4.7.2 Interview Procedures ...... 78 4.7.3 Data Processing and Analysis ...... 80 4.8 Reporting the Results ...... 81 CHAPTER 5 UNDERLYING PROCESSES OF MOBILE PHONE USE AND TRAVEL BEHAVIOR IN THE KUMASI METROPOLIS ...... 82 5.1 Introduction ...... 82 5.2 Statistical Analysis Methods ...... 83 5.3 Organization of Chapter ...... 84 5.4 Background Characteristics of the Study Participants ...... 84 5.4.1 Residential Location Distribution ...... 85 5.4.2 Socio-Demographic Characteristics ...... 88 5.5 Analysis of the Measures of the Intensity of Mobile Phone Use in Kumasi . . 91 5.5.1 A Portrait of Mobile Phone Ownership in Kumasi ...... 92 5.5.2 Usage of Mobile Phone in Kumasi ...... 96 5.6 Extracting Mobile Phone Use Factors – A Principal Component Approach . 102

x 5.7 Analysis of the Measures of Travel Behavior in Kumasi ...... 106 5.7.1 Trip Distance and Duration ...... 107 5.7.2 Mode of Travel ...... 110 5.7.3 Trip Purpose ...... 113 5.8 Extracting Travel Behavior Factors – A Principal Axis Factoring Approach . 116 5.9 Summary of Chapter ...... 119 CHAPTER 6 A STRUCTURAL ANALYSIS OF MOBILE PHONE USE AND TRAVEL BEHAVIOR IN KUMASI ...... 121 6.1 Introduction ...... 121 6.2 Statistical Analysis Methods ...... 121 6.3 Organization of Chapter ...... 122 6.4 Speciﬁcation of the Measurement Model ...... 123 6.4.1 Measurement Model for “Intensity of Mobile Phone Use” ...... 123 6.4.2 Measurement Model for “Travel Behavior” ...... 129 6.5 Causal Relationship between Mobile Phone Use and Travel Behavior . . . . 132 6.5.1 Structural Equation Model of Mobile Phone Use and Travel Behavior without Covariates ...... 132 6.5.2 Structural Equation Model of Mobile Phone Use and Travel Behavior with Covariates ...... 133 6.6 Summary of Chapter ...... 134 CHAPTER 7 QUALITATIVE MECHANISMS UNDERLYING MOBILE PHONE USE AND TRAVEL BEHAVIOR NEXUS IN KUMASI ...... 137 7.1 Introduction ...... 137 7.2 Organization of Chapter ...... 137 7.3 Characteristics of Participants ...... 138 7.4 Data Coding and Analysis ...... 139 7.5 Qualitative Results ...... 140 7.5.1 Attributes of Mobile Phone Use ...... 142 7.5.2 Dependence on Mobile Phone Usage ...... 146 7.5.3 Impacts of Mobile Phone Use ...... 153

xi 7.5.4 Underlying Mechanisms of Mobile Phone Use Impact on Travel Behavior159 7.6 Summary of Chapter ...... 164 CHAPTER 8 CONCLUSIONS ...... 166 8.1 Summary ...... 166 8.2 Limitations and Future Research ...... 174 APPENDIX A SURVEY INSTRUMENT FOR QUANTITATIVE STUDY . . . . . 176 APPENDIX B TRAVEL DIARY FOR QUANTITATIVE STUDY ...... 192 APPENDIX C SAMPLE SIZE DETERMINATION FOR QUANTITATIVE STUDY 194 APPENDIX D INTERVIEW GUIDE FOR QUALITATIVE STUDY ...... 195 APPENDIX E SUMMARY OF INTERVIEW THEMES ...... 198 REFERENCES ...... 200 BIOGRAPHICAL SKETCH ...... 212 CURRICULUM VITAE

xii LIST OF FIGURES

3.1 Geographical Location of Ghana in the Sub-Regional Context ...... 30 3.2 Subscription rates of fixed-telephone and mobile phones in Ghana (1995-2014) . 40 3.3 Geographical Location of Kumasi in the National, Regional and District Contexts 41 3.4 Amount and rate of built-up land change in the KMA (1986 – 2014) ...... 42 4.1 Visual Model for the Sequential Explanatory Mixed Methods Design Procedures 52 4.2 Distribution of proxy communities within study clusters/sub-metros ...... 63 5.1 Map showing the broad urban zones in the Kumasi Metropolis ...... 86 5.2 Distribution of households within the urban zones in the Metropolis ...... 87 5.3 Relationship between Type of Phone use and (A) gender (B) Age (C) Education and (D) Income ...... 95 5.4 Relationship between Frequency of Using Mobile Money and (A) gender (B)Age (C) Education and (D) Residential Location ...... 99 5.5 Relationship between Frequency of Using Social Media and (A) gender (B) Age (C) Education and (D) Income ...... 101 5.6 Relationship between Average Distance of Travel and (A) Age (B) Education (C) Residential Location and (D) Vehicle Ownership ...... 108 5.7 Relationship between Non-Motorized Transport as the Primary Mode and (A) Age (B) Education (C) Income and (D) Residential Location ...... 112 5.8 Relationship between Private Car as the Primary Mode and (A) Age (B) Educa- tion (C) Income and (D) Vehicle Ownership ...... 113 5.9 Distribution of Trip Purposes among the Study Participants ...... 115 6.1 Hypothesized CFA Model of the Intensity of Mobile Phone Use (left) and Travel Behavior (right) ...... 123 6.2 Path Diagram of the Initial CFA Model for ”Intensity of Mobile Phone Use” . . 125 6.3 Path Diagram of the Modified CFA Model for “Intensity of Mobile Phone Use” 127 6.4 Path Diagram of the Final CFA Model for “Intensity of Mobile Phone Use” . . 128 6.5 Path Diagram of the Final CFA Model for “Travel” ...... 131 6.6 Identified causal relationship between mobile phone use and travel behavior . . . 133 6.7 Path Diagram of the Full Structural Equation Model ...... 135

xiii LIST OF TABLES

2.1 Summary of empirical studies on the impacts of telecommunication on travel . . 27 4.1 Description of study variables ...... 56 4.2 Description of housing areas in the study area (Kumasi) ...... 62 5.1 Background characteristics of study participants ...... 90 5.2 Attributes of Mobile Phone Adoption in Kumasi ...... 93 5.3 Socio-demographic variables and mobile phone ownership variables. Values represent Pearson χ2 p-values ...... 94 5.4 Frequency of Mobile Phone Use ...... 97 5.5 Initial eigenvalue estimates of principal component analysis on mobile phone use items ...... 103 5.6 Original factor loadings of the 12 variables on the ﬁrst four components . . . . . 104 5.7 Rotated factor loadings of the 12 variables ...... 105 5.8 Per trip distance traveled per day ...... 106 5.9 Per trip distance traveled per day ...... 111 5.10 Socio-demographic variables and Purpose of Travel variables. Values represent Pearson χ2 p-values ...... 115 5.11 Initial eigenvalue estimates of principal component analysis on travel behavior items ...... 117 5.12 Original factor loadings of the 9 variables ...... 118 5.13 Rotated factor loadings of the 9 variables on the ﬁrst three components . . . . . 119 6.1 Standardized Solution for “Intensity of Mobile Phone Use” ...... 129 6.2 Standardized Solution for “Travel Behavior” ...... 131 7.1 Characteristics of the Study Participants ...... 139 7.2 Coding Guide ...... 141

xiv CHAPTER 1

PURPOSE STATEMENT AND RATIONALE OF THE STUDY

1.1 Purpose Statement

This study is a part of a broader concern with understanding the relationship between telecommunication and transportation, but within a developing country context. In recent years, the debate on the trade-oﬀ between telecommunications and transportation has gained considerable attention particularly in sustainable transport and transport economics scholarship. Central to the argument on this relationship lies the common characteristics and several parallels conceptual, physical and analytical that exist between telecommunications and transportation (Choo and Mokhtarian, 2007). In spite of the large amount of conceptual and empirical research on this subject, the direction of the relationship remains unclear (Mokhtarian, 2009). While various authors posit that telecommunication reduce demand for transportation (see for example Fadare and Agunloye, 2015; Choo et al., 2005;

Nie et al., 2002), others have argued that telecommunications stimulate and increase the need to travel (Mokhtarian, 2009, 2002). These studies however have often been confined to countries that have experienced gradual evolution of the use of technology as they developed, rather than in the arena of latecomers who have bypassed some of the technological stages that developed countries went through. Perhaps, an investigation into the relationship between telecommunications and transportation within a developing country context, where adoption of technology has been revolutionary albeit underserved in transportation infrastructure, might tie down the dilemma. More specifically, the purpose of this study is to examine whether means of telecommunication can reduce household trip generation in the city of Kumasi, Ghana. This research will have important implications for transportation planning and policy, through improving knowledge of telecommunication influences as well as the consequent potential behavioral shifts on households’ travel.

1 1.2 Rationale of the Study

For the first time in recorded history, there are more people worldwide living in urban than rural areas (DESA, 2013) . According to (DESA, 2013) and (Heilig, 2012), about 67 percent of the world’s population is expected to live in urban areas, specifically cities, by 2050, and much of this growth is expected to be experienced by developing economies (Cobbinah et al., 2015). Urbanization is an eminent phenomenon in both developing and developed countries and it manifests itself in increase in cities’ population, and the subsequent outward physical expansion of urban areas (Kneebone, 2014). As cities expand to the periphery, inhabitants or residents especially those in the high-income bracket tend to live farther away and commute daily to transact businesses and access basic facilities and services at the city centre (Kneebone, 2014), exacerbating urban sprawl. Due to the movement of people to and from the city centre day in and day out, there is the flow of information, ideas and practices. Sprawl has an effect on outer city inhabitants in the sense that they would have to travel longer distances to their places of work, which are normally concentrated at the city centre. Since there is a direct correlation between distance and cost, increased demand for travel therefore means an increased transport cost, and its associated debilitating effects on road accidents, energy consumption and pollution(Litman, 2016). In developing economies, however, the situation is worse. Efforts to address this problem have in recent times focused on the role of telecommunication in: (1) providing real time travel information to drivers (Litman, 2013); and (2) as an alternative to travel (Button and Stough, 2006). Telecommunication and transportation are closely interrelated, and are seen as alternative forms of communication (Choo and Mokhtarian, 2007). It has been argued that reducing the impact of sprawl and its subsequent effect on journey to work and travel cost, there is the need to improve ICT and other telecommunication systems. This notion is premised on the strong spatio-temporal representation about the development of telecommunication technologies, given that, these technologies allow the transmission of services at a distance

2 making it possible for physical movements to be substituted by virtual relationships (Aguiléra et al., 2012). Telecommunication or ICT has helped developed economies to reduce travel times and cost. This case is evident in the fact that ICT infrastructure has enabled interaction and interconnectivity between and across homes, office buildings and transportation systems. For example, people now have the luxury of not making any face-to-face trips to businesses since it is rather convenient and efficient to work from home. In addition, modern telecommunication technologies, such as real-time traffic information and mobile telecommunications allow road users to anticipate, avoid and respond to traffic delays (Litman,

2013).

Thus, as popularity of ICT increases, people’s travel behaviour and ultimately the use of transport systems and the spatial conﬁguration of built form could change fundamentally.

Owing to the foregoing discussion, an understanding of the inﬂuences of telecommunications on transportation, and more speciﬁcally on travel, is important in promoting sustainable mobility, particularly in developing countries such as Ghana, where the problems of transportation are more prevalent.

Ghanaian cities have grown very fast and this is reﬂected in their spatial extent with no clear policy to control their growth. One implication of this is that, trip lengths have increased for various purposes. For instance, work trip lengths have increased signiﬁcantly over the last decade in the major cities of Accra, Kumasi, Tema, Sekondi-Takoradi and Tamale

(Adarkwa and Poku-Boansi, 2011). The journey to work has now become more hazardous and it is characterized with traﬃc congestion, vehicular accidents, high consumption of fuel, increased expenditures for households, which negatively impact on economic productivity and the environment. Regrettably, the role of ICT in addressing these transport problems have not been explored or utilized, despite its recent phenomenal growth and widespread application in cities of developing countries such as Kumasi. In Ghana for instance, mobile phone subscription has experienced a phenomenal growth from 0.7 subscriptions in 2000 to

3 108.2 subscriptions per 100 persons (Pick and Sarkar, 2015). Until now, most of the attempts

(both conceptually and empirically) to understand the role of ICT on trip making patterns of households and industries have focused on advanced economies such as the United States

(Choo et al., 2002), the United Kingdom (Selvanathan and Selvanathan, 1994) and Japan

(Sasaki and Nishii, 2010).

While these studies have derived interesting and useful results, their conclusions are limited because they are conﬁned to developed countries that have experienced a steady adoption, diﬀusion and use of telecommunication technologies. In many other parts of the world, particularly in developing countries, they have bypassed some of the technological stages that developed countries went through; for instance, skipping the land line phone step to go directly to ubiquitous utilization of mobile phones. This process is known as “leapfrogging”

(Pick and Sarkar, 2015). Considering these, an understanding of the relationship between telecommunications and transportation in developing countries is particularly important.

What, for example is the relationship between ICT usage – mobile telephone – on the trip making patterns of households? Does the use of mobile phones, for example, necessarily result in fewer household trips, including journey to work? In economic sense, what is the potential that the use of mobile phones can help reduce household trips so that fuel can be conserved and productivity increased instead of the current situation where almost for all trip purposes, face-to-face interaction is preferred? The study attempts to answer these questions.

This study will constitute the ﬁrst known attempt to comprehensively assess the causal eﬀects of telecommunication on travel within a country than can be considered as leapfrogging in the context of telecommunication adoption. As such, it will also constitute one of the

ﬁrst known applications of mixed methods in this context. This study may prove signiﬁcant in contributing to the underdeveloped area of research related to the relationship between telecommunication and transportation, and in posing numerous pertinent questions to guide

4 future research. The main significance of this study lies in the fact that few studies have explored the relationship between telecommunication and transportation in a setting that can be considered leapfrogging the more traditional forms of telecommunication such as the landline phone onto more directly ubiquitous mobile phone. Conducting the research within such a context may provide valuable insight and help clarify the complexity and ambiguity that had existed in the current scholarship. Findings from a research of this kind will make a significant contribution to the efforts of government to design and operate a more efficient transportation system, one that will be more finely attuned to citizens’ transport uses and needs, and one that will promote sustainability practices in the midst of new development in a rapidly advancing country. This research may also address the much-needed coordination of effort by government agencies, specifically the Ministries of Telecommunication and Transportation, which is sorely lacking.

1.3 Organization of the Study

The organization of the study is as follows: Following this introduction, Chapter 2 reviews major studies on telecommunications and transportation interaction. This chapter first reviews the trade-off focusing on the history of telecommunication technologies and their socio-economic consequences, then discusses the conceptual relationship, followed by the empirical relationship between telecommunication and transportation, and finally summarizes the literature with a focus on its limitations. Chapter 3 introduces Ghana and Kumasi, with the aim to provide the context within which the research is framed. Key insights into the economic, social, and spatial organization of the study area are examined. These contexts are explained within a historical perspective spanning from the eras of colonization, independence, succession of military leaders, up to the constitutional age. Then, based upon these, the chapter concludes with refined research questions that would guide study.

5 Chapter 4 sets out the overall methodology adopted to address the research questions. More specifically, the chapter focuses on the strategies that were followed to generate and analyze the data necessary to answer the research questions. Key considerations addressed here are the research design, data measurement, data types and sources, sampling techniques, data collection, as well as statistical analysis themes. Chapter 5 presents an empirical analysis of the underlying processes influencing the structure of mobile phone use and travel behavior in the study area using data obtained through a cross-sectional survey. The chapter first presents the results of a descriptive analysis of the characteristics of the study participants, their mobile ownership and usage, as well as their travel behavior. It also presents the results of a principal axis factoring analysis of factors identified as important to the study participants in influencing their use of mobile phone and how they traveled within Kumasi. Chapter 6 is presented in two parts. First, it describes and presents the results of a confirmatory factor analysis that sought to develop a composite measure for the intensity of mobile phone use and travel behavior. Based on this, the second part presents the results of structural equation models of mobile phone use and travel behavior. To explain the statistical results, especially from the point of view of the study participants, the penultimate chapter, Chapter 7, presents the results of a qualitative analysis. Finally, Chapter 8 discusses the conclusions and suggest some directions for future research.

6 CHAPTER 2

STUDIES OF TELECOMMUNICATION – TRANSPORT NEXUS

2.1 Introduction

Telecommunications and transportation have common characteristics as both serve as means of sharing information between people, albeit through diﬀerent modes (Choo and Mokhtar- ian, 2007). In a sense, the physical transport and telecommunications are seen as subsystems of the communication systems (Salomon, 1986). These commonalities in functions and close interrelations have induced a growing interest in research that seek to understand the existence and the types of relationship between telecommunications and transportation. These studies, conducted both conceptually and empirically, have often produced contrasting results. The focus of this chapter therefore, is to review the existing literature (state of knowledge), and to frame the study’s expected contribution to knowledge about the interactions between telecommunications and transportation.

To help adequately appreciate this interaction, the chapter begins by identifying the diﬀerent technologies and applications of telecommunication that have a bearing on travel patterns from a historical perspective. Subsequently, the conceptual considerations of the relationships between these measures of telecommunication and transportation are discussed.

The aim of this section is to oﬀer ideas that converge to produce a conceptual framework within which the interactions between telecommunications and transportation are discussed.

In the next section, empirical studies that have attempted to test and illustrate the aspects of the interactions between the demand for telecommunications and the demand for travel are presented. Since these empirical studies have often been conducted at two levels – aggregate and disaggregate – the section will be presented in that manner by ﬁrst reviewing the studies that have explored the relationship at the aggregate level and follow it with studies on the relationships at the disaggregate level. The chapter proceeds by discussing the methods

7 that have been employed in the empirical studies. This section is important as the contrasting results that have emerged from the several studies that have assessed the relationships between telecommunications and travel may result from the differing approaches employed (Salomon, 1986). In the final section, key findings of the current literature and limitations of especially the empirical studies are summarized.

2.2 Historical Highlights and Growth in Communication Technologies

The global adoption and diﬀusion of telecommunication technologies has been rapid over the past few decades. However, it has had a long history and experienced substantial changes in technology since the invention of the telegraph in the nineteenth century. These changes have impacted on the social and economic systems which in turn have aﬀected the behavior patterns of people, organizations and governments in several ways (Pick and Sarkar, 2015). This section traces the development of telecommunications from a global perspective that will be useful in understanding the theoretical, conceptual and empirical relationships between telecommunications and transportation discussed in the subsequent sections. Al- though telecommunication operates through a unique interface between technology, economic and policy/regulation (Sterling et al., 2006), this section focuses on the history of telecommunication technology. Also, despite the many technologies invented within the period of this historical review, the discussion here is limited to the optical and electrical telegraph, the telephone, the wireless telegraphy, the internet and mobile telephony. Early means of communication included talking drums to mimic the tone and prosody of the human speech, and smoke signals to transmit news or gather people in a common area in the form of visuals, among the Greeks, Persians and Romans (Huurdeman, 2003). The use of these similar methods of communication continued in England, North America, West Africa and parts of Asia in the sixteenth and seventeenth centuries (Sterling et al., 2006). However, it was not until the late eighteenth and early nineteenth century that a

8 more systematic approach to communication in the form of telegraph emerged, following the advent of electricity.

Although, permanent ground systems that allow transmission of numbers and letters through signals had developed several years earlier, the ﬁrst documented large scale use of such systems was a mechanical semaphore signaling system (also referred to as optical telegraph) developed by Claude Chappe in the late eighteenth century (Dilhac, 2001). Trans- mission through this system was done using regulators made of movable wooden paddles mounted on towers and positioned at angles in increments of 45 degrees to indicate diﬀerent letters and numbers. These systems required the building of stations normally 10 to 15km apart to form a line before a telegraph could be placed (Huurdeman, 2003). The semaphore systems were widely used in France, with lines connecting major cities of Paris and Lille, and in Italy, Belgium, Germany and Holland (Dilhac, 2001). The operation of the semaphore also required good visibility, which was only made possible under conducive weather conditions and during daylight (Huurdeman, 2003). These requirements made optical telegraph cumbersome and excessively costly.

To go around these drawbacks of the optical telegraph, and with a great desideratum to ﬁnd eﬀective means of communicating especially complex messages, series of discoveries were made including electrochemical telegraph and electromechanical telegraph, all of which culminated in the development of electrical telegraph system in the US by Morse and in the Great Britain by Cooke and Wheatstone in 1838 (Huurdeman, 2003). These discoveries were made more possible by the invention of electricity in 1800. The electrical telegraphy was a code-based system of “dot”, “dash” and “space”, and consisted of a battery and a key at the transmitting end and an electromagnet at the receiving end (Sterling et al.,

2006). Through this system, message text could be instantly transmitted at a distance.

The electrical telegraph proved simpler over the optical telegraph and allowed for complex messages to be transmitted. However, similar to the optical telegraph, electrical telegraph

9 was an expensive luxury, thus had limited usage only by government agencies, such as the

U.S. Post Oﬃce. Morse and his group, faced with the challenge of self-capitalization, which inhibited their capacity to build regional telegraph networks licensed their patent to private companies (Sterling et al., 2006).

The commercialization of the telegraph led to the development of lines connecting major cities in the US, including New York – Philadelphia, New York – Boston and New York –

Buﬀalo, all in 1846. This expansion was experienced elsewhere in many countries in Europe as well as their colonies in the 1850s (Sterling et al., 2006). Subsequently, the demand for telegraph services increased signiﬁcantly among businesses and government departments. During this period of improved communication facilities, the notion of the relationship (particularly physical parallel) between telecommunication and transportation also emerged (de Sola Pool,

1977). To further expand telegraph lines amidst capital investment constraints, close associ- ations were developed between telegraph companies (such as the New York and Mississippi

Valley Printing Telegraph Company – which was renamed Western Union in 1856) and the rapidly expanded railway lines by following the existing rights of way of rail roads in 1850

(Sterling et al., 2006). As a result, the expansion of rail networks in turn stimulated the expansion of telegraph lines, thus suggesting that telecommunications and transportation depended on a common integrated network to deliver their services (Sterling et al., 2006).

Still faced with the challenges of high prices due to increased demand and limited competition, as well as the requirement of trained operators associated with the telegraph, the need to further improve means of communication by making the telegraph service more ﬂexible heightened. After several experiments ranging from the invention of the telephone device, the development of liquid (acid) and carbon button transmitters, to the development of a centralized power supply system, by many inventors between 1860 and 1880, the ability to transmit voice over wire (voice telephony) was ﬁnally invented by Alexander Bell in 1876

(Sterling et al., 2006). Further improvements in this service in the form of the conversion of

10 the manual switchboards to electromechanical switchboards, as well as the ability to transmit voice over longer distance than the initial 20 mile coverage limit, saw widespread use of the telephone by businesses and individuals worldwide with about 10 million of the devise in use at the end of 1910 (Sterling et al., 2006). The development of the telephone and its wider application generated renewed but complex notion of the relationship between telecommunication and transportation. Within the early years after the invention of the new technology, it was anticipated to facilitate the conduct of businesses from a distance, thus eliminating travel and reducing the load on transportation (de Sola Pool, 1977). Counter proposition to this argument was the notion that, the telephone would increase travel, as growth in the use of the service encouraged the creation of business districts and the diﬀusion of residences

(de Sola Pool, 1977).

In addition to progress in the domain of two-way voice communication, the twentieth century begun with a substantial development in line and radio transmission, known as broadcast communications (Huurdeman, 2003). To substitute for a wire network typical of the telephone, and to decrease the cost associated with real-time point-to-point communication, Guglielmo Marioni invented the wireless telegraphy using radio transmitters to produce signals over much longer distance (Balbi and John, 2015). Subsequently localities particularly in the less economically developed parts of the world that had yet to be reached by the wireline networks were connected. By 1916 radiotelegraphy stations had been installed and were in operation in other parts of the world outside the U.S. and the Great Britain, including Gambia, South Africa, Seychelles and Mauritius in Africa, as well as China and

Singapore in Asia.

Many other technologies were invented following radiotelegraphy, and included the television in 1923, transistors in 1947, integrated circuit in 1958, and the microprocessor in 1969.

These innovations further initiated remarkable and profound change in the future development of communication technologies. One of such key technologies was the mobile telephony

11 introduced in the U.S and developed commercially in Japan in 1979 (Pick and Sarkar, 2015).

The flexibility and convenience that come with the mobile phone for the transmission of two-way communication, has seen its subscription increased significantly over the last 2 decades. By 2013, mobile-cellular telephone subscription worldwide was estimated at 6.7 billion (Balbi and John, 2015). Another significant milestone in this period was the introduction of data communications, to connect digital computers following their emergence.

To accomplish this, techniques such as packet switching, and protocols such as the TCP/IP were developed. These standards are now used to interconnect computers globally and form the underlying protocol for the internet (Sterling et al., 2006). In 2013, an estimated 2.7 billion people used the internet (Pick and Sarkar, 2015). In recent times, data and voice communications have been combined to allow for the transmission of audio, video, voice and data signals across a single network. Mobile broadband service, driven by these technologies, are now widely used by cellular service providers, thus making the transmission of packets of information – webpages, e-mails, video streaming and music ﬁles – a possibility from any location.

As seen in this section, major telecommunication technologies have been introduced spanning almost two centuries. Also, it can be seen that the ability to transmit voice signals from a distance has improved steadily since its inception. Large amount of data are now able to be transmitted over a telecommunication network. These technologies have impacted on society in several ways by changing people’s lifestyle and the way organizations conduct businesses: conference participants are now able to participate while at remote locations (teleconference); employees of organizations are able to work from home and other locations outside the work place. Other sets of activities provided for people through telecommunication include online shopping (teleshopping), telebanking and the use of telecommunication to transmit entertainment to multiple remote locations (tele-entertainment), as well as telemedicine in the rural communities. These changes in lifestyle of people and organizations in turn produce a

12 change in the demand and supply of transportation (Choo and Mokhtarian, 2007). However, and as noted in the preceding paragraphs, there is no consensus on the types of relationships that exist between the measures and applications of telecommunication and transportation. The next section reviews these complex sets of interrelationships from a theoretical perspective.

2.3 Conceptual Considerations of Telecommunication – Transportation Rela- tionship

To understand the impacts of telecommunications on transportation, the main thrust of this chapter, this section focuses on the conceptual relationships between the two. Choo and Mokhtarian (2007) identify a bi-directional relationship between telecommunications and transport, implying a reverse causality between them. From a supply-demand or an economic perspective, four types of relationships can be identiﬁed. That is, transportation impacts on the demand for, and supply of telecommunication, while in reverse, telecommunication impacts on the demand for, and supply of transportation. The ensuing paragraphs provide an explanation of these relationships.

2.3.1 The Impact of Transportation on the Demand of Telecommunication

Transportation may be said to inﬂuence the demand of telecommunications when the development of telecommunication technologies and services are directly adjunct to the transportation industry. Examples of these are obvious in both historical and advanced telecommunication technologies. In terms of historical example, the development of the telegraph was prompted by the need for real-time reliable transmission on the status of trains (Mokhtar- ian, 1990). Advanced telecommunication technologies, particularly vehicle based mobile communication devices including navigation devices, dispatch services, tracking and location devices, as well as ship-to-shore and ground-to-air capabilities, which were occasioned

13 from the need for information on passenger and freight traffic highlights the impact of transportation on the demand for telecommunications. In recent years, Ghana has experienced an upsurge in the development of computer software applications appropriate to the country’s complex transportation system as the sector makes strides. Where other offshore applications have failed, locally developed applications such as “Mo’Go” has now been able to incorporate the nuances and specific complexities of the transportation system. This is a clear example of technology facilitating the use of transportation.

2.3.2 The Impact of Transportation on the Supply of Telecommunication

This type of impact relates to the physical parallel that exist between telecommunication and transportation. Telecommunication companies at the infant stage of their operations may need a partner in transportation companies to provide an existing right of way to exist.

These partnerships are often in the form of lessor-lessee or joint venture relationships in which the transportation agency gives start-up telecommunication companies the right to lay ﬁber-optic cable in the transportation right of way (Mokhtarian, 1990). Again, there are historical and advanced examples to this eﬀect. As indicated in earlier sections, the expansion of telegraph lines in the 1850s was achieved after an agreement had been reached between telegraph companies and railway companies to allow for telegraph line to follow the existing rights of way of rail roads (Sterling et al., 2006). In recent years, local telephone systems and cable television delivery systems are normally superimposed on ready-made transportation networks. In most developing countries including Ghana, improvements in transportation through the construction of roads and their related activities have made it possible for telecommunication companies to also develop their infrastructure base. In

Ghana, the ongoing construction of the 695 km (432 mi) Eastern Corridor road has enabled the Ministry of Communications, through Huawei Company, to lay its ﬁber optic cable along the same road corridor with a 165 km (102.5 mi) diversion to Ho, the Volta Regional capital

14 from the national capital, Accra. The e38 million (US$ 44.17 million) project has facilitated the connection of about 120 communities along the route to the ﬁber optic line which seeks to expand communication access (Okyere et al., 2018).

2.3.3 The Impact of Telecommunication on the Supply of Transportation

This type of relationship hinges on the operational efficiency of the transport system from improvements in telecommunication technologies, thus making this impact relevant to transport planners and system managers. The level of service (LOS) of transport system is enhanced by telecommunication when the technologies support more efficient use of existing transport networks (Choo and Mokhtarian, 2007). In a sense, the availability of telecommunication promotes more efficient use of existing capacity and decreases delays while inhibiting the need to construct expensive new infrastructure. Example here is the Advanced Traveler In- formation Systems which provides travelers traffic condition information and forecasts, trip detours and timing, as well as real-time information on incidents and hazards (Litman, 2013). Other telecommunication applications that support passenger and freight traffic and consequently enhance transport system efficiency include highway emergency call boxes, remote video surveillance of vehicle lanes, automated vehicle and guideway technologies, in-vehicle navigation devices and radio determination satellite (Ben-Elia et al., 2008). In Ghana, the on-going US$ 95 million Bus Rapid Transit initiative known as the “Aayalolo” has introduced the implementation of Vehicle Fleet Management System to support traffic as well as enhance efficiency through vehicle scheduling, vehicle monitoring, vehicle location message trigger and management system. This is expected to enable the buses to operate more efficiently and be monitored from a central depot (Okyere et al., 2018).

2.3.4 The Impact of Telecommunication on the Demand of Transportation

Telecommunication technologies inﬂuence demand for transportation when the need to travel by individuals is deferred, eliminated, enhanced or modiﬁed as a result of the use of such

15 technologies. This hypothesized relationship dates back with the arrival of the telephone, and more recently with the advent of the internet and the mobile phone (Aguil´eraet al.,

2012). This type of relationship produces two primary categories of the interactions between telecommunication and transportation – substitution and complementarity (Mokhtarian and

Tal, 2013). These broad relationships are informed by economic theory, particularly price eﬀects (Mokhtarian, 2002). According to the price eﬀect, as the price of one commodity decreases, the demand of the other commodity falls, implying a substitute relationship between the two commodities. On the other hand, two commodities are said to be complementary if the demand for one commodity increases as the price of the other commodity decreases.

The first hypothesized interaction of substitution assumes that the availability of telecommunication technologies diminishes the need to travel (Salomon, 1986). It is suggested that, telecommunication technologies allow for individuals and businesses to do at a distance what they did when previously in close reach to each other, thus improving the competitiveness of telecommunication with transport (Aguiléraet al., 2012). Substitution therefore is defined as the total or partial elimination of trips (Lee and Meyburg, 1981), while allowing for high quality interactions, including teleconferencing, telecommuting, teleshopping, telebanking and telemedicine at a distance (Mokhtarian, 2002). The realization of this hypothesized interaction is attractive to transport and urban planners due to its potential of reducing road congestion and its associated effects of fuel consumption, emissions and accidents. This view has however attracted much criticism on the premise that many issues, which could impact on such interaction between improvement in telecommunication technologies and transport, are ignored. According to (Salomon, 1986), the substitution hypothesis neglects the complementary role of telecommunications and transportation given that enhanced use of telecommunications may increase the demand for transport.

The second hypothesized interaction thus, is that of complementarity. This type of interaction assumes that telecommunications stimulate travel (Mokhtarian, 1990), informed by

16 two distinct relationships. Salomon (1986) identifies these two categories as enhancement and efficiency. The enhancement interaction occurs when improvements in telecommunication technologies generate additional travel between two nodes (Dal Fiore et al., 2014). In a sense, telecommunication stimulates travel when its use, such as phone calls or a meeting over the Internet, prompts a trip. The efficiency interaction on the other hand occurs when one service contributes to the efficiency of the other. In terms of the telecommunication- transportation relationship, this type of interaction hypothesizes that through the application of telecommunication technologies, efficiency of the transportation system is achieved Salomon (1986). An example of this is the use of mobile phones by individuals to schedule or modify personal meetings, thus requiring travel. In recent times, the use of intelligent transportation system also stimulates travel by providing real-time traffic information for road users (Mokhtarian, 1990). Other types of interactions between telecommunications and transportation suggested in the literature are modification, where the use of telecommunication modifies the use of transportation, and neutrality, where the use of telecommunication leaves transportation unaffected (Choo and Mokhtarian, 2007). Following from these sets of complex conceptual relationships, recent years have seen significant research to test and illustrate these aspects of the relationship between telecommunications and transportation (see for example Selvanathan and Selvanathan, 1994; Plaut, 1997; Choo et al., 2002, 2005; Choo and Mokhtarian, 2007; Wang and Law, 2007; Sasaki and Nishii, 2010; Lila and Anjaneyulu, 2013). The next section is devoted to a review of these empirical studies.

2.4 Empirical Studies of Telecommunication – Transportation Relationship

A great deal of empirical studies has been conducted to illustrate the complex aspects of the relationship between telecommunications and transportation. These studies have often taken an aggregate or disaggregate perspectives, and have consequently informed conclusions on

17 the nature of transportation impacts by telecommunications. It is noteworthy that, studies reviewed here are restricted to those conducted over the last three decades; a period which has experienced rapid prevalence and expansion of information technologies globally including the internet and mobile phones (Pick and Sarkar, 2015). This section therefore is devoted to a review of such studies. It is worth noting that the review done here is by no means a comprehensive inventory of all empirical studies on the subject. However, it does provide an overview of the diﬀerent types of studies conducted and the contrasting results they have generated.

2.4.1 The Aggregate Level

At the aggregate or macro level, empirical studies on this subject often plot measures of telecommunication and transportation in the geographical area at given time or over the same period of time. A distinction in empirical studies of this nature can be drawn between a single application to telecommunications (such as teleshopping, telecommuting, teleconferencing, telemedicine) and comprehensive approach where diﬀerent forms of telecommunications are looked at with an object to unveil the interaction between telecommunication and transportation in a broader context (see Table 2.1). To put these studies into perspective they are further examined based the nature of the relationship between telecommunication and transportation – substitution, complementary or neutrality (where the use of telecommunication result into no signiﬁcant changes in the volume of trips made).

The ﬁrst two aggregate studies reviewed here heavily draws from Mokhtarian (2002).

Both studies are conducted from economic perspectives but with a diﬀerent focus. Sel- vanathan and Selvanathan (1994) studied the relationship between telecommunication and transportation with a focus on annual consumption expenditure (in constant prices) per capita of three kinds of goods: public transport, private transport and communication (postal and telephone services). Using a simultaneous equation model to analyze time (1960-1986)

18 series data from the United Kingdom and Australia, they found positive cross-price elastic- ities among all three goods, indicating a pairwise substitution relationship between public transport, private transport and communication. The implication of this is that an increase in the price of one type of good leads to a decrease in the quantity demanded of that good, but increases the demand for the other goods.

Unlike many other empirical studies, Plaut (1997) examined the relationship between telecommunication and transportation with the industry as the unit of analyses, rather than the end consumer or individual commuter, emphasizing that more than half of all expenditure on communication and transportation services in Europe are made by the transportation service industry. With this as a backdrop, and using an input-output analysis, direct and total input coefficients for communication and transportation services across all industrial sectors of nine European countries were correlated. While direct coefficient is equivalent to the purchase of inputs made directly per $1 of output for a given industrial sector, total coefficients measure both direct and indirect inputs purchase made per $1 of output. Correlations of both direct and total input-output coefficients for communication and transportation sectors in all nine countries revealed positive and significant relationships, indicating complementarity. In other words, an industrial sector purchasing a certain measure of communication would tend to purchase the same measure of transportation. An inherent limitation of this study however is the difficulty to obtain separate coefficients for each industrial sector, since the correlation between transportation and communication services was taken across 44 industrial sectors (Choo and Mokhtarian, 2007).

In contrast with the above studies which examined the relationship between telecommunication and transportation from an economic perspective (prices and expenditure of communication and transportation services), Choo et al. (2002, 2005) measured the relationship between these two services in units by focusing on Vehicle Miles Travelled (VMT), airline Passenger Miles Travelled (PMT) and total miles traveled per capita as measures of

19 transportation and number of telecommuters as a measure of telecommunication (telecommuting). Using a two-stage multivariate time series analysis of aggregate data in the United

States ranging from 1966 to 1999 for all variables but telecommuting, and 1988 to 1998 for telecommuting, the study found that telecommuting tends to have limited but significant effect on reducing travel. Although this study makes use of composite indicators as measures of transportation, just like the first two studies, it fails to account for confounding variables which may influence the magnitude and direction of the relationship between telecommunication and transportation. Assessing true causality thus becomes difficult with these aggregate studies.

