Empirical Studies of an Internet and Service Based Economy: The Case of the Kurdistan Region of Iraq 9819933889, 9789819933884

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Perspectives on Development in the Middle East and North Africa (MENA) Region

Nabaz T. Khayyat Goran M. Muhamad   Editors

Empirical Studies of an Internet and Service Based Economy The Case of the Kurdistan Region of Iraq

Perspectives on Development in the Middle East and North Africa (MENA) Region Series Editor Almas Heshmati, Jönköping University, Jönköping, Sweden

This Scopus indexed book series publishes monographs and edited volumes devoted to studies on the political, economic and social developments of the Middle East and North Africa (MENA). Volumes cover in-depth analyses of individual countries, regions, cases and comparative studies, and they include both a specific and a general focus on the latest advances of the various aspects of development. It provides a platform for researchers globally to carry out rigorous economic, social and political analyses, to promote, share, and discuss current quantitative and analytical work on issues, findings and perspectives in various areas of economics and development of the MENA region. Perspectives on Development in the Middle East and North Africa (MENA) Region allows for a deeper appreciation of the various past, present, and future issues around MENA’s development with high quality, peer reviewed contributions. The topics may include, but not limited to: economics and business, natural resources, governance, politics, security and international relations, gender, culture, religion and society, economics and social development, reconstruction, and Jewish, Islamic, Arab, Iranian, Israeli, Kurdish and Turkish studies. Volumes published in the series will be important reading offering an original approach along theoretical lines supported empirically for researchers and students, as well as consultants and policy makers, interested in the development of the MENA region.

Nabaz T. Khayyat · Goran M. Muhamad Editors

Empirical Studies of an Internet and Service Based Economy The Case of the Kurdistan Region of Iraq

Editors Nabaz T. Khayyat Technology Management, Economics and Policy program Seoul National University Seoul, Korea (Republic of)

Goran M. Muhamad Economics and Finance University of Kurdistan Hewler Erbil, Iraq

ISSN 2520-1239 ISSN 2520-1247 (electronic) Perspectives on Development in the Middle East and North Africa (MENA) Region ISBN 978-981-99-3388-4 ISBN 978-981-99-3389-1 (eBook) https://doi.org/10.1007/978-981-99-3389-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nabaz T. Khayyat and Goran M. Muhamad

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Part I Information and Communication Technology Readiness 2

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Assessing Innovation Capability and Technological Readiness of KRG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aveen F. Mustafa

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Examining Customer State Preferences of Mobile Services in the Kurdistan Region of Iraq: A Conjoint Analysis Approach . . . Laila M. Halee

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Part II

Information and Communication Technology Adoption

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Investigating the Factors Affecting Mobile Money Adoption in the Kurdistan Region of Iraq: The Case of Newroz Telecom FastPay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Herdy Wahid Mam

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Adoption of Social Media in Small and Medium Enterprises . . . . . . 165 Krman K. Abdalqadir

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Effects of Social Media Reviews on Customers’ Purchase Intention in Erbil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Tawar Qaderi

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Investigating the Determinant Factors of Telemedicine Adoption in the Kurdistan Region of Iraq . . . . . . . . . . . . . . . . . . . . . . . 233 Zainab A. Jaff

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Contents

Part III Lean Services and Internet Banking 8

Transforming Lean to Service: Application to the Kurdistan Banking Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Nagham Haidar

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Consumers’ Current State Preferences for Internet Banking Services: The Case of the Kurdistan Region of Iraq’s Private Banks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 Hamsa Awni Lazar

10 Summary, Conclusion, and Policy Recommendations . . . . . . . . . . . . . 407 Nabaz T. Khayyat

Chapter 1

Introduction Nabaz T. Khayyat and Goran M. Muhamad

1.1 General Overview This book is a collection of eight studies covering several areas pertinent to the current technological and banking services situation in the Kurdistan region of Iraq (KRI). The economy of the Region is characterized by depending mainly on oil revenue constituting around 90% of the government’s fiscal revenue. The public sector has an excessive role in the economy in which the public spending to GDP is more than 70%. As the economy is not diversified, there is a large gap between the demand and supply of local production of tradeable goods, leaving the KRI economy highly dependent on imports. Another structural challenge in the KRI economy is its dependence on cash and a weekly financial system. Although the region has witnessed extraordinary growth in its economy from 2008 to 2014, the region has slid into the depths of a recession from 2015 to 2019. To strengthen the economy of the region, it is imperative to study the potential drive engines of economic development and offer relevant policies and recommendations based on empirical evidence. In this regard, this book will address two important factors that contribute to the economic development of the region including information and communication technology and the banking sector and its offered services. The book has been divided into three areas of research: Information and Communication Technology (ICT) Readiness, ICT Adoption, and Lean Services and Internet N. T. Khayyat (B) Technology Management, Economics, and Policy Program, College of Engineering, Seoul National University, San 56-1, Sillim-Dong, Kwanak-Gu, 151-742 Seoul, Republic of Korea e-mail: [email protected] G. M. Muhamad University of Kurdistan Hewler, 30 Meter Avenue, Erbil, Kurdistan Region, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. T. Khayyat and G. M. Muhamad (eds.), Empirical Studies of an Internet and Service Based Economy, Perspectives on Development in the Middle East and North Africa (MENA) Region, https://doi.org/10.1007/978-981-99-3389-1_1

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Banking. This book bridges the link between researchers, academicians, and practitioners. It is a good up-to-date handbook for researchers, policymakers, and governmental and non-governmental organizations interested in studying contemporary Kurdish issues, the rehabilitation, reconstruction, and development of the Kurdistan Region. The next section will present a summary of the studies covered in this book.

1.2 Information and Communication Technology Readiness Technological Innovation is widely recognized as being one of the main drivers of economic growth and sustainable development. Kurdistan Region has been suffering from a weakness in technological innovation in the development of its economy. This is mainly due to the underdeveloped technological infrastructure and less educational focus on technology-related fields. The author of Chap. 2 assesses the innovation capability and technological readiness of the Kurdistan Regional Government (KRG) entities. The author attempts to develop an index measure to assess the technological readiness of the KRG entities and to propose different policies and actions to the KRG-based on the developed index. In doing so, the author collects primary and secondary data from 108 governmental entities to measure 27 indicators of technological capabilities using Principal Component Analysis (PCA) based on the non-parametric approach method. Kurdistan Region plugged into the world communication network and brought GSM technology in late 1999. Since then, mobile telecommunication became a dynamic competitive environment due to continued technological change, rapid economic growth, and competitive markets. Mobile service providers started to offer mobile users enormous services and applications via fast-speed internet to run daily business and manage daily life’s needs such as paying bills, receiving interesting information, and attractive mobile applications. Nevertheless, the political discontent, economic crises, and regulators in Kurdistan Region have affected the telecommunication sector and created a challenging environment for Telecom operators. Chapter 3 author attempts to estimate the customer preference toward the different telecom services by finding the best package that could cover the needs of mobile service users in the Kurdistan Regions of Iraq. Several 16 mobile service bundles are designed and offered to be evaluated by mobile consumers in the Kurdistan Region. The user preferences of each choice are tested by interaction with socio-demographic attributes. The analysis is conducted by conjoint approach and discrete choice logit models as rank ordered and mixed logit models.

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1.3 Information and Communication Technology Adoption Mobile phones are the most used devices in modern times. The transaction value of mobile money has increased globally from 68 billion in 2012 to one trillion US Dollars in 2021. It has found great success in transforming the financial sector and boosting the economy in developing countries. However, this figure is very little in Kurdistan Region and Iraq, while more than 87% of Iraqi citizens are mobile cellular subscribers, and only 4 percent of those are financially included. The author of Chap. 4 investigates the factors affecting mobile money adoption in the Kurdistan region of Iraq. The author sees mobile money as a financial tool that has the potential to transform the financial sector in Kurdistan by providing accessibility at a low cost and thereby boosting the economy of the Region. The author applies a mixed method approach to analyze a case study of FastPay to figure out how people feel about this new payment system and what are their concerns. However, mobile money has not been utilized to its full potential in Kurdistan. This is mainly because people’s awareness level is very low and lack trust in the financial sector. In recent years, the market has seen a large adoption of Information and Communication Technology by Small and Medium Enterprises (SMEs) due to the essential factors of cost-effectiveness, diffusion of digital services in the forms of software and programs, and the integration of smartphones and tablets in forms of information technology convergence. This has facilitated an increase in the use of Web technology by SMEs and provides them with a great opportunity to grow and develop, support them to develop new products, and gain new market share. Chapter 5 deals with the adoption of social media in small and medium enterprises in the Kurdistan region of Iraq. It specifically investigates how SMEs are using social media for marketing and other purposes, and how they can get benefit from the available social media platforms. The Kurdistan region of Iraq is one of the regions that is still developing. While SMEs have a significant role in the economic development of the Region, SMEs in the region are still in the first stage of introducing social media to their businesses. Therefore, social media is expected to increase customer intimacy and operational excellence in Kurdistan. In this chapter, the author is mainly focused on the adoption of social media by SMEs, in an attempt to identify the main reasons for using social media and how SMEs are satisfied. To do that, the author obtained data from a questionnaire consisting of 24 key questions, covering most of the questions that related to social media and user satisfaction, and then she applied a logistic regression model to analyze the survey data. Social media in the Kurdistan Region of Iraq is very popular. Youth groups and students are the main segments using social media, and Facebook is the most used platform accounting for 87% of people in Erbil. Since the Facebook platform approaches a massive number of users, its impact on influencing customers’ purchasing decisions has dramatically increased. This has made social media marketing in the business sector become one of the most common and important tools companies employ to attract customers, and also provide the means for customers to generate online content in the forms of reviews for the services being

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demanded. The author of Chap. 6 investigates the effects of consumers’ Facebook reviews on people’s purchasing intentions. She puts special emphasis on the restaurants in Erbil, the capital of the Kurdistan Region of Iraq. The author analyzes data collected from primary and secondary sources on 49 restaurants in Erbil and shows how online reviews on their Facebook pages affect the overall number of their clients. A mixed methodology including survey questionnaires and interviews was conducted to acquire the necessary information, illustrate key findings, and elaborate some business recommendations. Chapter 77 author discusses the possibilities of adopting telemedicine in the Kurdistan Region and proposes a fundamental framework for its implementation. Telemedicine, telehealth, and e-health are interchangeable terms used to describe the electronic transfer of medical services or the use of telecommunications in health care and health information. Telemedicine services include providing and supporting health care using ICT by transmitting patient data and facilitating productive interaction between patients and doctors to improve treatment results at less cost. However, one of the main long-lasting issues in the Kurdistan region is the poor quality of healthcare services, especially in rural areas. People in rural areas having difficulties accessing adequate health services due to poverty and lack of necessary infrastructure, as a result, they have to travel long distances, incurring high costs for medical consultations. Nonetheless, this problem can be solved partially by adopting telecommunications to provide medical services remotely. The author examines the factors that are affecting the use of telemedicine services among healthcare stakeholders and determines the level of using telemedicine services in KRI hospitals. A survey questionnaire is conducted among several physicians and nurses in different hospitals in the region. The author applies Principal Components Analysis (CPA) to compress a dataset onto a lower dimensional for the variables, and then a multinomial logit regression analysis to evaluate the individual effects of the latent variables on the level of telemedicine use.

1.4 Lean Services and Internet Banking The banking industry generally in Iraq and specifically in Kurdistan region has been significantly damaged because of the political and economic situation the region has experienced. To improve the competitiveness of banks in KRI, the author of Chap. 8 suggests the implementation of Lean thinking originating from the manufacturing sector, as one of the prominent methodologies of business improvement, effective investment, building customer value, and making the competition of quality even better. The chapter attempts to investigate whether Lean thinking can be transferred from the manufacturing context to the banking service in the Kurdistan Region. In doing so, 14 banks are surveyed using correlation analysis to determine which Lean principle(s) and critical success factors CSFs are already established as a practice, and which settings determine their successful application in Kurdistan. The challenge

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for the service sector and the banking sector, in particular, is to borrow improvement systems from the manufacturing sector and translate them so that they fit with the unique characteristics of service provision. However, these challenges are much more severe in the Kurdistan region as neither the manufacturing nor the banking sectors have the driving principle that shapes the necessary business strategies. Kurdistan region of Iraq was ranked as one of the most underdeveloped banking industries in the Middle East and North Africa (MENA) region. The banking sector in KRI is a part of the banking sector of Iraq as the region does not have economic sovereignty. The Kurdistan region adopts the e-banking service and implements it in financial institutions as a critical element of national development. However, KRI has encountered numerous issues driven by various reasons, most importantly, the political situation and security struggles, banks are characterized by their dependence on the economic situation of the region, low financial infrastructure associated with shortages of skills and technology, and weaknesses in the banking administrative capacity. As such, banks ought to develop and bring in new policies and strategies that are eager to promote the economy of the region. The author of Chap. 9 investigates the factors that affect the use of Internet banking in KRI. She attempts to provide a framework for banks in KRI to improve their service efficiency without significant investments. In doing so, a mixed method of quantitative and qualitative of a questionnaire from 67 banks and interviews from major private banks to perform Conjoint Analysis (CA) is used.

Part I

Information and Communication Technology Readiness

Chapter 2

Assessing Innovation Capability and Technological Readiness of KRG Aveen F. Mustafa

Technological Innovation is widely recognized as being one of the main drivers of economic growth and sustainable development. Technological development must have the capacity to compete with other government ventures for the limited funds that are available. This implies that the research builds up needs and allocates its restricted resources among competing programs to amplify the capacity of the sector to attain the country’s technological, economic, and social objectives. This study focuses on the assessment of Innovation Capability and Technological Readiness for the Entities of the Kurdistan Regional Government (KRG). The aim of this research is to develop an assessment tool (an index) to assess the innovation capability and technological readiness of the entities of the Kurdistan Regional Government so as to propose different policies and actions to KRG based on the developed index. Twenty-seven indicators were used for the estimation rate of innovation capability and technological readiness, while only 22 of them have been subjected to a principal component analysis, which was further categorized into 10 principal components. Regrettably, no study has been conducted to assess the technological capabilities of the KRG entities. Consequently, the findings and the outcomes will most likely assist as the first research in this area, and guide the policymakers in the region and the IT department of KRG in deriving appropriate policies and regulations. Moreover, this study will also help the scholars to examine and investigate this area of interest by equipping them with dependable scientific measurements for assessing and evaluating technological readiness and innovation capability in the KRG entities. Few assumptions were needed for this research to be conducted. It’s assumed that a comparative analysis across entities within KRG is meaningful. Despite the variation in the size of KRG entities, measuring technological capabilities across these entities still proves useful. As such, the study has some limitations, First, the accuracy of the A. F. Mustafa (B) Consultancy Corner | AVRO Company, Erbil, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. T. Khayyat and G. M. Muhamad (eds.), Empirical Studies of an Internet and Service Based Economy, Perspectives on Development in the Middle East and North Africa (MENA) Region, https://doi.org/10.1007/978-981-99-3389-1_2

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study is based on the information that is collected through the survey, questionnaire, and the analyzed data. Second, difficulties in measuring the role of technology on economic development, since the topic is too broad, and finally, the lack of incentives in providing full information from the authorities.

2.1 General Overview 2.1.1 Introduction Innovation is defined as a specific tool of entrepreneurs and the means for exploiting the change as an opportunity for a different business or service (Drucker 1985a, b). It is nothing but a process of coming up with new ideas leading to higher convenience for human existence. In other words, innovation is a gradual process of converting the opportunity into new ideas which will be further employed for the development of new practices, leading to technological advancement (Musandiwa and Ngwakwe 2020). The interrelationship between science and technology and innovation is significant and they both positively influence each other. The existing literature suggested that the rate of innovation and contribution from science and technology has not been satisfactory in several developing nations in Asia, Middle East, South America, and Africa. Though some types of methodologies were used for measuring the extent of innovation in a few studies, they lack clarity in identifying the extent of innovation in developing countries and their relative status with that of developed nations. The principle of innovation supports many aspects of science and technology in products that help state global challenges, newer and faster methods of production, and delivering high-quality services that could potentially raise productivity. The booming in this aspect will have higher and better job opportunities for individuals. Many scholars emphasized the positive relationship between the rate of innovation and economic growth (Galindo-Martín et al. 2019; Maradana et al. 2019; Tabrizian 2019). As the level of innovation expands in a region, the rate of economic well-being will be varied accordingly. Proposing new economic growth determinants and finding new feasible drivers of economic growth are at the center of focus for research related to sustainable economic growth. Some researchers argued about the role of policies that emphasize trade openness, enhance competition, and productivity improvement, will positively affect the sustainability of economic development. However, some other scholars have recommended policies targeting innovativeness at the local level, specifically those related to science and technology (Kendiukhov and Tvaronaviciene 2017). The capacity and the capability to generate and construct economic value are essential to the ambition and expansion of firms, industries, and countries. The matter that could raise is how to set out resources to create in a preeminent way to spread and keep innovation alive. Moreover, it will deal with the handling of the funding

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made through science exploration, technology, research, and development. It is also enhancing capabilities with the eventual aim of gaining rewards in terms of wealth incensement and increased living standards (Malefane 2020). Technology and the economy are factors that have a direct impact on each other. Kvochko (2013) identified five common technological effects on the economy: 1. 2. 3. 4. 5.

Direct Job Creation, Contribution to GDP Growth, The emergence of New Services and Industries, The workforce Transformation, and Business Innovation.

According to Weaver et al. (2017), for their economic development, developing countries seek technology transfer using networking, foreign direct investment (FDI), joint ventures, or technology licensing. Harnessing technology is seen as amplifying productivity and enabling the country to accomplish a better output in the same number of labor hours. Technology is also viewed and considered as a critical factor for boosting essential infrastructure such as education, healthcare, transportation, and telecommunications. In the wave toward development, the various impacts of technology might be neglected, with potentially harmful outcomes for the environment and human health. Technology is a factor used to reach the desired economic growth of the country, but to sustain development, it is required to choose the right decisions on suitable technology methods and economic development (Pradhan et al. 2018). Historically, as stated by Pascali (2017), technology has been a significant factor in economic development as the advent of new technology into manufacturing processes increased productivity, enabling a better output for each labor hour (Pascali 2017). This leads to a rise in the national output and the national income, where the manufacturers have access to international markets for their products, and shows the high mutual relation between economic development and technology. Khayyat claims that this process requires advancement in technological capabilities to employ and engross more urbane technologies. Thus, this advancement in technological capabilities needed to build a national innovation system, its activities should include but not be limited to hardware and software purchases, industrial design and engineering activities, employing up-to-date machinery, equipment, and other capital goods, in-house software development, and finally the ability to conduct reverse engineering (Khayyat 2019). The current study assesses the innovation capability and technological readiness of the Kurdistan Regional Government (KRG) Entities to help the government to identify the degree of innovativeness of its entities. This will lead to deriving appropriate policy initiatives for different entities so as to contribute to the region’s economic development. Assessing technological readiness as described by Webster and Gardner (2019) looks at the specific contexts of innovation engagement and how innovation is adapted in which, it will be then adopted.

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The findings of the study are expected to identify the rate of technological readiness in each KRG entity.

2.1.2 Study Objectives This study aims at developing a tool to assess the innovation capability and technological readiness of the KRG’s different entities. The objectives of the research are to develop an index measure to assess the technological readiness of the KRG’s entities and to propose different policies and actions for KRG based on the developed index.

2.1.3 Significance of the Study Regrettably, no study has been conducted to assess the technological capabilities of the KRG entities. Consequently, the findings and the outcomes of this study will most likely assist as the first research in this area, and guide the policymakers in the region and the IT department of KRG in deriving appropriate policies and regulations. Moreover, this study will also help Kurdish scholars to examine and investigate this area of interest by equipping them with dependable scientific measurement for assessing and evaluating technological readiness and innovation capability in the KRG. Furthermore, for the policymakers in the KRG, the outcome of this study will feed them with guidelines to monitor and assess the technological readiness of the entities in the region. The finding will also help the policymakers to divide results into different groups and provide them with the services required.

2.1.4 Assumptions and Limitations of the Study This research has been conducted based on the following assumptions. First, it’s assumed that a comparative analysis across entities within KRG is meaningful. It is discussed that despite the variation in the size of KRG entities, measuring technological capabilities across these entities still proofs useful. Due to some factors mentioned below, the study has some limitations including: • The accuracy of the study is based on the information that is collected through the survey, questionnaire, and analyzed data. • Difficulties in measuring the role of technology on economic development, since the topic is too broad. • The lack of incentives in providing full information by authorities.

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2.1.5 Study Structure The study is structured into five sections: Section 1, ‘Introduction’, introduces the reader to the background of innovation and its relation to science and technology and their capabilities. Moreover, the background of the study is briefly elaborated on in this section, followed by research aims and objectives, the significance of the study, and assumptions and limitations. Section 2 is the ‘Literature Review’ which examines the current literature for information relevant to this study, starting with an introduction, followed by theories and technology adoption models, theory limitations, and previous indices about the technology and innovation. Section 3 is the ‘Methodology’, which includes the research design of the study, followed by the introduction section, research purpose, and type of research which consists of descriptive, exploratory, and explanatory research types. The learned experience from the literature review enhanced the design of a questionnaire, an overview of the participating entities, and a description of all the materials and equipment that are included in the current study. Finally, the section concludes with the data collection method section. Section 4 is the ‘Data Analysis and Results’. This is where the data is analyzed and discussed in thirteen subsections following the descriptive statistics section. In addition, the section discussed the construction of the principal component analysis and correlation relation, and concluded with the entity classification section. Section 5 of the study is the final section, ‘Summary, Conclusion, and Policy Recommendation Section’. It includes 5 subsections, starting with the introduction section, a summary of the study and its purpose will be stated. At the end of the section, the researcher’s conclusion and recommendations are made.

2.2 Literature Review To support the factors that are relevant for assessing the innovation capability and technological readiness of KRG entities, the following theories about the aim of this research were reviewed: • • • •

Theory of Acceptance Model (TAM) Technology Readiness (TR) Technology Readiness Acceptance Model (TRAM) Theory of Innovation Diffusion (TID).

In reference to these theories, researchers assess innovation capability and technological readiness, study the factors such as usefulness, time-saving, quick, ease of access, technology acceptance, technology accessibility, innovation capability, content quality and system, flexibility, and so on.

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2.2.1 Theory of Acceptance Model (TAM) The Theory of Acceptance Model (TAM) is a particular and parsimonious framework for forecasting and describing people’s adoption of information technology in work settings (Davis et al. 1989). TAM suggests users, who accept the use of new system are directed by the user’s intention for the system use, which is impacted by the user’s beliefs in regards to the systems perceived usefulness. Moreover, it is described as the degree to which a person believes that the use of a specific system will amplify and strengthen his/her performance and perceive user-friendliness that refers to the degree and scale where a person believes that the use of a specific system will be effort-free. The perceived ease and the user-friendliness is hypothesized to be a finder of the perceived usefulness, although both of the aforementioned beliefs are impacted by external variables, such as training, support, perceived accessibility, social impact process, and cognitive instrumental process (Muchran and Ahmar 2019; Venkatesh et al. 2003), TAM has been replicated and extended to describe diverse behaviors in technology adoption (Lin et al. 2007). Nevertheless, studies exploring how and why these two cognitive beliefs grow and evolve that are considered relatively inadequate (Athapaththu and Kulathunga 2018). TAM can be considered one of the most widely used research models that were developed by Davis (1989a, b). As elaborated more on, by Davis (1989a, b) and Malhotra and Galletta (1999), the model also recommends that when users are presented with a new technology, a list of factors impact their decision about how and when they will use it, such as: • External Variables—the external variables could cover perceived usefulness, perceived ease of use, and demographic factors. • Perceived usefulness (PU)—This concept has been defined by Davis et al. (1989) and Venkatesh and Davis (2000) as “the degree to which a person believes that using a particular system would enhance his or her job performance”. • Perceived ease of use (PEOU) was defined by Davis et al. (1989) and Venkatesh et al. (2003) as “the degree to which a person believes that using a particular system would be free from effort”. The previously described perceptions merged the effects on the adoption of new technologies (Adams et al. 1992). Subjective norms and perceived behavioral control were found to have an essential impact on the usage of Information Technology user behavior. As stated by Turban et al. (2018), perceived usefulness could influence actual use esteem attitude, if the technology used offers direct interest, satisfaction, and well-being to the user. In addition, like all the other powerful and dominant models, TAM has been widely criticized. “It has been stated and declared that the factor has created an illusion of knowledge accumulation” (Benbasat and Barki 2007), “there is no transparent and comprehensible pattern with respect to the external variable choice that is considered”

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(Hussein 2017; Legris et al. 2003), and “that the external variable to a greater extent generates a state of theoretical disorder, disorganized, and confusion in which it is not a transparent version of the numerous repetitions of the Theory of Acceptance Model (TAM) is the most commonly accepted theory among them” (Benbasat and Barki 2007).

2.2.2 Technology Readiness (TR) Technology readiness (TR) is referred to as people’s tendency to engage and use modern technology methods for fulfilling and achieving aims in home life and at work (Liljander et al. 2006; Ramírez-Angulo and Duque-Oliva). In spite of the fact that many literatures have studied the user reactions to the new technologies or technology e-related services, the scholarly research on people’s readiness for using technology-based systems is scarce. As claimed by Rojas-Mendez et al. (2017), technological readiness could be classified into four clear and well-defined components: (1—optimism, 2—innovativeness, 3—discomfort, and lastly, 4—insecurity). Their descriptions are stated below: 1. Optimism: a positive perspective of technology belief in increased control, flexibility, and efficiency in life owing to technology. 2. Innovativeness: a propensity for being the first to use modern technology methods (a propensity for being though leaders and technology pioneers). 3. Insecurity: mistrust of technology and doubtfulness regarding its ability for working properly. 4. Discomfort: recognition of lack of control across technology and a feeling of being submerged by it. Optimism and innovativeness are the positive initiatives of the Technology Readiness Index (TRI); they are the factors that persuade users for using technological products/services, and to carry a positive point of view in the direction of technology. The strong relationship between the positive initiatives in the Technological Readiness designating a person’s openness with regard to technology. On the other hand, discomfort and insecurity are the negative initiatives, such as inhibitors; which make the users reluctant or make them have less intention toward new technology adaptation (Acheampong et al. 2017). Even though Technological Readiness has been emphasized as a highly potential theory (Tsikriktsis 2004). Main criticism in regard to Technological Readiness (TR) is its instability. Shin and Lee (2014) have been able to prove and give evidence that partially or fully supports its different components, others such as Gelderman et al. (2011) have found a week or even nonessential support for the theory. It has been emphasized that scholars need to discover Technological Readiness (TR) (Parasuraman and Colby 2001). For instance: if the Technological Readiness (TR) concepts need to be combined and merged with each other (Gelderman et al. 2011), if the Technology Readiness (TR) concepts need to be merged and combined

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with other technology acceptance theories (Hsu et al. 2013), or if the Technology Readiness (TR) concepts need to be separated from the overall Technology Readiness (TR) construct (Walker et al. 2002).

2.2.3 Previous Indices About Technology and Innovation Technology is the group of approaches, skills, methods, and processes utilized in goods or services productions or in objective achievements, like scientific investigation. The technology could be the knowledge of techniques and processes, or it could also be comprised of machines to permit functioning without detailed knowledge of their operations (Kim and Nelson 2000). As stated and claimed by Fagerberg and Verspagen (2002), nowadays, technology has become one of life’s most needed necessities. It makes life much easier by providing ease of access, flexibility, time-consuming, and many other facilities and benefits that it provides users. Technology started by innovating and exploring, and it is still being explored. Due to innovation and investigation, many authors have different ideas and views about innovation and technology. Different scholars have defined the innovation from different perspectives, below are the most used and accepted definitions: Drucker (1985a, b) defined innovation as the particular device of industrialists and the means for taking advantage of the modification as a chance for a different business or facilities. Another definition of innovation is provided by Damanpour (1991) as any operation or exercise that is recent and brand new to organizations, including equipment, products, services, processes, policies, and projects. The definition of innovation is changed over time, for example, innovation is described by Tidd (2001); in different confrontations, innovation is a reasonable procedure of converting the chance into fresh ideas that will be involved in the growth of new practices resulting in technological betterment. Moreover, Afuah (2003) argued that revolution is the application of new technical and administrative knowledge and skills to provide a brand-new product or service to customers and consumers. It is a procedure of actions that rises up with new thoughts that will be promoted to higher ease of use that satisfies customers. The word ‘innovation’ originates from the Latin word ‘innovare’, signifying ‘to make something new’ Amidon (2003). The most straightforward meaning of innovation is that of “the effective misuse of new thoughts DIUS (2008) or “a thought, practice, or question that is seen as new by an individual” Rogers (1995). There is likewise a meaning of fruitful innovation: “the creation and usage of new procedures, items, administrations and techniques for the conveyance which result in noteworthy enhancements in results productivity, adequacy or quality” Mulgan (2003). This definition consolidates social and authoritative advancements. Most meanings of advancement can be identified with at least one of six measurements: (1) freshness; (2) development protest; (3) arranges in the development procedure; (4) fundamental impact; (5) level of examination; and (6) results.

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Previous Indices Researchers and other scholars have developed different indexes to assess technological capabilities and readiness. Below is a survey of the most widely used indexes: The events are stated based on the chronological order of the periods. Another study by Archibugi and Coco (2004) published under the name “A New Indicator of Technological Capabilities for Developed and Developing Countries”, includes the creation of technology, technological infrastructures, and development of human skills. For the study’s data collection, data was collected among 162 countries, and 162 countries were examined. In addition, Archibugi and Coco (2004) used different attempts that are considered to measure technological capabilities. Which includes, Technology Achivement Index (UNDP 2001a, b) and the Industrial Development Scoreboard (UNIDO 2002), Technology Index of the World Economic Forum’s Global Competitiveness Report (WEF. World Economic Forum 2002), and the last one that was used in the methodology is Critical Analysis by (Lall 2001). Equally important, in Archibugi and Coco (2004)’s study, the examination led to the inclusion of some indicators and to the exclusion of others. In the case of technology creation and innovation, resources assigned to the research and development possibly represent and entitle a better indicator than the composition of patents and scientific papers, not only—but also, for the majority of the developing countries, data is unavailable or unreliable. Furthermore, the study has shown that the data was reported on three technological infrastructures, for instance: internet, telephony, and electricity. However, information about capital goods stock such as machinery and equipment has not been provided. As for the study’s data analysis, it showed that data was unavailable or unreliable for some of the countries that were examined. Moreover, it was believed that consuming electricity might be a healthy representative, which allows for staying completely free and independent from any indicator that is expressed in monetary value (Archibugi and Coco 2004). Conclusively, in accordance with human resources, perfect and absolute indicators could be job qualifications, which permits capturing learning by doing from the work process, yet again, these data are available for a list of more restricted countries where they are barely contrastable. According to Archibugi and Coco (2005)’s published article under the name “Measuring technological Capabilities at the Country level: A survey and a menu for choice”, it is aim was to compare their methodologies, similarities and differences, and results. For the study’s data collection, 75 countries were chosen and the chosen data got distributed into 2 groups in accordance with the number of patents that were produced. Among the 75 selected countries, 21 were core countries while 54 were noncore countries. In addition, Archibugi and Coco (2005) used different attempts that are considered to measure technological capabilities. Which includes; The World Economic

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Forum (WEF. World Economic Forum, 2002) Technology Index, The United Nations Development Program (UNDP 2001a, b) Technology Achievement Index, Archibugi (Archibugi and Coco 2005), The United Nations Industrial Development Organization (UNIDO 2002) Industrial Development Scoreboard, and lastly, The science and Technology Capacity Index developed by RAND corporation and associated partners. Finally, the results provided a broadly comparable ranking of countries, although a few essential dissimilarities do emerge. To conclude, a new set of data will be used to gain a better understanding of the complicated relationships between technology, development, and welfare. In a recent study, Khayyat and Lee (2015) in their article titled “A measure of technological capabilities for developing countries” developed a new index to assess the technological capabilities of nations. The assessment criteria were based on measuring the rate of innovativeness as a tool to measure technological capabilities. The authors in their study have focused on a group of 61 developing countries. They incorporated different indicators for their assessment based on non-parametric approach, specifically, they applied the principal component analysis PCA to rank the countries under the study in terms of technological capability. In doing so, the states were categorized into three groups according to their revolution level. Furthermore, Khayyat and Lee (2015) found that factors such as Patent, FDI, learning, and resident-specified training have favorable effects on revolution degrees. The maximum degree of revolution was observed in China, monitored by Estonia and Malaysia, while the lowest revolution degree was described in Iran, Bangladesh, Tadzhikistan, and Cambodia.

2.3 Methodology In this section, the research methodology is clarified and discussed. The first section includes the research type, research purpose, the target population, and the sample for the study is explained. In addition, the data collected is presented. Afterward, the questionnaire and the data collection technique are shown and stated. This section also covers the descriptive statistics of the data under study.

2.3.1 Theoretical Framework The difference in innovation intensity occurs across different countries (Wadho and Chaudhry 2018). The power of a nation’s common innovation infrastructure is influenced by national innovative capacity and the environment for innovation in a nation’s industrial firms. Likewise, the external environment in which a firm runs its business will also impact the capability of innovation (Ferreira et al. 2019; Lin and Ho 2009). On the other hand, governmental support is another essential environmental feature for technological innovation, via functional regulation, it can have both positive and negative key factors on the innovation adoption.

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Haini (2019) emphasized the positive effect the internet access and its penetration on the innovation rate. It was discovered that the internet was a fresh data and statement knowledge that has an opportunity to progress the associations and networks among several creativities, and accordingly, it will permit the firms to communicate and get together with partners from distant parts of the world much easier. As a result, innovation networks will get stronger. Accordingly, the KRG entities have been used and compared along with their innovation capabilities and technological readiness in this report. Hence, related to the hypothetical deliberations concerning technological readiness and innovation capability, an attempt has been done to create additional combined methods for developing an index under the name of Kurdistan Region Government’s Index (KRGi). Moreover, the above and previously used theoretical consideration in the study’s literature shows that many internal and external factors influence the innovation capability rate, technological readiness, and technology use. Though the current methods, with the variables and help from the index, can address some of the technological readiness and the innovation capability to some extent. Based on the results and findings from the collected data that was analyzed, an index was developed under the name ‘KRGi’, which takes all the prioritized factors into consideration that has an effect on measuring the innovation capability and the technological readiness of the KRG entities. According to the newly developed index, the KRGi, a new classification for grouping and classifying the entities is made.

2.3.2 Data Collection Method This study is based on primary and secondary data collection. In order to assess the technological capability of the Kurdistan Region Government entities, a number of 108 governmental entities have been selected to collect data from. These entities are from the general directorate level to the directorate level in the Erbil governorate of the Kurdistan region. The data is collected from the entities that are located in Erbil governorate, as it is the capital city and most of the general directorates are based in the capital. The primary data that has been collected from the entities, while for the secondary data collected, the group of data has been taken from Regional Government (KRG)—Information Technology (IT) Department, the indicators were: IT Organizations, IT Security, IT Operations, IT Infrastructure, and Business Application. Although, the collected data from the IT department was not for all the entities. The index consists of 27 questions that include the factors of measuring technological capabilities. Later on, in the methodology section, they will be elaborated on more and they will be classified into groups based on their correlations. The priority setting analysis will provide data on the entities’ current technological capacity, and accordingly, the government to address the needs of these entities based on the outcomes. Table 2.2 in Appendix 1, shows the list of entities and their codes that have taken place in the data collection process.

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2.3.3 The Non-parametric Approach: The Principle Component Analysis Measuring and assessing the innovation capabilities and technological readiness of KRG entities in this study was based on selecting and choosing 27 indicators that have been subjected to a principal component analysis methodology. The 27 mentioned indicators have been stated at the end. The section is clarifying Principal Component Analysis (PCA) as this study is analyzed based on a non-parametric approach method, which is a strategy that is used to measure, demonstrate, and break down ordinal or nominal data with little example sizes. Dissimilar to parametric models, non-parametric models do not require the modeler to make any presumptions about the appropriation of the population, as it is a free strategy. The main idea of (PCA) is to lower the dimensionality of a data set including a large number of interconnected variables while retaining the present variation in the data set. This is accomplished by modifying a new set of variables, the principal components (PCs), which are unrelated, and which are ordered so the first few retain most of the present variation in all of the original variables (Jolliffe 1986). Furthermore, a PCA method is used through conducting the data that has been collected in the entities of the Kurdistan Regional Government. This will be used as a major philosophy to obtain quantitative data that could be statistically analyzed. The research tool is a questionnaire based. In this study, there are 27 indicators for the estimation rate of innovation capability and technological readiness, while only 22 of them have been subjected to the principle component analysis, as 5 of them are considered demographic factors, which were further categorized into 10 principle components. It is the easiest of the real eigenvector based on multivariate analysis and it discovers the data’s interior arrangement in a way that best describes the modification in the data. From the 10 constructed components, 7 components showed eigenvectors greater than 1, and the findings have also displayed that 7 components were meaningful, see (Table 2.3) in Appendix 1. To summarize the table (Table 2.5), it is giving cumulative weight for each principle. For instance, principle 1 weights 0.2033, principle 2 has a weight of 0.3574, principle 3 has a weight of 0.4446, principle 4 weights 0.5216, principle 5 has a weight of 0.5840, principle 6 has a weight of 0.6354, and principle 7 has a weight of 0.6858. The eigenvalues of principles 1–7 are greater than 1. While the weight of principle 8 is 0.7252, principle 9 is 0.7606, and finally, principle 10 has a cumulative weight of 0.7952. To add up, it can be said as principles 1–7 has an eigenvector greater than 1, while principles 8–10 have an eigenvalue less than 1. The rationale for this criterion is straightforward. Any observed variable contributes one unit of variance to the total variance in the data set. Any component that shows an eigenvalue greater than 1, such as principles 1–7, is considered a great amount of difference that has been added by one adjustable. Such a component, therefore, accounts for an expressive quantity of variance and is

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worthy of being engaged. While others have tiny representations of differences in data, and as a result, they were discarded. KRGi (Kurdistan Region Government Index) is an index for calculating the rate of innovation capability and assessing technological readiness in the KRG entities in regard to the routine of the essential indicators that have been used, such as employee average age, number of computers, type of connectivity, internet accessibility, and etc. The mentioned factors will be useful and the governmental policymakers will benefit from them for developing indices or progressing the current index. The KRGi takes into thought several weighing factors interrelated to innovation capability and technological readiness. However, some methodologies were developed for assessing and evaluating innovation rate, innovation capability, and technological readiness, they are disjointed in approach and as a result, a combination of all significant weighing factors is the current time need. The proposed KRGi index is a combination of 10 principles which were derived from 27 sub-indices of innovation and technology see (Table 2.6) in Appendix 1. It is a border coverage of many number variables which estimate the rate of innovation capability and technological readiness.

2.4 Data Analysis and Results In this section, the data will be analyzed and the results will be found. The following sections of this section will include the data collection method, which describes its method of collecting the data, and the index construction, which will elaborate on constructing an inverted index or indexing.

2.4.1 Descriptive Statistics In this section, a descriptive statistics summary of the data will be presented. For more elaboration on the data, graphical charts and tables will be used. The collected data from the survey provided data from different levels of their readiness for using technology in the entities in the Kurdistan Regional Government. The total number of entities that have been selected to participate in the survey was 111, while only 108 have been chosen as there was an inaccuracy in 3 entities in the Erbil governorate. The below subsections will describe the data in more detail.

Website Availability According to the survey data results, 35% of the entities do have a website (i.e. 38 entities have a website, while the other remaining 70 entities do not).

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Email Address Availability Based on the collected data from the survey, 54 entities have official email addresses, while the remaining 54 entities do not. Which shows an equal rate of 50–50% of the availability and non-availability of the email address in the Erbil governorate entities.

Information Technology Department Availability As shown from the results of the data collected from the held survey, the outcome shows the IT department availability and non-availability counts of KRG entities that are located in Erbil governorate, which 86 of the entities do have an IT Department. On the other hand, only 22 entities do not have an IT department.

Social Media Account Availability The KRG entities located in Erbil Governorate’s availability and non-availability of social media accounts have been observed within the held survey. 57 entities from the 108 selected entities do have social media accounts. However, 51 entities do not have a social media account.

Research Center Availability In line with the findings of the held survey, the result shows that 41 entities which means 37% do have a research center. Yet, 67 entities (62% of the entities) do not have a research center.

Using Current Researches from Universities The rate of entities using current researches from universities have been observed through the survey that has taken place, and it shows that only 27 entities use current research from universities. While, 81 entities, or (80%) of the entities do not use available researches from the universities in the Kurdistan region.

Oversees Training Programs Availability After the survey was done, the findings about the availability and non-availability counts of KRG entities that are located in Erbil governorate, which provide their employees with oversee training, shows that 57 entities (53% of the entities) do

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provide oversee training programs to their employees, while 51 entities (47% of the entities) do not provide their employees with any oversee training programs.

Human Capacity Building Program Availability According to the results stated from the held survey among the 108 chosen entities in Erbil governorate of the Kurdistan Region (KR), the availability and non-availability counts of KRG entities that are located in Erbil governorate, which provide their employees with human capacity building programs, show that only 75 entities (69% of the entities) do provide their employees with human capacity building programs. However, 33 entities (30% of the entities) do not provide their employees with any human capacity building programs.

Diplomatic Agencies Providing Training Courses According to the survey that was held for this study, the findings about the availability and non-availability counts of KRG entities that are located in Erbil governorate, in which the diplomatic agencies provide the entities with human training programs, show that 47 entities (43% of the entities) are provided with training courses by the foreign agencies, while 61 entities (56% of the entities) are not provided with any training courses by the diplomatic agencies and the foreign offices.

Entities Collaborating with Other Governmental Entities As reported in the survey’s findings, the collaboration level of the entities with other governmental entities and of Erbil governorate in the Kurdistan region shows that 89 entities, or (82% of the entities) are collaborating with other governmental entities, while 19 of the entities (18% of the entities) do not have any collaboration with other governmental entities.

Entities Collaborating with Universities Conforming to the survey among the 108 KRG entities in Erbil Governorate, the findings show the level of collaboration between the KRG entities of Erbil governorate with the universities, where their rate is equal between the entities that do collaborate with universities and the entities that do not collaborate with universities. The rate is 54 by 54. Table 2.1 summarizes the explanations mentioned above.

24 Table 2.1 Summary answers to the surveyed questions

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Website

Available

Not available

38 entities

70 entities

Email address

Available

Not available

54 entities

54 entities

IT department

Available

Not available

86 entities

22 entities

Available

Not available

Social media accounts

57 entities

51 entities

Research center

Available

Not available

41 entities

67 entities

Using current researches

Yes

No

27 entities

81 entities

Oversee training programs

Available

Not available

57 entities

51 entities

Available

Not available

Human capacity building

75 entities

33 entities

Providing training courses

Yes

No

47 entities

61 entities

Collaboration with universities

Yes

No

89 entities

19 entities

Yes

No

54 entities

54 entities

Collaboration with governmental bodies

Type of Internet Connectivity Figure 2.1 shows the types of internet connectivity of the entities of the Kurdistan region, which are located in the Erbil governorate. The table shows that 8 entities from the 110 selected entities are using Tishk Net as their connectivity type. 20 entities from the 110 selected entities are using Newroz Telecom as their connectivity type. 30 entities from the 108 selected entities are using Fiber Optic as their type of connectivity. Finally, the most used and the oldest network that is being used by 50 entities among KRG entities located in Erbil governorate is Tarin Net.

Average Number of Employees with Number of Entities The average number of employees from the 108 selected entities is compared with the number of entities in this subsection. Data entities could be observed in the below figure (Fig. 2.2).

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Fig. 2.1 Internet connectivity types of KRG entities located in erbil governorate

Fig. 2.2 Average number of employees with number of entities

The above figure shows the average number of employees with the number of the 108 selected entities where the data is clarified and elaborated. The figure shows that 33 entities have a range from 0–50 employees. In addition, 45 entities have a range of 51–100 employees. Moreover, 19 entities have employees with a range of 101–150. Lastly, the 11 remaining entities have a range of 151 + employees.

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2.4.2 The Principle Component Analysis KRGi has been predicted by considering the 10 principles. These principles have included 27 sub-indices as presented in (Table 2.5) in Appendix 1. This certainly presents the better ranking of innovation capability and technological readiness than other measuring devices as it considers the overall major impacting factors of innovation. Among the 111 chosen KRG entities, only 108 of them have been studied. Ministry of Electricity—General Directorate of Diwan, has recorded the highest KRGi value of 1.12 which states that it is having the highest potential for technological readiness and innovation capability; see Table 2.6 in Appendix 1. Ministry of Electricity—General Directorate of Diwan is followed by the Ministry of Education—General Directorate of Diwan (1.02), and Ministry of Martyrs and Anfal Affairs—General Directorate of Diwan (0.86) in terms of KRGi value that reflects the scope of the higher status of the technological readiness and innovation capability. On the other hand, the lowest KRGi value (-0.81) was recorded for the Ministry of Labor and Social Affairs— General Directorate of Administration and Finance. Ministry of Labor and Social Affairs—General Director of Inspection, Ministry of Housing and Construction— General Directorate of Human Resources, Ministry of Finance—General Directorate of Taxes have also registered the lowest level of KRGi value which shows that these entities have less favorable conditions for using technology and innovations. As it is collected and displayed from several primary data sources of literature that the rate of innovation capability and technological readiness is more in the Ministry of Electricity—General Directorate of Diwan, than Ministry of Education—General Directorate of Diwan, Ministry of Anfal and Martyrs Affairs—General Directorate of Diwan than the other directorates and entities in the KRG. Hence, KRGi could be considered a more accurate method than previous methods of measuring and assessing innovation and technology in the entities of the Kurdistan Region’s Government.

2.4.3 Correlation Matrix The correlation matrix revealed that a positive correlation was found between some of the indicators (see Table 2.7) Appendix 1. The correlation matrix displayed the positive interaction that was found between some of the measures. The strong relationship between IT operations and business applications is due to the importance of using IT in business in the modern world now. Likewise, there is a high correlation between human capacity building and the research center, this is because of the direct relationship between the increasing skills of each individual and enabling those individuals to work in a research center. On the other hand, the weak correction between

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internet connectivity and the number of computers reveals the fact that government entities do not provide internet access to all personal computers.

2.4.4 Classification of the Entities The entities that have been chosen and participated in this study have been classified into three groups based on the KRGi score as follows: First Group: Scientifically High Potential Entities to technology, innovation capability, and technological readiness, are those entities who have scores that are greater than 0.5, only 15 entities have been categorized under this category, which begins with the Ministry of Electricity—General Directorate of Diwan and ends with Ministry of Interior—General Directorate of Diwan. A suitable and satisfactory policy recommendation for this group could be the installation of new programs for these entities that show readiness for technology and are ready for implementing any modern method that might help in assessing technological readiness or innovation capability. Second Group: Scientifically Moderate Potential Entities to technology, innovation capability, and technological readiness, are those entities who have scores ranging from 0 to 0.5, 34. The list begins with the Ministry of Agriculture—General Directorate of Water and Water Resources and ends with the Ministry of Finance— General Directorate of Customs. A suitable and satisfactory policy recommendation for this group could be providing more training sessions which could be national or international, that employees can benefit from. Third Group: Scientifically Low Potential Entities to technology, innovation capability, and technological readiness, are those entities who have scores that are less than 0, 58 entities have been categorized under this category, which begins with Ministry of Finance—General Directorate of Accounting and ends with Ministry of Labor and Social Affairs—General Directorate of Administration and Finance. A suitable and satisfactory policy recommendation for this group could be from the government, by providing them with different training programs, motivating and encouraging them, making them implement technology in their day-to-day tasks, and progressing the KRG entities as a whole.

2.5 Summary, Conclusion, and Policy Recommendations This section summarizes the purpose and objective of the study, the major findings, the conclusion, and policy recommendations for future research scholars and policy makers. The study has provided answers to the following research questions: 1. What are the technological readiness level of KRG entities? 2. What factors enhance the technological readiness of the KRG Entities?

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2.5.1 Summary of the Study The study is titled ‘Assessing Innovation Capability and Technological Readiness in KRG’. It consists of five sections. Below is a summary of each section. Section 1 is an introductory section, which mainly describes the topic, its background, research aim/objectives, research questions, contributions, and limitations of the study. Section 2 deals with the literature review, where theories of technology adoption and innovation were discussed. This section gives the reader idea about existing research and opinion regarding the topic under study. Section 3 is the ‘Methodology’. In this section, the research purpose, types of research, theoretical framework, research strategy, and data collection method were elaborated. Section 4 is the ‘Data Analysis and Results’. In this section, the collected data from the 108 observed entities are analyzed. Principal Component Analysis (PCA), correlation relation, and classification of entities and their grouping are stated. Lastly, the final section of the thesis is Sect. 5. Which is the ‘Conclusion and Summary’ section. In this section, a brief introduction, a summary of the thesis, the purpose of the thesis, and recommendations for future scholars and policy makers are given. In addition to that, the research questions are answered as well.

2.5.2 The Purpose of the Study The purpose of this study was to develop an index to assess the innovation capability and technological readiness of the entities of the Kurdistan Regional Government. The index consisted of 27 questions and 27 variables, but only 22 of them were subjected to a principal component for the index construct. In addition to that, the survey was conducted in 108 entities from the Kurdistan regional government.

2.5.3 Policy Recommendations Interview with Key Staff at KRG Based on the results obtained from the quantitative part of this study, interview with relevant staff (IT experts) at KRG has been conducted to support the quantitative results, and to able to explore strategies that needed to be adopted by the KRG, to target the two groups of entities identified by this study, i.e., the high ranked and the low-ranked entities for technological readiness.

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The experts have been contacted via email and face-to-face to conduct the interview. The questions (see Appendix: 2: 1 IT Experts’ Questionnaire) were provided ahead and aimed at obtaining in-depth answers, which would answer the research questions. Three experts have participated in the interview and based on their insights and opinions, in addition to the researcher’s analysis supported by the quantitative results, the following recommendations are provided by KRG Department of IT (DIT), Ministry of Electricity (MOElec.), and Ministry of Planning (MOP). In the interview with the KRG Department of IT (DIT), the head of the department stated two different recommendations for the KRG entities according to the findings of the studied research. The first group is the high-ranked group and the second group is the low-ranked group. The group of KRG entities that are ranked high according to the KRG IT department are the entities that have ministers with good leadership skills and whose employees have realized and acknowledged the importance of technological innovation and development throughout the Kurdistan Region. Ruling and regulating a ministry or entity successfully starts with a well-educated and professional decisionmaking leader. These successful entities that have implemented technological initiatives are worth the modern projects that have been designed for as well as an increase in the budgeting system and the amount of money set for such entities. Nowadays, people live in an era where no governmental bodies, public offices, and organizations are unable to function properly without having an IT department that operates and manages the majority of the computer work and keeps track of archives. However, in 22 of the KRG entities, no IT departments are operating, which is a drawback for the government as a whole. The low-ranked group of entities is the group that has really poor quality information technology and is lagging behind the technological advancements that have evolved throughout the region through recent years. According to international standards, the majority of the entities fall in the lowest rank of the list. The following stated recommendations apply to all governmental bodies and are set in a way that is not time-consuming. The first recommended strategy is to set up and establish an IT department that must start serving in the shortest time possible. The establishment of an IT department in the entity is one of the most significant steps to be taken while on the path of being a professional and well-developed entity in the region. The IT department of each entity must work closely and collaborate professionally with its leader and decision-maker to present the best to the government as a whole and its citizens. Further, the staff and employees of the IT department must be highly skilled and trained to be suitable for the position. These IT departments must run efficiently to serve the most citizens they can and must have a sufficient building and foundation to fulfill the applications, governance, and policies.

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In an interview with the Ministry of Electricity, the head of the IT Department emphasized several recommendations for the KRG entities according to the findings of the research for, both, high-ranked and low-ranked groups. During the past decade, Information Technology (IT) was in its foundational stage and partially implemented as a supporting function in government institutions. At present, IT has infiltrated all government tasks and has become a part of the government, which means that without technological use, it is highly unlikely to find an entity to function properly. As such, the IT department became an unseparated part of the institution. It is very fundamental for the preeminent leaders and pioneers of this field to play a vital role. They must become mentors for their spreading awareness and sharing their experiences within the governmental bodies. It is highly recommended that esteemed leaders focus on both infrastructure and developing human resources. As a result, institutional stability and sustainability cannot be forgotten. The government itself cannot successfully promote information technology, however, it can play a prominent role in transforming it into that. The public–private partnership (PPP) between the government and the regal entities is imperative to push for more developments. The Ministry of Electricity witnessed changes in the transformation of some fields such as electronic ID and biometric registration. The government’s role in spreading awareness and developing the skills of its employees should be taken into consideration by providing diverse professional training programs to leaders without high education as none of the high-minded leaders could succeed in fulfilling any objectives. The government should prioritize long-term strategic IT projects as part of promoting high-ranked entities. For the entities that are developing slowly, it is highly recommended that the government builds up a strategy and conducts a comprehensive study to look into the factors that have an impact on these entities lag. Moreover, the ministry would have to propose different approaches for each entity based on its performance. Some entities might need professional HR and IT staff to communicate with the elite members of the organizations, collaborate with the different units, and comply with the strategy. Furthermore, it’s also suggested that the multilayered capacity building programs need to be implemented in a way that a friendly IT infrastructure environment is built to forge ahead. Finally, in the interview with the Ministry of Planning—IT Department, the head of the department presented two distinctive recommendations for the KRG entities according to the findings of the studied research. The first group is the high-ranked group and the second group is the low-ranked group. Ministry of Planning—IT Department advocates that the high-ranked entities take more responsibility in building bridges between other entities compared to the less developed entities technology-wise. They can share their experiences and development stories with other entities to follow the same path. The pioneering entities in technological development should examine other opportunities and develop different approaches for the IT transformation of the organizations. They can play a leadership role in the country. It is also recommended to

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think about the public interest and be accountable for society’s shift into technology development. The government is not the only sole responsible player, other organizations and firms must take care of the remaining responsibilities. It is their common duty and all related parties are responsible for taking care of the entities which are left behind. The first step to be taken is to survey the structural and HR department of these entities. The next step is to start setting up an appropriate formula for each entity to cope with the alteration. For instance, some entities in the government have already adapted to the technological and infrastructure development and staff capacity. Those entities can play a significant role, with the support of the government, in sharing their experience with less developed governmental bodies and other organizations.

2.5.4 Government Role in Future Research Recommendations Based on the findings of the current study, it is recommended that the below points need to be focused on more for future research: • Digital transformation requires proper planning based on sound research resulting in policies, roadmap, and plans of action for multiple domains. Adherence to a government-wide strategy is crucial for a stable, smooth, effective, and efficient digital transition. • KRG’s Department of Information Technology (DIT) in cooperation with Price Water Corporation (PwC) has conducted intensive analysis and research to assess KRG’s entities’ situation, capabilities, and problems. These findings resulted in a roadmap and blueprint. • DIT, as a governmental entity, like other fellow organizations faces great obstacles but has spared no efforts in pushing the leadership to be more supportive of the government’s digital transformation phases. • Based on the roadmap, DIT drives support, and prioritizes projects in eight fields: IT, Capacity building, IT Infrastructure, IT Governance, IT, applications, IT Participation, IT Policy, and IT Investment. These ideally should crystallize in G2G (Government to Government), G2B (Government to Business), G2E (Government to Employee), and G2C (Government to Citizen) in terms of services powered by well-developed infrastructure, managed by skilled human resources, in compliance with policies and strategies, and aligned with pre-defined goals and measured in milestones. • In the next four years, Government will focus on some key projects within the mentioned fields and frameworks. These currently, for instance, are HRMS (enterprise human resource management systems), Payroll, E-Payment, SOC (Security operation center), and data centers.

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• In interaction with the services, mobile applications will play a significant role. Projects falling under the category of IoT (Internet of things) such as automation of power bill calculation/ collection are being considered as of writing. • DIT acknowledges the hardship and difficulties that are being faced, however, it is the department’s responsibility to stay firm and be consistent because there are no other options. The governments of developed countries are already digital, developing counties are transforming now. Kurdistan is blessed with a motivated, well-educated, and extremely talented young generation. With the right support and vision from the top leadership, DIT has absolutely no excuse or reason to stay behind. So far, most of the efforts regarding technological advancement in the government sector are based on individual initiatives instead of institutional plans. The government’s individual institutions propose their policy initiative and work on implementing them without collaborating with the IT leadership and other prominent IT entities. • Digital renovation needs suitable planning based on all-encompassing policies for various fields. The absence of a comprehensive government strategy is an obstacle to technological advancement because the existence of such a strategy is critical for a stable and competent digital transition. • KRG made some efforts with international companies to start building a national strategy for the IT transformation but in its first stages stopped developing due to many obstacles and the absence of readiness of many government institutions. The government’s plan should focus on capacity development, good governance, awareness, education, infrastructure, and investment. • The government needs to focus on some strategic projects such as human resource management systems, Payroll, E-Payment, and data center infrastructure. • It is the government’s responsibility to remain strong and reliable as there are no other options. Kurdistan is filled with highly motivated and talented people, so with the correct support from the leadership, the region will have completely no reason to lag. • KRG is the umbrella for certain technical advancements; so policy initiatives should come from the government, executive, and legislative branches. The government should create a board of high-ranking entities and technology leaders to ask for advice, consultation, and draft plans and policy initiatives. • Government also can benefit from the technological advancements of private companies and partner with them to have the most effective advancement. Many countries around the world are reaching out to private companies because the technological advancement in the private sector is way faster than the government. Therefore, the government can benefit from the technological advancements of the high-tech companies and start putting a plan for implementation and adaptation. • The most effective way for KRG is to start its policy initiatives on ministries. KRG can focus ministry by ministry on technology advancement and at the same time

2 Assessing Innovation Capability and Technological Readiness of KRG

•

• • • • •

33

should put a long-term plan for the services that are connected to all population, especially ministerial services such as electricity, municipality, water resources, etc. Particular parameters or indices that decide the innovation capability and technological readiness more approximately like the number of computers/employees, internet accessibility, type of connectivity, local and oversee training availabilities need to be more focused on while developing indices regarding technological readiness and innovations capability. Entities need to make strong ties and have good relationships with universities and benefit from the current and existing research done by scholars. Special focus needs to be given to encourage and motivate entities for using technology, conduct programs, and training sessions for promoting innovation and technology. Entities need to work on digitalizing their work and use technology at their work as it is time-saving and gives quicker accessibility to the required document or data. Employees need to get trained and get the knowledge so they can be productive at their work, and it motivates them to innovate in the field. KRG IT Department needs to have data for all KRG entities.

2.6 Conclusion The current study has developed a new measurement tool to assess innovation capability and technological readiness for KRG entities from the KRGi. It is not very close to the current methods for assessing innovation like the technology index (WEF), technology achievement index (UNDP), industrial development score card (UNIDO), ArCo (Archibugi and Coco), and science and technology index (RAND Corporation), which were measurement tools for technology rather than innovation. In the case of KRGi, particular factors affect innovation in KRG entities. The new method of innovation was approximated for 108 entities of KRG that are located in the Erbil governorate. The highest rate of technological readiness was noticed in the Ministry of Electricity—General Directorate of Diwan followed by the Ministry of Education— General Directorate of Diwan, and Ministry of Anfal and Martyrs Affairs—General Directorate of Diwan among the 108 chosen entities under this study. The lowest rate of technological readiness was noticed and reported in the Ministry of Labor and Social Affairs—General Directorate of Administration and Finance, Ministry of Labor and Social Affairs—General Directorate of Inspection, and Ministry of Housing and Reconstruction—General Directorate of Human Resources.

34

A. F. Mustafa

The future recommendations for the enhancement of innovation capability rate and technological readiness assessment have been recommended based on the KRGi score. Hence, the KRGi proved to be a better measurement tool for innovation capability and assessing technological readiness in KRG entities, which could also be used for approximating the innovation capability and the technological readiness in other governorates of KRG entities.

Appendix 1: Descriptive Statistics and Variables Used See Tables 2.2, 2.3, 2.4, 2.5 and 2.6. Table 2.2 Entity names and codes Entity name

Code

Department of Foreign Relations

1

High Council of Women Affairs

2

Mine Action Agency

3

Department of Coordination and Follow up

4

Ministry of Martyrs and Anfal Affairs, GD of Diwan

5

Ministry of Martyrs and Anfal Affairs, GD of Service

6

Ministry of Martyrs and Anfal Affairs, GD of Investment

7

Ministry of Endowment & Religious Affairs, GD of Diwan

8

Ministry of Endowment & Religious Affairs, GD of Enowment and Religious Affairs (Erbil Branch)

9

Ministry of Justice, GD of Diwan

10

Ministry of Justice, GD of (Judiciary Justice Departments

11

Ministry of Justice, GD of Real Estate Registry

12

Ministry of Justice, GD of Administration and Finance

13

Ministry of Agriculture, GD of Erbil Water

14

Ministry of Agriculture, GD of Veterinary

15

Ministry of Agriculture, GD of Water and Water Resources

16

Ministry of Agriculture, GD of Diwan

17

Ministry of Agriculture, GD of Dams

18

Investment Board, GD of Erbil Investment

19

Investment Board, GD of Industry & Minerals

20

Investment Board, GD of Administration & Legal Affairs

21

Investment Board, GD of Assessment & Finance

22

Ministry of Trade and Industry, GD of Industrial Development

23 (continued)

2 Assessing Innovation Capability and Technological Readiness of KRG

35

Table 2.2 (continued) Entity name Ministry of Trade and Industry, GD of Divan

Code 24

Ministry of Trade and Industry, GD of Planning & Following Up

25

Ministry of Trade and Industry, GD of Trade

26

Ministry of Trade and Industry, GD of Company Registration

27

Ministry of Labour and Social Affairs, GD of Supervision and Development

28

Ministry of Labour and Social Affairs, GD of Social Safety (security) Insurance Net

29

Ministry of Labour and Social Affairs, GD of Reform and Social Affairs

30

Ministry of Labour and Social Affairs, GD of Inspection

31

Ministry of Labour and Social Affairs, GD of Administration and Finance

32

Ministry of Labour and Social Affairs, GD of Diwan

33

Ministry of Natural Resources, GD of Diwan

34

Ministry of Natural Resources, GD of Following Up

35

Ministry of Natural Resources, GD of Oil & Minerals

36

Ministry of Natural Resources, GD of Administration and Finance

37

Ministry of Natural Resources, GD of Investment

38

Ministry of Electricity, GD of Diwan

39

Ministry of Electricity, GD of Transportation

40

Ministry of Electricity, GD of Erbil Electricity

41

Ministry of Electricity, GD of Division

42

Ministry of Electricity, GD of Control and Communications

43

Ministry of Electricity, GD of Power and Electricity Investment

44

Ministry of Electricity, GD of Planning and Projects Follow up

45

Ministry of Finance, GD of Diwan

46

Ministry of FInance, GD of Retirement

47

Ministry of Finance, GD of Taxes

48

Ministry of Finance, GD Customs

49

Ministry of Finance, GD of Legal Affairs

50

Ministry of Finance, GD of Budget

51

Ministry of Finance, GD of Accounting

52

Ministry of Finance, GD of Specialized and Trade Banks

53

Ministry of Finance, GD of Insurance Companies

54

Ministry of Interior, GD of Diwan

55

Ministry of Interior, GD of Joint Crisis Coordination

56 (continued)

36

A. F. Mustafa

Table 2.2 (continued) Entity name Ministry of Interior, GD of Inspection Board/ Administration

Code 57

Ministry of Interior, GD of Inspection Board/ Army

58

Ministry of Interior, GD of Passports and ID of KRG

59

Ministry of Interior, GD of Police

60

Ministry of Interior, GD of General Police Academy

61

Ministry of Interior, GD of Violence Against Women

62

Ministry of Interior, GD of Traffic Police

63

Ministry of Culture and youth, GD of Erbil Culture

64

Ministry of Culture and youth, GD of Erbil youth

65

Ministry of Culture and youth, GD of Sports and Physical Education

66

Ministry of Culture and youth, GD of Libraries

67

Ministry of Culture and youth, GD of Journalism/ Printing

68

Ministry of Culture and youth, GD of Journalism/ Publishing

69

Ministry of Municipalities and Tourism, GD of Diwan

70

Ministry of Municipalities and Tourism, GD of Erbil Municipality

71

Ministry of Municipalities and Tourism, GD of Tourism

72

Ministry of Municipalities and Tourism, GD of Water

73

Ministry of Municipalities and Tourism, GD of Planning

74

Ministry of Municipalities and Tourism, GD of Mapping

75

Ministry of Municipalities and Tourism, GD of Archeology

76

Ministry of Higher Education and Scientific Research, GD of Diwan

77

Ministry of Higher Education and Scientific Research, GD of Research and Development

78

Ministry of Higher Education and Scientific Research, GD of Scholarship and Cultural Relations

79

Ministry of Higher Education and Scientific Research, GD of Engineering and Projects

80

Ministry of Higher Education and Scientific Research, GD of Planning and Following Up

81

Ministry of Higher Education and Scientific Research, GD of Quality Assurance and Trust

82

Ministry of Transport and Communications, GD of Diwan

83

Ministry of Transport and Communications, GD of Transportation

84

Ministry of Transport and Communications, GD of Planning and Following Up

85

Ministry of Transport and Communications, GD of Erbil International Airport (EIA)

86 (continued)

2 Assessing Innovation Capability and Technological Readiness of KRG

37

Table 2.2 (continued) Code

Entity name Ministry Transport and Communications, GD of Earthquake and Seismic Alerts

87

Ministry of Transport and Communications, GD of Posts and Communications

88

Ministry of Housing and Reconstruction, GD of Diwan

89

Ministry of Housing and Reconstruction, GD of Roads and Bridges

90

Ministry of Housing and Reconstruction, GD of Technics

91

Ministry of Housing and Reconstruction, GD of Human Resources

92

Ministry of Health, GD of Diwan

93

Ministry of Health, GD of Erbil Health

94

Ministry of Health, GD of Planning

95

Ministry of Planning, GD of Diwan

96

Ministry of Planning, GD of Administration and Finance

97

Ministry of Planning, GD of Planning

98

Ministry of Planning, GD of Budget Investment and Project Planning

99

Ministry of Planning, GD of Strategic Planning

100

Ministry of Planning, GD of Assistance and Coordination

101

Ministry of Planning, GD of Human Resources Development

102

Ministry of Education, GD of Diwan

103

Ministry of Education, GD of Erbil Education

104

Ministry of Education, GD of Planning

105

Ministry of Education, GD of Examinations

106

Ministry of Education, GD of Supervision

107

Ministry of Education, GD of Elementary and Middle School

108

Ministry of Education, GD of Highschool

109

Ministry of Education, GD of Institutes

110

Ministry of Education, GD of Curriculums and Programs

111

Table 2.3 Variables and labels Variable Label

Mean

Std dev 34.5254

X1

College degree

42

X2

Average age

39.9537

X3

Number of employee

X4

He rate

0.1402

X5

Number of computers

X6

Type of connectivity

Minimum Maximum N 4

205

108

49

108

90.6574 66.0139 13

450

108

0.095

0

1.2143

108

39.3519 35.2524

7

270

108

2.1111

0

3

4.1679 27

0.989

108 (continued)

38

A. F. Mustafa

Table 2.3 (continued) Variable Label

Mean

Std dev

Minimum Maximum N

X7

Website

0.3704

0.4852

0

1

108

X8

Email address

0.5185

0.502

0

1

108

X9

IT dept

0.7963

0.4046

0

1

108

X10

Info system

1.7407

0.7021

0

3

108

X11

Intra switching

0.9352

0.2473

0

1

108

X12

Electricity access

0.7222

0.45

0

1

108

X13

Colab with gov entitiy

0.8241

0.3825

0

1

108

X14

Colab with uni

0.3981

0.4918

0

1

108

X15

Use research from uni

0.25

0.435

0

1

108

X16

Colab with intl body

0.5

0.5023

0

1

108

X17

Research center

0.3796

0.4876

0

1

108

X18

Social media

0.537

0.501

0

1

108

X19

Human capacity building

0.6944

0.4628

0

1

108

X20

Training courses

0.7963

0.4046

0

1

108

X21

Oversee training

0.5278

0.5016

0

1

108

X22

Diplomatic agency provide training 0.4352

0.4981

0

1

108

X23

IT organization

0.2685

0.6782

0

3

108

X24

IT security

0.1019

0.3039

0

1

108

X25

IT operations

0.1574

0.4138

0

2

108

X26

IT infrastructure

0.2222

0.5525

0

3

108

X27

Business application

0.1389

0.3734

0

2

108

Table 2.4 Eigenvalues of the correlation matrix No

Eigenvalue

Difference

Proportion

Cumulative

1

−4.67654155

1.13258018

0.2033

0.2033

2

3.54396137

1.53977316

0.1541

0.3574

3

2.00418821

0.23230545

0.0871

0.4446

4

1.77188276

0.33728598

0.0770

0.5216

5

1.43459678

0.25143754

0.0624

0.5840

6

1.18315924

0.02326860

0.0514

0.6354

7

1.15989064

0.25370738

0.0504

0.6858

8

0.90618326

0.09390190

0.0394

0.7252

9

0.81228136

0.01578757

0.0353

0.7606

10

0.79649379

0.0346

0.7952

−0.020399

−0.024147

−0.214950

−0.213876

0.399722

0.385775

X26

X27

0.010700

−0.188481

0.379701

X25

−0.017291

−0.006198

−0.171763

−0.213834

0.402502

0.374097

X23

X24

0.058356

0.027785

0.022481

0.046714

−0.001716

−0.114291

−0.064888

0.324895

0.151116

X22

−0.218991 −0.104175

−0.208083

0.124355

0.354378

0.032205

0.167416

X20

X21

−0.088165

−0.252829

0.267027

−0.053624

0.090071

0.022649

0.000707

0.009004

0.031177

−0.134248

−0.086928

0.485048

0.361387

0.134317

0.064518

0.186528

−0.110939

0.090749 −0.077340

X19

0.529532

0.463502

−0.236300

−0.096984

−0.020182

0.224500

0.102253

0.006212

−0.003574

0.071421

0.041125

0.175836

0.077352

−0.037877

−0.436861

−0.208548

0.089315

−0.366019

−0.235076

0.433664

0.425381

0.134189

−0.108313

0.078938

0.073487

−0.301635

0.113309

0.020677

0.014378

0.068527

−0.051275

0.408287

0.372498

0.178863

−0.015129

−0.130776

0.253519

0.148006

−0.138184

−0.431324

−0.057843

0.162017

−0.064151

0.035954

−0.441941

−0.062732 0.257001

0.011353 0.003993

−0.144212

−0.234905

0.042887

0.233157

Prin7 −0.108024

−0.140190

0.091114

Prin6

−0.399520

0.032994

0.324052

0.066565

0.299903

X17

0.123464

X16

0.179501

0.120988

−0.076392

−0.092709

−0.211249

0.421712

0.251100

−0.141754

0.214903

0.261792

0.149099

−0.080193

0.137005

−0.261101

0.267568

Prin5 −0.056671

0.146443

0.015109

0.340758

0.272057 0.009833

0.055576

−0.063686

0.403322

0.170090

0.120283

−0.137780

Prin4 0.441672

Prin3

−0.070552

X18

0.135310

0.170352

X14

X15

0.131502

X13

0.254026

0.059894

0.059945

−0.009059

−0.121028

X11

X12

−0.178241

−0.095738

X10

0.203082

0.270031

0.149572

0.119718

X8

X9

0.215324

0.291059

0.157944

0.121822

X6

0.188684

Prin2

X7

0.077636

Prin1

X5

Principles

Table 2.5 Eigenvectors

−0.026404

−0.031894

0.001562

−0.005944

−0.017272

−0.068587

−0.094919

0.105846

0.144695

−0.244799

−0.086961

0.176002

0.035847

−0.034442

−0.106759

−0.368693

0.660209

0.333670

−0.181401

−0.070704

0.191899

0.237786

0.159194

Prin8

Prin9

0.053208

0.030444

0.026552

−0.003363

0.003624

−0.169212

−0.058953

−0.410325

−0.000174

−0.124778

0.013704

0.104739

0.001505

−0.244962

0.615838

0.234821

0.194100

0.070160

0.202894

−0.270189

0.172983

0.019094

−0.294705

Prin10

−0.017913

0.012127

0.031699

0.034532

−0.096395

−0.053912

−0.069411

0.133970

−0.180722

0.159186

−0.085386

−0.122925

0.132665

0.032988

−0.199181

0.146361

−0.130008

0.131135

0.068569

−0.128152

0.045653

0.766360

−0.396951

2 Assessing Innovation Capability and Technological Readiness of KRG 39

0.748 0.489

0.590 −0.184 −0.283

1.527

1.130

2.079

0.979

1.101

−0.444

2.488

0.017

0.328

−0.007

0.160

0.089

0.300

0.463

−0.065

−0.012

34

46

45

44

35

62

77

4

56

41

55

60

61

98

43

21

93

86

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

0.098

1.246

0.118

3.534

1.395

1.451

1.461

1.417

0.707

0.920

2.357

−1.769 1.329

−0.054

1.288

1.842

1.337

0.036

−1.266

−0.543

0.913 −0.507

−0.113

0.101

0.829

0.559

0.342

−0.090

−0.253 0.820

1.125

0.441

−0.555

−0.491

−0.147 −1.802

1.004

0.188

0.739

0.977

0.927

0.661

1.439

1.495

−1.126

−0.145

0.551

Prin5

−0.404

0.286

1.075

0.791

0.512

0.206

1.419

1.065

1.616

0.932

1.529

−1.271

1.211

−0.003

0.273

1.524

0.904

1.206

−0.809

1.268

1.221

−0.014 0.550

0.546

−1.006

1.317

−0.518

0.493

0.295

1.368

2.713

−0.413

96

5

0.989

−0.089

4

−0.315

−0.922

2.615

0.630

3.958

Prin4

5

0.473

103

Prin3

0.132

3

Prin2

2

3.182

39

1

Prin1

Name

Obs

Table 2.6 The KRGi index

0.975

−0.889

−0.403

−0.886

−0.211

0.382

0.506

1.265

0.412

0.540

0.498

−0.082

0.248

0.690

−0.961

0.310

0.538

0.491

−0.051

0.233

1.038

−0.585 0.570

0.391

0.078

1.032

0.502

1.007

−0.240

−0.049

−0.192 0.345

−0.492

0.583

1.315

−0.246

0.986

−0.281

Prin7

−1.020

−0.099

−0.017

0.076

2.371

0.609

Prin6

0.000

−0.095

−0.090

−0.353

1.945

0.592

0.338

0.695

0.033

0.720

−0.282

−0.508

−0.217

0.036

0.001

0.131

1.302

0.272

0.044

0.571

−1.084

0.591

Prin8

−0.051

0.920

0.450

1.464

−2.807

0.301

0.026

1.096

0.077

−0.834

−2.768

0.756

0.423

−0.090

−0.073 −0.017

−0.118

0.415

0.082

0.873

1.461

−0.766

−0.223

−1.018

0.506

−1.548

0.103

−2.166

0.593

−0.141

Prin10

0.312

0.051

0.166

−0.220

−0.039

0.116

−0.340

−0.069

0.490

−0.147

0.976

−0.045

0.381

−0.675

Prin9

(continued)

0.415

0.438

0.441

0.470

0.479

0.485

0.489

0.502

0.520

0.529

0.550

0.561

0.565

0.568

0.585

0.601

0.689

0.762

0.804

0.869

1.026

1.120

KRGi

40 A. F. Mustafa

0.677

−0.540

−0.067

9

72

8

37

37

38

39

40

76

−0.482

17

36

44

−0.175

−0.378

83

35

11

0.068

−0.538

75

34

43

0.185

1.133

70

33

26

−0.171

0.099

38

32

106

0.023

0.051

71

31

42

2.124

41

−0.418

1.229

−2.271

18

30

0.455

1.065

0.777

−1.238

−1.478

1.571

−0.126

1.090

−0.348

0.064

−0.047

0.345

0.008

0.382

0.633

1.584

1.342

0.381

−0.287

0.277

1.540

0.000

0.269

−0.355

0.777

1.427

0.890

0.057

1.294

−0.072

105

29

−0.352

0.966

28

1.096

−2.051

1.649

−0.020

−0.734

1.563

0.104

1.656

1.824

−0.376 1.269

0.396 −0.216

−1.847

−0.113

0.255

1.091

−0.757

−0.195

−1.117

0.151

−0.166

−1.765

0.470

0.130

−0.494 0.120

1.707

0.995

−0.150

−0.668

1.008

1.548

1.308

−0.585

−0.991

−0.786

1.562

−0.861

1.647

−1.167

−1.024

−1.990

−0.518

−0.280

−1.479

−0.358

−0.154

−1.035

−0.772

−0.397

−0.052

1.075

2.109

0.511

−1.675

0.441

−0.223

−0.332 0.187

0.317

Prin7

−0.455

Prin6

0.315

−0.794

0.169

−0.643

0.400 −0.498

1.286

0.325

−1.386 −1.328

−0.011

−0.725

−1.225

1.558

−1.963

0.643

0.520

1.016

Prin5

0.165

−0.032

1.799

3.483 −0.162

0.902

0.861

0.266

−0.298

Prin4

−0.493

−0.143

111

27

63

0.881

−0.069

104

26

0.686

0.178

1.344

0.787

0.683

Prin3

0.062

1.409

0.146

−0.258

1

1.216

Prin2

40

0.115

Prin1

25

42

24

Name

Obs

23

Table 2.6 (continued)

−0.451

1.390

0.332

0.456

0.744

1.947

−1.111

0.661

−0.118

−0.347

−0.238

0.233

0.010

−1.520

−0.030

−0.327

−2.173

−0.870

−2.327

−0.380

0.350

0.205

Prin8

−0.499

−1.387

0.632

0.373

−1.404

0.244

−0.639

0.340

−0.135

0.863

1.287

−0.630

1.935

−1.127

0.721

0.568

0.123

0.049

−0.673

0.948

−0.344

0.293

Prin9

−0.025

0.723

−0.243

0.837

1.433

0.689

−0.856

−0.212

0.074

0.783

−0.674

−2.188

−0.608

−1.169

1.283

−0.204

1.034

−0.736

−0.279

0.411

0.513

0.050

Prin10

(continued)

0.046

0.054

0.054

0.074

0.081

0.086

0.122

0.128

0.128

0.134

0.136

0.187

0.227

0.231

0.259

0.272

0.273

0.316

0.337

0.359

0.400

0.412

KRGi

2 Assessing Innovation Capability and Technological Readiness of KRG 41

24

49

50

−0.259

25

48

0.362

64

56 0.051

−0.317

0.997

0.405

0.268

0.434

−0.274

−0.461

−0.283

0.222

0.221

0.219

58

57

81

80

79

82

12

108

58

59

60

61

62

63

64

65

66

0.968

−0.558

0.815

0.218

−0.728

−0.252

0.971

0.976

0.979

0.185

−0.492

49

107

57 0.854

−2.338 −2.357 −2.366

−0.106 −0.103 −0.102 −1.588

−1.324

−0.028

−2.328

1.320

−0.515

−0.108

−0.010

−0.751

−0.632

−1.536

−0.535

−0.513

0.271 1.215

0.956

0.079

−1.337

−0.797

−1.295

−0.525

0.629 −1.949

1.866 −0.025

0.524

0.882

0.191

1.269

−0.586

0.519

Prin4

1.050

0.292

97

55

−0.467

59

54

0.372

87

53

−0.326

78

52

1.186

−0.544

−0.713

74

51

0.356

0.112

−0.451

52

−0.234

0.292 −1.441

0.115

−1.453

1.616

−1.312

−0.693

−1.377

1.917

50

47

Prin3

−0.446

89

46

Prin2 −1.476

−0.310

51

0.520

Prin1

Name

Obs

45

Table 2.6 (continued)

−0.539

0.068

−0.938

−0.939

−0.942

−0.943

−0.166

−0.243

−1.194

−1.328

−1.608

−0.719

0.215

1.390

−0.503

0.390

0.069

−2.230

−0.588

0.088

0.653

0.175

Prin5

1.152

−0.984

−0.924

−0.922

−0.917

−0.915

0.097

1.271

2.228

0.585

−2.623

−0.513

0.724

−0.033

−1.037

0.276

1.683

0.520

−0.657

0.990

−0.435

1.187

Prin6

0.560

1.343

−0.230

−0.233

−0.239

−0.242

−0.180

1.189

0.071

1.379

−1.674

−1.039

0.384

0.326

−0.791

−0.624

−1.055

1.818

2.129

0.356

−1.970

0.594

Prin7

−1.364

1.102

0.331

0.336

0.345

0.350

0.721

−0.108

0.228

−0.016

−2.327

0.951

0.538

−1.507

0.188

−0.408

−0.218

0.984

1.959

0.758

−0.321

−0.194

Prin8

0.120

0.856

−0.439

−0.448

−0.467

−0.476

0.548

0.613

0.006

0.421

−0.088

−0.204

1.219

−0.294

−1.045

−0.185

−2.728

0.470

−2.801

0.648

−0.746

−0.104

Prin9

−1.040

1.197

0.991

0.978

0.953

0.941

0.509

1.188

0.744

−2.149

1.116

0.381

1.188

−0.961

0.692

−0.885

1.719

−0.280

−0.191

0.780

−0.586

0.738

Prin10

(continued)

−0.165

−0.163

−0.155

−0.153

−0.150

−0.148

−0.134

−0.129

−0.076

−0.073

−0.061

−0.037

−0.034

−0.026

−0.015

−0.003

−0.003

0.001

0.017

0.025

0.026

0.031

KRGi

42 A. F. Mustafa

0.441 −0.568

−0.389

−0.088

−1.238

−0.697

−0.273

−0.140

−0.803

−1.357

0.004

−2.124

−0.015

−1.001

−0.385

−0.070

−0.353

−1.609

−1.392

−0.617

−0.404

−1.083

−0.649

−0.689

−0.503

−0.918

−1.070

−0.328

0.454

−0.416

−0.990

−0.495

−0.411

−0.618

−1.225

−1.090

73

20

16

90

23

53

66

28

19

29

14

95

30

88

68

65

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

2.318

1.766

1.681

−0.574

−1.174

−0.332

1.266

−0.088

1.475

−0.665

−0.817

−1.408

2.113

0.867

1.185

−0.054

−1.083

100

−0.064

−0.499

72

−0.912

−0.755

−1.619

−2.119

67

0.552

−0.218

110

69

102

0.414

−0.412

109

68

0.146

Prin3

71

−0.162

−0.606

36

70

Prin2

Prin1

Name

Obs

67

Table 2.6 (continued)

−0.515

−0.232

−0.683

−0.823

0.204

0.043

−0.882

−0.370

0.757

0.092

0.715

0.999

0.133

−0.953

0.232

−0.960

−0.119

−0.527

−0.564

−1.491

−0.740

−0.226

Prin4

−0.029

0.603

1.198

−0.250

−0.451

−1.071

0.050

0.824

−3.419

0.458

0.855

−1.092

0.307

−0.962

0.776

−0.878

1.533

1.394

−0.735

0.194

0.166

0.889

Prin5

0.847

0.408

−0.669

−0.193

0.406

2.211

−1.354

0.049

−2.202

0.157

1.540

1.253

1.355

1.143

1.458

0.130

−0.474

0.021

−0.191

0.603

1.047

−0.708

Prin6

0.350

1.187

−0.525

0.777

−2.070

−1.095

0.073

0.989

2.267

1.188

−0.720

−2.113

0.498

−1.513

0.315

−1.856

−1.046

−0.187

0.726

0.241

1.092

−0.314

Prin7

0.220

0.255

−0.543

0.619

0.655

−1.349

0.576

0.086

−2.278

0.794

−0.091

−0.147

−0.131

−0.728

0.464

−0.258

−0.445

−0.858

0.446

−1.109

−1.373

0.237

Prin8

−1.825

−1.198

0.919

−0.092

−2.671

−0.263

0.323

0.861

−0.105

−1.220

−1.370

0.874

−0.981

1.637

−0.690

1.433

0.411

0.378

0.085

−0.174

0.398

0.915

Prin9

−0.538

−0.512

1.426

−1.464

1.829

−0.503

−1.147

−1.782

0.977

1.136

−0.063

0.580

1.583

−0.973

−1.120

−0.821

0.481

−1.171

0.699

−0.448

−0.061

0.051

Prin10

(continued)

−0.369

−0.367

−0.361

−0.354

−0.353

−0.324

−0.312

−0.307

−0.294

−0.291

−0.285

−0.274

−0.259

−0.255

−0.252

−0.252

−0.229

−0.228

−0.227

−0.225

−0.217

−0.200

KRGi

2 Assessing Innovation Capability and Technological Readiness of KRG 43

−0.554 −0.591 −0.514 −0.178

−0.379

−0.204

−3.138

−0.307

−0.992

−1.713

−0.761

−0.448

−0.619

−0.634

−0.161

−0.761

−1.535

−1.283

−1.014

−0.681

−0.773

−1.174

−1.346

−0.746

−0.535

1.415

−0.484

0.112

−1.327

−0.753

−0.675

−0.599

−0.784

−0.433

−0.862

−1.147

−1.059

−0.945

−0.687

−0.686

−0.853

−0.893

22

6

33

7

10

15

27

54

84

99

47

69

91

85

101

48

92

31

32

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

−0.465

−0.833

−0.575

−1.546

−0.824

0.120

0.061

−0.943

−1.548

−1.047

−0.429

−0.848

−0.589

1.639

−0.897

−0.913

−0.515

−0.691

13

Prin3

Prin2

Prin1

Name

Obs

89

Table 2.6 (continued)

−0.229

−0.607

−0.696

0.197

0.565

−0.749

0.022

−0.879

−0.238

−0.712

0.508

0.281

0.767

0.576

0.129

0.161

0.668

0.754

−0.308

−0.092

Prin4

−1.307

−0.310

−0.512

−0.397

−1.297

1.053

0.431

1.167

−0.561

1.094

0.341

−1.483

0.563

−1.048

−1.190

−0.338

−0.650

−1.464

0.601

0.779

Prin5

−1.188

−1.267

−1.881

−0.621

0.272

−0.412

0.176

0.135

0.359

0.011

−2.946

1.289

−1.345

1.360

−0.232

−2.255

−1.759

−0.611

0.357

−0.063

Prin6

−0.033

0.369

0.710

−0.288

0.385

0.600

0.962

−0.337

−2.088

−0.338

0.169

−1.461

−0.655

0.810

−2.348

0.697

1.288

−0.673

−0.534

−0.292

Prin7

1.097

1.386

1.912

0.548

0.361

−0.270

1.471

−0.750

−0.044

−0.478

−3.020

−0.155

−3.122

−0.353

0.988

2.632

−1.387

1.248

−1.077

0.564

Prin8

1.104

−0.049

0.390

−1.025

1.829

−1.495

−0.929

1.057

−0.929

1.041

−1.115

1.777

−0.458

−0.063

0.868

0.705

−2.314

2.477

0.805

1.120

Prin9

−1.584

−1.251

−0.405

−1.675

1.093

0.045

1.039

−1.636

−1.284

−0.818

1.526

1.282

−1.338

−0.433

−0.448

0.430

0.034

−0.030

0.869

1.027

Prin10

−0.815

−0.751

−0.641

−0.595

−0.592

−0.543

−0.540

−0.517

−0.495

−0.478

−0.468

−0.466

−−0.442

−0.431

−0.424

−0.399

−0.386

−0.383

−0.380

−0.375

KRGi

44 A. F. Mustafa

0.22

0.43

0.32

0.21

0.00 −0.12

X7

X8

X9

X10

0.34

1.00

X8

1.00

X9

0.07

0.26

X15 −0.12

0.18

0.02 −0.09

X16

X17

0.15

0.14

0.13

0.11

0.05

0.04

X24

X25

X26 −0.03

X27

0.05

0.30

0.11

0.22

0.04

0.31

0.17

X21

X22

0.05

0.10

X20 −0.02

X23

0.01

X19 −0.20 −0.13

0.02

0.04

0.08

0.00

0.04

0.22

0.30

0.14

0.00

0.01

X18 −0.05

0.46

0.23

0.16

0.10

X14

0.00

0.25

0.25

0.11

X13

0.08

0.11

0.15

0.10

0.08

0.19

0.14

0.20

0.11

0.05 0.04

0.00 −0.10

0.04 −0.04 −0.10 −0.24

0.00 −0.04

0.06

0.02 −0.05 −0.04 −0.20 −0.01

0.31

0.39 0.11

0.03 −0.02 −0.08 −0.21

0.07 0.28

0.00 0.13

0.08 0.19

0.10 0.25

0.06

0.31

1.00

X17

0.31

1.00

X18

0.01

0.35

0.43

−0.02

0.00

−0.02

0.01

0.00

X21 X2

X23 X24 X25 X26 X27

0.00 0.10 0.07 1.00

0.17 0.76 1.00

0.21 1.00

1.00

X20

0.09

0.12

0.00 0.05 0.07 0.85 0.86 0.76 0.89 1.00

0.00 0.04 0.02 0.89 0.81 0.83 1.00

0.16 −0.03 0.09 0.03 0.78 0.76 1.00

0.09 −0.06 0.01 0.07 0.77 1.00

0.14

0.03 −0.07 0.01

0.03 −0.06 0.02

0.16

1.00

X19

0.01 −0.06

0.03 −0.01

0.05

0.03

0.12

0.13

0.05 −0.17 −0.10

0.10

0.22

0.32

0.04 0.16 −−0.03

0.18 0.18

0.28 0.23

0.24 0.20

0.07 −0.06 0.08

0.00

0.08

0.21 −0.26

0.21 0.36 0.10 0.25

1.00

X15 X16

0.45 1.00

1.00

X14

0.01 −0.08 0.20

0.10 −0.09 −0.01 −0.24

0.05 −0.05

0.30 −0.32

0.10

0.07 −0.05

0.09 −0.09

0.15

0.04

0.03 −0.02

0.14 −0.32

0.04 −0.04

0.15

0.32

0.15

0.19 −0.14

0.04 −0.08

0.14 −0.16

0.29 −0.03

0.15 −0.07

0.19 −0.06

0.06 −0.21

1.00

X13

0.18

1.00

X12

0.32 −0.13

0.09

1.00

X11

0.31 −0.35 −0.02 −0.12

0.07 −0.03

0.26

0.04

0.14

0.14

0.10

0.15

0.06

0.09

X12 −0.01 −0.12

1.00

X10

0.15 −0.04

0.01 −0.12 −0.25

0.34

0.43

1.00

X7

X11 −0.07 −0.05 −0.03 −0.03

0.22

1.00

0.40

1.00

0.18

X5

X6

X6

X5

Table 2.7 The correlation matrix of the sub-indices of the KRGi for 108 entities

2 Assessing Innovation Capability and Technological Readiness of KRG 45

46

A. F. Mustafa

Table 2.8 Interview questionnaire No

Question

1

What is your recommendation for those who are ranked high according to our index?

2

What is the role of the government in promoting those entities that are ranked high?

3

What the government should do for those who are lagging behind?

Appendix 2: Qualitative Part See Table 2.8.

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Gelderman CJ, Paul WT, Van Diemen R (2011) Choosing self-service technologies or interpersonal services—the impact of situational factors and technology-related attitudes. J Retail Consum Serv 18(5):414–421 Haini H (2019) Internet penetration, human capital and economic growth in the ASEAN economies: evidence from a translog production function. Appl Econ Lett 26(21):1774–1778 Hsu C-L, Chuan-Chuan Lin J, Chiang H-S (2013) The effects of blogger recommendations on customers’ online shopping intentions. Int Res 23(1);69–88 Hussein Z (2017) Leading to intention: the role of attitude in relation to technology acceptance model in e-learning. Procedia Comput Sci 105:159–164 Jolliffe IT (1986) Principal component analysis and factor analysis. In Principal component analysis (pp 115–128). Springer Kendiukhov I, Tvaronaviciene M (2017) Managing innovations in sustainable economic growth Khayyat N (2019) Technology management and policy of kurdistan region of Iraq. In: Anaid A, Tugdar EE (eds) Iraqi Kurdistan’s statehood aspirations: a political economy approach. Springer International Publishing, Cham, pp 87–98 Khayyat NT, Lee J-D (2015) A measure of technological capabilities for developing countries. Technol Forecast Soc Chang 92:210–223 Kim L, Nelson RR (2000) Technology, learning, and innovation: experiences of newly industrializing economies. Cambridge University Press Kvochko E (2013) Five ways technology can help economy. Retrieved from https://www.weforum. org/agenda/2013/04/five-ways-technology-can-help-the-economy/ Lall S (2001) Competitiveness indices and developing countries: an economic evaluation of the global competitiveness report. World Dev 29(9):1501–1525 Legris P, Ingham J, Collerette P (2003) Why do people use information technology? A critical review of the technology acceptance model. Inf Manag 40(3):191–204 Liljander V, Gillberg F, Gummerus J, Van Riel A (2006) Technology readiness and the evaluation and adoption of self-service technologies. J Retail Consum Serv 13(3):177–191 Lin C-Y, Ho Y-H (2009) RFID technology adoption and supply chain performance: an empirical study in China’s logistics industry. Supply Chain Manage Int J 14(5):369–378 Lin CH, Shih HY, Sher PJ (2007) Integrating technology readiness into technology acceptance: the TRAM model. Psychol Mark 24(7):641–657 Malefane MR (2020) Industrial policy, trade openness and economic growth nexus: an exploratory review. Proceed Eng 2(02):169–178 Malhotra Y, Galletta DF (1999) Extending the technology acceptance model to account for social influence: theoretical bases and empirical validation. Paper presented at the Systems sciences, 1999. HICSS-32. Proceedings of the 32nd annual Hawaii international conference on Maradana RP, Pradhan RP, Dash S, Zaki DB, Gaurav K, Jayakumar M, Sarangi AK (2019) Innovation and economic growth in european economic area countries: the Granger causality approach. IIMB Manag Rev 31(3):268–282. https://doi.org/10.1016/j.iimb.2019.03.002 Muchran M, Ahmar AS (2019) Application of TAM model to the use of information technology. arXiv:1901.11358 Mulgan GaA, D (2003) Innovation in the Public Sector Musandiwa TJ, Ngwakwe CC (2020) Effect of technology adoption on new product innovation. Manag Global Trans Int Res J 18(3) Parasuraman A, Colby C (2001) Techno-ready marketing: how and why consumers adopt technology. Free Press Pascali L (2017) The wind of change: maritime technology, trade, and economic development. Am Econ Rev 107(9):2821–2854 Pradhan R, Mallik G, Bagchi TP (2018) Information communication technology (ICT) infrastructure and economic growth: a causality evinced by cross-country panel data. IIMB Management Review Ramírez-Angulo PJ, Duque-Oliva EJ Technology readiness and E-Loyalty in B2C E-Commerce. ACR, 42

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Rogers EM (1995) Diffusion of innovations. Free Press , New York Rojas-Mendez JI, Parasuraman A, Papadopoulos N (2017) Demographics, attitudes, and technology readiness: a cross-cultural analysis and model validation. Marketing Intelligence & Planning Shin S, Lee W-J (2014) The effects of technology readiness and technology acceptance on NFC mobile payment services in Korea. J Appl Bus Res 30(6):1615 Tabrizian S (2019) Technological innovation to achieve sustainable development—renewable energy technologies diffusion in developing countries. Sustain Dev 27(3):537–544 Tidd J (2001) Innovation management in context: environment, organization and performance. Int J Manag Rev 3(3):169–183. https://doi.org/10.1111/1468-2370.00062 Tsikriktsis N (2004) A technology readiness-based taxonomy of customers: a replication and extension. J Serv Res 7(1):42–52 Turban E, Outland J, King D, Lee JK, Liang T-P, Turban DC (2018) Electronic commerce 2018: a managerial and social networks perspective. Springer UNDP, U. N. D. P. (2001a) Industrial development report, making new technologies work for human development UNDP, U. N. D. P. (2001b) Making new technologies work for human development, 264 UNIDO, U. N. I. D. O. (2002) Competing through innovation and learning, 203 Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage Sci 46(2):186–204 Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS quarterly, 425–478 Wadho W, Chaudhry A (2018) Innovation and firm performance in developing countries: the case of Pakistani textile and apparel manufacturers. Res Policy 47(7):1283–1294 Walker RM, Jeanes E, Rowlands R (2002) Measuring innovation-applying the literature-based innovation output indicator to public services. Public Administration 80(1):201–214 Weaver P, Jansen L, Van Grootveld G, Van Spiegel E, Vergragt P (2017) Sustainable technology development, Routledge Webster A, Gardner J (2019) Aligning technology and institutional readiness: the adoption of innovation. Technol Anal Strateg Manag 31(10):1229–1241 WEF. World Economic Forum (2002) The global competitiveness report

Chapter 3

Examining Customer State Preferences of Mobile Services in the Kurdistan Region of Iraq: A Conjoint Analysis Approach Laila M. Halee

3.1 General Overview 3.1.1 Introduction Over the last decades, with the advent of new technologies and magnificent changes in telecommunication, communication abilities have increased dramatically; it could have a remarkable role as a major contributor to the rapid growth of the economy, in fact, telecommunication could turn the world into a more globalized place in current time. Telecommunication has turned into one of the pillars of the business environment. Due to continued technological change, mobile telecommunication become a dynamic competitive environment. New communication and information technologies have enhanced communication patterns, by making easier and cheaper communication across geographic locations and time, Telecommunication service as a data service allowed consumers to have easy access, to digitalized content without constraints such as space and time. Almost two decades ago, before the evolution of the internet and other data network, Telecommunication service in Iraq was limited to PSTN. Technology has developed rapidly and could bring a disparate range of services to mobile service consumers and organizations. The technologies are defining the majority capabilities of the services they provide. In the year 2000, mobile communication technology in the Kurdistan region of Iraq was limited to the second generation (2G) that supported voice calls and short L. M. Halee (B) Iraq-Erbil, Bakhtiyari, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. T. Khayyat and G. M. Muhamad (eds.), Empirical Studies of an Internet and Service Based Economy, Perspectives on Development in the Middle East and North Africa (MENA) Region, https://doi.org/10.1007/978-981-99-3389-1_3

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messaging (SMS), in 2006, the 2.5 generation was introduced which provided lowspeed internet emerged. Mobile service providers in Iraq, as in other countries, were competing on acquiring new customers; retain customers that may churn their network. They were generating revenue through promotions of Voice and SMS services and Recharge offers to increase transactions of SMSs and competing on cheaper call tariffs. After the evolution of the internet, and the introduction of 3G technology, the high-speed internet in Iraq, in the year 2015, a simple mobile phone turned into a pervasive device in daily life. Mobile service providers started to offer mobile users enormous services and applications via fast-speed internet, to run daily business and life’s needs easier such as paying bills, receiving interesting information, and attractive mobile applications. However, after the introduction of the 3G technology, voice service as the main revenue driver of the telecommunication market started to decline, while data service revenue slowly raised. New technologies such as 4G LTE, and the evolution of the latest generation 5G, with appearing of these new technologies, it will bring high-speed mobile internet and competition in the Telecom market will be more solid. As soon as all mobile operators have the same coverage availability in all governorates with similar technologies, they will offer identical products and services to mobile service consumers. In recent years, the political discontent in Iraq has affected the telecommunication sector and created a challenging environment for Telecom operators.1 Customer demands and average revenue per user (ARPU) declined dramatically in 2014. The main reasons were negative externalities such as the ISIS war, economic crises, and regulators in Iraq. This situation had similar impacts on all operators.2 Customers started to be more suspicious of their pocket money and choose services more precisely in a cautious matter, mainly based on their expenditure on mobile services. Offering cheaper internet services by Unlicensed-internet service providers in Kurdistan territory, and paying no Tax to the government, made the challenge for mobile operators inflexible.3 Continued operating without affording license payments and investing in their technologies. They started to give cheaper internet service than mobile service operators in the region. Data consumers have started using OTT (over the top applications such as messenger, WhatsApp, Viber, skype…etc.), hence, mobile service users are minimizing the use of mobile call and short message services. With the evolution of artificial intelligence, adoption of big data analytics, and applying smart tools such as data mining in understanding consumer behavior, a high level of manual process in optimizing or designing new campaigns could be decreased. They design individual offers for customers by analyzing their usage 1

Fitch Solution-Iraq Telco Report-10 years. Asiacell and Zain Finance report-2014 -2015. 3 www.cmc.iq. The Iraqi Communication and Media Commission (CMC) did not grant license to internet service providers such as Fast Link and Newroz Telecom in 2018). 2

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and estimating their preferences. Applying these tools for real-time data analytics is costly, but Telecom companies have started to use more prediction and real-time data analytics to increase the switching costs of mobile service consumers. Telecommunication companies mostly rely on demand forecasts to validate the substantial investment needed to guarantee capacity available at the right time. Abundant preceding studies have focused on the mobile communication sector and customer preference, however, changing technologies and harder competition between service providers continue to open new empirical and academic research prospects. This chapter employs a conjoint analysis to study mobile service attributes such as call service, SMS service, internet service, and price, and combine them in the forms of different bundles to estimate customer preferences and forecast mobile consumer usage in the Kurdistan region. To date no study has been conducted in this field, this study considers the first attempt to study the mobile service sector in Iraq, which investigates about mobile service attributes with conjoint analysis. This study highlights the impact of different attributes on consumer decisions, which are considered by consumers as an important and appropriate factor in their subscription decision. The study offers practical guidance that supports the methodological use of conjoint analysis in mobile telecommunication field across various mobile service providers.

3.1.2 Problem Statement In the current competitive and complex business environment of the telecom market in Iraq and the Kurdistan region of Iraq, it is challenging for mobile service providers to entice new customers with attractive services/products, and increase loyalty for long-term sustainable growth. Telecommunication service providers are trying to increase their market share by offering services that can cover customer desires of mobile services, meanwhile having a satisfying return on investment and business profit for the operators. Most mobile services are promotional marketing activities of several services such as voice, recharge and internet, rather than concentrating on a definite service targeted at a specific segment of customers. To validate a service delivery strategy that leads to secure customer satisfaction, loyalty, and business success, as indicated by Khayyat and Heshmati (2012), it is imperative to determine that the provided service is ideal and desirable by mobile service consumers. Mobile service providers, emphasize on up-to-date behavior of consumers, to design new products or services that fulfill their needs (Smith 2020). Lawson (2021) asserts that the intention of developing a new product is to respond to customer requirements, gain a competitive advantage, and increase profits, but introducing a new product is an expensive and risky process. Tascioglu et al. (2019) claim that understating the purchasing behavior of consumers is critical for understanding and predicting the next purchase by a

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customer, through knowing customer preferences, mobile service providers can improve promotion plans to survive in a competitive market. Zhu (2007) argued that satisfying Mobile service consumers, especially the youth segment that represents the majority of mobile service users is a challenge for mobile service providers. The youth segment is getting more knowledgeable and smarter, and their demand for new and creative services is raising. They choose service/product/ offers more precisely and consider various factors before choosing a mobile service therefore, it is crucial for mobile service providers to analyze the customer preference that designates the reasons or factors that affect people’s choice for selecting a service or product. As indicated by Chaichana (2014), consumer preference is the most significant and influential factor in determining business success; it helps business to develop a new product, supports business to target their products toward specific consumers segment and recognize reasons that lead to the success of some products over others. Some characteristics of consumers such as Socio economic are observed factors, which can explain the criteria for choosing a product, but there are some unobserved factors that are unknown to researchers how people make a different choice than others. Through this research, observed factors that may influence consumer preferences of mobile services are studied and analyzed with discrete choice models of consumer preferences.