In an eﬀort to account for true causality, Choo and Mokhtarian (2007) developed a comprehensive model that considers causal relationships among VMT as a measure of transportation, local telephone calls as a measure telecommunication, land use (measured by suburbanization rates), economic activity (measured by prices of gasoline and consumer price index for private transportation) and other socio-demographic variables. With this framework as a backdrop, and using a structural equation modeling of aggregate time series data in the United States ranging from 1950 to 2000, the study found system-wide net complementary eﬀects between telecommunication and transportation. This suggests that as demand for telecommunication increases, demand for travel also increases, and vice versa.

2.4.2 Disaggregate Level

Although the aggregate studies reviewed in the foregoing oﬀer a broad picture of the system wide relationships between telecommunication and transportation, they fail to provide detailed information on the impacts of telecommunication on travel as well as the behavioral explanations behind such relationships. To remedy this limitation in the subject, recent studies on the relationships between telecommunication technologies and transportation have been examined at the individual or micro level. Here, travel or transportation measures

20 as well as measures of telecommunication are both obtained through activity diary or log

(Mokhtarian, 2002).

Using data from the 2002 third travel characteristic survey as well as an attachment survey which recorded information on the experience with the various forms of telecommunication, Wang and Law (2007) used an activity-based framework to comprehensively examine the relationships among the use of ICT, time allocated for subsistence activities, travel behavior and other demographic characteristics for the city of Hong Kong. Applying structural equation modeling technique, Wang and Law (2007) decomposed the relationships between the study variables into direct and indirect components. Regarding the direct interaction, the study found a complementary effect between ICT use and travel. This suggests that the use of any form of ICT (the internet, email, teleconferencing and videophone) con- currently increases the propensity to make a trip and the time spent for travel. Similarly, through the analysis of indirect effects, the study found that ICT had a generation effect on travel time by first inducing more time for out-of-home subsistence activities such as recreation.

In a single application study, Weltevreden (2007) explored an indirect relationship between telecommunication and transportation through an analysis of the impact of teleshopping (online shopping) on traditional shopping at city centers in the Netherlands using a sample of about 3200 internet users. Interestingly, the study identified both substitution and complementary effects of teleshopping on traditional shopping and consequently on travel. The study found a limited but significant negative effect of teleshopping on city center shopping albeit in the long run. Consumers who bought online directly from manu- facturers rather than the traditional retailers cited convenience as the major reason, although for limited number of products. However, in the short run, online buying may have a positive influence on city center shopping. Many buyers use the internet as a channel to gather information such as locations and prices before they make trips to the city center to purchase

21 from the traditional retailers, indicating a complementary relationship among teleshopping, city center shopping and travel. While Schellenberg (2005) observed a similar substitution eﬀect in Germany, a complementary eﬀect was rather observed by Cao et al. (2010) in Minnesota between teleshopping and traditional shopping. Just like with many single application studies, these studies fail to measure the system-wide impact on transportation by telecommunication, which may distort the nature of the relationship.

Sasaki and Nishii (2010) reported on the communication, activity and travel behavior of a sample of over 150 households in the city of Kofu, Japan. Using an ordered regression model to analyze the interaction among individual demographic characteristics, the number of trips, as well as the use of telecommunications, and a structural equation model to identify and analyze latent factors that may inﬂuence the relationship between telecommunications and trips, the study found a substitution eﬀect between all four types of telecommunication and travel. In other words, Sasaki and Nishii (2010) found that, the average duration of trips decreased anytime the number and use of telecommunication increased.

Using a 9-day dataset for the city of Harbin in China, Yuan et al. (2012) explored the relationship between the use of mobile phones and three different aspects of physical movement including radius, eccentricity and entropy to represent the scale, shape and randomness respectively, of individual travel behavior. Generally, they found significant correlation between the use of mobile phone and all three indicators of travel. Specifically, mobile phone usage has a positive relationship with movement radius and eccentricity, but negative link- age with movement entropy. These complex interactions suggest that, as the use of mobile phone increases, the activity space of users becomes larger, they tend to visit non-linearly distributed locations, and becomes much difficult to predict their patterns of movement. While the study reveals interesting and important conclusions, it is difficult to infer causality.

Lila and Anjaneyulu (2013) reported a study in which 201 employers sampled from diﬀerent organizations in the city of Bangalore, India, completed travel activity diary and

22 a log of their use of ﬁxed line phones, mobile phones and personal computers. Using a multinomial logit modeling technique, the study found a signiﬁcant negative relationship between teleworking or telecommuting and trip length, explained by commuters’ preference to working from home especially during peak hours.

2.5 Theories on Technology Adoption and Use

The empirical studies discussed in the foregoing have been inspired by several theories on technology adoption and use. Drawing on Pick and Sarkar (2015) and Lee (2007), in the ensuing sections, two of these theories found to provide insight into understanding the eﬀect of technology use on travel are discussed: Innovation Diﬀusion Theory (Rogers, 2003) and

Technology Acceptance Model (Davis, 1989).

2.5.1 Innovation Diﬀusion Theory

The innovation diffusion theory, also known as the adoption-diffusion theory, was formulated by Everett Rogers to comprehensively understand how innovations are adopted and diffused to members of a society over time (Pick and Sarkar, 2015). In this theory, innovation is the focus (Pick and Sarkar, 2015), and it is referred to as a new or unfamiliar idea or technology within a specific geographical area or a social system. The adoption of this innovation, according to the theory, is influenced by several factors categorized into four: (i) characteristics of the adopters in terms of their propensity to adopt; (ii) influence of mass media and interpersonal communications; (iii) exogenous socio-economic influences; and (iv) characteristics of the innovation itself (Pick and Sarkar, 2015). These attributes play important role as they form the basis by which individuals evaluate the usefulness of an innovation, and consequently their decision on whether to adopt it or otherwise. Communication channels play a crucial role in this process, as they provide the medium through which the individuals

23 obtain information about the perceived usefulness of an innovation (Pick and Sarkar, 2015).

Overall, these characteristics inform the rate of adoption and diﬀusion of an innovation.

Rogers (2003) conceptualizes that the process by which innovation diffuses within a social system begins with few innovators and end with laggard adopters. In between this spectrum, innovation diffuses rapidly then slowly tapers off (Pick and Sarkar, 2015). Based on this, and the characteristics of the adopters themselves, Rogers (2003) identified five stages of innovation, namely: innovators, early adopters, early majority, late majority, and laggards. According to Rogers, innovators are the first to adopt an innovation, with substantial resources financial and technical, to create or bring in an innovation from outside their social system. Early adopters, represent opinion leaders who are well connected and informed about the new technology, making them comfortable to adopt new ideas. Accord- ing to Zhang et al. (2015), innovators and early adopters constitute about 16 percent of the population in any given social system. The next two groups – the early majority and late majority – constitute a significant proportion (68 percent) of the population and are open to new ideas except that the latter does so with reluctance, until individuals in the early majority group have tried the innovation and successfully adopted it. The laggards are more conservative, have limited resources, and often lack awareness of the perceived usefulness of the innovation. These make them skeptical of change to the extent that they even tend to become non-adopters (Zhang et al., 2015).

Based on Roger’s theory, Rueda-Sabater and Garrity (2011) developed a framework for digital divide and categorized into three stages, which has since been modiﬁed by Pick and

Sarkar (2015) into ﬁrst adopters (stage 1), converging adopters (stage 2), and belated or leapfrogging adopters (stage 3). Pick and Sarkar (2015) have applied this framework to study the concept of digital divide at diﬀerent geographical levels – country, state, province, and city. Of concern to this study is stage 3 in Pick and Sarkar’s framework which applies to countries with weak economies, poor infrastructure, and low literacy, relative to the

24 developed countries in stage 1. Once these identiﬁed barriers have been overcome, Pick and

Sarkar (2015) conceptualizes that these countries enjoy a rapid rate of adoption by skipping the more traditional technologies such as PC and ﬁxed telephone lines, and moving onto modern forms such as internet and mobile phones. As will be discussed in the next chapter,

Ghana, the study area, ﬁts into this stage of the innovation adoption-diﬀusion cycle. Despite the large literature on the subject, only a handful of them have attempted to investigate the relationship between ICT and travel within a “leapfrogging” context.

2.5.2 Technology Acceptance Model (TAM)

TAM was formulated by Fred Davis in the 1980’s to originally explain the factors underpinning the adoption of technologies in the workplace. Davis’ (1989) TAM was based oﬀ the Theory of Reasoned Action (TRA), formulated a decade earlier by Fishbein and Ajzen

(1975) to explain the actual behavior of an individual. According to the TRA, the behavior of an individual is inﬂuenced by the intention to engage in that behavior (referred to as behavioral intention), along with the belief the person associates with that given behavior.

Davis (1989) demonstrated that attitude influences behavioral intention which in turn affect individual’s adoption of information technology. In other words, the thoughts that a person has in mind pertaining to the use of technology affects his/her attitude towards its use

(Fishbein and Ajzen, 1975).

In his Technology Acceptance Model, Davis (1989) postulates two important predictors of a person’s behavioral intention to adopting a new technology, namely perceived usefulness and perceived ease of use. The former refers to “the degree to which a person believes that using a technology will be free from physical and mental effort” (Davis, 1989). Perceived usefulness on the other hand refers to the “the extent to which a person believes that using a particular technology will enhance her/his job performance” (Davis, 1989). In other words, while perceived usefulness conceptualizes the extent to which technology satisfies the specific

25 needs of a person, perceived ease of use focuses on one’s ability to use the technology effec- tively to accomplish a task. Several studies — including Schierz et al. (2010), Mallat et al. (2009), Ha and Stoel (2009), Kuo and Yen (2009), and Khalifa and Ning Shen (2008) — have found a direct relationship between these two predictors of behavioral intention. According to these studies, perceived ease of use influences perceived usefulness, which in turn affects attitude and behavioral intention. Perceived ease of use, however, can directly influence attitude and behavioral intention (Davis, 1989). Drawing on these two dimensions of the TAM, this study investigates how the use of mobile phone by residents of a “leapfrogging” country affect their travel behavior. Usefulness within the context of this study is defined as the extent to which an individual perceives that using a mobile phone will enhance their travel, while ease of use is defined as the extent to which a person believes that using the mobile will free them from physical travel.

2.6 Summary of Chapter and Limitations of the Existing Literature

This chapter has reviewed past literature on the relationship between telecommunication and transportation from historical, conceptual and empirical perspectives. Through a consideration of the various applications of telecommunications (both comprehensive and single applications), this chapter has found that many of the studies reviewed here reﬂect the complexity of the relationships between telecommunication and transportation. Variations in the outcomes of these studies may well be attributed to the diﬀerences in their scope, data requirements, time (longitudinal or cross-sectional) as well as the methodological approach. However, the hypothesis that telecommunication will substitute for personal travel appears to dominate, especially from the empirical studies reviewed. Table 2.1 summarizes these studies based on the scope of study (aggregate and disaggregate); approach to telecommunication (comprehensive and single applications); main independent variables; nature of transport impact; and the modeling approach (Andreev et al., 2010).

26 Table 2.1. Summary of empirical studies on the impacts of telecommunication on travel Reference Scope of Approach to Main Nature of Modeling Study Telecommunica- Independent Transport Approach* tion Variables Impact Selvanathan and Aggregate Comprehensive Postal and Substitution SEM Selvanathan (1994) data telephone services Plaut (1997) Aggregate Comprehensive Telecommunication Complementary SEA data services Choo et al. (2002) Aggregate Single application Telecommuting Substitution Two-Stage data Multivariate Time Series Analyses Choo et al. (2005) Aggregate Single application Telecommuting Substitution Two-Stage data Multivariate Time Series Analyses

27 Choo and Aggregate Single application Local phone calls Complementary SEM Mokhtarian (2007) data Wang and Law Disaggregate Comprehensive E-mail, internet Complementary SEM (2007) data service, teleconferencing and videophone Weltevreden (2007); Disaggregate Single application Teleshopping Substitution, SEA Schellenberg (2005); data Complementary Cao et al. (2010) Sasaki and Nishii Disaggregate Comprehensive Fixed lines, mobile Substitution Ordered regression (2010) data phones and personal model, SEM computers Yuan et al. (2012) Disaggregate Singular Mobile phone Substitution Singular equation data application approach Lila and Anjaneyulu Disaggregate Single application Telecommuting Substitution DCM (2013) data *SEM: Structural equation modeling; SEA: Singular equation approach; DCM: Discrete choice models Even though the existing body of research offers extensive insight into the transportation effects of telecommunications, several shortcomings have been identified, related to both aggregate and disaggregate studies. Aggregate studies that have examined the relationship between telecommunication and transportation, although conducted comprehensively to account for other factors that could influence the direction of the relationship (see example Choo and Mokhtarian, 2007), have often precluded examination at the sub-national level, thus ignoring the behavioral understanding of the phenomenon. A way to go around this, at least partially, is through disaggregate studies (see example Lila and Anjaneyulu, 2013; Yuan et al., 2012). However, these disaggregate studies fail to measure the systems-wide effect of telecommunication on the different modes and purposes of transportation. These shortcomings obscure the true causality between telecommunication and transportation as well as the reasons that drive such relationship, if any. Additionally, as mentioned earlier, past researches have focused on developed countries where the adoption and diffusion of ICT have increased steadily, at the expense of developing countries where the technological process has been that of leapfrogging. Methodologically, previous studies, both aggregate and disaggregate, have presumed telecommunication as a binary construct in terms of users and non-users. In practice, however, given the recent upsurge in the penetration and use of telecommunication, even in developing countries, users of telecommunication may be differentiated by their experience and intensity of use (Wang and Law, 2007). To address these shortcomings of the current literature, this proposal is distinguished from disaggregate studies by system-wide effects rather than single effect of telecommunication on travel; and from aggregate studies by sub- national behavioral patterns, rather than single or multi-national patterns where it is difficult to understand other correlates such as location/urban form and socio-economic attributes which may provide an explanation into the nature of the linkages between telecommunication and transportation.

28 CHAPTER 3

CONTEXT OF THE STUDY

3.1 Introduction

An important theme of this study is the concept of leapfrogging – which Pick and Sarkar (2015) define as the idea that developing countries can skip or bypass some of the technological stages that developed countries went through – and its role on transportation in Ghana. To be able to appreciate this concept as a characteristic of Ghana’s technological development process, and drawing mainly from secondary data, this chapter first, presents an overview of telecommunication development in Ghana applying a historical approach. Given that telecommunication and transportation have developed in a dependent manner over the centuries, in which the latter was a precursor of the former (Black and Nijkamp, 2005), the discussion here also takes cognizance of the role of transportation in the development of telecommunication in Ghana, and traces its development also. It is expected that these discussions will bring to the fore key insights into the economic, social and spatial organization, which influenced the development of these two forms of communication in Ghana. Positing the development of transportation and telecommunication in this manner not only ensures an overview of historical perspectives but provides an overarching context which helps situate the study area (Kumasi). Subsequently, the last section of this chapter focuses on the city of Kumasi with emphasis on its role in the country, which has invariably contributed to its present spatial structure and consequently transportation problems.

3.2 Locational and Demographic Characteristics

As a prelude to the discussion of the development of telecommunication and transportation in Ghana, this section ﬁrst brieﬂy introduces the country in terms of its geographical, economic

29 and population features. Ghana, a West African country, shares boundary with Togo to the east, Cote d’Ivoire to the west, Burkina Faso to the north and the Gulf of Guinea to the south. The country covers a total land area of 238,533 km2, approximately the size of the US State of Oregon, spreading about 357 km from west to east, and 672 km from north to south, and is demarcated into ten administrative regions (Cobbinah et al., 2016) (see Figure 3.1). Ghana (hitherto referred as the Gold Coast) has had a long history. Settled ﬁrst by the Portuguese in 1497, and subsequently the Dutch, the French and the Danes, the English ﬁnally took over in the nineteenth century until Ghana gained independence in 1957 (Berry, 1995).

Figure 3.1. Geographical Location of Ghana in the Sub-Regional Context Note: (Figure based on Cobbinah et al., 2016)

Today, it is noticeable that several changes have occurred including developments in ICT and infrastructural facilities such as hospitals, universities, road construction and the development of major telecommunication installations throughout a major part of the country; particularly the southern portion. Despite this, its major foreign exchange earner is cocoa. In 2013, the country had a population of 27.3 million people and presently a little over 50%

30 of all Ghanaians live in urban areas (Ghana Statistical Service, 2013), although the level of urbanization varies from region to region. Greater Accra (90.5%), Ashanti (60.6%), Western (42.4%), Central (47.1%) and Brong Ahafo (44.5%) are more urbanized than the northern regions such as Upper West (16.3%), Upper East (21.0%) and Northern (30.3%). The current level of transportation and telecommunication development, among the development of other infrastructural facilities, to a very large extent, has concentrated in the resource – rich areas of southern Ghana (Adarkwa, 2012). This pattern has created a north- south development gap which has been very difficult to break away from partly because of efficiency and effectiveness considerations. The next section traces such development spanning from the pre-colonial era to the present telecommunication and transportation development situation in Ghana.

3.3 Development of Telecommunication and Transportation in Ghana

A review of the telecommunication sector in Ghana reveals clearly the importance that the nation attaches to this sector in view of its enormous potentials. This is in view of the remarkable eﬀorts made thus far towards making Ghana the hub of the telecommunication industry in Africa; having been described in various circles as the “Microcosm” of Africa’s developing world and the fact that the country oﬀers an environment that is business friendly and encourages growth because of its stable democracy, diverse culture and a vibrant economy (Sankaran et al., 2011). In short, these are the features which have guided the growth and development of the telecommunications sector from a modest beginning in 1881. The telecommunications industry in Ghana has gone through several stages in its history.

3.3.1 Pre-Colonial and Colonial Era

Historically, the development of transportation and telecommunication in Ghana dates back to the colonial era, especially in the late nineteenth century, similar to the situation in many

31 developing countries. As a way to establish political control and facilitate governance as well as link areas of potential agricultural (e.g., cocoa, timber and oil palm) and mineral (e.g., gold and diamond) production centers to world markets, it became apparent that better means of communication were needed in the colony (Gould, 1960; Noam, 1999; Allotey and Akorli, 1999; Sey, 2008). Specific to telecommunication, the first telegraph line was installed in 1881 between the Cape Coast Castle (which housed the governor of the colony) and the Elmina Castle (Allotey and Akorli, 1999). Following the change in the seat of government from Cape Coast to Accra in 1887, the telegraph line was extended to the Christiansburg Castle. Prior to 1890, all the colonial castles and fort towns including Accra, Prampram (in the Greater Accra region), Winneba, Saltpond (in the Central region), and Sekondi, Discove and Shama (in the Sekondi-Takoradi region) were all covered by telegraph lines. More significantly, all these centers were in the southern part of the country, specifically along Ghana’s coast, and served as trade centers; further corroborating the objective of the colonial masters to use telecommunication as a mechanism to facilitate the economic and political management of Ghana. To consolidate and strengthen telecommunications in the southern sector, the first and second manual telephone exchanges were installed in Accra in 1892 and in Cape Coast in 1904 respectively (Allotey and Akorli, 1999). In view of the growing importance of the Ashanti region (particularly Kumasi and Obuasi) as an agricultural and mineral production hub of the country, as well as the potential markets represented in the Northern region (salt trade in Salaga, kola nut trade in Kintampo), the telegraph lines were accordingly extended to these areas in 1901. By 1912, a total of about 1,492 miles of telegraph lines had been constructed to link 48 telegraph offices spread throughout the colony (Allotey and Akorli, 1999), bringing an end to the era of the telegraph as a means of communication. The development of the telegraph in Ghana followed similar but earlier developments experienced elsewhere in the world – the United States in the 1840s and Europe in the 1950s.

32 As the country developed both physically and economically, the need to further strengthen control by the colonial masters became more important leading to the introduction of improved telecommunication technology (two-way voice communication) and the physical expansion of its infrastructure in the country. The telephone was introduced into Ghana during the period of the World War I, and between 1914 and 1920, 4 main trunk telephone routes including the Accra – Kumasi, Accra – Takoradi, Kumasi – Takoradi and Kumasi – Tamale, were installed in the whole country using hard wired copper (Allotey and Akorli, 1999). Fur- ther improvements within the same period were experienced in the form of the installations of “1 + 1” and “1 + 3” carrier equipment on the telephone routes to augment their capacity.

Consequently, the total number of telephone exchange lines connecting the regions of the coast (south), the central and the northern parts of the country grew to about 1,560 by

1930. The manual switchboards were subsequently replaced with automatic switchboards to allow for better service and longer voice transmission leading to the installation of the

ﬁrst automatic telephone exchange in the capital of Accra in 1953. The need for broadcast communication or radio transmission also called for an upgrading of the original trunk telephone routes linking the major cities through the installation of a forty-eight and twelve- channel VHP network (Allotey and Akorli, 1999) in 1956, just before the colony gained its independence.

Underpinning these improved telecommunication facilities in Ghana in the colonial era was a system of roads and rails. The primary roles of these early transportation facilities were first to facilitate the flow of export commodities (gold, cocoa, oil palm, rubber, kola nut) by linking the hinterlands and production centers in the forest areas (central and northern belts) to the coastal part of the country, and second as administrative networks of tracks to facilitate the going about of colonial officers in the colonial districts (akin to the roles of the telegraph and telephone lines) (Gould, 1960). Subsequently, however, these facilities stimulated the deployment of the early means of communication as the telegraph lines and

33 telephone cables followed the existing right of way of rails and roads. The development of transportation in the colony occurred in a series of stages, just as the development of telecommunication, albeit transportation facilities preceded means of telecommunication, beginning with the development of trade roads.

Although trading between the local population and Europeans begun when the Por- tuguese arrived in the then Gold Coast in the ﬁfteenth century, the ﬁrst roads were not developed until 1874 after the British had established control in the colony (Gould, 1960).

The main trade routes between the important centers mentioned earlier and the administrative centers in the coast (the Kumasi – Cape Coast road in 1874 for example) were cleared and deﬁned without any major engineering works. Following the appointment of an inspector of trade roads in 1890, and the creation of the Roads Department in 1894, the construction of engineered roads with deﬁnite trunk and feeder pattern begun on a large scale in the colony. Notable were the improved Kumasi – Cape Coast road which had on it about 35 newly constructed bridges, the Kintampo – Mampong road in 1897, Accra – Apedwa and

Saltpond – Oda in 1890 (Gould, 1960). These roads penetrated the forest areas around the

Ashanti region, and acted as feeders to the coastal centers.

The lack of facilities to transport bulk commodities such as minerals, rubber, palm oil and timber, and the need to further develop the colony economically, brought about the development of bulk transportation in the form of railways. Thus, the mining towns of Obuasi and Tarkwa were linked with railways to the coastal centers of Takoradi and Accra in 1901 and 1902 respectively. These rail networks in all directions converged on Kumasi, making the city a traversing point from all parts of the country. In view of this, Kumasi housed several oﬃces, stores and warehouses belonging to the major European trading companies headquartered on the coast. The railway lines expanded steadily into one more mining center of Prestea in 1911 (to form the western line of Sekondi – Tarkwa – Prestea) and cocoa production centers such as Nsawam in 1911 and Koforidua in 1915 from the capital to

34 form the eastern line of Accra – Akwapem – Koforidua. After several attempts, the direct line from Accra to Kumasi was ﬁnally completed in 1923. This was followed by another important rail development between Huni Valley and Oda in 1926 forming the central line.

During the Second World War, the global need for mineral ores inﬂuenced the development of what could be known as the last line before independence – the Dunkwa – Awaso line – in 1941 to tap bauxite deposits (Chaves et al., 2013).

Interestingly, during the period of railway development and expansion, the trade roads mentioned earlier had its original primary role of connecting production centers to the coastal towns changed to functioning as feeders to the railway. Meanwhile, the road networks were considerably improved to adequately perform its feeding roles, under the newly created departments of Public Works (PWD) and the District. This was seen in the considerable and extensive development of roads infrastructure with improved surfacing materials such as laterite and gravels. This resulted in the further improvements of important roads like the Takoradi – Kumasi via Brofoyedu and Fomena in 1937, Cape Coast – Kumasi road via

Dunkwa and Obuasi in 1939, and the Accra – Kumasi link through Nkawkaw. Noticeably, by independence, the road sector had reverted to its original role as a primary transportation facility (Jedwab and Moradi, 2011). Although there seems to be some modest development of telecommunication and transportation infrastructure in the colony by the Europeans prior to independence, many of these were concentrated within the south and middle belts, to the neglect of the northern part of the colony, amounting to a general underdevelopment of telecommunication and transportation infrastructure (Jedwab and Moradi, 2011; Allotey and Akorli, 1999).

3.3.2 Post Colonial (Independence) Era

In 1957, Ghana gained independence, an era which brought new dynamism to the country’s infrastructure base including telecommunication. In the same year of independence, the

35 first government launched a 7-year development plan, which facilitated the installation of a second automatic exchange in Accra. At the end of the plan period, Ghana had more than 16,000 telephone subscribers. The rapid growth in domestic and international trading activities, made possible by improvements in transportation, called for and facilitated the installation of new telephone exchanges in Koforidua, Ho, Sunyani, Kumasi and even in the northern city of Tamale. The administration of telecommunication was also transferred from the P.W.D to a dedicated Post and Telecommunications Department to facilitate the rapid improvements in the sector (Allotey and Akorli, 1999). Meanwhile to stimulate this expansion, the country’s first president, Kwame Nkrumah embarked on heavy investments in roads throughout the country. More important was the special attention given to the northern part of the country to address the north south im- balance. These improvements were timely as the infrastructure left behind by the colonial masters was inadequate especially in the northern part of the country. The system of state control left by the colonial masters was however not abolished following independence since it was in tune with the more general economic and political ideology of centralized planning or socialism favored by Nkrumah (Noam, 1999). Nkrumah believed in the transformation of the economy along the lines of socialism, thus, both economic and political administration was centralized. During this period however, political opposition was stifled, several constitutional safeguards were abolished, and Ghana eventually declared as a one-party state by Nkrumah in 1964 (Berry, 1995). In 1966, Nkrumah was overthrown in a military coup d’état.The next 25 years saw the country plagued with a series of coups and political dissent interspersed with civilian governments between 1969 and 1972 as well as 1979 – 1981. Unfortunately, neither the military nor civilian governments within this period could substantially improve the sector, as the colonial masters and Nkrumah had bequeathed. In fact, the country’s telecommunication and transportation infrastructure deteriorated especially during the era of the Supreme Mil- itary Council (SMC) (1972 – 1979) (Berry, 1995). This notwithstanding, there were many

36 signiﬁcant projects undertaken by the Ghana Post and Telecommunication (GP&T) under the Provisional National Defense Council (1981 – 1991) regime. These projects, earmarked to improve and modernize telecommunication as well as introduce competition into the sector were collectively known as the First Telecommunication Project (FTP). Although the accomplishments were marginal, the FTP funded collaboratively by the Bank of Ghana, the

World Bank, African Development Bank, and the governments of Canada and Japan, saw the installation of 12 new electronic telephone exchanges, the construction of a telex and earth station, as well as the installation of microwave radio links completed by 1985 (Allotey and Akorli, 1999).

A Second Telecommunication Project (STP) funded by the World Bank was also initiated by the GP&T in 1987. The STP just like the FTP did not achieve all components of its stated goals modernizing and expanding the existing network capacity (Tobbin, 2010). However, the following were achieved by the projects’ completion date in 1992: installation of a new international telephone switch, the rehabilitation of cable networks and the satellite earth station in Accra, as well as the installation of a 1000-line telex switch (Allotey and Akorli,

1999). Despite the modest achievements of the STP, Ghana’s tele-density of 0.31 per 100 inhabitants was among the lowest globally and even on the continent, with Libya (9.0),

Zimbabwe (1.3), the Ivory Coast (0.5) and Togo (0.33) all having higher tele-densities.

The trend of development of the telecommunication and transportation in Ghana, as espoused in the foregoing sections, corroborates the notion of interdependence between the two sectors. The organization and governance of the transport sector in Ghana lends credence to the fact that these major sectors of the economy are closely related and explains why they were governed under one ministry for a very long time. It was noticeable that initially the right-of-way for roads and railways were used for the development of telegraph and telephone facilities. This is very much in agreement with what happens in advanced countries, as described by (Mokhtarian, 1990). Consequently, at the macro level, and using length of roads

37 and telephone access lines as indicators of transportation supply and telecommunication supply respectively, the nature of the relationship between the two sectors in Ghana tends to support the idea of complementarity (Okyere et al., 2018). The modest investments in telecommunication facilities in particular in the pre and post-colonial era paved the way for important institutional reforms in the sector which consequently saw the growth in mobile telephony, as discussed in the next section.

3.3.3 The Constitutional and Present Era

Following a referendum in 1992, Ghana was ushered into a constitutional era which also introduced the 4th Republic. During this period, divergent concepts of reforms in the telecommunication sector had emerged, albeit slow, dismantling the centralized and monopolistic system that had characterized the sector since the colonial era (Tobbin, 2010). These models of reforms followed the lead of the developed countries such as the United States, Britain and Japan. To facilitate the process of reforms, several policies, programs as well as institutions were established. First was the introduction of the Accelerated Development Program (ADP) in 1994 with an overarching objective to create a competitive and open access telecom market to attract both domestic and international investments to boost quality of service but at lower cost (Shirley et al., 2003). Second was the authorization of a national network operator – Ghana Telecom – and the subsequent privatization/corporatization thereof in 1996 (Shirley et al., 2003). To avoid repeating the “mistakes” of the pre-constitutional era, the telecommunication industry was de-monopolized by the introduction of a second network operator (SNO) (Tobbin, 2010). Another signiﬁcant step in the reform process was the establishment of a regulator the National Communication Authority (NCA) – by an act of Parliament, ACT 524 in 1996. The NCA was tasked to generally foster competition and balance the interest of the major stakeholders in the telecommunication industry including the ﬁrms, consumers and the society (Alhassan, 2003). In 2004, the last major step in the reform process was the development

38 of a National Telecommunication Policy (NTP) (Tobbin, 2010). The NTP was to provide the framework within which modern telecommunication such as mobile telephony, the internet and multimedia services were to evolve. The objective here was to achieve national penetration of telecommunication services to at least 25 percent and 10 percent to the urban population and the rural areas respectively.

Following from these and several other reforms, the total number of mobile telephone operators had risen to 4 by 2001, with ﬁxed-line telephone services provided by Ghana

Telecom and Westel. By 2009, the number of mobile telecom operators had increased by

2 contributing to a considerable increase in the number of mobile phone subscriptions to

63.77 per 100 inhabitants from a meagre 0.69 per 100 inhabitants in 2000 (International

Telecommunication Union, 2015). Presently there are 6 main mobile telecommunication operators in Ghana including Airtel, Expresso, Glo Ghana, Millicom Ghana (Tigo), MTN and Vodafone (formerly Ghana Telecom). The mobile phone subscription presently has experienced the most rapid growth of rate of 114.82 per 100 inhabitants, bypassing the mobile subscription rates of some developed countries such as the United States (95.5) (International

Telecommunication Union, 2015).

Two important points about the historical trends of telecommunication in Ghana as presented in this chapter are worth emphasizing. First, Ghana represents a developing country that might fit well into a path of leapfrogging the more traditional telecommunication technologies such as the fixed telephones and personal computers particularly in terms of the diffusion of such technologies to members of the society, albeit the infrastructure base for some of these technologies had existed since the colonial times. Generally, nation’s telecommunication technologies globally have evolved along the following stages first adopters, con- stituting mainly developed countries; converging adopters; and belated adopters or “leapfrog- gers” (Pick and Sarkar, 2015). Ghana exemplifies leapfrogging especially since 2004 given widespread usage of mobile phone over the fixed telephones (see Figure 3.2).

39 140 Phone Subscriptions

120

100

20 Number of subsription per 100 people 100 per subsription of Number

years

Fixed-Telephone Subscriptions Mobile Telepone Subscriptions

Figure 3.2. Subscription rates of ﬁxed-telephone and mobile phones in Ghana (1995-2014) Note: Figure constructed from available ﬁgures obtained from ITU (1995 – 2014)

Figure 3.2 illustrates the combined trend in the growth of fixed telephone and mobile phone subscription rates in Ghana. Mobile telephone subscription has grown rapidly over the years bypassing the use of fixed telephones, which has remained significantly low albeit it recorded higher subscription rates (average of 1.2 in 100 people) relative to mobile phones

(averagely 0.8 in 100 people) before 2004. The marginal inroad made by the ﬁxed telephone sub sector is partly due to inadequate infrastructure necessary for reliable connection (Sey,

2011). Between 2004 and 2014 the compound annual growth rate of mobile phones was about 80% compared with 5.0% of fixed telephones. The drastic diffusion of the use of mobile phones has been attributed to its handier infrastructure, the flexibility it affords, the ease of subscription and its relatively cheaper cost of ownership (Frempong, 2009). In addition, its ubiquity especially amongst the youth lies in its transformation as symbol of

40 prestige to a multi-purpose technology for social and economic activities. Unfortunately, mobile telephony subscription is skewed in favor of the urban areas. Second is the role of Kumasi in the historical pattern of telecommunication and transportation in Ghana. At the crossroad of the motor and rail transport system, facilitated by the deployment of various forms of telecommunication, Kumasi performed a range of functions unrivalled by many other urban centers in the country. As a bulk breaking point for many agriculture commodities, the city played an important role in the distribution of goods and other services in the country and beyond.

Figure 3.3. Geographical Location of Kumasi in the National, Regional and District Contexts Note: (Figure based on Acheampong, 2017)

Presently, the Kumasi Central Market (located at the center of the city) - the largest trading center in the sub-region – extends its catchment to include other West African countries including Togo, Burkina Faso, the Ivory Coast, Benin and Nigeria. What has fostered

41 Kumasi’s role as a “central place” especially in terms of economic significance in Ghana and West Africa is its strategic location geographically (see Figure 3.3) as well as its rich natural endowments. Over the period of the city’s existence, its remarkable presence in the socio-economic development of the country has encouraged a steady influx of migrants from other parts of Ghana and beyond, contributing to a larger floating population of northern traders especially and foreign nationals (Cobbinah and Amoako, 2012).

Figure 3.4. Amount and rate of built-up land change in the KMA (1986 – 2014)

To this end, the population of Kumasi has increased considerably over the last century from about 3000 in 1901 to an estimated 1.7 million in 2010. The current population is projected at 2.2 million (Oduro et al., 2014), with an estimated growth rate of 5.4 percent per annum. This rate of population growth is the fastest in the country and represents about twice the national and regional annual growth rates of 2.4 percent and 2.6 percent respectively. This unprecedented population growth has necessitated a substantial demand

42 for housing within the city resulting in an annual housing growth rate of 8.6 percent (Owusu-

Ansah and O’Connor, 2010). This has invariably aﬀected the physical structure of Kumasi resulting in the rapid spread of the city’s built-up area. Built on the ruins of the burnt town of a few square km in the late twentieth century, Kumasi has assumed a size of approximately

250 square km and it is fast expanding from its historical core to engulf the neighboring districts of Atwima Kwanwoma, Aﬁgya Kwabre, Ejisu-Juabeng, Atwima Nwabiagya and

Bosomtwe. Figure 3.4 illustrates the historical urban expansion patterns of Kumasi since

1986. At an average annual expansion rate of 4.5%, the built-up area of Kumasi increased by three-folds from 51 km2 in 1986 to 177.501 km2 in 2014 (Acheampong, 2017).

The rapid increase in the built-up area has been exacerbated on one hand by the concentric spatial structure of the city which has facilitated developments in all directions (Owusu-

Ansah and O’Connor, 2010), and on the other hand by the prevalence in the use of modern forms of telecommunication which has removed psychological and physical barrier to suburbanization. However, while residential activities have diﬀused massively into the suburbs, workplaces continue to be concentrated in the Central Business District (CBD) of the city.

The present structure of the city inﬂuenced among others by its historical economic role in Ghana has brought in its wake several challenges including transportation. Trip lengths for all purposes have impliedly increased substantially because of the rapid growth in the city with very little eﬀorts made thus far to decentralize activities from the Central Business

District (CBD) to the sub metropolitan areas. It is estimated that the duration for a typical journey to work has increased from about 35 minutes in 2000 (Adarkwa and Tamakloe, 2001) to 45 minutes in 2008 (Poku-Boansi, 2008) to approximately an hour (Poku-Boansi et al.,

2012). Ordinarily, such increases should have been manageable if the city had adequate plans to cater for the traﬃc and manage it. However, this is yet to be eﬀectively done, hence, journey to work has now become more hazardous than it has ever been and in the process, has resulted in a fair amount of congestion, vehicular accidents, high consumption

43 of fuel, increased expenditures for households with a generally negative impact on economic productivity and economic growth since a lot of time is non-productive time is spent in traffic. Given the role the city has played in the development of transportation and telecommunication, as well as its unique location, Kumasi provides an interesting case study of the relationships between modern forms of telecommunication and household travel, as well as a means whereby the problems of transportation in Ghana as a whole, can be approached. From the foregoing observations, it does appear that, both forms of communication (mobile phone and physical travel) are developing in differing directions in Kumasi. While ICT technologies in the form of mobile telephony is rapidly growing and improving, the problems of transport in the form of congestion and its associated effects are exacerbating. Amidst this conundrum, mobile telephony is undeniably impacting significantly on several aspects of people’s lives. Increasingly, it is becoming an important tool for activities such as communication, business, shopping, entertainment, and banking, which generally require physical travel. In a sense, mobile phones provide “virtual mobility”. Thus, it appears that the widespread usage of mobile phones and improvements in its technology could help amelio- rate the problems of physical travel by simply reducing the need to travel or enhance the efficiency of the current transport system. However, the nature of the relationship, as well as the extent the use of mobile phone affects travel to my knowledge, has not been extensively explored. This study is aimed at investigating the impacts of mobile phone use on household travel behavior in Kumasi. To achieve this objective, the following research questions have been proposed:

i. How does the use of mobile phone aﬀect travel behavior in Kumasi, Ghana?

ii. Is the net impact of mobile phone use on travel behavior in Kumasi one of substitution, complementarity, or neutrality? iii. Are the relationships between the use of mobile phone use and travel behavior diﬀerent among location and socio-demographic factors?

44 iv. How can the statistical results on the relationship between mobile phone use and travel behavior be explained from the participants’ perspectives?

The next chapter provides the overall methodology that will be adopted to address the empirical questions of this study.

45 CHAPTER 4

RESEARCH DESIGN AND METHODOLOGY

4.1 Introduction

In Chapters 2 and 3, a review of previous researches relevant to the study and the contex- tual scope of the study, respectively, were presented. While the literature review provided an overview of the general direction of the study as well as identiﬁed gaps in the existing scholarship, Chapter 3 provided an overarching context within which the study is situated. In the light of these, two main objectives were formulated for this study. These objectives reﬂect generally the empirical aspects of the study which would examine the nexus between telecommunication and transportation in a developing country perspective, as well to explain in-depth why certain factors are important to the nature of the relationship between telecommunication and transportation. To reiterate, the principal objective of the empirical research as stated in Chapter 3 was to:

• Examine empirically, the relationship between telecommunications and transportation within a developing country context – Ghana – and more speciﬁcally, to explore how the use of mobile telephony may inﬂuence the travel behavior of households and individuals.

In line with the research objective, four central research questions were derived to be pursued in this study, and are identiﬁed as follows:

i. How does the use of mobile phone aﬀect travel behavior in Kumasi, Ghana?

46 iv. How can the statistical results on the relationship between mobile phone use and travel

behavior be explained from the participants’ perspectives?

The focus of this chapter therefore is to set out the overall methodology adopted to address the empirical research objective as well the accompanying research questions. More speciﬁcally, this chapter focuses on the strategies to generate and analyze the data necessary to answer the research questions. Key considerations addressed here are the research design, data measurement, data types and sources, sampling techniques, data collection, as well as statistical analysis themes. These considerations, particularly the sampling design and sample size determination steps, are important because the data are based on a sample of households which would be generalized for the city of Kumasi, thus going through such a process ensures that the sample size is representative enough for generalizations.

4.2 Chapter Organization

This chapter is organized into eight sections. Following this section is a description of the research design adopted for the study, with a focus on its nature, reasons why it was selected for the study, and how it fits in addressing the research questions. In section four, a definition of the target population, as well as it characteristics is presented. A detailed description of the study variables and their accompanying data types are provided in section five. As will be elaborated later, the study is conducted in two phases – a quantitative phase and a qualitative phase. Thus, in the sixth section, the step-by-step procedure to conducting the quantitative phase is described, including the development of the research instrument, sampling techniques adopted, the administration of the research instrument, as well as a plan to process and analyze the quantitative data. Section seven outlines the data collection and analysis plan for the qualitative data. The chapter concludes with a summary of the methodological issues addressed in this chapter.

47 4.3 Research Design Strategy

There is a variety of research design strategies that can be used to study the problem of the relationship between telecommunication and transportation within an urban context such as Kumasi. The empirical literature that exists on the subject has often been framed within the two main research approaches – quantitative and qualitative research methodologies. While the quantitative studies have focused on explaining the causal relationships between single application of telecommunication and travel (Choo et al., 2002, 2005; Lila and Anjaneyulu, 2013) or multiple applications (Selvanathan and Selvanathan, 1994; Plaut, 1997; Sasaki and Nishii, 2010; Kim and Goulias, 2004), a growing number of qualitative studies, particularly attitudinal surveys (Mokhtarian and Salomon, 1997; Handy and Yantis, 1997), have primarily focused on identifying the factors that influence the desire of household members to engage in any of the applications of telecommunication such as telecommuting. Generally, in quantitative research, an investigator relies on numerical data to “predict results, test a theory, or find the strength of relationship between variables, or a cause and effect relationship” (Chilisa and Kawulich, 2012), using a belief system grounded in a postpositivist worldview (Creswell, 2013, p. 23). The post-positivism worldview derives from the works of “Comte, Mill, Durkheim, Newton, and Locke” (Smith, 1983, cited in Creswell and Creswell, 2017, p. 6), and other recent writers including Phillips and Burbules (2000). This philosophical worldview is a relaxed form of positivism, which is based on the view that knowledge claims are based strictly on experience and verifiable facts. Post-positivism concurs with positivism on the notion of the existence of an external reality; however, post- positivists recognize that our claims of knowledge cannot be based on an absolute or objective realism considering researchers’ human limitations (Chilisa and Kawulich, 2012), and that we can only approximate the truth. From this viewpoint, quantitative researchers make their knowledge claims about the world by developing numeric measures of the “external reality” in the form of variables that are causally related to determine the magnitude and frequency

48 of their relationships. While quantitative research is indispensable in terms of establishing causality drawing on large numbers, it is far removed from understanding the behavior of participants about which data are obtained.

Qualitative studies provide avenues to fill this gap inherent in quantitative studies by offering in-depth information about participants’ experiences and perspectives. As a general term, researchers using a qualitative approach collect data from participants in a natural setting with a view to “exploring and understanding the meanings these individuals ascribe to a social phenomenon” (Creswell and Creswell, 2017, p. 4). Informed by the constructivist paradigm (Lincoln and Guba, 1985), most qualitative researchers pose broad and open-ended questions to research participants with the objective to elicit responses that reflect the subjective meanings of their study participants’ experiences of the (social) world in which they live, thus taking their research subjects’ point of view (Creswell and Creswell, 2017). Unlike the quantitative method, which uses deductive logic to test theories and hypotheses, qualitative researchers move from specific observations to broader generalizations and theories

(Creswell, 2013). In addition, using the qualitative method affords the researcher the flexibility to modify her/his data collection strategy to reflect better new developments observed in the field. Despite the many benefits ascribed to qualitative research (see Creswell, 2013, p. 47–48), studies employing this approach are susceptible to sample bias and subjectivity which consequently limit the generalizability of the obtained data to the sampling frame and study population (Creswell, 2014).

Evident from the foregoing discussion is that, as stand-alone modes of inquiry, neither quantitative nor qualitative method is suﬃcient to address complex social phenomena such as the interaction between the uses of telecommunication and travel, particularly in the context of an African city. As such, there has been a growth of interest in research methodologies that leverage the strengths and neutralizes the weaknesses of both quantitative and qualitative research designs. Many are the terms that have been used to describe this

49 methodology including “multimethod” and “mixed methodology”. But it is the more recent term – “mixed methods” – (see Tashakkori and Teddlie, 2003, 2010, for an extensive discussion on mixed methods) that has received widespread use, and has been recognized as a third research choice which integrates quantitative and qualitative data, drawn from both approaches within one study (Creswell and Creswell, 2017). In a mixed methods research study, investigators collect both quantitative and qualitative data for deductive and inductive purposes respectively (Creswell and Creswell, 2017). In other words, quantitative data is collected with the aim to test and improve theories, while understanding of the theories from the perspective of real world people are advanced from the collection of qualitative data. According to (Tashakkori and Teddlie, 2003), ”mixed methods” is preferred when: (i) a stand-alone approach is not sufficient to answer a research question (especially one that is both confirmatory and exploratory in nature); (ii) the objective of a study is to provide an in-depth understanding of a complex social phenomenon; and (iii) a researcher wants a complete understanding of a phenomenon from differing viewpoints.

In the light of the foregoing, and to present a more complete understanding of the research problem, this study employed the mixed methods design. In choosing to mix both quantitative and qualitative methods, I considered the complexity inherent in the telecommunication- transportation nexus subject, as well as the objectives and questions formulated for this research. More speciﬁcally, I used this research design to examine the study objectives using questionnaires and life experiences of participants to improve the clarity of the telecommunication and transportation nexus debate. This allowed for more insightful and experiential contributions from the study participants (Anane, 2014).

Creswell et al. (2003) identify three main factors that need to be considered while designing a mixed methods study: timing, weighting, and mixing. “Timing” refers to whether the collection and analysis of quantitative and qualitative data are done in sequence or concur- rently (that is, both types of data are collected and analyzed simultaneously). When data

50 are collected sequentially, further consideration is given to which aspect of the implementation process comes ﬁrst. “Weighting” refers to the extent of treatment of one type of data in a single study. In practical terms, the researcher can put equal weight on both types of data, or can give priority to one over the other. “Mixing” the data generally, raises two questions:

1) at what point in a mixed methods study does mixing occur? and 2) how does it occur?

While the ﬁrst question might be easier to address, the second often requires the researcher to determine if both types of data are connected, separated, or merged, and at what points along the quantitative – qualitative continuum these processes occur (Creswell et al., 2003).

Based on the above factors, Creswell et al. (2003) identify six main strategies to conducting a mixed methods study (refer to Creswell, 2009, pp. 208–216 for a full discussion of these six strategies). In designing this mixed methods study, I used the “sequential explanatory strategy”, which comprises two distinct levels: first the collection and analysis of quantitative data, and second the collection and analysis of qualitative data with the aim of explaining or elaborating on the quantitative results obtained in the first phase. Thus, in terms of timing, the collection of quantitative data comes first. In this study, I first obtained (using questionnaires) and analyzed quantitative data to identify the potential impact and the extent thereof of mobile phone use on travel behavior, and purposefully selected key informants from the quantitative sample for the qualitative phase (see Creswell, 2013, on purposeful sampling). Although these two stages are important to gaining a complete picture of the research problem, in this study, priority was given to the quantitative data. The quantitative part represents the major aspect of the data collection and analysis of the study with the view to examine the trends and patterns that exist between the use of mobile phones and the travel behavior of households. A smaller qualitative component followed in sequence and it was used to identify factors that were important in influencing the type of relationship between the main study variables that were revealed from the quantitative phase.

51 Phase Procedure Product

▪ Cross-sectional face-to-face ▪ Numeric data QUAN Data survey (N=384 households Collection /661 Individuals)

▪ Data Processing (SPSS 20.0) ▪ Edited, coded and clean data QUAN Data ▪ Frequencies (SPSS 20.0) ▪ Measures of central Analysis ▪ Exploratory Factor Analysis tendency (SPSS 20.0) ▪ Extracted factors ▪ Confirmatory Factor Analysis ▪ Measurement Model (LISREL 8.7) ▪ Structural Equation Modeling (LISREL 8.7) ▪ Full structural equation model Cases ▪ Purposefully selecting

Selection participants for case studies ▪ Cases (N=24) (N=24), 8 from each group

▪ Text data (interview ▪ Individual in-depth transcripts) qual Data Collection interviews with the 24

participants ▪ Text data (interview ▪ In-depth interviews with 3 transcripts) government agencies

▪ Codes and themes ▪ Coding and thematic qual Data Analysis analysis ▪ Similar and different themes

▪ Within-case and across-case theme development ▪ Visual data display ▪ NVivo 11.4.1 software

Interpretation ▪ Explanation of the meaning ▪ Discussion of Entire of quantitative results ▪ Recommendations for future Analysis ▪ Interpretation of the meaning studies of cases

Figure 4.1. Visual Model for the Sequential Explanatory Mixed Methods Design Procedures Source:Figure Adapted 4.1. Visual from Model Ivankova for the and Sequential Stick (2007) Explanatory Mixed Methods Design Procedures Source: Adapted from Ivankova and Stick (2007)

5261

With regard to “mixing”, it bears mentioning that the quantitative and qualitative data were connected between the analysis of the quantitative phase and the collection of data of the second phase. In a sense, a preliminary analysis of the quantitative data was used to identify the participants as well as the interview questions in the follow-up qualitative phase. The sequential explanatory strategy is distinguished from the others primarily because the two levels identified build on each other so that they are distinct, easily recognized stages of conducting the study (Creswell, 2014). A visual model that illustrates this mixed methods strategy is presented in Figure 4.1. The visual model uses some form of notation to describe the strategy. “Quan” and “Qual” in the figure stand for quantitative and qualitative respectively. Capitalization of the “QUAN” indicates that priority was given to the quantitative phase of the study. The ensuing sections present a more detailed procedure of how data was collected, analyzed, and interpreted, under each of the two phases of the study, the quantitative procedure first, followed by that of qualitative. Before a discussion of the procedures, however, it is necessary to have a clear idea of the population studied, as well as the data types obtained. Thus, in the sections that follow, I begin with a definition of the target population, identification of the study variables, as well as the types of data that were collected.

4.4 Target Population and Unit of Analysis

Study population, as explained by Trochim and Donelly (2008), is the group sampled from and generalized to in a study. It comprises individuals, households, organizations, or any element of interest that is being studied (Blair et al, 2014). In accordance with the objective and research questions, households and individuals in the household residing in the Kumasi Metropolis constituted the target population for this study. A household in this context is an individual or group of individuals who live together in the same housing unit, share the same house-keeping arrangements, catered for as one unit and recognize one person as its

53 head (Ghana Statistical Service, 2013). Given this definition, the total number of households in Kumasi is 440,283 with an average of about 4 persons per household (Ghana Statistical Service, 2013). Having defined the population, the next step was to set its boundaries (Blair et al., 2013). Although a household in general may consist of several individuals including a man, his spouse, and child(ren), the individuals of interest in the research were those of voting age (that is, persons 18 years and older). In a more specific operational term, the target population for the study comprised individuals who are at least 18 years of age, and have their principal place of residence in the Kumasi Metropolis. Thus, these individuals served as the source of primary data for the study. It is worth mentioning that, institutions and agencies with competencies in making various policies that shape transportation, telecommunication and physical organization of func- tional land uses in the country were consulted for purposes of data collection. These were not units of analysis per se. Instead, they were sources of primary information to inform especially the qualitative phase of the study. As will be explained later, the information from these institutions provided a starting point to gathering the data from the selected individuals for the qualitative phase. In view of the fact that reliable aggregated data on measures of telecommunication and transportation are scarce in Ghana and more specifically Kumasi, a study to collect individual level data on mobile phone use and travel patterns was necessary. The next section identifies the main study variables on which data were collected.

4.5 Study Variables and Data Types

To obtain the relevant data from the target population defined in the previous section, it was important to translate the main research question into specific variables. The study hypothesizes that, amidst other factors such as location and socio-demographic, the use of mobile phone has an impact on travel behavior. Consequently, the main variables identified in the study were travel behavior (the dependent variable), the intensity of mobile phone

54 use (main independent variable), as well as location and socio-demographic characteristics

(control variables).

In the travel behavior literature, there is no single indicator that measure the concept, and thus, has been measured in several ways: price/cost of travel (Boarnet and Crane,

2001), travel modes (Soltani et al., 2005; Srinivasan and Rogers, 2005; Næss and Jensen,

2004; Cervero, 2002), fuel consumption and distances travelled (Goudie, 2002), daily trip numbers and duration (Giuliano and Dargay, 2006; Newbold et al., 2005), purpose of travel

(Best and Lanzendorf, 2005; Anable, 2005), among others (see Curtis and Perkins, 2006, for a comprehensive review of these studies). Of interest is the use of these single travel behavior measures in the previous studies that have examined the relationship between telecommunication and transportation: including vehicle kilometers/miles travelled (Jamal et al., 2017; Choo and Mokhtarian, 2007), purpose of travel (Lila and Anjaneyulu, 2016), travel distance (Lila and Anjaneyulu, 2013), and number of business air trips (Denstadli et al., 2013). As mentioned earlier and evident from this discussion, there is no single commonly accepted measure, at least to my knowledge, of travel behavior. As will be elaborated later in this chapter, this study uses all these indicators to develop a composite measure of the dependent variable. Thus, travel behavior is treated as a latent construct and the identiﬁed indicators as observed variables (see Table 4.1).

Regarding the main independent variable – intensity of mobile phone use – it is used as a measure of telecommunication because of its ubiquity in the study area. Other technologies such as the ﬁxed-telephone, computers and the internet have lagged far behind mobile telephony in terms of penetration rates in the study area (Pick and Sarkar, 2015; Sey, 2011;

Tobbin, 2010; Over˚a,2006). In a review of scholarly communication research, (Boase and

Ling, 2013, p. 509) identiﬁes two common measures that operationalize intensity of mobile phone use: “frequency and duration”. Frequency measures the number of times the mobile phone is used for a speciﬁc purpose (e.g., calls, SMS), while duration measures the amount of

55 Table 4.1. Description of study variables Main Indicators Description Construct/ Variable Travel Behavior Travel frequency Daily frequency of trips made Travel distance Daily average distance travelled Travel duration Daily average duration of travel Mode of travel Primary mode of travel Purpose of travel Purpose of travel Intensity of Frequency of use Frequency of using mobile phone Mobile Phone Use Type of phone Type of mobile phone used Number of phones No. of phones used by respondents Number of networks No. of SIM cards or networks subscribed Type of activities Types of activities conducted on the phone Number of activities Number of activities conducted on the phone Demographic Age Age of participant Gender Gender of participant Education Highest educational level of participant Employment Employment status of participant Household Size Household size a participant belongs to Income Monthly income of the household Car ownership Availability of automobile in household Land Use Residential location Residential location time spent on undertaking certain activities on the mobile phone. Aside these two measures, other speciﬁc measures of the concept (intensity of mobile phone use) that were used in the study are types and number of mobile phone used, number of networks subscribed to, as well as number and types of activities conducted on the mobile phone. Given that the eﬃcacy of these additional indicators has not been fully evaluated in the communication scholarship, the extent to which they are linked to the “intensity of mobile phone use” construct would be determined using exploratory factor analysis (see section 4.6.3). In a similar treatment of the travel behavior variable, intensity of mobile phone use is treated as a latent construct.

Table 4.1 provides a list of the observed indicators under each latent construct.

56 Considering the role played by land use/urban form and socio-demographic factors in determining telecommunication use and travel behavior (Choo and Mokhtarian, 2007; Wang and Law, 2007), both variables were also used in this study as controls. Important socio- demographic factors that inﬂuence travel behavior include household composition, gender, age, educational level, employment status, income, and car ownership (Van Acker and Wit- lox, 2010; Curtis and Perkins, 2006). With regards to the urban form aspect, key components include: density, diversity and design (Cervero, 2002 cited in Curtis and Perkins, 2006), and residential location (Goudie, 2002; Næss and Jensen, 2004, cited in Curtis and Perkins, 2006). In this study, however, only the residential location component was used, given the dearth of up to date aggregate land use distribution data at the metropolitan scale. All the indicators discussed in this section formed the main measures on which data were collected and analyzed. In the sections that follow, the procedures implemented under each phase of the research design are discussed.

4.6 Quantitative Phase

This section focuses on the speciﬁc procedures that were followed to obtain and analyze the data on the study variables under the quantitative stage. Speciﬁc issues considered here are the questionnaire design and pretesting, sample selection and data collection, as well as the methods for processing and analyzing the quantitative data.

4.6.1 Questionnaire Development

As mentioned earlier in Section 4.3, a questionnaire is the most important instrument used by the quantitative researcher to collect data. Thus, in this study, the variables identiﬁed from the research questions in Table 4.1 were translated into a structured questionnaire for the purposes of data collection. The design of the questionnaire was strongly inﬂuenced by the need to reduce as much as possible the cognitive burden and the time required to complete the

57 survey (e Silva et al., 2017), due to the literacy level of the study population. Consequently, the great majority of questions were formulated as close-ended response choices, as well as rating scales questions (i.e., matrix scale questions), to reduce the need to write input data, with some few open-ended questions. The open-ended questions included respondents’ age, household size, income, among others, that required short and precise numerical answers.

It bears mentioning that, some of the information asked, specifically the travel information, in this survey required different techniques to collect precise and detailed data. The travel information was obtained using a travel diary, to collect detailed travel behavior data for all individuals within the target population during a specific 24-hour period (see Brown et al.,

2013, for a detailed description of this methods). The survey consisted of over 90 questions divided into 6 parts. A brief description of the thematic sections is presented below:

i. Introductory page: The ﬁrst section of the survey served as the introduction and

contained an abridged version of the informed consent, as well as the goal of the study.

Other relevant information associated with the participants such as the location of their

residence and a unique identiﬁer were also listed on the introductory page. Data on

the location of participants’ residence provided a geographical context for the collected

data, particularly the aspects related with mobile phone use and travel activities.

ii. Household socio-demographic information: The second section of the question-

naire contained questions used to obtain socio-demographic characteristics of household

members including gender, age, ethnicity, highest level of education attained, marital

status, employment status and type, family type and size, tenancy status and type of

house, as well as car ownership status. These data would provide the basis to diﬀeren-

tiate households and individuals as heterogeneous entities and to determine how these

attributes shape their use of mobile phones and travel behavior.

58 iii. Household travel data: The third part of the questionnaire contained questions used

to elicit information about an individual’s actual travel experiences in a travel day. A

travel day began at 4am in the previous day and ended 4am the following day. The

speciﬁc questions focused on whether a household member made a trip on a travel day,

and details of the trips including: departure and arrival location, departure and arrival

time, primary mode and purpose of trip, total distance covered and cost of trip. It

is important to mention here that each participant prior to the actual data collection

was provided with a travel diary/log, which served as a guide to them in answering the

questions contained in this section. iv. Mobile phone use data: Information on participants’ ownership and use of mobile

phone constituted the fourth part to the questionnaire. Under this section, questions

were formulated to obtain data on mobile phone ownership, type and number of mobile

phones, as well as number of subscribed networks. Questions on the speciﬁc activities

undertaken on the mobile phone were also included. The activities/uses about which

questions were asked included phone calls, SMS, e-mail, online purchase, mobile banking,

mobile money1, getting driving directions, social media, and entertainment (radio, T.V,

gaming, etc.). There was an option for the respondents to list and answer questions on

other activities that were not identiﬁed in the survey. Respondents were subsequently

asked to assess the frequency of using the listed activities on their mobile phones on an

8-point Likert scale ranging from “many times daily” to “never”.

v. Mobile phone use and travel relationship: This part to the questionnaire aimed at

examining, from the participants’ experiences, the impact of the use of mobile phone on

their travel. Respondents who answered questions about speciﬁc mobile phone activities

1Mobile money is a mobile based technology that allows users to receive, store and spend money using their mobile phones. It is sometimes referred to as a “mobile wallet” or by the name of a speciﬁc service provider, such as MTN mobile money, TIGO Cash, Airtel Money, and Vodafone Cash, in Ghana.

59 were asked here to determine how they would have This part to the questionnaire aimed

at examining, from the participants’ experiences, the impact of the use of mobile phone

on their travel. Respondents who answered questions about speciﬁc mobile phone activ-

ities were asked here to determine how they would have undertaken those transactions

if they were unable to use the mobile phone for such uses at the time. Next, respon-

dents were asked to express their level of agreement to speciﬁc statements about the

overall impact of mobile phone use on their travel experiences. The statements included

“increased the number of my trips”, “increased my travel time while driving”, “reorga-

nization of my travel time and space”, “awareness of travel alternatives”, and “increased

my comfort of travelling”. A 5-point Likert scale ranging from “strongly disagree” to

“strongly agree” accompanied these statements. vi. Household income: The ﬁnal set of questions focused on the household’s monthly

income and the chief income earner in a household. The total monthly income, which

was mainly reported by the head of a household, comprised the income of all working

members of the household. The questions on household income were consciously included

in the ﬁnal part of the survey to avoid biasing responses and reduce survey drop-out

levels (e Silva et al, 2017).

4.6.2 Sample Selection and Data Collection

The previous section has focused on the development of speciﬁc sets of questions aimed to obtain information about the study’s target population with regards to their use of mobile phone and travel experiences. This section presents a standard protocol for drawing a probability sample of respondents to be contacted for the purposes of data collection.

60 Sampling Technique

In view of the geographical size of Kumasi (approximately 254 square km) and the total number of households (over 440,000), a study of this nature could not have included the entire target population. Moreover, a sample size based on the study population to ensure sample representativeness would be too large for data collection, irrespective of the error margin. Consequently, the sampling process presented here aims at attaining a sample size that would be appropriate to elicit a reasonable quantitative response enough to generalize for the whole of Kumasi. In this regard, the study adopted a multi-stage sampling technique including a cluster (area) sampling, a simple random sampling of suburbs within the city, and a systematic random sampling of households within a selected suburb. Albeit this sampling method could be complex and difficult, it combines the various methods to address the sample needs of the study in the most effective and efficient ways possible (Trochim and Donnelly, 2008). The three-stage sampling process is discussed seriatim. The process began with a cluster sampling, that is, to divide the study area into clusters. The study relied on the existing administrative zones, known as sub-metros, and created 10 clusters2. It is worth mentioning that, the sub-metropolitan divisions as used in the study was only useful for data collection purposes since they provide the basis for population distribution in the Metropolis. As will be discussed in Chapter 5, the sub-metros would be reclassified into broad urban zones to enrich the spatial dimension of the relationship between the main study variables. In the second stage, communities were sampled from each cluster, using a simple random sampling strategy. To facilitate this, the communities in the sub-metros were first grouped based on income. Generally, the ownership and use of mobile phones are known to be correlated with household income, with trends in the intensity of its use higher along income

2 Albeit, in 2012, Asawase sub-metro was carved out from the Kumasi Metropolis to create the Asokore Mampong Municipal Assembly, it was included in this study since the sample frame was based on the 2010 Population and Housing Census.

61 Table 4.2. Description of housing areas in the study area (Kumasi) Housing Areas Description High Income These areas are dominated by single family apartments and homes, and are occupied by senior government oﬃcials as well as high income residents. Medium Income These areas are also dominated by single family homes (known as estates), but are built by government agencies, and are managed as a rental or on hire purchase. Low Income These areas, also known as the indigenous sector, are dominated by multi-family compound housing. Housing density is typically high in these areas. The “Zongos” are included in this sector. The Central The CBD serve as the primary business hub for the entire Business District metropolitan area. It is characterized as the city’s major (CBD) employment center. Source: (Okyere, 2012) brackets. This is also true for trip generation, where travel behavior theory recognizes that daily travel choices are related to socio-economic characteristics of the built environment particularly income or housing areas (Frank and Pivo, 1994). In a developing country context especially, such as the context for this study, income/housing area is found to be the strongest predictor of travel behavior (Leck, 2006). The number of communities sampled from each sub-metro however was conditioned on how heterogenous the cluster was with regards to income. Thus, in a more heterogenous cluster with distinct income groupings, three communities were randomly sampled to represent each of the income groups – low, medium and high income. On the other hand, in a very homogenous cluster, only one community was sampled. Consequently, these locations provided a true representation of each cluster, and consequently the Metropolis. In all, a total of 24 proxy locations were sampled for data collection in the metropolis (see Figure 4.2). In a sense, the total number of household within these proxy locations served as the sampling frame or accessible population from which the sample size was determined. A general description of the various income/housing areas in Kumasi is provided in Table 4.2.

62 Figure 4.2. Distribution of proxy communities within study clusters/sub-metros

The third stage in the process concerned itself with a method to determine an appropriate sample size from the sampling frame. In determining the appropriate size for the sample, the study took into consideration the sample frame and the level of precision (Fowler Jr, 2013).

Although a variety of methods exist for determining representative sample for proportions including the one developed by Cochran (1963) for very large frame population, and the

ﬁnite population correction (fpc) approach for relatively small frame population (Fowler Jr,

2013), this study used the more simpliﬁed method provided by Yamane (1967, cited in Israel,

2009) to determine the sample size of households for each community. The Yamane’s formula to sample size determination is shown in Equation (4.1):

N n = (4.1) 1 + N(e)2

63 Where n is the sample size for each proxy community, N is the target population (total household for each proxy community), and e is the level of precision. The level of precision required was such that a 95% conﬁdence interval is no wider than 5% (i.e., 0.05). Plugging the respective sample frames and a level of precision (e) of 0.05 into Equation (4.1), the minimum sample size for each proxy community was determined (see Appendix A for the list of communities and their corresponding sample sizes). It is important to mention that the 2010 population and housing census data (Ghana Statistical Service, 2013) was used to obtain the sample frame for the study communities. In all, a total household sample of 384 was determined for data collection in Kumasi metropolis.

Data Collection Procedure

The preceding sections set the stage for the collection of data from the identiﬁed sample of households. The process however began with the recruitment and training of ﬁeld assistants, as well as the testing of the research instrument. These steps and the actual data collection are explained in detail in the ensuing paragraphs.

With 24 proxy communities evenly distributed over the metropolis, field assistants were recruited and trained to assist in conducting the interviews. Five field assistants were recruited with each assigned to two clusters (or sub-metros). The field assistants were all

Masters students in the Planning program at the Kwame Nkrumah University of Science

Technology – the researcher’s previous university in Ghana. Two factors were important in the recruitment of the field assistants: first was their rich experience with questionnaire administration; and second was their familiarity with the study area given they all reside in the metropolis. Three training sessions – two before the instrument testing (discussed in the next paragraph) and one after the testing – over a 2-week period were organized for the research assistants. The first two training session involved introducing to the field assistants the motivation and objectives of the study, as well as reviewing the questionnaire item by

64 item. The aim was to explain each question and the response it was intended to elicit to them. Also, a one-day debrieﬁng session was held after the pretesting of the questionnaire to address: question-by-question problem identiﬁcation; problems of respondent resistant; and any other weakness in the instrument, before the actual data collection.

As mentioned in the previous paragraph, the survey instrument was pretested following the ﬁrst two training sessions for the ﬁeld assistants. This was done to primarily assess the adequacy of the research instrument to measure the study constructs (Blair et al., 2013).

Pretesting the questionnaire was also done to aﬀord the ﬁeld assistants the opportunity to gain further understanding and familiarity with the instrument before the primary data collection. It was anticipated during the pretesting stage that, questions that were poorly conceived, did not elicit appropriate response, or did not yield any response, would be

flagged. The questionnaire was pretested on about 10% of the total sample to include three persons of diverse characteristics from each cluster, so that the questions and response categories are given a reasonable test. It bears mentioning that, during pretesting, the exact procedures planned for the primary data collection were followed. The results of the pilot survey influenced the final questionnaire in several ways. It provided enough information to develop closed-ended questions for questions that were initially framed as open-ended.

Also, the response categories of some of the questions were found to be less than adequate.

Modifications were made to accommodate these and other identified weaknesses in the survey instrument. The modified questionnaire was consequently used for the main data collection.

Following the training of field assistants and adopting the modified research instrument, initial contacts were made with the prospective respondents. In selecting these households, the field assistants made use of the existing road network system to subdivide the proxy communities into residential blocks. From each residential block, a house was randomly selected as the starting point. Subsequent houses were selected using the systematic random sampling technique (Trochim and Donnelly, 2008). By this method, every kth element or

65 house (determined by the formula: N/n, where, N and n are the total households and

sample size respectively for each proxy community) was selected until the required sample

size allocated to a residential block within a study community was reached. The value of

k varied for each proxy community. The use of systematic random sampling over the other

probability sampling techniques, at this stage of the data collection process stems from the

fact that, it is highly representative of the population since the respondents are randomly

ordered, unless certain characteristics of the population are repeated for every kth respondent, which is highly unlikely. In the study context, several unrelated households might inhabit a house. Thus, in such instance, households who were available and willing to participate in the study were selected. Where more than one household was willing to participate, the ﬁeld assistants were asked to list them and randomly select one. In single household occupied houses however, the identiﬁed household was automatically selected, unless they declined their participation.

The purpose of the initial contact with the identified households was to provide a compact preface to the survey by explaining to the respondent the objective and other details of the study. In a sense, it was to give the identified households sufficient information about the study to satisfy the needs of informed consent, as well as obtain cooperation. In the process of making initial contacts, especially after the participation had been elicited, travel diaries were handed out to every member of the household who was at least 18 years. The travel diary was designed to capture information on the origin and destination of their trips, departure and arrival times, primary mode of transport used, and purpose of their trips. Each household was assigned a travel day within the week and were asked to carry with them the diary on such days and record the required information. As mentioned in Section 4.6.1, the travel diary was to serve as a guide in answering the questions under the travel behavior section of the questionnaire. A convenient day and time was agreed between the interviewers and interviewees for the actual data collection. All eligible household members were interviewed

66 face-to-face during which the interviewer read the interview questions to them and manually recorded their responses on the questionnaire. From the 384 households sampled, a total of 724 persons – comprising adults aged from 18 years who were at home as of the time of the survey – were interviewed. The study procedures as described here were approved by the Oﬃce for Research at The University of Texas at Dallas.

4.6.3 Data Processing and Analysis

In the previous sections, the procedures for collecting primary data from the study sample were discussed. This section, which is the final step in the quantitative phase of the study, focuses on the techniques that were used to process and analyze the data. Processing of the obtained data involved primarily editing, coding, cleaning and preparing the data analysis file. The field assistants edited the questionnaires immediately after data collection. They met with a supervisor each day after the completion of the questionnaire to edit and check each questionnaire properly. This ensured that problems experienced during the data collection were identified early enough and corrective action taken if necessary. Once all the questionnaires were checked and turned in, they were signed by the field assistants and read- ied for coding and data entry. Coding involved the assignment of numbers to the responses given to each questionnaire item (Blair et al., 2013). Thus, each questionnaire item was given a designated amount of column space, and every response category for each questionnaire item was a given a designated code number. These codes were contained in a data entry template (developed with SPSS v20) and were provided to the field assistants for data entry. Each respondent’s responses were individually entered into the data entry template. Before data analysis began, the coded responses to each question were cleaned for consis- tency. The cleaning of the dataset involved two main steps: basic and advanced cleaning. In the basic cleaning process, frequency counts for each variable was run to check for mislabeled, out-of-range or system-missing data. In the advanced data cleaning process, inter-item con- sistency was checked using cross-tabulations. This was done especially on linked questions:

67 for instance, if a respondent indicated that they were not using mobile phone, then the response to questions about the activities conducted with the mobile phone should always be “Not applicable (coded as 997)”. The data processing activities, which included editing, coding, and cleaning, took approximately 8 weeks to complete.

Basic descriptive statistics including measures of central tendency, as well as inferential statistics including exploratory factor analysis (EFA), conﬁrmatory factor analysis (CFA) and structural equation modeling (SEM) were used to describe the data and examine the causal relationship between the main study variables. An overview of the EFA, CFA and

SEM adopted for the data analysis is presented in the following sections.

Exploratory Factor Analysis (EFA)

The ﬁrst statistical analysis technique employed in the study was the Exploratory Factor

Analysis (EFA). EFA is a technique in factor analysis typically used in situations where the relationship between observed indicators and latent construct are uncertain, and as a result determines the extent to which the latent constructs are linked to the observed indicators

(Byrne, 2013). That is, EFA aﬀords the researcher to see below the surface and explore the minimal number of factors that account for covariation among the observed indicators.

The main variables in the study travel behavior and intensity of mobile phone use are latent or unobservable constructs. However, as discussed in section 4.5, the researcher has some knowledge about the underlying structure of the travel behavior construct (based on existing empirical research), but not that of the intensity of mobile phone use construct.

Notwithstanding, EFA was employed on both the travel behavior and the intensity of mobile phone constructs to explore if indeed, it measures the intended covariation among the questionnaire items. In this study, 12 items obtained on ﬁve main variables and18 items obtained on six main variables (see Table 4.1), were used to measure the various dimensions of travel behavior and intensity of mobile phone use respectively.

68 EFA becomes useful in this regard as it helps group the items into few factors based on their relationships (Byrne, 2013), as well as determine the extent to which the observed indicators were related to intensity of mobile phone use. Mathematically, EFA uses a set of equations that maximize the multiple correlation of factors to each item, which is formally written as:

i1 = piAA + piBB + piC C + ··· + µi, (4.2) where: i is the response to an item; A, B and C are the factor scores; ps are the weights used to best reproduce the original standardized item response; and u is the residual for an item when the ﬁt is not perfect (Gorsuch, 1997). In using EFA to determine the dimensions of the independent construct, the study adopted the four-step general protocol recommended by (Kline, 2013) including checking for sample adequacy, selecting a method for factor extraction, deciding on the number of factor to retain, and selecting a rotation method. These steps are described below. An important component of EFA concerns the minimum sample size required to complete the analysis. Generally, EFA is a large sample technique, thus the larger the sample the better (Kline, 2013). In the early application of EFA, the adequacy of sample size was based on the sample to variable ratio (N : p), where N is the number of cases and p is the number of indicators. In the literature, rules of thumb for sample to variable ratio range anywhere from 5:1, 10:1, 15 : 1 or 20 : 1, although the 10 : 1 ratio is probably the most common recommendation (Kline, 2013). Evidently, there is no clear agreement about the optimal sample to indicator ratio for EFA. In many statistical packages such as SPSS, standard tests for checking sample adequacy are provided. Williams et al (2010) recommend the use of the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO), which “computes the ratio of the squared correlation between variables to the squared partial correlation between them” (Acheampong, 2017). The resulting index ranges from 0 to 1, with a value greater than 0.5 considered adequate for factor analysis (Williams et al., 2010).

69 The next step in EFA is to select a method of factor extraction. There are about seven extraction methods in the literature (see Mulaik, 2009, Chapter 7, cited in Kline, 2013), although the most basic choice is between principal component analysis (PCA) and principal axes factoring (PAF) (Kline, 2013). PCA and PAF vary only in that PCA does not have the error term (u) in Equation (4.2). Thus, the researcher in using PCA assumes that the variables are almost perfectly reliable and correlate highly with at least one other variable

(Kline, 2013). In SPSS, PCA assumes the default extraction method especially for initial factor solution. (Gorsuch, 1997) however argues that items by their distribution and categorization, and the fact that they may contain more than one factor make them unlikely to be highly reliable. Hence it is appropriate to include the error term in Equation (4.1) in EFA, in which case PAF is preferred. PAF becomes more appropriate when the assumption of assumption of multivariate normality is violate, that is, the distribution of the data is significantly non-normal (Fabrigar et al., 1999, in Costello and Osborne, 2005). It helps to know that the practical differences between PAF and PCA are often insignificant especially when variables have high reliability and when the sample size is large enough. The significance of these extraction methods in EFA lies in the fact that they produce factor loadings for every item on every extracted factor. In EFA, a simple structure where most items load largely on one factor but small on the others, is the most preferred (Kline, 2013).