3.1.3 The Evolution of the Mobile Telecommunication in Iraq Iraq has one of the most exploited telecommunication markets; it has a highly competitive market with three main mobile network operators and more than five internet service providers. The following statistics are provided based on a recent market research report of4 Fitch solution Telco report published in 2019: • Mobile penetration in Iraq is around 90%. • The mobile sector almost completely relay on prepaid services, average revenue per mobile user in Iraq is lower than in the Middle East and North Africa. • Iraq launched the 4G in 2019. • Continued positioning of national mobile networks will permit mobile operators to compete on innovations, price, service and quality. • Mobile subscribers in Iraq will reach 51.6 million subscribers in 2028. As shown in Table 3.1 total mobile service users will reach more than 43 million customers in the next four years and 3G data service will increase to 29 million customers.

4

Fitch Solution-Iraq Telco Report-10 years forecast is a Macro research product of Fitch Solution Group Ltd,UK company. Fitch solution is integrated to Business Monitor International (BMI).

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Table 3.1 The telecom sector status in Iraq Telecoms sector- historical data (IRAQ 2017–2022) Indicator Total mobile phone subscribers (‘000)

2017

2018

347.313 378.140

2019

2020

2021

2022

39.309.6

407.779.3.3

42.215.6

43.611.2

3G subscriptions (‘000)

12.925

17.404.8

21.129.5

24.404.5

26.893.8

29.314.2

Total mobile phone subscribers/100 inhabitants

90.7

96.1

97.3

98.3

99.1

99.7

6974.5

6618.8

6373.5

6203.8

904.3

860.9

850.6

837.8

822.3

2.3

2.1

Monthly blended ARPU,IQD

9020.8

Total fixed voice 1011.3 subscribers (‘000) Total fixed voice subscribers/100 inhabitants

2.6

Total broadband subscriptions (‘000)

408.1

Total broadband subscriptions (‘000)/100 inhabitants

1.1

8.005

445

1.1

2.00

2.00

1.9

439.2

470.9

505.4

530.1

1.1

1.1

1.2

1.2

F-Fitch solution forecast-Resource regulator-Operators-Fitch solutions

Average revenue per user declined from 9,000 IQD to 7,000 IQD. The challenge will be harder for the mobile operators, to increase the revenue. The Kurdistan region as an autonomous region in Iraq has seen incredible growth in mobile penetration, after the establishment of the regional government in 1992, the government promoted and expanded the communication services as satellitebased communication services and systems, connecting people to other areas and countries. As Khayyat and Heshmati (2012) mentioned, the region plugged into the world communication network and brought GSM technology in late of 1999. After the Iraq war in 2003, which had a significant impact on the region as starting new investments as construction and communication sectors, mobile telecommunication market turned to a successful experience story in the Kurdistan region. In5 December 2003, the first mobile license was issued for three defined zones in Iraq. 5

www.cmc.iq.

54 Table 3.2 Market share of telecom operators in Iraq

L. M. Halee

The main mobile network providers

Market share 2019 (%)

Asiacell

37

Zain

43

Korek telecom

18

Source Asaicell, Korek and Zain Websites-According number of subscribers of each operator

One License for southern region, one for the central of Iraq and one license for the Kurdistan Region. The nationwide license was awarded in August 2007 (Khayyat and Heshmati 2012). Thus, the winning bidders’ operators that could get the nationwide license, started operating in all regions of Iraq. These operators are Korek telecom which was limited to Erbil, Duhok and partially Sulymaniah, Asiacell which was operating mostly in Sulymaniah and southern regions and Iraquna and MTC Atheer which were operating in southern regions, Iraquna and Atheer merged to form the Zain Iraq after Zain group started investing in Iraq telecommunication sector.

3.1.4 Mobile Service Providers in the Kurdistan Region With the augmented use of mobile service users, across the developing world, the Kurdistan region of Iraq played a significant role in telecom service industry. This industry uses the latest technology infrastructure. Mobile service operators are enriched with 4G technology. The telecommunication mobile service sector in the Kurdistan Region of Iraq is one of the developing sectors that provides services to satisfy customer expectations. Korek Telecom and Asiacell launched prepaid services as call service offering call service and short SMS service in 2000 by adopting GSM technology in 2000.6 They developed their technology and expanded coverage, providing other major mobile services such as roaming services in 2005, data service with 2G technology in the year 2007 and hundreds of value added services and mobile applications (Table 3.2). In the broadband sector in the Kurdistan region according to the history of available internet offers and short time between offering new packages, there is a strong competition between the key players including Fast link, Newroz telecom and O3 Telecom.

6

http://www.asiacell.com, http://ww.korektel.com.

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Zain Iraq 7

Zain Iraq is a part of Zain group, a telecom group with coverage in 8 countries across middle-east and Africa. Presently Zain Iraq is considered as top Mobile service provider serving 16 Mobile subscribers.8 Zain is pursuing its network expansion northward to the Kurdistan region, while Korek and Asiacell are expanding their network southward.9

Asiacell Asiacell has more than 14 million customers, the first telecom operator that could achieve nationwide coverage. It launched 3G in Jan 2015, and 99% of its subscribers are prepaid. Asiacell offers different packages to benefit different segments of mobile users as women and youth. Ooredoo started its ownership of Asiacell in 2012. In 2013 Asiacell was listed in the global stock Exchange market.10

Korek Telecom Korek Telecom is a shared limited telecom company that started as a regional operator in the North of Iraq in the year 2000. In 2007, Korek telecom awarded a nationwide mobile operator license. Currently, it provides mobile coverage to all of Iraq with a wide range of services for business and individual mobile users, Korek’s main competitive advantage in the market is offering high quality service with best value in 2011, Korek Telecom signed a strategic partnership with France Telecom-Orange and Agility to leverage its leadership position in Iraq.11 Korek launched 3G (The third generation of Internet) with other operators in 2015, started offering internet service with high-speed with 4G. Presently (2019) Korek serves more than 7 million customers and has over 18% of the market share of Iraq’s mobile telecom market.

7

www.iq.zain.com. Zain group Finance report press realse-www.zain.com. 9 www.telcomatraining.com/list-of-mobile-network-operators-of-iraq. 10 www.Ooredoo.com-Ooredoo is a Qatari and international telecommunication company operating across North Africa, South Asia and Middle East and has 164 million customers across 12 countries. 11 Agility is Kuwaiti traded global logistic provider, operating across 100 countries. Agility company provides transportation, warehouse, supply chain management services.France telecom-Orange is multinational and Forth largest telecommunication in Europe with 256 million customers. www. orange.com. 8

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3.1.5 Aim of the Research With the introduction of 4G networks, bundles that contain text messages, free call minutes and limited internet quota became prevalent among telecommunication companies. The principal purpose is to estimate the customer preference toward the different telecom services by creating packages or bundles through applying choice-based conjoint analysis, and finding the best package that could cover the needs of mobile service users in the Kurdistan Regions of Iraq. In addition identify the factors that influence customer preferences. The objectives of this research are as follows: 1. To identify factors including socio-demographic variables that may affect consumers’ choice. 2. To identify the most preferred mobile applications that can attract mobile consumers in Kurdistan region of Iraq. 3. To identify the best packages (Voice and data) that can benefit Mobile consumers and match their needs.

3.1.6 Delimitation of the Study This research is delimited to mobile service providers in Kurdistan regions, Erbil, Duhok and Sulymaniah. The sample of this study is delimited to 273 respondents. All respondents were individual mobile service consumers in Kurdistan region, mobile service users of the three main mobile service operators. Respondents were limited to public, private sector and students. Employees of Telecommunication industries were not involved in sample to avoid any effect of industry information on respondent’s perceptions.

3.2 Previous Literature This section covers three parts. First, it reviews research regarding technology acceptance models. Second, it embarks on the evaluation of previous relevant studies for Bundling in the Telecommunication sector and finally, part three provides a systematic literature review of conjoint analysis for relevant academic research.

3.2.1 The Technology Acceptance Model The Technology acceptance model (TAM) is considered one of the most applicable and widely used research models, as the theoretical basis for forecasting factors that

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Fig. 3.1 Technology Acceptance Model (Davis 1989)

influence the adoption or use of new technological innovations. It is developed and proposed by Davis (1989), to explain and study the acceptance of technology by individuals in the working environment, this model explains that when a new technology is presented to users, some factors determine their decision about when and how they will use it. Hong and Tam (2006) stated that TAM has been widely accepted as a powerful model, to illustrate the factors that impact user’s usage behavior and adoption across the diversity of information technology. TAM proposed that an individual forms an attitude to an object based on some definite beliefs, which led to framing an intention to behave with respect to that object. Davis (1989), and Malhotra and Galletta (1999), summarized TAM as shown in Fig. 3.1. • External Variable In this model can comprise socio-demographic factors, Perceived ease of use, and perceived usefulness Perceived usefulness and preserved ease are two major concepts of TAM: • Perceived usefulness (PU): Refers to the degree to which an individual thinks that using a specific system will improve his or her job performance or productivity (Venkatesh 2000). • Perceived ease of use (PEOU): Refers to the degree to which an individual thinks that using a specific system would be free of effort (Venkatesh 2000). • Attitude toward use: It refers to the degree of desire to an individual has on using the technology or system • Behavioral Intention: It is a combination of perceived usefulness and Attitude toward use. • Actual use: It is calculated by the behavioral intentions of the individual user It is theorized that user perceptions about ease of use and usefulness of the technology can determine attitudes toward using new information technology such as mobile phones, it is essentially the key basis of the behavioral intention and eventually determines the actual use of the technology (Davis 1989). Moreover, TAM has been used in many different fields for research studies, to examine and understand the fundamental inspirations that lead users to adopt new technologies such as mobile services and the adoption behavior of users. They have examined additional functional dimensions as hedonic and experimental aspects. They applied the Technology acceptance model and extended some aspects.

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In this study, Mobile service attributes such as calls, short messages, and internet services are alternatives to consumers. Each of the services has a different perception and acceptance by mobile service consumers.

Call Service The prospective of video call service market amplified rapidly with the appearing 3G mobile service. Lin and Liu (2009), investigated adoption behavior of video call services in Taiwan through applying media richness theory and an extension of TAM model as perceived price and perceived critical mass to explain mobile video call service adoption. The model was tested empirically by using data from online surveys to target mobile users. Result of his study revealed the most significant factor is perceived critical mass of video call service users. The second fundamental factor was perceived usefulness, the third one was perceived price and the fourth factor was perceived enjoyment.

SMS Service Short message service, is a low cost mobile service of telecommunication services that after appearing mobile messenger application as the top service is declining. SMS service became a requisite for young generation who use SMS more than call service to preserve their social relationship through exchanging messages over the mobile (Rheingold 2007). SMS is being broadly used, not only for chattering and information service, but also for advertising, for marketing advertising and e-government services is comprehensively popular. Kim et al. (2008), investigated the factors that related to the adoption of SMS services in Korea. Through applying technology acceptance model and its extensions as perceived value, perceived enjoyment, perceived ease of use and perceived usefulness. Result of the study showed that the main motivation of SMS service adoption is perceived enjoyment that can influence improving value perceptions of perceived value of usefulness and ease of use. Kim et al. (2013), in their study, with technology acceptance model, inspect student intention to adopt SMS service and examine the gender differences as a moderating factor in SMS adoption for m-learning in universities. They evaluated the model with data collected from 225 SMS users. Result of the study revealed that perceived usefulness contributes as a stronger persuasive factor in user adoption of SMS for learning than perceived ease of use. Furthermore, they found out that Female students valued ease of use, significantly higher than Male students on intention to adopt SMS service. Afzal et al. (2015), attempt to analyze the factors that determine consumer intention to adopt SMS by youth of Pakistan. The researcher hypothesized in his study that perceived usefulness, perceived ease of use, perceived cost and perceived enjoyment positively influence customer intention to adopt SMS service. The finding of

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the study after analyzing collected data by mail questionnaire of 329 undergraduates and graduates of different universities supported all hypothesis except perceived cost.

Data or Internet Service Kim and Han (2009), argued the key drivers of mobile data service acceptance, they proposed an extended theoretical framework including not only utilitarian value but also a set of perceived values as12 hedonic value, social values that people consider as remarkable factors that impact decision making process of pay as you go services as mobile data service. The result of their study, after testing their proposed model, through surveying 287 potential users of mobile data services, showed that social values and utilitarian have the highest impact on mobile service acceptance, while hedonic value has the lowest impact in adopting data service decision. Moreover, this study could reveal the moderating role of gender and age in adopting the decision process of data service. Al-Debei and Al-Lozi (2014), investigated the best predictor toward the adoption of mobile data service in Jordan, they examined the perceived value of Data consumer, and used a multidimensional model of (enjoyment value, Utilitarian value, uniqueness value, epistemic value and economic value). The majority of their interviewed sample were university students. The result indicated that utilitarian and economic value are the best predictors for the adoption of Mobile data services. Furthermore, technology was the greatest influencer on people’s perception about utilitarian and economic value dimensions. Additionally, they suggested more research to discover how consumer perception can change across different types of Mobile data services. Pagani (2006), proposed a theoretical business-oriented model to investigate the most important factors in making decisions in the adoption of high-speed internet service. Through developing of an explorative survey of twelve business corporates, hypotheses are expressed and a model creates. The main data was collected from 1545 business companies in Europe and USA. The research result revealed that interest or consumer awareness plays a major role in influencing intention in adoption of data services and customer satisfaction was the most important attribute for corporates in adopting data service. Kongaut and Bohlin (2016), investigated the development of mobile internet service adoption in the last decade and key drivers that currently are shaping adoption of mobile data services in Sweden. They collected data through an annual survey by telecommunication regulatory. Result revealed that consumers with higher education, higher income and lower age are the potential adopters of the internet service. Kitchen et al. (2015), examined the critical characteristics that may affect intention to adopt long-term evolution mobile services in Malaysia.13 He used the extension of technology acceptance model by including other perceived values as perceived 12

Kim and Han (2009) Hedonic value shows “pleasure, enjoyment and Concern associated to the use of service or product”. 13 LTE offers an outstandingly higher speed capability compared to 3G mobile network.

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processing speed and image. The result of the study revealed that attitude is the most critical factor in forecasting intension toward adopting long-term evolution services over other factors such as perceived processing speed, perceived usefulness, image and personal innovativeness. Qi et al. (2009), analyzed reasons of using mobile data services in China. They presented an extended technology acceptance model by including data subscribers’ experienced attributes in mobile data service consumption; such as voice service experiences, innovation experience and brand experience that could affect customer attitude. They tested the model by data collection of 802 data mobile users. The finding of the study showed that brand experience and subscriber’s perceived ease of use are the most significant factors that could affect subscribers in data service adoption. In addition, two other factors as innovation experience and voice service experience influence subscriber’s usage behavior significantly. Rao Hill and Troshani (2007), came up with a conceptual framework about the existing technology acceptance theories, and in doing so; they proposed a new framework for the acceptance of mobile services. The main finding of their research was that user demographic as age and Gender can affect the ease of use, perceived utility, social impact and influencing conditions to adopt the mobile services. Koenig-Lewis et al. (2010), examined the barriers for adopting mobile banking service, by extending TAM model, and adding the other factor as trust, suitability, reliability, perceived cost and risk on behavioral intention. The data collected by an online survey in Germany; the result of his study exposed that these factors are substantial in the adoption of mobile banking service. Suitability was a major factor for perceived ease of use and usefulness and reliability. Gao et al. (2014), inspected acceptance of mobile information service by expanding the Technology acceptance model as Mobile service acceptance Model. Research’s hypothesis was tested by data collected from mobile service users. Research results from mobile data users collected data, indicated that both personal initiative and characteristics have a remarkable impact on the intention of using extended mobile information system. As a summary of this part, it can be highlighted that according to the applied TAM model in mentioned studies, there are different observed factors that can affect consumer decision in adopting call service, SMS service and internet service. The general one which was mentioned in majority of studies was perceived usefulness. Furthermore, there are some other factors as customer awareness, gender, education and age groups, which have a vital role in the degree of adoption of mobile service. In this study, impact of these observed factors is studied on customer preferences.

3.2.2 Customer Preferences and Conjoint Analysis Conjoint analysis is the well-known technique for identifying consumer preferences in decision makings of multi-attribute of the product or services. This technique has

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been applied broadly in marketing of different businesses, to calculate consumers ‘willingness to pay and optimization of product or service bundles sale. Mishra (2015), applied conjoint analysis technique to design mobile service packages by identifying customer preference and through hierarchical group analysis extracted similar and noticeable consumer segments from conjoint analysis output. Klein and Jakopin (2014), utilized conjoint analysis to examine user’s perception of mobile service bundles utility. The attributes that they used in their proposed bundling including but not limited to Internet access, minutes of talking, text messaging and price of mobile device. Their findings after analyzing the perceptions of 116 respondents mentioned that pricing was the most important and text messaging was the least important among the presented attributes. Nevertheless, internet access and minutes have a vital role and in contrast, messaging is the least imperative attribute in customers’ assessment of mobile offers. Tallberg et al. (2007) studied the impacts of handset bundling on data service adoption in Finland. The finding showed that handset bundling is a risky tool for steering the market but it can have a positive impact on the adoption of new services if carefully tuned by the regulator. Kim (2005), used the conjoint analysis technique to estimate consumer preferences for mobile services in Korea. He estimated consumer preference for 3G services such as video calls, multimedia mobile internet applications, and roaming services. His result of interviewing 250 respondents showed that consumers give a higher value to video calls over roaming services and multimedia mobile internet services. Sobolewski and Kopczewski (2017), applied preference discrete choice experimentation to estimate willingness to pay toward fixed-line telecommunication services and inspect complementarity and substitutability among numerous component services based on projected assessments. Research results designated that broadband was the top valued, and pay TV and fixed-line broadband were tremendous preservatives in evaluations representing complementarity. Mobile with fixed telecommunication services would be substituted in mobile and voice powered by unlimited voice and data plans. Nikou et al. (2012), applied conjoint analysis to validate the substitutability of voice and SMS services by internet-based services such as skype and WhatsApp. They assessed the willingness of people to use these services and whether concerns such as privacy, reliability, and security affect their decision. Research findings indicated that consumers are attracted to in-group communication as the most important attribute of these internet-based services, meanwhile for all services security and reliability, and interoperability are esteemed as imperative necessities. Zubey et al. (2002), used conjoint analysis to suggest attributes of VoIP that best meet consumer needs. The analysis looked at attributes of IP and PSTN Telephony as price, reliability, accessibility, quality, and services and made recommendations for the consumer on which attributes should be included in the product to be sold in the market. Based on attributes importance rate reliability adds the highest importance among consumer preference. Zhu (2007), examined internet service attributes such as price, brand, speed and application of internet service by conjoint analysis to find consumers’ preferences.

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The conjoint analysis result showed that the speed of internet service was the most significant attribute affecting consumers’ choices. Kim et al. (2017), used conjoint analysis to examine the attributes and consumers’ marginal willingness to pay for OTT services in China and Korea. He discovered that the overall WTP of China consumers was 3.4 USD per month and for Korean consumers was 3.1 USD per month-for OTT services. Hurtado (2016), applied the conjoint analysis technique to recognize and create a ranking criterion of the factors that students take into consideration when selecting an internet service provider, research results showed that the price and attachment of a mobile phone in the package are the most valuable attributes for students. Confraria et al. (2017), utilized conjoint analysis in order to identify consumer willingness to pay for mobile plans in Portugal. Attributes of plans were specified as the market share of the service provider, having friends and family on the same network, commitment period to the operator, call rates, and monthly recharge. The result presented for consumers being on the larger network (market share of the operator) and reducing the commitment period to the operator were the highest preferences among the offered plans. Dagli and Jenkins (2016), estimated willingness to pay for improved mobile services such as upgrading 4G and roaming services, they employed choice experiments for their study in Cyprus. They investigated attributes of improvements as increasing internet speed and internet quality with 4G, unlimited internet usage, and unrestrained roaming in two countries. Result of the study revealed consumer’s preferences were unrestrained roaming in two neighboring countries followed by increasing speed and unlimited usage attributes. Shin et al. (2011), used conjoint analysis to identify customer demand for launching mobile number portability (MNP) service in Uzbekistan. MNP refers to a consumer’s right to have the same mobile number when switching to a different mobile network. They estimated customer preference for other attributes such as service quality, call service, Price, discount calls on the same network, and network Provider Company. Analyzing results of 115 responses to their survey exhibited that consumers did not consider mobile number portability, as a substantial service upgrade, and still service quality and price were the most valuable characteristics for mobile service consumers in Korea. Takano (2016), studied customer preferences of Japanese mobile phone consumers, the author applied a conjoint analysis technique to a sample of mobile consumers over 15 years old. Analysis outcome showed that demand for the mobile network would be increased, if operators increase transmission speed, decrease the price of mobile sets, promote family applications with discounts, and if consumers do not change their mobile operator. Research result recommends mobile service operators provide more enticing mobile phone services by altering characteristics such as transmission speed and family discount offers. To inspect the essential condition for using a mobile device in higher education as an education tool for omnipresent learning, Lee (2013), used the conjoint analysis method. Based on the research result, the author suggests that businesses and

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policymakers develop a special mobile device for educational purposes for the effective distribution of omnipresent learning. Because students would like to use large screen-window-based devices and have access to a mobile network for educational purposes. To understand the relative importance of essential features in decision models of the consumer when assessing intention to use mobile services, Luo et al. (2013) utilized choice-based conjoint analysis in a technology acceptance tradeoffs research. They extracted the relative importance of each feature that could affect the adoption decision. Research results presented that reasonable usefulness is the threshold that service providers should effort to achieve and simple design does not vary expressively from the design with reasonable complexity. Kwak and Yoo (2012), evaluated consumers’ preference for 4G technology; they conducted 500 face-to-face interviews in Seoul, South Korea. They applied a Choice experiment to assess the willingness to pay for 4G attributes such as the quality of service, video-on-demand service, internet rates, broadcasting channels number, and accompanying services. The author found that consumers were quite ready to pay for 4G services. Their estimation for monthly willingness to pay was 4.03 USD for quality of service, 1.75 USD for video-on-demand, for broadcasting channel 0.06 USD, and for accompanying services 1.45 USD. As depicted in mentioned studies, the ultimate goal of conjoint analysis is to answer the research questions. In addition, to solve the study‘s problem, Conjoint analysis is an efficient technique to analyze customer preferences for mobile services and answer questions of the current study. This study employs conjoint design to test and analyze mobile service attributes, discover which attribute has a positive impact on consumer’s decisions, what factors affect their decision, and what are their desirable combinations of mobile service.

3.2.3 Bundling in Telecommunication In this study, to predict the customer preference for telecom services, several different bundles are designed, mobile service consumers of voice, data, and short message services validated these bundles, to choose the best one that satisfies their monthly or weekly mobile service needs. Bundling is a prevalent applied marketing strategy to increase fixed revenue and market share, reduce churn rate and enhance customer service in the Telecommunication sector (Bouwman et al. 2007). Kim (2005), argued that telecom operators should design a bundle based on consumer preferences and set an optimum price for bundles under a long-term profitability strategy. According to Lawless (1991), when a firm comprises more products in a bundle, it will be more challenging for competitors to duplicate the same bundle. García-Mariñoso and Martínez Giralt (2008), emphasize that generally, service providers implement bundling when they think characteristics of a few products or

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services together can attract many consumers as a package more than offering them individually. Pereira and Vareda (2013), investigated the impact of telecommunication bundles on regulations and competition. The research findings highlighted that behavior and technological fluctuations need a modification of regulatory value and bundles asserted to have considerable importance in telecom markets. Yang and Ng (2010), studied a mixed bundling problem in the Telecommunication business in China market. They intended to find a solution to increase the revenue of service providers by specifying the optimum price. They presented an algorithm and compared the revenue of three plans; mixed bundle where consumers can subscribe to the bundle and get a mobile phone at a discounted price, the bundle separately, and individual sale. Their modeling method specified the optimum bundle price of a promotional mobile phone with a subscription to the package. Their finding showed that a mixed bundling strategy could provide better revenue than selling the bundles individually. M. Yang (2013), identified the factors that influence subscriber retention in the South Korea mobile telecommunication market. Research studies specified the substantial role of mobile bundling in increasing customer retention. Although it depends on the services that include in the bundle. To know the impact of bundling on increasing the switching costs of individual telecommunication users and making them less likely to switch from their main service provider. Lee (2017) conducted a detailed survey in Korea. Results of the survey displayed the positive impact of bundling on decreasing switching to another provider. Internet consumers, which subscribed to bundles of their main service provider, were less likely to switch to competitors. A distinguished number of studies have focused on Forecasting Consumer valuations for Telecommunication service attributes and it was an interesting topic for researchers since Telecommunication services especially the internet advent in 1990. Each study is dedicated to finding the most important attribute, preferred package, and most important factor that affects the business success and customer retention. Until now, Research in the mobile telecommunication sector are limited, particularly; customer preferences of mobile service are inadequate, specifically in Iraq. After the literature review, it has been noticed that there are some similar researches which have been conducted in developing countries on forecasting customer preference of mobile services, but in Iraq, no previous research has engaged in a proper survey to gain the opinions of mobile service users as implementations on their preferences toward mobile services in Iraq. To reduce this research gap, the current study focuses not only on the accuracy of how individual conjoint measurement is performed for forecasting customer mobile preference but also on how mobile service bundling can show customer needs, and which package can cover customer requirements of a mobile service provider.

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3.3 Methodology and Model Specification This section outlines the methodological steps undertaken toward achieving the objectives of the research. It provides research methodology, details of the theoretical foundations of the applied conjoint approach, a perspective on Research design, sampling, and data collection, questionnaire design, at the questionnaire and the data collection technique is presented, in addition, the credibility and ethical considerations, finally, an expressive analysis of the data is discussed.

3.3.1 Questionnaire Design The questionnaire of this study consists of three parts: Part one- respondents profile includes inquiries about customer socio economic characteristics such as gender, age, monthly income, marital status, education level, occupation and living city. Age is categorized into four categories, (1) 18–24, (2) 25–35, (3) 36–49, and (4) more than 50 years old. Education level is categorized into five categories. (1) Primary, (2) Secondary, (3) Institute, (4)University-Bachelor, (5) masters degree and higher education, The occupation is categorized into four categories, (1) unemployed, (2) Government Employee, (3) Private sector Employee, (4) Academics. The living city is questioned in three categories, representing the main governorates of the region for target sample of the study,(1) Erbil,(2) Duhok,(3) Sulymaniah. Monthly income of the respondents is questioned in four categories based on the range of salaries in the government and private sectors. (1) 0–500,000 IQD, (2) (2)500,000 to 1,000,000 IQD, (3) 1,000,000 to 2,000,000 IQD, (4) More than 2,000,000 IQD. Part two of the questionnaire is about customer mobile service usage characteristics and preferences on mobile applications. Mobile service uses are questioned to mention their monthly expenditure on mobile service in five categories, based on recharge credit cards available in the market for three operators. According to the Fitch report mentioned in section one, the average revenue per user was fallen to 7,000 IQD. Therefore, the researcher put less than 5,000 IQD as the lowest range of mobile service expense per month and first category. Other categories are (2) 5,000–7000 IQD, (3)7000–10,000 IQD, (4) 10,000–15,000 IQD, (5) More than 15,000 IQD. Part three: Conjoint Survey. In this part, respondent is questioned to rank his/her preferred mobile service bundles based on the needs of monthly and weekly mobile services usage. As stated previously, the main aim of this study is to estimate customer preference for mobile services. Hence, according to research questions and support of previous literatures on bundling and applying conjoint analysis, the researcher used the conjoint method to design bundles and find the customer preferences of

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mobile service, in addition, mobile service bundles that can fit customer needs based on the profile of the customer. Creating attractive bundles should not be guided only by competition and internal requirements but also by customer needs. In this survey by ranking bundles, customers will indicate their needs from a mobile service provider, mobile service bundles according to14 weekly and monthly service usage that can benefit consumers and applicable for mobile service providers. The Questionnaire was offered to the mobile service consumers in Kurdish and English languages.

3.3.2 Foundation of Conjoint Analysis The main idea of this study is to estimate the customer preference for mobile services through offering bundles that are not commercially launched in the market yet. Toubia (2018) indicated that conjoint analysis has turned into an important marketing research tool, which is being used extensively in marketing to analyze consumer trade-offs, understand how consumers make decisions and predict consumer behavior. Generally, Conjoint analysis is a technique a market researcher describes a product or service through a set of attributes, joining the different levels of attributes to determine which attributes are important for consumer when wants to select a product or service (Kim et al. 2005). Developing a new product from idea to market is a costly and time-consuming process, it needs involvement of entire telecom functions such as Technical, marketing, Human Resource and Finance. Each function has its role in providing a service or offer to provide customer with a service that can cover his\her needs in a profitable way. However, before providing any new service or offer, it requires business companies as Telecom industries, to understand which feature of their products or service are most valued by the customers. Therefore, marketers prefer to have an idea of product success rate in the market as concept testing. Concept testing can be a suitable start of product development process. It consists of giving product explanation with a price to an appropriate target of consumers and getting their responses by a survey (Keller and Kotler 2022; Kotler and Keller 2006). It is critical for marketers to understand the factors that impact the choice of individuals from a group of services, from marketing viewpoint. Consumer’s choice can be examined in five steps, need of customer, collecting data, look for product or service, evaluation of alternatives, and purchase or subscribe customer experience evaluation (Kotler and Keller 2006). Conjoint analysis allows business to precisely study consumer behavior and make decisions based on actual insights from data, which supports developing business strategies to deliver a competitive advantage. 14

Researcher applied her study of current available call, data and SMS service by main three operators, quota and price, and designed weekly and monthly bundles.

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Conjoint Analysis supports marketing managers in sorting the comparative importance of a product’s attributes. It gets the overall decision about a set of profiles or alternatives and then decomposed the original assessments into separate utility scales which the original decisions can be reconstructed (Eggers et al. 2021). Green et al. (2001), defined conjoint analysis as the favorite methodology for finding out how consumers make compromises among competing products it can be a powerful tool for business decisions. Valkama (2014), indicated in his research that conjoint analysis allows researchers to hide the exact purpose of the research from participants and can entice a large number of respondents due to its time effectiveness and answering easiness. Since 2000, researchers and academics have found that Conjoint analysis is helpful in solving critical industry problems, by using this method, researchers have gained great achievements by making conjoint analysis views more directed and related to business reality (Hauser and Rao 2004). In this study, each choice base conjoint analysis is selected as a proper approach to estimate consumer’s preference, as it is the ideal statistical conjoint analysis technique, to enumerate consumer’s preferences, for products and services In addition, it could resemble the choice process of consumers in the market better (Orme 2006).

3.3.3 Attributes and Attributes Levels In conjoint approach of the study, the relevant attributes and their levels according to the stated research questions are identified, and based on attributes and their levels, profiles of combination of attributes and levels are made. As shown in Table 3.3 Table 3.3 Attribute and levels Attributes

#

Level

Description

Call service

1

3 h per day

3 h—same operator

2

10 h

10 h—same operator

Data service

SMS service

Cost

3

Unlimited

Unlimited minutes—same operator

1

60 MB

60 MB0 internet daily

2

1 GB

1 GB internet

3

800 MB

Only social media-but no call service

4

Unlimited

Unlimited with fair usage specified by operator

1

50 SMS

50 SMS onnet-same operator

2

150 SMS

150 SMS onnet-same operator

3

200 SMS

200 SMS onnet-same operator

1

5,000 IQD

Cost per package

2

10,000 IQD

Cost per package

3

30,000 IQD

Cost per package

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In conjoint analysis, attributes and levels identity as related properties on which a product or service could differ from others. In each attribute, levels should be mutually exclusive. level of each attribute is combined with levels of other attributes and creates several profiles. These profiles are considered as representative descriptions of product profiles (Jang and Choi 2018). In previous conjoint analysis studies of customer preference of mobile service which are mentioned in chapter two literature, researchers have utilized different types of attributes. For instance, Klein and Jakopin (2014), used attributes as internet access, minutes of talking, text messaging and price of mobile device in their proposed bundles. Kwak and Yoo (2012), investigated attributes of 4G technology as service quality, video-on-demand, number of broadcasting channels, internet rates and supplementary services. Kim et al. (2005), selected 3G mobile service attributes as video call, multimedia mobile internet application and roaming service. Shin et al. (2011) Studied customer preference of attributes as service quality, call service, network provide and discount on calls on same network. In this study through an extensive and detailed review of the literature on finding mobile service preferences studies, moreover, the researcher studied all offered bundles for each service provider in the market. In addition, researcher validated all attributes and levels through short focus group discussions with a few industry experts in pricing and marketing to design realistic bundles as alternatives in conjoint survey. As exhibited in Table 3.1, four attributes are defined for conjoint survey as follows: The first attribute is “Call service” which is the main service of any mobile operator. According to available offers by three main providers in the market,15 levels are set as close values to the characteristics of scenarios that service providers could offer as Voice bundle to customers. This attribute, 3 h, 10 h and unlimited. In designing a combination these three levels are set according to the cost of the bundle, in order to be more representative, as weekly, daily and monthly usage per bundle. The second attribute is “Internet or data service”, with four levels, 60 MB per day,1 GB internet, 800 Mega Byte for social media usage but no call service. This level gives enhancement to the call service. Currently, internet users are using over the top services for Whatsapp, Viber, Facebook and many other applications for voice call. Having a bundle, a combination with Voice call service minutes, may entice those customers that need internet for voice call and browsing social media. And gain revenue for operators for both internet and voice call services. The third attribute is short message service, which has three levels, 50 SMS, 150 SMS and 200 SMS, these levels are designed to be combined in bundles and customer who has the high usage of SMS could select the most appropriate one based on his/ her needs.

15

Asiacell, Zain, Korek Call offers are available in market. http://www.Asiacell.com, http://www. korektel.come, http://www.iq.zain.com.

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The fourth attribute is the “Cost of the Package or bundle” which is a significant factor that affects customer preference on mobile packages. As mentioned in the literate review Klein and Jakopin (2014), mentioned cost as the most important factor that affects customer perception and willingness to pay on bundles. There are three Levels for cost attribute, 5000 IQD, 10,000 IQD, and 30,000 IQD which are set according to weekly and Monthly prices of packages.

3.3.4 Conjoint Profile Cards (Bundles) and Orthogonal Design In choice base conjoint approach, after defining the attributes and related levels, a set of alternatives as product profiles, which are composed of multiple attributes, are designed. The respondent is asked to evaluate and rank these profiles. This evaluation implies that respondents make trade-offs between the different characteristics of products by considering all attributes concurrently to state his/her preferences. After defining attributes and levels of the service or product, they are combined to create possible product profiles to be evaluated by respondents. In the current study the combination of all attributes and levels introduced in Table 3.3, would yield a tremendously large number of cases (3*4*3*3 = 108) as possible service profiles. There are 108 product profiles to be evaluated by respondents, because the evaluation of high number of product profiles, would be a really tedious task, and the quality of respondents could be affected by respondents’ exhaustion (Pignone et al. 2012). Hence to control respondent’s fatigue, reduce the number of these combinations (profiles), and maintain orthogonality among levels, it is common to use a fractional factorial design, to present a proper fraction of all possible combinations of attributes and levels. Orthogonal or fractional factorial design is a statistical technique that is used to generate the minimum number of profiles. The resultant set is called orthogonal array that considers only the main effect of each attribute level. In the current step of the study, the software program (SAS) has been employed to reduce and choose the best orthogonal combinations. Details can be found in Appendix 3.2. Based on the used SAS function 36 choice sets as the best profiles defined. After all choice sets have been analyzed, studied carefully and validated by experts as product managers of mobile services, 4 realistic choice sets each contains four profiles or bundles, in total 16 profiles are addressed which resemble a realistic bundle to be analyzed by respondents, and find the potential trade-offs between attributes. Overall and based on earlier experiences, 16 profiles can be managed in 15 to 20 min. The created sixteen bundles are mutually independent; hence, redundancy in the representation of data is controlled (Schueler et al. 2022).

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According to Okazaki (2006), it is important to examine consumer demand for mobile services in terms of consumer characteristics because consumer preference changes according to his/her characteristics and background. Hence, demographic such as gender, age, education level, income, living place, and occupation are involved in the survey to confirm a board of consumers’ base is covered.

3.3.5 Pilot Test of the Questionnaire In the initial stage of the data collection process, before administration of the survey, to verify the accuracy of the questionnaire, ensure easiness and obstacles of questions and check ambiguous expressions a draft English version and Kurdish version of the questionnaire as a pilot test validated by different level of educations and ask their opinions about clarity of questions. Through this pre-testing of the questionnaire, in the Kurdish version, Researcher found that the way of choosing bundles was not clear. Some respondents selected only one bundle as their preferred package. Hence, the questionnaire has been adjusted and an instruction guide added for participants of the survey about the evaluation and ranking of bundles.

3.3.6 Data Collection Method and Sampling To collect data and answer research questions, a sample of the population should be selected, defined sampling as the process of selecting units of populations as organizations or people, by studying the sample, the result can be fairly generalized back to the population in which they were selected. Fink (2003), indicated that a good sample is a subset or proportion of population-just like a population but smaller. Generally, sampling methods are classified as either probability or non-probability (Salant et al. 1994). In non-probability sampling, the sample is selected from the population but it is not possible to determine how it differs from the population. In this method, the sampling is according to the research’s judgment and it has types as Judgment sampling, convenience sampling ad quota sampling. In the probability sampling method, the sample can be random and representative and each member of the population will have an equal chance of being selected to be in the sample According to StatPac,16 in this method, the degree of error can be calculated. The degree of error means to which level a sample might vary from the population. As the sample is greater, the sampling error is decreased. It is the most applied method for the survey-based research. To guarantee the representativeness of the sample, Researcher is required to make estimations of population’s characteristics

16

http://www.statpac.com, http://www.surveys/sampling.htm.

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based on the selected sample of the population. Types of Probability sampling can be generalized to Simple random sampling and Stratified sampling. Stratified random sampling, is a form of probability sampling in which a sample is randomly chosen, from identified subgroups of the population. In this study, the best sampling method is the probability stratified method, which can increase the representativeness of the sample. However, data for the market share of telecom companies are available based on a country-level, thus applying the stratified method may not reflect the actual market share for the Kurdistan region, hence, simple random sampling has been adopted. Taken into consideration, in the Kurdistan region, Korek and Asiacell are the major Telecom service providers. The considered sample for this study was limited to Mobile users of the main mobile operators in Erbil, Duhok and Sulymaniah. Data were collected between March to April 2021 in three regions: Erbil, Duhok, and Sulymaniah. In reachable areas, a paper survey was distributed. Kurdish and English questionnaires in different segments such as universities; Government and Private sector, and request people to fill the questionnaires in their preferred languages; except employees of main mobile operators (Korek, Zain, and Asiacell) do not reflect any insight about operators in the result. Furthermore, to overcome geographical constraints, in unreachable area as Sulymaniah, online survey is used. At the end of the process, a number of 273 usable 300 respondents were collected. According to Orme B (2010), determining the proper sample size is based on the experience of the researcher. However, the researcher notes the range of conjoint survey sample size usually is 150 to 1200 Respondents. To ensure the readability sample size should be large enough, the number of observation is 4368. According to Johnson (2003), the rule of thumps for sample size in conjoint analysis can be applied: Conjoint sample size = (NTA/C) > 500. N = size of respondents 273. T = Number of Task or profile 4. A = Number of alternatives per task 4. C = biggest number of levels per each attribute 4. In this study, there are 273 respondents, 4 profiles, 4 alternatives per profile, and the maximum number of level is 4. Conjoint sample size = (273*4*4)/4–1092 > 500. According to this formula, 273 is a sample of conjoint representatives.

3.3.7 Model Specification The conjoint analysis details in the previous section, can be summarized to: – Defining attributes and levels of the mobile service as Voice call, Internet service, SMS service and Cost with explained levels are defined

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– Designing the alternatives for respondents via combing attribute and their levels. In this study 4 choice sets, each includes four alternatives, totally sixteen alternatives are offered to respondents to be ranked – After data collection, According to how respondents evaluate the profiles, the utility of each product attribute is calculated by regression analysis, and the result is the significance of attributes and utilities for each attribute level (Garver et al. 2010). Utility means the individual decision of preference of each attribute, which is unique to each respondent and is used to get a precise estimate for the probability that decision makers select a definite product perception (Edelenbosch 2014). When several attributes of mobile service come together to define the total utility of a bundle perception, the achieved utility scores are equivalent to regression coefficients and deliver a quantitative measure of the preference for separate parts of the mobile services that are allocated to the multiple attributes, the superior values indicate superior preference. The data is analyzed with models at three different levels of aggregation. The first model is Rank ordered logit model that assumes, the second used model is rank ordered logit with socio-demographic interaction and the third used model is Mixed logit model that assumes each individual consumer has its own preference.

Foundations of Discrete Choice Models The interesting research questions that posed at the beginning of the study included: What are the factors influence consumer choice of alternatives which are mobile service bundles? Assume that telecom operators want to design a service bundle for mobile users in Kurdistan Region, what are the significant attribute to be considered? To determine the preference of individual consumers over a discrete set of items as products or services, researchers frequently depend on a survey, and asking respondents to select the most preferred option out of a set of presented alternatives, to estimate the preferences in such survey, a standard discrete choice model can be applied. Numerous studies from marketing research choice and econometrics have designated that consumer preference is modeled by discrete choice methods (Chaichana 2014). Mangham et al. (2009), have stated the discrete choice experiment as a quantitative method for provoking individual preferences that allows researchers to expose individual’s value of chosen attributes of a service or product by asking them to state their choices among available alternatives. Srinuan et al. (2012), explained discrete choice theory as the study of individual’s behavior when he/she has to make a choice from a restricted number of choices based on utility maximization rule. Discrete choice analysis is a technique for estimating the probability of new service or product adoption in market. For instance, consumer preference of mobile service bundle is a dependent variable and has correlations to some factors such as call service, data service, SMS service and cost of the package. From the Discrete

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choice analysis framework, this study applied rank ordered logit model, rank ordered interacted with socio economic factors and mixed logit model.

Random Utility The utility theory supports conjoint analysis (Lancaster 1966). After ranking profiles from the most preferred to the least preferred alternative, utility from each product combination can be used to estimate the most significant product attribute and the most preferred levels. (Orme 2006). It is used as the theoretical basis for studying consumer preferences trough using discrete choice models (McFadden 1973). Random utility models have been developed, to explicate choices between discrete mutually-exclusives alternatives, and could obtain substantial industry and academic interest (Baltas and Doyle 2001). McFadden (1986), proposed that behavior of consumers in market is the result of utility maximization. In Random utility, the hypothesis is that consumers select their preferred alternatives based on utility maximization as the aim of their decision (Fritz 2010). Following this, Utility maximization supports consumers to decide on the best choice, which based on its rule, is supposed to afford consumers with a great deal of utility. Koppelman and Bhat (2006), stated that according to the utility maximization rule, there is a function that comprises attributes of alternatives with characteristics of individuals and describes the individual’s utility for each alternative. Accordingly, it is assumed that each mobile service consumer observes the utility related with each attribute of the mobile services and chooses the one with the greatest possible perceived utility. Train (2009) and Train (2002), conceptualized that random utility has two components: The first component includes observed factors by researchers and second component includes unobserved factors which is unmeasurable and random component. It is a consequence of changes in consumer’s attitude, perceptions and other factors. Hence there is inadequate information about all unobserved factors that affect individual consumer’s choice decision, in Random utility model, the total utility for available alternatives i for consumer n is decomposed into two parts as follows: Uni = Vni + εni

(3.1)

In Eq. (3.1) Uni subscript represents the utility of individual consumer n obtained from alternative i, subscript i is the mutual exclusive alternatives, Vni stands for the deterministic component of utility and is a function of all observable attributes of alternatives attributes and it is a fixed term, εni is stochastic component of utility it is treated as random, εni Captures all unobserved determinants of the utility which are not included in Vni (Ida and Sakahira 2008). According to Train (2009), the deterministic component or observed utility encompasses the utility, obtained from characteristics of a product, service or goods and individual’s characteristics as follows: Uni = Vni + εni = V (xni , sn ) + εni

(3.2)

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where xni stands for a vector composed of attributes of alternative i to the n th consumer and sn stands for the vector composed of characteristics of n th consumers. If we hypothesize a linear relationship in the observed or deterministic component in Equitation (3.2), we can get: Uni = βni x ni + αni snt + εin

(3.3)

In Eq. (3.3) β stands for consumer preference and a represents the degree of impact on the observed utility by attributes of the mobile service and characteristics of the individual correspondingly. In unobserved component, = ε{εn1 , εn2 , εn3 . . . εni }., assuming that respondents choose an alternative with the aim of utility maximizing of the random utility function, the probability that n th consumer select the alternative i from the set of available alternatives j is written as:   Pni = P Uni ≥ Un j , ∀ j = i = P(Vni + εni ≥ Vn j + εn j , ∀ j = i )

(3.4)

where Pni can be explained as the probability that an alternative is selected which counts on the joint distribution of the difference between random errors f(εn ). According to the assumed distribution of the unobserved or each random part εni − εnj is less than Vni − Vnj the can be rewritten as follows (K. E. Train, 2009): Pni = Pr op(Vni − Vn j ≥ εn j −ε ni ∀ j = i)

(3.5)

The Logit Model The logit model is the favored model among the different discrete choice models as it is consistent with random utility theory. Usually, to analyze data from conjoint choice experiments, Standard logit model is applied (Louviere and Woodworth 1983). After data collection, the utility of each attribute and level of a service or product can be estimated. There are different models available for this and there is no unique way of data analysis. Logit models that can be implemented according to the type of the used design and type of research. Binary choice logit model, Multinomial logit model, ordered choice logit model and random parameter logit or Mixed logit model. Rank ordered and mixed logit models are broadly been employed to analyze discrete choice models (Ida and Sakahira 2008).

The Rank Ordered Logit Model Beggs et al. (1981), introduced rank ordered logit model to test and estimate the user preferences when a user is ranking a discrete set of attributes. Fink (2003), defined rank ordered logit model as a standard discrete choice model that can be used to analyze the preferences of individuals over a set of alternatives, where the

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preference are perceived through a conjoint approach study. According to K. E. Train (2009), it is resulting from a random utility model. Therefor it can be explicated as if there are m discrete choices and the respondent n ranks choice i as the first preferred choice, it can be assumed that utility of choice i is greater than other choices thus Unm < Uni . In this study, as explained in previous sections of alternative choice sets, in one choice set, there are four choices so respondent can rank choices from 1 as the most preferred to 4 as the least preferred choice. The unique choice of respondents which has been ranked as 1, based on the i utility as Uni as described in Eq. (3.1). Chaichana (2014), stated, that in rank ordered logit model, when respondent select the most preferable alternative among a set of alternatives, the random utility identifies the likelihood Li, then the root of likelihood from the utility function as Eq. (3.1) can be specified as below: ⎡ Li =

⎤

j ⎢ ⎥  exp{μi j} ⎢ ⎥ ⎢ j ⎥ ⎣ ⎦ j=1 δi jk exp μik

(3.6)

K =1

δi jk is 0 if Uik≥ U ji and 1 if Uik≤ U ji . Hence the Eq. (3.6) equation represents the likelihood of respondent. It is described as when the respondent selects the best choice from choice set, and will continue choosing and ranking the rest choices until the last choice (Allison and Christakis 1994). Therefore this process explains that when a respondent chooses one alternative from a set of alternatives and gives it the highest rank, the rest alternatives don’t relate with the selected choice. Which clarified (AII) independent from irrelevant alternatives. In the rank ordered logit model according to the standard logit model, the probability of ranking r a set of alternative choices (r1 , r2 . . . ., rk ) by individual j, where, rk stands for the selected alternative in a ranking order, The probability of rank ordered logit model can be expressed as Eq. (3.7) (Alsebaeai 2013). ⎡

⎤

j−1 ⎥ v  ⎢  ⎢ en ⎥ Pr ob(ranking r1 , r2 , . . . , rk ) = pr ob Ur 1 > Ur 2 . . . > Ur j = ⎢ j ⎥. ⎣ ⎦ K =1 v em m=k

(3.7) When vn represents a vector of alternative assigned rank k, and if the estimation has the S choice, consequently, the equation can be rewritten as follows: ⎡

⎤

S ⎢ ⎥ v    ⎢ en ⎥ Pr ob Ur 1 > Ur 2 . . . Ur j = ⎢ j ⎥ ⎣ ⎦ t=1 v n e m=k

(3.8)

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Ahn et al. (2012) have expressed the likelihood of ranked order logit model, as below, where β represents the vector parameter corresponding to interaction term. InL(β) =

p t=1

In[

 j−1 K =1

[j

evn

m=k

evm

]

(3.9)

Hence the current study employs the ranked order logit model to analyze and determine the consumer preferences of mobile services, a choice set of mutually exclusive alternatives in a design of mobile service bundles is offered to consumers to be ranked from the best to the worst bundle. In addition, another model is estimated to examine how socio-demographic factors impact respondents’ preferences the same ranked order logit model has been interacted with socio economic factors to find the impact of consumer characteristics on their presence.

The Random Parameter Logit Model (Mixed Logit Model) Mixed logit is a statistical model for examining discrete choice model, researchers mention it in different studies with different names as17 Mixed discrete choice models, Mixed multinomial logit models, discrete choice with random coefficients or random parameter logit model. This model assumes the coefficients follow a normal distribution across attributes. This has been developed by McFadden and Train (2000), as a highly flexible model with the ability of approximating any random utility model of discrete choice. Mixed logit is broadly applied in ICT as an innovative product choice analysis using stated preference methods (Manandhar 2012). Mixed logit model relaxes the IIA property and considers attributes of alternatives and characteristics of respondents to estimate the probability of a choice (Boever et al. 2011). Since the deterministic part of utility is measurable, as mentioned above: Uni = Vni + εni = βni xni Following this, according to Train and Sonnier (2005), the probability of mixed logit model can be specified under a diversity of behavioral specifications, each derivation provides a specific interoperation. In the mixed logit model, the random coefficient or unobserved factors that affect consumer’s decision to choose a product can be divided into two parts: εn = ηn + σn

(3.10)

where the subscript ( ηn ) represents the first unobserved or stochastic part that is heteroskedastic over respondents and alternatives, and interrelated over alternatives. The subscript (σn ) represents the second stochastic part, which is identically and 17

www.stata.com.

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independently dispersed over alternatives and consumers. Like any random utility model of discrete choice family, the utility for consumer n from alternative i can be defined as (Manandhar 2012): Uni = Vni + εni = βni xni + εni

(3.11)

where xni is a vector of observed (by researcher from an source) deterministic explanatory variable which includes characteristics of respondents and attributes of alternatives, explanation of the decision background and choice task itself in choice. Where βn and εni are assumed as stochastic influnces, and are not observed by the researcher. To allow the error to differ with respect to sample in the population βn is assumed to have the density f (β)n , the choice probability with respect to the random error framework will be: Pni = L ni (β) f (β)dβ (3.12) In Eq. L ni (β) is the probability of logit choice at parameter β and f (β)dβ represents a density function: L ni (β) =

evni (β) k  evk(β)

(3.13)

k=1

where, Vni (β) is deterministic part of the utility with linear utility in β, Vni (β) turns to βn x ni , and according to Train and Sonnier (2005), “Mixed logit probabilities are the integrals of standard logit probabilities over a density of parameters”. Hence the choice probability can be written as follow: Pni =

eβ xni  βx nk ke

 f (β)dβ

(3.14)

The mixed logit model’s power is not exhibiting IIA property and it can overcome taste variation issues in normal logit model.

3.4 Data analysis and Presentation of Results This section provides the empirical results and presents descriptive statistics of the data and, general preferred bundles by respondents and model estimations to the reader, supported with frequency figures and geographic chart. It covers estimation results of applied rank ordered logit models and mixed logit model to analyze the

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Table 3.4 Study sample

Subject

Description

Target sample

Mobile service users-living in three Kurdistan regions

Sample size

273 respondents

Survey period

Survey was conducted in April 2019

Survey mechanism

Interview and online survey

consumer’s preferences of the target sample of mobile service users who reside in Kurdistan region.