Once the factors have been extracted, the next step is to determine how many of them to retain. Various statistical criteria exist for determining the number of factors to retain. A default criterion in most EFA statistical packages is the Kaiser criterion, also known as the eigen value > 1.0 rule. Each extracted factor has an associated eigen value, which represents the amount of variation in the indicators explained by that factor. With this criterion, factors with eigen values greater than 1 are retained. Two main problems are identiﬁed with the Kaiser criterion: ﬁrst, the estimation of eigen values is a function of sampling error; and second, it tends to produce too many factors (Velicer and Jackson, 1990, cited in Kline,

70 2013). A variation to the Kaiser criterion is the Scree test, which is aided by a scree plot.

A scree plot involves two steps: ﬁrst is plotting a line graph with eigen values on the y-axis and the possible number of factors on the x-axis; and second is visually examining the graph to locate the point where the drop in eigen values over successive factors levels out (Kline,

2013). With this criterion, the number of retained factors corresponds to the number of eigen values prior to the last signiﬁcant drop in the plot. A major drawback in the Scree test approach is the subjectivity in its interpretation, although it is greatly reduced when sample size is large (Gorsuch, 1983).

The last consideration in EFA is the method of rotation. The basic idea of rotation is to maximize high loadings and minimize low loadings with the view to achieving the simplest possible structure. That is, the loadings or coefficients for the rotated factors should move towards 1.0 or 0. This, in a sense, makes the interpretation of the factor solution less difficult and more obvious. (Field, 2013) identifies two broad classes of rotation techniques: orthogonal and oblique. Orthogonal means the rotated factors are uncorrelated with one another, akin to the initial solution. There are alternative methods for orthogonal rotation, and include the varimax and quartimax. Orthogonal varimax, which yields the simplest structure, is the most widely used rotation method in EFA. With regards to oblique rotation where the factors can covary, oblique promax is the most common rotational method.

Conﬁrmatory Factor Analysis (CFA)

As in EFA, Conﬁrmatory Factor Analysis (CFA) is used to examine the covariation among a set of observed variables to gather information on their underlying latent constructs (Byrne,

2013). In contrast to EFA, however, CFA is appropriate in situations where the relationship between the observed indicators and the underlying factors are known a priori, either from theory, empirical research or information from the EFA. Thus, EFA is a necessary companion to the CFA. Given that CFA focuses on ways in which observed indicators are mapped to

71 particular factors, it is often recognized as the measurement model of the full structural equation model which examines the causal relations among the factors (see the next section for a discussion of the structural equation model). In this study, CFA was used to develop a measurement model for the travel behavior and intensity of mobile phone use constructs. The CFA measurement model may be speciﬁed in terms of exogenous notations (in this study, the travel behavior variables) and endogenous notations (in this study, the intensity of mobile phone use variables) denoted as Y and X variables respectively. The notations of the CFA model for these sets of variables are described as the following (Byrne, 2013):

y = Λyξ + δ (4.3)

x = Λxη + ε (4.4) where x is a q × 1 vector of observed exogenous variables, y is a p × 1 vector of observed endogenous variables, ξ is an n × 1 vector of latent exogenous variables, η is an m × 1 vector of latent endogenous variables, δ is a q × 1 vector of measurement errors in x, ε is a p × 1 vector of measurement errors in y,

Λx is a q × n regression matrix that relates the n factors to each of the q variables,

Λy is a p × m regression matrix that relates the m factors to each of the p variables. Given that the CFA is embedded in the full SEM, both follow similar modeling procedure, which are discussed in the next section.

Structural Equation Model (SEM)

Structural equation modeling (SEM) has been a popular methodology in studies on the relationship between telecommunication and transportation in particular, and in travel behavior

72 studies in general over the last four decades. Mokhtarian and Meenakshisundaram (1999) used a number of structural equations to estimate the interrelationships between three major types of communication including personal travel, transfer of objects, and electronic transfer of information. Kim and Goulias (2004) employed SEM to explore the relationships among diﬀerent activities, indicators for travel time and frequency, as well as telecommunication technologies. Wang and Law (2007) also used SEM to examine the complex relationships among the use of ICT, time use and travel behavior in Hong Kong. Finally, Choo and

Mokhtarian (2007) used SEM of time series data in the US to empirically investigate the relationship between local telephone calls and vehicle miles travelled.

In general, SEM is a statistical methodology that adopts a conﬁrmatory or theory-testing approach to the multivariate analysis of a structural theory of a given phenomenon (Byrne,

2013). More specifically, SEM represents a causal process among endogenous variables, and between endogenous and exogenous variables, similar to dependent and independent variables respectively (Schreiber et al., 2006). A full SEM with latent or unobservable variables conveys two important parts – (1) a measurement model, through a CFA (discussed in the previous section) which depicts the extent to which the latent variables are measured by the indicator variables in the hypothesized model, and (2) a structural model, which specifies the interrelationships among latent variables as well as the causal effects on the endogenous variables by the exogenous variables in the hypothesized model, akin to the running of multiple regression equations (Schreiber et al., 2006).

SEM, however, is distinguished from generation of multiple regression procedures in several ways, including its conﬁrmatory approach rather than exploratory, its tractability to carry out inferential analysis due to the speciﬁcation of patterns of interrelations between variables a priori, its ability to correct for measurement error, as well as its capacity to incorporate both observed and latent measurements (Byrne, 2013). Considering these desirable features, coupled with its popularity in travel behavior research, SEM was applied

73 in this study. It is important to note that unlike the application of SEM in the previous

travel behavior literature where latent variables are precluded due to operational diﬃculties

(see Van Acker and Witlox, 2010; Golob, 2003) and insigniﬁcant multicollinearity among

variables (Wang and Law, 2007; Choo and Mokhtarian, 2007), this study utilized the full

SEM with latent variables. A major limitation of models involving only observed variables

is that measurement error is not taken into account. These models assume that all the mea-

sured variables are perfectly valid and reliable, which is unlikely to be the case (Lomax and

Schumacker, 2004), especially with survey data. Thus, the key variant of the SEM used in

this study that sets it apart from the other travel behavior studies is the inclusion of latent

variables. The general notation for structural equation model is written as:

η = Bη + Γξ + ζ (4.5)

where:

ξ is an n × 1 vector of latent exogenous variables,

η is an m × 1 vector of latent endogenous variables,

B is an m × m matrix of coeﬃcients that relates the m endogenous factors to each other,

Γ is an m × n matrix of coeﬃcients that relates the n endogenous factors to the m,

ζ is an m × 1 vector of residuals representing errors in the equation relating n and ξ.

In the special case where there are no relationships between endogenous variables or there

is only one endogenous variable (travel behavior as is the case in this study), the B matrix

reduces to zero. Then the structural equation model reduces to the following form:

η = Γξ + ζ (4.6)

An important step following the speciﬁcation of the CFA (in the previous section) and

SEM models (hereafter referred to as the full SEM) is model identiﬁcation. Statistical

identiﬁcation is the investigation of whether the full SEM model can be mathematically

74 estimated (Choo, 2004), to the extent that the unique set of parameters contained in the

model are consistent with the data (Byrne, 2013). Thus, the full SEM is identiﬁed and

estimable if only there exist unique estimate for every model parameter. The model on the

other hand cannot be identiﬁed if several sets of various parameter estimates could equally

ﬁt the data. Based on the degree of correspondence between the data and the structural

parameters, a model may be just-identified, overidentified, or underidentified (see Byrne,

2013, p. 29, for a discussion of the types of identiﬁcation). A statistical concept central

to the principle of identiﬁcation is the degrees of freedom of the model, which is equal to

the diﬀerence between the number of observations and the number of known parameters to

be estimated. The number of observations is calculated as: k(k + 1)/2, where k refers to

the number of indicators in the model. In practice, models with positive degrees of freedom

(dfM ≥ 0) are overidentified and are considered necessary condition for identification, albeit not sufficient condition (Kline, 2013). It is important to mention that overidentification can

also be achieved by imposing constraints on particular parameters (Byrne, 2013).

Once the identiﬁcation requirement of the full SEM model is met, the next step is to

estimate the parameters of the model. The focus of the estimation process is to get parameter

values such that the diﬀerence between sample and the model is minimal. The existing

statistical packages for SEM oﬀer a variety of methods for estimating model parameters.

LISREL3 8.7, for instance, offers a choice of about seven parameter estimation methods, albeit the default method is the maximum likelihood (see Jöreskog and Sörbom, 1993a, cited in Byrne, 2013 for a detailed discussion of these estimation methods). The ML estimation method assumes multivariate normality and analyzes only covariance matrices, based on an iterative process (Kline, 2013). In estimating the full SEM parameters in this study, however, the Weighted Least Squares (WLS) method was used. The WLS requires an asymptotic

3LISREL, an acronym for Linear Structural Relations, is a statistical software package used in SEM for observed and latent variables.

75 covariance matrix, which means that the analysis is based on two matrices – the polychoric matrix4 and the asymptotic covariance matrix (Byrne, 2013). Considering the types of data used in this study are of mixed scale including dichotomous (e.g., travel mode, travel purpose, mobile phone use purpose), ordinal (e.g., frequency of travel, frequency of mobile phone use) and continuous (e.g., average travel duration), WLS was preferred (Jöreskog and Sörbom, 1993a,b, cited in Byrne, 2013). The next step in the modeling process is the test of model fit, where the goodness of fit between the hypothesized model and the sample data is determined. In CFA or SEM, the goodness of fit means how close the population covariance matrix implied by the model is to the sample covariance matrix. Generally, goodness of fit of the estimated model is measured by the chi-square (χ2) statistic, where lower values (and non-significant p-values) are desirable. The χ2 statistic however has been criticized especially on its sensitivity to sample size. Large samples are crucial to obtaining precise estimates, given that the analysis of covariance structures is based on large sample theory. Thus using χ2 as a goodness of fit test is likely to render the findings of fitted model unrealistic (Byrne, 2013). Considering this problem, Jöreskog and Sörbom (1993b) have suggested the use of test of fit statistics that provide more of a descriptive approach rather than a significance testing approach (see Van Acker and Witlox, 2010, for a discussion on alternative SEM model fit indices). Therefore, in assessing the goodness of fit of the CFA model in this study, the Root Mean Square Error of Approximation (RMSEA) was used. According to Brown et al. (2013, p. 137–138, cited in Byrne, 2013, p. 122), RMSEA begs the question, “how well would the model, with unknown but optimally chosen parameter values, fit the population covariance matrix if it were available?” This discrepancy is expressed per degree of freedom, thus, making RMSEA sensitive to the number of estimated parameters in the model, and not the size of the sample. RMSEA values less than .05 are indicative of good fit. Among the

4LISREL computes the polychoric correlation matrix for variables that are ordinal or of mixed scale.

76 available software packages, LISREL 8.7 was used to estimate the CFA and SEM models in

this study.

The ﬁnal step in the process is model interpretation. SEM provides three types of eﬀects

between the variables – direct effects, indirect effects and total effects (Byrne, 2013). While

direct eﬀects emerge between variables without any intervening variables, the indirect eﬀects

of a variable on another are mediated by intervening variables (Kim and Goulias, 2004). The

sum of the direct and indirect eﬀects result into the total eﬀects. In interpreting the SEM,

however, the focus is on the total effects of X on Y , although it can be of great interest to compare the direct and indirect effects. It is worth noting that standardized structural coefficient estimates are generally used in interpreting the SEM.

As mentioned earlier, the study was conducted in two phases. To understand the results produced from the various statistical techniques used in the ﬁrst phase, a follow-up qualitative phase was conducted. In the next section, the activities conducted under the qualitative phase are presented.

4.7 Qualitative Phase

The overarching goal of the qualitative phase was to understand, from the participants’ view, how certain factors inﬂuenced the use of mobile telephony and consequently household travel, by studying their experiences of use of the mobile phone. The following sections describe the procedures that were followed in the collection and analysis of the qualitative data.

4.7.1 Participants

In conducting the qualitative phase of the study, the Case Study approach, speciﬁcally the multiple case study, was adopted. The focus was to explain in-depth the results from the quantitative phase. More speciﬁcally, the case study was preferred over the other qualitative

77 research approaches since the purpose of the qualitative phase was to gain in-depth understanding of participants’ experience, from their perspective, of the use of mobile phones, so as to understand the different factors that explain how the use of mobile phone affects participants’ travel. The unit of analysis primarily is an individual(s) who is(are) a member of a household, and their activities, with respect to their use of mobile phones and travel patterns. The participants for the qualitative phase were drawn from the quantitative sample of households. Accordingly, and because the idea here is to understand the quantitative results by obtaining information from fewer participants, cases for this phase were very small relative to the quantitative phase sample size. The selection of the participant sample followed the approach — criterion sampling — used by Lee (2007). In using this purposeful sampling technique, study participants are selected based on a set of criteria developed by the researcher (Lee, 2007). A preliminary analysis of the quantitative data using the EFA revealed 3 principal patterns of mobile phone use based on socio-demographics and location characteristics. These classes of mobile phone use comprise: (a) outer city heavy phone users; (b) inner city young adults phone users who primarily use the phone for social purposes; and (c) adult males who use the phone for business purposes and mobile money transactions. Based on the above criteria, representative sample was selected after a careful review of each participants’ responses in the survey (Lee, 2007). In all, 24 participants, eight from each group, were interviewed. Data were collected from the identified classes/groups on their experiences and patterns of using mobile phones as well as the perceived impacts of mobile phone use on their travel behavior, using semi-structured interviews. Details of the demographic characteristics of the participants are described in Chapter 7.

4.7.2 Interview Procedures

Data were collected from the identiﬁed classes/groups on their experiences and patterns of using mobile phones as well as the perceived impacts of mobile phone use on their travel

78 behavior, using semi-structured interviews. Before data collection, the sampled participants were contacted to schedule the interview. Once a date, time and location were agreed, the interviews were conducted. To create a relaxed atmosphere and to encourage dialogue, potential participants were given the flexibility to choose the place of interview (Thompson et al., 1989). As a result, the interviews were mainly conducted at participants’ homes or their work places. Importantly, at the beginning of each interview, the consent form, which contained the rights of the study participants, were read and explained to them. If the participants chose to continue, they were asked to sign the informed consent form. It bears mentioning that this procedure was approved by The University of Dallas Institutional Review Board. The interview protocol contained questions related to the background information of the participants including their number of years of using mobile phone, the type of mobile phones, number of phones, and number of networks (see Appendix D). These questions were closed-ended, thus they were asked in the manner of survey questions. After participants had provided responses to the background questions, questions that bordered on the main objective of the qualitative study were asked. The main issues that were covered included: 1) participants’ experience with the use of mobile phones; 2) the perceived impact of mobile phones on participants’ travel experiences; and 3) perception of the factors that explain how mobile phone use affects their travel. Although the interviews were guided by a checklist of questions, they were deliberately conversational in nature and did not have any particular order across the participants. Each interview session took approximately 30 minutes. In addition to the participants at the household level, officials of three important government institutions with competencies in formulation policies that shape the telecommunication and transportation sectors in the country were consulted for interview. These institutions comprised the Ministry of Telecommunication, Ministry of Transportation, and Land Use and Spatial Planning Authority. Although these institutions were not necessarily unit of analyses, they were interviewed to find out their perception on the impacts of

79 mobile phones on transportation and their roles in helping facilitate such interface, if any.

These interviews were also aided by a semi-structured checklist and were conducted in a more discursive manner. It needs to be mentioned that the household level and institutional interviews were audio-recorded with the permission of the interviewee.

4.7.3 Data Processing and Analysis

The data obtained from the participants essentially centered on their personal experiences with the use of mobile phones as well as their opinions on the perceived impact of mobile phone use of their travel behavior. Given that these interviews were conducted largely in

Twi (the local dialect spoken by residents of the study area), the data analysis began by translating and transcribing all of the audio recorded interviews into English. The data of all 24 participants were analyzed using the open coding process (Strauss and Corbin,

1990, in Lee, 2007), which included labeling concepts (or codes), deﬁning and developing categories, based on their properties (Khandkar, 2009). It was an open process because the data were explored without making any prior assumptions about what could have been discovered (Strauss and Corbin, 1990, in Lee, 2007). Speciﬁcally, codes were created by labeling important information from sections of the data (words, sentences, paragraphs).

Codes with common properties were grouped into categories, and consequently into themes.

Data of the ﬁrst participant was analyzed using the forgoing process to identify preliminary themes. Using the initial sets of themes, the data from the remaining participants were analyzed. The four-step process to analyzing the transcribed data is outlined below, drawing on Ivankova and Stick (2007) and (Creswell and Maietta, 2002).

1. Preliminary exploration of the data by reading through the transcripts and writing

memos;

2. Coding the data by segmenting and labeling relevant text;

80 3. Aggregating similar codes together to develop categories and themes; and

4. Connecting and interrelating themes.

Following this process, a matrix table was created to visualize the code scheme in terms of the amount of relevant information shared by the participants and the themes (see Ap- pendix E). It is worth mentioning that the transcripts were coded in NVivo (Version 12; QSR International, 2018), a qualitative analytical software. Details of the process are described in Chapter 7.

4.8 Reporting the Results

The analysis of the data are presented in two chapters. Results of the quantitative data analysis are reported in Chapters 5 and 6. Speciﬁcally, Chapter 5 is devoted to a description of the study sample as well as, results from the EFA and CFA with the view to examining the measures of mobile phone use and travel behavior constructs. Results from SEM analysis are presented in Chapter 6. Chapter 7 is devoted to a presentation of the results from the qualitative analysis.

81 CHAPTER 5

UNDERLYING PROCESSES OF MOBILE PHONE USE AND TRAVEL

BEHAVIOR IN THE KUMASI METROPOLIS

5.1 Introduction

In the previous chapter, a detailed discussion of the overall design and methodology addressing the empirical analysis of the relationship between mobile phone use and travel behavior in Kumasi was presented. More speciﬁcally, Chapter 4 discussed details of the research design, the study variables and data types, the development of survey and interview questions, the sample selection and data collection procedure, as well as data processing and statistical methods adopted. The focus of this chapter is to address aspects of the quantitative phase of this study. As articulated in section 3 of the previous chapter, this study is conducted in two phases: quantitative and qualitative. The quantitative phase focused on the following research questions, of which the ﬁrst could be characterized as the primary question and the remaining two as the secondary questions.

i. What relationships exist between mobile phone use and travel behavior and in the Ku- masi Metropolis?

• What underlying processes inﬂuence the intensity of mobile phone use in the Ku- masi Metropolis?

• What underlying processes inﬂuence the travel behavior of households in the Ku- masi Metropolis?

Given that in this study, the intensity of mobile phone use and travel behavior are treated as latent constructs, it is imperative to ﬁrst determine the structure of these constructs before an analysis of their interrelationships are examined, as will be discussed in the next chapter. To this end, this chapter presents analyses of the underlying processes inﬂuencing the structure of mobile phone use and travel behavior in the Kumasi Metropolis.

82 5.2 Statistical Analysis Methods

Relevant statistical analysis methods including simple descriptive statistics, bivariate analysis, and exploratory factor analysis (EFA) are employed in this chapter. Detailed discussion of these methods (in particular the EFA) including their mathematical representations and assumptions can be found in Section 4.7 of the previous chapter. Basic descriptive statistics such as measures of frequency and central tendency were used to examine the characteristics of the study sample, their mobile phone ownership and usage, as well as the various measures of travel behavior. To understand the relationship between the main study variables and the socio-demographic attributes of the study participants, a bivariate analysis (using Chi-square and Spearman Rho correlation) was employed. In the context of this study, Chi-square (χ2) test was used to understand whether there existed statistically significant differences in the measures of mobile phone use and travel behavior across the different demographic groups. It bears mentioning, however, that the (χ2) test was used only when one or both of the variables of interest are measured on a nominal scale. In the case where both variables under consideration are measured on an ordinal scale, the Spearman Rho correlation was computed to estimate the size of the effect between variables. Additionally, figures in the form of graphs and maps were used to provide a graphical evidence of the analysis. As described in the previous chapter, the main study constructs are treated as multi- dimensional given the number of variables that characterize them. To evaluate the extent to which these variables correspond to mobile phone use and travel behavior in the context of the study area, an exploratory factor analysis, specifically a Principal Axis Factoring (PAF) was employed. PAF was preferred to a Principal Component Analysis (PCA) because of the nature of data used in this study which are largely categorical (dichotomous and ordinal) in scale, and violate significantly the assumption of multivariate normality. A Shapiro-Wilk test on the mobile phone use and travel data revealed that the distribution of the data was

83 far from normal. Generally, EFA atheoretically extracts few factors or components from the various variables associated with the constructs based on a method of rotation and variable communality.

5.3 Organization of Chapter

The results of the analysis on the underlying processes of the study constructs follows in five interrelated sections. In the first section, the background characteristics differentiating the households and individuals’ use of mobile phone and travel behavior are presented. As will be discussed, travel behavior most especially, depends on the location of residence relative to various facilities, as well as a number of individual characteristics of the traveler (Næss and Jensen, 2004). Thus, in this section, the results of two key factors residential location distribution and socio-demographic – characterizing the study participants are presented. This is followed with a discussion of the various measures summarizing the characteristics of mobile phone use and travel behavior in the study area. Taking together, these sections serve as a prelude to the main thrust of the chapter. Subsequently, in the third section, results of a principal component exploratory factor analysis that investigate the underlying structure of the study constructs are presented. The penultimate section presents the results of a confirmatory factor analysis that provides a test of the overall goodness-of-fit of the underlying structure of the study constructs. The last section summarizes the chapter, and provides a transition to the next chapter.

5.4 Background Characteristics of the Study Participants

As mentioned above, there are two key factors that diﬀerentiate people’s travel activity (Næss and Jensen, 2004), and to some extent mobile phone use. The ﬁrst captures the spatial attributes including residential location of travelers and mobile phone users, while the other concerns with socio-demographic characteristics including age, gender, household

84 composition, education status, participation in the workforce, income and car ownership.

This section presents a descriptive analysis of these factors in the Kumasi Metropolis. The spatial attributes are presented ﬁrst, followed by the socio-demographic characteristics.

5.4.1 Residential Location Distribution

Although a number of spatial attributes exist especially in the literature on transport geography including land use variables, in this study, data were collected only on the residential location component, akin to the approach used by Næss and Jensen (2004) in his study on residential location and travel behavior in the Copenhagen metropolitan area. As mentioned in Chapter 4, households who make up the study participants were sampled from 24 proxy communities in the Kumasi Metropolis. To simplify their distribution, and drawing on Acheampong (2017), these residential locations were categorized into three broad zones of unique physical and socio-economic characteristics. These zones are deﬁned as three successive circular zones which extend from the core of the city (Acheampong, 2017).

The ﬁrst zone is referred to as the “historical core” and it is delineated as the area enclosed by the city’s inner ring-road (see Figure 5.1). This zone covers approximately 11 percent of the total land area of the metropolis and comprises the traditional neighborhoods of the metropolis (Acheampong, 2017), notable among which are Adum, Asafo, Bantama, Asawase,

Manhyia, and Amakom. This zone also contains the central business district (including markets, business and ﬁnancial institutions), oﬃces of public institutions, health institutions, and a major transport terminal, among others. The concentration of high-order facilities and activities in this zone make the “historical core” the commercial and administrative hub of the metropolis as well as a major employment center. It is not surprising therefore that the majority of the actual distribution of respondents’ trips end in the historical core, as will be discussed later in this chapter. Also, it bears mentioning that this zone serves as a nucleus around which the remaining two zones have evolved.

85 The other two zones are the “inner-suburban” and “outer-suburban” and they are delineated using density analysis (Acheampong, 2017). The inner-suburban zone covers a total land area of 38.7 km2 of contiguous high to medium density development immediately surrounding the historical-core. It comprises approximately 19 percent of the total land area of the metropolis and extends between the historical core and the outer-suburban zones. Finally, the outer-suburban zone covers a total land area of 145 km2 of medium to low density contiguous urban land immediately surrounding the inner-suburban zone. The outer- suburban zone on the other hand comprises peri-urban settlements within the metropolis where development density is generally low.

Figure 5.1. Map showing the broad urban zones in the Kumasi Metropolis Source: Author’s construct, based on Acheampong (2017)

It can be inferred from the above that, the physical development pattern of the metropolis follows a concentric pattern with the historical core acting as the nucleus (Oduro et al., 2014), as shown in Figure 5.1. Figure 5.1 also depicts the location of the residential areas

86 selected for the study. As can be seen the communities are fairly distributed among the three broad zones. Regarding the residential location distribution of the participant households, however, there is considerable variation among the three zones in the study area. Out of the 384 households who participated in the study, about 27 percent resided in communities located in the historical-core. The distribution of households with regards to their residential location increased by about 12 percentage points in inner-suburban zone, but reduced slightly to 32 percent in the outer sub-urban zone, as illustrated in Figure 5.2.

Outer

32% Historical 28.70%

39.3%

Inner

Figure 5.2. Distribution of households within the urban zones in the Metropolis Source: Based on ﬁeld Survey, January – April 2017

Similar ﬁndings were reported by Acheampong (2017) in the city where about 27 percent of households had their homes located in the historical-core neighborhoods, with the remaining living in the inner-suburban (43 percent) and outer-suburban (30 percent) zones of the metropolis. Given that factors such as travel time, travel cost and ease of access to amenities and opportunities are important in inﬂuencing households’ residential choice in an urban area (Rivera and Tiglao, 2005), one would expect that majority of the households in Kumasi

87 would live in and around the historical-core in the light of the multitude of functions the zone performs. Findings from this study however suggest otherwise as it is the case in other urban systems in many countries. Generally, greater dispersion in residential activity from city centers to suburban areas is fostered by improved transport infrastructure and availability to automobile (Duranton and Puga, 2014), which blurs the boundary between economic activity centers and residential homes, as well as a relaxation of the space-time constraint. Although this could explain the pattern of residential development in other urban systems, it is hard to think same for the Kumasi Metropolis especially given the poor conditions of the city’s roads and its unreliable and unregulated public transport services (Poku-Boansi and Cobbinah, 2018). With the rapid physical expansion of the city coinciding with the massive adoption of mobile phone in the early 2000 as discussed in Chapter 3, it seems intuitive that the use of mobile phone has removed a major psychological barrier to suburbanization and thus, could foster the diﬀusion of residences to the suburbs of Kumasi. In the subsequent analysis, reference is made to these broad zones with a particular interest in exploring how they relate to respondents’ socio-demographic characteristics, as well as their mobile phone use and travel behavior.

5.4.2 Socio-Demographic Characteristics

A great deal of empirical research has discussed the importance of a range of socio-demographic factors that inﬂuence on one hand mobile phone use, and travel behavior on the other hand. The most important of these variables include age, gender, household composition, educational status, employment status, household income, and car ownership. These variables diﬀerentiate the population under study, which in turn, highlight variations in their use of mobile phone and travel behavior attributes. In view of this, this study elicited data on these variables on households and individuals in the Kumasi metropolis. A summary of the socio- demographic characteristics of the respondents who participated in the study are presented

88 in Table 5.1. Overall, 384 households containing 661 persons aged over 18 participated in the study. One person in the household, who in many instances was the household head, acted as the informant for the household level background information, while each person in the household reported for her- or himself individual-level information.

The individual level characteristics included gender, age, educational attainment, employment status, and type of occupation, and are presented seriatim. The participants of the study were aged from 18 years and above, with an average cohort between 25 and 39 years, from which about 51 percent were males and the remaining females. The level of education of the respondents was expectedly low. Only about 12 percent reported as having earned or enrolled in a tertiary degree, with approximately 46 percent of the respondents having attained only basic level of education. With regards to employment status, 76.1 percent of the respondents were employed with approximately 2 percent retired. In addition, about 13 percent indicated that they were unemployed, a ﬁgure which is slightly lower than that of the city (19 percent) and the country (18.5 percent) as provided by the 2010 population and housing census (Ghana Statistical Service, 2013).

Information about the type of occupation was also gathered among the respondents who were employed. The data shows that the employed are distributed among artisans (25.9 percent), small business (36.3 percent), organized trade (10 percent) and salaried employers

(21.1 percent). This information shows the dominance of the informal sector within the context of the study area which is consistent with the national situation, where about 80 percent are employed by the informal sector (Haug, 2014; Osei-Boateng and Ampratwum,

2011).

Data were also obtained on the household level characteristics which included household size, type of family, income levels and vehicle ownership. The average household size of respondents was approximately 4 persons. This average household size in the sample has implication on the family structure, as the most common family type among the households

89 Table 5.1. Background characteristics of study participants Characteristic Response Level Percent of Sample Mean Individual Characteristics (N = 661) Gender Male 52 Female 48 Age (in years) 18 – 24 13.3 32 25 – 39 47.0 40 – 54 27.1 55 and over 12.6 Educational Attainment No education 1.5 Primary 46.3 Secondary 40.5 Tertiary 11.6 Employment Status Employed 76.1 Unemployed 12.7 Homemaker 1.7 Retired 2.0 Full-time student 7.5 Household Characteristics (N = 384) Household Size 1 27.1 3.45 2 12.5 3 18.5 4 17.4 5+ 24.5 Family Type Single person 27.1 Nuclear 70.3 Extended 2.6 Income (in GHS)* Below 500 14.6 750 500 – 1000 50.5 1001 – 2000 22.9 2001 – 3000 7.6 3001 – 5000 3.1 Over 5000 1.3 Vehicle Ownership Yes 24.5 No 75.5 Number of Vehicles Owned 1 92.9 2 7.1 Source: Based on ﬁeld Survey, January – April 2017 *The average exchange rate over the study period (January – April 2017) was 4.2 GHS/US$.

90 was the nuclear, accounting for about 70 percent. The households were also differentiated by their levels of income. The total household income was obtained from the household head and it was taken as the estimated monthly income of all individual working members within a household aged 18 years and above. With an average of two adult-working members per household, the average monthly household income was estimated to be GHS 750 (US$ 179), which is significantly lower than the national average of GHS 1217 (US$ 289) (Ghana Statistical Service, 2013). The implication for mobile phone use and travel behavior is that household members are relatively not in position to afford the more sophisticated cell phones and expensive modes of travel such as private cars. Another important variable that differentiated the households was vehicle ownership. From the sample of households, only 33 percent owned vehicle. Interestingly, about 14 percent of these vehicles were two-wheel motorcycle. Of the households owning vehicle, about 76 percent owned one vehicle, 17 percent owned two vehicles while about 6 percent owned three vehicles. To examine the representativeness of the study sample to the general population of the Kumasi metropolis, these characteristics of the sample were compared to the profile of the population provided by the 2010 population and housing census. Overall the sample was representative of the population characteristics of the Kumasi metropolis, especially in terms of age, gender, household size and education. Having presented the locational and socio-demographic characteristics of the study participants, the next sections present the analyses of the various measures summarizing the characteristics of mobile phone use and travel behavior among the study participants.

5.5 Analysis of the Measures of the Intensity of Mobile Phone Use in Kumasi

In the foregoing section, relevant background locational and socio-demographic information based on which households and individuals are diﬀerentiated, have been presented. This section begins the analysis of the measures of the main study variables. Here, analysis of the

91 various measures that characterize the use of mobile phone use among the study participants are presented. These measures are the ownership of mobile phone (including type of phone, number of phones and number of networks subscribed to) and the usage of mobile phone

(including frequency and duration, cost of mobile phone use, number of activities performed on the mobile phone, among others. The subsequent sections are devoted to a discussion of these measures as well as how they vary among the various socio-demographic and locational characteristics of the study area.

5.5.1 A Portrait of Mobile Phone Ownership in Kumasi

This section of the chapter examines the general state of mobile phone ownership in the study area, with a focus on the type of mobile phone device, the number of devices owned, as well as the number of mobile networks respondents are subscribed to. Table 5.2 presents the attributes of mobile phone adoption in the study area. As discussed in Chapter 3, mobile phone subscription in Ghana has experienced a rapid rate of growth, particularly between 2004 and 2014, and presently stands at about 115 subscriptions per 100 persons

(International Telecommunication Union, 2015). This ubiquitous mobile phone use in the country is expectedly reﬂected in the study area as the survey showed that almost 96 percent of the respondents owned a mobile phone device.

Among the respondents that owned a mobile phone, about 64 percent owned a smart- phone1 of some kind, and could accommodate the more sophisticated mobile phone applications. Notably, about 81 percent of mobile phone users had just one mobile phone device, while approximately 20 percent subscribed to two or more mobile networks, suggesting that most of the respondents with multiple subscriptions are using them with a single mobile phone device. Albeit the incidence of multiple subscriptions is rather rare in most advanced

1The use of Smartphone here refers to mobile phones that can access the internet and applications.

92 economies, it is not uncommon in developing countries, especially in Sub-Sahara Africa (see James and Versteeg, 2007, cited in Sey, 2011). Generally, individuals hold more than one network subscription in Ghana for two primary reasons: reliability in in-network calls and to reduce the cost of local calls (Sey, 2011).

Table 5.2. Attributes of Mobile Phone Adoption in Kumasi Characteristic Category Frequency Percent of Sample Ownership of mobile phone Yes 635 96.1 No 26 3.9 Total 661 100 Type of Mobile Phone Cell phone 226 35.6 Smart phone 409 64.4 Total 635 100 Number of mobile phones 1 515 81.1 2 99 15.6 3 13 2.0 4+ 8 1.3 Total 635 100 Number of networks subscribed to 1 412 64.9 2 165 26.0 3 52 8.2 4 4 0.6 5 2 0.3 Total 635 100 Source: Based on ﬁeld Survey, January – April 2017

To understand the relationship between mobile phone ownership A bivariate association of the identiﬁed mobile phone ownership attributes and some of the important individual and household-level attributes, such as age, educational attainment, income levels, and residential location are examined. An examination of the correlation between these variables were looked at because of their established relationship between them in the existing literature (see for example Wesolowski et al., 2012). The socio-demographic variables that revealed statistical signiﬁcance with respect to the ownership of mobile phone use variables are: age versus owning a mobile phone (own),

93 Table 5.3. Socio-demographic variables and mobile phone ownership variables. Values represent Pearson χ2 p-values Socio-Demographic Mobile Phone Ownership Variables Variables Own Type Number Network Location 0.585 0.758 0.348 0.235 Gender 0.102 0.001* 0.000* 0.000* Age 0.000* 0.000* 0.426 0.017* Employment 0.000* 0.985 0.000* 0.000* Education 0.128 0.000** 0.001* 0.000* Income 0.579 0.029* 0.086 0.089 Note: (*) represent weak eﬀect size; (**) represent moderate eﬀect size based on Cramer’s V.

type of phone (type), and number of networks subscribed to (network); gender versus type,

number of phones (number), and network; employment status versus own, number and

network; education versus type, number and network; and income versus type (see Table 5.3).

Despite the statistical signiﬁcance of the relationships between these variables, their eﬀect

sizes were weak, except for education versus type of phone (Cramer’s V statistic = 0.362;

p < 0.000), which had moderate eﬀect size. A summary of the results of the relationship between type of phone used and the statistically signiﬁcant socio-demographic variables is shown in Figure 5.3. Concerning gender, mobile phones are common among males and females alike, although males were found to be more likely to use smartphones, on an average.

The χ2 analysis revealed that gender2 had a signiﬁcant eﬀect on the type of mobile phone used by respondents (χ2 = 11.059; p < 0.001). From Figure 5.3a , most males (about 70 percent) in the study metropolis tend to use smartphones.

Regarding age, mobile phone ownership is particularly high among the older members of the household. About 99 percent and 98 percent of 50 – 54 and 55 and over year-olds respectively own a mobile phone, while only 83 percent of the respondents aged 18 – 24 are mobile phone owners. On the type of mobile phone owned, the χ2 test found a signiﬁcant

2Females were coded to be zero, (0) and males to be one (1).

94 Figure 5.3. Relationship between Type of Phone use and (A) gender (B) Age (C) Education and (D) Income Source: Based on field Survey, January – April 2017 association between ownership of smartphone and age. Figure 5.3b suggests that older household members have less affinity towards smartphone in the metropolis. For example, only 48 percent of adults aged 55 and over use smartphones while that of adults aged 18 – 24 and 25 – 39 stands at a notable 81 percent 71 percent respectively. The analysis found no significant differences in mobile phone ownership across categories of educational attainment (see Table 5.3). Differences in educational attainment are however reflected in the type of mobile phone used by the study participants. Results from the χ2 test (χ2 = 83.654; p ≈ 0.000) show that the highly educated are more likely, on an average, to own a smart phone in the study area. For instance, smartphone ownership increased linearly with each additional level in educational attainment – primary (44.8 percent), secondary (72.4 percent) and tertiary (94.8 percent). Concerning income, results of the χ2 test analysis found

95 no marked diﬀerence across the various income classes in terms of mobile phone ownership.

Strikingly, 97.8 percent and 99.5 percent of individuals living in households with a monthly

income of less than GHS 500 (US$ 120) and GHS 500 – GHS 1000 (US$ 120 – US$ 238)

respectively reported owning mobile phone. This is not entirely surprising in the study area

considering that about 21 percent of mobile phone users do not own smart phone, and thus

use cell phones which are relatively aﬀordable. The type of mobile phone used, however,

varied with income as it increased linearly as income increased (χ2 = 12.5; p < 0.029).