3.4.1 Descriptive Statistics As shown in Table 3.4, the survey which was distributed over the three targeted governorates, Erbil, Duhok and Sulymaniah, could get 300 respondents of mobile consumers. After data cleansing and removing the incorrect papers, 273 respondents were recorded as the total respondents. In this part, descriptive statistics has been used to describe data via applying frequency distribution of each group and some important statistics.18 Descriptive statistics is a simple way to describe and interpret raw data through grouping them and present in an expressive way. Table 3.5, lists the number of 20 variables with Nlogit software that have been utilized in this study. The descriptive statistic table illustrates the means of the variables along with the standard deviations, minimum, maximum, and number of records for each variable. There are 4 choice sets, with four alternatives per each choice set, each individual has ranked 16 different bundles which leads to have 4368 observations. Author has added two more variables as Group set and Package numbers to recognize each bundle easier.

3.4.2 The Socio-demographic Variables The survey result provides data on different demographic characteristics such as gender, age, education, income, marital status, occupation and living city, which represent the respondent’s profile.

18

https://statics.lared.com/statistical-guides/descriptive-inferential-statics.php.

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Table 3.5 Descriptive statistics of variables All observation in current sample Variable

Mean

Std. dev

Minimum

Maximum

Cases

Missing

CSERVICE

0.9375

0.826892

0

2

4368

0

DSERVICE

1.5

1.11816

0

3

4368

0

SSERVICE

0.875

0.780714

0

2

4368

0

COST

1.125

0.780714

0

2

4368

0

RANKING

2.5

1.11816

1

4

4368

0

GENDER

0.703297

0.456857

0

1

4368

0

AGE

1

0.651925

0

3

4368

0

EDUCAT

2.39927

0.978785

0

4

4368

0

OCCUPAT

1.33333

0.809078

0

3

4368

0

CITY

0.831502

0.746871

0

2

4368

0

INCOME

1.15018

0.931533

0

3

4368

0

MARRIED

0.527473

0.499302

0

1

4368

0

MEXPEND

2.29304

1.29476

0

4

4368

0

NLINE

0.989011

1.46648

0

5

4368

0

MLINE

0.908425

0.494386

0

2

4368

0

YSUBSCRI

2.74359

0.663211

0

3

4368

0

PMSERVCE

1.72894

1.81381

0

4

4368

0

MPAPP

2.6337

2.34694

0

8

4368

0

GROUPSET

1.5

1.11816

0

3

4368

0

PACKAGE

1.5

1.11816

0

3

4368

0

CONTROL

1

0

1

1

4368

0

Gender The Gender profile of the respondents is presented in Table 3.6. This demonstrated that (30%) of respondents are Female, and Males account for (70%) of the respondents. Mobile service providers can offer some mobile applications to the different genders specifically. For instance, design a new customized bundle for females and offer them a Mobile application that can attract their interest. As Shown in Fig. 3.2 the favorite Mobile application, for both categories is Music mobile application Table 3.6 Gender of respondents

Respondents gender Female

Respondents # 81

Respondents % 30

Male

192

70

Total

273

100

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Fig. 3.2 Male and female favorite mobile application

Table 3.7 Age group of respondents

Respondents age

Respondents #

Respondents %

18–24 years old

55

25–35 years old

166

61

36–49 years old

49

18

3

1

273

100

More than 50 years old Total

20

followed by the Education application in the female group as a second application, and followed by the Sports mobile application with a very close difference from the first one, by the Males group of respondents. (The total answer can be fide in mobile Favorite application 4.9).

Age The demographics profile by age is revealed in Table 3.7, the age variable has four ranges. In Kurdistan, the user of Mobile are below 18 years old, but only respondents above 18 are allowed to participate in the survey. (61%) of the respondents are in the range 25–35 years old, (20%) are in the range 18–24 years old, (18%) are in the range 36–49 years old, and in last category, 1% are above 50 years old. The highest age group is 25–35 years old and the lowest is above 50 years old. Within the age group, the majority of respondents as shown in 4.1.5, Majority of respondents were employees of the Government and Private sectors, therefore it can have the effect of a sample on the Age group.

Level of Education Table 3.8 demonstrate the demographic profile by the education level of respondents. As can be noticed, the majority of respondents, (55%) are those with a Universitybachelor, (19%) institutes diplomas, (16%) are those with high school and secondary diplomas and very few (5%) are primary level.

3 Examining Customer State Preferences of Mobile Services … Table 3.8 Education level of respondents

Respondents education

Respondents %

13

5

Secondary school

44

16

52

19

149

55

15

5

Total

273

100

Occupation of respondents

Respondents #

Respondents %

Institute Masters degree and higher education

Unemployed

50

18

Government employee

91

33

Private sector employee

123

45

Academics Total

Table 3.10 Marital profile of respondents

Respondents #

Primary

University-bachelor

Table 3.9 Occupation profile of Respondents

81

9

3

273

100

Marital status

Respondents #

Respondents %

Single

129

47

Married

144

53

Occupations As shown in Table 3.9, the occupations of respondents grouped in four sectors, (33%) of respondents were government employees and (45%) are private sector employees. (5%) are academic employees and (18%) are unemployed.

Marital Status As shown in Table 3.3.10, 53% of respondents were married and 47% of rest were single.

Monthly Income The monthly income is categorized into four groups as presented in Table 3.11, (30%) are respondents with the lowest earning monthly of 0-500 K. (33%) are respondents with monthly income of 500 K to 1 million IQD. (30%) of respondents 1Million

82 Table 3.11 Monthly income of respondents

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Monthly income

Respondents # Respondents %

0-to 500,000 IQD

81

30

500,000 to 1,000,000 IQD

90

33

1,000,000 to 2,000,000 IQD 82

30

20

7

More than 2,000,000 IQD

Table 3.12 Monthly mobile expense

Monthly mobile service expenses

Respondents #

Respondents %

Less than 5000 IQD

29

10

5,000 IQD-7,000 IQD

51

19

7,000 IQD-100000 IQD

70

25

10,000 IQD-15,000 IQD

60

22

More than 15,000 IQD

64

23

273

100

Total

to 2 million IQD monthly income, and (7%) of respondents are with high monthly income above 2 million IQD.

Monthly Mobile Service Expenditure As shown in Table 3.12, based on the amount of credit cards of mobile operators, five categories are defined as monthly expenses on mobile service. (25%) expend 7,000 to 10,000 IQD per month. (23%) above 15,000 IQD,(22%) of respondents expend 10,000 to 15,000 IQD per month, (19%) expend 5,000 to 7,000 IQD and the lowest expenditure is less than 5,000 IQD by (10%) of respondents.

Living City As presented in Table 3.13, according to the study’s aim, three regions could be covered. The majority of respondents were Duhok by 41%, a very low difference Erbil comes by 38% as the second one. The lowest number of respondents are from Sulymaniah which is 21%. As mentioned in Chapter three, Data Collection Sulymaniah Geographical was not easy to be covered, and an online survey has been utilized to collect data.

3 Examining Customer State Preferences of Mobile Services … Table 3.13 Living city of respondents

Living city of respondents

Respondents #

Respondents %

Erbil

103

38

Duhok

113

41

57

21

Sulymanih

Table 3.14 Second line users

Table 3.15 Main line of respondents

83

Number of line

Respondents #

Korek only

165

60

Asiacell only

37

14

Zain only

12

4

Korek and Zain

34

12

Korek and Asiacell

Respondents %

18

7

Zain and Asiacell

0

0

Korek, Zain and Asiacell

7

3

Total

273

100

Main line of respondents

Respondents #

Respondents %

Asicell

47

17

Korek

204

75

Zain

22

8

Total

273

100

Number of Lines and Main Line of Respondents As mentioned in introduction, some mobile service users have other lines as second lines to use offers of other lines (Table 3.14). Study area is Kurdistan regions, which is the customer base of Korek telecom as mentioned in the introduction. Therefore, as it can be noted in Table 3.15, it is acceptable to have majority of respondents as Korek line users as stated in Introduction chapter, Korek has its main base in Kurdi.75% are using Korek Line as main line, (17%) are using Asiacell Line as main line and (8%) are using Zain line as their main Line.

Experience with Main Adopted Line As for customer experience with their main line, majority of the respondent (75%) mentioned that they have their main line for more than 4 years. (10%) have their main line for 2 years to 3 years. (4%) 1 year to 2 years, and (3%) had experience

84 Table 3.16 The most used service by respondents

Table 3.17 The main service usage

L. M. Halee

Respondents #

Experience with main line

Respondents %

Less than 1 year

8

3

1 year to 2 year

10

4

2 years to 3 years

26

10

More than 4 years

229

84

Main service usage

Respondents #

Respondents %

Call service

134

49

SMS service Internet service

0

0

39

14

Mobile application

6

2

Some or all of them

94

34

with their main line less than 1 year. Therefore, they have enough experience with their main line to decide what their service provider can offer.

The Main Service Usage As shown in Table 3.16 As the most used main service, (49%) mostly are using call service, it was mentioned in the introduction chapter that still Iraqi Mobile service users are call service users. (14%) are using internet services more than other services, and 34% were using all services together. There were no main users of SMS service (Table 3.17).

Favorite Mobile Application In the last question, the mobile application is categorized into six different mobile application types, to find the preferred mobile application to be used in bundling as extra services to mobile service consumers. The majority of respondents, (40%) mentioned music mobile application as their favorite application. (21%) mentioned sports applications, (14%) Mobile education applications, (10%) Style and Design, (9%) TV mobile and (7%) Game mobile application as their favorite mobile application (Table 3.18).

3 Examining Customer State Preferences of Mobile Services … Table 3.18 Favorite mobile application

Favorite mobile application

85

Respondents # Respondents %

Style and design application

26

10

TV mobile application

25

9

Education application

37

14

Game application

19

7

Music application

108

40

Sport application

58

21

3.4.3 Respondents’ Preferences in Each Profile As stated in chapter three, a set of choice sets have been ranked by consumers. There were four choice sets with four-unique choices per each one, each respondent is questioned, to evaluate and rank bundles per each choice set from the best to the worst to specify the preferred Bundle. Preference is calculated manually using Excel program.

Consumer Preferred Bundles-Rank 1 The first choice set‘s preferences can be viewed in Table 3.19: as the first rank by respondents. The most preferred package is the Monthly package which offers unlimited call minutes for 1 month, 800 MB internet to use social media and 150 SMS with cost 125, this package can persuade customer with unlimited calls and internet quota which is satisfactory for social media because customer will avoid using internet for calling through over the top application and instead will use the call quota of its bundle, the second preferred bundle comes with 124 votes, with offering 3 h per month, daily 60 MB for 1 month and 150 SMS for 1 month. This package has the lowest price at 5,000 IQ|D which is satisfactory for those consumers that have low expenditure, the total internet quota is 1.8 GB for consumers which is a lower cost compared to the other bundles.

Consumer Preferred Bundles-Rank 2 The second package from choice set 2 has the highest votes for second preferred bundles. This package is a weekly package that offers unlimited minutes, daily 60 MB internet and 150 SMS per week. The price of the first preferred package is 10,000 IQD (Table 3.20).

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Table 3.19 First ranked bundles Attributes

Choice set 1 package 1 monthly

Call service-inside your network

Choice set 2 package1 monthly

Choice set 3 package 1 weekly

Choice set 4 package 3 monthly

3 h per month Unlimited for 1 month

Unlimited per week

3 h per week for 1 month

Data service

Daily 60 MB 800 MB per month-for for 1 month social media (facebook, whatsapp, viber, instagram) without call service of these apps

60 MB per day

800 MB per month-for social media (facebook, whatsapp, viber, instagram) without call service of these apps

SMS Service-inside your network

150 SMS for 1 month

150 SMS per week

50 SMS per month

150 SMS for 1 month

Cost

5000 IQD

10,000 IQD

5,000 IQD

10,000 IQD

Rank 1

124

125

103

111

Table 3.20 Second rank bundled Attributes

Choice set 1 package 3 weekly

Call service-inside your network

Unlimited per week 3 h for 1 Month

Unlimited per week 10 h per month

Data service

Unlimited per week Daily 60 MB for 1 month

60 MB per day

Daily 60 MB per day for 1 month

SMS service-inside your network

50 SMS per week

150 SMS per week

150 SMS per month

Choice set 2 package 2 monthly

50 SMS for 1 month

Choice set 3 package 1 weekly

Choice set 4 package2 monthly

Cost

10,000 IQD

5,000 IQD

5,000 IQD

10,000 IQD

Rank 2

92

90

86

106

Consumer Preferred Bundles-Rank 3 Table 3.21 presents choice set-package 3 as the third preference of the respondents. This bundle provides 10 h of minutes, with unlimited internet and 150 SMS for one month, followed by low difference package 2 in choice set 1 with 102 votes. They are similar offers in terms of price, these bundles are considered the less preferred packages for consumers according to the ranking. The main reason can be the high price.

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Table 3.21 Third Rank Bundles Attributes

Choice set 1 package 2 monthly

Choice set 2 package3 monthly

Choice set 3 package 4 monthly

Choice set 4 package 2 monthly

Call service-inside your network

3 h per day for 1 month

10 h for 1 month

3 h per week for 1 month

10 h per month

Data Service

800 MB per each week for social media(facebook, whatsapp, viber, instagram) without call service of these apps

Unlimited for 1 month

1 GB for 1 month

Daily 60 MB per day for 1 month

SMS Service-inside your network

200 SMS for 1 month

150 SMS for 1 month

200 SMS 150 SMS for per month 1 month

Cost

30,000 IQD

30,000 IQD

10,000 IQD

10,000 IQD

Rank 3

102

104

83

89

Consumer Preferred Bundles-Rank 4 Table 3.22 list the lowest preferred bundles for respondent and the most unperformed bundle can be mentioned the choice set 1 bundle 4 with 129 votes. It is a monthly bundle with unlimited minutes and 1 GB which means 4 GB plus 50 SMS for one month. All the lowest preferred bundles are costly bundles. Table 3.22 Fourth rank bundle Attributes

Choice Choice set 2 set 1 package 4 package 4 monthly monthly

Choice set 3 package 3 monthly

Choice set 4 package 1 monthly

Call service-inside your network

10 h weekly

Unlimited for 1 month

10 h per week for 1 month

Unlimited for 1 month

Data service

1 GB weekly

1 GB for 1 month

800 MB per each week for social media(facebook, whatsapp,viber,instagram) without call service of these apps

1 GB for each week for 1 month

SMS service-inside your network

50 SMS for 1 month

200 SMS for 150 SMS for 1 month 1 month

50 SMS for 1 month

Cost

30,000 IQD

30,000 IQD

30,000 IQD

30,000 IQD

Rank 4

101

121

108

129

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3.4.4 The Estimation Results In this study, the ranked ordered logit approach is utilized. When a customer ranks the alternatives, according to the utility maximization rule he/she selects the preferred alternatives based on utility maximization as Ur 1 >Ur 2 . . . >Ur j . The below model can be drawn as congruent with random utility model: when consumer states the preferred choice, the dependent variable is Rank. The random utility model assumes that an individual n faces a choice among j alternatives in each of t choice sets in a survey and is questioned to rank the alternatives in order of preference, the individual utility is as below: Un jt = β nCser vr Cserv n jt + βn Dser vr Dserv n jt + βnSser vr Sser v n jt + βnCost Cost n jt + εni

(3.15)

In Eq. (3.15) Un jt stands for consumer ‘s utility per reach choice of alternatives j which is expressed through ranking, and is the dependent variable Cserv, Dserv, Sserv, and Cost are dummy variables of attributes vector associated with alternative j, to determine the effect of these independent variables on consumer choice as the dependent variable. βn is a vector of coefficients of attribute vectors. The error has a normal distribution over variables. As discussed in chapter three, ranked ordered logit model is defined through the contingent ranking in the conjoint survey. In second model we have socio-demographic interaction with customer choice, which test the effect on consumers’ choice: the random utility model interaction test with demographic attribute as follow: Un jt = βnCser vr Cser vn jt + βn Dser vr Dser vn jt + βnSservr Sser vn jt + βnCost Costn jt   + α0 + αGen Gen + α Age Age + α Edu Edu + α I nc I nc + α Occ Occ + αCit y Cit y Rank1   + γ0 + γGen Gen + γ Age Age + γ Edu Edu + γ I nc I nc + γ Occ Occ + γCit y Cit y Rank2   + δ0 + δGen Gen + δ Age Age + δ Edu Edu + δ I nc I nc + δ Occ Occ + δCit y Cit y Rank3   + τ0 + τGen Gen + τ Age Age + τ Edu Edu + τ I nc I nc + τ Occ Occ + τCit y Cit y Rank4 + εni

(3.16)

In this equation, characteristic attributes of respondents as gender (Gen), age, income (Inc), Occupation (Occ), and city interacted with choice ranks 1,2,3, and 4 to test either these attributes have any impact on choices or customer demographics does not have any impact on respondents choice.

The Ranked Ordered Logit Estimation Rank order logit model has been applied to test and estimate the consumer preferences by analyzing the difference between attributes and find consumer preferences across attribute level. When they ranked the mobile service bundles, which are a set of alternatives of a total of 273 usable survey respondents on the rank ordered choice

3 Examining Customer State Preferences of Mobile Services …

89

conjoint questionnaire were received for the preference analysis to estimate the rank ordered logit. Each respondent ranked the profiles or bundles as the most preferred (1). To the least preferred (4) from four choice sets. Resulting in 1,092 for rank ordered logit for 4 sets of estimation and in total 4,368 observations of 16 bundles or alternatives. As the first step using NLogit software (version 4.1) has been utilized to estimate the ordered logit probability model for the attributes. The estimation result by Rank ordered logit has been precisely proceeded to find parameters by estimating all attributes and understanding the parameters to map on the specification of utility model of mobile service in Kurdistan region. Estimates were obtained as summarized data in Table 3.23. As it can be noted in this Table 3.23, the only insignificant variables are Call service and SMS service that means drawing proper implications about the result is not possible. These two variables have a positive coefficient but relatively are small in size. As the P-value of call service coefficient is 0.9888 and P-value of SMS service is 0.2513, they are far away from significant levels. It should be close to zero value to be significant. According to this model, Data service and cost are significant attributes of mobile services. As mentioned in chapter three, Attributes and levels part, Data service attribute levels in choice sets 60 MB,1G,800 mb and unlimited which in each presented bundle had different validity as Daily, weekly and monthly. The negative sign Data service coefficient means that consumers would prefer the lowest attributes level as they prefer 60 MB over 1G, They prefer 1 GB over 800 MB, and prefer 800 MB over unlimited. And in general level 1 over level 4. It can be justified that offered bundles to respondents who have lower Quota of Data service are on daily basis and weekly basis which in total provide the higher Quota of internet per week or per month to consumers. The second significant variable is the Cost attribute. It has a positive coefficient, that surprisingly means that consumers prefer highest level of cost attribute which is 30,000 IQD, over level 1 which is 10,000, and level 1(10,000 IQD) over level 0(5,000), which is in contradiction to the utility maximization rule that people want the highest benefit with the lowest cost. Table 3.23 Estimation results of ranked ordered logit model Variable

Coefficient

Standard error

b/St.Er

P[|Z|>z]

Call service

0.0001

0.0097

0.14

0.9888

Data service

−0.0195

0.0069

−2.825

0.0047

SMS service

0.0098

0.0086

1.147

0.2513

Cost of bundle

0.1140

0.0089

12.745

0.000

Alternative 2

−0.3645

0.0194

−18.803

0.000

Alternative 3

−0.2632

0.0188

−14.034

0.000

Alternative 4

−0.1949

0.0199

−9.803

0.000

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For expectation about price, users want to pay higher price for bundles with higher internet quota. As mentioned data service is a significant factor in decision making of choosing bundles. As mentioned before in chapter one still call service is a revenue generating driver in Iraq telecom market. But the influencing attributes to choose a bundle that is mixed with the call, SMS and Data service, the quota of internet service that can encourage them to subscribe to the bundle while SMS and call service minutes have a lower role with insignificant value as presented in rank ordered logit model estimations. It can be justified that each of these costs has a very high quota of internet and voice, with the validity of monthly and weekly so it means that people are ready to pay more for monthly bundles with higher quota. In this model, the choice is the dependent variable which is the rank of bundles, as shown in Table there are three choices alternatives,1,2,3, although there are four choices in the survey and collected data. In the logit model alternative 0 is used as the base and reference to other alternatives. The presented data in table are related to the base alternatives. According to this model, all alternatives which are four choice sets have negative coefficients that mean respondents would prefer the bundle which is ranked as 1 over all these alternatives, so alternative 1 is the best alternative. This indicates that all those bundles have been ranked as 1 are the preferred bundles to the respondents. As mentioned before Rank 1 preferences were two main bundles with only one difference in votes. • First one is unlimited calls for one month, 800 MB per month-for Social media (Facebook, WhatsApp, Viber, Instagram) without call service of these apps plus 150 SMS for 1 month by 10,000 IQD per month • Second one is 3 h per month, daily 60 MB for 1 month and 150 SMS by 5,000 IQD/month To sum up, according to this model, call service and SMS service are the only insignificant attributes that don’t have an impact on consumer preferences decision, and data service and are a statistically highly significant factor in customer preference for mobile services bundles. And consumer prefers the highest rank as the most preferred bundle.

Model Estimates: Interaction with Socio-demographics Characteristics A number of 10 variables have been utilized to estimate the second model. For the interaction test with socio-demographic characteristics, and to find the answer to the question “How socio-demographics factors affects consumer choice of mobile service bundles?” the second estimated model is a discrete choice, multinomial rank ordered logit model. In this model choice is the dependent variable. The reliability of the model can be evaluated based on log-likelihood function which is equivalent to the value of Chi-square, in this model Chi-square value is 185.389. According to Chi-square table with degree of freedom 4, this model is highly significant. For more details see Appendix 3.2 Chi-square Table.

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Table 3.24 demonstrates the estimation results from interactions of sociodemographic characteristics such as gender, age, education level, occupation, living city and income on mobile service attributes such as Call service, Data Service, SMS service and cost. According to the results of the interaction terms it is shown that the sign and magnitude of the coefficient value are more or less the same as the Table 3.19, However, the size of the Call service and SMS service coefficients have been improved in this model compare to the first model but again are insignificant. As in the previous model, data service has a negative highly significant coefficient again. On the other hand, the cost again has a highly significant value. Justification of these attributes was given in the previous model. The obtained result shows the testing socio-demographic attributes with each individual ranking. The majority of interactions of choice Ranked 2 reference to Ranked 1 (The top preferences) with socio-demographic have negative significant values which means the lowest level of these attributes prefer the Reference Ranked 1 bundle as presented in previous sections. Table 3.24 Interaction with socio-demographic characteristics Variable

Coefficient

Standard error

b/St.Er

P[Z > z]

Call service

0.00268

0.00960

0.279

0.7803

Data service

−0.01883

0.00676

−2.787

0.0053

SMS service

0.01177

0.00849

1.386

0.1658

COST of package

0.11903

0.00889

13.384

0.0000

Rank2_male

−0.25963

0.03524

−7.368

0.0000

Rank2_age

−0.08897

0.02838

−3.135

0.0017

Rank2_education

−0.02643

0.01473

−1.794

0.0727

0.00589

0.02194

0.268

0.7885

−0.07242

0.02119

−3.418

0.0006

0.03744

0.02322

1.613

0.1068

Rank3_male

−0.06555

0.03433

−1.91

0.0562

Rank3_age

−0.01586

0.02712

−0.585

0.5587

Rank3_education

−0.05620

0.01422

−3.952

0.0001

0.03576

0.02109

1.696

0.0900

Rank3_city

−0.05238

0.02062

−2.54

0.0111

Rank3_income

−0.03882

0.02233

−1.739

0.0821

Rank4_male

−0.04035

0.03356

−1.202

0.2292

Rank2_occupation Rank2_city Rank2_income

Rank3_occupation

Rank4_age Rank4_education Rank4_occupation

0.00213

0.02643

0.081

0.9358

−0.06185

0.01393

−4.439

0.0000

0.00344

0.02068

0.166

0.8680

Rank4_city

−0.01680

0.02004

−0.838

0.4018

Rank2_income

−0.00043

0.02171

−0.02

0.9842

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Interaction of Gender with Rank 2, is highly significant with a negative value, Gender has two values Female and male, according to the estimation of this model, Female consumers tend to choose rank 0 over Rank 1 bundles, therefore bundles with Ranked 0 are preferences of Females. But males are tended to prefer bundles Ranked 1 over Ranked Zero. The Age interaction with Ranked 2, reference to Ranked 1-Negative coefficient of age indicates that the Younger age group of consumers are preferring Ranked 1 bundles over Ranked 2 while older people prefer to choose Ranked 2 bundles. The Education relations with Ranked 2, reference to Ranked 1, Education has a negative significant value that indicates people with less education prefer Rank 1 over Rank 2. It can be justified that people with less education have a lower income according to descriptive statistics, so they choose based on the cost of and Ranked 1 bundles are lower prices. Interaction of occupation and income are insignificant and it is not possible to draw any kind of proper implication about the result. The city interaction with Ranked 1, reference to Ranked 1 the coefficient of the city is negatively significant, City attribute has three levels as Erbil, Duhok and Sulymaniah, the estimation result shows that first Erbil consumer, second Duhok consumer most likely prefer Ranked 1 over Ranked 2 and Sulymaniah consumers tend to choose rank 2 over Rank 2 bundles. • The most striking findings of the interaction test of socio-demographics shows that demographic characteristic has an impact on individual ranking. The first preferred bundles • Unlimited calls for one month, 800 MB per month-for Social media(Facebook, WhatsApp, Viber, Instagram) without call service of these apps plus 150 SMS for 1 month by 10,000 IQD per month • 3 hours per month, Daily 60 MB for 1 month and 150 SMS by 5,000 IQD/month Are preferred by female, youngest (18–25 year) and low educated (primary, secondary), occupations of academics (as students) people from Erbil and Duhok, the income variable did not have any role in decision making and ranking the preferences. The second preferred bundles as presented before as 10 h per month, Daily 60 MB, and 150 SMS by 10,000 IQD by 10,000 voted by 108 votes as the 2nd preferred package are preferred by Men, older age group, 25–35 years old, higher education, people from Sulymaniah. The Rank 2 bundle as the second preferred bundle, are chosen by mainly Men, older age group, and higher education from Sulymaniah. As depicted in Table 3.4, the estimation result of socio-demographic interaction with Ranked 3 bundles reference to Ranked 1 and 2 bundles, shows that all utilized characteristics of respondents have significant relation with choosing Ranked 3 bundles. As the previous Rank relation: The gender interaction with Ranked 3, in reference to Ranked 1 and 2, gender has a negative significant interaction with ranked 3 as the previous bundle Female consumer prefer choosing bundles with ranked 0 and the male group prefers Ranked 2 bundles. The interaction of Rank 3 with age is insignificant and has been ignored.

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The education interaction with Rank 3, Education has a high negative significant value, therefore the less education levels prefer to choose the Rank 1 & 2 bundles and the higher education level prefers to choose Ranked 3 bundles. The occupation interaction with Ranked 3, reference to Ranked 1 & 2 -Occupation has a positive significant coefficient. In Occupation there are four levels as unemployed, Government employee, private sector, and Academic, from these levels, It can be stated that academics and private sectors prefer Ranks 1 and 2 bundles and unemployed prefer bundles Ranked 3 as their preference. Interactions of city with Ranked 3, reference to Ranks 1 and 2: the location or the living city has a negative and a significant interaction with Rank 3, that means consumers from Erbil preferred Rank 1, Duhok prefer Rank 2 and Sulymaniah prefers Ranked 3 bundles over bundles. The income interaction with Ranked 3, reference to Ranks 1 and 2- Income has negative significant value with the implication that lower income preferred Ranked 1 and 2 as their preference and highest income desire Ranked 3 bundles. As the last interaction of socio-demographics with Rank 4 bundles, to examine the effect of these characteristics on choice preferences, as shown in Table 3.19, all values are insignificant except education level that has high negative interaction with Rank 4 which implies that the lower education group of consumer prefer Ranked 1, 2 then 3 bundles over Ranked 4 bundles and higher education people prefer bundle Rank 4.

Mixed Logit Estimations This section presents the estimation result obtained from the mixed logit model. Following the estimation procedure of Train (2003), a number of 20,000 draws with Gibbs sampling is generated. The first 10,000 iterations (draws) are omitted and considered as burn-in, while the draws of every tenth iteration of the second 10,000 draws were retained after successful convergence. Finally, the retained 1000 draws are used to create inference in terms of means and standard deviation. For the estimation, the Gauss 6.0 Program with R codes are used for the random coefficients to estimate the mixed logit probability for attributes of mobile services for the 1,092 observations. In normal logit models, the error has a normal distribution over attributes variable, however, in the mixed logit model each independent variable will have its specific distribution and the error is allowed to be correlated over individuals. Priory assigned distribution for marginal utility of each attribute is assumed to be in normal, log-normal, and censored distributions. For the qualitative attributes (call service, data services, SMS services and bundle cost), dummy variables were used. In the case of the dependent variable (the ranking), the first option (rank 0) is set as the base alternative. The results are shown in Table 3.25. The results of the Bayesian procedure from both Bayesian and classical perspectives, in this study the classical perspective is followed for the interpretation purpose. The estimated means and their variance are presented, in which, most of the results are statistically significant.

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Table 3.25 Estimation of mixed logit model Variance W

Mean bn Variable Bundle cost

Mean 0.1192

Standard error 0.0493

Rank 1

Based alternative

Rank 2

−0.2902

0.0845

t-value

Variance

Mean

Standard error

2.4178

0.2341

0.1142

2.0499

−3.4343

0.5343

0.1132

4.7199

Rank 3

−0.2411

0.1031

−2.3398

0.9928

0.1791

5.5388

Rank 4

−0.2013

0.1012

−1.9891

0.8991

0.1451

6.1964

Call service

0.0099

0.0041

2.4146

0.0101

0.0012

8.4166

Data service

−0.0913

0.0129

−7.0771

0.2111

0.1001

2.1097

SMS service

0.0102

0.0031

3.2903

0.0789

0.0084

9.3928

The magnitude of the mean coefficient related to data service and cost is qualitatively increased in comparison to ranked order logit model. Cost is positive significant impact on customer preference, call service and SMS service are insignificant and don’t have any role in customer preference choice. Rank 2, Ranke 3 and Rank 4 have negative significant values that confirm the correctness of estimation and Rank 1 are the highest preference bundles. An appealing result is that due to the heterogeneity in preferences among respondents, bundle cost has a positive value, which is very impurely related to the most preferred rank clue in terms of implications. What customers need is a reliable data service with more Giga bites regardless of cost. The variance of all the attributes are highly significant which indicates the respondents’ behaviors show heterogeneity in their preferences. Thus, the hypothesis of no variance can be rejected and the use of the mixed logit model is justified. To summarize the results from all three models, it can be resulted that in general that people in Kurdistan region when evaluating bundles which are combinations of different services, consider the Data service and the cost of the bundles to choose the preferences bundles, hence bundles that include satisfying internet quota with higher validity even with higher cost can have more desire than unlimited number of minutes, it can be justified that when mobile consumers use internet service, they are able to employ different over the top applications to make phone calls to the network and other network and international calls. According to call rates of19 Asiacell, Zain and Korek, when a customer calls another line has to pay higher cost than calling a number in the same operators, calling to other network for each operator is costly and in service bundle combination, service operators offer Call minutes to the same operators, therefor when consumer use internet service for calling, all calls are same rate.

19

Available call rates as OFFNET calls on websites of these three operators that mean calls to other operators.

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3.5 Conclusion and Recommendations This section discusses the key findings analysis result, providing answers to the research question. Limitations and suggestions for further research are argued.

3.5.1 Summary of Study The recent development in telecommunication technologies has facilitated a wide range of services, leading to harder competition on the market. To sustain the growth, win consumers’ loyalty and to maintain their market position, mobile service providers, are implementing a number of plans to keep the current consumers and attract new ones. As combining a range of mobile services into a basket together rather than offering individual services. To increase the success rate of new products in market, providers prefer to know: which service has a higher impact on consumer decision to subscriber to the bundles? Which service should have a higher weight to design an effective and cost-efficient for the provider and satisfactory for consumers? What other factors determine consumer choice of bundles? This study examines customer preferences in the mobile telecommunication market in the Kurdistan region of Iraq. It has gone through some way toward enhancing business understanding of consumer needs, through conjoint analysis techniques, some satisfactory bundles which were close to available bundles in telecom market designed, customer preferences have been investigated according to the ranking of bundles by respondents. This study employed discrete choice models to measure the significance of mobile service attributes such as Call, SMS, Data service plus Cost in making usage decisions on choosing Mobile service bundles. A random sample of mobile service users in Erbil, Duhok, and Sulaymaniyah have been targeted by a conjoint survey. The survey was conducted through a paper questionnaire and an online survey. The obtained response number (273) was reasonable, considering participants were informed that the survey would take some time to complete and there was no incentive for participation. Each respondent is asked to choose his/her preferred alternatives from four presented alternatives in four choice sets. Results of regression discrete models as ranked order logit model ranked ordered interacted with sociodemographics factors and mixed logit models indicated that attributes with higher significant values play a critical role than those with smaller values in customer preferences of mobile service bundles.

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3.5.2 Conclusion According to the key findings of presented models, customer characteristics can have a significant impact on selecting a mobile service bundle, most mobile service bundles are designed for the usage behavior of the service users there are some available bundles for specific segments as students, corporates and business enterprises that mostly are designed for small segments. Very few bundles are available in the market for specific gender as males and females. The highest ranked bundles were preferred by females, Youngest (18–25 years) and low educated (primary, secondary), occupations of Academics (as students) people from Erbil and Duhok, income did not have any role in decision making and ranking the preferences. The majority of telecom mobile application is favored by youth age, as presented in this study, males like to have sport and game mobile application, Again the most preferred bundle can be attached to the favorite mobile application and launched for Male and younger ages. These models are suitable for launching and testing a set of bundles that company needs to invest in and increase the business success against the competitors in the market. The second preferred bundle as presented before as 10 h per month, Daily 60 MB, and 150 SMS by 10,000 IQD by 10,000 voted by 108 votes as the 2nd preferred bundle are preferred by men, older age group, as 25–35 years old, higher education, people from Sulymaniah. Socio-demographic influence customer preference significantly and telecom operators can depend on use consumer characteristics to cover all needs of consumers in different ages, gender, education, etc. As stated in section four, when consumers want to choose his/her favorite mobile service packages, some factors such as data service and the cost of the package have a fundamental impact on the choice preference. Consumers with the high probability that could be seen in rank ordered estimation mainly rely on the quota as the daily quota which is for daily internet usage for one month and one week, or 800 MB for social media are the most significant factor but again it doesn’t mean that call doesn’t have any role. Telecom operators can increase the usage of the voice service by attaching to an internet offer when the customer has a high number of minutes and most probably will use it. Meanwhile, for telecom operators, these weekly and monthly bundles can increase the loyal customers and fixed weekly and monthly revenue. As noticed customers prefer daily quota by weekly or monthly validity. A weekly or monthly quota which is charged by daily basis can be a good bundle for this kind of consumer which prefers the daily quota. As mentioned in descriptive statics the game and sport mobile applications are the most preferred mobile application for males and design and style are the mobile applications that are preferred for female consumers.

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3.5.3 Interviews with Decision Makers in the Telecom Sector The researcher has interviewed three decision makers in the Government and two in the Telecom operators to include their perceptions related to the key finding and aim of the current research.

A Key Decision Maker in Telecom Operator X What is your opinion about providing bundles of different services rather than individual service offerings in Kurdistan region? What factor should be considered in designing new bundles? Telecom operators can retain existing customers and attract new ones through providing the right service and bundle to the right customer. The bundle can be tailored based on customer needs. For this customer profiling and segmentation is critical. I think bundles are preferred due to the fact that they cover the right mix of services for a specific segment. However, this has to be done in a scientific way using existing data from business and operations support systems together with customer demographics. And the factors should be considered in designing a mobile service bundle are customer preferences, customer usage segments, and demographics. According to the key finding of this study, do you think internet service can be an important factor when mobile service users choose a mobile bundle of voice, data, and SMS in Kurdistan region? With the advancements in technology and social networks available, the internet nowadays can be considered the number one factor that a customer will choose a package based on. Customer preferences, customer profile and segment and demographics. In a very soon future, Voice will be replaced by internet as the main revenue driver for telecom operators in market, it is healing in KRG as well where the telecom operators are at the risk of becoming the internet pipe only and all the benefit go to Over The Top (OTT) players like Google, Facebook, Viber,…. Therefore telecoms should find new ways to monetize data on top of the connection they provide especially that IoT will enable more and more devices to connect. The focus should be to provide the right internet service with adequate speed and the best quality of service. Do you think telecom operators in Kurdistan region, should consider demographic characteristics (Age, Gender, income…etc.) of consumers? When do they want to offer a bundle? When Telecom operators want to design bundles they should consider customer demographics on a large scale. Because customer preferences change by their demographics. All such info is available during the customer registration process, therefore, telecom operators are well-positioned to utilize this valuable information.

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A Key Decision Maker in Telecom Operator Y What is your opinion about providing bundles of different services rather than individual service offering in Kurdistan region? What factor should be considered in designing new bundles? Obviously, telecom operators can retain customers with best service, for voice it has to be a very good voice channel, without any cut and for data, it should be the fastest internet. There are two types of customers, some people don’t believe in it and some like bundles to the renewing it most of the time. SMS is dying business because of the OTTs as WhatsApp, Viber and all these messaging tools available these days. Voice still is strong business however it is going down and the Internet is going up, hence, data is the most important thing ever but nobody would succeed without voice plus SMS it has to be there. When it comes to telecom, the quality of service is number one before anything else to be considered for bundle, having high quota of internet in bundle is very important, as much as you could put data in a package is very important when it comes to voice then if there be some SMS it could be attractive. According to the key finding of our study, do you think internet service can be an important factor when mobile service users choose a mobile bundle of voice, data, and SMS in Kurdistan region? Yes, Nowadays internet is a very important factor, very soon the future Internet of Things will be there, a new generation of high-speed internet as 5G required to support smart devices. I think Kurdistan market will be closer to having data replace voice because fastest internet as 4G was in Kurdistan for a long pervious compared to the rest of Iraq so In Iraq Kurdistan market is closer to having data replace voice because 4G in \Kurdistan will happen quicker compared to the rest of the country. Do you think telecom operators in Kurdistan region, should consider demographic characteristics (Age, Gender, income…etc.) of consumers? When do they want to offer a bundle? Of course, demographics are important, as our operator has specific bundles for female only as bundles and services based on demographics are important because you directly target a segment of the community. Indeed, each city has its own characteristics and specifications, some bundles you can offer in one city and in other cities you cannot, based on customer behavior, so they should be considered when a bundle is designed for consumers.

A Key Decision Maker in Ministry of Communication-Kurdistan What is your opinion about providing bundles of different services rather than individual service offering in Kurdistan region? What factor should be considered in designing new bundles?

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I believe the bundling of services can have a win–win situation for customer and operators if it is done in a correct way meaning high-speed data and good quality voice even if it is via different network operators. And important factors that should be considered are Good coverage, License and technology capabilities, high-speed internet, Call service voice (national and international), and unlimited internet usage volume. According to the key finding of our study, do you think internet service can be an important factor when mobile service users choose a mobile bundle of voice, data, and SMS in Kurdistan region? Yes sure, High-speed internet is part of the daily needs of the citizen within the Kurdistan region. With the existing 2G/3G licenses it is not possible to provide broadband internet. The existing operators should request new licenses for LTE or cooperate with exiting LTE holders to provide broadband internet. The other option will be taking over the existing LTE operators. This depends on Operators strategies but I think the main revenue driver from voice to internet for Iraq will be in 4 years but for Kurdistan region it started from 2017. Do you think telecom operators in Kurdistan region should consider demographic characteristics(Age, Gender, income…etc.) of consumers? When do they want to offer a bundle? Yes. I believe market segmentation and even different branding for a different market segment is a good strategy to provide right quality services to specific market groups. This means that operators should change their organization structure and strategy to focus on customers’ need. To collect the needs of a specific group’s requirements a benchmark and survey should be done.

3.5.4 Recommendation Asiacell, Zain and Korek are providing numerous individual services, launching these services need a budget, different operation process as technical license and implementation, regulation approval, communication, and commercial costs. Bundling can increase the efficiency of different services, through gathering customer needs in one bundle and increasing the awareness of different services. The estimations and models can be used by the telecom operator to test and find the impact of the new offers in the market and analyze the required changes and investment to increase the effectivity and efficiency of the bundle. If a telecom operator launches a new bundle, the preferences of the bundle can be examined before the main launch or second change, and invest in the most important factor in customer preferences and demographics to increase customer satisfaction and business success in the market. According to the research result as presented in the previous chapter and decision

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maker’s insights below recommendations are suggested for telecom operators in Kurdistan Region: – Design bundles for different customer demographics which lead to cover their needs according to their preferences and increase customer satisfaction, investment in communication channels preferred by those segments will create an efficient customer awareness, If they know that a bundle a preferred by female as the firs bundle of this study, they can attach more value-added service and preferred mobile application for Female segment. Mobile applications as style and design were the most preferred application for Females. – Design bundles and mobile applications for different age groups.as combining the preferred sport and game mobile application as mentioned in descriptive statistics with bundles of higher internet quota bundles. – The first attribute that significantly affects the choice of the customer is Data service levels. According to estimation results and confirmation by decision makers’opinion Data service has a critical role in people’s daily life and a significant role in affecting their decision on choosing bundles. Call service doesn’t play any role to entice a consumer to choose packages even with a high number of free minutes or even unlimited, SMS service. But lower data levels as Daily usage, then 800 MB are preferred by consumers. The second attribute that significantly affects choice of customer is cost, customer of Kurdistan region are price-oriented but they are ready to pay more for packages that have higher quota means people would prefer higher cost price packages the high-cost package individually as mentioned before the higher cost packages have high internet quota. A scenario that can be recommended to telecom operators according to this is: Providing monthly or weekly bundles with a higher quota of internet and daily charging. Because consumer prefers the lower daily data quota usage for a customer and higher price as daily charging which is called plus a fee. The consumer will be convinced that can get higher internet quota, with daily charging of internet and monthly or weekly subscription. Generally, the below scenarios can be considered in designing bundles of consumers in Kurdistan region: Bundle 1—10,000 IQD per month. – Unlimited voice call to same network for 1 month, – 800 MB per month for Social media (Facebook, WhatsApp, Viber, Instagram) without call service of these apps – 150 SMS for 1 month Bundle 2—5,000 IQD per month. – Three hours voice call to same network for 1 month – Daily 60 MB for 1 month – 150 SMS for 1 month Bundle 3—5000 IQD per month. – Unlimited voice per week

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– 60 MB per day – 150 SMS per week Bundle 4—10,000 IQD. – 3 h per week for 1 month, – 800 MB per month-for Social media(Facebook, WhatsApp, Viber, Instagram) without call service of these apps, 50 SMS per month by 10 IQD Bundle 5—10,000 IQD. – 10 h voice call to same network – Daily 60 MB per day for 1 month – 150 SMS per month

3.5.5 Suggestion for Future Studies This research is found to be insightful and comprehensive resource for operators and regulators, with reasonable results regarding customer preferences and mobile service bundles. The researcher believes that this research will serve as a base and starting point for further studies on the telecom sector and customer preferences analysis. It is recommended that future research investigate new experiments and focus on the following areas: – Examine customer preference by adding more attributes such as validity and provider operator on preferences, technologies such as 4G and 5G and design packages of coming internet generation, – Study youth preference in telecom market because youth segment requires more operator challenges as creativity to be attached to a bundle or specific service. – Study factors in determining customer preferences at country-level with a higher sample can be a new area for coming reaches in the telecommunication sector, to know customer intension and create more creative and satisfactory services that lead to customer satisfaction and business success. – This study was a conjoint analysis and discrete models, it is recommended that new models and approaches be applied to similar study and compare the results in a comprehensive sensitivity analysis. Additionally for future studies, more, cooperation is looked-for between academic and Telecommunication industries, telecommunication operators study customer needs through the recent technology tools and statistics of their usage behavior. These data can be used by academic researchers to customize models according to customer behavior of consumers to increase satisfaction and success rate of the product and service in the market.

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Appendix 3.1: Questionnaire Dear Respondent, I am a masters student at University of Kurdistan, Erbil. This master thesis aims to study about Telecom Sector, by that we mean all companies that provide Voice service and Data service in Iraq. All questions here relate to your preference of different mobile services. Completing this survey takes almost 7 min and completely is anonymous. All information will be used for research purposes and will be strictly confidential. There is no right or wrong answer, so please mark answers that you find as the best choice that matches your behavior or perspectives. Thanks. Part 1-Choose only one answer 1. What is your gender? • Female • Male 2. How old are you? • • • •

18–24 years Old 25–35 years Old 36–49 years Old More than 50 years Old

3. What is your education level? • • • • •

Primary Secondary school Institute University-bachelor Masters Degree and Higher education

4. What is your occupation? • • • •

Unemployed Government Employee Private Sector Employee Academics

5. Can you please state the city of your residence? • Erbil • Duhok • Sulymaniyah 6. How much is your monthly income (USD)? • 0-to 500,000 IQD • 500,000 to 1,000,000 IQD

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• 1,000,000 to 1,500,000 IQD • 1,500,000 IQD to 2,000,000 IQD • More than 2,000,000 IQD 7. What is your marital status? • Single • Married 8. What is your monthly mobile expenditure? • • • • •

Less than 5000 IQD 5,000 IQD-7,000 IQD 7,000 IQD-100000 IQD 10,000 IQD-15,000 IQD More than 15,000 IQD

9. How many mobile line do you have? • • • • • • •

Korek only Asiacell only Zain only Korek and Zain Korek and Asiacell Zain and Asiacell Zain, Korek,Asiacell

10. Which line is your main line? • Asiacell • Korek • Zain 11. How long have you been subscribed to your main line? • • • •

Less than 1 year 1 year to 2 year 2 years to 3 years More than 4 years

12. Which mobile service do you use the most? • • • • •

Call service SMS service Internet Service Mobile Application Some or all of them

Part 2-Imagine your main mobile service provider (Asiacell or Zain or Korek), wants to give you a package that covers most of your needs:

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This section has 5 different choice sets. Each choice sets has packages to cover your mobile service needs as a mobile user. Which package is the best for you? You can rank it as 1. For example, if package 4 is your preferred package, you can rank it as 1, if package 3 is your second preferred one you can rank it as 2, if package 2 is your third preferred one, Rank it as 3 and if package 1 is your least preferred package, you can rank it as 4. You can rank form most preferred to least preferred. Choice set 1 Attributes

Package 1-monthly

Package 2-monthly

Package 3-weekly

Package 4-monthly

Call service-inside your network

3 h per month

3 h per day for 1 month

Unlimited per week

10 h weekly

Data service

Daily 60 MB for 1 month

800 MB per each week for social media (facebook, whatsapp, viber, instagram) without call service of these apps

Unlimited per week

1 GB weekly

SMS service-inside your network

150 SMS for 1 month

200 SMS for 1 month

50 SMS per week

50 SMS for 1 month

Cost

5000 IQD

30,000 IQD

10,000 IQD

30,000 IQD

Rank package From 1 the most preferred to 4, The least preferred

[]

[]

[]

[]

Choice set 2 Attributes

Package 1-monthly

Package 2-monthly

Package 3-monthly

Package 4-monthly

Call service (inside your network)

Unlimited for 1 month

3 h for 1 month

10 h for 1 month

Unlimited for 1 month

Data service

800 MB per month-for social Daily 60 MB Unlimited for 1 GB for 1 media (facebook, whatsapp, for 1 month 1 month month viber, instagram) without call service of these apps

SMS service (inside your network)

150 SMS for 1 month

Cost

10,000 IQD

Rank package [] From 1 the most preferred to 4, The least preferred

50 SMS for 1 150 SMS for month 1 month

200 SMS for 1 month

5,000 IQD

30,000 IQD

30,000 IQD

[]

[]

[]

(continued)

3 Examining Customer State Preferences of Mobile Services …

105

(continued) Choice set 3 Attributes

Package 1-weekly

Call service-inside Unlimited per your network week

Package 2-weekly

Package 3-monthly

Package 4-monthly

3 h per day for 1 week

10 h per week for 1 month

3 h per week for 1 month

Data service

60 MB per day Unlimited internet for 1 week

800 MB per each week for 1 GB for 1 social media (facebook, month whatsapp, viber ,instagram) without call service of these apps

SMS service-inside your network

150 SMS per week

50 SMS for 1 150 SMS for 1 month week

200 SMS for 1 month

Cost

5,000 IQD

10,000 IQD

30,000 IQD

10,000 IQD

Rank package From 1 the most preferred to 4, the least preferred

[]

[]

[]

[]

Choice set 4 Attributes

Package 1-monthly

Package 2-monthly

Package 3-monthly

Package 4-weekly

Call service-inside Unlimited for your network 1 month

10 h per month

3 h per week for 1 month 10 h per week

Data service

1 GB for each week for 1 month

Daily 60 MB per day for 1 month

800 MB per month-for social media (facebook, whatsapp, viber, instagram) without call service of these apps

Unlimited per week

SMS 50 SMS for 1 service-inside your month network

150 SMS per month

50 SMS per month

200 SMS per week

Cost

30,000 IQD

10,000 IQD

10,000 IQD

5,000 IQD

Rank package From 1 the most preferred to 4, the least preferred

[]

[]

[]

[]

Part 3-Q13.If your mobile operator, wants to design a mobile application that fits your need which mobile application do you prefer to have? More than 1 answer is allowed • Music Mobile application • Sport Mobile application • Game Mobile application

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L. M. Halee

• TV mobile application • Style and Design mobile application • Education Mobile application Q13 If You Want to Use Mobile Entertainment Services, Which Entertainment Area is Your Preferred One? Choose only one answer • • • • • • • • • •

Music Sport Game TV mobile Style and Design Education Health Shopping Islamic Your mobile Number––––––– (optional. will be helpful)

Your participation in this survey is highly appreciated, please note the information that you have provided assuredly will not be used for any other purposes than for objectives mentioned at the beginning of this survey.

Appendix 3.2: Chi Square Table

Degree of Freedom 1

Probability of exceeding the critical value 0.99 0.000

0.95 0.004

0.90 0.016

0.75 0.102

0.50 0.455

0.25 1.32

0.10 2.71

0.05

0.01

3.84

6.63

2

0.020

0.103

0.211

0.575

1.386

2.77

4.61

5.99

9.21

3

0.115

0.352

0.584

1.212

2.366

4.11

6.25

7.81

11.34

4

0.297

0.711

1.064

1.923

3.357

5.39

7.78

9.49

13.28

5

0.554

1.145

1.610

2.675

4.351

6.63

9.24

11.07

15.09

6

0.872

1.635

2.204

3.455

5.348

7.84

10.64

12.59

16.81

7

1.239

2.167

2.833

4.255

6.346

9.04

12.02

14.07

18.48

8

1.647

2.733

3.490

5.071

7.344

10.22

13.36

15.51

20.09

9

2.088

3.325

4.168

5.899

8.343

11.39

14.68

16.92

21.67

10

2.558

3.940

4.865

6.737

9.342

12.55

15.99

18.31

23.21

11

3.053

4.575

5.578

7.584

10.341

13.70

17.28

19.68

24.72

12

3.571

5.226

6.304

8.438

11.340

14.85

18.55

21.03

26.22

(continued)

3 Examining Customer State Preferences of Mobile Services …

107

(continued) Degree of Freedom 13

Probability of exceeding the critical value 0.99 4.107

0.95 5.892

0.90 7.042

0.75 9.299

0.50

0.25

0.10

0.05

0.01

12.340

15.98

19.81

22.36

27.69

14

4.660

6.571

7.790

10.165

13.339

17.12

21.06

23.68

29.14

15

5.229

7.261

8.547

11.037

14.339

18.25

22.31

25.00

30.58

16

5.812

7.962

9.312

11.912

15.338

19.37

23.54

26.30

32.00

17

6.408

8.672

10.085

12.792

16.338

20.49

24.77

27.59

33.41

18

7.015

9.390

10.865

13.675

17.338

21.60

25.99

28.87

34.80

19

7.633

10.117

11.651

14.562

18.338

22.72

27.20

30.14

36.19

20

8.260

10.851

12.443

15.452

19.337

23.83

28.41

31.41

37.57

22

9.542

12.338

14.041

17.240

21.337

26.04

30.81

33.92

40.29

24

10.856

13.848

15.659

19.037

23.337

28.24

33.20

36.42

42.98

26

12.198

15.379

17.292

20.843

25.336

30.43

35.56

38.89

45.64

28

13.565

16.928

18.939

22.657

27.336

32.62

37.92

41.34

48.28

30

14.953

18.493

20.599

24.478

29.336

34.80

40.26

43.77

50.89

40

22.164

26.509

29.051

33.660

39.335

45.62

51.80

55.76

63.69

50

27.707

34.764

37.689

42.942

49.335

56.33

63.17

67.50

76.15

37.485

43.188

46.459

52.294

59.335

66.98

74.40

79.08

88.38

60

Not Significant

Significant

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Part II

Information and Communication Technology Adoption

Chapter 4

Investigating the Factors Affecting Mobile Money Adoption in the Kurdistan Region of Iraq: The Case of Newroz Telecom FastPay Herdy Wahid Mam

4.1 Introduction 4.1.1 Overview Mobile money is the most innovative tool that has been utilized by developing countries to transform the financial sector; by the same token, it boosts the economy of the country. Taking into consideration that mobile phones are the most used devices in the modern era, according to ITU, the number of individuals owning a mobile phone is increased by 11% from 2019 to 2022, it reached 4.81 billion in 2022 compared to 4.25 billion in 2019.1 According to GSM Association (March 2022), the transaction value of mobile money has increased from 68 billion in 2012 to one trillion US Dollars in 2021 (see Fig. 4.1). Mobile money has been utilized by developing countries for financial inclusion; also, it has found great success in transforming the financial sector and boosting the economy in those countries. However, a clear path for the adoption of mobile money by developing countries is still missing. Even though mobile money is a great financial tool and already found success and transformed some developing countries, replicating the same system in other developing countries is not an easy task. A study by Flores-Roux and Mariscal 1

ITU: Key ICT indicators for the world and special regions (totals and penetration rates). https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx.