About the relationship between mobile phone ownership and residential location, individuals own mobile phone at about the same rate irrespective of the urban zone in which their households reside. About 99 percent of the study participants living in the historical core own mobile phone, compared to 99 percent of residents in the inner sub-urban zone, and about 98 percent of outer sub-urban residents. With a χ2 p-value of 0.585 from the

Chi-square analysis, these diﬀerences are not statistically meaningful. This pattern holds true for the type of mobile phone use: 63.2 percent in the historical core zone, 65 percent in the inner sub-urban, and approximately 64 percent for those in the outer sub-urban zone.

Based on this finding, it is difficult to suggest that the adoption and pervasive use of mobile phone had a role in the massive diffusion of residences in the Kumasi metropolis, as argued by (Ioannides et al., 2008).

5.5.2 Usage of Mobile Phone in Kumasi

Having observed the distribution of mobile phone ownership among the household and individual level characteristics in the previous section, this section details the speciﬁc activities that mobile phone owners in the case study metropolis conduct on their devices. To gain a more ﬁne-grained view of how frequently they used their mobile phones, respondents were asked in the survey a series of questions about which activities and applications they conduct

96 on their phone, and how often they conducted those activities3. Generally, the ﬁndings reveal that certain applications appeal to a wide cross-section of mobile phone owners, including voice calls, text messaging and mobile money transfer. Others are widely used among certain categories of users but less popular among others. For instance, the use of emails and social media are extremely popular among the highly educated and younger mobile phone owners respectively but less common among the less educated and older owners. The third group of mobile phone applications are less pervasive across the various categories of mobile phone owners, such as using it for navigation, on-line shopping and mobile banking. Table 5.4 shows the frequency of use of mobile phone in the sample. The rest of this section details the frequency of use of the identiﬁed mobile phone applications, as well as its distribution among the diverse categories of mobile phone users in the study area.

Table 5.4. Frequency of Mobile Phone Use Frequency of Use (in Percent) Activity Never Occasionally Frequently Always Voice calls 4.7 0.5 4.1 90.8 Text messaging 60.5 16.2 5.7 17.5 Emails 82.0 5.0 4.7 8.3 On-line shopping 97.1 1.5 0.8 0.6 Mobile banking 95.9 0.9 2.1 1.1 Mobile money 45.2 24.5 14.2 16.0 Navigation 98.5 0.3 0.2 1.1 Social media 48.1 1.2 4.4 46.3 Radio/TV 75.2 3.9 2.1 8.8 Source: Based on ﬁeld Survey, January April 2017

A notable 95 percent of mobile phone owners made voice calls at least once in a month, making it the most widely-used basic activity. It is also the most frequently used mobile

3These activities ranged from the traditional uses of mobile phone such as voice calls and text messaging, to the more sophisticated activities including e-mailing, on-line shopping, mobile banking, social media, among others. The frequency of use of these activities were measured on a 7-point Likert scale: a) never; b) less than once a month; c) at least once a month; d) at least once a week; e) several times a week; f) every day; and g) several times a day, which were recoded into a) never; b) occasionally c) frequently; and d) always.

97 phone activity as about 91 percent of users reported making calls always. Except for resi-

dential location and employment status, using the mobile phone for voice calling is common

across the other demographic groups including educational attainment and income, as the

Chi-square and the Spearman’s Rho correlation analyses revealed insigniﬁcant relationships.

Mobile phone users residing in the peripheral areas of Kumasi are especially avid voice

callers, as about 98 percent of users in the inner sub-urban and 93 percent of users in the

outer sub-urban used voice calling always compared with about 91 percent of users in the

historical core (χ2 = 17.295; p < 0.008). This relationship was however weak (Cramer’s V

statistic = 0.117; p < 0.008). Similarly, the Chi-square analysis revealed that mobile phone

users who are employed tended to use voice calling more regularly (96 percent) compared

with the unemployed users (89 percent) (χ2 = 8.431; p < 0.038). This relationship was also

found to be weak (Cramer’s V statistic = 0.115; p < 0.038).

Voice calling is followed in popularity by mobile money transfer, engaged in by nearly

55 percent of mobile phone users in the study area. As an electronic payment system,

mobile money is widely used in Ghana, especially for the following reasons: payment of bills,

remittance transfer, trading, purchase airtime, among others (Mas and Radcliﬀe, 2010).

Its convenience in terms of cost, time, and the fact that it does not require the internet

or smartphone has made mobile money service more prevalent in Ghana, and so it is not

surprising that more than half of mobile phone users in the study area are engage in it. Also,

given the reasons for using the mobile money service, which by their nature are not done on

a daily basis, it is not surprising that almost 25 percent of phone owners used mobile money

occasionally.

Mobile money users exhibit modest diﬀerences across gender (χ2 = 13.027; p < 0.005), educational attainment (r = 0.115; p < 0.004), age (r = −0.078; p < 0.048) and residential

location (χ2 = 20.868; p < 0.002). Averagely, its use is prevalent among males (63 percent)

than females (51 percent) and the highly educated (66 percent) than the less educated (47

98 percent) mobile phone users. Regarding age, the distribution of using the phone for mobile money was a bell-shaped (see Figure 5.4b). Its use is prevalent among the middle-aged groups

(25 – 39 and 40 – 54) at least occasionally, while the youngest group (18 – 24) and the oldest group (55 and over) of mobile phone owners made less use of mobile money transfers. About

60 percent of males use their phone for mobile money transfer at least occasionally compared with 48 percent of females. In terms of residential location, majority of phone owners who used their device for mobile money transactions resided in the outer sub-urban zone (60 percent) compared with 53 percent and 58 percent among the inner sub-urban and historical core residents respectively (see Figure 5.4d). It bears mentioning that the eﬀect size for the above relationships was rather weak.

Figure 5.4. Relationship between Frequency of Using Mobile Money and (A) gender (B)Age (C) Education and (D) Residential Location Source: Based on ﬁeld Survey, January April 2017

99 Mobile phone activities such as social media and email vary substantially in terms of their overall popularity in the study area. While about 52 percent of mobile phone owners indicated using social media at least occasionally, only about 18 percent of owners used their phones to send and receive emails at least occasionally. Despite their diﬀerences in usage rates, social media and email share the common characteristic of having especially high rates of engagement among males, younger, highly educated, and high-income phone owners (see Figure 5.5). Males frequently used social media and send or receive emails at respectively 61 percent and 24 percent on their phone, compared with 46 percent and 14 percent of females. Similarly, users who had attained tertiary levels of education used social media (87 percent) and emails (54 percent) at least occasionally, compared with 23 percent and 6 percent of users who have attained only some form of primary education. The above patterns of relationship somewhat change when the use of social media and emails are conditioned on age. While young mobile phone owners used social media more frequently (80 percent of owners aged 18 – 24 used social media at least once in a day) compared to their older counterparts (only 25 percent of owners aged 55 and older used social media at least once in a day), the reverse is true for owners who used emails, on an average. The diﬀerence between younger and older mobile phone users is equally pronounced among those who use text messaging. Almost 45 percent of younger mobile phone owners between 14 and 24 years used text messaging at least occasionally compared with the those aged 40 – 54 (33 percent) and 55 and over (28 percent) on an average. When it comes to listening to radio or watching television on the mobile phone, however, the rates for younger and older mobile phone users are nearly identical. On an average, younger owners aged 18 – 24 and 25 – 39 reported listening to radio or watching television on their mobile phone at respectively 12 percent and 18 percent, while owners aged 40 – 54 and 50 and over did so at respectively 13 percent and 12 percent, on an average. The remaining mobile phone applications and activities, including navigation, on-line shopping, and mobile banking are rarely used among mobile phone owners. Only about 2

100 percent, 3 percent and 5 percent of owners used their phones for navigation, on-line shopping and mobile banking respectively. As will be discussed in the qualitative part in Chapter 7, these activities are less common among the study participants because of the absence of or poor delivery of certain infrastructure and services such as street and property address system, as well as a general sense of insecurity and lack of trust in the system.

Figure 5.5. Relationship between Frequency of Using Social Media and (A) gender (B) Age (C) Education and (D) Income Source: Based on ﬁeld Survey, January April 2017

The foregoing has described the variables that characterize mobile phone usage in the study area. The analyses have revealed that, in the context of the study area, mobile phone is used primarily for social interactions (through phone calls, text messaging and social media), but also for the arrangement of ﬁnancial remittances (through mobile money service) and for activity coordination.

101 5.6 Extracting Mobile Phone Use Factors – A Principal Component Approach

The ﬁndings presented above suggest that the intensity of mobile phone use as a construct is not unidimensional. Although Boase and Ling (2013) has identiﬁed frequency and duration as the two main dimensions of the intensity of mobile phone use, these measures have not been fully evaluated in the telecommunication literature. It bears mentioning, however, that

Jamal et al. (2017) and (Denstadli et al., 2013) for example have recently used frequency of smartphone use and frequency of videoconferencing respectively as measures of telecommunication use. Evidently, mobile phone use can be viewed from diﬀerent dimensions, and thus yield several categories, many of which have been discussed in the context of the study area in the previous section. To what extent do these categories correspond to the study participants’ mobile phone use? The ﬁrst attempt to answer this question is to subject the corresponding variables to an exploratory factor analysis, which is the focus of this section.

Respondents were interviewed on 12 related mobile phone use items. It is the focus of this section to identify which of these variables are important in explaining the intensity of mobile phone use within the study area. To identify and extract these factors, a principal axis factoring analysis was conducted on the set of mobile phone use items. In factor analysis, two measures – the Kaiser-Meyer-Olkin (KMO) and the Bartlett’s test of sphericity – are paramount Williams et al. (2010). These two tests are necessary condition for a successful factor analysis. First, results of the KMO which measures the sampling adequacy, indicated a score of 0.720, greater than the threshold of 0.5 which is considered adequate for factor analysis (Williams et al., 2010). Similarly, the Bartlett’s test of sphericity was signiﬁcant

(χ2 = 1815.627; p < 0.000), indicating that the correlation matrix of the variables involved in the factor analysis is not an identity matrix, which is desirable.

Following from these tests, the exploratory factor analysis was run to obtain ﬁrst, initial eigenvalues for each item in the mobile phone use data. These values are the variances of

102 each item/factor, and the total variance in the set of data is proportional to the number of variables used in the analysis.

Table 5.5. Initial eigenvalue estimates of principal component analysis on mobile phone use items Extraction Sums of Rotation Sums of Initial Eigenvalues Squared Loadings Squared Loadings % of Cum. % of Cum. % of Cum. Factor Total Variance % Total Variance % Total Variance % 1 3.179 26.492 26.492 3.179 26.492 26.492 2.114 17.613 17.613 2 1.897 15.804 42.297 1.897 15.804 42.297 2.040 17.002 34.614 3 1.278 10.651 52.947 1.278 10.651 52.947 1.676 13.965 48.579 4 1.025 8.539 61.486 1.025 8.539 61.486 1.549 12.907 61.486 5 0.856 7.136 68.622 6 0.752 6.268 74.891 7 0.691 5.762 80.653 8 0.673 5.606 86.259 9 0.522 4.347 90.606 10 0.470 3.917 94.523 11 0.339 2.822 97.345 12 0.319 2.655 100.000

Based on Eigenvalues greater than Kaiser’s criterion of one, four components were extracted. Table 5.5 presents the initial eigenvalues for each item. The ﬁrst component explained 26.492 percent of the variance, the second factor 15.804 percent of the variance while the third and fourth factors explained 10.651 percent and 8.539 percent of the variance respectively. Together these four factors accounted for 61.486 percent of the total variation in the mobile phone use data. The four-factor solution was retained for rotation. The rotation procedure was applied on the original loadings of the 12 variables on the ﬁrst four factors given in Table 5.6.

The varimax rotation method was applied to Table 5.6, that is, the original loadings were subjected to a clockwise rotation of 15 degrees (Abdi, 2003). The orthogonal varimax rotation method produces the simplest structure since each original item tends to be asso-

103 Table 5.6. Original factor loadings of the 12 variables on the first four components Factor Intensity of Mobile Phone Use Items 1 2 3 4 Number of phones 0.459 -0.612 Type of phone 0.694 Number of networks 0.521 -0.554 Frequency of phone calls 0.572 -0.405 Frequency of text messaging 0.562 Frequency of Email Frequency of on-line purchase 0.491 Frequency of mobile banking 0.501 0.545 Frequency of mobile money 0.532 0.442 Frequency of navigation 0.59 Frequency of social media 0.687 -0.402 Frequency of radio 0.464 0.511 Note: Absolute coefficient values below 0.4 have been suppressed in the Table ciated with one of the factors, and each factor represents only a small number of variables (Abdi, 2003). Although all items related to mobile phone device loaded onto a single factor, the factor structure – item loadings of the remaining three factors could not be regarded as clean (Costello and Osborne, 2005), as several items cross loaded between the factors. Based on the a priori factor structure, and guided by the scree plot, multiple factor analyses were run on the same items fixing the number of factors to extract at two, three, and five. After rotation, the three-factor loading table (see Table 5.7) was selected since it had few item cross-loadings and all factors had a minimum of three items. According to Costello and Osborne (2005), meeting these two minimum conditions suggest that the factor loadings in Table 5.7 has the best fit to the data. As shown in Table 5.7, the procedure yielded a new set of rotated solution with three factors. From the table, five items, namely: type of phone, frequency of text messaging, Email, mobile money transfers, and social media, clustered around the first factor. Given that that this factor is largely dominated by evaluations about mobile phone activities that can be considered basic, factor one is said to represent the underlying construct basic usage of mobile phone’. This factor

104 Table 5.7. Rotated factor loadings of the 12 variables Factor Intensity of Mobile Phone Use Items 1 2 3 Number of phones 0.781 Type of phone 0.573 0.461 Number of networks 0.790 Frequency of phone calls 0.435 Frequency of text messaging 0.579 Frequency of Email 0.506 Frequency of on-line purchase 0.464 Frequency of mobile banking 0.721 Frequency of mobile money 0.455 Frequency of GPS navigation 0.410 Frequency of social media 0.673 Frequency of radio Note: Absolute coefficient values below 0.4 have been suppressed in the Table has a corresponding eigenvalue of 3.179, and accounted for about 26.5 percent of the variance in the use of mobile phone among the study participants. Factor two reflects ‘characteristics of the mobile phone device’. On this factor, items such as the type of phone, the number of phones, and number of networks, were considered important to mobile phone users in the study area in their evaluations of the intensity of mobile phone use. This factor as a construct has a corresponding eigenvalue of 1.897, and accounted for 15.8 percent of the variance in the use of mobile phones among the study participants. The third and final factor was dominated by evaluation of mobile phone uses that would typically be referred to as ’advanced usage of mobile phone’, especially in the context of the study area. These activities, including on-line purchase, mobile banking, and GPS navigation, were rarely used among mobile phone owners, thus, it is not surprising that they cluster as one factor. This factor as a construct has a corresponding eigenvalue of 1.278, and accounted for 10.6 percent of the variance in the use of mobile phones among the study participants. Together, the three factors that were retained accounted for about 53 percent of the total variance in the travel behavior data.

105 5.7 Analysis of the Measures of Travel Behavior in Kumasi

Having described the measures and the underlying structure of mobile phone use (the main independent variable) among the study participants in the previous section, this section focuses on a description of the study participants in terms of their travel behavior (the dependent variable). As described in the previous chapter, in addition to the household and individual level characteristics, household members aged over 18 were asked to complete a trip diary for one weekday. This yielded approximately 2,233 trips reported by all the participants. The remainder of this section presents a description of the attributes of these trips including the trip frequency, travel distance, travel duration, travel mode choice and travel purpose, as well as these attributes disaggregated by locational and socio-demographic factors. It bears mentioning that the analysis is limited to trips that start and end in the Kumasi metropolis. In doing so, long distance trips and its accompanied characteristics which could have disturbed the outcome of the analysis were avoided. Also, about 11 percent of the study participants did not travel on the assigned travel day4, thus the analysis is limited to only those (589 participants) who made any form of travel on the travel date.

Table 5.8. Per trip distance traveled per day Distance Traveled Frequency Percent of Sample Less than 1 km 134 22.8 1 – 3 km 228 38.7 3.1 – 5 km 173 29.4 5.1 – 10 km 49 8.3 10.1 – 15 km 5 0.8 Total 589 100

The number of trips diﬀered quiet substantially between the various residential zones (χ2 = 27.910; p < 0.015), although the eﬀect size was small (Cramer’s V statistic = 0.154;

4Households were randomly assigned a travel date during the initial contact stage. Thus, a travel day began at 4am on the assigned travel date and ended at 4am the following day of the assigned travel date.

106 p < 0.015). Residents of communities in the historical core zone are likely to make more trips per day compared with the residents in the inner and outer sub-urban zones. While 62 percent of the historical core residents made trips over the study average (3.8 trips per day), 52 percent and 55 percent of inner sub-urban and outer-suburban residents respectively made such trips. Considering the dominant commercial and service functions of the historical core zone, this may not be surprising as residents in this zone will ﬁnd it more convenient, in terms of cost and time, traveling back and forth from their place of residence to the main activity centers. The analysis however did not ﬁnd much diversity in the number of trips per day across gender (χ2 = 13.483; p < 0.061), household income (r = 0.061; p < 0.139), employment status (χ2 = 2.670; p < 0.914), and vehicle ownership (χ2 = 8.413; p < 0.298).

5.7.1 Trip Distance and Duration

Trip distance was measured by the number of kilometers traveled irrespective of travel mode, while trip duration was measured in hours5 of travel. Of all the 2,233 recorded trips, the average distance per travel day was 3.2 km. A substantial proportion of the trips were completed in less than a km, while only about 1 percent was made between 10 and 15 km. Consistent with Poku-Boansi et al. (2012), a signiﬁcant 68 percent of the trips in the study area were completed between 1 and 5 km. The average trip length was cross-tabulated against the location and socio-demographic variables, and Figure 5.6 provides a graphic evidence for their relationships. The Person Chi-Square analyses found a weak positive association between trip length and residential location (χ2 = 31.345; p < 0.000; Cramer’s V = 0.163; p < 0.000). On an average, residents of the two sub-urban zones (outer and inner) made longer trips while those of the historical core tended to travel shorter distances. For instance, while about 12

5Trip duration was originally measured in minutes of travel. However, because of certain high values which could have inﬂuenced the outcome of the analysis, the unit of trip duration variable was rescaled from minutes to hours.

107 percent and 14 percent of outer sub-urban and inner-suburban trips averaged between 5 and

10 km, only 4 percent of the historical core trips averaged that distance.

Figure 5.6. Relationship between Average Distance of Travel and (A) Age (B) Education (C) Residential Location and (D) Vehicle Ownership Source: Based on ﬁeld Survey, January – April 2017.

A similar pattern of relationship was observed between average trip length and gender, age, educational attainment, employment status, income and vehicle ownership. Albeit, age was positively correlated with trip length (r = 0.106; p < 0.010), the distribution of average travel distance was almost bell-shaped. The average trip length for the two middle-aged groups (25 – 39 and 40 – 54) generally made longer trips, while that of the youngest and oldest cohort was generally short. This age diﬀerence is partly explained by the fact that the two middle-aged groups constitute the more active labor force and that they undertake longer trips for work-related activities usually concentrated in the historical core. This is further corroborated by the variation in the average travel distance when it comes to employment

108 status: about 40 percent of the employed made average trip length more than 3 km, while

the same trip length was made by only 25 percent of the unemployed. Employment status

is related to educational attainment as well as income, as they showed comparable ﬁndings.

From the correlation analysis (r = 0.169; p < 0.000), the highly educated were more

involved in long distance trips, with about 55 percent making an average of 5 km per day.

Conversely, the less educated tended to make shorter trips, as a signiﬁcant 75 percent un-

dertook trips less than 3 km per trip per day (see Figure 5.6). According to Van Acker and

Witlox (2010), this diﬀerence in educational attainment could result from the fact that the

highly educated by their special skillset obtain specialized jobs which are concentrated in the

historical core zone, which in turn involve long distance commuting. With the convenience

attached to owning an automobile, it was not surprising to observe that travelers who owned

a vehicle tended to undertake longer trips averaging more than 5 km (13 percent) compared

with travelers whose households did not own a vehicle (7.7 percent) (χ2 = 13.604; p < 0.009;

Cramer’s V = 0.152; p < 0.009).

Regarding duration, the average time spent in a trip per day was 0.5 hours or 30 minutes

(SD = 0.27 hours; minimum = 0; maximum = 2.3 hours). It is important to note that the analyses of duration of travel as done here is independent of trip mode. Thus, the time spent in a km of trip by walking and by car were not treated diﬀerently. Based on this information, and especially when compared with the average duration of work trips (45 minutes) in

Kumasi as recorded by Poku-Boansi et al. in 2012, it would not be out of place to suggest that traffic conditions in the metropolis has fairly improved. Notwithstanding, there was variation in the average duration of trips among the location and certain socio-demographic characteristics of the study participants. With respect to gender, males tended to undertake trips with slightly longer durations than females, albeit this difference was not significant

(χ2 = 72.341; p < 0.132). Similarly, trip duration was not statistically diﬀerent among the categories of age (χ2 = 154.273; p < 0.918), employment status (χ2 = 65.161; p < 0.302) and

109 vehicle ownership (χ2 = 65.916; p < 0.280). These were particularly surprising considering that these factors were signiﬁcant in the variation of average distance of travel, which in many ways correlated positively with average travel duration.

Conversely, average travel duration diﬀered quiet substantially between the various categories of household income (χ2 = 411.257; p < 0.00) and residential location (χ2 = 151.182; p < 0.029). Household income plays a relatively large part in the variations in travel duration. Respondents within households in the lowest income group spent on average 20 percent less hours of travel than the study average while the highest income group spent about 10 percent more. Much of this can be explained by people in lower income groups being more likely to be economically inactive which is associated less time spent traveling. With respect to residential location, the average number of hours traveled by residents in the historical core was 0.16 hours and 0.25 hours less than the inner sub-urban and outer sub-urban zones respectively, which was expected.

5.7.2 Mode of Travel

While the above has focused on the amount of trips - frequency, distance and duration – undertaken by the study participants, this section goes further to describe the modes used to complete those trips. To obtain this information, respondents were asked during the survey to indicate the transport mode – walking, bicycle, motorcycle, car, public transport

(trotro/taxi) – used for each trip undertaken on the assigned travel date preceding the interview.

Based on the responses, their primary mode of travel was established. Determining the primary mode of travel was guided by the following conditions: if one mode of transport was reported, that mode was simply used as the respondent’s primary mode; if two or more trip modes were reported, and had equal frequencies, the one with the most distance covered was determined as the primary mode; and if two or more trip modes (with unequal frequencies)

110 Table 5.9. Per trip distance traveled per day Travel Mode Frequency Percent of Sample Non-motorized (Waking/Bicycle) 223 37.9 Motorcycle 3 0.5 Public Transport (Trotro/Taxi) 260 44.1 Private Car 103 17.5 Total 589 100 Source: Based on field Survey, January – April 2017 were reported, the one with the highest frequency was considered as the primary mode. Table 5.9 summarizes the results of this process. Most of the respondents had used some form of motorized transport in conducting their daily travel needs. While about 18 percent had reported using private car, about 44 percent had used public transport regularly in their daily travel. It is important to note that public transport as used here does not refer to the public/government owned large occupancy buses or mass-transit as used in some developed countries such as the United States. In the context of the study area, these are privately owned mini-buses (locally referred to as “Trotro”) and shared taxi services that are managed by unions – albeit a substantial proportion operate outside of the unions – and offer commercial services usually between lorry terminals and parks. Interestingly, about one third of the respondents used non-motorized mode (predominantly walking) as their primary mode of travel. This according to Poku-Boansi and Cob- binah (2018) is because of the generally poor accessibility within the metropolis. Figure 5.7 shows there exist significant relationships between the mode of travel (in particular car use and non-motorized transport) and the socio-economic level of a person and the residential location the person lived. In terms of gender and age, it was observed that women on average were inclined to travel by non-motorized transport (walking in particular), whereas car use tended to be high among men. Older participants (aged over 40 years) had a greater reliance

111 Figure 5.7. Relationship between Non-Motorized Transport as the Primary Mode and (A) Age (B) Education (C) Income and (D) Residential Location Source: Based on field Survey, January – April 2017 on the car but a significant reduction in the use of non-motorized transport compared to their younger counterparts. Regarding the availability of private car, expectedly, the use of car seemed on average higher among households owning cars, compared to households without them (χ2 = 94.051; p < 0.000; Cramer’s V = 0.400). Key stages within the household cycle such as educational attainment, employment status and household income provided comparable findings with respect to car use and non- motorized transport. Households consisting of highly educated, actively working, and high- income members were most likely to use cars. Conversely, households with less educated, non-active, and low-income members were least likely to use cars, but favored using non- motorized transport. Finally, residents living in the historical core zone were more likely to use non-motorized (particularly walking) and public transport than residents located in the peripheral areas in the metropolis. Although it is evident from this analysis that household

112 Figure 5.8. Relationship between Private Car as the Primary Mode and (A) Age (B) Edu- cation (C) Income and (D) Vehicle Ownership Source: Based on field Survey, January – April 2017 attributes (including age, gender, educational attainment, income and car ownership) and characteristics of residential environments have significant relationship with modal choice (especially car use and non-motorized transport), the correlations between them are relatively weak. Thus, other factors could be important in influencing the modal choice of residents in the study area, and according to Dieleman et al. (2002), the purpose of a trip is of significant importance. Consequently, the next section is devoted to an analysis of the purpose for which trips are undertaken in the study area.

5.7.3 Trip Purpose

As generally understood, travel is a derived demand, thus people travel not for its own sake but to fulﬁll their psychological, social and economic needs or obligations. As a conse- quence, to understand what prompts travelers to behave in a particular way, it is important to understand the purpose of their trips. The study participants reported 14 diﬀerent out-

113 of-home activities6 for which they undertook their trips. Given the large number of the response categories, for clarity purposes and drawing on Dal Fiore et al. (2014), they were classiﬁed into three purpose categories, namely: mandatory, maintenance and discretionary.

The mandatory purposes included work and school related trips, the maintenance purposes included shopping and healthcare visit, and the discretionary purposes included recreational and social activities. It is worth mentioning that the original data as recorded from the respondents were not mutually exclusive, as respondents reported as traveling for one purpose, say mandatory, may also have traveled to undertake discretionary activities in their trip chain. Therefore, the data were recoded to obtain travel purpose information for one person per a given day of travel, using the same sets of condition as used for the determination of primary mode of travel in the previous section. Figure 5.9 provides a distribution of trip purposes among the study participants.

As presented in Figure 5.9, travel within the metropolis is generally for mandatory purposes (in particular work-related activities), accounting for about 67 percent of respondents.

This is not surprising considering that the travel dates assigned to the study participants were generally on weekdays. Notwithstanding, a notable 22 percent of respondents traveled for maintenance purposes, including shopping and healthcare visits, whereas about 10 percent of respondents traveled for discretionary purposes, including recreational and social visits.

As done previously for the other travel behavior measures, Chi-Square test and Spear- man’s Rho correlation were computed to examine the general linear relationships as well as the statistical signiﬁcance between the purpose of travel and the socio-demographic proﬁle of respondents (see Table 5.10). The socio-demographic variables that show statistically

6 The 14 trip purposes included: work, work related meeting, drop-oﬀ, transit, school, buy goods, buy services, buy meals, general errands, recreation, visit friends/relatives, healthcare visit, religious activities, and community/social activities.

114 Discretionary

Maintenance 10.2%

22.4%

67.4%

Mandatory

Figure 5.9. Distribution of Trip Purposes among the Study Participants Source: Based on ﬁeld Survey, January – April 2017

signiﬁcant diﬀerences with respect to travel purpose are gender, employment status, and household income.

Table 5.10. Socio-demographic variables and Purpose of Travel variables. Values represent Pearson χ2 p-values Socio-Demographic Purpose of Travel Variables Variables Mandatory Maintenance Discretionary Location 0.630 0.718 0.033* Gender 0.002* 0.000* 0.000* Age 0.000* 0.106 0.001* Employment 0.000** 0.000** 0.000* Education 0.101 0.635 0.085 Income 0.012* 0.926 0.000* Vehicle Ownership 0.765 0.275 0.299 Note: (∗) represent weak eﬀect size; (∗∗) represent moderate eﬀect size based on Cramer’s V.

Other things being equal, men were more likely to travel for mandatory purposes, while women on average made more journeys for discretionary and maintenance purposes shopping

115 in particular. This is partly explained by the fact that women are generally “responsible for most household maintenance tasks” (Van Acker and Witlox, 2010). In terms of employment status, the actively working household members on average were more likely to travel for mandatory purposes. Conversely, the non-working household members tended to travel for maintenance and discretionary purposes. When it comes to income levels, individuals in households on higher incomes were more likely to travel for mandatory reasons, while those on less income tended to travel for discretionary purposes particularly social visits.

This section has described the variables that characterize travel behavior in the study area. The analyses have revealed that, in the context of the study area, travelers make an average of about 4 trips per day, and an average of 3.2 km per trip per day. Most of the travelers use some form of motorized transport, predominantly trotro or shared taxi to complete these trips. Furthermore, these travels are mostly for mandatory purposes including work and school, and usually take respondents an average of 30 minutes to arrive at their destinations.

5.8 Extracting Travel Behavior Factors – A Principal Axis Factoring Approach

Akin to the mobile phone construct, the above suggest that travel behavior is not unidimensional. Previous studies have diﬀerentiated between the amount of travel (frequency, distance and duration), modal share, and to some extent travel purpose, as the various dimensions of what prompt travelers to behave in a particular way (see for example Næss,

2005). It is important to note that, irrespective of the acknowledgment of these dimensions, studies of travel behavior have often used a single indicator, say travel distance, as a measure of the concept or construct. However, evidently, travel behavior can be viewed from diﬀerent dimensions, and thus yield several categories, many of which have been discussed in the context of the study area in the previous section. This section presents the results of an

116 exploratory factor analysis which determines the variables that are important in explaining travel behavior within the study area. Respondents were interviewed on 10 related travel behavior items. The exploratory factor analysis procedure as outlined in Section 5.6 for extracting the intensity of mobile phone use factors was largely followed in extracting the travel behavior factors. The factor analysis was done via a principal axis factoring extraction method, followed by a varimax rotation method. Result of the KMO measure of sampling adequacy indicated a score of was 0.500 (values closer to 1 are better). The Bartlett’s test of sphericity also showed a signiﬁcant result (χ2 = 1241.885; p < 0.000), indicative that there is enough correlation among the variables to run a meaningful EFA.

Table 5.11. Initial eigenvalue estimates of principal component analysis on travel behavior items Extraction Sums of Rotation Sums of Initial Eigenvalues Squared Loadings Squared Loadings % of Cum. % of Cum. % of Cum. Factor Total Variance % Total Variance % Total Variance % 1 2.247 28.089 28.089 1.931 24.139 24.139 1.404 17.553 17.553 2 1.468 18.346 46.434 1.203 15.033 39.171 1.328 16.596 34.148 3 1.354 17.056 63.490 0.840 10.494 49.665 1.241 15.517 49.665 4 1.006 12.578 76.068 5 0.804 10.048 86.116 6 0.543 6.782 92.898 7 0.361 4.515 97.413 8 0.207 2.587 100.000 9 0.319 2.655 100.000

Based on the work of Næss (2005), the process attempted to extract three factors to reﬂect the dimensions of amount of travel, mode of travel and purpose of travel. It was revealed from the initial analysis that, the communality of two of the variables exceeded a value of one (an indication of the presence of ultra-Haywood case); thus, the procedure failed to produce a solution. These indicators, namely, public transport as a primary mode and discretion

117 purposes were therefore excluded from the analyses (McDonald, 2014). Consequently, the exploratory factor analysis was run on nine variables to obtain first, initial eigenvalues for each item. Although four factors obtained eigenvalues greater than one, only the first three were retained for rotation. As mentioned earlier, this was guided by literature, as well as information from the scree test, which is a graph of the eigenvalues. These three factors together explained about 63.5 percent of the variance in the dataset. The rotation procedure was applied on the original loadings of the nine variables on the first three factors given in

Table 5.12.

Table 5.12. Original factor loadings of the 9 variables Factor Travel Behavior Items 1 2 3 Travel frequency 0.49 Average travel distance 0.871 0.428 Average travel duration 0.545 Average travel cost 0.674 Purpose of travel: Mandatory 0.674 -0.572 Purpose of travel: Maintenance 0.750 Primary mode of travel: Non-motorized 0.67 Primary mode of travel: Motorcycle Primary mode of travel: Private car Note: Absolute coeﬃcient values below 0.4 have been suppressed.

Results of the varimax rotation method on the original factor loadings are shown in

Table 5.13. It can be observed from Table 5.13 that two items, namely, mandatory purpose of travel and maintenance purpose of travel, clustered around factor one. The negative sign on the maintenance purpose variable was expected since the study participants traveled less for such reason especially during the week. On the basis of these items, factor one was named ‘purpose of travel’. This factor corresponded to an eigenvalue of 2.247 and accounted for about 28 percent of the variance in the behavior of travelers in the study area.

The second factor had three items – two positives and one negative. The positive loadings

118 included average travel distance and private car as a mode of transport, while the negative loading was non-motorized transport. Given the dominance of travel modes in factor two, it was named as ‘mode of travel’, and it accounted for about 18 percent of the variance in the behavior of travelers in the study area. Finally, four items evaluated by the study participants namely; travel frequency, average travel distance, average duration of travel, and average travel cost clustered around the third factor.

Table 5.13. Rotated factor loadings of the 9 variables on the ﬁrst three components Factor Travel Behavior Items 1 2 3 Travel frequency 0.547 Average travel distance 0.689 0.678 Average travel duration 0.473 Average travel cost 0.674 Purpose of travel: Mandatory 0.837 Purpose of travel: Maintenance -0.825 Primary mode of travel: Non-motorized -0.720 Primary mode of travel: Motorcycle Primary mode of travel: Private car 0.413 Note: Absolute coeﬃcient values below 0.4 have been suppressed in the Table.

This factor therefore reﬂects the ‘amount of travel’ engaged in by the study participants.

As a construct, this factor accounted for about 17 percent of the variance in the behavior of travelers within the study area, and corresponded to an eigenvalue of 1.354.

5.9 Summary of Chapter

The focus of this chapter was to present an empirical analysis of the underlying processes inﬂuencing the structure of mobile phone use and travel behavior in the study area using data obtained through a cross-sectional survey. A descriptive analysis was ﬁrst conducted to examine the characteristics of the study participants, their mobile ownership and usage,

119 as well as their travel behavior. The analyses revealed that, generally, mobile phone is used primarily for social interactions (through phone calls, text messaging and social media), but also for the arrangement of financial remittances (through mobile money service) and for activity coordination. In terms of the travel behavior of participants, generally, travelers make an average of about 4 trips per day, and an average of 3.2 km per trip per day. These trips are generally done using trotro or shared taxi, with a substantial amount done through walking. In addition, these travels are mostly for mandatory purposes, predominantly to work, and usually take respondents an average of 30 minutes to arrive at their destinations. Using a principal axis factoring analysis, three main factors were identified as important to the study participants in influencing their use of mobile phone and how they traveled within Kumasi. The mobile phone use factors were: characteristics of the mobile phone device, basic usage of mobile phone, and advanced usage of mobile phone. With regards to travel behavior, the factors comprised: purpose of travel, mode of travel, and amount of travel. Overall, the exploratory factor analysis provided useful empirical insight into the underlying structure of the mobile phone use and travel behavior data influenced by latent variables. Evidently, this analysis is exploratory, and as a result is prone to errors (Costello and Osborne, 2005; Kornberg and Clarke, 1992). To address this issue, a confirmatory factor analysis (CFA), which allows error terms for the variables/indicators to covary, was conducted. The next chapter presents the results of the CFA, as well as the analysis of the patterns of relationship between mobile phone use and travel behavior in the study area. Thus, this chapter only provides a backdrop for the next chapter.

120 CHAPTER 6

A STRUCTURAL ANALYSIS OF MOBILE PHONE USE AND TRAVEL

BEHAVIOR IN KUMASI

6.1 Introduction

A major output of the previous chapter was the identification of the minimal number of factors that account for the covariation among the intensity of mobile phone use and travel behavior constructs. This provided some knowledge of the underlying structure of the latent constructs. Based on this knowledge, the relations between the observed variables and the latent constructs was postulated, as shown in Figures 6.1a and 6.1b. In this chapter, the “two-step” approach to structural equation modeling is utilized to first, test the hypothesized structure, and second, to examine the relationship between the latent constructs. More specifically, this chapter has two separate, but complimentary objectives: (1) to obtain a factor model of the intensity of mobile phone use and travel behavior that fit the responses provided by the study participants; and (2) to examine how the factors influencing the intensity of mobile phone use affect the factors influencing travel behavior in a multivariate analysis.