H. W. Mam (B) SafinClub, Ranya, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. T. Khayyat and G. M. Muhamad (eds.), Empirical Studies of an Internet and Service Based Economy, Perspectives on Development in the Middle East and North Africa (MENA) Region, https://doi.org/10.1007/978-981-99-3389-1_4

113

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H. W. Mam

1200 1000 1000 795 800 631 600

533 380

400

291

221 160

200 68

101

0 2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

Fig. 4.1 Mobile money transaction value worldwide

(2010) shows that no identifiable determinant serves as a bedrock for mobile banking success, in the same fashion, replicating those conditions does not guarantee success. Equally as important, the technology Acceptance Model (TAM) (Davis, 1989) is the most used tool for predicting the adoption and acceptance of any emerging technology around the world. This is also true in developing countries, as mobile money found its first success in the developing countries’ context (Munyegera and Matsumoto 2016). However, more recent studies recommend combining different models to better predict the acceptance of mobile money (Baabdullah et al. 2019; Al-Okaily et al. 2020). Najib and Fahma (2020) indicated that it is not only crucial to building users’ initial trust, but also trust is a big factor in the success or failure of any technology in developing countries. This is the reason for the current study that uses the TAM model in combination with the initial trust model (ITM) to predict the adoption of Mobile Money in the Kurdistan region of Iraq. A study conducted by Must and Ludewig (2010) indicates that regulatory and initial investment restrictions prevent the potential of mobile money and the adoption process. Simultaneously, mobile money can help to reduce the poverty rate by creating new jobs, increasing the savings rate, and increasing access to financial products. The author also recommends the government’s involvement in subsidizing the local mobile money infrastructure and helping to create a decentralized network of trusted agents. Furthermore, the current study will continue to the body of literature by adding determents such as personal awareness and government intervention to the TAM model, to have a tailored model that best suits Kurdistan as a developing region.

4 Investigating the Factors Affecting Mobile Money Adoption … Table 4.1 Mobile cellular subscribers in Iraq

Year

Value (%)

2013

75.29

2014

80.43

2015

81.63

2016

95.77

2017

94.27

2018

92.27

2019

89.91

2018

87.10

115

Source www.worldbank.org

4.1.2 Mobile Money in the Kurdistan Region of Iraq Iraq and Kurdistan are the most middle-eastern countries using smartphones to connect to the rest of the world. A study by the world bank showed that more than 87% of Iraqi citizens are mobile cellular subscribers (see Table 4.1). However, only 4 percent of those are financially included (Globalfindex 2017). Hence, mobile money as a financial tool has the potential to completely transform the financial sector in Kurdistan by providing accessibility at a low cost. Although mobile money has not been utilized to its full potential in Kurdistan, however, mobile money is not a new service in the region. Telecommunication providers have tried to promote this technology before, such as Asiacell by Asiahawala. In fact, FastPay, provided by Newroz telecom, has recently made the most significant impact with its cutting-edge technology.2 In addition, E-commerce is gaining a lot of attention in the region, especially through social media networks. Hundreds of new start-ups are utilizing the new trend to conduct their businesses. Certainly, it is not by coincidence; E-commerce has a fair amount of advantages over the traditional way of business (Sin et al. 2016). Although this might be true, E-commerce in Kurdistan is still conducting their transactions by cash due to a few reasons. First, as it is mentioned above. A very small number of citizens in Kurdistan are financially included. Second, the level of awareness is very low. Despite the fact that mobile money has plenty of advantages, the adoption process is very slow. Third, last but not least, the people of Kurdistan have lost trust in the financial sector and it will take time and energy for people to consider any financial options rather than cash (Bradosti and Singh 2015).

2

https://www.newroztelecom.com.

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H. W. Mam

4.1.3 FastPay Business Model and Smart Phones The world is rapidly adapting to the mobile phone, as Dr. Michio Kako puts it “Smartphones are becoming an extension of our physical body” Taking into consideration the fact that worldwide two billion people remain unbanked (Ibtasam et al. 2017). Seems like using smartphones as a means of financial inclusion is a perfect solution. Mobile phone money has been utilized on mobile devices in the past, but it is only in recent years that a new application of mobile money has emerged, specifically by leveraging the capabilities of smartphones. Mobile phones are getting cheaper and cheap mobiles are getting smarter. Thus, it would not be long before the whole population of the world has at least one smartphone (see Fig. 4.2). Furthermore, smartphones are far more convenient for mobile money adoption, for example, FastPay in the Kurdistan region is utilizing smartphones to provide more services and for a larger population. It is also intuitive, as it provides interoperability, for example. All users of Asiacell, korek telecom, and Zain can use FastPay by simply plugging in their phone numbers. It is worth noting that traditional mobile phones were slower and restricted, while smartphones are faster, more secure, and more versatile. Ibtasam et al. (2017) investigated Smartphone-based mobile money applications, the study found that the learnability of mobile money applications is an important component, especially for people with low literacy. In addition, a study conducted by Lien et al. (2015) shows that smartphones are superior to traditional phones in terms of more flexibility, more accessibility to persons with low literacy levels, and more security. The paper also argues that designing the interfaces and architectures of the application are solutions for these problems. Accordingly, FastPay by taking advantage of smartphones has created an initial application for both android and IOS users. In illustration 1, the Android version of the app and its features is demonstrated. First of all, on the loading page of the application, you can see all the FastPay services including some future vision features, such as Government and Utility bills.

Smartphone Users in Billions Smartphone users in Billions

1.57

2014

1.86

2015

2.1

2.23

2016

2017

2.53

2.71

2.87

2018

2019

2020

Fig. 4.2 Wordlwide smartphone users (Retreived June 2019) Source www.statista.com

4 Investigating the Factors Affecting Mobile Money Adoption …

117

Fig. 4.3 FastPay loading screen and agents’ network

Furthermore, you can also see your account balance. Language features are at the top right corner of the loading screen, the available languages are Kurdish, English, and Arabic (Fig. 4.3). According to Rea and Nelms (2017), agent network is an important aspect, and the failure and success of mobile money depend on it. If mobile money does not have a wide separated agents network, where customers can deposit and withdraw money, it defeats the purpose and benefits of mobile money. Similarly, FastPay has a wide separate agent network that covers all four governorates in Kurdistan. To access the map, you simply press on the nearest reseller and it gives you the location of each agent on Google maps, thus one can not only see the location of the agents, but also how to get there and locate the nearest agent (see Fig. 4.4). When it comes to online shopping, FastPay is still restricted to buying cards online. FastPay offers three different services, and those are Internet Recharge, Mobile Recharge, and Online Cards. Under the Internet, Recharge customers can buy (Fastlink, Newroz 4 g, Tishiknet, FTTH, ADSL, and fancy net). Those are all internet providers. However, under mobile recharge, customers can buy four different mobile cards and those are Asiacell, Korek, Zain, and Reber. Lastly, the online cards

118

H. W. Mam

Fig. 4.4 FastPay mobile recharge, internet recharge, and online cards

are large percentages of the business opportunity for FastPay, during the data collection process the participants were asked what they are using FastPay for and 84% of the participants indicated that they are using FastPay for buying online cards. Under online cards, FastPay provides rechargeable online cards for all the apps such as iTunes, Google play, PlayStation Store, XBOX, Amozon.com, Nintendo eShop, Skype, Minecraft, and Zynga. Taking into consideration that the people in Kurdistan do not have a lot of options when it comes to buying online cards, FastPay seems a good option, thus a lot of people using it (see Fig. 4.5). Depositing and withdrawing money are two important functions that determine the functionality of mobile money (Kendall et al. 2011). Depositing money is fairly simple. After finding the nearest reseller, the user will plug in her number and the amount she wants to deposit and hit send. Withdrawing money has a similar structure, the user writes down the agent’s phone number, and at the bottom, she writes down

Fig. 4.5 FastPay withdraw money, deposit, and send

4 Investigating the Factors Affecting Mobile Money Adoption …

119

the amount she wishes to withdraw, and thus the agent will give her the same amount in cash. Another functionality of FastPay is sending money, this is also very simple. You write down the phone number of the person you are wishing to send money to and the amount at the bottom and hit send. Nevertheless, FastPay is still in its early stages of penetrating the market, and its services are still limited. However, FastPay claims to open the biggest online shopping in the near future, where you can buy products, delivered to your house and pay via FastPay mobile money. This is a big potential for FastPay. If they can achieve this, they will be the first of this kind in the Kurdistan region.

4.1.4 Aim The aim of this study is to investigate mobile money adaption in the Kurdistan region of Iraq more closely, with the help of previously conducted research and from the consumer perspective a model has been developed. The aim is to understand the factors that affect mobile money adoption in the Kurdistan region. In this regard, the mobile payment FastPay has been taken as a case study. To figure out how people feel about this new payment system and what are their concerns, and also to contribute to the existing literature.

4.1.5 Significance of the Study Mobile money is an innovative tool that has been used by developed countries to step into a cashless economy; most importantly, it has completely transformed some developing countries’ financial systems. Despite that, mobile money has failed in some other developing countries (Asongu 2018). Identically, Kurdistan as a developing region is trying to utilize mobile money for financial inclusion; however, user acceptance is still unclear. The contribution of this study is to propose a model which combines the Technology Acceptance Model (TAM) with the Initial Trust model (ITM), to predict user acceptance in the Kurdistan region of Iraq. In doing so, additional variables (factors) of prediction are included in the model. The rich literature on mobile money shows that combining different models is a more accurate approach in predicting user acceptance (Afshan and Sharif 2016). However, no research has considered the government intervention factor, especially in developing countries’ context.

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4.1.6 Limitations Taking into consideration that mobile money is relatively a new service in the Kurdistan region; recently FastPay has received some attention from the public. Despite that, it steadily penetrates the market. One obvious limitation of the study is the sample size. As we talked about it above, mobile payment is a new service in Kurdistan which is why finding FastPay users is not an easy task. Another limitation is the geographical coverage, although, Erbil is the capital, Kurdistan consists of four governorates (Erbil, Slemani, Duhok, and Halabja). The gathered sample for this study only consists of Erbil governorates users.

4.1.7 Summary In this section, an overview of mobile money is demonstrated, and the implications and the future forecast regarding mobile money are briefly discussed. The direction of the present study is explained by analyzing the existing literature. Furthermore, the emergence of mobile money in the Kurdistan Region of Iraq (KRI) is clarified, in addition, by illustrating some statistical data, the claims and the direction of the study are strengthened. Moreover, the business model of FastPay is described; additionally, the implication of the FastPay mobile app is discussed and graphically presented. Equally as important, the implication of mobile money in the smartphone era coupled with some statistical data is argued. Additionally, the aim of the study is elaborated on, and research objectives together with research questions are planned. And lastly, the significance of the study and perceived limitations of the study is presented.

4.2 Literature Review In this section, the author focuses on the theoretical background of the study, and the theoretical background has been presented as relevant to the research problem. The main resources for this study arise from academic research databases, such as Elsevier, JSTOR, social science research network, and Google scholars. The conceptual model for this study comes from reviewing more than 100 books, Journal articles, peer reviews, institutional and annual reports, research, and several websites. The subtopics presented help to deliver the message of the research, by showing the opportunities and challenges of mobile money are presented as follows: Cashless economy, FastPay business model and the emergence of mobile money.

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4.2.1 Introduction According to a report conducted by World Bank in cooperation with Harvard University in 2008, mobile money is the money that can be used and assessed by mobile phones. Albeit mobile money is a relatively new financial service, however, it already has transformed the financial sector in some African countries. Besides that, there are no limits to the implications of mobile money; the report indicates that countries like South Africa, Kenya, the Philippines, Japan, and a lot more are using mobile money for financial transactions and services. The services that are taking advantage of mobile money are domestic and international remittance, bill payment, payroll deposit, loan receipts, and purchase of goods and services ranging from groceries to pay bus tickets and micro insurances. The possibilities of mobile money are tremendous, especially in developing countries for finical inclusion, Also, as a tool for a cashless economy in developed countries (Kingiri and Fu 2019). A study conducted by Asamoah et al. (2020) shows that mobile money can help economic activities in two ways, first, it can reduce the cost and risk that comes with cash. Second, it uses the already-built infrastructure that connects billions of people around the world, and the majority of those who do not own a bank account, in another term they are financially excluded. The report also shows that when Vodafone and Safaricom introduced M-pesa mobile money to Kenya in March 2007, in less than a year, they gained more than 2,37 million users. In a literature review on mobile money and payment carried out by Diniz et al. (2011), the authors show an important distinction between mobile transactions, mobile payments, mobile banking, and mobile money. As such, they provided adequate definitions as follows: Mobile transactions: refers to any transaction that is carried out through mobile devices, whether it is a financial transaction or not. Mobile payments: are payments that are enabled through handheld devices; such as mobile phones or any other portable device. Mobile payments are not necessarily carried out through network telecommunication networks. Mobile banking: mobile banking is a channel that allows consumers to use mobile devices to connect to their financial accounts in such a way that allows them to conduct all financial services such as deposits, withdrawals, and conduct payments. Mobile money: mobile money, which is also the case for the present research, is a type of money that have the characteristics of cash in terms of liquidity, acceptability, and anonymity. It is worth noting that, mobile money is different from other types of electronic payments such as credit cards, debit cards, and smartcards. Not to forget about cryptocurrency; which is a completely different digital payment that utilizes completely different methods to conduct transactions. Cryptocurrency does not use or backed up by any bank institutions, rather it uses cryptography techniques to authenticate the transactions (Narayanan et al. 2021).

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4.2.2 Success and Failures of Mobile Money A study conducted by Etim (2014) indicates that mobile money success comes from the Vodafone service M-pesa for the Kenyan people. In fact, in less than a year, Mpeas brought financial inclusion for millions in Kenya. While the Kenya story was a huge success for the Kenya economy and mobile money in general, other parts of Africa such as Ghana and Nigeria fall behind by only a 10 percent adoption rate. While Asongu and Odhiambo (2017) stated that mobile money has found great success in African countries, however, the literature does not show why mobile has had great success in some African countries like Kenya, but not so much in other countries, like Zambia and Egypt. Mobile money in Egypt, as an Islamic country, has not found great success with a penetration rate of 1%. Also, the mobile money users in Egypt are the same as Kenyan users (mainly university educators and male users). However, the early adopters’ theory and concepts are not presented in Egypt. Additionally, Mobile money is aligned with Islamic financial Rules (Badran 2017). A study conducted by Kingiri and Fu (2019) show the importance of early adopters in the vast adoption and success of mobile money banking in Kenya. According to the study, before the launch of M-Pesa in Kenya in 2009, only 19% of the population had access to financial services, while the rest of the population remained unbanked. M-pesa as mobile money took this as an opportunity, hence showing the world that it is possible to extend financial services to the poor unbanked population through early adopters to push technology for free. Etim (2014) also shows that, while M-pesa as mobile money has found great success in Kenya, the remaining African countries such as Ghana and Nigeria have less than a 10 percent adoption rate. The study concluded that while most of the Nigerian population used mobile phones to stay connected to their friends and families, they were rarely used to perform more complex taxes such as mobile banking and money transfer. Although there are a lot of mobile money services in Ghana, however, cash is still the main form of payment for day-to-day transactions. While there has been a recent increase in knowledge and awareness of mobile money, its adoption has not shown any significant progress. The primary obstacle hindering mobile money adoption in Ghana is the persisting issues related to regulations and partnerships. Simultaneously, the lack of education also acts as a barrier to widespread adoption (Dzokoto and Appiah 2014). A user acceptance of mobile payment system on social networks have been analyzed by Liébana-Cabanillas et al. (2018), to explain the acceptance behavior, the authors added two extra factors to the original TAM model, trust and perceived risk. According to the authors, men only consider a payment system useful if it’s easy to use at the same time, while women show that, they have a higher intention of use regarding their attitude toward the system. According to Rea and Nelms (2017), agents network is an important factor that determines the success and failure of mobile money. The research emphasizes agent networks, if the mobile money

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provider does not have a widely separated network, the system will face the same challenges as traditional banks. The adoption of Mobile telephony and its user satisfaction in the Kurdistan Region has been analyzed by Khayyat and Heshmati (2012), in order to predict the adoption of mobile telephony, the authors used a set of theories, for example, the technology acceptance model (Davis et al. 1989), Theory of reasoned action TRA (Ajzen and Fishbein 1977), Theory of planned behavior TPB (Ajzen 1991), Innovation Diffusion Theory IFT (Rogers 1995), and Unified Theory of Acceptance and use of technology UTAUT (Venkatesh et al. 2003). Furthermore, to investigate user satisfaction, these theories have been utilized: Customer satisfaction and its Measurement, Customer purchasing process, Perceived value, and Quality of service. According to the research, the rate of user satisfaction with mobile telephony in the Kurdistan region is up to 79%. A study was conducted by Marumbwa (2014) to show and explore the moderating effects of socio-demographic variables on consumer acceptance and use of mobile money transfer services (MMTs) on the frequency of use (FOU) in southern Zimbabwe. The study shows that age, gender, and income are negatively affecting mobile money transfer (MMT) user acceptance, in other words, higher age is correlated with dissatisfaction, while education levels and employment statues are key ingredients to predict the frequency of use of mobile money transfer application, also the acceptance of mobile money transfer (MMT).

4.2.3 Mobile Money and Economic Development According to Asongu (2018), the mobile money market’s worth was 655.8 million USD in 2014, and it is estimated to reach 1.3 billion dollars by 2019. This is only evidence that mobile money in Africa will continue to prosper; this will open new opportunities for mobile money development. This is also important in how the study of mobile money is aligned with the Sustainable Development Goals (SDGs) of the United Nations plan which replaced the Millennium Development Goals (MDGs). In essence, mobile money helps to reduce/mitigate income inequality (Asongu 2018). According to Kirui et al. (2013b), the impact of mobile phone-based Money transfer services in agriculture has significantly affected the rural areas in Kenya. They collected data from 379 multi-stage randomly selected households and showed that the use of mobile money to transfer money has positively affected the household input by $42 annually, at the same time, household agriculture commercialization by 37% and an increase by $224 of household income. The impact of mobile money transfer services on the performance of microenterprises has been examined by Wanyonyi and Bwisa (2013), and the findings indicate the significance of mobile money transfer (MMT). According to the authors, the use of mobile money transfer for (business to business) B2B and (customer to

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business) C2B transfers shows a noticeable performance increase in small and local enterprises. In a recent development regarding mobile finance, most of the researchers and practitioners acknowledge that mobile money has the ability to transform the financial sector in terms of reducing transaction costs, at the same time increasing financial inclusion for the unbanked population in developing countries (Grey 2017). Another study by Murendo and Wollni (2016) indicates the positive relationship between mobile money and the reduction in the level of food security. Taking both the food security index and food expenditure into consideration, the results specify that the use of mobile money and the volume transferred are associated with food security reduction, at the same time, the use, frequency of use, and the volume of mobile money transferred are related to an increase in food expenditure. Thulani et al. (2014) conducted research on mobile money as a strategy for financial inclusion in rural communities in Zimbabwe. They found that the usage of mobile money in unbanked rural areas is very high, however, using mobile money for savings and loans is not adopted, and rather, they are using the traditional ways of savings and borrowing. The research concluded that mobile money providers need to increase awareness among the users in order to encourage them to migrate from the traditional way to this safe and secure to save their income. Although mobile money shows big promise to boost Small and Medium Enterprises (SMEs), at the same time boosting the economy and financial inclusion, however, Ackah (2016) indicates that mobile money has no effect on the growth of SMEs in Ghana, hence the Author advises that mobile money providers provide sensitization on the effective use of mobile money in the business to boost the growth of SMEs. Another crucial factor for mobile money adoption in Ghana is the necessity for telecommunication companies to offer transaction costs are reasonably priced. Furthermore, commercial banks are concerned as mobile money is emerging strongly in all kinds of markets. Kamukama and Tumwine (2012) Confirm that the commercial banks in Uganda’s liquidity ratio have fallen short by more than 20% representing a ratio of total liquid assets to total deposit liability. According to the research, mobile money is responsible for more than 36.7% of that change. Hence, research conducted by Kamukama and Tumwine (2012) recommends a partnership between commercial banks and mobile money providers, this joint venture approach will benefit commercial banks in two ways. First, it will give commercial banks the opportunity to extend their physical reach to remote and poor areas. Second, commercial banks should take advantage of the products that regular mobile money providers cannot, for example, insurance services, credit, and loans where banks have a competitive advantage over mobile money providers. Additionally, this will build a strong bond between the bank and clients; in return, it will increase the cash-in or cash-out transactions (Kamukama and Tumwine 2012). Yet another study by Munyegera and Matsumoto (2014) investigated the effects of mobile money on the welfare of rural households in Uganda. The results from the study reveal that mobile money adoption can increase the household per capita by more than 69%. The justifications come from the facilitation of remittances; user

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households are more likely to receive remittances more frequently than non-user households. Kikulwe et al. (2014) also came to the same results, while investigating the effects of mobile money on smallholder farms, and household welfare in Kenya. Using penal survey and regression models, the study shows that mobile money has a positive impact on household income directly. Farmers can make more farm purchases input, market a large portion of their products, and have higher profits than non-users of mobile money. The results further suggest that mobile money can help to reduce poverty, and access constraints and boost the development of rural areas. Despite the fact that mobile money is a great financial tool and it already found success and transformed some developing countries, replicating the same system in other developing countries is not an easy task. According to a study conducted by FloresRoux and Mariscal (2010), there is no definitive set of variables that can be identified as the fundamental factors for the success of mobile banking. Similarly, attempting to replicate those conditions does not guarantee success. Asongu and Nwachukwu (2016) find that there is a positive relationship between mobile money phones and inclusive development. In 2012, Swedish banks witnessed a big potential for mobile money. Six large Swedish banks in cooperation with the Central bank of Sweden have developed a mobile payment system called Swish. Six years later, currently, the majority of swedes prefer to use swish rather than other means of payment systems such as Internet banking or mobile banking (Jakobsson 2016).

4.2.4 Cashless Economy Omotunde et al. (2013) defines a cashless economy as an economy where transaction payments are paid through credit and debit payments for goods and services rather than cash. The advancement of information technology brings new innovations to the world every day, according to a study conducted in 2016 on cashless payment and economic growth, which rounds the benefits of a cashless economy by naming a few here. A cashless economy discourages robbery and other cash-related crimes, as people naturally will carry fewer cash, robberswill have less incentive to rob someone. Second, businesses using cashless payments increase transaction efficiency, increase revenue, and reduce operating costs. Cashless payments are also more hygienic than cash for food vendors (Tee and Ong 2016). It is important to point out the value of financial inclusion for economic development. Numerous body of literature shows that financial development is an indication of economic growth as well (Mohan 2006). And that takes us back to the first point; cashless payment methods such as mobile money bring financial inclusion for the unbanked and financial inclusion helps with economic development. Despite the disadvantages of cash, cash remains persistent. A study supported by MasterCard displays that, in developing countries, the dependency on cash is still up to 90%. The same study demonstrates four key factors that hinder developing

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countries to go cashless, and those are as follows: 1-Access to financial services, 2-Macro-economic and cultural factors, 3-Merchant scale and competition, and 4Techology and infrastructure field (Thomas et al. 2013). Similarly, access to financial services is a key factor for developing countries to decrease their dependent on cash. Comparatively, Sweden as a developed country also uses mobile money to step toward a cashless economy (Jakobsson 2016). Globally, the advantages of a cashless economy are recognized. Countries like Singapore, Sweden, Canada, and France have invested big to go cashless and are leading the world by example (see Fig. 4.6). Moreover, India is also attempting to go cashless, a move to fight against the massive political corruption that is happening in that country. A study by Kaur (2017) shows that the circulation of cash inspires corruption, Thus, a cashless economy reduces corruption and the circulation of fake money. According to the responses gathered in a questionnaire, 55% of the respondents agreed that a cashless economy is crucial for the growth of the nation and should be implemented on India. Consequently, the transition from a cash-based economy to a cashless economy is not an easy task. Developed countries recognize the importance of this economic phenomenon; as a result, they have made efforts to transform their financial sector into an economy where physical cash is not needed at all. It is important to realize that mobile money plays an important role in a cashless economy in both developed and developing countries. Another argument is that the countries that reduce their dependence on cash are more likely to develop more quickly, countries like Mexico, Greece, Peru, Egypt, and Nigeria are still heavily dependent on cash, thus there less developed compared to the country of top the chart such as Singapore, France, Canada, Sweden (see Fig. 4.7). The consulting firm Booz and Company in 2011 predicted that, by 2015, the social commerce market would reach $30 billion in annual sales. 7-out of 10 internet users are actually also social network users (LiébanaCabanillas et al. 2018). Dolan (2009) believed that limited interoperability of mobile money in a region affects the efficiency of the service, and thus makes it harder for consumers to adopt. Fig. 4.6 Estimated percentage for consumer transaction payment conducted by non-cash methods. Source Mastercard.com

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The adoption of mobile money by small and medium enterprises can help those businesses to achieve their business objectives adequately, especially in unbanked regions. In a way that those businesses which adopt mobile money perform significantly better (Masocha and Dzomonda 2018). Furthermore, according to David-West et al. (2018), financial inclusion especially helps poor people in remote areas, it can help to reduce poverty at the same time get more people to participate in economic growth. Also stated in the Globalfindex (2017), the more people have access to the financial system the more businesses can open or expand, more investment in children’s education, the ability to absolve financial shocks, combat financial risks, also empowering women economically to have control over their finances.

4.2.5 Mobile Money is Empowering Kurdistan like any other developing country is a male-dominated society. The struggle to involve women in the development of the country is constant, the government on one side and the NGOs and social movements on another side are working on improving the situation and involving the other half of the society for economic development. Taking that into consideration, it is well-documented that mobile phones and mobile money can empower women in developing countries (Asongu and Odhiambo 2017). In another study by Authors Kirui et al. (2013a), mobile money can empower women through financial inclusion, the study indicates when women control their financial accounts, it is less likely for their counterparts to use or take control of their income. Additionally, financial management in the household has been investigated by Al Surikhi (2012). The author interviewed women from different levels and educational backgrounds; the study finds that mobile money can improve the efficiency of household management. Similarly, in Pakistan, a study conducted by Ibtasam et al. (2017) shows cases that mobile money on smartphones can bring independence to women.

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Comparatively, mobile money is beneficial for the improvement of health services for minorities and the poor (Asongu and Nwachukwu 2016), Mobile money can enhance business opportunities (Mishra and Bisht 2013), reduce agricultural waste by improving decision-making and reduce mismatching demand–supply (Aker and Fafchamps 2010), connected the rural and urban together (Asongu and Nwachukwu 2016). Taking all factors into account, mobile money has not only empowered individuals but has also significantly transformed the lives of millions across the globe. Moreover, it holds immense potential for macroeconomic and microeconomic development at both the national and business levels. Not to forget, the increased use of mobile money is associated with sustainable economic development and is negatively correlated with the poverty rate in Asia and Eastern Europe (Asongu 2018).

4.2.6 Summary Access to financial services is decisive for economic development, as it improves resource mobilization. Identically, mobile money as a financial tool brings financial services to millions in developing countries. Notably, the first success of mobile money goes back to 2007 in Kenya. As Vodafone provided M-pesa in a short time, it completely transformed the financial sector in Kenya. Moreover, developed countries use mobile money as a replacement for cash. That is to say, Sweden introduced Swish a type of mobile money to the people and the level of acceptance is outstanding. In any case, studies show that mobile money payment is preferable over other non-physical means of payment such as mobile banking and internet banking. Additionally, mobile money also found success in developed countries. Mobile money is a great financial tool and has transformed the financial sector of some developing countries, at the same time, helps some developed countries to step in a cashless society. However, mobile money adoption remains unpredictable. The literature shows there are strong indications that political stability and government support can be missing links for the acceptance and adoption of mobile money.

4.3 Methodology 4.3.1 Introduction This section deals with different models that have been used to predict technology acceptance. Among other models, a specific set of models has been chosen to investigate mobile money adoption in the Kurdistan region. Furthermore, the author suggests a modified TAM model that is inspired by reviewing of the literature and the

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unpredictability of mobile money adoption. Hence, the new model adds new factors to the original TAM model. Additionally, this section covers the methodology selected for this study. Also, the population sampling technique, the data collection, and the statistical analysis methods are explained.

4.3.2 Research Approach To put it simply, this research tackles the factors that affect technology acceptance, more specifically the factors that directly affect mobile adoption in Kurdistan as a developing region. Thus, the author has examined the literature attentively in order to address the adoption factors. Given that the literature shows mobile money adoption is unpredictable, the research takes on a qualitative form in the first stage. By analyzing the literature, the researcher found that TAM is an important model to predict technology acceptance, by combining TAM with ITM and introducing an additional variable to the original TAM, a novel model has been developed. Moreover, a set of questionnaires has been written to test the model. In this stage, the research takes on a quantitative form, by analyzing the primary data that has been collected from the questionnaire. It is fair to say that the research approach is mixed research of qualitative and quantitative. More specifically, it is exploratory research. Cameron (2009) indicates that exploratory research is a type of mixed research that starts with qualitative and finishes with quantitative. Zhou (2011) shows that mixed method is widely used and preferred among applied social science and evaluation, Cameron (2011) also indicates that there are a few social researchers that use a single type of research; most of the social researchers prefer mixed methods to tackle their problems. A handbook of mixed methods in social and behavioral science by Bazeley (2003) also indicates that the mixed method is emerging and is particularly useful in applied social science and evaluation.

4.3.3 Sample and Data Collection According to Johnson and Turner (2003), there are six major types of data collection in social and behavioral science, which are questionnaires, interviews, focus groups, tests, observation, and secondary data. Accordingly, this research utilizes three techniques of data collection. First, questionnaires have been developed from scratch, and the primary data has been collected. Second, interviews have been developed and conducted with the key personnel individuals in the field. Last but not least, the researcher has effectively used secondary data to shape and develop the research model.

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For this research, a sample of 120 responses was collected from randomly selected FastPay users, it’s worth noting that mobile money is still relatively new in the region, thus the targeted population is also small. The convenience sampling method is being used. Etikan et al. (2016) indicate that a convenience sampling method is good for a homogenous population, whereby all the members of the population have similar characteristics. By the same token, this method has been utilized by other technology adoption studies (Featherman and Pavlou 2003; Kitchenham and Pfleeger 2002; Yang 2005). Using a traditional hard copy questionnaire, the questionnaire has been distributed in both English and Kurdish languages. The researcher utilized agents’ shops as a means to reach the users of mobile money. These shops served as locations where mobile money consumers deposited money into their accounts. At these shops, the users were approached and asked to complete the questionnaire. The duration of the data collection was 8 weeks (March 1st to April 29th 2019). Similarly, a study conducted by Churchill et al. (2010) indicates that SEM models can do well with a small sample of 50–100 respondents. With this in mind, we established that the sample size is sufficient to test our hypothesis. In the data screening process, 13 respondents were removed from the sample for incomplete answers; additionally, 17 univariate outliers were excluded from the data analysis. At last, a sample of 90 questionnaires is analyzed.

4.3.4 Research Purpose The purpose of this research is to understand the factors that affect the user adoption of mobile money. Although mobile money has received a lot of attention in recent years both from academicians and businesses, there is no clear path for mobile money adoption, the literature on mobile money adoption shows that, while mobile money has found great success in some countries, it has failed in some other countries. Hence, exploring other variables that impact technology acceptance, especially mobile money acceptance has profound importance. For example, TAM has been utilized in combination with other models such as the theory of reasoned action (Fishbein and Ajzen 1975), the theory of planned behavior TPB (Ajzen 1991), and the innovation diffusion theory IDT (Rogers 1995). In other studies, new factors have been added to TAM such as initial trust. Nevertheless, mobile money adoption remains unpredictable. For this reason, exploratory research is conducted by adding new factors to TAM.

4.3.5 Theoretical Framework The examination of literature in the field of information systems (IS) and the adoption process of any emerging technology, shows that researchers are commonly employ the social psychology adoption models as the groundwork for their research

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design (Harrison et al. 1997). Studies on mobile money adoption have received their fair share as well (Hughes and Lonie 2007; Mas and Morawczynski 2009). Moreover, among other models, the Technology Acceptance Model (TAM) has been used frequently by researchers to analyze the adoption of mobile money by developed and developing countries (Lule et al. 2012; Osei-Assibey 2015; Tobbin 2010). Hence, the research is dealing with technological acceptance in a developing country which is why the Technology Acceptance Model has been utilized for the research. In the same fashion, new factors have been added to the TAM model in order to have a tailored model that best fits the Kurdistan region or the same model can be tested in other developing countries.

Technology Acceptance Model (TAM) As various technological advancements continue to emerge at an ever-increasing rate and become integrated into our daily lives. Yet, the success and failures of these technologies are highly dependent on the user acceptance and rejection of these technologies. Thus, the Technology Acceptance Model by Davies (1989) based on the psychological theory of reasoned action and planned behavior is the most used model to predict user acceptance (Lee et al. 2003; Maranguni´c and Grani´c 2015). Figure 4.8 shows the TAM Model. A study carried out by Maranguni´c and Grani´c (2015), indicates that although TAM model is a sophisticated model, however, there is still room for improvement. It has been suggested that other environmental factors should be taken into consideration, especially cultural differences. Thus, the researchers suggest extensions to the original TAM model (Chau 1996, Hu et al. 1999). Hypothesis H1. Perceived usefulness positively affects mobile money adoption H2. Perceived ease of use positively impacts mobile money adoption

Perceived ease of use Attitude towered using

Eternal Variables Perceived usefulness

Fig. 4.8 Technology acceptance model

Behavioral Intention

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The Initial Trust Model (ITM) In addition to the technology acceptance model (TAM), the initial trust model (ITM) has received it is a fair amount of attention from scholars as well, together with other models to predict the Information technology acceptance (Afshan and Sharif 2016). Also, researchers found that trust is an important factor for technology acceptance, thus a new modified TAM model was developed by adding the trust factor to the original TAM model (Gefen 2004, Gefen et al. 2003; Wu et al. 2011). H3. Initial trust positively affects the user’s intention to adopt mobile money. Modified Technology Acceptance Model (TAM) Without a doubt, TAM is one of the most used models to predict the acceptance of a new and emerging technology. Also, TAM has developed a lot since it is first proposed by Davies in 1989. The TAM is combined with other models such as the Initial Trust Model (ITM) (see Fig. 4.9). TAM itself has left the room to add new factors that you find best suit the situation and the technology (Maranguni´c and Grani´c 2015). Taking that into consideration, previous studies on mobile money adoption indicate the importance of regulation quality and government support for the success of mobile money. However, no previous study has used government support as a variable to test the relationship between government support, adoption intention, and government support and initial trust. Thus, the present research proposes a hypothesized model that takes the perceived government support as an added variable to the original TAM. Additionally, to test the perceived government support, four questions have been developed as follows. First, in order to test whether the government should exploit the services of mobile money or not, two questions are included in the questionnaire: 1. Do you accept your salary to be deposited into your mobile money account? 2. Do you want to pay your utility bills via mobile money? Second, in order to test the regulation quality, one question is included as follows: 3. Do you think the government should reduce the dependence on cash money And finally to gauge the overall level of support and increased awareness, the respondents were asked specific questions as part of the survey. 4. Do you think the government should promote mobile money to increase the financial services. H4. Government Support positively influences user intention to adopt mobile money. H5. Regulation quality is a positive impact on perceived government support. H6. Subsidization positively influences perceived government support. H7. Increased awareness positively impacts perceived government support. H8. Government support positively affects Initial trust.

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Perceived Usefulness

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Fig. 4.9 Hypothesized model of the study

4.3.6 Research Design The research has been undertaken in the Kurdistan region of Iraq, specifically in Erbil province. Moreover, the FastPay services are mostly used in the region. The targeted audience is FastPay users that have a clear understanding of mobile money services. In order to collect the primary data, questionnaires have been developed. The questionnaire has been carried out face-to-face, online, and through phone calls. It’s worth mentioning that the targeted audience has been selected randomly. Furthermore, the interviews are conducted with FastPay managers to investigate the adoption factors.

4.3.7 Research Paradigm A research paradigm is a set of beliefs and rules that are accepted among scientists on how a problem should be approached and interpreted (Kuhn 1962). There are three main branches of research paradigm, and those are positivist, constructivist, and pragmatist. The first one interprets reality as there is only one reality and this reality can be measured. In other words, the first school of thought is more likely to use quantitative methods to describe reality. The second school of thought thinks

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that there is more than a single reality; therefore, it is more likely that this school of thought is more likely to use the qualitative method, and the third school of thought believes that reality is constantly negotiated, debated, and interpreted, that is why there is not a specified method to solve the problem, but rather whichever solves the problem the best.

4.3.8 Epistemological Consideration According to Ritchie et al. (2013), epistemology is dealing with the way we perceive the world around us, in addition, it focuses on the basis of our knowledge. The authors show that there are two types of epistemological viewsin which we acquire knowledge. The first school thinks that the basis of our knowledge comes from the observation of the world around us, also known as the bottom-up or inductive view. The second school thinks that the basis of our knowledge comes from our theoretical concepts then we test those theories for validity. In other terms, it is a top-down view or also known as the deductive approach. It is fair to say that the author of this study has a deductive approach. As hypotheses are developed first, then the hypothesis is tested and investigated.

4.3.9 Ethical Considerations The inspiration behind the study is the concerns for the economic situation in the Kurdistan region and the potential of mobile money to transform the financial sector in Kurdistan. For this reason, the findings of this research have profound importance. In the data-collecting process, the consent of each individual participant has been asked before participating in the survey. Likewise, the purpose of the study was clearly stated and explained. The respondents’ anonymity and confidentiality is considered, and the option of reaming anonymous is given to the participant. In addition, participation in the survey was voluntary, each participant signed a consent form to show willingness and the freedom of choice. And lastly, this research is not supported or funded by any organization.

4.3.10 Summary In this section, the methods that the study is utilizing are discussed, and the reasoning behind the method choice is also explained. The chosen methodology is backed up by scholarly reviews of previous studies, especially the best methods that deal with social science studies is considered.

4 Investigating the Factors Affecting Mobile Money Adoption …

135

The author puts light on the background of the theories that the conceptual model of the study is built on. The theories that deal with technology acceptance have been thoroughly explained, Technology Acceptance Model (TAM) has been chosen as a foundation, and the model allows and even suggests for further extensions to tailor the model to the time and the place of the study. The modified TAM model is illustrated in detail and the hypothesis is developed accordingly. Questionnaires are also developed accordingly to test the proposed model.

4.4 Data Analysis and Presentation of Results 4.4.1 Introduction In this section, the primary data that has been collected through the questionnaire is thoroughly analyzed. In the demographic segment, information regarding age, gender, occupation, income, and expenses is collected, henceforth the data will be demonstrated in Tables and figures. By using Microsoft Excel, the significance of the variable will be shown, especially the significance of the variables such as perceived ease of use, perceived usefulness, attitude toward using mobile money, behavioral intention, perceived trust, and government support. Additionally, in the last part, the answers to the open-ended question will be discussed.

4.4.2 Demographics The questionnaire for this study covers measures of demographic variables such as gender, age, occupation, monthly income, and expenses. The sample size was 90 respondents. According to Table 4.2, male accounts for 84%, considering the sex ratio in Erbil according to the Ministry of Planning is 1/1. In terms of age, 73% of respondents were arranged from 25–40 years old, 21% of the respondents are from the younger group from 18–25 years old, while only 5% of the participants are +40 years old. The occupation of a large proportion of the sample is self-employed by 57% of the participants, while 33% are employees and 8.9% of the population are students. When it comes to the monthly income expenses, $500–$1000 accounts for 47.7% of the population, 0–$500, $1000–$2000, and +$2000 account for 15%, 23%, and 13% accordingly. Whereas in monthly expenses, 36% account for 0–$500, 45% of the participants’ monthly expenses is between $500–$1000, while $1000–$2000 and +$2000 account for 15% and 2%, respectively.

136 Table 4.2 Demographic variables and distribution

H. W. Mam

Variable name Gender Age

Occupation

Monthly income

Monthly expenses

Levels

Percentage (%)

Male

76

84.4

Female

14

15.6

18–25

19

21.1

25–40

66

73.3

+40

5

5.5

Student

8

8.9

Employed

30

33.3

Self-employed

52

57.8

0–$500

14

15.6

$500–$1000

43

47.7

$1000–$2000

21

23

+$2000

12

13.3

0–$500

33

36.6

$500–$1000

41

45.6

$1000–$2000

14

15.6

+$2000 Purpose of use

Number

2

2.2

Transfer money

28

31.1

Online shopping

84

93.3

Other business

12

13.3

Age The age profile of the respondents is categorized into three groups, the first group is between 18–25, the second group is between 25–40, and the third group is 40 and above. 21.11% of the participants are in the first group; additionally, 73.33% of the participants are in the second group, lastly, 5.5% of the participants are in the third group. The second group 25–40 has the highest percentage, while the age group 40 and above has the lowest percentage (see Fig. 4.10).

Occupation The occupation of the participants is divided into three groups, the first group student corresponds to 8% of the participants, and second group employed corresponds to 33.33% of the participants, lastly self-employed corresponds to 57.77% of the participants. Self-employed has the highest participant rate, while students have the lowest rate (see Fig. 4.11).

4 Investigating the Factors Affecting Mobile Money Adoption … Fig. 4.10 Age distribution

70

137 66

60 50 40 30 20

19

10

5

0 18-25

Fig. 4.11 Occupations of the participants

25-40

+40

60 52 50 40 30 30 20 10

8

0 Student

employed

self employed

Income Income is a crucial factor, as people have more purchasing power, the intention to utilize mobile money increases. Moreover, the participants have been divided into four groups, according to their monthly income, the first group corresponds to 15.55% of the participants, and their income level is between 0–$500. The second group corresponds to 47.77% of the participants with an income level of $500– $1000. The third group corresponds to 23.33% of the participants, and their income is $1000–$2000. While last group corresponds to 13.33% of the participants and their income is $2000 and above (see Fig. 4.12).

Expenses The figure below illustrates the monthly expenses for the individual participants, the survey was divided into four groups. The first group with a range of 0–$500 corresponds to 36.66% of the participants, the second group with a range of $500– $1000 corresponds to 45.55% of the individual participants, the third group with a range of $1000–$2000 represents 15.55% of the participants, and lastly, the fourth

138

H. W. Mam 50 43

45 40 35 30 25

21

20 15

14

12

10 5 0 0-$500

$500-$1000

$1000-$2000

$+2000

Fig. 4.12 Income of the participants

45

41

40 35

33

30 25 20 14

15 10 5

2

0 0-$500

$500-$1000

$1000-$2000

$+2000

Fig. 4.13 The expenses of the participants

group which is $2000 and above, represents of only 2.22% of the participants (see Fig. 4.13).

Purpose of Use Although mobile money relatively is new in Kurdistan, it is only recently that FastPay is attracting a lot of attention, and at the same time, it penetrates the market at a fast rate. Again, the range of services that FastPay provides to the users is still not very.

4 Investigating the Factors Affecting Mobile Money Adoption …

90

139

84

80 70 60 50 40 30

28

20

12

10 0

Transfer

Online

Other business

Fig. 4.14 Mobile money purpose of use

If you look at the FastPay application that comes for both Android and IOS devices, you will see a variety of services. However, not all of the services are active. For example, FastPay claims that it will open the biggest online shopping in Kurdistan. Also, it shows features such as “Government” and “utility bills”. Yet, at the present moment, those services are not active. Other services that are already active in FastPay mobile money are as follows: deposit, withdraw, send money, receive money, mobile recharge, internet recharge, shop payment, online shopping, and online cards. It is worth noting that in online shopping, you can only order food, as FastPay in partnership with Lezzoo EATS services provides food delivery to your doorsteps, likewise, it provides payments via FastPay. In the survey, individuals were asked, what is the purpose of using mobile money? According to the data that is been collected, 28 percent of the respondents are using mobile money for money transfers, and 84 percent of the respondents are using mobile money for online shopping. it is important to explain that online shopping includes (buying online cards, Mobile recharge, Internet recharge, and online shopping). 28% of the respondents indicated that they are using mobile money for other business rather than money transfer or online shopping. Some of the users are using mobile money for business. For example, some of them are agents and a small percentage are using mobile money for gambling. As mobile money allows them easy access to master cards, thus they can access gambling sites (see Fig. 4.14).

4.4.3 Likert Scale Questions In this section, the author will demonstrate the results of the Likert scale questions in the questionnaire. For this purpose, this study has used 5 point Likert scale as follows:

140

H. W. Mam

1—Strongly disagree, 2—Disagree, 3—Neither agree nor disagree, 4—Agree, and 5—Strongly disagree. Then, the collected data has been added to an Excel sheet; there the data is converted to figures in order to present it in an understandable format.

Perceived Ease of Use PEOU questions 1. 2. 3. 4.

Overall, mobile money is easy to use. Learning to operate a mobile money application is easy for me. It is easy for me to deposit money using mobile money. I find it easy to use mobile money to conduct transactions.

In the questionnaire design, the author has added four variables to test the ease of use of FastPay services, in a Likert scale fashion. For the first question, the respondent was asked if they find mobile money easy to use. 34% of the respondent strongly agreed with the statement, 28% of the participants ticked on Agree and 33% answered neither Agree nor disagree. While disagree and strongly disagree is only 3% and 1%, respectively. In the second question for testing the ease of use variable, the respondents were asked if learning the mobile application is easy. Again the answers were positive, 43% strongly agree, 34% agree, 22% percent neither agree nor disagree, and 0% both disagree and strongly disagree. In the third question, the participants were asked if it is easy to deposit money using mobile money. The answers were as follows: 33% strongly agree, 37% Agree, 22% neither agree nor disagree, and 1% both disagree andstrongly disagree. Furthermore, the last question to test the ease of use of FastPay, the respondents were asked about the easiness of the transaction process the answers once again are positive. The outcome of the question were as follow, 40% strongly agreed, 34% agreed, and 22% neither agreed nor disagreed, in a similar fashion, the lowest percentages disagreed and strongly disagreed by only %2 and 1%, respectively. The data shows that the ease of use of FastPay mobile money is easy and intuitive. When the four variables related to ease of use are added together, the answers related to strongly agree and agree are the highest, where strongly agree is more than 150% and agree is more than 130%. In contrast, the strongly disagree and disagree have the lowest values at 3% and 6%. While neither agrees nor disagrees has more than 110% (see Fig. 4.15).

Perceived Usefulness PU Questions 1. Mobile money is useful to me. 2. Mobile money allows me too conveniently to shop online. 3. Mobile money improves my transaction efficiency.

4 Investigating the Factors Affecting Mobile Money Adoption …

141

160% 140% 40% 120%

34%

100%

60% 40% 20%

33%

22%

80%

37% 1% 1% 0% 1%

2%

Question 4 Question 3

28% 43%

1% 0% 3%

22% 33%

28%

34%

disagree

neither agree nor disagree

agree

Strongly agree

34%

Question 2 Question 1

0% Strongly disagree

Fig. 4.15 Perceived ease of use

4. Mobile money allows me easily purchase online cards. Perceived usefulness is another variable in the conceptual model; in order to test the perceived usefulness of FastPay services, four questions were asked. In the first question, the respondents were asked if they think mobile money is useful for them. In the data analyses, you will see that 36% of the respondents strongly agree with that statement, 38% of the respondents ticked on agree, and 13% of the participants answered neither agree nor disagree. While only 2% of the answers are disagree and only 1% of the answers are strongly disagree. Another question is related to the online shopping application of FastPay; when the participants were asked if they find FastPay convenient for online shopping, 29% of the participants answered strongly agree and 46% answered agree, on the other side, 4% of respondents answered disagree and only 1% answered strongly disagreed, while neither agree nor disagree has 20% share. The third question is considering the transaction efficiency of FastPay, thus 26% answered strongly agree, 42% answered agree, and 31% answered agree, while disagree and strongly disagree are only 1% and 0%, respectively. The final question to test the usefulness of FastPay services is dealing with buying online cards. When the contributors were asked if they find buying online cards feature useful, the strongly agree and agree together contribute 86% of the answers, i.e., 50% and 36%, respectively, while neither agree nor disagree is 13% and none of the participants answered disagree, and only 1% answered strongly disagree. The data shows, when all the variables related to perceived usefulness added together, the strongly agree point has 140% of the answers, 160% answered agree, more than 80% answered neither agree nor disagree, and only 7% and 3% answered disagree and strongly disagree accordingly (see Fig. 4.16).

142 Fig. 4.16 Perceived usefulness

H. W. Mam 180% 160% 36%

140%

120% 42%

100% 13%

80% 60% 40%

20%

1% 0% 1% 1%

0% 1% 4% 2%

31%

Strongly disagree

disagree

neither agree nor disagree

0%

50% Question 4 26%

46%

29%

Question 2 Question 1

20% 23%

Question 3

38%

36%

agree

Strongly agree

Attitude Toward Using Mobile Money For the measurement of the attitude toward using mobile money, the following questions have been asked: 1. Using mobile money is a good idea. 2. I like using mobile money. 3. I want to know more about mobile money. When the respondents were asked about the goodness of mobile money, 60% of the respondent strongly agreed with that statement, 27% of them answered agree, 11% of answered neither agree nor disagree, while 0% and 1% answered disagree and strongly disagree, respectively. Additionally, when participants were asked if they like using mobile money, 36% ticked on strongly agree and 42% ticked on agree. On the other hand, disagree gets only 1% and strongly disagree 2%, and 19% of the respondents answered neither agree nor disagree. The third and final question to measure the attitude toward using mobile money is about mobile money awareness. When the respondents were asked if they want to know more about mobile money, 52% strongly agree with that statement and have the curiosity to know more about this new innovative technology, also 29% of the participants answered agree. Only 11% answered neither agree nor disagree, lastly 3% and 4% answered disagree and strongly disagree accordingly. It is worth mentioning that when all three questions regarding the attitude toward using mobile money added together, strongly agree have the biggest share by more than 150%, agree by 99%, neither agree or disagree by 41%, by contrast, disagree and strongly disagree have only 4% and 7% accordingly (see Fig. 4.17).