6.2 Statistical Analysis Methods

In line with the objectives of this chapter, an SEM analysis was performed using data obtained through the cross-sectional survey. As mentioned in Chapter 4, SEM was preferred over the other statistical techniques including regression models because of the complexity involved in modeling phenomena using multiple observed variables of latent constructs, as it is the case in this study. As noted earlier, a two-step approach was employed to achieve the objectives of this chapter. First, a measurement model was speciﬁed using a conﬁr- matory factor analysis (CFA) to test the relation between the observed variables and the

121 corresponding dimensions of the latent constructs. This was followed by the specification of the structural model to determine the relationship between the intensity of mobile phone use and travel behavior in the study metropolis. Discussions of the mathematical representations of the CFA and SEM as well as the step-by-step modeling procedure can be found in the seventh section of Chapter 4. At each stage of the modeling process, the specified models were assessed to determine the extent to which they fitted the data using primarily two fit statistics, which are displayed with the path diagrams. These were the Chi-Square exact-fit test and the Root Mean Square Error of Approximation (RMSEA). Given that Chi-Square test is sensitive to sample size — it is artificially inflated by large sample and generally renders the findings of fitted model unrealistic (Byrne, 2013) — the RMSEA was the preferred measure of the models’ fitness. For a discussion of RMSEA, see Bryne (1998, 112). MacCallum et al. (1996) have suggested the following cut-off points for RMSEA values, in terms of how the hypothesized SEM best fits the observed data: (i) RMSEA greater than 0.10 indicate a poor fit; (ii) RMSEA between 0.08 and 0.10 indicate a “mediocre” fit; (iii) RMSEA between 0.05 and 0.08 indicate “reasonable errors of approximation in the population”; and (iv) RMSEA less than 0.05 indicate best fit. Estimation of the CFA and SEM was performed in LISREL, version 8.80. Because of the nature (categorical and ordinal) of the observed indicators LISREL’s Weighted Least Squares (WLS) estimator was utilized in both stages of the analyses.

6.3 Organization of Chapter

This chapter presents results of the second part of the quantitative analyses. The chapter is organized into six sections. Following this section, section three tests the validity and reliability of the hypothesized structure to the data based on the results from the previous chapter, using a Conﬁrmatory Factor Analysis (CFA) in section four. The CFA is speciﬁed for the two constructs under study: the intensity of mobile phone use and travel behavior. In

122 section five, a full structural model is estimated to examine the causal relationship between the two constructs. This is done at two levels – first an examination of the relationship without the identified demographic variables, and second, with an examination of the relationship but with the demographic variables. The last section, section six, summarizes the chapter and provides a transition to the next chapter.

Figure 6.1. Hypothesized CFA Model of the Intensity of Mobile Phone Use (left) and Travel Behavior (right)

6.4 Speciﬁcation of the Measurement Model

6.4.1 Measurement Model for “Intensity of Mobile Phone Use”

Drawing on the results of the EFA, this section examines whether a model with basic use of mobile phone, characteristics of the mobile phone device and advanced usage of mobile phone dimensions could be used to measure “intensity of mobile phone use”. As hypothesized

123 and depicted in Figure 6.1, questions concerning the traditional ways of using the mobile phone would load on one dimension while those relating to the characteristics of the mobile phone device as well as to the more sophisticated use of mobile phone load on the other dimensions. Note that the indicator type of phone is hypothesized to load on both basic use of mobile phone and characteristics of mobile phone device dimensions, largely because certain activities such as emailing and social media can only be used on smartphones with internet communication capabilities. To test the validity and reliability of the hypothesized structure to the data, a CFA was employed with the view to establish a model that passed the RMSEA fit test. The output of this process is the measurement model in Figure 6.2, formulated in a matrix form as shown in Equations (4.3) and (4.4) in Chapter 4). The data were coded so that individuals with higher scores on the device, basic, and advance dimensions owned multiple sophisticated phones, and were heavy users of mobile phone for basic and advanced activities, respectively. The path diagram in Figure 6.2 displays the standardized factor loadings (or regression weights) for each of the observed indicators and the latent construct. The Chi-Square exact-fit and the RMSEA test of fitness are also displayed. In addition to the information displayed in the path diagram, LISREL also provides additional information that bear on the analyses in the form of an output file, including T-values, squared multiple correlation coefficients (R2)1 for the observed indicators, and modification indices.2 As hypothesized, the three items related to “device” load on a common factor, while five items including type of phone load on the “basic use of mobile phone” factor. However, the factor loadings of two out of the three “advance use of mobile phone” items are greater

1The R2 describes the amount of variance in the observed variable accounted for by the latent construct or the common factor. As a reliability measure, R2 is scaled between 0 and 1, with values closer to one implying that the indicator in question is a better measure of the latent construct of interest.

2Modification indices provide the expected drop in the chi-square value if the parameters in question are freely estimated, and are often used in the inductive re-specification of CFA models (cf. Muthénand Muthén 1998-2006: 507).

124 0.13 numbph

0.14 typeph 0.93 0.14 0.20 netph 0.90 device 1.00 0.89 0.68 sms 0.21 0.57 0.88 emails 0.34 basic 1.00 -0.06 0.49 0.76 mobmon 0.94 0.62

0.11 social advance -0.23 1.00 1.23 eshop -1.68 1.49 1.66 ebank

1.52 gps

Chi-Square=889.38, df=31, P-value=0.00000, RMSEA=0.205

Figure 6.2. Path Diagram of the Initial CFA Model for ”Intensity of Mobile Phone Use” than one (in absolute value): -1.68 for the ”ebank” item, and 1.49 for the “gps” item. In a completely standardized solution, it is generally believed that estimated coeﬃcients of both observed and latent variables in the CFA or full structural model must be less than one, and in cases where they are not the solution is said to be inadmissible (J¨oreskog, 1999). This becomes more of a statistical issue particularly when the residual variances of the variables in question are negative, often referred to as “Heywood cases” (Byrne, 2013). Since the estimated residual variances of the two items (“ebank” and “gps”) in Figure 6.2 are non- negative, albeit their standardized loadings are greater than one, typically, they would not

125 be deleted from the model (J¨oreskog, 1999). However, the “advanced” construct with its observed indicators were targeted for removal from the initial model because of their R2 values. The “eshop”, “ebank” and “gps” variables have corresponding R2 of near zero, implying that the “advanced” factor explains little to no variance in these variables, which invariably suggests that these indicators are not reliable. This is not surprising considering that the indicators in question are very skewed (Bollen, 1987), and thus, provide little information

(refer to Table 5.4). Additionally, with an RMSEA of 0.205, the covariance matrix of the initial factor model failed to replicate the variance matrix of observed variables – in other words, the initial model is not a good ﬁt to the data.

It was therefore likely that a two-factor model was more appropriate to measure the intensity of mobile phone use in Kumasi. To test this possibility, the model in Figure 6.2 was re-estimated without the “advance” factor and its related indicators. This change in the factor structure produced a two-factor model illustrated in Figure 6.3, with a mediocre fit to the data (RMSEA = 0.091). The “device” factor was maintained in the modified model, while the “basic” factor was renamed to “usage” to reflect the fact that the factor relates to indicators encompassing the various activities conducted on the respondents’ mobile phones.

Given that the “advance” factor had been removed, there was no need to maintain the distinction between the “basic” and “advance” use of mobile phone in the modiﬁed model.

Although the initial model’s ﬁt improved substantially in the modiﬁed model, the estimated variance of the “netph” was negative.

Given the high reliability of the “netph” indicator (R2 = 0.98), it was not targeted for removal. Instead, a “corrective action” was taken by constraining the variance of “netph” to be equal to the variance of “numbph” (Byrne, 2013). This is consistent with the advice given in Bentler and Chou (1987, in, Byrne, 2013) against deleting indicators with negative variance estimates from the model. This was enough to correct the negative variance of

“netph” (see Figure 6.4). Additionally, an examination of the modiﬁcation indices indicated

126 0.46 numbph

0.74 0.22 typeph 0.23 device 1.00 1.07 -0.15 netph 0.79 0.28

0.46 sms 0.74 usage 1.00 0.79 0.37 emails 0.53

0.99 0.71 mobmon

0.01 social

Chi-Square=77.17, df=12, P-value=0.00000, RMSEA=0.091

Figure 6.3. Path Diagram of the Modified CFA Model for “Intensity of Mobile Phone Use” that the modified model in Figure 6.3 could be slightly improved if the type of phone indicator (typeph) was correlated with the social media indicator (social) by freeing their error co- variances. The “typeph” indicator positively covarying with the “social” indicator at 0.33 was not surprising given that social networking often gets carried out on smartphones by respondents in the study metropolis. Although there was the presence of other significant error covariance such as between text messaging (sms) and sending emails (emails), they did not make substantive sense. Placing an equality constraint between variance of “netph” and variance of “numbph”, as well as freeing the correlated error co-variances of “typeph” and

127 numbph 0.20

0.89 typeph 0.54 0.39 device 0.90 0.59 netph 0.20 0.46 1.00 mobile

0.44 sms 0.37 0.79 usage 0.33 0.84 emails 0.30 0.57

0.88 mobmon 0.68

social 0.23

Chi-Square=51.54, df=12, P-value=0.00000, RMSEA=0.071

Figure 6.4. Path Diagram of the Final CFA Model for “Intensity of Mobile Phone Use”

“social” produced a revised model that provided a good ﬁt to the data (RMSEA = 0.071).

Two other important measures of model fitness that are provided in LISREL’s output file are: normed fit index (NFI) (Bentler and Bonett, 1980), and comparative fit index (CFI)

(Bentler, 1990). The ﬁnal CFA as illustrated in Figure 6.4 above, has an NFI of 0.98 and

CFI of 0.99, indicating a very good ﬁt (values closer to one are better).

Table 6.1 provides the standardized factor loadings and other important information about the ﬁnal factor model. The standardized factor loadings represent the correlation between each indicator variable and corresponding factor. Considering ﬁrst the indicators of the “device” dimension, they are 0.89 for number of phones (numbph), 0.39 for “typeph”,

128 and 0.90 for “netph”. With regards to the indicators of the “usage” dimension, the standardized loadings are 0.79, 0.84, 0.57 and 0.88 for “sms”, “emails”, “mobmon” and “social” respectively. The standardized loadings of all the indicators in the ﬁnal model were statistically signiﬁcant (p < 0.01). The contributions of these indicators to their respective factors were also assessed using the R2 estimates provided in Table 6.1. From the R2 information, each observed variable had between 32 and 80 percent of their variances explained by the latent constructs. Although there was considerable overlap in the content of the “device” and “usage” dimensions, the dimensions were distinct and only correlated with one another at 0.26. This positive correlation suggests that the use of multiple and smart mobile phone device comes with a modest but noticeable increase in the usage of mobile phone activities. This relationship is reinforced by the positive loading of the typeph indicator on the “usage” dimension. Consequently, it can be inferred from this analysis that two nearly orthogonal dimensions underlie the intensity of mobile phone use in Kumasi metropolis.

Table 6.1. Standardized Solution for “Intensity of Mobile Phone Use” Indicators R2 Device Usage Number of phones -0.80 0.89 Type of phone -0.46 0.39 0.46 Number of networks -0.80 0.90 Text messaging -0.63 0.79 E-mailing -0.70 0.84 Mobile money -0.32 0.57 Social networking -0.77 0.88 LISREL Estimation by Weighted Least Squares All coeﬃcients are standardized and signiﬁcant at p < 0.01 Squared multiple correlations for the observed variables in parenthesis

6.4.2 Measurement Model for “Travel Behavior”

Although a factor structure was hypothesized for the travel behavior construct as depicted in Figure 6.1, some of the relationships in the conceptual model could not be included in the

129 final model of travel due to non-identifiability. Thus, this model includes only the “amount” dimension of travel behavior. A CFA model was therefore estimated for the travel behavior construct using only the indicators that measure the amount of travel including frequency of travel (freqtrav), distance of travel (disttrav), duration of travel (durtrav), and cost of travel (cost). Table 6.2 presents the standardized factor loadings and other important information about the final factor model, and graphically shown in Figure 6.5. As shown in the figure, the CFA model depicts the latent construct, and its observed indicator variables shown in rectangular box with an arrow pointing from the latent variable to the indicator variables. The degree of fitness between the model and the observational data in the measurement model was assessed using the RMSEA. An RMSEA of 0.005 supports the validity of the constructed scales. The model was also identified which implies that the hypothesized direction of effects among the model variables support the data. Next, the parameters of the identified model were estimated using the weighted least square estimator (WLS). As explained in Section 6.2, the WLS was used because of the multivariate non-normality condition of these indicator variables. The standardized factor loadings represent the correlation between each indicator variable and corresponding factor. From Table 6.2 and Figure 6.5, the factor loadings of the various indicators measuring the endogenous latent variable (travel) loads positively onto the latent travel variable. The lambda values of frequency of travel, distance travelled, duration of travel and cost of travel are 0.54, 0.47, 0.30 and 0.68 respectively. All estimated parameters were statistically significant (p < 0.01). Factor loadings suggest that all indicators are strongly related with the latent factor travel behavior, especially the cost of travel and frequency of travel indicators. Also reported in Table 6.2 is the R2 (shown in parenthesis) which was used to assess the variation in each indicator explained by the latent construct. The R2 take on values of 0.29, 0.22, 0.09 and 0.46 for frequency of travel, distance travelled, duration of travel and cost of travel, respectively.

130 freqtrav 0.71

0.54

disttrav 0.78 0.47 1.00 Travel

0.30

durtrav 0.91 0.68

cost 0.54

Chi-Square=2.03, df=2, P-value=0.36184, RMSEA=0.005

Figure 6.5. Path Diagram of the Final CFA Model for “Travel”

Table 6.2. Standardized Solution for “Travel Behavior” Indicators R2 Component Score Coefficient Frequency of Travel -0.29 0.54 Travel Distance -0.22 0.39 Travel Duration -0.09 0.90 Travel Cost -0.46 0.54 LISREL Estimation by Weighted Least Squares All coefficients are standardized and significant at p < 0.01 Squared multiple correlations for the observed variables in parenthesis

Now that measurement models for the two-dimensional intensity of mobile phone use and the one factor travel behavior constructs have been specified, the next and final stage of the modeling process proceed to ask the primary question of whether respondents’ use of mobile phones influence their travel behavior. Results of this causal analysis are presented in the next section.

131 6.5 Causal Relationship between Mobile Phone Use and Travel Behavior

As discussed in Chapter 4, the structural equation modeling method is used to estimate the impact of the intensity of mobile phone use on travel behavior. Based on the conceptual model, two structural equation models were developed: one, to estimate the relationship between mobile phone use and travel behavior without the covariates, and two, to estimate same but with the covariates. Through the same estimation procedure described in

Section 6.2, the ﬁnal models were achieved.

6.5.1 Structural Equation Model of Mobile Phone Use and Travel Behavior

without Covariates

In the second stage of analysis, structural equation model is used to examine the hypothesized relationships that exist among the latent endogenous variable (travel), usage and device variables leaving out the control variables. This structural equation modeling helped in the estimation of the model and to examine the significant effects of the various causal relationships among the various variables before the introduction of control variables such as demographics. Figure 6.6 graphically summarizes the identified causal effects, based on the conceptual model.

The RMSEA of 0.067, which is less than the 0.08 threshold, indicates an overall model

ﬁt which is considered as reasonable. The chi-square value is 152.84. All the indicators used as displayed above in Figure 6.6 have signiﬁcant factor loadings on the latent variables

(device and usage) with the exception of the indicator emails which did not have a significant factor loading onto device. An increase in device is associated with 0.34 in travel, indicating complementary effect. An increase in usage of mobile phones is associated with an increase of 0.39 in travel, also indicating a complementary effect.

132 0.21 numbph

freqtrav 0.62 0.89 0.56 typeph 0.38 device 0.61 0.89 0.20 netph 0.34 disttrav 0.70 0.45 0.55 Travel

-0.08 0.39 sms 0.39 0.24 0.78 0.31 usage durtrav 0.94 0.62 0.91 0.20 emails 0.61

0.91 0.62 0.62 mobmon cost

0.18 social

Chi-Square=152.84, df=39, P-value=0.00000, RMSEA=0.067

Figure 6.6. Identiﬁed causal relationship between mobile phone use and travel behavior

6.5.2 Structural Equation Model of Mobile Phone Use and Travel Behavior

with Covariates

In the previous section, structural equation model of the relationship between mobile phone use and travel was developed, but without the demographic variables. This, however, does not provide a complete view of the causal relationship between the two since the model ignores a number of important variable that are important to explaining the relationship between telecommunication and transportation. Thus, in this section, a full structural equation model which examines the hypothesized relationships that exist among the latent endogenous variable (travel), usage and device variables including the control variables, is analyzed.

The structural equation modeling helped in the estimation of the overall fit of the model used and the significant effects of the various causal relationships among the various variables.

133 The model fit of the hypothesized structural model was tested using the CFI, GFI and RMSEA. The comparative fit index of 0.98 and the goodness of fit index of 0.99 are an indication that the model is a good fit, since the fit indices satisfy the 0.90 threshold value (Jaccard and Wan, 1996). The RMSEA of 0.093 value which less than the 0.10 threshold, indicates an overall model fit which is considered as mediocre in nature (MacCallum et al., 1996). The structural model is a mediocre fit to the data based on the CFI, GFI and RMSEA values. Amidst the demographic variables, the two dimensions of mobile phone use still had a complementary effect on travel, albeit reduced effects. Device has a positive effect on travel (β = 0.36, p < 0.05). The usage of mobile is associated with a positive effect on travel (β = 0.20, p < 0.01). The results also found that certain socio-demographic measures (age and income) logically significantly affect travel behavior. As expected, age is associated with a decrease on travel (β = −0.22, p < .01). Likewise, participants with more income is associated with an increase in travel (β = 0.40, p < 0.01). These results support the established knowledge that daily trip numbers and duration increase significantly for the younger and high-income travelers. Participants’ level of education, gender, family type, vehicle availability, and residential location, however, did not yield significant effects on travel behavior.

6.6 Summary of Chapter

This chapter presented results of the second part of the quantitative analyses. More specifically, it presented results on the specification of measurement models for the mobile phone use and travel behavior constructs, and results of the causal relationship between the two constructs. In measuring the intensity of mobile phone use, measures such as type of phone, frequency of mobile phone use, using mobile phone to text (SMS), using mobile phone for email, using mobile phone for mobile money, using mobile phone for social media, number of phones used, and number of mobile networks, were found to be significant. Likewise,

134 0.27 numbph 0.85 device 0.24 typeph 0.41 0.94 0.60 0.12 netph usage -0.47 0.80 0.36 sms 1.18 freqtrav 0.61 0.74 agel 0.36 -0.10 emails 1.07 0.20 genderl -0.22 0.62 0.46 mobmon 1.00 0.00 disttrav 0.53 0.69 -0.14 social hhsizel 0.33 Travel 1.00 -0.30 0.00 age 0.30 1.00 famtyp -0.22 0.00 gender -0.01 durtrav 0.91 0.80 1.00 empl 0.40 0.00 hhsize -0.21 1.00 0.00 famtype educl -0.01 cost 0.36 0.00 emp 1.00 incomel 0.00 educ 1.00

0.00 income vehavail 1.00

0.00 vehavail reslocl 1.00 0.00 resloc Chi-Square=746.64, df=111, P-value=0.00000, RMSEA=0.093

Figure 6.7. Path Diagram of the Full Structural Equation Model the analysis revealed that, travel frequency, distance of travel, duration of travel, and cost of travel, are good measures of travel behavior. Having identified the underlying factors of travel behavior and mobile phone use, the next step in the analysis examined the inter- relationship between these two constructs within a full structural equation model at two levels – first without demographic variables, and second, with demographic variables. At the first level, the two dimensions of the intensity of mobile phone use construct – device and usage – were found to affect travel behavior positively. This relationship was mirrored in the second level where the relationship between the two constructs was examined taking into consideration several demographic measures, including age, gender, educational level,

135 family type, vehicle ownership, income, and location. In sum, it is found that causal eﬀects between telecommunications and travel in the study metropolis are mostly complementarity. These results are consistent with the complementary relationships found between the two in Lila and Anjaneyulu (2016) in India, Denstadli et al. (2013) in Norway, and Choo and Mokhtarian (2007) in the United States. In the next chapter, factors that could explain the positive relationship between mobile phone use and travel behavior are examined from the perspectives of the study participants, using the materials from the qualitative study.

136 CHAPTER 7

QUALITATIVE MECHANISMS UNDERLYING MOBILE PHONE USE

AND TRAVEL BEHAVIOR NEXUS IN KUMASI

7.1 Introduction

Thus far, the analyses have focused on the quantitative phase of the study and have shown some quite clear indication of the effect of mobile phone use on the travel behavior of residents. But it is also important to get knowledge about the mechanisms through which the use of mobile phones and its applications influence travel behavior. Thus, this chapter turns to the material from the qualitative interviews to explore the mechanisms that underlie the relationship between mobile phone use and travel behavior discovered from the previous chapter. Although this chapter focuses on understanding the quantitative results, it also presents findings on participants’ experiences in their usage of mobile phones and their perception of the impact of the use of mobile phones on their travel behavior. The reason for analyzing the second phase of the study in this manner was to allow for comparison between the quantitative results and the qualitative findings. Overall, 24 semi-structured interviews were conducted with selected participants from the quantitative study, based on the case study approach, specifically the multiple case study, and guided by the pragmatism interpretive framework.

7.2 Organization of Chapter

This chapter is organized into seven major sections. Before delving into the major themes arising from the qualitative analysis, it is useful to provide some background information about the participants. Thus, Section 7.3, following this section, presents a description of the characteristics of the participants, including their distribution by age, location, sex and occupation. Section four focuses on the procedure through which data was analyzed for the

137 study. The last section presents a discussion of the qualitative results in four subsections based on the identified themes: attribute of mobile phone use; dependence on mobile phones; impact of mobile phone; and the underlying mechanisms on the impact of mobile phones on travel behavior. The penultimate section presents a summary of the key findings in the form of a framework matrix, while the final section concludes the chapter.

7.3 Characteristics of Participants

Participants in the study were adult mobile phone users aged at least 25 years. The ages of participants range from 25-39 cohort to the cohort 55 and over. It was not until the early

2000s that mobile telephony became proliferated in Ghana. The age range of participants provides a useful ground for understanding the focus or objective of this study as the substitutionary and complementary impact of the use of mobile telephony can be well explained by people who were adults and working before the proliferation of mobile phones in Ghana. The participants are made up of 44 percent females and 56 percent males living within the three delineated residential location zones. A higher proportion of the participants (44 percent) occupied the outer sub-urban areas of Kumasi, followed by 30 percent in the inner sub-urban areas of Kumasi. Only 26 percent of the participants resided in the historical core centers of the study area, akin to the residential location distribution of the quantitative sample in Figure 5.1 in Chapter 5. Most of the participants were employed in various occupations including artisans, organized business (fabric and jewelry wholesale trade), salaried employees (banking, police force and education institution) and those engaged in small businesses

(barbering salon). There was a case of one participant on retirement from active work and two students. Background information about the participants are summarized in Table 7.1.

138 Table 7.1. Characteristics of the Study Participants Participant’s ID Age Sex Location of residence Occupation 1 40-54 Female Historical core Organized business 2 40-54 Female Outer sub-urban Salaried employee 3 40-54 Female Inner sub-urban Organized business 4 55 and over Male Inner sub-urban Artisan 5 25-39 Female Inner sub-urban Organized business 6 25-39 Male Historical core Artisan 7 40-54 Female Historical core Small business 8 40-54 Male Outer sub-urban Salaried employee 9 25-39 Male Outer sub-urban Student 10 55 and over Female Outer sub-urban Salaried employee 11 25-39 Male Outer sub-urban Student 12 25-39 Male Outer sub-urban Artisan 13 25-39 Female Outer sub-urban Small business 14 25-39 Male Outer sub-urban Artisan 15 55 and over Male Inner sub-urban Retired 16 25-39 Male Inner sub-urban Salaried employee 17 25-39 Female Historical core Small business 18 25-39 Male Outer sub-urban Salaried employee 19 40-54 Male Inner sub-urban Salaried employee 20 25-39 Male Outer sub-urban Artisan 21 40-54 Male Inner sub-urban Artisan 22 25-39 Female Historical core Small business 23 55 and over Female Historical core Small business 24 40-54 Male Inner sub-urban Artisan Source: Field Study, January – February 2018 Designed by exporting the “case classiﬁcations” sheet in NVivo.

7.4 Data Coding and Analysis

Inﬂuenced by the goal of the qualitative study, various coding strategies were combined in the analysis of the interviews, and it included attribute coding, thematic coding and process coding (Adu, 2016). Attribute coding was used to code speciﬁc features of the research sites and the study participants, such as age, gender, location of residence and occupation, as presented in Table 7.4. With regards to thematic coding, phrases or sentences in the interview transcripts were used to capture the meaning of an aspect of the data.

139 Finally, in terms of process coding, certain observable activities were coded from the data to depict the behavior of the participants in terms of their mobile phone use and travel characteristics. Based on these, a codebook was developed to align with the interview guide (see Table 7.2). Overall, this procedure yielded the coding of 263 statements from the 22 interview transcripts (refer to Appendix E). Given that there existed several patterns among the initial codes, the codes were categorized into 42 codes, and this formed the first phase of the categorization procedure. The second and final phase of the categorization procedure involved the identification and definition of themes (4) and sub-themes (15), also referred to as primary code and secondary code respectively, as shown in Table 7.2. As mentioned in Chapter 4, transcripts were coded in NVivo. When coding was completed, analysis was conducted to address four primary themes: attributes of mobile phone use; dependence on mobile phone; impacts of mobile phone use; and underlying mechanisms of mobile phone use impact on travel. Each of these topics is address seriatim in a multiple case study format to enhance and highlight similarities and differences between the study participants. It is important to note that respondents’ own words are provided to illustrate the themes evident in the data and to provide context for the findings. As much possible, quotations that are captured here are those, in the view of the researcher, which aptly support the positions that are being argued in the study.

7.5 Qualitative Results

The qualitative data analysis yielded several themes based on the research questions, as illustrated in Table 7.2. Four themes were extracted from the procedure in Section 7.4, namely: (1) attributes of mobile phone use; (2) dependence on mobile phone usage; (3) impact of mobile phone use; and (4) underlying mechanisms of mobile phone use impact on travel behavior. The remainder of this section is devoted to the description of these themes, with verbatim evidence from the data to support them where necessary. Table 7.2 presents

140 Table 7.2. Coding Guide Primary Code Secondary Code Freq* Description Attribute of Mobile phone 19 This node stores information about number of years participants have mobile phone use ownership used mobile phone. Network 23 This node stores information about the number of phone networks subscription participants are subscribed to. Number of mobile 23 This node stores information about the number of phones used by phones used participants.

Dependence on Advanced usage of 27 This node stores information about participants use of phone for mobile phone mobile phone advanced functions including online banking, online shopping, GPS navigation, social networking, etc. Traditional usage of 44 This node stores information about participants use of phone for mobile phone advanced functions including calls, SMS, gaming, calculator, mobile money, etc.

Impact of mobile Complementary 10 This theme suggests that the use of mobile phone has increased the 141 phone use impact number of trips or enhanced the trips made by participants. Pre-mobile phone 34 This node stores information about how participants carried out their use regular activities (that they have mentioned under theme “dependence on mobile phone”) prior to them adopting mobile phone. Substitutionary 40 This theme suggests that the use of mobile phone has reduced or impact eliminated participants’ physical travel.

Underlying Nature of business 1 The identified impact of mobile phone use on participants’ travel is mechanisms influenced by their nature of business. Poor infrastructure 8 The identified impact of mobile phone use on participants’ travel is and service delivery influenced by the lack of or poor infrastructure and service delivery in the Kumasi/Ghana. Security and trust 9 The identified impact of mobile phone use on participants’ travel is influenced by insecurity and lack of trust in the current system. Technological 25 The identified impact of mobile phone use on participants’ travel is barriers (network influenced by technological barriers such as poor network coverage. connectivity) *Frequency (Freq) here refers to the number of times a significant statement was coded into a theme. an overview of the themes derived for the qualitative results. Each theme has been further

sub-sectioned into diﬀerent themes geared towards exploring how the use of mobile phones

may inﬂuence the travel behavior of individuals interviewed in the study.

7.5.1 Attributes of Mobile Phone Use

A rapid growth rate has been noted among mobile phone subscribers in Ghana (Interna-

tional Telecommunication Union, 2015). This theme therefore examines the characteristics

of mobile phone use in the study area. Three sub-themes were identiﬁed in the attributes

of mobile phone use theme: mobile phone ownership, network subscription, and number of

mobile phones used. Detail description of these themes are provided below.

Ownership of Mobile Phones

It has been established in literature that mobile telephony in Ghana experienced a sharp

increase in the early 2000s with the advent of mobile phone brands such as “Motorola”,

“Siemens” and “Nokia” as well as the MTN network service provider (MTN has undergone

diﬀerent stages of ownership and management – from Spacefon, then to Areeba and now

MTN). Participants conﬁrmed that they had started to use mobile phones within the period

of proliferation in Ghana. The following quotes illustrate the fact that mobile phone use

became prominent in the last two decades. P19, a banker and lecturer in his mid-forties

commented when asked how long he has used mobile phone: “That is way back in 2001. I

was using Motorola, you know, the big one.” Another participant (P4) who is over 55 years

of age and works as a mechanic also maintained that he started to use mobile phones shortly

after the major network service provider MTN came into operation in the country: “I started

using Mobile phone 6 months after MTN network came into the country.”

The views as expressed by these participants only underscore the fact that telephone use predated mobile telephony as was the case in the more developed nations. However,

142 what is diﬀerent in the current study is that the rate of expansion or growth in the use of mobile phones has been unprecedented – giving rise to “leapfrogging.” Before the last two decades, the telephones were preserve of only the elite – the few who were privileged to use telephone at the time. Telephones were used mainly at government oﬃces and a few government residences as well as private residences. And at the time, it was only the state network provider (Ghana Telecom) that provided services for the landlines or telephones. Another interesting phenomenon back then was the use of communication or business centers. Even though a section of the crop of mobile phone users did not own their personal phones they relied on the services of the communication centers to reach out to family members, loved ones and business partners. As at the time, only voice calls were the predominant use of the phone because the mobile devices came as feature phones, which did not have more sophisticated and smart uses. Other phones used by the communication centers came in the form of portable landlines. In summary, it is seen that participants owned their mobile devices since the early 2000s, but it is also noteworthy that ownership and use of mobile phones were almost synonymous. Within the period of mobile ownership proliferation also came the proliferation in use of mobile services irrespective of the private ownership of a device.

Network Subscription

In this section, the number of mobile devices used by participants, mobile network subscriptions and the reasoning behind such subscriptions are discussed. It is worth noting that, some mobile devices support more than one SIM network while others do not. As much as possible, caution is exercised here not to interpret the use of a single mobile device to necessarily mean subscription to only one mobile network service provision. As of the time of this study, there are six network service providers namely MTN, Vodafone, Tigo, Airtel, Glo and Expresso. Current developments show that Tigo and Airtel are on the verge of merging and Expresso is becoming extinct with very sharp declines in subscription.

143 From the interviews, it was made evident that all participants are subscribed to at least one mobile network service provider with one participant owning three mobile devices while six others owned two mobile devices. Sey (2011) attributes such practice in Ghana to three primary reasons: reliability in in-network calls; reduction in the cost of local calls; and controlling points of contact, which were confirmed by the participants. Regarding the issue of network reliability, residents in the study area had the perception that the reliability of network connection could not be ensured with only one network. Thus, multiple subscription afforded residents the flexibility to switch networks to prevent disruption in communication. This could explain the unprecedented growth in mobile phone subscription over the years in Ghana (Pick and Sarkar, 2015). For many participants, multiple subscription reduced the risk of getting disconnected because of connection mishaps in critical situations. P4, who is subscribed to two networks, and works as an artisan in the inner sub-urban area of Kumasi, illustrated this in the following words: “The network connection in some locations are poor or not strong, so I use the two to compensate each other. Especially, the connection in big cities like Accra and Kumasi are exceptionally strong and good, but when it comes to small towns like Mankranso and the like, you can have one of the networks having a good network than the other. So, that’s why I use those two networks.” In terms of communication cost, telecommunication companies in Ghana generally offer cheaper rates for in-network calls, given the high competition among them. To leverage this offer, many of the study participants, especially those who are highly cost-conscious subscribe to multiple numbers for cheaper calls. P6, who is a middle-aged artisan remarked, “Most people I do contact or call frequently do use these two networks....It is also very cost effective to make calls from the same network to another than otherwise. So, I mostly do contact MTN with MTN and Vodafone with Vodafone because of the low cost I would be enjoying.” This experience was shared by P18: “...for in Ghana, when you are dealing with cross network, it is expensive. Vodafone to Vodafone is easy, MTN to MTN, easy. So, sometimes the decision to make a call and the phone to use depends on the number of the recipient.”

144 The ﬁnal motivation for participants to have multiple networks subscription is to control how they are contacted. Typically mobile phone users, especially business owners and pro- fessionals, take caution in the manner they hand out their phone numbers for future contact.

In line with this, some participants (P2, P18 and P19) subscribed to more than one network with the aim to separate private and business contacts. These participants are able to ﬁlter out the types of call they receive by identifying which phone number they use. An illustrative example, P18, a middle-aged lecturer in a university remarked:

“Usually when you have the two, there is one number that you have to use as a private line, so unless you are very close to me, you won’t get it. It doesn’t have to do with the reliability of the network. I was using the Vodafone as private because, most of the people I was dealing with were on Vodafone network and it looked like it was cheaper to be on that network.”

Number of Mobile Phones Used

The use of multiple networks in most cases comes with a cost. Mobile phones may usually have a single Subscriber Identification Module (SIM) slot, or multiple SIM slots. For this reason, people may purchase one or more mobile equipment for the use of the different network operators. As indicated early on, business owners may prefer to differentiate the contacts they have with the use of different SIM cards. They thus may purchase different phones for these cards. It was noted that many of the participants making use of three or more network operators had two mobile phones; while those with a single network operator had one mobile phone. For participants with two network operators, either one or two mobile phones were used based on the SIM card slots in the phone.

The foregoing themes – ownership of mobile phones, network subscriptions and mobile equipment used by participants – have revealed interesting ﬁndings into the experiences of participants on their reasons for mobile phone acquisition, and some of the factors that inform their behavior towards the use of mobile phones such owning multiple phones and

145 subscribing to multiple networks. This is intended to provide context for the discussion of the remaining themes.

7.5.2 Dependence on Mobile Phone Usage

Mobile phones have many characteristics that attract both the young and old. These characteristics, some of which have been described in the previous theme, could lead to excessive dependence (Nikhita et al., 2015). This theme concerns itself with describing how participants have relied on the mobile phone in their everyday activities. To appreciate the importance of mobile phones to the study participants, it is important to ﬁrst turn to the pre-mobile phone use era and describe how participants conducted their activities before adopting mobile phone.

Prior to the use of mobile phones, people used all sorts of media, including phone booths, communication centers, and even the postal services to reach out to family members and friends who lived farther away. But clearly, there were lot of diﬃculties. An illustrative example, P3, who runs an organized business with over 15 years of experience in using mobile phones, remarked:

“Prior to my use of smart phone, I used to go on these business trips myself, mostly on Monday evenings. I would go to the VIP bus station and take one of the buses, which cost GHS 550 (US$ 130), for Togo. The bus would usually make a transit in one of the neighboring border towns before eventually arriving in Togo the following day. Once I arrived, I would ﬁnd a place to lodge before going to the market to buy the needed products.”

It was even more diﬃcult and complicated when it came to remit or sending money either for family/friend support or for purchase of services. Participants (P8, P11, and P20) who supported family members who lived outside of their region of residence used “unorganized” vehicle delivery services to remit funds. For example, P8, whose family lived in a remote part of the country remarked:

146 “I had to travel to the bank to remit money to my family and friends or go to the Ghana Post to undertake such transaction prior to my using a mobile phone. In many instances, I had to join long queues to do such a transaction. Also, you could take it to one of these bus stations and ask the driver to deliver the money. But this was risky and not reliable or convenient.”

Similarly, P20 commented:

“With regard to remitting money to my family, I had to go to the bus terminal in Kejetia and give it to a vehicle traveling to the Northern region. In the process I had to take the vehicle’s number plate and give it to my relative, who will use it to identify himself for the money. As you can imagine, the drivers were not liable to any loss, so this process of sending money was a big risk.”

From the foregoing, participants were clearly challenged and unsecured in the conduct of many of their primary activities. With its advanced functions and capabilities, the introduction of mobile phones represented a revolution in the ability of participants to conduct activities virtually, that hitherto could only be done using the channels indicated earlier.

Two sub-themes emerged from analyzing how participants have relied on mobile phones in the conduct of their primary activities: advanced usage and traditional usage of mobile phones.

Traditional Usage of Mobile Phones

In the context of this study, traditional uses represent activities that do not require an internet-enabled phone to be conducted. As a result, although “Mobile Money”1 service is a more recent application in Ghana, it is considered traditional because its subscription and usage does not require the internet. Other mobile phone activities in this category are voice communications and Short Messaging Service (SMS). Following the adoption of mobile phones by participants, reaching out to family and friends as well as sending money, among other things, have been more convenient and aﬀordable. This has changed the norm of doing

147 a lot of things. As expressed by some participants (P2 and P20) who have family members living farther apart, the mobile phone with its calling capability has made reaching out to family and friends easier and has somewhat blurred the psychological boundary that had existed between them and their relatives. P2 remarked:

“Now, Mobile phone has made communication very simple. Aside the beneﬁts to my business, I can communicate with my siblings overseas very often, and even on video, as if they are physically close to me. We were ﬁrst restricted to only telephone calls and or telegram, which was cumbersome and inconvenient to say the least.”

P20 also remarked:

“I use the mobile phone to primarily stay connected with my family (father and sibling) to check up on them since they all live in the Northern region.”

Also, business transactions through the mobile phone have become important in this era. Business owners often depend on mobile phones as a means of staying in contact with their clients (both first time and recurring) and their suppliers. Participants expressed that mobile phones have been important to their businesses in terms of the convenience it affords in keeping up with their suppliers and the retention of clients. Three participants (P1, P3, and P12) reported that they have had to rely on the mobile phones in ordering merchandise from their suppliers. P12, an artisan (an electrician to be specific) in the outer sub-urban area of Kumasi, remarked:

“...Oh yeah! I can call some of my suppliers at Adum, ‘Hey Kalala, I need some cables’, and give details and speciﬁcations about the type of goods I need because I have already established that rapport with them, they send the goods to me via a vehicle and I also transfer the total amount for the goods through the mobile.”