4 Investigating the Factors Affecting Mobile Money Adoption … Fig. 4.17 Attitude toward using mobile money

143

160% 140%

52%

120%

100% 29%

80% 60%

4%

40%

2%

20%

1%

Question 3 Question 2

42%

3% 1% 0%

0% Strongly disagree

36%

disagree

11% 19% 12% neither agree nor disagree

Question 1 60%

27% agree

Strongly agree

Behavioral Intention To test the behavioral intentions of the individual participants, three different questions have been developed and used in the questionnaire. The questions were as follows: 1. I will frequently use mobile money. 2. I will recommend mobile money to others. 3. I will continue using mobile money in the future. When the participants were asked about frequently using mobile money, 20% of the respondents answered strongly agree, 41% of the participant ticked agree on the box, furthermore, 23% answered neither agree nor disagree, 11%answered disagree and only 4% answered strongly disagree. On the second question to evaluate the behavioral intention, the participants were asked whether they recommend mobile money for others as well; for this question, 39% answered strongly agree, 42% answered agree, and 17% answered neither agree nor disagree, while disagree and strongly disagree received only 1% and 1%, respectively. For the third and final question, the participants were asked if they want to continue to use mobile money in the future; for this question, 38% of the respondents strongly agreed, 52% answered agreed, while neither agree, disagree, and strongly disagree received 8%, 1%, and 1%, respectively. Similarly, when all the questions regarding behavioral intention were added together, the results showed 97% answered strongly agreed, 135% of the respondents answered agreed, 48% answered neither agree nor disagree, and 13% answered disagree, while strongly disagree received only 6% (see Fig. 4.18).

144

H. W. Mam 160%

140% 120%

52%

100% 80% 60%

Question 3 38%

40%

1% 1%

20%

4%

1% 1% 11%

0% Strongly disagree

disagree

42%

8% 17% 23% neither agree nor disagree

Question 2 Question 1

39% 41% agree

20% Strongly agree

Fig. 4.18 Behavioral intention

Perceived Trust Trust is another very important variable that the study focuses on. In order to test this variable, three questions were included in the questionnaire design, and the questions are as follows: Question 1—Mobile money seems reliable. Question 2—Mobile money seems secure. Question 3—Mobile money was created to help the client. When the participants were asked if mobile money seems reliable, 29% of the participants strongly agreed with that statement, a huge percentage of 40% of the population also agreed with that statement, 28% of the respondents neither agreed nor disagreed, and only 3% of the respondents disagreed, while 0% of the participants strongly disagreed. The second question regarding the trust variable is concerned with the security of mobile money. When the participants were asked if they think mobile money is secure, 26% of the respondents strongly agreed, 32% of the respondents agreed, 28% neither agreed or disagreed, 13% of the repondents disagreed, and only 1% of the repondents strongly agreed. It is worth mentioning that the security of mobile money seems relatively concern for consumers. The third statement regarding the trust variable is whether mobile money is created to help consumers; for that statement, 39% of the respondents answered strongly agree, 42% answered agree, 16% answered neither agree nor disagree, while disagree and strongly disagree received only 1% and 2%, respectively. Surprisingly, when all statements regarding trust were added together, strongly agree is responsible for 94% of the answers and agree is accountable for 114% of the answers, while neither agree nor disagree and disagree and strongly disagree account for 72%, 17%, and 3%, respectively (see Fig. 4.19).

4 Investigating the Factors Affecting Mobile Money Adoption … Fig. 4.19 Perceived trust

145

140% 120%

100%

42%

80%

39% 16%

60%

40%

2%

20%

1% 0%

1% 13% 3%

32%

28% 28%

Question 2

26% 40%

Question 3

Question 1

29%

0% Strongly disagree

disagree

neither agree nor disagree

agree

Strongly agree

Government Support Another dimension of the conceptual model and questionnaire design is government support. With the intention of measuring government support, four questions are designed. In a Yes and No structure, the participants were asked the questions as follows: 1. 2. 3. 4.

Do you accept your salary to be deposited into your mobile money account? Do you want to pay your utility bills via mobile money? Do you think the government should reduce the dependent on cash money? Do you think the government should promote mobile money to increase the financial services?

74% of the participants answered yes when they were asked if they think the government should pay salaries through FastPay; for this statement, only 26% did not like the idea. On the second question when the respondents were asked if they want to pay their utility bills with FastPay, a large percentage of 92% answered yes, and only 8% answered no. The third question regarding the government support for mobile money, the participants were asked if they think the government should reduce the dependents on cash and again 90% of the respondents answered to that question as a yes, and only 10% did not like the idea. Identically, when the majority of the participants ticked yes when they were asked if the government should promote mobile money and use it to increase financial services, 96% answered yes to this last question, whereas only 4% answered no (see Fig. 4.20).

Open-Ended Question and Discussion It is worth mentioning that the questionnaire has an open-ended question, where the participants were asked what their suggestions for the government are and how the government can support mobile money. The answers are discussed and elaborated below.

146 Fig. 4.20 Government support

H. W. Mam 100%

96%

92%

90%

90% 80%

74%

70%

60% Yes

50%

No

40% 30%

26%

20% 8%

10%

10% 4%

0% 1

2

3

4

A large group of the participants suggests that physical stores accept FastPay as an option rather than just focusing on cash; another respondent lights on the awareness, she thinks that with proper awareness, mobile money can penetrate the market faster. Certainly, if individuals possess the necessary information about mobile money, their usage and frequency of using it will undoubtedly increase. Another group of the participants’ concern is related to security, they think that the government should increase security so it can be more reliable and trustworthy. Likewise, another individual points out that there should be private–public partnerships. The argument is that the government should take advantage of the effectiveness and efficiency of private banks and private companies because the government banks went bankrupt back in 2014, thus the people of Kurdistan have lost trust in the Banks (DeWeaver 2015). A group of the participants show the importance of infrastructure, first, the necessary infrastructure should build so that the adoption process is smooth across both the urban and the rural areas. A study conducted by Mothobi and Grzybowski (2017) also assures the importance of infrastructure, the findings of the study shows the availability of infrastructure affects the adoption rate. The outcomes of the research indicates that, the adoption of mobile money is higher in areas where physical infrastructure is better. A collection of the comments indicate the necessity of reducing the dependence on cash; with this in mind, promote mobile money services.

Descriptive Statistics of Variables To test the proposed model, structural equation odelling (SEM) has been utilized by using maximum likelihood estimation. The covariance of the conceptual model and the relationship between the latent variables has been examined using structural odelling techniques. Table 4.3 shows the definition of the variables used.

4 Investigating the Factors Affecting Mobile Money Adoption …

147

Table 4.3 Definition of studied variables Variables

Definitions

Perceived ease of To which degree the user thinks, using mobile money is free of struggle use (Davis 1989) Perceived usefulness

To which degree the user thinks mobile money can enhance her/his job performance (Davis 1989)

Attitude toward using

The attitude of the user toward whether the user rejects or accepts the technology (Davis 1989)

Behavioral Intention

The behavioral attitudes that indicate the intention to use the system (Davis 1989)

Initial trust

The willingness of an individual to take initiative without prior experience with the system (McKnight and Chervany 2001)

Government support

The government support factor directly affects the user intentions toward using the system

4.4.4 The Sample Quality The reliability and validity of the designed questionnaire were estimated as part of the priori condition before counting the scores of each variable and testing the model.

Validity and Reliability Analysis The degree of consistency between multiple measures of variables needs to be assessed in order to conclude the questionnaire’s reliability. A Cronbach’s alpha approach is applied to test each factor. Lee Cronbach developed Alpha in 1951, in order to provide researchers with a tool to measure the internal consistency of a test or a scale. A Cronbach alpha value range between 0–1 indicates higher reliability between variables. The acceptance of the reliability is the correlation of the experiment with itself. For example, the index of measurement is produced by subtracting the correlation of the test variables from 1:00. For example, if a test has 0.90 test reliability, the error variance will be 0.19 (0.90*0.90 = 0.81; 1.00 – 0.81 = 0.19). A value of α greater than 0.7 is accepted, while 0.8 and above is preferable. Any value for α below 0.7 recommends re-designing the questionnaire with some adjustments (Cronbach 1951). In addition, the internal consistency of the items is measured by: how many of the factors in a test measure the same conceptual model. That is why it is also concerned with the relatedness of the indicators within the concept. According to Tavakol and Dennick (2011), before conducting the test, the internal consistency of the test should be measured for the purpose of examining the validity of the test. Table 4.4 shows the reliability test results. By using the SPSS program, the validity and reliability of the study are tested, the results show that all Cronbach alpha values are above 0.7, and thus they are

148 Table 4.4 Reliability test

H. W. Mam

Variable (latent variable) No of indicators Cronbach’s alpha Ease of use

4

0.897

Usefulness

4

0.789

Attitude toward using

3

0.910

Behavioral intention

3

0.843

Trust

3

0.824

Government

4

0.798

considered within the acceptable threshold level. Ease of use, attitude, and trust are within the preferred level.

Confirmatory Factor Analysis Additionally, by using the AMOS program, a confirmatory factor analysis (CFA) is conducted to confirm the validity and reliability of the test. A total of 21 indicators are added to test 6 latent variables: Perceived ease of use (PEOU), Perceives usefulness (PU), Attitude toward using (ATU), Behavioral intention (BI), Perceived trust (PT), and Government support (GS). The goodness-of-fit is measured by using the following commonly used to test model-fit measures. The minimum discrepancy is divided by its degrees of freedom (CMIN/DF), goodness-of-fit (GFI), comparative fit index (CFI), normalized fit index (NFI), and square error of approximation (RMSEA). According to Marsh and Hocevar (1985), an acceptable CMIN/DF is between 2 and 5, Byrne (2013) also indicates that the ratio should not exceed 3. Accordingly, CMIN/DF ratio for this research is between 1 and 3. Furthermore, in social science studies, convergent validity is used to measure the relationship between variables. In other words, to show the theoretical correlation between variables are correlated when tested. The present study tests the convergent validity using all of the standardized factor loading, construct reliability (CR), and average variance extracted (AVE). When it comes to the factor loadings of the model, the results show that all values are above the threshold of 0.70 (Tucker and Lewis 1973). Those high values suggest that the indicators tend to meet at a point. In addition, the AVE assessments of latent variables are higher than 0.50; indicating a good correlation (Phonthanukitithaworn et al. 2015). Lastly, the values for construct reliability (CR) are higher than 0.70,; an indication that the latent variables are represented well by their represented indications (see Table 4.5).

Government support

Trust

Behavior intention

Attitude

Usefulness

Ease of use

Recommended values

Latent variable

0.75

0.74

0.7

GS2

GS3

GS4

0.75

PT3

0.78

0.69

PT2

GS1

0.73

0.84

BI3

PT1

0.79

BI2

0.78

0.65

ATU3

BI1

0.58

ATU2

0.81

PU4

0.69

0.84

PU3

ATU1

0.78

0.75

PU1

0.71

PEOU4

PU2

0.76

0.73

PEOU2

PEOU3

0.79

0.746

0.78

0.70

0.871

0.845

Ui J

(8.3)

where j is the specific chosen alternative under examination, and J is any other alternative. Hence, Eq. (8.3) illustrates the probability of choosing alternative j to be equal to the probability that the utility of alternative j is greater than (or equal to) the utility associated with alternative j after evaluating each and every alternative from the set J. ) ( )| |( Prob j = Prob. β ' Vi j + Ei j ≥ β ' Vi J + Ei J ∀ j ∈ J = 1, 2 . . . j . . . J ; j /= J (8.4) β represents all the variables that are estimated, while V represents the variables (controlled and demographic) that have an impact on the employee’s choice of job objective. This equation calculates the estimated maximum likelihood function (probability) based on the actual choices from the data set, to observe patterns of choice made by the random individual (Brown 2003). However, to avoid the error of independent and identical distribution (IID) the multinomial logit model is used (Hensher et al. 2005).

312

N. Haidar

The Multinomial Logit Model This section attempts to construct an econometric model using regression analysis with discrete dependent variable (discrete choice modeling) based on the employee’s indicators to identify his/her objective. Since the dependent variable Y has more than one category (discrete choices), a multinomial logit regression is used. Multinomial Logit model is used to model a relationship between a dependent variable and one or more independent variable. The dependent variable Y is a discrete variable which is represented by a set of mutually exclusive choices or categories. The independent variables X include ordinal variables, which allow for quantifiable variation i.e., age, and experience, as well as nominal variables which only allow for non-quantifiable variation between the variables (i.e., gender, position, sector) (Hair 2005). Dummy variables are used to quantify the nominal variables (gender, position, and sector). Dummy variables (also known as indicator variables) is a useful method in regression analysis to numerically quantify the information contained in variables that are not measured numerically, i.e., sex, region, and occupation (Suits 1957). In this analysis, gender, Position, and Sector have two dummy variables representing sex, management position, and sector whether private or public respectively. This model has the employee’s objective as the dependent variable Y and eight controlled variables along with five socio-demographic variables Xs as detailed below. The dependent variable Y of employee’s objective has four discrete alternatives as specified below: 1. 2. 3. 4.

Get the daily working assignment done (Y0 which is also the reference group) To increase my productivity compared to previous performance (Y1) To increase my knowledge about my tasks (Y2) To increase value of the final service provided to the customer (Y3).

These alternatives are denoted with Y0 (which is the reference) Y1, Y2, and Y3 respectively. Indicators of any alternatives are associated with the below controlled variables: 1. I think the departmental goal helps the bank to achieve its mission (X9) 2. I have a clear understanding of how my function helps the department to achieve its goals (X10) 3. Employees are encouraged to give suggestions on changing how the work should be done (X16) 4. Work Processes are continuously updated based on the front line employee’s suggestions for improvement (X17) 5. I can openly suggest to my manager to cut on the non-value-adding steps related to my job (X20) 6. I think more training can help me serve more customers (X29) 7. I think I need more feedback on my work (X32) 8. My manager’s main objective in my job (X35).

8 Transforming Lean to Service: Application to the Kurdistan Banking …

313

Further, the model of employee’s objective is also associated with the sociodemographic variables which are: X1. Gender X2. Age X3. Experience X4. Position X5. Sector These factors and their associations with employee’s objective are thoroughly explained in literature review section. For the sake of clarity, Annex C3 provides a conversion Table of reference number of each variable from the questionnaire and the reference number used in the construction of the model. To analyze employee’s objective based on the different organizational, departmental, and cultural attributes in the banking sector in Kurdistan, a model is constructed using logistic regression methodology, and the statistical package NLOGIT4.0 software, to examine the correlation and association between the measured attributes on the employee’s objective. A multinomial logit model is constructed to examine the effect of goal clarity, employee engagement, Training, feedback, and managerial “open door policy” on the identification of job objective for bank employees in Kurdistan Region of Iraq.

8.4 Data Analysis and Discussion This section presents the results and analysis of the applicability of Lean implementation in Kurdistan Banking industry. The objectives of this chapter are (a) to investigate the introduction of Lean thinking to the banking sector in Kurdistan, (b) to explore the CSFs of Lean thinking in the banking sector in Kurdistan, (c) to examine the characteristics of banking industry regarding Lean principles, and (d) how the characteristics hinder or facilitate the application of Lean thinking. Based on those objectives and the relevant literature, the hypothesis was constructed. The analysis, findings and discussion are presented based on the hypotheses test and research questions. Table 8.13 presents the correlation matrix for all the variables used in all the analyses respectively. Table 8.14 in Appendix 8.3 presents the estimated model parameter for the multinomial logit model (Indicating employees’ objective).

8.4.1 Description of the Variables Table 8.16 in Appendix 8.3 presents the descriptive statistics of the raw data obtained from the survey and the variables used in this study to identify the impact of these variables on the Lean application in the banking industry in Kurdistan Region of Iraq. Based on the standard deviation shown in Table 8.16, there is high heterogeneity of the data set with little desperation. The heterogeneity of the data set suggests the

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N. Haidar

adequate use of variable groups. Analysis of the groups of the variables is explained in this section. The questions in the survey are categorized according to the critical success factors of Lean thinking application. In addition to demographic constructs, the chapter has the construct of goal and objective clarity and planning and goal setting. It also includes the construct of employee independence, and engagement. It further has the constructs of waste analysis, understanding customer value, training and education, motivation as well as feedback. These constructs in this research model are better fitted to the aim of this chapter and the requirements for the application of Lean thinking in the banking sector in Kurdistan Region of Iraq (see Table 8.15 in Appendix 8.3 for the descriptions of the main variables used in the study).

8.4.2 Correlation Analysis A correlation analysis was performed (see Appendix 8.3 Table 8.13 for the linear correlation coefficient of all the survey questions). Specific analysis of novelty in the correlation analysis is presented in this section. As discussed in the design of questionnaire section, the questionnaire is intended to explore and investigate Lean introduction, practice and readiness to adopt a Lean methodology in Kurdistan banking sector. As the literature review suggests, the critical success factors CSFs are addressed with some specific questions taking into consideration the service characteristics of banking sector. The correlation between the CSFs (which is addressed with one or more questions in the survey hence group analysis) provides a basis for the correlation among the factor groups. There is a positive correlation coefficient of 0.404 between the clarity of department goal and frequency of updating the departmental plan (see Table 8.13 in Appendix 8.3). In other words, the more the bank updates the plans, the more the front line employees are aware and clear regarding department objectives and how their specific tasks contribute to achieving that objectives. Moreover, a correlation of 0.505 exists between long-term plan being achievable and the employee’s ability to understand the overall mission of the bank and the department. As shown in Table 8.5, in general, there is a positive correlation between the ability of the employees to understand the strategic mission and objectives of the bank, and their departmental mission and objective in particular, with their understanding of customer value. Departmental goal clarity in Q5 has a coefficient of 0.44 with the employee’s ability to meet the customer need in Q21 and Q22. A very high correlation of 0.53 exists between employee’s understanding of their tasks and how they contribute to the departmental mission in Q5 with their willingness to meet the performance quota in Q18. The ability of employees to meet the customer need is linked to the communication of goals and objectives. This supports the critical success factors of Lean present in the banking industry in Kurdistan. Goal clarity and understanding customer value are two Lean CSFs that are established in the current practice of banks.

8 Transforming Lean to Service: Application to the Kurdistan Banking …

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Table 8.5 Correlation coefficient between goal clarity and understanding customer value Customer value Goal clarity Correlation

Q1

Q2

Q3

Q4

Q5

Q16

0.18

0.07

0.12

0.11

0.18

Q17

0.18

0.26

0.32

0.2

0.14

Q18

0.23

0.22

0.33

0.44

0.53

Q19

0.22

0.1

0.19

0.22

0.32

Q20

0.13

0.13

0.28

0.2

0.22

Q21

0.25

0.34

0.34

0.38

0.44

Q22

0.25

0.32

0.34

0.23

0.44

Table 8.6 shows the correlation between the two CSFs planning/goal setting and employee engagement. There is a negative correlation of −0.042 between the frequency of updating the work plans Q28 and the updating of work processes Q12. This is an odd correlation it has small statistical significance, and it is out of the scope of this analysis. However, there is a negative correlation of −0.056 between the frequency of updating the work plans Q28 and the identification of waste in the process Q14. The correlation is weak, but its negative direction indicates that the less frequently the department is updating the plans the more waste is identified in the process. This analysis is reasonable and further supports the practice of the Lean CSF of waste identification and continuous work planning (Kaizen). It further supports the applicability of Lean in the banking sector in Kurdistan, on the ground that there is an existing understanding of the crucial role continuous planning and goal setting plays in defining customer value and detecting waste in the banking service process design. There is a strong positive correlation of 0.37 between the update of the department plans and employee’s engagement in the process. Meaning the more the department updates the plan, more employees are engaged in the design of the plans and Table 8.6 Correlation coefficient between planning/goal setting and employee engagement Employee engagement Planning/goal setting Correlation

Q6

Q7

Q8

Q11

0.33

0.25

0.37

0.038

Q12

0.37

0.17

0.13

−0.042

Q13

0.22

0.39

0.11

0.144

Q14

0.16

0.26

0.03

−0.056

Q15

0.4

0.18

0.38

0.052

Q28

316 Table 8.7 Correlation coefficient between waste analysis and employee’s engagement

N. Haidar

Waste analysis Employee’s engagement Q11

Correlation

Q12

Q13

0.25

0.15

Q14

0.07

0.19

Q15

0.42

0.31

objectives. This relationship supports H3 and H5 with the bank’s readiness to engage employees on the work process design to minimize waste and boost employee morale. Table 8.7 shows a positive correlation between the level of employee engagement and the level of waste in the processes in general. Meaning, the more employees are engaged, less waste is identified in the process. There is a correlation coefficient of 0.42 between encouraging employee’s suggestions of work process design in Q11 and cutting on non-value-added (NVA) activities in Q15. Meaning, the more employees are encouraged to give suggestions, the more NVA is cut down in the work processes. This finding supports the bank’s readiness to take employees as an active participant in designing the value process mapping and waste identifications. Table 8.8 below shows that individuals in management positions have a better understanding of the mission and objectives of the bank and department than people who are not in management positions. This further proves the hierarchical structure of banks identified in the literature, particularly in Kurdistan. The correlation is weak but positive. On a departmental level, the goals of the department have a correlation of 0.16 with the manager position. This does not support the practice of the CSFs in banking when it comes to vertical information flow, which is one of the critical factors of Lean application. Information in banking sector in Kurdistan, mostly flow horizontally and does not get to the front line employees. As the review of the literature suggests, one of the characteristics of banking is that it primarily deals with information. Hence, a vertical flow of information that transcends hierarchical positions is very crucial. This supports the adjustment of the Lean practices and principles to the characteristics of banking. Table 8.9 shows a positive correlation coefficient between education/training and employee independence and engagement. A coefficient of 0.465 exists between the Table 8.8 Correlation coefficient between position and goal clarity

Correlation Goal clarity

Management position QA 1

0.055

QA 2

0.048

QA 3

0.163

QA 4

0.084

QA 5

0.060

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Table 8.9 Correlation coefficient between education/training and employee independence and engagement Motivation and training Employee independence and engagement Correlation

Q9

Q10

Q11

Q12

Q23

0.26

0.12

0.33

0.290

Q24

0.28

0.27

0.21

0.194

Q25

0.37

0.26

0.28

0.173

Q26

0.29

0.10

0.04

0.130

Q27

0.29

0.28

0.40

0.465

Table 8.10 Correlation coefficient between value-added activities and continuous process improvement Value Added activities Continuous process improvement Correlation

Q7

Q12

Q15

Q19

Q9

0.07

Q11 0.25

0.37

0.184

0.16

Q13

0.12

0.25

0.15

0.265

0.25

Q14

0.05

0.07

0.19

0.189

0.13

Q16

0.07

−0.06

0.02

0.178

0.48

Q21

0.29

0.28

0.4

0.465

0.44

engagement of employees in the update of work processes Q12 and the level of feedback given to the employee Q27. Meaning, the more the employee is given feedback, the more they are engaged in the design of the work processes. The correlation of 0.33 between employee suggestions of updating work processes and continuous employee training strongly suggests that training engages employees in the work processes. This implies that trainings in the banking industry in Kurdistan are mostly related to the day-to-day work processes. Moreover, there is a strong correlation of 0.46 between feedback Q27 and employee engagement in the update of work processes. This is a reasonable relationship, and the more feedback given to employees the more engaged they are in the process design. The correlation between value-added activities and continuous improvement is shown in Table 8.10. In general, there is a positive correlation between the two CSFs of Lean application in Kurdistan banking industry. The correlation of 0.37 between continuous work process update Q12 and the need to go out of the process to find a creative solution suggests that the work processes are not updated according to employee’s input. This is because if work processes are updated according to employee’s suggestions, this should eliminate any need for frequent and ad hoc change of processes to meet the need of the customer.

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N. Haidar

This is a pitfall to the Lean application in the banks because it implies that the work processes are not standardized which is very critical in Lean thinking. To achieve the least level of waste, there should be minimal variation in the service as well as in the process of delivering the service. However, the need to address this issue of standardization of work processes is one of the contingencies of applying Lean to banking sector, and it supports the necessity of adjusting the principles to banking settings. Further, the strong correlation of 0.44 between Q19 (Understanding customer value is a challenge) and Q21 (The need to tailor the resources available to fulfill a customer order) is a reasonable relationship. This implies that the resources (mainly information in a banking setting) given to an employee to fulfill a customer order does not specifically address the customer value. This is one of the main principles of Lean thinking to address customer value in each and every step of the value chain, which is not the case in Kurdistan banking industry. This is further addressed in section five of this study.

8.4.3 Multinomial Logit Model for Employee’s Choice of Objective This section describes the relationship between the employee’s objective as the dependent variable Y and eight controlled variables along with five sociodemographic variables denoted with Xs to justify whether the critical success factors influence employee’s objective and socio-demographic factors. Using a multinomial Logit Model the following relation model between Y and Xs has been specified: Y ( j ) =α0 + β1 X 1 + β2 X 2 + β3 X 3 + β4 X 4 + β5 X 5 + β9 X 9 + β10 X 10 + β16 X 16 + β17 X 17 + β20 X 20 + β29 X 29 + β32 X 32

(8.5)

where Y is the employee’s objective at alternative j and it is the dependent variable, α0 is the intercept which captures the difference between alternatives and the reference group of each independent variable, while the controlled variables and demographic variables are denoted by X9, X16, X17, X20, X29, X32, X10, X5, X1, X2, X4, X3. The parameters to be estimated are denoted with βs. To calculate the association of each of the independent variables from the controlled factors and demographic factors on the employee’s choice of four alternatives regarding job objective. The below model is constructed: Yi j =α0 +

1 E j=0

β j X 1i j +

4 E j=0

β j X 2i j +

1 E j=0

β j X 4i j +

1 E j=0

β j X 5i j

8 Transforming Lean to Service: Application to the Kurdistan Banking …

+

5 E

β j X 9i j

j=0

+

5 E

5 E

β j X 10i j +

j=0

β j X 20i j +

j=0

5 E

β j X 16i j +

j=0 5 E

β j X 29i j +

j=0

5 E

319

β j X 17i j

j=0 5 E

β j X 32i j +

j=0

3 E

β j X 35i j

(8.6)

j=0

where i is the individual and j is a specific alternative under consideration. To examine how the specified model fits to explain the variations in the data survey, Table 8.11 presents the predicted and actual frequencies and percentages based on the actual number of observations of each alternative. According to the results in Table 8.11, completing the work assignment is the most chosen objective alternative by employees in the banking sector of Kurdistan Region; the model has estimated 85% of the actual outcome. The second highest frequency alternative is increasing customer value, and the model has estimated 78% of the actual outcome. To test the fit of the model, we calculate the predictive values for the four alternatives: (39 + 7 + 8 + 25)/130 = 60.8% which implies that 60.8% of the data fits well and is correctly predicted by the estimated model. To test the validity of the model, the Chi Square statistics is reported from the estimation of likelihood ratio (LR) of 107.75 with 39 degrees of freedom. This number is significantly above the standard Chi square critical value, indicating that the model is significant at 0.99 significance levels (Greene 2008; Rice 1989; Scheffe 1947). Fitting the regression results with the coefficients of the significant variables affecting the employee’s choice of objective following inferences can be drawn based on the results shown in Appendix 8.3 Table 8.14. The goal clarity of the department affects the employee to choose increasing productivity as their main objective at their job. As shown in Table 8.14 Appendix 8.3, a coefficient of 0.538 exists between goal clarity of the department QA5 and having productivity (Y1) as the main objective. This supports the fact that goal Table 8.11 Frequencies of actual and predicted outcomes-multinomial logit model Predicted alternatives of employee’s objective Actual alternatives

Complete job assignments

Increase productivity

Increase knowledge

Increase customer Total value

Employee’s Objective

Y0 0

Y1 1

Y2 2

Y3 3

Y0

39 (85%)

1

5

1

46

Y1

7

7 (32%)

6

2

22

Y2

13

5

8 (27%)

4

30

Y3

4

0

3

25 (78%)

32

Total

63

13

22

32

130

320

N. Haidar

Table 8.12 Effect of demographic variables on the employee’s choice of job objective

Demographic Variables

Y =1

Y =2

Y =3

Constant

−0.211

−2.891

−3.623

Sector

−1.466

−0.453

−0.323

1.520

1.365

1.400

−0.266

−0.318

0.837

Position

0.800

0.414

0.112

Experience

0.228

0.446

−0.382

Gender Age

clarity of the department influences the choice of employee’s objective to focus on the productivity level. The coefficient between employee engagement in the planning and goal setting QA11 and QA12 are 0.23 and 0.028, respectively, when the objective is increasing productivity Y1 employee engagement in the planning and goal setting are selected. The coefficients are statistically insignificant indicating that employee engagement in the objective of banks in KRI would not affect productivity. The negative and positive coefficients shown in Table 8.12 of gender, age, position, sector and experience justify the fact that the demographic factors affect the choice of employee’s objective. Below is a graphical representation of response rate to employee’s objective. Figure 8.5 shows the employee’s objective in his/her job, above figure shows that 35% chose alternative, 1 (To get the job assignments done), 17% alternative 2 (to increase productivity), 23% alternative 3 (To increase knowledge), and 25% alternative 4 (to increase customer value) respectively.

50 45 40 35 30 25 20 15 10 5 0

46

My Main Objective in My Job is 1. Get my working assisgnment done

30

32

22

2. To increase my productivity compared to previous performance 3. To increase my knowledge about my tasks

1. Get my working assisgnment done

2. To increase my 3. To increase my 4. To increase value productivity knowledge about my to the final service provided to the compared to previous tasks customer performance

Fig. 8.5 Response rate to employee objective alternatives

4. To increase value to the final service provided to the customer

8 Transforming Lean to Service: Application to the Kurdistan Banking … Fig. 8.6 Response rate of lean introduction

321

Have you ever heard of Lean System? 10

YES NO 120

The implementation of Lean thinking in banking sector of Kurdistan region is possible. Figure 8.6 shows that Lean has not been formally introduced in the banking sector in Kurdistan with a response rate of only 8% of (Yes) and 92% of (No). However, some of the Lean critical success factors CSFs are practiced in the banks in Kurdistan, but they have not been exactly labeled as Lean practice. The correlation analysis showed that vertical information flow, standardization of processes, and breaking through the hierarchical organization structure are among the main pitfalls in front of a Lean transformation in the banking sector in Kurdistan. As such, the banks have to focus on to improve to make a transformation to Lean possible. Nevertheless, Lean thinking has been applicable to the banking sector in Kurdistan with the existing understanding of employee role, system view of service delivery, and waste identification as one indicator of process efficiency and service quality. In order to adjust the Lean principles of manufacturing to resemble characteristics of banking industry, the main focus has been on principles that drive Lean transformation in Kurdistan banking industry such as standardizing processes, automation of basic processes, and vertical information flow. The multinomial logit model analysis indicates that the banks in Kurdistan have the proper level of employee independence, engagement, and understanding of their task and objective to deliver customer value. The capacity to embark on a Lean journey in regard to employee objectives and understanding of the overall process is justified.

8.5 Conclusion and Recommendation The banking industry generally in Iraq and specifically in Kurdistan Region has been significantly damaged because of the political and economic situation under the previous regime before 2003. This has significantly negatively affected the banking industry in Iraq and in Kurdistan. The applicability of Lean thinking to service in general and banking sector in particular is not thoroughly studied in the existing literature. Many scholars call on

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N. Haidar

transferring and translating the Lean principles from the manufacturing sector to the service context. This study investigated this aspect and the current practice of the critical success factors in the banking sector in Kurdistan region of Iraq and addressed the problem of Lean applicability in service context in general and banking sector in particular. The aim of the chapter was to investigate the current practice of banks in Kurdistan in terms of internal efficiency and effectiveness in addressing the wide array of customer financial needs in the country. In this regards the critical success factors of Lean thinking as one of the improvement systems were examined. The analysis was based on a survey of 130 usable questionnaires in 14 private and public banks in Erbil with the application of a descript choice modeling. The results suggested that the banking industry in Kurdistan is practicing some of the success factors of Lean transformation without recognizing the practice as Lean transformation. The results of the multinomial logit model indicated that employees of the banks in Kurdistan have a proper level of understanding of customer value and are aware of the nonvalue-added activities related to their tasks. The banking sector has a proper level of employee capacity in terms of engagement and independence to take on a Lean transformation journey. Nevertheless, the correlation analysis results suggested a lack of holistic application of Lean tools and techniques in the banking sector in Kurdistan. That is, the transformation lacks strategic direction and long-term commitment to internal efficiency and continuous improvement. The banking sector in Kurdistan lags behind in vertical information flow, flat organizational structure, automation of basic processes and standardization of processes. The development of the financial institutions in the region is a critical development issue as well as a policy issue. The government’s policy guidance should facilitate the growth of private banking industry and competitively provide safeguard to the industry to increase trust of the potential customer base in the country. On a long run, the KRG should privatize the banking industry to ensure a level of quality of the service is competitive. This chapter has shown that the industry is already improving, however not competitively compared to other Middle East countries. Besides the change of the policy and governmental guidance, the internal processes of the banking sector in Kurdistan are not competitive. Lean transformation is one of the suitable improvement systems suggested by this study to expedite the growth of this sector by focusing on internal efficiency, customer satisfaction, and effectiveness in service delivery. Lean is specifically suitable to the banking sector in Kurdistan due to the characteristics of banking transactions. Since Lean does not require significant investment and can put in place reliable processes that can run the enormous amount of transactions happening in a bank, a transformation of Lean is very suitable in banks in Kurdistan. Further, the organizational structure of the banking sector in Kurdistan should focus on a vertical flow of information to ensure the benefit of a Lean transformation. For this purpose, Kaizen meetings to engage all lines of authority in the process is recommended to managers. Finally, automation is an area where Kurdistan banks lag

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behind, and it is recommended for managers to invest in this area for a competitive service delivery.

Appendix 8.1: Survey Questionnaire (English)

Background of the Questionnaire This questionnaire is developed to be the empirical foundation for this study concerning Lean in the financial service industry. The author for this study is Nagham Haidar, who is a student at University of Kurdistan—Hawler Master program in Business Management. The aim of the survey is to assess the Applicability of Lean in the organization Definition of Lean thinking Lean thinking is a business methodology that aims to provide a new way to think about how to organize human activities to deliver more benefits to society and value to individuals while eliminating waste A. General information Gender

0 Male

0 Female

Age

0 < 20

0 20–29

0 Yes

0 No

0 30–39

0 40–49

0 50–65

Years worked at this Bank? Are you in a leading position (Manager Position)? What Department Are you working at? Instructions Please tick the alternative that suits your opinion best. Number (5) represents strongly agree while (1) represents strongly disagree. Please answer all questions, if you don’t have an opinion or don’t understand the question chose X B. Please express your opinion on the following Lean practices related to your organization on a five-point Likert scale i.e., 1. Strongly Disagrees 2. Disagree 3. Normal 4. Agree 5. Strongly agrees X. No opinion Sr. NO

Mission and Strategy

1

I perfectly understand the mission and objectives of the bank

2

The Mission and Strategy of the bank is perfectly communicated to all employees

3

The departmental goals are clear to me

1

2

3

4

5

X

(continued)

324

N. Haidar

(continued) Background of the Questionnaire 4

I think the departmental goal helps the bank to achieve its mission

5

I have a clear understanding how my function helps the department to achieve its goals

Planning/Goal setting 6

The short term plans are often updated

7

Updating the short term plans is a group work in which every employee has opinions as to what should be the next priority

8

Long-Term plans are realistic and reachable

Employee Independence 9

There is a need to go out of the predesigned process and creatively look for solutions in my job

10

My job requires a lot of independent decision making without referral to the upper line of management:

Employee Engagement 11

Employees are encouraged to give suggestions on changing how the work should be done

12

Work Processes are continuously updated based on the front line employee’s suggestion for improvement (continued)

8 Transforming Lean to Service: Application to the Kurdistan Banking …

325

(continued) Background of the Questionnaire Waste Analysis 13

I can do my job quicker if not for some unnecessary procedures

14

In my job, I think there are additional steps that do not add value to the service and slows down my performance

15

I can openly suggest to my manager to cut on the non-value-adding steps related to my job

Understanding Customer Value 16

Understanding the customer need is always a challenge

17

To deliver perfect service to the customer, there has to be less number of customer assigned per employee

18

Meeting the performance quota is the ultimate goal for me

19

Customer value is the trigger to the designing of work processes

20

I can predefine the needs of the customer before making an order

21

I try to tailor the need of the customer to the resources available to complete the order (continued)

326

N. Haidar

(continued) Background of the Questionnaire 22

I would take additional responsibilities to perfectly meet the need of the customer to complete the order

Training and education 23

There are continuous training for employees to acquire new skills?

24

I think more training can help me serve more customers

Motivation and Feedback 25

There is always opportunities to learn new things

26

I mostly receive feedback that helps me to improve

27

I think I need more feedback on my work

C. Please select the option that suits your opinion best 28. How often the short term plans are updated 1. Every week 2. Every two week 3. Every month 4. Every three month 5. Others, [please specify]: 29. My main objective in my job is to: 1. Get my working assignment done 2. To increase my productivity compared to previous performance 3. To increase my knowledge about my tasks 4. To increase value of the final service provided to the customer 30. My manager’s main objective in my job is to: 1. Get my working assignments done 2. To increase my productivity compared to previous performance 3. To increase my knowledge about my tasks 4. To increase value of the final service provided to the customer D. Open Questions (continued)

8 Transforming Lean to Service: Application to the Kurdistan Banking …

327

(continued) Background of the Questionnaire 31. Have you ever heard of Lean 0 YES System?

0 NO

If yes, what does Lean mean for you? ____________________________________________________________________________ _________ If Yes, What are the positive aspects of Lean? ____________________________________________________________________________ _________ If Yes, What are the Negative aspects of Lean? ____________________________________________________________________________ ____________________________________________________________________________ _________________ Thanks for your participation We would like to thank you for your participation; the responses will help us during the process of writing our thesis. All collected data will be handled confidential and will be destroyed after it has been analysed

Appendix 8.2: Graphs of Survey Responses See Figs. 8.7, 8.8, 8.9, 8.10, 8.11, 8.12, 8.13, 8.14, 8.15, 8.16, 8.17, 8.18, 8.19, 8.20, 8.21, 8.22, 8.23, 8.24, 8.25, 8.26, 8.27, 8.28, 8.29, 8.30, 8.31, 8.32, 8.33, 8.34, 8.35, 8.36, 8.37, 8.38, 8.39, 8.40, 8.41, 8.42 and 8.43 Survey Participant by Bank 30

25

25 20 13

15 10 5

1

11

24 15

12 4

0

Fig. 8.7 Survey participant by bank

4

5

4

3

5

4 0

0

328

N. Haidar Participant Gender Ratio

90

84

80 70 60 46

50

Male

40

Female

30 20 10 0 Male

Female

Fig. 8.8 Participant gender ratio

Age of Participant 60

54

49

50

39 30

40>49 50>59

20 12 10

10

5

0 39

Fig. 8.9 Age of participant

Appendix 8.3 See Table 8.13 See Table 8.14 See Table 8.15 See Table 8.16

40>49

50>59

>60

>60

8 Transforming Lean to Service: Application to the Kurdistan Banking …

329

Years of experience of the Participant 60 50

48 1-5

6-10

37

40

11-15 16-20

30

21-25

22 20

26-30 31-35

10 2

3

3

4

16-20

21-25

26-30

31-35

36-40 2

0 1-5

6-10

11-15

36-40

Fig. 8.10 Years of experience of the participant

Management Position Ratio 100 89

90

80 70 60 50

Manager

41

Not Manager

40

30 20 10 0 Manager

Fig. 8.11 Management position ratio

Not Manager

330

N. Haidar

Sector Ratio of the Banks 70

65

65

60 50 40

Private

30

Public

20 10 0 Private

Public

Fig. 8.12 Sector ratio of the banks

I perfectly understand the mission and objectives of the bank? 50

46

45 40 35 30 25

24

22

24

20 15 9

10

5

5 0 Strongle Disagree

Disagree

Normal

Agree

Strongly Agree

Fig. 8.13 I perfectly understand the mission and objectives of the bank

No Opinion

8 Transforming Lean to Service: Application to the Kurdistan Banking …

331

The Mission and Strategy of the bank is perfectly communicated to all employees 38

40

37

35 30 24

25 20 15

12

10

9

10 5 0 Strongle Disagree

Disagree

Normal

Agree

Strongly Agree

No Opinion

Fig. 8.14 The mission and strategy of the bank is perfectly communicated to all employees

The departmental goals are clear to me 45

42

40 34

35 30

24

25 20 15

13 9

10

8

5 0 Strongle Disagree

Disagree

Normal

Fig. 8.15 The departmental goals are clear to me

Agree

Strongly Agree

No Opinion

332

N. Haidar

I think the departmental goal helps the bank to achieve its mission 50

46

45 40

33

35 30 25

21

20 14

15 10

8

8

5 0 Strongle Disagree

Disagree

Normal

Agree

Strongly Agree

No Opinion

Fig. 8.16 I think the departmental goal helps the bank to achieve its mission

I have a clear understanding how my function helps the department to achieve its goals 60 49

50 40

36

30 21 20

12 10

6

6

0 Strongle Disagree

Disagree

Normal

Agree

Strongly Agree

No Opinion

Fig. 8.17 I have a clear understanding how my function helps the department to achieve its goals

8 Transforming Lean to Service: Application to the Kurdistan Banking …

333

The short term plans are often updated 37

40

35

35

30 23

25 20 15

15 11

9

10 5 0 Strongle Disagree

Disagree

Normal

Agree

Strongly Agree

No Opinion

Fig. 8.18 The short term plans are often updated

Updating the short term plans is a group work in which every employee has opinions as to what should be the next priority 40

36

38

35 30 23

25 20 15

15 10

8

10 5 0 Strongle Disagree

Disagree

Normal

Agree

Strongly Agree

No Opinion

Fig. 8.19 Updating the short term plans is a group work in which every employee has opinions as to what should be the next priority

334

N. Haidar Long-Term plans are realistic and reachable

50 45 40 35 30 25 20 15 10 5 0

45 37

20 13 8

Strongle Disagree

7

Disagree

Normal

Agree

Strongly Agree

No Opinion

Fig. 8.20 Long-term plans are realistic and reachable

There is a need to go out of the predesigned process and creatively look for solutions in my job 40

34

35

34

30 25

21

18

20 15

12

11

10 5 0 Strongle Disagree

Disagree

Normal

Agree

Strongly Agree

No Opinion

Fig. 8.21 There is a need to go out of the predesigned process and creatively look for solutions in my job

8 Transforming Lean to Service: Application to the Kurdistan Banking …

335

My job requires a lot of independent decision making without referral to the upper line of management 40

37 33

35 30 23

25 20

15

15

12

10

10 5 0

Strongle Disagree

Disagree

Normal

Agree

Strongly Agree

No Opinion

Fig. 8.22 My job requires a lot of independent decision making without referral to the upper line of management

Employees are encouraged to give suggestions on changing how the work should be done 40

37

35

31

30 23

25 20 15

12

14

13

Strongly Agree

No Opinion

10 5

0 Strongle Disagree

Disagree

Normal

Agree

Fig. 8.23 Employees are encouraged to give suggestions on changing how the work should be done

336

N. Haidar

Work Processes are continuously updated based on the front line emplyee's suggestion for improvement 50 43

45 40 35 29

30 23

25 20 15

12

11

12

Strongly Agree

No Opinion

10 5

0 Strongle Disagree

Disagree

Normal

Agree

Fig. 8.24 Work processes are continuously updated based on the front line employee’s suggestion for improvement

I can do my job quicker if not for some unnecessary procedures 40

36

35 30

27

26

25 20

17 13

15

11

10 5 0 Strongle Disagree

Disagree

Normal

Agree

Strongly Agree

Fig. 8.25 I can do my job quicker if not for some unnecessary procedures

No Opinion

8 Transforming Lean to Service: Application to the Kurdistan Banking …

337

In my job, I think there are additional steps that do not add value to the service and slows down my performance 40 33

35

35

30 25

20

19

20

15

15 10

8

5 0 Strongle Disagree

Disagree

Normal

Agree

Strongly Agree

No Opinion

Fig. 8.26 In my job, I think there are additional steps that do not add value to the service and slows down my performance

I can openly suggest to my manager to cut on the non-value-adding steps related to my job 38

40

34

35 30 25 20 15

14

13

Strongle Disagree

Disagree

15

16

Strongly Agree

No Opinion

10 5

0 Normal

Agree

Fig. 8.27 I can openly suggest to my manager to cut on the non-value-adding steps related to my job

338

N. Haidar

Understanding the customer need is always a challenge 45 40 35 30 25 20 15 10 5 0

41 30 23 15

12

9

Strongle Disagree

Disagree

Normal

Agree

Strongly Agree

No Opinion

Fig. 8.28 Understanding the customer need is always a challenge

To deliver perfect service to the customer, there has to be less number of customers assigned per employee 45 40 35 30 25 20 15 10 5 0

40 36

17

15

10

Strongle Disagree

Disagree

Normal

Agree

Strongly Agree

12

No Opinion

Fig. 8.29 To deliver perfect service to the customer, there has to be less number of customers assigned per employee

8 Transforming Lean to Service: Application to the Kurdistan Banking …

339

Meeting the performance quota is the ultimate goal for me 45

39

40

32

35 30

26

25 18

20

15 10 5

11 4

0 Strongle Disagree

Disagree

Normal

Agree

Strongly Agree

No Opinion

Fig. 8.30 Meeting the performance quota is the ultimate goal for me

Customer value is the trigger to the designing of work processes 37

40

38

35 30 25 20 15

10

17

16

13

9

5 0 Strongle Disagree

Disagree

Normal

Agree

Strongly Agree

Fig. 8.31 Customer value is the trigger to the designing of work processes

No Opinion

340

N. Haidar

I can predefine the needs of the customer before making an order 45

41

40 35

31

Strongle Disagree

30

Disagree

25

20

20 15

17

Normal Agree

11

10

Strongly Agree

10

No Opinion

5 0 Strongle Disagree

Disagree

Normal

Agree

Strongly No Opinion Agree

Fig. 8.32 I can predefine the needs of the customer before making an order

I try to tailor the need of the customer to the resources available to complete the order 50 43

45

39

40 35

Strongle Disagree

30

Disagree

25

Normal 17

20 12

15 10

14

Agree Strongly Agree

5

No Opinion

5 0 Strongle Disagree

Disagree

Normal

Agree

Strongly No Opinion Agree

Fig. 8.33 I try to tailor the need of the customer to the resources available to complete the order

8 Transforming Lean to Service: Application to the Kurdistan Banking …

341

I would take additional responsibilities to perfectly meet the need of the customer to complete the order 50 45 40 35 30 25 20 15 10 5 0

46 34

Strongle Disagree Disagree Normal

15 11

14

Agree

10

Strongly Agree No Opinion

Strongle Disagree

Disagree

Normal

Agree

Strongly No Opinion Agree

Fig. 8.34 I would take additional responsibilities to perfectly meet the need of the customer to complete the order

There are continuous trainings for employees to acquire new skills? 40 35 30 25 20 15 10 5 0

37 29 19

Strongle Disagree Disagree 17

17

Normal 11

Agree Strongly Agree No Opinion

Strongle Disagree Normal Disagree

Agree

Strongly No Agree Opinion

Fig. 8.35 There are continuous trainings for employees to acquire new skills

342

N. Haidar

I think more training can help me serve more customers 45

39

40 35 27

30

30

Strongle Disagree Disagree

25 20

16

10

Agree

12

15

Normal

Strongly Agree

6

5

No Opinion

0 Strongle Disagree Disagree

Normal

Agree

Strongly Agree

No Opinion

Fig. 8.36 I think more training can help me serve more customers

There is always opportunities to learn new things 38

40 35

32

30

Strongle Disagree

25

25

Disagree

20 15

Normal

14 11

10

10

Agree Strongly Agree

5

No Opinion

0

Strongle Disagree

Disagree

Normal

Agree

Strongly Agree

Fig. 8.37 There is always opportunity to learn new things

No Opinion

8 Transforming Lean to Service: Application to the Kurdistan Banking …

343

I mostly receive feedback that helps me to improve 45 39

40 35

32 Strongle Disagree

30

24

25

Disagree Normal

20 15

14 10

11

Agree Strongly Agree

10

No Opinion

5 0 Strongle Disagree

Disagree

Normal

Agree

Strongly Agree

No Opinion

Fig. 8.38 I mostly receive feedback that helps me to improve

I think I need more feedback on my work 38

40 33

35 30

Strongle Disagree 25

21

20

17

15 10

Disagree Normal Agree

12 9

Strongly Agree No Opinion

5 0 Strongle Disagree

Disagree

Normal

Agree

Fig. 8.39 I think I need more feedback on my work

Strongly No Opinion Agree

344

N. Haidar

How often the short term plans are updated? 40

37

35

32

30 25 20 20

22

1. Every week

19

2. Every two week 3. Every month

15

4. Every three month

10

5. Others, [please specify]:

5 0 1. Every 2. Every two 3. Every week week month

4. Every three month

5. Others, [please specify]:

Fig. 8.40 How often the short term plans are updated?