Three participants (P4, P20, and P23) also demonstrated how mobile phones have been useful in maintaining business contact with their clients. For example, P20, who operated a barbering salon commented:

“...I also have my phone number on my shop’s banner, so if anyone comes to my shop and I am not around, they are able to reach me through the mobile phone. This aﬀords me the opportunity to re-arrange with the client to a convenient time to attend to them, without losing them completely.”

148 The usage of mobile phone has also brought a greater degree of respite, especially to those who had difficulties in transferring money, with the advent of the mobile money service, as illustrated by the reports of many of the participants (P4, P6, P7, P12, P15, P16 and P20). For example, P20 explained: “Performing some activities has now become quite easier, especially with the transfer of money. Now if I want to transfer money to someone, all I need is to have some money available on Mobile Money wallet, look for the contact and send the money, all from my mobile phone without the need to travel to the transport terminal or to anywhere.” The use of mobile money transfer was highly ranked among all participants. This was due to the benefit of reducing waste in time and energy, especially in business transactions, as reported by the participants. P7, an adult who operates a small business in the CBD of Kumasi, found relief in the use of mobile money transfer as expressed in the following words: “It was really a tussleBut with the mobile money service, the supplier doesn’t have to even come to this shop, and after raising the money, I transfer it to them. Occasionally, I would have to travel to Asafo Market to transfer the money through a Public Transport driver. A mere glimpse of the total amount on you could increase the delivery charges. But with the mobile money, the charge is always fixed. There were a lot of emotions attached, because you could call and the driver informs you, ‘Oh I haven’t delivered it yet’. Imagine the unsettling nature of such venture.” Also, participants emphasized convenience in accessing mobile money vendors who are liter- ally all over the place. The trips to banks, which are usually in central business locations are reduced or entirely eliminated, thus saving time, energy and cost. For example, P16 quoted: “There are times I forgo going to these banks to save myself from long queues. Sometimes you can even go to an ATM machine and it will not be working. So, I prefer saving money on my mobile wallet to have easy access to it as and when I need it. So, yes, I use the mobile money service very often than the regular traditional banks.” Generally, all participants agreed that mobile phones are used for communication. Whether it is to connect with family, friends or clients, mobile phones play an important part in connecting people. P16, who has used mobile phone for 10 years, summarized best the dependence of participants on the traditional functions of mobile phone when he explained his primary use of the mobile phone:

149 “I think the three major things I use my mobile phone for is for mobile money transfers, making phones calls and for text messaging.”

Advanced Usage of Mobile Phones

Despite the apparent dominance of the traditional usage of mobile among the study participants, as presented in the previous theme, some residents of the study metropolis had found a more advance way to using the mobile phones for sophisticated activities including web browsing, social networking, email, mobile banking, online shopping and GPS navigation.

This was especially the case for participants who owned internet enabled mobile phones

(Smartphones) . Participants, particularly salaried employees and students (P9, P16 and

P19), used web applications such as Google to search for information. For example, P9, a graduate student with 10 years of smartphone use, remarked: “I also use it for searching for items that I’m not familiar with, deﬁnitions, locations, video tutorials”. Similarly, P19, a lecturer with a private university in Kumasi, commented: “...browsing for information, sometimes for the work I do, for teaching....”

In addition, social networking was found to be integral among the participants, especially the younger ones. From research purposes, to brand awareness and entertainment, through to crisis communication, social media was regarded as an important feature by participants

(P6, P8, P10, P12, P14, P16 and P22). P14 emphasized the dependence on social media:

“...I use it for social media. You know it is the trend now and when I am sitting idle, I hop on to one of these social platforms and have a decent chat with one of my friends or sometimes even strangers.”

This was reiterated by P10:

“I use the social apps like WhatsApp, Facebook, the Facebook messenger and checking on Instagram. I have my Bible app on it which I use at church gatherings, for browsing, and for taking pictures.”

A recurring theme for using social media among these participants is to develop and maintain relationships with friends and families, exchange and source information, and for

150 entertainment. An illustrative example, P20, emphasized the importance of using social media to maintain connection with others remotely:

“I can even video chat with them to not only stay in touch but to reach out with a nearly physical presence. Also, I receive pictures and other media ﬁles on my mobile phone through the mobile app, WhatsApp, from friends and family. I can also get real-time feeds of programs and occasions I am not able to attend via Facebook Live.”

Although usage of social media was often reported by younger participants, some older adults also saw the importance of the feature. For example, P15, who is over 55 years and retired, explained how he relies on social media for entertainment:

“...it makes me socially active, especially when no one is around the house, all I do is to jump on to one of these social media platforms on the mobile phone. For example, people send to me pictures and videos, which are interesting and or funny. But I always delete the disturbing ones. These videos get me certainly entertained.”

Social media was even used by some participants, especially those into organized business, as a marketing tool to advertise their products.

Other applications of the smartphone, including email, online shopping, internet banking and GPS navigation, were also used by some study participants, although on a limited scale.

These applications were limited to certain characteristics of the participants based on literacy level, type of occupation, and age. Three participants (P2, P8 and P18) reported that they use emails quiet regularly because of the nature of their jobs. For example, P18, who doubles as a consultant and a lecturer commented:

“Sometimes you might be on the run, our work is such that we don’t stay at a particular place. Then you have to do something. Sometimes when there is a discussion you also comment to prove that you are not far from around. And that’s one of the gaps that this mobile app, the network, has managed to bridge. In the past you have to be stationary for you to access your e-mails and all of that. But now it’s easy. You can contribute to discussions, even on another department’s work that they did. We have a staﬀ e-mail account where we do that.”

Three participants (P2, P9 and P18) also use their phones for internet banking. P18 appears to be an active user as expressed in his quote:

151 “...now with most of the banks having Mobile App, I do my transactions online and to some extend purchases. So, I do transactions, whether banking or to buy....” P9 used internet banking just for checking balance and receiving alerts as she explained: “I can also check my account on the mobile phone with the bank’s mobile app. I use the bank’s mobile app to check my account balance, and usually they give update of my account’s balance. Whenever my account is credited with my salary, I am alerted. And when I withdraw or receive money, alerts pop-up.” Despite the government of Ghana’s efforts to develop a street addressing system by introducing the Ghana Digital Property Address System (GhanaPostGPS) in 2017, GPS aided navigation is still under-utilized in the country, because of challenges such as poor service provision of transport infrastructure (Sewell and Desai, 2016), especially at the community level. Realizing the difficulty, only one participant reported the use of GPS enabled navigation. P9, who is also a Google map contributor, explained: “...When I’m traveling, I use my map a lot. That even motivated me to be on the networks. Because, it comes with..., you have to use data for the map. I use it for driving directions and trying to find locations which I am not familiar with. And also, I am a Google Contributor...so, everywhere I visit, I make sure I pin them on the map, so that another person can use it next time.” In addition to the infrastructure challenge, issues such as trust and security (these challenges would be discussed under the “Underlying Mechanisms of Mobile Phone Impact on Travel Behavior” from participants’ perspective) have affected negatively the use of online shopping. Just as GPS navigation, only P3 reported actively using online shopping: “I have Amazon, which [sic] I buy books. I’ve used it to buy for a doctor friend of mine, because there was no option getting anybody to buy. And then I also use Tabao, to buy things. It is an alternative to Alibaba, which charges in dollars, but the Tabao charges in Yuan.” From the foregoing, the advanced features of the mobile phone used mostly by the participants are web browsing, social networking, and to an extent emails, as succinctly captured by P8: “I surf the internet with it (mobile phone) for research, and for social networking sites, especially Facebook. I do not have to go to one of these internet cafésto surf the internet

152 or use social media. I even check my e-mails right on the phone. I also use WhatsApp too on my mobile phone for messaging as well as sending and receiving media ﬁles.” Dependence on these applications, including the traditional uses, in many ways inﬂuence the lifestyle of the participants, including their travel behavior. In this regard, the use of mobile phone to perform the aforementioned activities could reduce, eliminate, increase or enhance the travel experience of participants. The next theme therefore examines the impact of mobile phone use on travel behavior of participants.

7.5.3 Impacts of Mobile Phone Use

The main thrust of this study was to examine how mobile phone use affect travel behavior in the study metropolis. Two different meanings of the impacts of mobile phone use on travel were identified: substitutionary impact and complementary impact. These sub-themes are key to understanding this relationship.

Substitutionary Impact of Mobile Phone Use

Within the context of this study, the substitutionary impact of mobile phone suggests that mobile phone use eliminates or reduces travel. Participants indicated that the use of mobile phone allowed them to save time by eliminating travel to certain activity centers as they could transact such activities and services through mobile phone applications such as Mobile Money, e-mail and social networking, among others. The presentation here follows the structure of the previous theme and presents the substitutionary impact of, ﬁrst, traditional uses of mobile phone, and second, the advanced uses of mobile phone. From the previous section, the primary traditional activities participants use their mobile phone to perform are voice communication, SMS and mobile money transfer. All participants who used mobile phone for voice calling perceived that mobile phone has reduced their number of trips in many ways. For example, P4 quoted:

153 “The use of phone has reduced the number of trips that I used to make. If I have any family issue or I need to link up with a friend, all I do is to establish a connection through the Mobile phone. This morning, I called some of my relatives to check up on them, which I would have otherwise traveled to do, and this saved the energy, time and cost required to do this very same activity.”

Similarly, P15 commented:

“I used to make several trips to undertake certain activities, but now, I can use the mobile phone to convey the message about whatever needs to be done, for it to be undertaken by someone in my stead. It has really reduced my number of trips.”

This opinion was also conﬁrmed by P4, whose relatives live in a diﬀerent geographical area:

“....Again, I could make up to three trips to check up on my relatives. Now, all I do is to pick up my mobile phone and call them and we have a chat.”

In a more signiﬁcant manner, participants whose mobile phone activities were advanced in nature also explained how their use of mobile phone had an inverse relationship with their travel activities. In particular, participants who used the mobile money transfer feature of the mobile phone opined that it has reduced greatly their number of trips. For example, P8 indicated that:

“The mobile phone has really reduced the number of trips that I make. Before the access to mobile phones, in order to undertake certain activities, I usually had to move or travel from one point to another. Spent a lot of time joining queues in order to undertake certain particular activities. It was really time consuming and costly. Now, with the mobile phone, I can sit in my oﬃce, not make a trip to anywhere and conduct most activities on the phone. All I would need is money on my mobile wallet if I want to transfer....”

Sharing in the experience of P8 on the impact of mobile phone use, P13 remarked:

“Now, if I need money I won’t travel to the bus station for that process that I used to do to get money, I will just receive it on my phone through Mobile Money. And if I want to send money too to my family, I won’t make a trip to the bus station to send them the money through the driver, I will just send it to them through mobile money right on my phone, in seconds and they receive it.”

Similarly, P9 emphasized the importance of the mobile money service by comparing his experience with traditional banks prior to using the service (Mobile Money). He (P9) remarked:

154 “I don’t also go to the bank, look for a friend who is a worker at the bank, ask him to cash my cheque for me because I am in a hurry. I just use Mobile Cash and go my way.”

Social networking was another important application that had negatively impacted on travel, from the perspective of the participants. On illustrating how video live streaming and video call on mobile phone has reduced and eliminated trips to social activities that otherwise could not be avoided, P19 remarked:

“If there is a special ceremony such as a naming ceremony or wedding that I cannot attend for reasons such as distance, friends are able to share live feed with me in real time to make up for my absence. In fact, the introduction of mobile phone (especially smart phone) has really brought about convenience in my daily activities.”

P2 is a salaried employee in the outer sub-urban area of Kumasi, and she indicated that,

“It has rather reduced it. Because I was in town last week, but I couldn’t ﬁnd all the type of things that we wanted. So, I communicated with my sister using the mobile phone, through WhatsApp...and she got the things where she is. Apparently, if this wasn’t possible as I said, I would have come back from town and gone back again today. Now, all that has been solved and two or three trips have been forfeited, not only at my end, but at hers too.”

Among the participants who used smartphones, the perception of the substitutionary role of mobile phones on travel was felt even more for those who conduct business remotely within the city, especially those into organized trade, who work remotely and across geographical boundaries. For example, P2, a fabric merchant who gets her business supplies from four neighboring countries, mentioned that the use of mobile phone has saved her money, time and stress:

“As you can see I sell cloths, and the mobile phone serves me very well in my business. I mostly receive my supplies from Togo and the other neighboring countries, but I don’t travel physically to these countries as I used to....So, it helps not to tire me out traveling all the way to see the designs before selecting. Because of the mobile phone, it can last more than 3 months, and I haven’t been on a business trip.”

P4 also indicated the eﬀect of the mobile phone in reducing the number and cost of his business trips:

155 “Total cost of transporting around has subsided. Last week, I was supposed to make a trip to Accra. I communicated with the person through the phone and he sent the goods to me. All I did was to pay for cost of transporting only the goods, which saved the cost of personally traveling forth and back, plus the goods.”

Just as small business and organized business traders, salaried employees whose work conﬁned them to an oﬃce space, and do not have the luxury of time to spend moving from one point to the other to physically access certain important activities, also appeared to have saved some time and cost from their use of mobile phone. For example, P16, a banker who works from open to close six days a week explained how smartphone allows him to carry out a lot of his activities remotely:

“Now I can also sit in the comfort of my oﬃce and access the internet without having to go to the internet caf´e.”

In a similar vein, P19 explained how mobile phone has reduced his number of trips after he had identiﬁed the extent of his trip reduction:

“For me, I believe it has replaced between 70 and 90 percent of my travel....For the browsing, at any given time, sometimes I’m at home and I need to, and I don’t have Wi-Fi but I use my phone hotspot. If I am working on any paper I have to come to the oﬃce, but with my phone, I only need to have credit on it. I could stay at home, instead of traveling to the oﬃce.”

From the experiences of many of the participants as presented in this section, the use of mobile phones had reduced the number of trips undertaken. However, some of the participants expressed other opinions on how certain mobile phone applications have impacted on their travel. While some felt mobile phone has made their travel easier, others opined it has increased the number of their trips. The next section presents the ﬁndings on these participants.

Complementary Impact of Mobile Phone Use

Although evidence from the above themes suggests that the use of ICT by the participants supports the “ease of use” notion as theorized by Davis (1989) in the Technology Acceptance

156 Model, where individuals believed that the use of a particular technology has freed them from “physical and mental effort,” few participants in the sample noted otherwise. According to these participants, the use of the mobile phone has not significantly reduced their physical travel needs, in fact mobile phone use seemed to have increased or enhanced their travel. P1, who sells jewelry and gets her supplies from over 10 different sources, noted extensively why her use of mobile phone has not had any negative effect on her travel behavior: “I occasionally use mobile phone as a channel to order goods. I must say this is not done frequently. You know, there are some businesses that ordering of supplies are wholly done online. But it is difficult to entirely resort to this business mode with the kind of business I am engaged in. I usually get my goods not from one place. With the watches alone, I get my supplies from ten different places. Sometimes, a supplier may have a good quality of a kind of product, while other kinds may not be of such good quality. That means my presence is required to physically inspect my supplies before I purchase them. So, you see, I can’t really say I am sitting here and I will order for it to be delivered, you need to go and do proper selection.” Some participants also noted that activities such as voice calling prompted personal meetings, which indirectly created trips. This is particularly the case where users start off a conversation on the phone but end up meeting personally either because the information about which they are talking is sensitive or a business transaction could only be completed in person. An illustrative example is P11 remark: “...Although the fellow is my friend, he needed to see me personally. He was at an area that was not near here. So, he called and asked whether I’m in the house and I said yes. He wanted to come over to discuss a personal matter which I have alluded to, so I had to invite him over. Other than that we could have discussed everything over the phone.” Other participants also found motivation to travel, knowing that they don’t have to be home to undertake certain activities. For instance, P12, who is an outer sub-urban artisan, noted that: “If I am not home and I might miss a news update at some particular times, I could just log on to a TV app on the mobile phone wherever I am and follow the news right there. For this reason, travel becomes easier.” The complementary effect of the mobile phone was also experienced by some participants because they could afford to arrange or plan their travel in the comfort of their home. This

157 to an extent makes travel more eﬃcient through the limitation of the number of trips one would have to undertake. P16 remarked:

“When it comes to traveling, the mobile phone has helped a lot. For instance, when I want to travel to Accra and I want to take a plane, I can call someone at the airport and maybe book an appointment for me. I don’t have to necessarily go to the place before I can do that. With that it limits the number of hours to go from home to that place....So, I think it has enhanced my travel.”

Thanks to mobile phones, participants, especially the non-vehicle owning ones, could arrange for ridesharing cabs such as Uber. P9, made these observations:

“The mobile phone can promote transportation, because currently, Uber is in Ghana, Uber is in Kumasi and we use the phone to promote, you know, plan our trips and then book. And then even sometimes Airline, you can book, sometimes when I’m traveling to Accra or outside. There are times they call you if you are late. So, it facilitates. If I want to travel, and I am not driving, I can call STC2 and then book on the phone instead of going there. And I can also go on-line, know information about STC and book on-line, on my phone. So, it facilitates.”

Another way through which mobile phone use enhanced the travel experience of participants was through the GPS navigation system. The GPS enabled navigation system provided real-time traﬃc information for road users. Information such as route alternatives and road conditions reduces travel time, the inconvenience of searching, as well as the fear of getting lost. P9 explained how maps had enhanced the eﬃciency of his travel:

“For maps, most of these locations are there, they even give you alternate routes and then your drive time, so you are able to plan that if I leave Wa, I can be able to get to Tamale at this time, get the address of accommodation and call ahead, you can even know the prices on-line. Not necessarily reduced it, because it has decreased the time, because you don’t stop to ask for directions, don’t drive around looking for locations. And it enhanced it because you know that from point A to point B....You can go to three locations within a day, so you plan, so it has enhanced it I would say.”

The shorter routes or increased convenience may reduce “generalized transport cost,” which invariably induces demand.

2STC is a Ghanaian joint state and private owned company that operates inter city transport services (Source: Wikipedia, 2018).

158 From the analyses, it can be noted that although mobile phone use has numerous impacts that may substitute for travel in most cases, it still tends to complement travel behavior at the margin. In order to understand how each of these relationships occurs, the remaining section presents results on the underlying mechanisms of mobile phone use and travel behavior relationship identiﬁed by the participants.

7.5.4 Underlying Mechanisms of Mobile Phone Use Impact on Travel Behavior

This theme as it suggests was to understand from the participants’ viewpoint why and how their use of the mobile phone affected their travel behavior in a particular way. Based on the analysis of the qualitative data, four factors were identified as providing an explanation of how the mobile phone affected travel. The identified mechanisms include: the nature of participants’ business; lack of or poor infrastructure and service delivery in the study metropolis; insecurity and lack of trust in the system; and technological barriers arising from poor network coverage.

Nature of Business

It has been evident from the analysis that the use of and dependence on mobile phones has been increasing. Among the participants, dependability on the mobile phone in most daily activities was due to the nature of their business. Some businesses may demand the physical presence of a business owner while others do not. For the former, a business owner may be able to assess the situation, and if indeed their presence is needed, they could attend to the client. Mobile phone use therefore cuts down unnecessary costs that could be incurred.

Hagan, who is an artisan indicated that:

“When I am done with a work I’m doing for someone, I mostly communicate it through pictures to show the person the progress of the work or how complete the work is, who might be not close to the site. Sometimes, I might be at a site working, and someone might be in need of my services. I don’t have to make a trip to reach an agreement with

159 the person wherever he/she might be. Just a phone call away and we talk through it and reach an immediate agreement on the phone.” The use of mobile phones in this instance, substitutes for travel. However, in some cases, the nature of one’s business would demand that they meet their client in person for a proper assessment of the situation after starting off a conversation on the phone. Agya, who is a mechanic, indicated that: “I mainly use the phone for communication. I am a Moto Mechanic and people could call me through the phone to sometimes seek for assistance. If I have any idea as to how to solve it, I instruct them on the phone or if they are nearby and its day time, I rush to their aid in person....” In businesses where there is the need for constant communication with clients and sometimes suppliers, business owners depend on the mobile phone to keep their clients (including potential clients) satisfied while expanding their business. This was evident in the words of P20: “I also have my phone number on my shop’s banner, so if anyone comes to my shop and I am not around, they are able to reach me through the mobile phone. This affords me the opportunity to rearrange with the client to a convenient time to attend to them, without losing them completely.” Small businesses and artisans in particular also depended on the mobile money service to make and receive payments for their supplies and services, respectively. Small, who is an artisan, that, “Paying my rendered services currently doesn’t always have to be through traditional systems, ever since I subscribed to the mobile money service. My clients can always remunerate my services through my mobile phone wherever our respective locations are. Cashing the digital payment out is easy, mostly due to mobile money vendors at convenient locations.” Participants may also depend on mobile money transactions to decrease their cost of travel with their suppliers as noted by Hagan: “I can call some of my suppliers at Adum, ‘Hey Kalala, I need some cables,’ and give details and specifications about the type of goods I need because I have already established that rapport with them, they send the goods to me via a vehicle and I also transfer the total amount for the goods through the mobile.”

160 Poor Infrastructure and Service Delivery

Infrastructure and service provision are generally poor in many areas of the study metropolis and Ghana as a whole. Lack of portable roads and the underdevelopment of the addressing system are among the many infrastructural challenges undermining the use of important internet-enabled applications such as on-line shopping and GPS navigation. For this reason, these applications were not utilized to their fullest capabilities. As a result, these services are less common among the study participants. This sentiment was explained extensively by P18: “Maybe because we don’t have a better street naming and addressing system, so, it’s difficult to move. You are unable to decide, for example, if I want to make a journey, which of the roads will give me the best travel time. So, in terms of that you are unable to plan your trip, based on the route selection and you are unable to tell, for example, what is happening on the roads. You don’t even know. But if you had that opportunity, then it’s easy for you to travel. But if we had a very comprehensive system, then if I sit in the house and I want to go to Adum, I can decide. In Kumasi, you might not find that useful now, because virtually everyone is running through one spine. They are all going through Tech Junction. But when you have that level of connectivity, that you are able to decide, that okay, it is better to go this way. Even though, this way on the map might be even be longer, you probably will be able to make it because of the volume of traffic. Those are the decisions that we would want to be able to take on basis of an efficient application, but we don’t have that.” Because of this challenge, the public transport system in the study metropolis, is not able to leverage innovative transport technologies such as ITS, which have consequently rendered the system inefficient. P18, again remarked: “I am not a fan of public transport because of probably the reliability and other things, but I should be able to decide that, if I want to take a bus or I want a bus coming from Accra to, let’s say Ayebi or somewhere there, our trips are such that they are connected very well, but you should be able to decide when the next available bus will be. So those are the things that would make movement very efficient, in a way that people can avoid the situation where they have to spend long hours waiting at terminals. In the morning when you go and they are queued, people queue for two or three hours to catch a bus.” The unavailability of this infrastructure and services limits the use of the identified internet- enabled applications, and consequently reduce the participant’s motivation to travel.

161 Security and Trust

Many participants revealed that issues of security and trust were major barriers to exploring fully mobile phone applications such on-line shopping. Fraudulent activities are generally on the rise and without vigilance, one could a substantial amount of money. P8 expressed these concerns when he was asked about the use of on-line shopping:

“No, I do not buy things on-line. There are so many fraudulent activities going on around here. I am not sure I would transfer money to someone I have little or no means of identifying and trusting. I am afraid even to engage in this on-line shopping. Even on social media, a lot of people have pictures or details which are not theirs. So, I am mostly skeptical engaging them....”

P9 conﬁrmed this challenge:

“People also perpetuate fraud on the phone. They sell items but give a false picture. Somebody took a picture of a suit that I was selling and then put it as his DP (Display Picture). He was also selling a diﬀerent suit then gave the person a diﬀerent one.... The person used my image. There was one time that someone got my number. I didn’t know how. Because of this, people just forward your number because they are using smartphones or get them someway somehow. So, those issues of trust are there....”

Issues of insecurity and trust are compounded by the absence of a national identiﬁcation system, which makes it almost impossible to bring perpetrators to book. P8 remarked:

“...But if there were to be such good and connected identiﬁcation systems in place, it would have been easier and convenient to be involved as such.”

Because of these issues, some participants expressed their intention to purchase from on- line sites, but could not for the fear of being defrauded. This in turn induces travel demand as participants would prefer physically purchasing from shops, although ordinarily on-line shopping would reduce shopping trips. An illustrative example, P16 explained:

“There are times I really wish to do on-line shopping, but some sites are there to defraud people. So, because of these fraudsters, I feel insecure to do so. This is the challenge that inhibits me from doing so.”

To overcome this barrier, one participant (P18), found a way to purchase from on-line shops using the banking system, although it is not always eﬀective:

162 “Where I am buying from, if I’m buying online, I am not too sure whether that site is secure. So, the problem might not come from my bank, but the problem might come from the site I am buying from, whether there is enough security. And we all know our issue of ‘Sakawa’3, so it’s diﬃcult.”

Technological Barriers

The final barrier that participants identified as a factor that could explain why and how, for instance, they are not able to enjoy fully the benefits certain mobile phone applications, was related to technology, particularly problems of network connection. Although Ghana has seen significant growth in mobile phone users as well as mobile network companies over the past two decades, network coverage is largely restricted to the major cities, and even in these areas, connections are not reliable. Many participants indicated that poor network connection hindered their business operations and even their personal relationships. For example, P11 explained how poor network connection affects his business operation:

“Strong and consistent network connection is a challenge we face out here. A good and strong network is necessary to facilitate the smooth running of most of our transactions over the mobile phone. If this connection slacks even in a bit, it hinders progress of business operations, as well as constant personal connections. Even a good network connection is necessary for a smooth mobile money transaction.”

This was shared by P14, who perceived that poor network connection impacted on his personal relationship:

“There are times you need someone in the moment and it’s very crucial that very moment. You call and get the feedback the person’s phone is switched oﬀ. You ask the person later and they tell you it wasn’t so. This is one of the very setbacks with the use of the mobile phone that needs to be really addressed...to help with our day-to-day activities.”

Network coverage even worsened for participants whose relatives and friends lived in remote areas. P15 explained:

3Sakawa is a Ghanaian term for illegal practices that combine modern internet-based fraud with fetish rituals (Source: Wikipedia, 2018).

163 “There are times, you really want to convey a vital piece of information to someone at a different location in the country, you are on the phone with him and there’s a sudden disconnect in the network, regardless if you have enough call credits. If this is solved and strong and firm network connection everywhere in the country is ensured, this would really help, especially in my business. I am working with someone in Prestea. If he doesn’t find a higher ground to stand (on) to receive the call, it’s very difficult establishing contact with him.”

For this reason, some participants have subscribed to multiple networks in order to avoid unwanted hitches during a call. P16 explained why he has three networks on two phones:

“...primarily because of poor network coverage, especially in the remote towns. The poor telecommunication network coverage makes calls difficult to go through if you find yourself in any of these places. Since you may not have an idea which of these networks works better or poorly in a specific locality, it is best to have multiple networks. In such a situation I keep switching until I receive a good signal. Other than that, I can choose to use one network. Thus, I use these three networks because of these hindrances.”

7.6 Summary of Chapter

The focus of this chapter was to present an analysis of the qualitative interviews obtained from 24 participants, with the view to understanding the underlying mechanisms inﬂuencing mobile phone use impact on travel behavior. Although the focus was to identify perceptions of the qualitative participants to explain the results of the quantitative results, the approach to data collection and analyses took into consideration the research questions of the overall study, thereby prompting an inquiry into the following issues: mobile phone ownership and usage, impacts of mobile phone use on travel, and mechanisms that underlie mobile phone use

– travel relationship. Based on these issues, following the transcription and analyses of the data, four primary themes and 15 sub-themes emerged. The analyses of the ﬁrst theme on attributes of mobile phone use revealed that all participants had been using mobile phones for years with some dating back their adoption to the advent of the ﬁrst mobile communication company in Ghana in the early 2000. The second theme – dependence on mobile phones

– revealed two primary forms of mobile phone use: traditional uses and advanced uses.

164 Participants use mobile phones for more traditional purposes such as phone calls, texting and mobile money transfer, as opposed to the more advanced uses such as internet banking and online shopping. There was an increased dependence on mobile phone use due to its benefits in reducing costs, while saving time. Generally, mobile phone use had led to a reduction in the number of trips individuals may have to make in their day-to-day activities, therefore serving as a substitute, although in some few instances, complementary effects of mobile phone were observed. Four underlying mechanisms were found to have influenced the nature of the relationship between participants’ use of mobile phones and their travel behavior. The identified factors included the nature of one’s business, where in the midst of all features of mobile phones, business owners still needed to be present physically to sort, select, and acquire merchandise from suppliers; poor infrastructure and service delivery in the country which has undermined the full utilization of some mobile applications like on-line shopping and bank transactions, even as traditional phone uses are on the rise; insecurity and mistrust in the system due to increased fraudulence; and technological barriers due to poor network connection and reliability, which also do not afford participants to leverage fully the benefits of mobile phones, which invariably affect their travel behavior.

165 CHAPTER 8

CONCLUSIONS

8.1 Summary

The study began with an investigation into the relationship between mobile phone use and household travel in light of the broader concept of sustainable development. In the last few decades, governments have made strenuous efforts towards achieving global sustainable and inclusive development goals such as smart cities, green economy and sustainable development goals (SDGs). In Africa, projects such as Ghanas Hope City, Rwandas Vision city, Nigerias Eko Atlantic, and Ethiopias first smart parking system, are among the initia- tives these countries are implementing to achieve these development goals (Okyere et al., 2018). These efforts have prompted a growing interest, especially in the last three decades, in research that that seek to understand the relationship between telecommunication and transportation. Understanding the relationship between telecommunication and transportation have become more important considering the several parallels that exist between the two as forms of communication (Choo and Mokhtarian, 2007). As indicated in Chapter 1 and demonstrated further through the review of literature in Chapter 2, albeit the previous studies have highlighted the closely interrelated and complex nature between the two modes of communication, only a handful of them have attempted to investigate the relationship between ICT and travel within a leapfrogging context Based on the research gaps and current research directions, this study adopted a mixed methods approach to study the relationship between telecommunication and transportation within a country that fits into the leapfrogging stage of the innovation-diffusion cycle. In line with this objective, four central questions were derived to be pursued in this study, and are identified as follows:

i. How does the use of mobile phone aﬀect travel behavior in Kumasi, Ghana?

166 ii. Is the net impact of mobile phone use on travel behavior in Kumasi one of substitution,

complementarity, or neutrality? iii. Are the relationships between the use of mobile phone use and travel behavior diﬀerent

among location and socio-demographic factors? iv. How can the statistical results on the relationship between mobile phone use and travel

behavior be explained from the participants’ perspective?

In order to understand the relationship between telecommunication and transportation within a developing country context, the research questions were addressed at the level of the aggregate level with a larger sample of residents as well as at the level of the individual resident. While at the aggregate level, knowledge about the nature of the relationship between the two variables of interest can be discovered, it is not in itself sufficient to uncover the mechanisms that explain the aggregate result. Therefore, as described in Chapter 4, this study has been based on quantitative as well as qualitative research methods. To my knowledge, this is the first time both quantitative and qualitative methods have been used in a single study to examine the relationship between telecommunication and transportation. The first three research questions were pursued in the first phase of the study and were addressed using quantitative methods (survey). It was argued that the findings derived from this phase of the study would provide a portrait of the relationship between telecommunication and transportation within a developing country perspective and offer a basis to compare the findings with that of previous studies that have focused on developed countries.

The last research question was the focus of the second phase of the study and was addressed using qualitative methods (interviews) to generate and analyze the data, with the view to explaining the results from the ﬁrst phase.

167 Based on a descriptive analysis of the survey1 in Chapter 5, several measures of the

study constructs2 were identified. These measures which represent the different dimensions of the constructs were subjected to an exploratory factor analysis (EFA) using a principal component method to reduce the individual measures into fewer dimensions. Subsequently, a confirmatory factor analysis (CFA) was carried out to verify the factor structures identified from the EFA. As presented in Chapter 5, the CFA identified two dimensions (device and usage) of mobile phone use and a single factor (amount of travel) for travel behavior. It is important to state that, the usage factor differentiates the experience and intensity of use of mobile phone which was measured on the survey in ordinal scale. This addresses some of the limitations in previous studies like Wang and Law (2007) that use a binary variable to measure the usage of ICT. Such studies are unable to capture the variations in the impacts on travel associated with the different experiences of ICT usage. Following the specification of the EFA and CFA models, the causal relationship between the intensity of mobile phone use and travel behavior was examined empirically using structural equation modeling (SEM).

Based on the study questions, two structural equation models were developed: one without controls, and the other with control variables.

Although the interaction between telecommunication and travel in previous research remains unclear, the complementarity thesis appears to dominate (Mokhtarian, 2009). Sim- ilarly, in this study, although the eﬀect was moderate, the survey results show a positive relationship between the two, that is, participants who use the mobile phone more often

1A cross-sectional survey was conducted in the study metropolis between January and April 2017, to obtain data on travel behavior, mobile phone use, as well as locational and socio-demographic characteristics from a random sample of households (384) and individuals (661). Details of the sampling and data collection procedures are described in Chapter 4.

2Intensity of mobile phone use and travel behavior were conceptualized as latent constructs. Indicators associated with the intensity of mobile phone use were number of phones, type of phone, number of networks, frequency of phone calls, frequency of text messaging, frequency of email, among others. Indicators associated with travel behavior were travel frequency, average travel distance, average travel duration, among others.

168 and for many applications tend to travel more. This effect on travel was true for both dimensions of mobile phone use. The device dimension was associated with 0.34 effect on travel. Likewise, the usage factor had a 0.39 effect on travel. To capture a more complete view of the overall relationship between the variables of interest, as well as testing its ro- bustness, standard demographic and location controls were added to the initial model. The estimated model showed that both factors of the intensity of mobile phone use maintained their significant relationships with travel behavior, although the RMSEA as a test of model

fitness marginally increased to 0.093. With regards to the influences of the control variables, people who are older tended to travel less as compared to the younger people in the study metropolis, which was expected. Also, the income variable had a positive effect on travel behavior. This suggests that higher income individuals in the study metropolis generated more and longer trips. While age and income were found to be significant, the quantitative analysis discovered that residential location, level of education, employment status, vehicle availability, family type, household size, and gender did not have any significant effect on travel behavior. This suggested that these traits of respondents do not in a significant manner influence travel behavior in the study metropolis.

Overall, the quantitative results support the hypothesis that as the use of telecommunication intensifies, demand for travel increase. These findings support recent analyses on the relationship between telecommunication and transportation but conducted in different geographical regions. For example, Lila and Anjaneyulu (2016) in India, Denstadli et al.

(2013) in Norway, and Choo and Mokhtarian (2007) in the United States.

In the follow-up interviews it was discovered that the participants believed the eﬀect of mobile phone use on their travel was substitutionary, which contradicted the conclusion from the survey analysis. Although the purpose of the qualitative study originally was to explain an outcome of interest from the quantitative phase, we resolved not to select cases for the interviews based on their value to the survey results. Rather, we selected cases based on their

169 mobile phone use, demographic and locational characteristics, which were determined from the EFA. This was to allow for comparison between the results of the qualitative interviews and those of the quantitative surveys for the same study area. This comparative analysis is useful to address, for example, whether random sampling procedure according to the household survey is matched by the results of qualitative interviews drawn using purposive sampling approaches. As mentioned in Chapter 4 and demonstrated in detail in Chapter 7, the qualitative study was conducted as a sub-sample of the larger household survey. The impact of mobile phone on travel was assessed by asking respondents a series of questions.

First, they were asked to think about the last time they used the mobile phone to conduct an activity, and to speculate on what or how they would have conducted that same activity without the mobile phone. They were also asked to think about their current uses of mobile phone and to speculate on how they performed those activities before they adopted the mobile phone. Moreover, participants were asked to reﬂect on the activities they can do now that they could not do hitherto their adoption of mobile phone.

The evidence from the qualitative interviews pointed to the fact that, although few participants felt their travel had enhanced, generally, mobile phone use had led to a reduction in the amount of travel individuals may have to make in their day-to-day activities, therefore serving as a substitute. These results showed that, clearly, there was a conﬂict between what people did as in the quantitative survey and what they believed was happening. This apparent discrepancy could be explained by several reasons, some of which are fetched from the present study. Diﬀerences in unit of inquiry and the level of analysis could be one explanation for the observed discrepancy between the results of the two approaches. While survey was administered at the household level, the qualitative study ended up investigating one individual and did not extend the unit of inquiry to include all eligible household members.

On a per capital level, as was observed from the interviews, participants saved travel time, had their number of trips diminished, and even eliminated as a result of using mobile phone

170 services such as mobile money, voice communication and social media. Meanwhile, from the account of many of the participants, these saved trips were borne by or transferred onto other members of the household or the society who are now easily contactable (as a result of mobile phone) to perform activities on their behalf. For example, one participant (P18) in explaining how this phenomenon occurs in the study area remarked: “Yes, on my part, it doesnt allow me to move, and I am able to get whatever I have to get. And thats only not making the journey, but the journey is someway somehow made by somebody if I want to buy bread, under normal circumstance, I would drive and come to the commercial area and buy it. I am unable to do that, all I do is call any of the taxi drivers, campus drivers; can you buy bread and bring it to me?” Additional information could have been gathered if all the eligible household members were interviewed. All these other individuals within the household or the society are factored when measuring the aggregate or average effect. Under these circumstances, it appears the more intensive use of mobile phones makes more travel. Thus, although travel appears to be increasing at the aggregate level, it is the disaggregate impacts that are observed from the qualitative interviews. It could therefore be suggested that the effect of mobile phone on travel at the disaggregate level may be at best a transient phenomenon. This notion illustrates the point Mokhtarian (2002) made about the differences between aggregate and disaggregate studies that examine telecommunication transportation nexus: ”substitution can be happening at the margin, simultaneous with generation and complementarity happening overall”. Another confound for the divergence of the survey and interview results is the difference in coverage between the two methods. While the whole suite of mobile phone applications and services were accounted for in the survey and thus led participants to address all topics related to the effects of mobile phone on travel, within the context of the interview participants had the flexibility to speak to or even ignore a topic as they chose. Thus, the perceived effects of mobile phone on travel from the qualitative data were highly contextualized and were limited to the few mobile phone applications or services that participants were heavily dependent

171 on. Consequently, there were relatively less data to use when examining the participants perception on the impacts of mobile telephony on their travel behavior. In contrast, in the quantitative analysis, the multiple mobile phone applications3 which may separately trigger different effects on travel were averaged out to determine the aggregate effect. To maximize the likelihood that survey and interview results will align, further analysis of the quantitative data is needed to examine the differential effect of each of the mobile phone applications, rather than the average or total effect, on travel. Additionally, the differences in measuring travel activities across the two methods could explain the divergence in the results. While in the survey participants used a one-day travel diary to record their trips, within the survey participants were asked to provide a retrospective account of their activities without a log. Considering that some participants travel patterns and behaviors may involve a chain of trips and, it could be difficult, if not impossible, to measure accurately without a guided travel diary. The conflict between respondents subjective assessment of the impacts of mobile phone on their travel and their actual behavior raises several questions about strategies for conducting mixed methods. It must be said that, the study does not in any way invalidate the utility of mixing quantitative and qualitative methods. It does however add weight to arguments put forward by researchers like De Lisle (2011) about the complexities in implementing mixed methods for purposes of triangulation as qualitative data can provide new, additional or even conflicting perspectives. There is an inherent assumption that results from the two methods should be similar considering that their data come from the same sample. The study has however shown that interview data may carry different messages than survey data, thus in a mixed methods study especially where qualitative data is used to explain or support quantitative results, researchers must first analyze data from both instruments separately using methods appropriate for each.

3These mobile phone applications were combined into a single composite indicator called usage using conﬁrmatory factor analysis (see details in Chapter 6).

172 Although the divergence in the results from both methods do not provide the basis for clear conclusions, the preponderance of evidence from the study tends to support the complementary thesis rather than the substitution thesis. Within the context of the study area, the positive relationship between telecommunication and transportation is affected by several factors including the nature of ones business, where in the midst of all features of mobile phones, business owners still needed to be physically present to sort, select, and acquire merchandise from suppliers; poor infrastructure and service delivery in the country which has undermined the full utilization of some mobile applications like on-line shopping and bank transactions, even as traditional phone uses are on the rise; insecurity and mistrust in the system due to increased fraudulence; and technological barriers due to poor network connection and reliability, which also does not afford participants to leverage fully the benefits of the mobile phones. To the extent that findings from the qualitative data have provided a behavioral understanding of the results from the quantitative data, the study has provided a comprehensive view of the relationship between telecommunication and transportation, thus addressing the limitations of the vast majority of previous studies.

The analysis point to several policy recommendations. To leverage the complementarity relationship, it is important to first address the barriers facing users to fully explore the capabilities of the mobile phone. Ghana, for instance, can take advantage of the ongoing installation of fiber optic cable along its major corridor roads to provide the network infrastructure necessary to support ITS applications such as traffic control devices, traffic monitoring systems, integrated corridor management, advanced emergency braking systems, among others, based on accurate real-time information about traffic and vehicle conditions.

This would also mean the Government of Ghana invests in a comprehensive street and property addressing system which currently is sorely lacking. This is expected to result into higher efficiency in the use of resources and better management of traffic flow and accidents, which are critical to ensuring sustainable transportation.

173 8.2 Limitations and Future Research

The findings and conclusions drawn from the study may have been influenced by several limitations that are worth noting. Firstly, considering the dearth of reliable aggregate data over time in Ghana, the study relied on cross-sectional survey to investigate the quantitative research questions. Generally, this type of data structure weakens the validity of research results, particularly in establishing causal relationships, considering the fact that its capacity to control for unwanted influences does not extend beyond a specific attribute of a unit of analysis. To minimize the effect of this, the study used an ordinal scale to measure the intensity of mobile phone use to capture the variations in mobile phone use and its associated impacts on travel and employed a SEM to model the relationship between mobile phone use and travel. Using SEM allowed the study to deal with unwanted variation caused by confounding factors since the model incorporated both observed and unobserved measures, making it possible to draw valid conclusions based on the data (Byrne, 2013). Thus, the limitations of the data do not invalidate the findings of the quantitative results. That said, the current study may be extended using a longitudinal design in which the participants are surveyed over time to reveal in-depth how changes in mobile phone use influence changes in physical mobility over time. Secondly, the present study only measures the impact of mobile phone use on travel at the aggregate level and not the specific elements of that impact. As pointed out by Denstadli et al. (2013) and Choo and Mokhtarian (2007), this is also the case in previous studies exploring the relationship between ICT and travel where they fail to separate the different impacts. Although results from this study, in particular the quantitative phase, point to an overall positive relationship, the study does not give clear answers with respect to the degree to which mobile phone applications such as mobile money, social media, or email, increases or changes travel behavior. Although such analysis was possible in the present study, it was not feasible because of the size of the sample. Results from the qualitative

174 data suggest that different mobile phone uses provide unique communication opportunities and potentially different interfaces with travel, but these relationships need to be further investigated quantitatively. Using larger samples, future research may improve the analysis by differentiating the various types or applications of mobile phone use on travel. A much more detailed analysis of the quantitative data in this way will also create the basis to compare the survey results with the interview data results. Lastly, having modelled the relationship between mobile phone use and travel in a single geographical unit, it would be desirable in future to replicate the approach used in this study for other metropolitan areas in Ghana. By extending the study to cover other geographical units, the stability of the estimates may be improved and also permits segmentation by geographical area, to determine whether the relationships identified here differ by area within the country.

175 APPENDIX A

SURVEY INSTRUMENT FOR QUANTITATIVE STUDY

Survey of Mobile Phone Use and Household Travel, 2017

To examine the effects of mobile phone Usage on household’s travel behavior

Dennis Kwadwo Okyere

Data Collection Instrument

School of Economic, Political and

Policy Sciences

The University of Texas at Dallas

Dallas, TX. USA

800 W Campbell Rd, Richardson,

Texas. 75080

[email protected]

176 INTERVIEWER: PLEASE IDENTIFY YOURSELF BY NAME AND ORGANIZATION AND THEN READ THE FOLLOWING STATEMENT EXACTLY AS WRITTEN

CONSENT STATEMENT

The purpose of this study is to examine whether the use of mobile phones can reduce or otherwise households’ need for travel within the city of Kumasi.

You are invited to participate in this study by completing a brief survey in your home about your experience with the use of mobile phones as well as your travel patterns. Please keep in mind that this research has no known risks, and your participation is voluntary. Your participation will only be needed once, to answer a few survey questions, and should last twenty to thirty minutes. Approximately 384 households in Kumasi will participate in this study.

During the survey, we would like to ask you about your experiences in the use of mobile phones as well as your travel experience. In addition, certain demographic and economic information about you, including age, education, marital status, occupation and income will be asked. We may also ask other members of your household about similar issues.

If you choose not to respond to any of the questions in this questionnaire, you are free to do so. If you decide to answer some or all of the questions, we will use the information you give us only for the purposes of research and publication.

Your name and other personal information will be retained by the researcher and the University of Texas at Dallas.

For more information about the study please contact Dennis Kwadwo Okyere Tel. 0244 461434 Email: [email protected]

1. Do you agree to be interviewed?

No = 0 Interviewer’s Yes = 1 HS1 Initials

2. Do you agree to let young people in your household to be interviewed?

No = 0 Interviewer’s Yes = 1 HS2 Initials

3. Interview Date 4. Interview Start Time

AM=1 PM=2 DAY Month Year Hours Minutes

177 Part 1 – Household Demographic Information Community ID Household ID

1. Household Head

1.1 Record gender of respondent (Do not ask, code only) 1.6 What is your marital status?

Male = 1 Married = 1 Separated/Divorced = 4 Female = 2 ID1 Single = 2 Don’t Know = 999 Widowed = 3 Refused = 998 ID6

1.2 Will you tell me your year of birth please (Enter last two digits of year) 1.7 Which of these best describes your current employment status?

Year of Birth: Don’t Know = 999 Employed = 1 Retired = 4 Refused = 998 Refused = 998 I ID2 Unemployed = 2 Student = 5 Homemaker = 3 Don’t Know = 999 ID7

178 1.3 If Q1.2 above is unknown, could you tell me which age band you fall into? 1.8 If employed, what is your occupation?

16 – 17 = 1 55 or over = 5 Agricultural = 1 Salaried emp = 5 18 – 24 = 2 Don’t Know = 999 ID3 Artisan = 2 Pension = 6 25 – 39 = 3 Refused = 998 Small business = 3 Don’t know = 999 40 – 54 = 4 Organized trade = 4 Refused = 998 ID8

1.4 Ethnicity? 1.9 Type of family?

ID4 Single Person = 1

Nuclear = 2 ID9

Extended = 3 Don’t Know = 999 Refused = 998 ID4 1.10 What is the size of your household?

1.5 What is your highest level of education achieved? Household Size: ID10

Primary = 1 Tech/Voc. = 4 PhD = 7 JSS = 2 Bachelors = 5 Don’t know = 999 SHS = 3 Masters = 6 Refused = 998 ID5 Part 1 – Household Demographic Information Community ID Household ID

1.11 What is the ownership status of the house in which you live? 1.12 What type of house do you have? (Do not ask, code only)

Owner = 1 Caretakers = 4 Detached = 1 Apartment/Flat = 4 Rented = 2 Don’t Know = 999 ID11 Semi detached = 2 Free occupants = 3 Refused = 998 Compound = 3 ID12

2. Household Roster Household = Consists of all the people who live together in one residence and share a kitchen.

Interviewer Note: Use response codes in section 1 – Household head 2.1 2.2 2.3*** 2.4 2.5 2.6 2.7 2.8 2.9 2.10

179 Household Name Relationship Sex Birth Age Marital Education Employment Occupation ID to Head Year Status HM1 HM2 HM3 HM4 HM5 HM6 HM7 HM8 HM9 02 03 04 05 06 07 08 09 10 11 12 Response code for 2.3***

Wife/Husband = 2 Grandchild = 5 Father/Mother in-law = 8 Other relatives = 11 Son/ Daughter = 3 Father/Mother = 6 Nephew/Niece = 9 House help = 12 Daughter/Son in-law = 4 Brother/Sister = 7 Brother/Sister in-law = 10 Others = 13 Part 2 – Mobile Phone Use Data Community ID Household ID

3. Now I want to ask you about your household’s access to and experiences with the use of mobile phones

3.1 Does anybody in your household have a mobile phone?

No = 0

Yes = 1 MP1

3.2 If yes, we would like to find out about the members who use mobile phones, the number of phones they have, the networks they are subscribed to, as well as the amount of money they spend on mobile phones in a week

3.2a 3.2b 3.2c 3.2d 3.2e 3.2f 3.2f Household Name Does HH ID use How many active Type of Which cell phone How much a week Member mobile phone? No=0 phones does HH ID Mobile providers are HH do you spend on

180 ID Yes=1 use? Phone ID subscribed to? your mobile phone (in GHS) MP1 MP2 MP3 MP4 MP5 MP5 01 02 03 04 05 06 07 08 09 10 Response code for Mobile Phone Networks: Response code for Type of Mobile Phones: MTN= 1 Glo Ghana = 4 Don’t Know = 999 Regular = 1 Don’t Know = 999 Vodaphone = 2 Airtel = 5 Refuse = 998 Smart = 2 Refuse = 998 Tigo = 3 Expresso = 6 Part 2 – Mobile Phone Use Data Community ID Household ID

3.3 Please complete the the table below by the activities conducted and the frequency of using mobile phones.

Mobile Phone Activities 3.3a 3.3b 3.3c 3.3d 3.3e 3.3f 3.3g 3.3h 3.3i 3.3j 3.3k HH Phone Instant E-mail Online Mobile Payment of Getting Social Radio/TV Other (Specify) ID Calls Messages Purchase Banking Bills/Transfer Driving Media of Bills Directions MP7 MP8 MP9 MP10 MP11 MP12 MP13 MP14 MP15 MP16

F T F T F T F T F T F T F T F T F T F T 01 02 03

181 04 05 06 07 08 09 10 Response code for Frequency of Mobile Phone Use (F) Response code for Type of Purposes (T) Many times daily = 1 Many times a month = 5 Don’t Know = 999 Private Purposes = 1 Don’t Know = 999 Daily = 2 Monthly = 6 Refuse = 998 Business Purposes = 2 Refuse = 998 Many times a week = 3 Less Frequently = 7 Educational Purposes = 3 Weekly = 4 Never = 8 Part 3 – Household Travel Data Community ID Household ID

4.1 Are there any registered vehicles used by your household, and usually parked here overnight?

No = 0 Yes = 1 VH1

4.1 If yes, please complete the table below:

4.1a 4.1b 4.1c 4.1d 4.1e 4.1f 4.1g Vehicle Number What is the make What is the What type of fuel of What is the Who is the How much a and model of this body type? (Use fuel does it usually production registered week (in GHS) vehicle? the body type use? (Use the fuel year? owner? (Use do you spend on code below) type code below) ownership the vehicle, code below) including fuel? VH2 VH3 VH4 VH5 VH6 VH7 VH8 182 01

Vehicle Body Type Code: Fuel Type Code: Registered Owner Code: Car = 1 Taxi = 5 Gasoline = 1 Household/Person = 1 Don’t Know = 999 SUV = 3 Trotro = 6 Diesel = 2 Lease = 2 Van = 4 Motorcycle = 7 LPG = 3 Institution = 3 Truck = 4 Others = 8 Don’t Know = 999 Others = 4

Part 3 – Household Travel Data Community ID Household ID

4.2 Now we would like you to share with us your household’s actual travel experiences on an assigned Travel Day guided by your travel log. (Interviewer Note: This section must be completed only by participants who have completed the Travel Log on an assigned day)

A Travel Day began at 4am on an assigned travel date and ended at 4am on assigned travel date + 1 day

4.2a 4.2b 4.2c 4.2d Household Where was Household member at Where was Household member at 3AM on Did Household member make any trips on the Member ID 3AM on assigned travel date (when assigned travel date + 1 day (when the assigned travel date, even if it was a short trip? the travel day began)? travel day ended)? No = 0 Yes = 1 TD1 TD2 TD3 TD4 01

183 02 03 04 05 06 07 08 09 10 Response Code for TD2: Response Code for TD3: Home = 1 Don’t Know = 999 Home = 1 Don’t Know = 999 Work = 2 Refused = 998 Work = 2 Refused = 998 Another Place = 3 Another Place = 3 (Please specify) (Please specify)

Part 3 – Household Travel Data Community ID Household ID

4.3 We Would like to find out why a household member did not make any trip on an assigned date.

4.3a 4.3b 4.3c 4.3d Household Why didn’t household member make What is the main reason why household What is the main reason why household member Member ID any trips on the assigned date? member didn’t need to go anywhere all was unable to travel? day? TD5 TD6 TD7 TD8 01 02 03 04

184 05 06 07 08 09 10 Response code for TD6: Response code for TD7: Response code for TD8: No need to go anywhere = 1 Was not scheduled to work/personal day = 1 Had no available transportation = 1 Wanted to travel, but was unable to = 2 Worked from home for pay = 2 Sick or caring for another HH member = 2 (for personal reasons) (telecommuted or home-based) Waiting for a delivery/visitor = 3 Refused = 998 Students were on vacation = 3 Bad Weather = 4 Other = 4 Other = 5 Refused = 998 Refused = 998 4.4 Thank you for your answers so far. Now we will ask you to provide details about the trips in Section 4.2

4.4a 4.4b 4.4c 4.4d 4.4e 4.4f 4.4g 4.4h 4.4i 4.4j 4.4k 4.4l HH Member Trip No. Dep. Location Arrival Location Dep. Arrival Main Main trip Park at Cost of Which HH Total cost ID Time Time purpose of mode used Arrival Parking (in Member ID of trip (in Trip Location? GHS) travelled with GHS) No=0 you? Yes=1 TD9 TD10 TD11 TD12 TD13 TD14 TD15 TD16 TD17 TD18 TD19 TD20

185

Trip Roster (Continued)

186

Part 3 – Household Travel Data Community ID Household ID

4.5 We Would like to find out some more details about your assigned travel date, whether you made a trip or not.

4.5a 4.5b 4.5c 4.5d 4.5e 4.5f Household If you made a trip on the If you did not make a trip on On your assigned date, did If yes to TD24, please Did you purchase Member ID assigned date, was your the assigned date, was that you work from home for estimate the number anything online to be travel typical of a normal typical of a normal day? pay during any part of the of hours worked at delivered to your day? day? home? home? No=0 No=0 No=0 No=0 Yes=1 Yes=1 Yes=1 Yes=1 TD21 TD22 TD23 TD24 TD25 TD26 01 02 03 187 04 05 06 07 08 09 10

Part 4 – Mobile Phone Use and Travel Relationship Community ID Household ID

5. In this section, we are interested in knowing about the impacts of mobile phones on your travel experience.

5.1 How would you have undertaken the following transactions if you were unable to use the phone at the time.

Alternative Activity to Mobile Phone Usage 5.1a 5.1b 5.1c 5.1d 5.5e 5.5f 5.5g 5.5h 5.5i 5.5j 5.5k HH Phone Calls Instant E-mail Online Mobile Payment of Getting Social Media Radio/TV Other ID Messages Purchase Banking Bills/Transfer Driving (Specify) of Bills Directions MT1 MT2 MT3 MT4 MT5 MT6 MT7 MT8 MT9 MT10 MT11 01

188 02 03 04 05 06 07 08 09 10 Response code for Alternative Activity to Mobile Phone Usage: Would not have undertaken activity otherwise = 1 Would undertake a non-trip alternative = 2 Would have undertaken a trip = 3 Don’t Know = 999 Refused = 998 Part 4 – Mobile Phone Use and Travel Relationship Community ID Household ID

5.2 What main mode of transport would you have most likely used if you would have made a trip to undertake the transaction?

Mode for Trip Alternative to Mobile Phone Use 5.1a 5.1b 5.1c 5.1d 5.5e 5.5f 5.5g 5.5h 5.5i 5.5j 5.5k HH Phone Instant E-mail Online Mobile Payment of Getting Social Media Radio/TV Other ID Calls Messages Purchase Banking Bills/Transfer Driving (Specify) of Bills Directions MT11 MT12 MT13 MT14 MT15 MT16 MT17 MT18 MT19 MT20 MT21 M K M K M K M K M K M K M K M K M K M K 01 02

189 03 04 05 06 07 08 09 10

Response code for Alternative Activity to Mobile Phone Usage (M): Response code for Kind of Alternative Trip (K): Walking = 1 Taxi/Trotro = 5 Entirely new trip = 1 Bicycle = 2 Public Transit/Bus = 6 Part of an existing trip = 2 Motorcycle = 3 Don’t Know = 999 Depends on prevailing situations = 3 Private Vehicle = 4 Refused = 998 Don’t Know = 999 Refused = 998

Part 4 – Mobile Phone Use and Travel Relationship Community ID Household ID

5.3 Do you agree or disagree with each of the following statements about the overall impact of your use of mobile phone on your travel experiences?

5.3a 5.3b 5.3c 5.3d 5.3e 5.3e Household Increased number of trips Increased travel time while Reorganization of travel Awareness of travel Increased the comfort Member ID driving time and space alternatives of traveling

MT22 MT23 MT24 MT25 MT26 MT27 01 02 03 04

190 05 06 07 08 09 10

Response code for Kind of Alternative Trip (K): Strongly Agree = 1 Strongly Agree = 5 Agree = 2 Don’t Know = 998 Neutral = 3 Refused = 998 Disagree = 4

Part 5 – Miscellaneous Community ID Household ID

6. Who is the chief income earner in this household? (Interviewer instructions: Please use household member id)

Chief Income Earner: INC1

7. And finally, could you tell me your total monthly household income?

Less than 500 = 1 Over 3,000 = 5 500 – 1,000 = 2 Don’t Know = 999 1,001 – 2,000 = 3 Refused = 998 INC2 2,001 – 3,000 = 4

On behalf of the Investigators, I’d like to thank you for participating in this study.

We may be re-contacting some people sometime in the future to ask them a few additional questions. Could we have your permission to contact you again to learn a bit more about your travel experience and your use of mobile phones?

No = 0 Yes = 1 MISC

Finish Time: Interview Length:

AM=1 MINS PM=2 Hours Minutes

INTERVIEWER DECLARATION

I HAVE CONDUCTED THIS INTERVIEW. TO THE BEST OF MY KNOWLEDGE, IT IS FULL AND ACCURATE RECORDING, AND HAS BEEN COMPLETED IN ACCORDANCE WITH THE INTERVIEW INSTRUCTIONS.

INTERVIEWER: ------

SIGNATURE: ………………………………………………………………………………

DATE: …………………………………………………………………….

191 Household Travel Log Community ID Household ID Household Member ID RVLDAYFRQATTTV STUDY QUANTITATIVE FOR DIARY TRAVEL

We would like you to share with us your household’s actual travel experiences on an assigned travel day using the below travel log.

Instructions: 1. Each household member in the survey has been assigned a travel day. Your household’s Travel Day is (SPECIFY TRAVEL DAY). A Travel Day begins at 4am and end at 4am the following day. 2. Keep this packet with you on your travel day. Use the travel log to record every place you go throughout the day. Be sure to include short trips.

1. Where did you go? 2. How did you get there? 3. What did you do 4. How far did 5. How much did it there? you travel cost? Start Here What time did How did you get What time did you Use the activity list code Use the travel How much (in GHS) did you you arrive at there (use travel leave this place? below distance code spend on this travel?

this place? mode code) below B APPENDIX Place 1: Where were you at 4:00 am on your assigned travel day? Provide place name and address if applicable:

192 Place 2: Where did you go next? Provide place name and address if applicable:

Place 3: Where did you go next? Provide place name and address if applicable:

Place 4: Where did you go next? Provide place name and address if applicable:

Place 5: Where did you go next? Provide place name and address if applicable:

Place 6: Where did you go next? Provide place name and address if applicable:

Place 7: Where did you go next? Provide place name and address if applicable:

Household Travel Log Community ID Household ID Household Member ID

1. Where did you go? 2. How did you get there? 3. What did you do 4. How far did 5. How much did it there? you travel cost? What time did How did you get What time did you Use the activity list code Use the travel How much (in GHS) did you you arrive at there (use travel leave this place? below distance code spend on this travel? this place? mode code) below Place 8: Where did you go next? Provide place name and address if applicable:

Place 9: Where did you go next? Provide place name and address if applicable:

Place 10: Where did you go next? Provide place name and address if applicable:

193 Response code for Travel Mode: Response code for Trip Activity/Purpose: Response code for Distance: Walking = 1 Regular Home Activities = 1 Less than 1km = 1 Bicycle = 2 Work from Home = 2 1 – 3km = 2 Motorcycle = 3 Work = 3 3.1 – 5km = 3 Private = 4 Work Related Meeting/Trip = 4 5.1 – 10km = 4 Taxi/Trotro = 5 Drop Off/Pick Up = 5 10.1km – 15km = 5 Public Transit/Bus = 6 Transit = 6 Above 15km = 6 Don’t Know = 999 Attend School as a Student = 7 Don’t Know = 999 Refuse = 998 Buy Goods = 8 Refuse = 998 Buy Services = 9 Buy Meals = 10 General Errands = 11 Recreational Activities = 12 Visit Friends/Relative = 13 Healthcare Visit = 14 Religious Activities = 15 Community/Social Activities = 16 Others = 17

APPENDIX C

SAMPLE SIZE DETERMINATION FOR QUANTITATIVE STUDY

Sub-Metros Total Households (2017)* Communities Total Households (2017)* Sample

Nhyiaeso 50,516 Ahodwo 2,380 6 Santase 7,239 15 Dakodwom 802 5

Subin 70,185 Asafo 10,045 36

Kwadaso 87,883 Odeneho Kwadaso 2,372 10 Nsima 3,001 10 Asuoyebua 8,059 25

Asokwa 52,798 Asokwa 8,206 6 Kyirapatre 3,652 6 Atonsu-Agogo 24,456 15

Oforikrom 107,007 UST 1,523 5 Kentenkrono 1,470 10 Old Ayigya 14,945 40

Asawase 108,373 Parkoso 601 10 Asawase/Zongo 22,906 46

Manhyia 61,112 Buokrom Estate 4,902 10 Dichemso 10,410 22

Tafo 54,323 Pankrono Estate 16,511 14 Moshie Zongo 17,019 14

Suame 60,347 Kronom 7,663 16 Old Suame 7,357 15

Bantama 95,589 Suntreso Extension 1,884 14 Ohwim 1,588 12 Ampabame 2,961 23

TOTAL 748,126 181,952 384 Note: *Projected Estimates projected using the 2010 census ﬁgures

194 APPENDIX D

INTERVIEW GUIDE FOR QUALITATIVE STUDY

State: Date: / /2018 Interviewee Name: Interviewer Name: Place of Interview: Length of Interview: (minutes)

Question 1. When did you start using mobile phone? Answer: Probe: Number of active phones owned; number of networks subscribed to; type of mobile phone use. Answer: Probe: Reasons for using multiple phones or networks [if applicable] Answer:

Question 2. What activities do you use the mobile phone for? Answer: Probe: Answer: Probe: Answer: Probe:

Question 3. So, how were you performing the activities you have mentioned prior to your use of the mobile phone? In other words, how would you have performed these activities without the mobile phone?

195 Answer:

Probe:

Answer:

Probe:

Question 4. Are there some things you can now perform that you could not do hitherto, because of the use of mobile phone?

Answer:

Probe:

Answer:

Probe:

Answer: Probe:

Question 5. Are there things you used to do that you dont do now because of the mobile phone?

Answer:

Probe:

Answer:

Probe:

Question 6. From your responses thus far, what impact would you say your use of mobile phone has had on your travel behavior?

Answer:

Probe:

Answer:

Probe:

196 Question 7. What do you see as the main challenges inhibiting your use of mobile phone to its fullest potential? Answer: Probe: Answer: Probe: (End of the Interview) Thank you very much for your participation and valuable information.

197 APPENDIX E

SUMMARY OF INTERVIEW THEMES

Nodes

Name Files References Created On Attribute of mobile phone use 23 65 8/22/2018 8:30

Mobile phone ownership 19 19 8/22/2018 10:2 Network subscription 23 23 8/22/2018 10:2 Number of mobile phones used 23 23 8/22/2018 10:2

Dependency on mobile phone 23 71 8/22/2018 8:42

Advanced usage of mobile phone 16 27 8/22/2018 8:44 Traditional usage of mobile phone 20 44 8/22/2018 8:44

Impact of mobile phone use 22 84 8/22/2018 8:46

Complementary impact 6 10 8/22/2018 8:47 Neutral 0 0 8/22/2018 8:48 Pre Mobile phone use 20 34 8/25/2018 1:14 Substitionary impact 20 40 8/22/2018 8:47

Interview questions 0 0 8/25/2018 9:53 Underlying mechanisms 17 43 8/22/2018 8:54

Nature of business 1 1 8/22/2018 8:55 Poor infrastructure and service delivery 3 8 8/22/2018 8:54 Security and trust 5 9 8/22/2018 8:55 Technological barriers (network connectivity) 17 25 8/23/2018 7:02

198 Nodes

Created On Created By Modified On Modified By 8/22/2018 8:30 PM DKO 8/23/2018 7:03 PM DKO

8/22/2018 10:27 PM DKO 8/25/2018 12:28 PM DKO 8/22/2018 10:28 PM DKO 8/25/2018 12:28 PM DKO 8/22/2018 10:28 PM DKO 8/25/2018 12:28 PM DKO

8/22/2018 8:42 PM DKO 8/22/2018 8:43 PM DKO

8/22/2018 8:44 PM DKO 8/25/2018 2:19 PM DKO 8/22/2018 8:44 PM DKO 8/25/2018 1:40 PM DKO

8/22/2018 8:46 PM DKO 8/22/2018 8:47 PM DKO

8/22/2018 8:47 PM DKO 8/25/2018 3:57 PM DKO 8/22/2018 8:48 PM DKO 8/23/2018 7:00 PM DKO 8/25/2018 1:14 PM DKO 8/25/2018 3:27 PM DKO 8/22/2018 8:47 PM DKO 8/25/2018 3:28 PM DKO

8/25/2018 9:53 AM DKO 8/25/2018 9:53 AM DKO 8/22/2018 8:54 PM DKO 8/22/2018 8:54 PM DKO

8/22/2018 8:55 PM DKO 8/25/2018 11:33 AM DKO 8/22/2018 8:54 PM DKO 8/25/2018 3:54 PM DKO 8/22/2018 8:55 PM DKO 8/25/2018 3:59 PM DKO 8/23/2018 7:02 PM DKO 8/25/2018 3:59 PM DKO

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211 BIOGRAPHICAL SKETCH

Dennis Kwadwo Okyere was born in Kumasi, Ghana. In 2010 and 2012, He earned a BSc in Human Settlements Planning and an MPHIL in Urban Planning, respectively, from the Kwame Nkrumah University of Science and Technology, Ghana. Between 2010 and 2011, Dennis worked as a Teaching Assistant at the same university. In January 2013, he moved permanently to the United States, and entered The University of Texas at Dallas (UTD) in 2014 to pursue his PhD in Public Policy and Political Economy. During his graduate studies at UTD, he worked as a Research/Teaching Assistant, and received certiﬁcation in time series and dynamic modeling from the University of Essex, England, in August 2017.

212 CURRICULUM VITAE

DENNIS KWADWO OKYERE [email protected]; www.linkedin.com/in/dennisokyere/ 800 West Campbell Rd, Richardson TX 75080, USA.

EDUCATION

Ph.D. Public Policy and Political Economy (Expected) December 2018 Cumulative GPA: 3.9/4.00 Dissertation: Eﬀects of Mobile Phone Use on Household Travel Behavior in Kumasi, Ghana Advisor: Professor Brian J. L. Berry Place: University of Texas at Dallas (UTD), Richardson, Texas

Master of Public Policy August 2016 Place: University of Texas at Dallas (UTD), Richardson, Texas

MPhil. Planning June 2012 Thesis: Sustainability of the Urban Transport System of Kumasi, Ghana. Advisor: Professor Kwasi Kwafo Adarkwa Place: Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

BSc. Settlements Planning May 2010 Senior Honors Thesis: Examining Urban Sprawl in the Kumasi Metropolis Place: Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

OTHER CERTIFICATIONS

Post Graduate Certiﬁcate: 3K Dynamic Models for Social ScientistsAugust 2017 Essex Summer School in Social Science Data Analysis Department of Government University of Essex

GRANTS AND FELLOWSHIPS

Full Scholarship for Essex Summer School in Social Science Data Analysis August 2017 Oﬃce of the Dean of Graduate Studies Dissertation Research Award May 12, 2017 Ph.D. research Small Grant, Oﬃce of V.P for Research, UTD November, 2016 Graduate Assistantship Award, University of Texas at Dallas Sep 2015 – Present PROFESSIONAL EXPERIENCE

Research Assistant to Dr. Vito D’Orazio Sep 2015 – Aug 2017 University of Texas at Dallas, Richardson, Texas Project: Updating the Militarized Dispute Data through Crowdsourcing, MID Developed a corpus of conflict related news articles between 2011 and 2017 using LexisNexis. Set up and conducted several crowdsourcing experiments using Qualtrics, in an effort to improve the process by which quality conflict data is collected. Organized and facilitated a Qualtrics workshop for graduate students at the school of Economic, Political and Policy Sciences, UTD.

Research Director Jun 2012 – Apr 2014 OAK Development Advisory, Kumasi, Ghana Led and supervised the development of Land Use Plans for three (3) communities in the Afrigya-Kwabre District of Ghana between 2012 and 2014. Led a team to prepare a completion report for the “Rutal Water and Sanitation Component (Component 2.3) of the DANIDA sponsored Local Service Delivery and Governance Program” in ﬁve (5) regions of Ghana in 2012. Supervised the conduct of “a Soci-Economic ImPact of Mini Grid Electriﬁ- cation System in Seven Island Communities on the Volta Lake in Ghana”, for the Kumasi Institute of Technology Energy (KITE) in 2012.

Research Assistant to Professor Kwasi Kwafo Adarkwa 2011 - 2012 Kwame Nkrumah Univerity of Science and Technology, Kumasi, Ghana Book Project: “Future of the Tree: Towards the Growth and Development of Kumasi” Coordinated regular meetings and events for contributors of the book. Created all the maps in the book using AutoCAD. Performed a substantial portion of the pre-press documentation of the book, including reviewing and copy-editing.

Planning Oﬃcer (Intern) Jun 2009 – Aug 2009 Town and Country Planning Department, Kumasi, Ghana Conducted on site reviews of zoning and development projects. Captured land related data and its management using ArcGIS and ArcMap. Performed editing and analysis activities using ArcGIS and ArcMap. Assisted in the preparation of reports and maps on land use and urban development planning. Participated and took minutes of the statutory planning committee meetings. TEACHING EXPERIENCE

University of Texas at Dallas, Richardson, Texas

• Teaching Assistant

– Civil War and Conflict Resolution Spring 2018 Coordinated all course logistics for Dr. Patrick Larue. Graded homework, quizzes and exams. Proctored mid-semester and final exams. Held weekly office hours. – American National Government Fall 2017 Delivered two lectures for Dr. Thomas Gray. Graded homework, mid-semester exams and final exams. Proctored mid-semester and final exams. Held weekly office hours.

Kwame Nkrumah University of Science and Technology Kumasi, Ghana

• Teaching Assistant

– Urban Transportation Planning and Research Methods Spring 2012 Delivered four lectures on Urban Transportation Planning and two lectures on Research methods for Professor Kwasi Kwafo Adarkwa. Graded mid-semester exams and final exams. Proctored mid-semester and final exams. Held weekly office hours.

• Teaching Assistant

– Rural Settlement Planning Workshop I and II Fall 2010 – Spring 2011 Coordinated all course logistics and studio sessions for Dr. Michael Poku-Boansi. Organized extra tutorial sessions for second year undergraduate students. Coor- dinated ﬁeld trips to study sites.

PUBLICATIONS

1. Okyere, D. K., Adarkwa, K.K. & M. Poku-Boansi (2018). Connecting the dots: Telecommunication and Transportation nexus in Ghana. Telecommunication Policy, Vol 42, Issue 10: 836–844.

2. Cobbinah, P. B., Okyere, D. K., & Gaisie, E. (2016). Population Growth and Water Supply: The Future of Ghanaian Cities. In Population Growth and Rapid Urbanization in the Developing World (pp. 231–252). IGI Global.

3. Poku-Boansi, M., Okyere, D. K. & Adarkwa, K. K. (2012). Sustainability of the Urban Transport System of Kumasi, Ghana. Regional Development Studies (RDS), Vol 16:117–140. PRESENTATIONS

• “Eﬀects of Mobile Phone Use on Household Travel Behavior in Kumasi, Ghana”, poster presented at the APPAM Regional Student Conference, Claremont, California March, 2018

• “Physical Development Processes and Transportation Challenges in Kumasi, Ghana”, paper delivered at the 5th GRASAG Research Conference, KNUST Kumasi, Ghana. April, 2012

RESEARCH IN PROGRESS

• Investigating the nexus between Intensity of Mobile Phone Use and Travel Behavior in a Leapfrogging Setting: the Case of Kumasi Metropolis, Ghana (with Brian J. L. Berry)

• Capturing Land Value Increments from Road Transport in the Kumasi Metropolis, Ghana (with Patricia Bosu and Eric Gaisie)

• Flood Risk: Modelling Extreme Rainfall in Accra, Ghana (with Francis Bilson Darku, Eric Gaisie, and Eric Adabor)

• Poverty Suburbanization in the Dallas Fort Worth Metropolitan Area (with Helina Sarkodie-Minkah and Eric Gaisie)

RESEARCH IN PROGRESS

• Methodological Training: Regression, Time Series Analysis, Bayesian Methods, Survey Research, Qualitative Research, Mixed Methods Research.

• Technical Skills: R, STATA, Mplus, LISREL, SPSS, LaTeX, Qualtrics.

ACTVITIES AND PROFESSIONAL AFFILIATIONS

Texas State Representative, Graduate Student Association of Ghana – USA 2016 – Present Student Aﬃliate, Transportation Research Board (TRB) 2017 – Present Student Member, Association for Public Policy Analysis and Management 2016 – Present Member, African Students Union – UTD, Richardson, TX 2014 – Present Member, Resurrection Power and Intercessory Ministry, Grand Prairie, TX 2014 – Present REFERENCES

Dr. Brian J. L. Berry Lloyd Viel Berner Regental Professor School of Economic, Political and Policy Sciences University of Texas at Dallas 800 West Campbell Road, GR 31 Richardson, Texas 75080 Phone: 972-569-7173 Email: [email protected]

Dr. Bobby Alexander Associate Professor of Sociology School of Economic, Political and Policy Sciences The University of Texas at Dallas 800 West Campbell Road Richardson, Texas 75080 Phone: 972-883-6898 Email: [email protected]

Dr. Harold Clarke Ashbel Smith Professor School of Economic, Political and Policy Sciences University of Texas at Dallas 800 West Campbell Road, GR 31 Richardson, Texas 75080 Phone: 972-837-5828 Email: [email protected]