My main objective in my job is to: 50 45 40 35 30 25 20 15 10 5 0

46

30

32

22

1. Get my working assisgnment done 2. To increase my productivity compared to previous performance 3. To increase my knowledge about my tasks

1. Get my 2. To increase 3. To increase 4. To increase working my productivity my knowledge value to the assisgnment compared to about my tasks final service done previous provided to the performance customer

Fig. 8.41 My main objective in my job is to

4. To increase value to the final service provided to the customer

8 Transforming Lean to Service: Application to the Kurdistan Banking …

345

My manager's main objective in my job is to: 74

80 70 60 50 40 30 20 10 0

1. Get my working assisgnment done

20

15

21

2. To increase my productivity compared to previous performance 3. To increase my knowledge about my tasks

2. To increase 3. To increase 4. To increase 1. Get my my productivity my knowledge value to the working assisgnment compared to about my tasks final service provided to the previous done customer performance

4. To increase value to the final service provided to the customer

Fig. 8.42 My manager’s main objective in my job is to

Have you ever heard of Lean System? 140

120 120 100 80

YES

60

NO

40 20

10

0 YES

Fig. 8.43 Have you ever heard of Lean system

NO

Gender

−0.06

−0.16

QA 13

−0.11

−0.09 0.04

0.02

−0.05

−0.05

0.01

−0.10

QA 11

−0.02

−0.14

QA 10

−0.05 −0.08

QA 12

−0.10

−0.07

−0.01

−0.08

QA 8

QA 9

0.02

−0.09

0.04

QA 7

−0.03 −0.08

−0.01

−0.04

−0.09

−0.04

QA 5

QA 6

−0.09

−0.11

0.01

QA 4

0.05

0.11

−0.02

0.07

0.19

0.11

0.11

0.06

0.06

0.06

0.08

0.16

−0.02 −0.09

0.09

−0.02

0.08

−0.01

QA 2

0.05

−0.05

1

Management position

−0.17

0.50

−0.33

1

Experience

QA 3

−0.08

−0.08

QA 1

−0.31

0.32

−0.02

0.03

Management position

Sector

1

0.65

−0.07

0.07

Age

Age

1

Years of experience

Gender

Table 8.13 Correlation coefficient

−0.05

0.01

0.06

0.08

−0.01

−0.02

0.06

0.04

0.06

0.01

0.08

0.05

−0.10

1

Sector

0.33

0.10

0.16

0.06

0.41

0.21

0.07

0.14

0.50

0.49

0.44

0.18

1

QA 1

0.27

0.43

0.41

0.29

0.24

0.28

0.40

0.36

0.29

0.40

0.50

1

QA 2

0.14

0.35

0.36

0.23

0.21

0.20

0.36

0.17

0.44

0.53

1

QA 3

0.28

0.34

0.29

0.24

0.43

0.36

0.28

0.26

0.64

1

QA 4

0.29

0.27

0.38

0.23

0.42

0.51

0.18

0.40

1

QA 5

0.26

0.26

0.35

0.02

0.19

0.34

0.32

1

QA 6

(continued)

0.12

0.43

0.39

0.03

0.07

0.41

1

QA 7

346 N. Haidar

−0.04

−0.01

0.03

QA 31

0.06 0.03

0.08

0.06

0.19

0.06

QA 29

0.00

0.07

QA 30

0.05

−0.05

QA 28

−0.01

−0.12

0.07

0.01

−0.03

QA 26

QA 27

−0.02

−0.02

−0.04

QA 25

0.09 0.03

0.03

−0.03

0.00

0.10

0.00

0.08 0.15

−0.04 −0.03 −0.01

−0.03

0.28

0.09

0.05

0.12

0.02

−0.03

−0.01

0.03 0.14

−0.12

0.08

0.02

−0.01

0.24

0.21

−0.03

0.03

0.06

0.09

−0.01

QA 23

0.02

0.21 0.12

0.06

QA 24

0.01

0.10

QA 22

0.22 0.02

−0.02 0.07

0.13

Sector

−0.05

Management position

−0.02

−0.01

−0.16

−0.07

0.16

−0.01

QA 20

QA 21

0.11

−0.11

0.11

QA 19

−0.06 0.12

−0.09

0.13

−0.06

−0.06

QA 17

QA 18

0.02 −0.06

0.03

−0.17

0.00

0.12

0.08

Experience

QA 15

0.08

QA 16

Age

Gender

−0.15

QA 14

Table 8.13 (continued)

0.13

0.18

0.13

0.26

0.22

0.32

0.20

0.35

0.27

0.25

0.25

0.13

0.22

0.23

0.18

0.18

0.22

0.25

QA 1

0.14

0.03

0.06

−0.03

0.16

0.19

0.21

0.23

0.33

0.32

0.34

0.13

0.10

0.22

0.26

0.07

0.41

0.04

QA 2

0.07

0.05

−0.02

0.09

0.13

0.25

0.18

0.29

0.33

0.34

0.34

0.28

0.19

0.33

0.32

0.12

0.33

0.22

QA 3

0.09

0.06

0.07

0.04

0.15

0.36

0.33

0.45

0.36

0.23

0.38

0.20

0.22

0.44

0.20

0.11

0.33

0.23

QA 4

0.17

0.10

0.05

0.10

0.29

0.43

0.42

0.48

0.37

0.44

0.44

0.22

0.32

0.53

0.14

0.18

0.48

0.26

QA 5

0.14

0.03

0.02

0.01

0.35

0.26

0.20

0.26

0.20

0.23

0.35

0.13

0.23

0.27

0.21

0.12

0.31

0.24

QA 6

(continued)

0.09

−0.13

−0.14

−0.02

0.16

0.13

0.13

0.16

0.23

0.21

0.29

0.25

0.27

0.35

0.19

0.07

0.33

0.05

QA 7

8 Transforming Lean to Service: Application to the Kurdistan Banking … 347

0.39

0.22

QA 13

0.25

0.17

0.33

0.37

QA 11

0.12

QA 10

QA 12

1

0.35

1

0.14

QA 9

QA 8

QA 8

QA 9

QA 7

QA 6

QA 5

QA 4

QA 3

QA 2

QA 1

Sector

Management position

Years of experience

Age

Gender

Table 8.13 (continued)

0.11

0.13

0.37

1

QA 10

0.25

0.44

1

QA 11

0.15

1

QA 12

1

QA 13

QA 14

QA 15

QA 16

QA 17

QA 18

(continued)

QA 19

348 N. Haidar

0.09

0.20

QA 31

0.03

0.08

0.06

0.04

QA 29

QA 30

0.05

−0.05

QA 28

0.29

0.22

0.31

0.24

0.37

QA 26

0.28

QA 25

0.28

0.26

0.17

0.26

QA 27

0.37

0.26

QA 23

QA 24

0.25

QA 22

0.15

0.18

0.37

QA 20

QA 21

0.16

0.33

QA 19

0.15

0.31

0.00

0.42

QA 17

QA 18

0.18

0.12

0.40

0.15

QA 15

0.26

QA 16

QA 9

QA 8

0.16

QA 14

Table 8.13 (continued)

0.05

0.18

0.14

0.11

−0.04 −0.03

−0.05 −0.02

−0.04 0.03

−0.04

0.03

0.13

0.17

0.19

0.29

0.29

0.40

0.19

0.13

0.33

0.04

0.28

0.04

0.28

0.21

0.33

0.48

0.28

0.09

0.19

0.21

0.10

0.31 0.02

0.42

0.19

QA 12

−0.06

0.07

QA 11

−0.04

0.04

0.10

0.26

0.27

0.12

0.25

0.30

0.22

0.10

0.16

0.09

0.02

0.38

0.03

QA 10

−0.01 −0.07

−0.02

−0.04

−0.06

0.34

0.30

0.31

0.40

0.32

0.03

0.18

0.17

0.13

0.23

0.21

0.15

0.19

1

QA 14

0.08

0.09

0.14

0.35

0.33

0.41

0.35

0.34

0.21

0.36

0.16

0.25

0.25

0.39

0.22

0.27

0.50

QA 13

−0.01

0.03

−0.02

0.05

0.35

0.43

0.41

0.53

0.35

0.58

0.46

0.26

0.41

0.36

0.20

0.18

1

QA 15

−0.07

0.20

0.20

0.01

0.13

0.16

0.05

0.14

0.06

0.29

0.23

0.37

0.48

0.18

0.52

1

QA 16

−0.08

0.07

0.00

−0.04

0.07

0.06

0.03

0.18

0.03

0.24

0.34

0.31

0.45

0.22

1

QA 17

0.12

0.13

0.01

−0.02

0.13

0.26

0.21

0.24

0.18

0.28

0.47

0.20

0.35

1

QA 18

(continued)

−0.02

0.07

0.08

0.13

0.24

0.28

0.16

0.30

0.23

0.47

0.44

0.46

1

QA 19

8 Transforming Lean to Service: Application to the Kurdistan Banking … 349

QA 13

QA 12

QA 11

QA 10

QA 9

QA 8

QA 7

QA 6

QA 5

QA 4

QA 3

QA 2

QA 1

Sector

Management position

Years of experience

Age

Gender

Table 8.13 (continued)

QA 20

QA 21

QA 22

QA 23

QA 24

QA 25

QA 26

QA 27

QA 28

QA 29

QA 30

(continued)

QA 31

350 N. Haidar

0.21

0.11

−0.02

0.01

0.12

0.52

0.30

0.16

0.26

0.20

0.18

0.13

0.07

−0.07

0.00

−0.06

QA 22

QA 23

QA 24

QA 25

QA 26

QA 27

QA 28

QA 29

QA 30

QA 31

0.32

0.37

0.43

0.23

0.51

1

1

QA 21

QA 21

QA 20

QA 20

QA 19

QA 18

QA 17

QA 16

QA 15

QA 14

Table 8.13 (continued)

−0.01

0.04

0.02

0.14

0.28

0.26

0.28

0.30

0.23

1

QA 22

0.03

0.11

0.07

0.06

0.32

0.48

0.56

0.48

1

QA 23

0.07

0.03

0.03

0.21

0.43

0.55

0.64

1

QA 24

0.08

−0.04

0.05

−0.04

−0.08

0.21

0.53

1

QA 26

−0.08

0.08

0.53

0.58

1

QA 25

−0.10

0.04

−0.03

0.12

1

QA 27

−0.02

0.04

0.03

1

QA 28

0.04

0.63

1

QA 29

0.07

1

QA 30

1

QA 31

8 Transforming Lean to Service: Application to the Kurdistan Banking … 351

352

N. Haidar

Table 8.14 Model parameter estimates for indicating employees objective Model

Variable Main

Sub

Y =1

Y =2

Y =3

Constant

Constant

−3.925

−2.891

−3.623

QA4

Reference

0.211

0.408

−0.007

0.538

0.1645

−0.231

0.237

0.196

−0.262

0.0289

−0.233

0.217

−0.332

−0.139

−0.062

1. Strongly disagree 2. Disagree 3. Normal 4. Agree 5. Strongly agree 6. No opinion QA5

Reference 1. Strongly disagree 2. Disagree 3. Normal 4. Agree 5. Strongly agree 6. No opinion

QA11

Reference 1. Strongly disagree 2. Disagree 3. Normal 4. Agree 5. Strongly agree 6. No opinion

QA12

Reference 1. Strongly disagree 2. Disagree 3. Normal 4. Agree 5. Strongly agree 6. No opinion

QA15

Reference 1. Strongly disagree 2. Disagree 3. Normal 4. Agree 5. Strongly agree

(continued)

8 Transforming Lean to Service: Application to the Kurdistan Banking …

353

Table 8.14 (continued) Model

Variable 6. No opinion QA24

Reference

0.386

0.086

0.107

0.033

−0.063

−0.131

0.856

0.792

2.295

−1.466

−0.453

−0.323

1.520

1.365

1.400

0.800

0.414

0.112

0.228

0.446

−0.382

1. Strongly disagree 2. Disagree 3. Normal 4. Agree 5. Strongly agree 6. No opinion QA27

Reference 1. Strongly disagree 2. Disagree 3 Normal 4. Agree 5. Strongly agree 6. No opinion

QA30

Reference 1. Get my working assignment done 2. To increase my productivity compared to previous performance 3. To increase my knowledge about my tasks 4. To increase value to the final service provided to the customer

Sector of the bank

Reference 1. Private 2. Public

Gender

Reference 1. Male 2. Female

Position

Reference 1. Manager 2. Not Manager

Experience

Reference 1. 1–5 Years 2. 6–10 Years

(continued)

354

N. Haidar

Table 8.14 (continued) Model

Variable 3. 11–15 Years 4. 16–20 Years 5. >20 more than Age

Reference

−0.266

−0.318

0.837

1. 20, 1 for 20–29, 2 for 30–39, 3 for 40–49, 4 for 50–65

Years of experience X3

Years of experience of the participant denoted numerically

Position

X4

Whether the participant is in a leading (management) position or not denoted with dummy variable 0 for manager 1 for not manager

Sector

X5

Sector of the bank, 0 for private, 1 for public/government-owned banks

QA 1

X6

Understanding the mission and objectives of the bank, using linker scale, with 0 denoting strongly disagree while 4 denotes strongly agree and 6 if no opinion

QA 2

X7

The Mission and Strategy of the bank is perfectly communicated to all employees, using linker scale, with 0 denoting strongly disagree while 4 denotes strongly agree and 6 if no opinion

Goal clarity

(continued)

8 Transforming Lean to Service: Application to the Kurdistan Banking …

355

Table 8.15 (continued) Variable

Label Description of the variable

QA 3

X8

The departmental goals are clear to employees using linker scale, with 0 denoting strongly disagree while 4 denotes strongly agree and 6 if no opinion

QA 4

X9

The departmental goal helps the bank to achieve its mission, using linker scale, with 0 denoting strongly disagree while 4 denotes strongly agree and 6 if no opinion

QA 5

X10

Clear understanding how employees function helps the department to achieve its goals, using linker scale, with 0 denoting strongly disagree while 4 denotes strongly agree and 6 if no opinion

QA 6

X11

The short term plans are often updated, using linker scale, with 0 denoting strongly disagree while 4 denotes strongly agree and 6 if no opinion

QA 7

X12

Updating the short term plans is a group work in which every employee has opinions as to what should be the next priority, using linker scale, with 0 denoting strongly disagree while 4 denotes strongly agree and 6 if no opinion

QA 8

X13

Long-term plans are realistic and reachable, using linker scale, with 0 denoting strongly disagree while 4 denotes strongly agree and 6 if no opinion

QA 9

X14

There is a need to go out of the predesigned process and creatively look for solutions in my job

QA 10

X15

My job requires a lot of independent decision making without referral to the upper line of management:

Classification of the variable

Planning/goal setting

Employee independence

(continued)

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Table 8.15 (continued) Variable

Label Description of the variable

Classification of the variable

QA 11

X16

Employees are encouraged to give suggestions on changing how the work should be done

Employee engagement

QA 12

X17

Work Processes are continuously updated based on the front line employee’s suggestion for improvement

QA 13

X18

I can do my job quicker if not for Waste analysis some unnecessary procedures

QA 14

X19

In my job, I think there are additional steps that do not add value to the service and slows down my performance

QA 15

X20

I can openly suggest to my manager to cut on the non-value-adding steps related to my job

QA 16

X21

Understanding the customer need is always a challenge

QA 17

X22

To deliver perfect service to the customer, there has to be less number of customer assigned to per employee

QA 18

X23

Meeting the performance quota is the ultimate goal for me

QA 19

X24

Customer value is the trigger to the designing of work processes

QA 20

X25

I can predefine the needs of the customer before making an order

QA 21

X26

I try to tailor the need of the customer to the resources available to complete the order

QA 22

X27

I would take additional responsibilities to perfectly meet the need of the customer to complete the order

QA 23

X28

There are continuous training for Training employees to acquire new skills?

QA 24

X29

I think more training can help me serve more customers

Understanding customer value

(continued)

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Table 8.15 (continued) Variable

Label Description of the variable

Classification of the variable

QA 25

X30

There is always opportunities to learn new things

Motivation/feedback

QA 26

X31

I mostly receive feedback that helps me to improve

QA 27

X32

I think I need more feedback on my work

QA 28

X33

How often the short term plans are updated: 0. weekly, 1. every two weeks, 2. every month, 3. every three month, 4. others

QA 29

X34

My main objective in my job is Objective to, 0. complete work assignment, 1. increase productivity, 2. increase knowledge, 3. increase customer value

QA 30

X35

My manager’s main objective in my job is to: 0. Complete work assignment, 1. increase productivity, 2. increase knowledge, 3. increase customer value

QA 31

X36

Have you ever heard of Lean system? 0, Yes, 1 No

Table 8.16 Descriptive statistics of the variables used in the logit model

Planning/goal setting

Variable

Mean

Standard deviation

QA29 (X34)

1.36923

1.20167

QA4 (X9)

2.81538

1.27459

QA11 (X16)

2.39231

1.40595

QA12 (X17)

2.41538

1.37409

QA15 (X20)

2.54615

1.44196

QA24 (X29)

3.01538

1.34097

QA27 (X32)

2.80769

1.41474

QA30 (X35)

0.869231

1.15059

QA5 (X10)

2.88462

1.18541

Sector (X5)

0.50000

0.501934

Gender (X1)

0.646154

0.480012

Age (X2)

1.75385

0.956913

Post (X4)

0.684615

0.466466

Exp (X3)

1.10769

1.1959

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Chapter 9

Consumers’ Current State Preferences for Internet Banking Services: The Case of the Kurdistan Region of Iraq’s Private Banks Hamsa Awni Lazar

9.1 Introduction Since the early twenty-first century, there has been a worldwide trend shift, directing toward an Internet-based information society. As the evolution of globalization, banking institutions worldwide tended to utilize the internet to reduce their operation costs. Internet banking was the success factor in achieving this goal, through its ability to make customers open a new banking account, download forms and process different activities such as loan request, money transfer, and online payment. Internet banking (IB) has been classified as a critical element of national development in many countries. For example, China is one of the most advanced internetoriented societies, and its achievements in IB are in line with the most developed countries. Such results were realized not only by a significant period in the development of IB but also by continued efforts of public and private sectors, that have helped the effective establishment of a national strategy for an internet-oriented society (Anirban bose 2018). In recent years many Middle East countries designated the Internet as an essential task and tried to use the internet as a driving force for their economic growth. These efforts have made the region an area with great potential for the IT market that has high possibilities and opportunities for future IT developments (Raudeliuniene et al. 2021). The information and communications market in the Middle East is expanding due to utilizing informatization in the public sector and efforts to vitalize the mobile communication business (Habibi and Zabardast 2020; Raudeliuniene et al. 2021).

H. A. Lazar (B) Accounts Payable Analyst, TAQA Iraq Oil and Gas Company, Erbil, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. T. Khayyat and G. M. Muhamad (eds.), Empirical Studies of an Internet and Service Based Economy, Perspectives on Development in the Middle East and North Africa (MENA) Region, https://doi.org/10.1007/978-981-99-3389-1_9

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There is still limited policy effectiveness in developing countries that have poor IB infrastructure. This is due to the lack of skilled human resources, the irrationality of government policies, and an inadequate social system. Meanwhile, there has been rapid growth in the use of new information and IB across the range of private and commercial sectors. Many banks have introduced e-banking systems as a means of reducing costs, improving services to citizens, and increasing effectiveness and efficiency in the private sector (Chaimaa et al. 2021). Electronica banking (E-Banking) uses information technology to provide better efficiency, in which, bank services are available to different e-banking stakeholders (e.g., citizens, businesses, employees, and agencies) and increase the convenience and approachability of banking services to citizens (Chaimaa et al. 2021). Kurdistan region of Iraq was ranked as one of the most underdeveloped banking industry in the Middle East and North Africa (MENA) region (Musa 2017). The banking sector in KRI is a part of the banking sector of Iraq. In general, banks in KRI are characterized by their dependence on the economic situation of the region. The reform of the banking system in the area is a challenge due to the political culture and security struggles. The KRI economy is mainly depending on oil export, making the economy and banking industry more fragile (Muhamad 2022). The sequence of wars and political challenges have caused the majority of the population of KRI not to trust the banking system. This has resulted in a substantial loss of untapped potential customer base. Figures show that more than 80% of the population does not have a bank account, which translates to 75% of the financial system in terms of assets, and 77% of GDP (Musa 2017). Furthermore, the lack of trust in the banking industry is traced back to the political and economic history of banking, which has paralyzed the development of this sector in Iraq and Kurdistan. In general, people do not trust banking because of their dependence on the booming and boosting of the economy, which in turn depends on the political agenda (Muhamad 2022). Kurdistan Region does not have economic sovereignty but rather it follows the Iraqi economic system. The Iraqi financial sector is dominated by the state-owned banks, accounting for approximately 93% of banking system assets (IMF 2017). These state banks are capital-deficient and have a weak loan portfolio. IMF necessitates that the state-owned banks that dominate the banking system in Iraq need to be restructured (Musa 2017). Despite the attempts of Central Bank of Iraq (CBI) to reform the banking system, the overall function of the banks in the economy remains minimal (IMF 2017). In regard to the private banks, they remained small and have been established in KRI relatively recently in the period following the collapse of Saddam Husain’s regime in 2003 (Muhamad 2022). Despite the political and economic struggle, there are some internal issues of banking structure and process designs in both the private and state banks such as (a) lack of complete and accurate data collection on banking transactions (IMF 2017), (b) low financial infrastructure associated with shortages of skills and technology (World Bank 2012), and (c) the weaknesses in an administrative capacity and data provision (IMF 2017). Thus, restructuring the state banks and the private banks in terms of administrative operations and internal processes, as well as

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creating an enabling environment for private banks developments are two major focus areas of growth within an economy dominated by oil revenue and public spending (IMF 2017). This chapter attempts to investigate the determinants of internet banking adoption in the Kurdistan Region of Iraq, and the sample is limited to the private banks in the capital city of Erbil. The context is to suggest an appropriate structure of Internet banking, with a reliable internet system, in which customers can gain trust and banks increase market share, which in turn will contribute to the growth of the economy in the region. In doing so, a questionnaire from 67 banks and interviews from major private banks to perform Conjoint Analysis (CA), which focuses on Discrete Choice Modelling (DCM), with rank-ordered logit model are used. This will provide a framework for banks in KRI to improve their service efficiency without significant investments. The current research will enrich the body of literature concerning studies on banking in the KRI, while Kurdistan’s context as a specific research target is not widely explored yet. Furthermore, the results can be used to propose policy perspectives for the private bank’s consideration and the related stakeholders in the Kurdistan Region of Iraq. The chapter is organized into five sections. Section 9.2 reviews relevant literature intending to determine critical factors that affect IB implementation in developing countries. Section 9.3 covers the research method used in this study and Sect. 9.4 presents statistical figures based on data collection. Section 9.5 concludes the chapter.

9.2 Perspective of Internet Banking 9.2.1 Historical Background of Internet Banking Before the evolution of online baking, the only available type of banking was the use of a teller to either access credit services or saved money in banks. Sundarraj (2011) locates the evolution of banking and the adoption of online banking to the year 1980. Since then, the banking sector has witnessed an increase in banking services and the incorporation of legislative reforms in the banking field. Internet banking came with the growth of internet in the 1990s when people recognized the efficiency and availability of banking services through the new form of the banking system. Over the years since its inception, the use of internet banking has been embraced by some because of its ability to have no human interactions. Other individuals have justified their choice for internet banking due to the security of the system even though cybercrime threatens the system (Nikkel 2020). According to Kesharwani and Singh Bisht (2012), few banks in the United States started using the system although internet banking was not clearly in the view of any bankers since 1981. Accordingly, New York-City bank, Chase Manhattan, Wells Fargo and Chemical and Manufacturers Hannover were among the first banks to

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embrace the new banking system. These banks started by offering home banking services to their customers through a system that was identified as a videotext system. In the United Kingdom, the bank of Scotland offered the first internet banking service to the Nottingham building society in 1983 through the home link. The home link referred to a method of banking where the customers of a bank were connected to the bank services through a television set and a telephone. The system allowed for the transfer of funds and payment of bills through the bank offering the service (Tipi et al. 2017). In the 1990s, the new system of banking having been accepted and integrated into the banking field incorporated the following services to the bankers: Viewing of bank statement, bill payments and bank transfers. These services were the only services provided at the moment traces the evolution of internet banking in a structured form creating a clear picture of the birth and growth of the new banking system since the’80s (Nikkel 2020; Tipi et al. 2017). Later, in 1994, Stanford offered the first form of an online banking website. The website allowed individuals to access banking services and their bank accounts online, viewing all their financial accounts in one place by opening an account aggregation software (Agrawal et al. 2020). Then, legislative measures are incorporated in the financial sector resulted in significant growth of internet banking despite barriers in the adoption of Internet banking due to the issue of cyber crime (Agrawal et al. 2020; Akinbowale et al. 2020). However, with the inception of the internet in the banking services, a modern, efficient, secure and quick way of accessing banking services was born with the development of technology.

9.2.2 Technology Acceptance Model (TAM) of Online Banking The adoption of new technology as stated by Ahmad (2018), has always been an issue, and the debate was high in the 1970s bringing about the approval of the technology acceptance model popularly known as TAM. Initial work of internet banking mainly emphasized on user’s trust. Several studies examined the association between consumer behaviour and internet banking by examining various variables (Juwahe et al. 2012; Laukkanen and Kiviniemi 2010; Rotchanakitumnuai and Speece 2003). This concept has been seen as the reason behind the use of technology acceptance model to deal with the challenges that are brought about by technology in the banking system. “Lifen Zhao et al. (2010) insist that the use of TAM in understanding the use of internet in the banking system of a developing country is quite useful as it incorporates the concept of evolution in the banking world. Extension in the technology acceptance model in the banking field helps in understanding and interpreting the technological changes in the banking field.

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A new model for Finish online banking acceptance has been developed by “Pikkarainen, Pikkarainen, Karjaluoto, and Pahnila “ 2004), the model extended the TAM and identified three more factors which included information on online banking, quality of internet connection and also security and privacy. A survey was done on 268 respondents in 2002, and the finding showed that perceived usefulness and information on internet banking on the website were the primary factors determining the adoption of online banking. The level of digital literacy has been considered to identify As part of the internet banking revolution, this is moving toward cost advantages in the banking sector. Al-Somali et al. (2009) were able to determine various reasons that motivate users to embrace internet banking in Saudi Arabia. In light of Technology Acceptance Model (TAM), they were able to deduce the perception of online banking and social influence, as well as education, resistance to change and trust and resistance, have a crucial effect on the attitude towards customer adoption in internet banking. Yee-Loong Chong et al. (2010) examined trust and government support, perceived usefulness and perceived ease of use to determine if these are the factors that affect e-banking adoption in Vietnam. The authors used correlation and multiple regression analysis to analyze 103 survey samples. The result showed that Vietnamese intention to use internet banking is positively associated with perceived usefulness, trust and government support, but in contrast to Technology acceptance model (TAM) observed ease of use was found as an insignificant. Xue e tal. (2011) investigated the adoption factors of internet banking in the United States. According to the authors, the transmission of online banking adopters was demand readiness of substitute networks, coproduction services lead to customer efficiency and saturation of local online banking. Furthermore, the authors emphasize that the fastest online banking adopters are those who have more tendencies for internet accessibility and have more transactions as they desire higher efficiency in online transaction methods after monitoring their characteristics, time zone and regional locations. The authors find that customers who live in high-quality areas of internet services are more committed to online banking features than those who are living in low branch density in other regions. Yousafzai and Yani-de-Soriano (2012) examined the behavior of internet banking service users by an integrated framework, a combination of the construct of technology readiness with TAM, and demographics like gender and age. The authors surveyed 435 internet banking users in the UK through Customer Specific Internet Banking Acceptance Model (CSIBAM). There was a secure connection between usefulness and behavior in youths of male descent with a high level of optimism, while for older females the linkage between ease of use and behavior was distinct with a high level of discomfort. Kesharwani and Singh Bisht (2012) observed security and privacy threat of internet banking adoption in India in light of the TAM model. The authors considered perceived risk as a key of the bank websites and perceived ease of use in services of internet banking context. Findings indicate an undesirable impact of perceived

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risk on the behavioral intention of internet banking adoption, while trust also harms perceived risk. Meanwhile, perceived ease of use is affected by a well-designed website and is useful in promoting more comfortable use and in reducing perceived risk issues on internet banking users.

9.2.3 Factor Affecting Online Banking Adoption Several studies have analysed factors affecting online banking adoption. Elhajjar and Ouaida (2020) examined factors affecting mobile banking adoption for Lebanese banking customers. A survey of 320 questionnaires were used to develop the analysis using structural equation modeling. The results indicate that the main factors guiding users toward adoption of mobile banking are perceived risk, perceived ease of use, perceived usefulness, digital literacy and resistance to change. Jeon et al. (2020) found that internet banking adoption has a positive impact on consumer innovativeness and can minimize consumer perceived risk. Similarly, Kavitha and Gopinath (2020) found a positive and significant relationship between internet banking and both perceived usefulness and perceived ease-of-use which affect the degree of loyalty and satisfaction of users. Banks and financial institutions have majorly embraced technology to provide services to their customers most efficiently. The advantage of using technology in their banking systems that has attracted banks to internet and technology incorporation is the enhancement of effectiveness and efficiency. Accordingly, the motivation of the customers towards mobile banking and the use of other internet services in banking further guarantees the success of the system. It ensures the adoption of mobile banking by the clients and other stakeholders in the banking institutions (Alkhawaldeh et al. 2022; Banerjee and Sreejesh 2022). The use of modern technology goes a long way in the innovative world incorporated in the banking sector and enables the customers to produce financial transactions independently. It also allows customers to complete transactions at the place and time that the individual prefers or chooses, thus proving to be very convenient (Alkhawaldeh et al. 2022; Jeon et al. 2020; Zhu et al. 2022). Zhu et al. (2022) also identifies the breakthrough in technological systems through more diversified communication channel as a significant factor that influences the growth of online banking. Technological advances can be traced in the fields of telecommunication and banking institutions. It goes ahead to identify the efficiency in internet provisions to the success of the mobile banking systems, which enables it to perform efficiently. The increase in the number of mobile subscribers in the banking world translates to the rise in the customer base of the online banking system. Mobile subscribers are attracted to online banking by the efficiency in mobile internet providers to reach every individual owning and using a mobile phone. The mobile internet providers allow that online transactions are completed quickly and by individuals in separate ends of the world.

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Several studies have examined the type of services that increase the adoption of online banking. Dauda and Lee (2015) studied the types of services that increase the adoption of online banking business in Nigeria. The findings indicated that banks need to increase efficiency and strength competitiveness by promoting smart and practical functions, adding entertainment and extra convenience to customers, such as making internet banking easy to use by creating a digital wallet and video banking, which enhances real-time contacting, consumer’s smartphones connected with ATMs, and digital currency. Ali et al. (2022) studied the factors motivating online banking users in Pakistan during Coved-19. The results concluded that the quality of services, system and information as well as trust have a noticeable effect on users’ intention to do more online banking. However, there are other factors that obstruct consumers to use internet banking. Arif et al. (2020) examined those obstructions in Pakistan using a survey of 300 customers. The findings show that the most significant barriers on the adoption of internet banking are the value barrier, risk barrier, and image barrier. These barriers are different from gender points of view as males and females have different decisionmaking processes. Previous studies identified tradition barriers in the use of internet banking (Khanra et al. 2020; Laukkanen 2016; Serener 2019). However, tradition barrier has no longer the case to reject Internet banking due to the daily needs of internet banking (Arif et al. 2020).

9.2.4 Customer Satisfaction in Online Banking The development of the banking field focuses on addressing and ensuring they satisfy the customer base. Customer value is an essential tool for ensuring the success of online internet banking by attracting customers who can access and transact through financial institutions. However, online banking have been connected to service quality, web design and security of the data and customer information (Tipi et al. 2017). This has often resulted in lower customer satisfaction, or many customers have not been satisfied at all with the operations of online banking. Basir et al. (2022) measured the relationship between customer satisfaction and several variables with online banking in Malaysia. Regression analysis was used to find out the reasons for the satisfied using of online banking. The results indicated that consumers are significantly concerned about security, privacy and service quality. Mwiya et al. (2022) examined the influence of digital space service quality of banking on customer satisfaction in Zambia. Correlation and multiple regression models were used to analyze 312 questionnaires from bank customers. The findings indicated that several factors related to electronic service quality such as security, privacy, website attribute, responsiveness, reliability, and efficiency affect customer satisfaction. An extensive literature survey has conducted by Kumar and Sharma (2017) on customer satisfaction in online banking in India. The paper investigated the way customer satisfaction has changed during the transition time from the traditional

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means of banking business to the modern one. The authors identify that the following factors determine customer satisfaction, service quality, online banking, customer relationship management, employee behaviour, less documentation process and costeffectiveness, tangible appearance, prompt service and appearance and less Interest rate. Furthermore, the uses of E-Banking have increased customer satisfaction and given new banks a competitive advantage, as it has become one of the vital banking services. Hammoud et al. (2018) surveyed the Lebanese banking consumers. The finding was that responsiveness and communication; reliability, efficiency, and ease of use as well as security and privacy have an essential effect on customer satisfaction. Customers can be satisfied when the banking sector focuses on service quality dimensions.

9.3 Methodology, Data and Model Specification This chapter used a combination of quantitative and qualitative research methods namely mixed methods to investigate about the determinant factors for internet banking adoption for private banks. A random sample was drawn to conduct a questionnaire survey. A total of sixty-seven (67) usable questionnaires were received out of (100) questionnaires. As for the qualitative part, an interview with three persons from the major private banks in KRI is conducted to support the empirical part of this study.

9.3.1 Data Collection This research uses a mixed method to answer research questions based on empirical data and interviews. The analysis mainly depends on quantitative analysis (questionnaire) to assess the Internet banking services, critical success factors (CSFs), and principles from the customer’s perspectives toward private banks in the Kurdistan region of Iraq. Customers of banks are surveyed; the collected data is used to assess the strategic and overall readiness for the implementation of internet banking. Using quantitative analysis helps to identify key enablers and obstacles of IB application in terms of the organization’s culture and employee capacity and change acceptance level. The selected banks that contributed to this research are mostly private. Identifying the measures and guidelines by which the needed data (best fit) will be collected comes as the next step that ought to be taken. The method of research gives the techniques to be employed in obtaining the required data for the study. Kothari (2004) explains that the data collection method to be used for the research, by the research would focus on two types of data sources

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identified (1) Primary Data Sources and (2) Secondary Data Sources. Each of the following will be discussed below:

Primary Data Primary data is the data that is collected for the particular research problem using steps that fit the research question (Hox and Boeije 2005). The information is the source of the numerical data needed for the analytical phase of the study. The particular form of data will be obtained by the use of two methods; (1) Structured Interview and (2) Survey Questionnaire. Structured interview The difficulty of the research question compelled the use of structured interviews as an appropriate approach, with several managers to get a clear comprehension of the most vital elements influencing the adoption of IB in KRI”. Managers that represent the professional class, within private banks sector will be required to gain insight, and point out the significant elements that influence the adoption. Survey questionnaire The primary method employed to source data for evaluation using statistical techniques will be the survey questionnaire. Hox and Boeije (2005) noted that “a survey is concerned with conditions or relationships that exist, opinions that are held, processes that are going on, effects that are evident or trends that are developing”. The responses of these individuals will give the numerical data that will be employed when identifying the major elements with high influence. The questionnaire comprises of three sections; the first part being the individual data of the participant as it was inquired. The second bit composes the conjoint survey that was divided into three sections. Respondents were chosen using a random sampling technique of different private banks in KRI. The third phase discusses the thoughts of the participants on the objectives of the research obtained from the responses. Participants were asked to give the degree to which the various variables influenced the adoption of IB on conjoint analysis way with five variables by ranking three different cards on each profile from 1 highest satisfaction to 3 the lowest.

Target Population The participants will include; • Customers—Individual or organization that requests the service • Managers—Is a professional with the expertise needed by the customer.

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The Survey Design A predefined list of respondents to be surveyed was decided. The survey was conducted in person by the researcher. The respondents are anonymous throughout the analysis of the data.

9.3.2 Model Specifications The random utility model is the model used to assess the collected data on a theoretical basis for identifying consumer preferences by employing discrete choice techniques. Banking services allow customers to have their valuation on future banking service characteristics. It is thus imperative to assume that each customer associates utility with each attribute of the future banking services and identifies the service with the greatest possible utility.

Discrete Choice Analysis The discrete choice analysis employed for this study is a ranked order logit and multinomial logit model approach. Many scholars prefer ranked order logit model for such studies. See for example: Yee-Loong Chong et al. (2010) who attempted to identify and determine the factors that influence their behavior and multinomial logit model was used to find the socio-demographic factors which affect their preferences.

Random Utility Model The utility model of McFadden (1973) is applied as a theoretical focus for assessing consumer taste and preferences employing discrete choice models. The maintained assumption of the model is that respondents choose their preferred alternative on the basis that it maximizes their utility. The model also implies that a function exists containing characteristics of alternatives and those of individuals that showcase an individual’s satisfaction valuation for each choice. We thus assume that each consumer notes the utility connected to each attribute of the internet usage and uses the ones with the largest possible satisfaction. In this utility model, the utility is defined into two parts: deterministic and stochastic (which is not observable) as follows: U n j = V n j + εn j

(9.1)

where subscript n stands for the nth consumer and j for the jth alternatives of choice situation. Unj is the utility obtained from alternative j by the nth consumer. V represents the deterministic part while ε represents the stochastic part of utility, which

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represents the factors influencing the satisfaction of another which is not considered in Vnj combined with other elements, which are significantly unobservable (Ben-Akiva et al. 1985). The deterministic part comprises the utility obtained from attributes of goods/services and characteristics of consumers as follows: U n j = V n j + εn j = (xn j,) + εn j

(9.2)

The vector is composed of attributes of xnj, which is an alternative j to nth consumer and sn being the vector composed of characteristics of nth consumer. Assuming linear relationship in the deterministic part in Eq. (9.2), we can obtain. U n j = βn j ' xn j + an j ' snt + εn j

(9.3)

here, B (consumer preference) and a represent the degree of influence on the deterministic part of utility by attributes of the internet access and characteristics of individuals respectively. In the case of the stochastic part, ε = (εn1, εn2,…) follows a joint distribution, which helps in making probabilistic decisions about the individual consumer’s choice. Hence, the probability that nth consumer picks the alternative I from the many available options of other options Jn is equal to: Pni = (U ni ≥ U n j, ∀ j /= i ) = (V ni + εni ≥ V n j+, ∀ j /= i)

(9.4)

Rearranging the terms: Pni = (εn j − εni ≤ V ni−, ∀ j /= i )

(9.5)

Therefore, the choice of an alternative through a probability scenario is based on the distribution of error term differences, i.e., the probability Pni is the function from integration over the distribution f (en). Several different models have been developed from various specifications of this density, according to the distribution assumed for the stochastic part of the utility (Petrin and Train 2003).

Discrete Choice Methods When users decide to adopt new products or services, we found that they choose from a set of alternatives and several studies from econometrics and marketing research choice show that discrete choice methods model the preference. Srinuan, Srinuan, and Bohlin (2012) note that the discrete choice theory can be identified or defined as the behaviour study of the individual that must choose from a limited number of choices which are maximized based on utility. The discrete choice analysis is one of a technique that uses for estimating the probability that a new product or service would be adopted in society, by predicting a dependent outcome from an independent variable. For example, internet adoption

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is a dependent variable and correlates with some factors such as service provider, data package, devices and location.

Multinomial and Rank-Ordered Logit Model The multinomial logit model (MNL), which is identified as the conditional logit model, is the most utilized random utility model based on Verma and Plaschka (2003), in which using this choice probability, the likelihood function is established, and parameters are estimated by maximum likelihood estimation (MLE). The model assumes an extreme value distributed error, closely approximated to the distribution model of normality and creates a locked view of the probabilistic choice model (Hensher et al. 2005). Moreover, the authors assume that error elements are identically and independently spread out across alternatives as well as cross individuals. The model has another restrictive assumption, which is the independence of irrelevant alternatives (IIA), that dictates that if g is a preferred alternative is out of the given choice set (g,h), the preference should not change towards h if a third alternative i is added, supplementing the choice set to (g,h,i), which means i is to be considered as an irrelevant alternative and should not change the choice between g and h. This assumption makes the MNL very simple to estimate; however, it leads to the restriction in reflecting the realistic substitution pattern caused by the change in attributes of other alternatives. Moreover, assuming the same coefficients overall consumers in case of heterogeneity in consumer preferences may mislead the implication (Koh 2007). An extension to multinomial logit model in the sense that the former uses ranked choices and the latter most preferred option is the Rank-ordered logit model. An extension to multinomial logit model in the sense that the former uses ranked choices and the latter most preferred choice is the Rank-ordered logit model. Unlike conventional choice model, if consumers are asked to rank their choice of alternatives rather than making just one choice, this captures more information about their preferences, resulting in more robust parameter estimation. Beggs et al. (1981) urge to use ranked data to estimate the characteristics of consumer choices from stated preference data as it provides a better view on the preference of a consumer than data from a choice experiment. Figure 9.1 clarifies the analytical framework of conjoint analysis survey.

9.3.3 Conjoint Survey To date, the conjoint analysis (CA) has been used in marketing research as an efficient and effective tool when constructing consumer preference (Green 1990). To apply the CA method, a conjoint survey must be designed first. This researcher created a conjoint survey questionnaire that asked participants to rank a set of alternatives for e-Banking implementation. The questionnaire is attached in the appendix.

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Internet banking attributes and levels determination

375

Orthogonal design using SAS

Conjoint Cards Socio-demographics

6 Other questions related to customer’s use of internet banking

Conjoint questionnaire design Stated

Literature review and facts

Questionnaire validation and reliability

Main conjoint survey (Rankordered)

preference data and socio demography

Fig. 9.1 The Analytical frame work of conjoint analysis survey (Beggs et al. (1981))

9.3.4 Attributes and Attribute Levels Identifying the attributes connected to the relevant research question and then allocating levels for each of these attributes is the first stage of a discrete choice experiment and a conjoint survey. Hensher et al. (2005) note a vital element of the structure, as the characteristics and their levels define the various scenarios that are considered in the research. Hensher et al. (2005) suggest taking precautions when recognizing ambiguity inherent in establishing the meaning of what attributes influence an individual’s choice in our case, the use of the Internet this study has known this, and made substantial efforts to work through the ambiguity to try and recover as much clarity in the attribute content (in terms of meaning and measurement) as is possible. Focus-group studies and questionnaire pre-tests have a significant role in securing the success of choice experiment studies in developing countries, particularly in establishing plausible and understandable scenarios to help in the survey. The pre-testing of the questionnaire helped note the challenges to participants in comprehending the contents of the questionnaire. The focus group also helped in structuring questions. For example, the focus group suggested placing choice cards at the beginning to be preceded by questions related to Internet access and usage information, and respondent characteristics so that respondents would focus on the choice cards more, and it would be interesting for them to answer questions in the second part. Questionnaire validation and reliability were made. From the survey, one crucial point was identified. Respondents chose options from the choice sets in a random manner because of their no capability in coping with the complex tasks and their desire to finish the job responses as fast as possible. The final questionnaire used in the survey underwent a few revisions following from focus groups.

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In the final version of the questionnaire, five attributes as exhibited in Table 9.1 were decided upon to describe the Internet and its service characteristics based on the literature survey, qualitative interviews with senior level employees in banks and discussions with people having access to the Internet and have bank accounts. The first attribute is “Language”, a dummy variable with three levels (English, Kurdish and Arabic). This attribute is placed on recognizing the consumers’ preference on the portal’s language of the banks. Consumer preferences for the adoption of Internet services in different ways. Information on consumer preferences for the IB can help the bank to select a language. The second attribute, “Access to internet banking account”, is a dummy variable with three levels, when the questionnaire validation and reliability were undertaken, these attributes were leveled with OTP (One Time Password) with a password, USB with a password and only password. However, the estimation result showed that the respondents were not quite familiar with the OTP, which was also needed the definition. Hence, for the respondents to quickly understand the access mode, it was leveled with it, followed by a proper explanation. Table 9.1 Attributes and their levels Attribute 1

2

3

4

5

Language

Access to internet banking account

Limit per Transaction

Transaction limit per day

Transaction cost

Level

Description

1

Kurdish/English

– Online service language may have Kurdish and English

2

Arabic/English

– Online service language may have Arabic and English

3

All

– Online service language may have Kurdish, Arabic and English

1

OTP (One Time Password) with password

– OTP is a 6-character code that it can only be used once with user’s password

2

USB with password

– Allows user to securely access online banking by using USB with password

3

Only password

– Only using username and password

1

Less than $1,000

– Limit on how much cash withdrawals can be made in a single transaction

2

$1,000–$5,000

3

More than $5,000

1

Up to $5,000

– Limit on how much cash withdrawals can be made in a single day

2

$5,000–$10,000

3

More than $10,000

1

Proportional

– Cost per online/wire transfer (cost on transaction) – Percentage of the amount

2

Fixed

– Fixed transaction fee

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The third attribute, “Limit per Transaction”, accounts for the banks at three levels: Less than $1,000, $1,000–$5,000, and up to $5,000, given the three levels available in the current market in Kurdistan. The standards were stated to find the high ability to choose by consumers willing to spend per each transaction. This study, however, concentrates on Kurdistan region, considering much less developed Internet infrastructure in Kurdistan than in the United States. The fourth attribute, “Transaction limit per day”, with three levels, is up to $5,000, $5,000–$10,000, and more than $10,000. This is the most important factor in determining consumers’ spending money in each of the other attributes identified in the study. Some researchers include the price to investigate consumer demand for Internet banking, mobile banking and mobile communication services (Savage and Waldman 2005; Scott James Savage and Waldman 2009). In this study, the first level of USD 5,000 represents a low-cost price, similarly the second, USD 5,000–10,000 a modest price, and the third, more than USD 10,000, is high, given the ability and income status of employees in the private sector. The levels of the transaction limit per day are constructed hypothetically but based on real limit rates stated currently by major bank account holders in Kurdistan. The fifth attribute, “Transaction cost”, the proxy to the reliability of the Internet service, indicates how often users face the confusion of whether the commission fee is fixed or proportional. This attribute shows the probability of choosing between to levels, whether fixed or proportional, based on their occupation and transaction frequency with banks to some extent. To sum up, five attributes are defined to establish the choice experiment, giving consideration to the requirements of a good comprehension of the population targeted and its view and experience (Coast and Horrocks 2007), based on an exploratory identification process: the results of questionnaire validation and reliability (Lancsar and Louviere 2008). To the best of the researcher’s knowledge, to fit the goal of the research, the identified attributes are relevant, and the assigned levels are realistic. As shown in Table 9.1.

9.4 Data Analysis and Presentation of the Results 9.4.1 Data Collection The demographic statistics of the data for this study were obtained through a conjoint survey using the current banking customers of 100 questionnaires that were distributed, a total of 67 of them were received corresponding to an initial response rate of (67%) in which, (58%) respondents are male and (42%) female. The age range is from 18 to 60, of which (24%) are in the field of 18–29, (48%) are between 30 and 39, (15%) are between 40 and 49, (6%) are between 50 and59, and (7%) are 60 or older. For analytical purposes, the age range of respondents is divided into “below 30” (24%) and at “least 30” (76%). The demographics of the respondents can be shown

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in Table 9.2. For the entire sample, there is a low percentage of those educated from primary to secondary school (9%). Similarly, there was also a small percentage of those with masters and doctoral degrees (28% masters and 5% doctorate). Table 9.2 Demographic statistic Characteristics of study subjects, N = 67 Characteristics %

Characteristics %

Characteristics %

Characteristics %

Age range (18–60 yrs)

Monthly income (USD$)

Transaction Frequency

Reason to use IB

18–29

24% 0–500 USD

3% 1–2/week

12% Time efficient

60%

30–39

48% 500–1,000 USD

2% ≥3

66% Cost efficient

38.80%

40–49

15% 1,000–1,500 USD

50–59 60 or older

15% No transaction 22% Privacy

50.70%

6% 1,500–2,000 USD

16% No. of Bank card

61% Security

11%

7% More than 2,000 USD

66% Credit/debit card

61% Bank service satisfaction

Gender

Marital status

Only banking access

Male

58% Single

37% Cards for online shopping

Female

42% Married

57% Card purpose usage

Others

Education Secondary

5% Most preferred bank

Diploma

9% KIB

Bachelor Master

6% Hotels/tickets

35.80%

42% Multiple branches

43%

Waiting time

35.80%

25% Customer care 28.30% 11% Information security

23.80%

70% Using abroad

27% Transaction Security

28.30%

58% BBAC

5% All of them

37% Transaction costs

29%

28% RT

2% Internet familiarity

Occupation

IBL

Unemployed

0% Byblos

Civil Servant

8%

Online shopping

31% Bank name and reputation

10% Not at all 13% Fairly Averagely

Queue management

40%

9% Work experience 12% Exp1 (0–5 yrs) 21% 9% Exp2 (6–10 yrs)

42%

Business

45%

Well

37% Exp3 (≥11yrs) 37%

Academic

16%

Very well

33%

Others

31%

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The majority of respondents are those with high school diplomas (58%). Two educational groups were focused: Education 1 refers to those educated up to diploma level (67%), while the rest of Education 2 belongs to the ones having the highest intellectual level of education from bachelor to doctorate (33%). The monthly income level of respondents was also categorized into 5 groups the low-income group earning up to $500 US dollars per month (3%) and 500 to 1,000 USD per month is (2%), 1,000 to 1,500 USD is (15%), 1,500 to 2,000 USD is (16%). And high-income group above 2,000 USD per month is (66%). As for the major banks, the smallest percentage used BBAC and RT Bank (8%), with KIB Bank (62%) and Byblos and IBL Banks (32%) in the middle. Customers use debit cards more (61%) as compared to Banking access cards (31%) and online shopping cards (42%). (8%) of the respondents were civil servants of which 21% of them have meagre experience (0–5 years) and almost 57% of their marital status is single. 70% of the respondents are very well familiar with the internet. About 9% of the respondents are not familiar with the internet at all. As shown in Fig. 9.2 respondents’ satisfaction toward banks in KRI is only 3% are very unsatisfied while, 9% are unsatisfied, and 40% are on average with 30% satisfied and 18% very satisfied. Figure 9.3 shows the intend of bank account holders to use internet banking, the highest percentage was 78% of will intend to use it within 6 months, while 10% was in one year, and the remain of 5% was one year later, and 7% is for no plan to use it at all. Internet banking project is in doubt whether will success or fail. Once we asked bank customers what is your opinion toward IB project in KRI only 5% said it will fail, while 55% said it will success, and the remaining of 40% said it will, with partial failure as shown in Fig. 9.4.

Fig. 9.2 Respondents satisfaction of private banks

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Fig. 9.3 Percentage of respondents for future use of internet banking

Fig. 9.4 Percentage of Respondents’ opinion toward whether internet banking project will success or not

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9.4.2 Descriptive Analysis This section presents a descriptive summary of the data, focusing on the sociodemographic analysis of the survey participants. With the help of frequency figures, as well as graphical charts are presented to visually summarize the data.

Gender Regarding the gender of the participants, there is a difference between male and female participants (see Table 9.3). The above table shows that male respondents were greater in number than female respondents. 58% were male while 42% were female.

Age In regards to age profile of the participants, as shown in Table 9.4, the variable of age is categorized into five groups. The first category is below 30 years, which corresponds to 24%, while category 30–39 is 48%, category 40–49 is 15%, category 50–59 is 6%, and the last category 60 years and above corresponds to 7% of the total participants. Within the age groups, the group 30–39 has the highest percentage; the age group 18–29 years is the second highest percentage of participants, while those below 60 years old have the lowest percentage of 6%. Table 9.3 Gender ratio of survey participants

Table 9.4 Age group of survey participants

Gender of the participants

Number of participants

Male

39

Percentage of participants (%) 58

Female

28

42

Total

67

100

Age

Number of Participants

39

32

48%

40 > 49

10

15%

50 > 59

4

6%

Percentage %

>60

5

7%

Total

67

100%

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Table 9.5 Years of experience of the participant

Years of experience

Number of participants

Percentage of participants (%)

0–5 years

15

21

6–10 years

28

42

More than 10 years

24

37

Total

67

100

Experience The experience was grouped based on the range of input answers (number of years). As shown in Table 9.5, the group of 0–5 is 21%, group of 6–10 is 42%, group more than 10 is 37%, Group more than 10 makes up the highest percentage of respondents, while group -of 0–5 years and above make up the lowest percentage of respondents.

9.4.3 Rank-Ordered Logit Model As mentioned in the previous sections, this study has 67 valid responses with complete data on the choice questions. Each respondent answers the choice questions by ranking alternatives in every choice set. There are three choice sets, and each Option has three alternatives. Therefore, the total observations for the rank-ordered Logit estimation are 603. This study employs LIMDEP software to estimate the rank-ordered logit model. The results of rank-ordered logit estimation include the parameter estimates, asymptotic-statistics of consumers for each attribute of the future banking services and relative importance of the characteristics. This study interprets the parameters as a marginal utility that is as a partial derivative, which means the change in efficiency for a unit increase in the variable. The statistically significant chi-square statistic (p < 0.005) indicates that the final model gives a considerable improvement over the baseline intercept-only model. This tells you that the model gives better predictions than if you just guessed based on the marginal probabilities for the outcome categories, as shown in Table 9.6. For the goodness of fit according to Table 9.7, 75.6% of data can be used using current model this table contains Pearson’s chi-square statistic for the model (as well as another chi-square statistic based on the deviance). These statistics are intended to test whether the observed data are consistent with the fitted model. The research Table 9.6 Model fitting information Model

−2 Log Likelihood

Intercept only

198.639

Final

142.093

Chi-Square

df

Sig.

56.546

8

0.000

9 Consumers’ Current State Preferences for Internet Banking Services … Table 9.7 Goodness of fit model

383

Chi-Square

Df

Sig.

Pearson

74.457

8

0.000

Deviance

75.627

8

0.000

starts from the null hypothesis that the fit is right. If it is not rejected (i.e, if the p-value is significant), then one concludes that the data and the model predictions are similar and that you have a good model. However, if the assumption of a good fit is rejected, conventionally if p < 0.05, then the model does not fit the data well. The results of our analysis suggest the model does not fit very well (p < 0.004). We have put Kurdish/English as a reference for the language, and Arabic has a negative coefficient, so respondents prefer Kurdish English over Arabic, and All (Kurdish, Arabic, English) has a positive coefficient, so they prefer it over the reference of Kurdish/English and Arabic as well. That means nobody prefers to have Arabic/ English as a language. As long as we have researched in Kurdistan region most of the respondents’ native language is Kurdish, and that goes with the same result of most preferred profile in CA was selected “All” as the language of the service. This is one of the reasons to adopt IB in the Kurdistan region. OTP with password option has been put as a reference, the coefficient of USB and password is positive, which means that respondents preferred USB and password over OTP, however, the coefficient of password is more significant which means if there is no OTP they prefer to have password and the coefficient of password is more significant than coefficient of USB which means respondents would like to have password over USB. Limited per transaction(LPT) has three different options LPT1 is for Less than $1,000 LPT2 is for $5,000–$10,000 and LPT3 is for more than $10,000, so we have put LPT1 as a reference, the coefficient of LPT2 is positive which means that respondents prefer to have LPT2 over LPT1, LPT3 has a negative coefficient which means respondents prefer to have LPT1 over LPT3, On the other hand, we have a transaction limit per day (TLP) that has three choices, TLP1 is up to $5,000 TLP2 is $5,000–$10,000 and TLP3 is more than $10,000, and we have put TLP1 as a reference, the coefficient of both TLPs is positive, but TLP3 has a more significant coefficient than TLP2 that means people prefer to have more than $10,000 over $5,000–$10,000 and TLP3 over TLP1. Transaction cost has two different options proportional and fixed, we have put proportional as a reference and fixed has a negative coefficient so that means respondents prefer proportional over fixed, which is the same result as we have found and will clarify it in the next section (see Table 9.8).

384 Table 9.8 Parameter estimates

H. A. Lazar

Variable

Coefficient

Intercept

1.394***

Std. error

P value

0.348

0.024

−0.450

0.228

0.048**

ALL

2.285

0.974

0.019***

USB

1.495

0.461

0.001***

PSWD

2.289

0.518

0.000***

LPT2

1.521

0.456

0.001***

LPT3

−1.962

0.862

0.023***

TLP2

0.815

0.758

0.001***

0.825

0.397

0.038***

−1.151

0.464

0.013***

ARABIC

TLP3 FIXED

9.4.4 Participants Preferences for Each Profile Each profile’s preferences can be viewed in different ways: by discussing about the importance of each attribute in each level using the respondent’s votes for each level inside the profile as per the following are the preferences obtained from each profile. (A) First Profile (Profile A’) Preferences The first profile A’ preferences can be viewed in the below mentioned ways. As the preferences were calculated manually using excel sheet program to record the data as illustrated in Table 9.9. According to the profile A, the levels related to the internet banking are the most important levels in the success factors that are affecting the e-banking adoption in private sector, as per the study question of “What are the factors that affect the adoption of IB in KRI”. They indicate that level 1 is the most preferred level by scoring 13 votes, while level 2 scored only 3 votes and level three scored 8 votes. As the attributes of the preferred level are the language is Kurdish/English, access to your internet banking is OTP with a password, limit per transaction is 1,000–5,000 USD; transaction limit per day is 5,000–10,000 USD and the transaction cost is Table 9.9 Profile attributes A Profile (A) Attribute

Level 1

Level 2

3

Language

Kurdish/English

Arabic/English

All

Access to your internet banking OTP with password OTP with password Only password Limit per transaction

$1000–$5000

Less than $1000

More than $5000

Transaction limit per day

$5,000–$10,000

More than $10,000

More than $10,000

Transaction cost

Fixed

Fixed

Proportional

Ranking

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Table 9.10 Profile attributes B Profile (B) Attribute

Level 1

Level 2

Level 3

Language

All

Arabic/English

Kurdish/English

Access to your internet banking

Only password

USB with password

Only password

Limit per transaction

Less than 1,000

1000–5000

1000–5000

Transaction limit per day

Up to 5,000

5,000–10,000

More than 10,000

Transaction cost

Fixed

Proportional

Proportional

Ranking

fixed. Adoption of e-banking projects and established as a proper and strong legal framework for the different operations of e-banking because it includes the protection of customer’s data and personnel information. In addition, Kurdistan bank roles at the current stage require to be developed to cover new legal issues and reforms which are major factors for the successful of e-banking implementation in Kurdistan. However, Mosoti and Mwaura (2014) emphasized that the success of e-banking adoption and its services in developing countries are highly dependent on the bank’s role in ensuring a proper legal framework for their operation. Establishing protections and legal reforms will be needed to ensure, among other things, the privacy, security and legal recognition of electronic interactions and electronic signals (Oni and Ayo 2010). (B) Second Profile B’ Preferences inside each level can be viewed differently from the first profile. The calculation of levels was obtained as depicted in Table 9.10. According to the respondents, the factors that affect the adoption of IB are listed in three levels as level 3 is the most preferred one by scoring 6 points as the most important attributes in the third level are language Kurdish/English, access to internet banking only password, limit per transaction 1,000–5,000 USD, transaction limit per day is more than 10,000 USD and transaction cost is proportional. With respect to level 1 have a very similar preference in the third level by scoring 5 points as the language is all (i.e., Kurdish, Arabic and English). Access to IB is the same only password, but limit per transaction is less than 1,000 USD, transaction limit per day is up to 5,000 USD less than level 3, and in contrast, transaction cost is fixed not proportional. “Level 2” have ranked 3 points as third preferred level and is given quite less importance in comparison with the “level 1 and 3”. This preference is not very similar to Profile A level 1in terms of the attribute of the This indicates that they have no variances toward the final decision of the e-banking adoption in Kurdistan. (B) Third Profile Preferences Inside Each Level: The third “Profile C” preference is also discussed in the same way as previous two profiles as shown in below Table 9.11.

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Table 9.11 Profile attributes C Profile (C) Attribute

Level 1

Level 2

Level 3

Language

Kurdish/English

All

Arabic/English

Access to your internet banking

USB with password

OTP with password

Only password

Limit per transaction

Less than 1000

More than 5000

1000–5000

Transaction limit per day

More than 10,000

5000–10,000

Up to 5000

Transaction cost

Proportional

Proportional

Proportional

Ranking

Table 9.12 Most preferred profile

Set

Most preferred

AL1

13

AL2

4

AL3

8

BL1

5

BL2

3

BL3

6

CL1

5

CL2

17

CL3

6

However, the rank for the levels scored by this group is presented in Table 9.6. According to the respondent’s e-banking services and the factors related to the adoption, level 2 ranked 19 scores and made it the most preferred level in “profile C” as attributes are Language is all, access to IB is OTP with password, limit per transaction is more than 5,000 USD, transaction limit per day is 5,000–10,000 USD and transaction cost is proportional. While level 3 ranked second by 6 points and level 1 slightly less than level 3 by 4 scores. The summarized table of the most preferred profile is made as shown in Table 9.12.

9.5 Conclusion and Policy Implication In this section, the conclusion regarding the analysis result will be shown, and the result would be used to interpret as a policy perspective for banks and related stakeholders. Policy implication from this study could be applied for improvement in bank services to drive the technology penetration rate in Kurdistan region.

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9.5.1 Conclusion To attain a global network which guarantees increased efficiency of the banking industry, mostly in countries that are developing, one vital role of management is pushing for the adoption and employment of e-banking systems for the purpose of efficient delivery of services which would in turn lead to increased market share thus more sales while meeting the customer expectations and satisfaction, attracting more customers who increase your market share, and keep existing ones as a way of maintaining the market share. The main objective of the study is to create the customer’s behavior in relation to online service preference for the days ahead. The research has employed conjoint evaluation models and noted methods of choice by use of random satisfaction model to identify the pattern of technology adopted in regards to preference by customers for online banking services in Kurdistan banking industry in future as a potential. The strong pillar for policy making has been incorporated in this study through the techniques using a random utility model and also using the link between the private bank services characteristics and individual demographics. The outcomes are also consistent with the multinomial and rank-ordered logit model. Consistency with previous studies has proven to be preferred under the environment of minimal market entry and in cases that depict low market information as in the scenario of products not yet in the market and which are new. The act reveals that future technologies can foster the adoption of online banking services at a high rate. Government laws and policies promote efficient and practical services especially self-services that are branded and expand a worldwide acceptance and use of e-banking systems which contribute to rising in the adoption of online services, as consumers prefer giving in-store payments fast and in a secure environment not having to find their credit or debit card. Results obtained from the study are likely and expected to have a significant influence and implications for product and service choice, marketing strategies, and operating plans for Kurdish banks and other financial institutions. The influencing vectors showcase both exogenous and endogenous elements that impact a bank; client’s choice of service, which can be said to influence future banking studies. The level of significance of each component has also been determined in consideration to the preference structure which is likely to give a well-structured foundation for reference in future studies on banking service marketing as well as in policy making areas. Limitations from the current study provide an opportunity for more research to be carried out on the same. To start with, the present study tended to focus on consumers’ preference on future online services, but online services are many in scope, comprising detailed analysis from the supply side. All these aspects of electronic banking incorporated in the study can be booked into future studies that will be important for a more in-depth evaluation. The core of the current, on the other hand, is accepted or adopted in the next online banking services, so that it excludes some actions that reveal the intentions of customers guided by experience and employment of the current online services

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that influence their perception to take upon the future online products and services. These studies, particularly discussing elements that do not allow the adoption of future online banking services, were also taken into consideration beyond the scope of this research. Finally, the data and information used are limited to foreseeable banking customers alone. However, more areas may depict the potential to give additional and significant opportunities; an example is microfinance institutions that serve low-income markets. Studies on consumers’ for choices and preferences for future online services that undertake a comparative analysis of developed and developing economies using different techniques and guidelines could give meaningful results and insights into the behavior and attitudes of customers for further studies. According to Shaikh and Karjaluoto (2015), “cross-cultural and transnational studies enable researchers to find out how particular social and cultural attributes of society impact the use of technologies and services among its members”. Besides, research that will investigate the behavior of smartphone or tablet users concerning the adoption of future online services will be interesting. Following Shaikh and Karjaluoto (2015), since this subset of consumers that have smartphone or tablet have convenient access to the use of internet; the necessary computer skills needed for using various online activities such as online shopping and online account management; and the required computer skills and the infrastructure of technology needed to engage in e-banking, focuses on the integration of wireless and internet technologies, they may be ready for the adoption of the future online services. LIMDAP ran the analysis outcome by using a total of 603 observations from 67 participants gotten from the same geographical location. In the next part, we will tend to showcase key findings and policy implications by focusing on the analysis result and exploring the existing policy for KRI, lack of current policy would be determined. The study will, therefore, aid in providing policy implications specifically from the estimation result.

9.5.2 Policy Implication The analysis result will be used to find policy implications that can be examined for adopting Internet banking penetration rate in Kurdistan. Regarding significant attributes such as language, access to an internet banking account, limit per transaction, transaction limit per day and transaction cost. The estimation result has been accurately proceeded to find parameters by estimating all attributes and interpreting the parameters to map the specification of the utility model of e-banking services adoption in Kurdistan. The utility among the Internet Service Provider can be inferred that the people in KRI prefer all three languages (i.e., Kurdish, Arabic and English) for mobile application once they use internet services, and they prefer to access their account by using OTP with password while they want to pay per transaction between 5,000 USD to 10,000 USD as well as transaction limit per day is preferable to be more than

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10,000 USD and the transaction cost to be proportional not fixed. And the percentage of using internet banking in the next six months is very high with 76% that means internet banking in Kurdistan is highly preferred, and people demand the service. This is the same situation as in many countries like Nepal and Vietnam from the studies of Manandhar (2012) that user is less preferable to use the Internet outside the homestead. Regarding the most preferred reason to use IB is time efficient and privacy that they more preferred it than time proficient and security has the lowest percentage of reasons to use IB service, the study result shows that the demand for IB is higher than expected and the level two of “profile C” is preferable, but the security reason is still concerned for them. The study found that lacking people’s awareness to use IB services is essential and this can be inferred that while people have a limitation of knowledge to inquire about services, the government should increase internet usage awareness to deliver IB services in KRI to achieve user’s satisfaction. As presumed earlier about transaction cost, consumers do not want to pay a high price for a commission fee, and researchers suggested the same that people prefer the quality of excellent service with a cost; thus cost has an essential role in the adoption of IB services. The critical issue is accessibility and affordability of IB services as Kurdistan government has to include IB as part of National Strategy to move the country forward by increasing both competency and competitiveness, reducing the inequality between urban and rural area. There have to be many IB projects that need to be created by the Kurdish government to strengthen IB capability of Kurdistan. For example, the e-signature access has to be permitted by the government to private banks aiming to promote IB access for people, reduce the gap of the digital divide, and improve quality of life and private sectors. The developed private banks start to establish online banking so that consumer can check the balance and the main page of bank access, not authorized to make any transaction domestically or internationally. The project mainly creates opportunities and reduces time by accessing information according to their interests as well as privacy would be enhanced through the mobile banking access” that has been designed to serve the goal of IB access for every bank in Kurdistan. Furthermore, e-banking services projects have been initiated to promote the use of ATMs by banks, which will develop electronics services to serve the public and transform the traditional ceremony into electronic services. From analysis result, it could be used as an approach for long-run development, the government should create a suitable policy to regulate IB market environment as in Korea that the Korean government eliminated regulations to encourage IB market competition and support emerging relative actors in the IB services value chain to reduce people uncertainty by providing clear vision market scope and coordinating and sharing information with banks and customers to ensure that IB services roll out optimal for user needs in term of data privacy and security with reasonable cost. For access facilities, banks should provide training for a customer before they handle the access of IB to them that teaches them how to use the service. For developing IB skills for sustainable development, Kurdistan has to improve Internet education programs

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with various courses, and target unreached groups such as the elderly, housewives and the disabled (“Luna” 2009). Moreover, the collaboration with the International organization also helps in providing a training course to customers who would train the trainer. The researcher recommends that government should consider income subsidization and training programs to drive fixed-line Internet as well. From a suggestion by several best practices, the government should consider subsidizing low-cost equipment such as inexpensive tablets and smartphones. The smartphone is a new trend for Kurdish people after third mobile generation network was launched and Kurdish people are interested in new technology, so they intend to use a smartphone with Mobile internet services as estimation result was shown in Table 9.2. As implied by the result, banks have an excellent opportunity to drive demand side by developing mobile-based contents which are customized based on level and type of user and also provide e-banking services as well as local materials that relevant to their needs. As the demand of IB is high, as suggested by the results the obstacle of mobile services and the smartphone was from price level so the government should consider about subsidy in case they provide IB services in a remote area with low population density. Srinuan et al. (2012) stated that a study on mobile Internet service emphasized on adequate management of frequency allocation, content and application development and competition to stimulate the growth of IB adoption. This is the definite advantage for the bank to drive IB penetration in Kurdistan and provides the service via smartphone. After the banks established the IB service, the estimation results implied that the people are willing to use IB service to access bank services thus developing IB is a sustainable way to increase the number of bank consumers as suggested in many countries. Internet providers in Kurdistan have to reconsider how to induce the people to utilize the internet and let the community manages everything by them to raise awareness of community collaboration and to increase their revenue. For instance, 70 the Indian government subsidized private agency for operating income of community center and subsidy planned to phase out in a few years. Srinuan et al. (2012) note that fixed wireless Infrastructure can be used to support the expansion of bandwidth capacity in the long-run, but investment cost is high, so in the area without an extensive fixed network, wireless technologies can serve as a dual role both for mobile services and for computer services to fixed locations. Moreover, the quality of service availability of services would be solving when government permits agencies to enable the Internet by providing a wireless network in the community. The private content providers intend to develop the appropriate contents, but for the initial phase, the government should step into providing contents for internet (Kardefelt-Winther 2014). As the descriptive statistics show that the people in Kurdistan access to internet almost for entertainment purpose, the relevant local contents match with people’s needs would motivate them to realize the benefit of internet access more than for entertainment then the bank, who has the responsibility to develop and promote software and content industry, would need to take actions to promote a local content,

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especially IB contents. Lack of investment for the ongoing project is occurring in many developing countries, and banks should consider Public Private Partnership (PPP) as an alternate option for a project like IB development. With collaboration between government and private sectors, the local community can benefit from IB project that includes incentives for the private sector to deliver projects and has a high potential to make the community more competitive and may eventually increase community capital as a result. Based on the above results, several implications have been drawn for management. First, the critical need for managers to focus on promoting these future online services as they have a strong impact on customer’s satisfaction or dissatisfaction depending on service features or demographic and characteristics groups has been emphasized. However, relative to other services, the service fee and real-time interaction are the most important services. First, in line with other researchers (Koenig-Lewis et al. 2010; Laukkanen 2007). Young people who use online banking regularly are early adopters of innovative technologies that will eventually filter through to older age groups, and with increased word of mouth, it can lead to a network effect that will reach critical mass adoption. Managers can take advantage of this and introduce services that add entertainment for youths or extra convenience in terms of ease of usage, e.g., digital wallets, real-time interaction (video banking), ATMs integrated with smartphones, website customization, and digital currency. Second, since the ATM is the most common channel used by most customers for transactions, ATM integrated services have a better chance of increasing adoption, especially in developing countries where there is low access to the internet, and low internet banking and mobile banking adoption. Third, even though website customization services cannot be priced explicitly, they can help in customer retention and improve satisfaction. Since the majority are interested in this service, banks can concentrate on developing a customized website with tag-based interactions that help users retrieve information about their past online banking activities for the majority while providing an official website for other groups. Fourth, promoting digital currency may not be an effective policy. Meanwhile, Bank A, Bank C, and other banks can be the chief promoters of digital currency targeted at those with less access to the internet and lower percentage of IB use. Business models based on P2P NFC should be promoted to help create new paymentdriven revenue streams, e.g., Apple Pay users across the US will soon be able to make peer-to-peer (P2P) money transfers to friends and family using a new feature set to be integrated into Apple’s iMessage platform through the latest iOS 11 operating system. The feature will also bring an Apple Pay Cash card account to the mobile payment service, which will enable users to make in-store purchases (Rian 2017). Fifth, there should be a policy for promoting comprehensive, flexible, and additional authentication transaction security solutions that will allow the banks to meet the needs of an escalating threat landscape and tailor mitigation methods to different risk profiles. Sixth, even though the biometric service is preferred less,

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Bank managers can introduce this by providing various options in terms of differentiated services since the majority that like the service is not very heterogeneous in their preference. Bank B, Bank C, and other banks can be the chief promoters of these services, which should be targeted at customers who are less familiar with the internet and access the internet at home or the office or using mobile phones or smartphones. Besides, other bank strategies to encourage potential adopters like offering incentives for adoption, providing customers with value-added promotion programs, and aggressive marketing could lead to increasing adoption.

9.5.3 Managerial and Overall Recommendation Based on interview results with senior level employees from three different private banks in Kurdistan, several recommendations have been drawn for management. To support the empirical part of this research interview has been conducted with three different banks, as shown below the questions have been asked: 1

Do you have IB service?

2

Why didn’t you provide IB?

3

Is it good to have IB service?

4

Can you provide it?

5

When you will be able to provide it?

6

What are obstacles?

7

What do you want from government to do for you?

8

This is the most preferred profile, what do you think about it?

9

Any other comments?

First, Bank A asks the government to provide an e-signature from corporate so they can start to adopt IB service and strengthen privacy threats of customers, which they are planning to, take at the end of this year 2019. And for the most preferred profile of CA survey, they have commented on transaction cost, that IB services have to be for free as they aim to reduce employees, paper works and less headache for management. Critical need for managers to focus on the language of future online services as they have recommended only the English language is enough as long as it’s an international language. Bank B also didn’t have IB service, but only provided access for checking on balance and statement of account SOA, as they don’t have any plan to adopt IB service because of security issues as they prefer to have human interaction while the transaction is made. The significant threat for Bank B is compliance and money laundry, to avoid these obstacles they recommend Central Bank to be more involved in transactions and all governmental institutions employees receive their salaries through banks, not in cash. And they recommend that transaction cost to be the lowest rate, but not for free as Bank A suggested.

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Bank C; have not adopted IB service yet. And have no near plan to adopt it, most probably in next year they will start to plan as they are recommending to have a mother company to provide the IB service for them and they just follow the rules, in order to reduce money laundry and as we are in war zone area, and we have still had cells of ISIS that need to be financed, so they ask the government to upload rules for anti-bribery processes and check with other developing countries who have already adopt IB service for their practices in order to secure the transactions. Awareness of people is another recommendation from Bank C, as they need to limit the IB only for educated people. As their condition to adopt IB service is informing customers in advance that if they made an online transaction it would not be received on the same day, it will need 24 h in order for the bank to check all the documents for the purchase by human interacting process and then after confirmation from management level the transaction will be made successfully. When the most preferred profile was shown to them, they recommended for language to be only Arabic and English, not necessary for all three languages together as they excluded the Kurdish language. And for access to IB, only password is enough without OTP because of old people they might forget their OTP device. And transaction limits per day it’s better to be 5,000 USD only. And for transaction cost, it’s better to be fixed, not proportional.

9.5.4 Recommendations It is recommended that future research should be forward-looking and focuses on the following areas: 1. Increasing number of respondents to cover Sulaimanya and Duhok, not only concentrating on the Kurdistan region. 2. Additional variables are needed to add to the attributes, by having more than three choice sets; research can have six choice sets to allow variation of the data for an individual. Furthermore, for future projects, cooperation is needed between academic researchers and internet banking services to achieve better customer oriented investigations. Establishments of internship programs and research funds supported by the industry will help in better understanding of the financial, regulatory and education needs of the industry and consumers. Future studies can broadly investigate the factors that affect customer satisfaction, such as education level, word of mouth, life cycles and usage patterns of customers, switching barriers, etc.

Appendix 1: Descriptive Statistics See Tables 9.13 and 9.14.

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H. A. Lazar

Table 9.13 Summary statistics of variable used Variable

Mean

Std.Dev

Minimum

Maximum

Cases

1

0.41791

0.493625

0

1

603

X2

1.25373

1.11184

0

4

603

X3

3.10448

0.736358

1

4

603

X4

2.71642

0.990409

1

4

603

X5

1.20896

0.820898

0

4

603

X6

3.4063

0.977705

0

4

603

X7

0.771144

0.73861

0

4

603

X8

0.3267

0.780408

0

4

603

X9

0.626866

1.26874

0

7

603

X10

0.253731

0.60751

0

3

603

X11

1.65672

1.24154

0

4

603

X12

1.33002

0.919814

0

4

603

X13

2.73134

1.27751

0

4

603

X14

0.641791

0.510076

0

2

603

X15

0.373134

0.643192

0

4

603

X16

0.447761

0.580847

0

3

603

X17

2.35821

1.57251

0

4

603

X18

1.04478

0.96946

0

3

603

X19

1.49254

0.983853

0

4

603

X20

0.149254

0.396344

0

2

603

X21

0.328358

0.470006

0

1

603

X22

0.358209

0.479872

0

1

603

X23

0.313433

0.464274

0

1

603

X24

0.432836

0.49588

0

1

603

X25

0.358209

0.479872

0

1

603

X26

0.283582

0.451111

0

1

603

X27

0.298507

0.457983

0

1

603

X28

0.283582

0.451111

0

1

603

X29

0.238806

0.426708

0

1

603

X30

0.41791

0.884235

0

3

603

X31

0.610282

0.488091

0

1

603

X32

0.38806

0.487713

0

1

603

X33

0.507463

0.500359

0

1

603

X34

0.41791

0.493625

0

1

603

X35

0.119403

0.324531

0

1

603

X36

0.492537

0.583147

0

2

603

9 Consumers’ Current State Preferences for Internet Banking Services …

395

Table 9.14 Correlation between the variables X2

X3

X1

1

−0.00285 0.20872

0.21223

−0.14207 −0.04264 −0.2293

0.07189

X2

−0.00285 1

0.13191

0.16049

0.46598

0.41081

0.0769

0.19913

X3

0.209

1

0.10219

0.26061

0.2109

−0.0934

0.07058

X1

0.13191

X4

X5

X6

X7

X8

–

X4

0.212

0.160

0.10219

1

0.01784

0.01454

−0.03437 0.11791

X5

−0.142

0.466

0.261

0.01784

1

0.11757

0.30091

X6

−0.043

0.411

0.211

0.015

0.11757

1

−0.02974 0.02386

X7

−0.229

0.077

−0.093

−0.034

0.301

−0.02974 1

0.06941 1

X8

0.072

0.199

0.071

0.118

−0.013

0.024

0.06941

X1

X2

X3

X4

X5

X6

X7

X8

X9

0.058

−0.01339

0.014

0.058

0.237

−0.169

−0.001

−0.134

−0.056

X10 −0.205

0.303

0.041

0.120

0.163

0.053

0.063

0.424

X11 0.015

0.117

0.170

−0.019

0.041

0.122

−0.108

−0.066

X12 0.157

0.150

−0.007

0.170

0.206

−0.051

0.075

−0.023

X13 −0.035

−0.089

0.284

−0.155

0.082

0.135

−0.065

−0.317

X14 −0.176

−0.103

0.060

−0.024

0.072

0.053

0.219

−0.081

X15 0.167

−0.028

0.202

0.002

−0.035

−0.123

−0.040

−0.005

X16 −0.184

0.264

0.205

0.039

0.399

0.179

0.030

0.270

X9

X10

X11

X12

X13

X14

X15

X16

X9

1.000

−0.052

−0.025

−0.037

0.040

−0.091

0.061

−0.098

X10 −0.052

1.000

−0.320

0.331

−0.374

−0.285

0.063

0.228

X11 −0.025

−0.320

1.000

−0.349

0.338

0.183

−0.326

0.089

X12 −0.037

0.331

−0.349

1.000

−0.395

−0.066

0.145

−0.025

X13 0.040

−0.374

0.338

−0.395

1.000

0.288

−0.151

0.042

X14 −0.091

−0.285

0.183

−0.066

0.288

1.000

−0.412

−0.114

X15 0.061

0.063

−0.326

0.145

−0.151

−0.412

1.000

−0.008

X16 −0.098

0.228

0.089

−0.025

0.042

−0.114

−0.008

1.000

X3

X4

X5

X6

X7

X8

X17 −0.212

−0.078

−0.045

−0.117

0.046

0.158

0.148

−0.230

X18 0.086

0.059

0.014

0.153

0.063

−0.070

−0.167

0.161

X19 −0.086

−0.101

0.011

−0.071

0.094

−0.050

−0.098

−0.251

X1

X2

X20 0.063

0.117

0.151

0.032

0.088

−0.080

0.117

0.180

X21 0.052

0.012

0.074

−0.024

0.054

0.230

0.174

−0.048

X22 −0.065

0.054

−0.106

−0.069

−0.039

−0.056

−0.106

0.086

X23 −0.11586 −0.18328 −0.22714 −0.06648 −0.21136 −0.01021 −0.02299 −0.0401 X24 −0.19052 0.1801

0.03972

X9

X12

X10

X11

X17 0.02964

−0.26744 0.42299

X18 0.01361

0.13298

0.00682

0.18144

X13

X14

−0.29892 0.39031

−0.13626 0.28332

0.25339

−0.05564 −0.05696

X15

X16

0.36526

−0.30974 −0.01221

−0.24377 0.00226

0.06907

0.04398 (continued)

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H. A. Lazar

Table 9.14 (continued) X1

X2

X3

X4

X5

X6

X7

X8

–

X19 −0.05613 −0.28447 0.13865

−0.14504 0.20061

0.23299

−0.12553 −0.07263

X20 −0.00799 −0.09545 0.2562

−0.13533 0.07932

0.117

−0.04289 0.1638

X21 −0.01983 −0.08284 0.16787

−0.009

0.07247

0.11727

0.03912

X22 −0.12388 −0.15844 0.08127

0.0027

0.08408

0.15862

−0.04627 −0.04003

X23 −0.08031 −0.07041 −0.12426 0.00633

−0.05944 −0.03015 −0.04185 −0.29953

X24 0.06703

0.08148

−0.00109 0.14519

−0.09933 −0.03617 0.10214

X17 X18

X19

X20

X21

X22

X23

X17 1

−0.17725 0.26264

−0.1339

0.24515

0.00798

X18 −0.17725 1 X19 0.26264

−0.19558 −0.28978 −0.13075 0.06187

−0.19558 1

−0.07382 −0.02702 0.19567

X20 −0.1339

−0.28978 −0.07382 1

X21 0.24515

−0.13075 −0.02702 −0.18327 1

0.00791

X22 0.00798

0.06187

1

X23 −0.07213 0.0352

−0.04659

−0.18327 0.11145

0.19567

0.11145

0.00791

0.18514

−0.1734

−0.12987 0.09915

0.15649

X24 −0.07213 0.06925 0.0352

0.05291

0.18514

0.11388

−0.1734

−0.10104

−0.12987 −0.1618 0.09915

−0.21286

1

−0.07075

X24 0.06925

0.05291

0.11388

−0.10104 −0.1618

−0.21286 −0.07075 1

X1

X3

X4

X5

X7

X2

X6

X25 −0.00188 −0.03053 −0.10609 −0.25775 −0.11442 −0.0558

X8 −0.06358 0.00636

−0.05829 0.01563

X26 0.20542

0.12457

0.09068

0.11337

0.16269

X27 0.1747

0.08589

0.08469

0.21989

−0.00712 −0.09695 0.14336

−0.02698

X28 0.00401

−0.02447 0.2257

0.07991

0.00121

−0.09845

0.08483

X29 −0.26166 −0.15944 −0.12712 −0.22862 −0.14269 0.19706

0.09041

0.03366

−0.06348 −0.14488

X30 0.14723

0.07445

−0.11309 −0.1888

0.02367

−0.12756 −0.12801 −0.02486

X31 0.00834

0.02028

0.0118

0.04189

−0.01573 0.13463

X32 0.07044

−0.40244 0.05343

X9

X10

X11

X12

0.01154

−0.07948

−0.05035 −0.20287 −0.08038 −0.12657 −0.05869 X13

X14

X15

X16

X25 −0.02565 −0.10716 0.05618

−0.06504 −0.06224 −0.08569 −0.14314 0.01361

X26 −0.07602 0.00977

0.09402

−0.04577 0.02865

X27 −0.11674 0.10346

−0.02982 0.08911

0.05237

−0.10767 0.0281

−0.09268 −0.05349 −0.07423 0.05872

X28 −0.10214 −0.15389 −0.01275 −0.22192 −0.02323 0.11734

0.04691

X29 −0.05605 −0.06111 0.26788

0.05023

−0.10732 −0.07022

−0.05926 −0.1316

−0.01177 −0.16118

X30 −0.11397 0.08058 X31 0.01157

−0.27731 0.06304

−0.10061 0.08749

−0.22057 −0.01006 −0.03125 0.01829

0.0281

−0.02121 −0.01224 0.10678

X32 0.04111

−0.1815

−0.07591 −0.11932 −0.02435 0.25922

0.01423

X17 X18

X19

X20

X24

X25 0.0278

0.12615

−0.12099 −0.28157 −0.05837 −0.10368 −0.23637 −0.08721

X26 −0.03806 −0.06327 −0.1468 X27 −0.1072

X21 0.09734

−0.06383 −0.19412 0.08359

X28 −0.03806 −0.02908 −0.04575 0.01373

X22 0.12418

X23

−0.12472 −0.28233 −0.08179

−0.03939 −0.21524 −0.0892 0.05367

−0.13942

−0.30655

−0.26285 −0.06819 −0.01496 (continued)

9 Consumers’ Current State Preferences for Internet Banking Services …

397

Table 9.14 (continued) X1

X2

X29 0.34019

X3

X4

−0.35115 0.1467

X5 0.05409

X30 −0.05408 −0.02187 −0.03078 −0.0503 X31 0.04367

0.18087

0.07522

X6

X7

0.05563

−0.12641 −0.00113 0.07593

−0.07893 0.10465

−0.08523 0.05188

X8 0.04457

0.0461 −0.01568

X32 −0.0256

0.02643

0.31761

−0.06811 0.0954

−0.0839

0.05617

X27

X28

X29

X31

X32

0.10465

0.2495

X25 1

0.15152

−0.01117 −0.05566 −0.0534

X26 0.15152

1

−0.0486

0.04496

−0.03817

−0.15476 0.05613

X25 X26

X30

–

0.04386

−0.19706 −0.03524 −0.10832 −0.02536

X27 −0.01117 −0.0486

1

0.16849

−0.21238 −0.12398 0.26863

−0.11788

X28 −0.05566 0.04496

0.16849

1

0.1136

0.11055

0.00224

0.05765

X29 −0.0534

−0.19706 −0.21238 0.1136

1

0.01242

−0.15856 0.2005

X30 0.10465

−0.03524 −0.12398 0.00224

0.01242

1

−0.03768 −0.09934

X31 0.2495

−0.10832 0.26863

−0.15856 −0.03768 1

0.05765

X32 0.04386

−0.02536 −0.11788 0.11055

0.2005

−0.09934 0.23163

X1

X3

X5

X6

X7

0.0326

0.09736

X2

X4

0.23163 1

X8

X33 0.10841

−0.12434 0.22105

0.04953

X34 −0.1044

0.07887

0.00307

−0.15472 −0.03139 0.08127

0.01673

−0.00573

X35 −0.03204 0.08163

0.19795

−0.03402 0.01843

0.05182

−0.03622 0.20065

X36 0.01085 X9

X10

X33 0.06327

0.1528

0.05404

0.08693

0.03449

0.04757

0.14644

X11

X12

X13

X14

X15

X16

0.11672

0.13649

X34 −0.06093 −0.00521 0.1369 X35 0.10839

0.22379

−0.08976 0.11073

−0.07809 0.36163

0.15525

−0.06702 0.06907

−0.17126 0.03992

0.09086

−0.05874 0.35803

−0.30361 −0.23658

−0.18231 0.11356

0.25881

X36 −0.37759 −0.01575 −0.01387 0.06189

−0.14317 0.09152

X17 X18

X22

X19

X33 −0.06041 0.138

X20

X21

0.09881

−0.00563 0.11671

X34 −0.05835 −0.10165 −0.05513 0.06273

0.05194

−0.2138

0.03314

−0.17193 0.00988

X23

X24

0.05111

0.02209

0.01709

−0.065

0.0146

−0.06837

−0.13878 0.2326

−0.0831

0.04887

−0.04298

X36 −0.14381 −0.09196 0.12368

0.06951

0.00896

0.38354

0.1467

−0.16976

X25 X26

X29

X30

X31

X35 0.2383

0.07801 X27

0.09644 X28

X33 −0.07342 −0.04251 −0.00974 0.02373

−0.03908 0.47822 −0.3295

0.13254

−0.06998 0.21036

0.36795

X34 0.06123

0.00401

0.04244

0.20542

−0.04873 0.01022

X35 0.20489

−0.12955 0.16214

0.27892

0.00967

0.15014

−0.17307 0.06405

X36 −0.04386 0.03647

0.17629

X33 X34

X35

X36

X33 1

0.16894

0.36277

0.26919

X34 0.16894

1

0.34126

0.16666

X35 0.36277

0.34126

1

−0.07428

X36 0.26919

0.16666

−0.07428 1

X32

−0.00836 0.02673

−0.00148 0.0102

398

H. A. Lazar

Appendix 2: Questionnaires (Internet Banking) Part One: Personal Information 1. What is your gender? a. [] Male b. [] Female 2. How old are you? a. b. c. d. e.

[] 18 to 29 [] 30 to 39 [] 40 to 49 [] 50 to 59 [] 60 or older

3. What education level do you have? a. b. c. d. e. f.

[] Primary school or less [] Secondary [] Diploma [] Bachelor [] Masters [] Doctorate

4. What is your occupation? a. b. c. d. e.

[] Unemployed… (Go to question number 7) [] Civil Servant [] Business [] Academic [] Others

5. How many years is your working/business experience? a. [] 0 to 5 years b. [] 6 to 10 years c. [] More than 10 years 6. How much is your monthly income (USD)? a. b. c. d. e.

[] 0 to 500 [] 500 to 1,000 [] 1,000 to 1,500 [] 1,500 to 2,000 [] More than 2,000

9 Consumers’ Current State Preferences for Internet Banking Services …

7. What is your marital status? a. [] Single b. [] Married c. [] Others 8. How many members are in your family? a. [] Small (1 to 5) b. [] Large (more than 6) 9. What is your major private bank? a. b. c. d. e. f. g. h. i. j.

[] Kurdistan International Bank (KIB) [] Byblos Bank [] Intercontinental Bank of Lebanon (IBL Bank) [] Bank Audi [] BBAC Bank [] IS’ Bank [] Standard Charter [] Regional Trade Bank (RT) [] Cihan Bank [] Ashur bank

10. Do you have a bank card? a. [] Yes b. [] No…(Go to question number 12) 11. What is the main purpose of using your bank card? a. b. c. d.

[] Hotels/tickets [] Online shopping [] Only Using abroad [] All above

12. What is the frequency of your transaction with your bank? a. [ ________/week] b. [ ________/month] c. [] No transaction 13. Internet Familiarity? a. [] Not at all b. [] Fairly c. [] Averagely

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H. A. Lazar

d. [] Well e. [] Very well Part Two: Conjoint Survey Please tick the most preferred package in profile (A)-shall I change tick to rank? Profile (A) Attribute

Level 1

Level 2

Level 3

Language

Kurdish/English

Arabic/English

All

Access to your internet banking OTP with password OTP with password Only password Limit per transaction

$1000-$5000

Less than $1000

More than $5000

Transaction limit per day

$5,0000-$10,000

More than $10,000

More than $10,000

Transaction cost

Fixed

Fixed

Proportional

()

()

()

Please tick the most preferred package in profile (B) Profile (C) Attribute

Level 1

Level 2

Level 3

Language

All

Arabic/English

Kurdish/English

Access to your internet banking

Only password

USB with password

Only password

Limit per transaction

Less than 1000

1000–5000

1000–5000

Transaction limit per day

Up to 5000

5000–10,000

More than 10,000

Transaction cost

Fixed

Proportional

Proportional

()

()

()

Please tick the most preferred package in profile (C) Profile (E) Attribute

Level 1

Level 2

Level 3

Language

Kurdish/English

All

Arabic/English

Access to your internet banking

USB with password

OTP with password

Only password

Limit per transaction

Less than 1000

More than 5000

1000–5000

Transaction limit per day

More than 10,000

5000–10,000

Up to 5000

Transaction cost

Proportional

Proportional

Proportional

()

()

()

9 Consumers’ Current State Preferences for Internet Banking Services …

401

• Now please mention the best option, according to your preference, from among the ones you have indicated as the most preferred in each set (A, B and C) If you think there are other attributes than what we used in this survey have to be considered, please specify?_________________________________________ Part Three: External Factors: 1. What type of internet banking service do you currently use? More than one option is allowed a. b. c. d.

[] Credit/debit card for a specific bank [] Only banking access [] Cards for online shopping [] I don’t use

2. How long have you used internet banking? a. b. c. d. e.

[] Three months [] Six months [] One year [] Two years [] Other, Specify______________

3. How often do you find internet banking not opening? a. [] Sometimes b. [] Many times c. [] Never 4. How satisfied are you with the bank’s service? a. b. c. d. e.

[] Very satisfied [] Satisfied [] Average [] Unsatisfied [] Very unsatisfied

5. Which banking services give you the most satisfaction? Choose up to 3 choices a. b. c. d. e. f. g. h. i.

[] Mode of transaction [] Comprehensiveness of online services [] Banks name and reputation [] Multiple Branches [] Waiting time [] Customer care [] Information security [] Transaction security [] Transaction costs

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H. A. Lazar

j. [] Queue management k. [] Others, Specify______________ 6. If you have never used internet banking, when do you intend to use it? a. b. c. d.

[] within 6 months [] in 1 year [] 1 year later [] No plan to use

7. The reason to use the internet banking service (please tick as many as you use/ would use it for? a. b. c. d. e.

[] Time efficient [] Cost efficient [] Privacy [] Security [] All above

8. In your opinion do you think internet banking project in Kurdistan will be successful? a. b. c. d.

[] Yes it will [] It will but with partial failure [] No, it will fail [] I don’t know

Thank you very much for your participation, please note the information which you have provided surely will not be used for any other purposes than for the objectives mentioned at the beginning of this survey.

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Chapter 10

Summary, Conclusion, and Policy Recommendations Nabaz T. Khayyat

10.1 Introduction Federal Iraq and its northern region, the Kurdistan Region of Iraq (KRI), are entering a period of fast technological modernization with several positive values, assisting the development of a new strategic focus on technology and information services. The economic activities associated with the different sectors of the economy and the use of information technology have become a driving engine of the steady economic growth of the country. Globalization challenged the regional government like in the whole world, to develop a strategy to accelerate the adoption and dissemination of information and communications technologies (ICT), technologies in support of the social, cultural, and economic future of the nation. The KRG recognizes the importance of embracing ICT to achieve its development goals. Stimulating economic growth, raising living standards, and modernizing cultural activity will be greatly facilitated with the introduction of advanced information infrastructure and the widespread introduction of ICT into everyday life. The objectives of this book were to analyze two important factors contributing to the region’s economic development: the current technological and banking services in the KRI and to evaluate the ICT Diffusion in the region. This chapter summarizes the three research areas conducted in this collective work: Information and Communication Technology (ICT) readiness, ICT adoption, and Lean services and Internet banking.

N. T. Khayyat (B) Technology Management, Economics and Policy Program, Seoul National University, Seoul, Republic of Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. T. Khayyat and G. M. Muhamad (eds.), Empirical Studies of an Internet and Service Based Economy, Perspectives on Development in the Middle East and North Africa (MENA) Region, https://doi.org/10.1007/978-981-99-3389-1_10

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10.2 Part One: Information and Communication Technology Readiness Chapter 2: Assessing Innovation Capability and Technological Readiness of KRG The study developed a new measurement tool to assess innovation capability and technological readiness for KRG entities, the tool is named the KRGi, where, the particular factors that are most affecting the innovation in KRG entities are studied and analyzed. The KRGi was approximated for 108 entities of KRG. The Principal Component Analysis (PCA) was applied to 22 indicators for the estimation rate of innovation capability and technological readiness, which were further categorized into 10 principal components. The score obtained was categorized into three groups of entities based on their innovation capabilities and their technological readiness. According to the results, the highest rate of technological readiness was noticed in the Ministry of Electricity followed by the Ministry of Education, and Ministry of Anfal and Martyrs Affairs among the 108 chosen entities under this study. The lowest rate of technological readiness was noticed and reported in the Ministry of Labor and Social Affairs and the Ministry of Housing and Reconstruction. Chapter 3: Examining Customer State Preferences of Mobile Services in the Kurdistan Region of Iraq: A Conjoint Analysis Approach The study highlights the impact of different attributes on consumer decisions, which are considered by consumers as important and appropriate factors in their subscription decision. The conjoint analysis technique is used to study mobile service attributes such as call service, SMS service, internet service, and price. It combines them in the form of different bundles to estimate customer preferences and forecast mobile consumer usage. The study’s main objective was to estimate the customer preference toward the different telecom services by creating packages or bundles by applying choice-based conjoint analysis, finding the best package that could cover the needs of the mobile service users, and identifying the factors that influence customer preferences. For the analysis, the author collected 273 observations stratified on the three governorates of the region and the three main telecom services operating there. The main finding of the study is that customer characteristics have a significant impact on selecting mobile service bundles. The majority of telecom mobile application is favored by the youth age, as presented in this study, male prefers to have sport and game mobile application, again the most preferred bundle can be attached to their favorite mobile application and launched for male and younger ages.

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10.3 Part Two: Information and Communication Technology Adoption Chapter 4: Investigating the Factors Affecting Mobile Money Adoption in the Kurdistan Region of Iraq: The Case of Newroz Telecom FastPay The author of this chapter investigated the mobile money adoption factors in the region, to achieve the objective, a case study of mobile money was taken, the mobile payment FastPay which is considered one of the most popular mobile money platforms in the region. A tailored model is proposed which combines the technology acceptance model (TAM) with the Initial Trust model (ITM), to predict user acceptance in the Kurdistan region of Iraq. A survey questionnaire is used for the primary data collection, a sample of 120 responses was collected from randomly selected FastPay users. Structural Equation Modeling is used to analyze the results, the findings indicate the surveyors, in general, are happy with the mobile payment and intend to continue using mobile money under the correct circumstances, however, they showed concerns regarding the security of mobile money, better infrastructure, trust, and awareness. Chapter 5: Adoption of social media in Small and Medium Enterprises Chapter 5 studies the use of social media tools in SMEs for their business objectives. Since SMEs have a significant role in economic development, it is essential to understand how SMEs get benefit from social media to achieve different business strategic objectives such as customer/supplier intimacy, promoting new products, and supporting decision-making. The research aim is extended to investigate the impact of social media adoption on a firm’s productivity. A quantitative method is used through conducting a stratified survey among Humanitarian agencies, non-governmental organizations, and service sectors in KRI. Several 99 observations were collected and analyzed using binomial and multinomial discrete choice analysis. Overall, the results of the study can be summarized as follows: • • • •

Facebook is considered to be the most used platform by most SMEs. Social media increase the productivity of SMEs. The main purpose of using social media by SMEs is to increase brand awareness. In general, SMEs are satisfied with the use of social media platforms.

Chapter 6: Effects of Social Media Reviews on Customers’ Purchase Intention in Erbil Chapter 6 investigates the effects of consumers’ Facebook reviews on people’s purchasing intentions. A special emphasis is on the restaurants in Erbil, the capital of the Kurdistan Region of Iraq (KRI). A mixed methodology of survey questionnaires (quantitative) and interviews (qualitative) was conducted, and 98 observations from 49 restaurants are collected

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and analyzed using multiple regression analysis. The key findings indicate that positive online reviews increase the number of customers. The findings further suggest that if the restaurants’ management team adopts an effective online strategy to respond to the reviews of their customers, they can exercise some influence on the fluctuations in the number of their clients. It is recommended for restaurants to successfully manage their online reviews and create a strong emotional bond with their customers. Chapter 7: Investigating the Determinant Factors of Telemedicine Adoption in the Kurdistan Region of Iraq This chapter investigates the possibilities of adopting telemedicine to propose a fundamental framework for its implementation. The chapter aims at determining the level of using telemedicine services in KRI hospitals and examining the factors that are affecting the use of telemedicine services among healthcare stakeholders. A survey questionnaire was conducted among several physicians and nurses in different hospitals in the region. A total number of 350 observations were collected. The results of the analysis reveal that the health providers were highly using telemedicine and regarded it as very applicable to inpatient consultation, while they did not agree that it was applicable in surgeries. Furthermore, the results showed that the health providers found telemedicine easy to learn and would improve their qualifications and professionalism.

10.4 Part Three: Lean and Banking Services Chapter 8: Transforming Lean to Service: Application to the Kurdistan Banking Industry Chapter 8 examines the possibilities of transferring lean thinking from the manufacturing context to the banking service in the Kurdistan Region of Iraq (KRI). Several 130 observations among 14 banks are collected and analyzed using correlational analysis and discrete choice method to determine which Lean principle(s) and critical success factors CSFs are already established as a practice, and which settings determine their successful application. The results conclude that banks in KRI are currently practicing some of the principles and CSFs of Lean, though there is a lack of some of the success factors to ensure a Lean transformation. The results of the multinomial logit model indicated that employees of the banks in Kurdistan have a proper level of understanding of customer value and are aware of the nonvalue-added activities related to their tasks. The banking sector has a proper level of employee capacity in terms of engagement and independence to take on a Lean transformation journey.

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Chapter 9: Consumers’ Current State Preferences for Internet Banking Services: The Case of the Kurdistan Region of Iraq’s Private Banks The study examined the determinant factors affecting Internet banking use in the KRI. A discrete choice ranked ordered model is used to analyze the conjoint survey of a random sample of 67 observations collected from the banking users in the region. Several characteristics such as Internet banking language, access to Internet banking, Limit per transaction (LPT), Transaction limit per day (TLP), and transaction cost are used for the analysis. The finding suggests that Interbanking banking users have no language preferences (i.e., Kurdish, Arabic, and English) for mobile applications. They prefer to access their account by using OTP with a password. And the percentage of using Internet banking in the near future is relatively high at 76% which means Internet banking in Kurdistan is highly preferred, and people demand the service.

10.5 Recommendations 1. Technology policy The regional development policy should aim at promoting national and regional competitive advantages. The policy should include different production factor demands, various support conditions, firm strategies, and structure, as well as the landlocked condition of the Kurdistan region. The Federal Government’s role in encouraging firms and having a vision for national development should build upon a national consensus to establish the basic directions and paradigms for sustainable economic development. The Kurdistan regional government should construct a system to create opportunities to strengthen capacities and to form the optimal environment to develop the economy. The current import-oriented economy should be used to foster a rapid acquisition of technological capability to lay the foundation for radical changes in the internal economic environment. Domestic producers exposed to international competition and the opportunity to cooperate with foreign firms should be fully utilized. The KRG’s liberal foreign direct policies should be used for technology and skill transfer in a more effective way. The elements of a new and progressive state policy should involve gaining a competitive advantage, raising creativity in cooperation among firms, and playing an active role in international cooperation. The recent years of intensive use of information technology in the region have eased technology transfer and business cooperation. The information sector is found to be a key strategic factor for the economy. Both the public and private sectors must cooperate to improve connectivity and its utilization in the production and delivery of services. Technology capability determines the Kurd’s capacity for development and this factor must be integrated into the structural policy of the regional government. Increased regional competition and the globalization of economic and technological activities offer special challenges as well as opportunities for Kurds to develop

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their society. Limited domestic resources can be strengthened by joint ventures and enhanced security and profitability for foreign investment. 2. A multi-channel policy approach The KRG must adopt a multi-channel policy approach to obtaining new technology from external sources. Increased investment in education to correct for inappropriate past investment to promote competition among the universities while minimizing state control and intervention in the administration of universities are measures with high priority. The introduction of more science and technology into basic education, and expanding the supply of skilled workers and vocational training as part of the labor market policy are better measures than the current mismanaged public employment programs. Other measures include expanding public and private investment programs, prioritizing and improving conditions for Small and Medium enterprises (SMEs), opening the domestic market, and creating a more FDI-friendly business environment. Competition and cooperation at all levels need to be harmonized. Priority should be given to the information industry and its use, not only in communications and production, but also in the formation of E-government to provide public services to firms and citizens. Labor costs, administrative bureaucracy, and intellectual property rights are a source of concern mostly for foreign firms. The KRI, with its rich natural resources but a weak tradition of industrial policy in a politically unstable region and less hospitable to FDI, needs careful policy and protective measures to support small domestic firms. The KRG must succeed in its reform programs to reduce the outflow of capital, increase the inflow of capital, promote reliance on domestic resources, and concentrate on capability in public institutions and productive sectors. 3. The financial market The value of the Iraqi Dinar changed for basically two reasons: fluctuation in the exchange rate, and an increase in the inflation rate. The latter is believed to be much lower in the Kurdistan region compared to the other Iraqi regions. The exchange rate fluctuation of the Iraqi Dinar—pegged to the US dollar—is mainly attributed to the volatility in the US dollar value and supply of the Petro-dollar. In the absence of tax revenues, oil revenues are an important source for the government to cover public sector wages and material expenses. The Iraqi banking system is deficient as could be measured by the very low level of savings, the volume of credits, and limited inter-bank relationships. However, many savings and national, regional, and foreign commercial banks are established in Kurdistan. Unfortunately, this expansion of banks is not matched by a modern system of business laws and regulations. The latter is a major handicap in the formulation of banking and business efficient policies. Consequently, the enormous business opportunities and potential that exist to develop the region’s market are crippled. The lack of a comprehensive and consistent system of business laws and regulations, one cornerstone, in a market economy hinders the promotion of savings and credits, the second cornerstone, of a well-functioning banking system.

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A Central Bank, a key player in the government’s monetary policy, affects the money supply and determines interest rates in the economy via different policy instruments, e.g., the discount rate, the reserve ratio, and the open market operations. The money supply and the interest rate affect consumption and investment demands. However, the Iraqi Central Bank has yet to be able to play this role as well as to stand lender of last resort when the financial system is threatened. Although the Kurdistan Regional Central Bank is not independent in the formulation of its monetary policies and their implementation, it should be possible to come up with accommodating procedures to develop credit channels of the monetary policy encompassing the regional financial market. The exchange rate, a measure of the domestic currency per unit of foreign currency, undeniably affects the Balance of Payment. It affects the flows of goods and services, transfers, and capital mobility. The exchange rate is floating allowing for continuous adjustment without the central bank intervention. Monetary policies such as exchange rates and interest rates are powerful monetary instruments that affect aggregate demand in the short run. However, fiscal policy may have led to a higher inflation rate and helped to crowd out local production. The weakening dollar causes instability in the inflow of resources to the region. In addition to reduced oil revenues, import becomes more expensive increasing the inflation rate. The decline in the value of the US dollar should have enhanced local production competitiveness, but due to dependency on oil revenues and the destroyed local production capacity, imports have flourished. The increased inflation rate causes higher wage demand and further lowers labor productivity.