19 Important to realize, quality of master data appeared to be a specific determinants that influence MDM adoption by local government in developing country. This is due to the lower quality of data in developing countries as opposed to the developed countries. Similarly, the moderation effect of population density on the relationship between demand and MDM adoption by local government revealed in this research also distinguished the importance of number of citizens or customers served by an organization in the adoption of IT in the context of developing countries. Hence, the results have a potential to be a reference for other research on IT adoption, particularly in the context of developing country. Third, the result of this research has a valuable practical contribution. The involvement of MDM and local government practitioners in verifying the initial conceptual model, validating the survey instrument and reviewing the proposed guidelines and strategy has made the finding reliable to be used in real-world phenomena. In addition, to evaluate the developed MDM adoption model in Malaysia local government, this research proposed a set of guidelines and strategy of MDM adoption for the Malaysian public sector (see Appendix N). The proposed guidelines and strategy of MDM adoption will assist the MDM implementation in the Malaysian Public Sector. This is due to the intention of developing more MDM initiatives in the Malaysian public sector has been established in Eleventh Malaysia Plan, 2016-2020 published by The Economic Planning Unit (2016) and the Malaysian Public Sector ICT Strategic Plan 2016-2020 developed by MAMPU (2016b). The findings of this research would be beneficial for the MDM initiators, such as MAMPU, the Ministry of Urban Wellbeing, Housing and Local Government, the Ministry of Rural and Regional Development, and state government. MDM initiators could understand the key constructs that must be considered for MDM adoption so that the implementation of this technology can be widely accepted by local government and other organizations in the future.
20 1.8 Research Scope The scope of this research is limited to the five main perspectives: IT adoption stage, IT adoption study, level of analysis, MDM cluster, and respondents. Table 1.4 shows the perspectives, perspective’ types and scope applied in this research. Table 1.4 Scope of the research Perspective Type Scope of this research IT adoption stage i. Pre-adoption ii. Post-adoption Pre-adoption IT adoption study i. Relational ii. Descriptive iii. Comparative Relational Level of analysis i. Individual ii. Organization Organization (department unit of Malaysia local government organizations) MDM cluster i. Business ii. Education iii. Health iv. Others Business Respondents Department units of Malaysia local government organizations i. Information Management Department ii. Town Planning Department iii. Business Licensing and Petty Traders Department The MDM adoption as a dependant variable in this research refers to the intention of Malaysia local government to participate in sharing their master data as data sources to the MDM initiatives. Generally, IT adoption stages can be categorised into two stages of pre-adoption and post-adoption (Lin, 2014). Pre-adoption refers to the initial decision of the organizations to adopt IT innovation. On the other hand, postadoption refers to the willingness of the organization to continue using the IT
21 innovation after the implementation stage (Kamal, 2006). This research focuses on the pre-adoption stage of MDM by Malaysia local government, particularly in business domain (i.e. business registration and licensing MDM initiatives). The nature of this research is a relational study of IT adoption. According to Hanafizadeh, Keating and Khedmatgozar (2014), studies on IT adoption are typically classified into three categories, namely relational, descriptive, and comparative studies. Relational studies aim to investigate causal relationship of variables that influence IT innovation adoption. Descriptive studies identify the characteristic and opinion of IT adopters, adoption challenges, and characteristics of adoption. Whereas comparative studies analyse IT adoption by focusing on the evaluation of major variables, which comprises three sets of studies: population, distribution channel, and methods. This research applied relational approach to investigate the relationship between the independent variables; technological, organizational, and environmental determinants, and the dependant variable; MDM adoption by Malaysia local government. This research investigates the determinants that influence the MDM adoption by Malaysia local government at the organizational level. IT adoption research mostly categorised into three main adoption levels, namely organizational, individual, and team level (Salahshour, Mehrbakhsh, & Dahlan, 2017). Organization term in this research refers to a department unit as an entity that consists a group of people to achieve the same mission, vision, strategies, and goals (Miles, 2012). The level of analysis in this research includes the departments of Malaysia local government organizations. Based on the Malaysian Government Online Services Gateway model (Figure 1.3, page 6); MDM is classified into several clusters, such as business, education, and health. This research only focuses on MDM on the business cluster, in particular, the BLESS initiative. Business cluster is among the most important domains in the Malaysian public sector, which contributes to the ‘Doing Business’ assessment that includes the aspects of business regulation and their implications for firm establishment and operations (World Bank, 2018). Hence, the research only involved
22 department of Information Management, City Planning and Business Licensing and Petty Traders from 155 Malaysia local government bodies. The selection of these departments as potential respondents is based on the master data entity managed by these departments. These departments are responsible for managing master data regarding business registration and licensing. Sampling frame shows that there is a total of 465 departments of Information Management Department, Town Planning Department, and Business Licensing and Petty Traders Department from 155 Malaysia local government (Johor State Government 2017, KPKT Selected Statistics 2015). 1.9 Structure of the Thesis This thesis is structured into seven chapters. Chapter 1 gives an overview of the research. It introduces the research background, which briefly introducing the MDM, the Malaysia local government, and MDM adoption scenario by Malaysia local government organizations. It then provides the problem background and problem statement of the research, research questions, and research objectives. And finally outlines the significance of the research and the research scope. Chapter 2 review the literature and highlights the knowledge gaps in extant research to justify the novelty of this research. The chapter starts with a discussion of the key concepts by explaining the key terms. Subsequently, the chapter reviews related theories of IT adoption at the organizational level. Then, the chapter describes two systematic literature review (SLR) that have been conducted to identify related works within MDM research area and IT adoption in local government context. The chapter analyses a knowledge gap of previous studies to justify the rationale of the current research and proposes a conceptual model for a new MDM adoption model for Malaysia local government. At the end of the chapter, an initial conceptual model is proposed by discussing the theoretical underpinning and matrix analysis between two SLR. Chapter 3 discusses the research methodology followed for the overall research process to fulfil the research objectives and obtain the expected outcomes. It begins
23 with a discussion of research philosophy, research roadmap design, and research stages. Chapter 4 explains the process of the conceptual model development. It discusses the expert verifications on the initial conceptual model, research hypotheses, and operational definition. Chapter 5 presents the empirical data analysis of the research. First, initial preparation is described, including response rate analysis, data cleaning, non-response bias test, common method bias test, and normality test. Second, descriptive analysis of the demographics is presented. Third, the measurement model analysis is discussed, including internal consistency reliability, convergent validity, and discriminant validity. Fourth, the structural model analysis is discussed, including the collinearity, path coefficient, coefficient of determination, effect size, and blindfolding and predictive relevance. Fifth, since this research involves assessing the moderating effect of population on the relationship between citizen demand and MDM adoption, a moderation analysis is also presented. At the end of the chapter, the summary of hypotheses testing is presented. Chapter 6 presents the discussion of empirical findings of Chapter 5 and model evaluation process. The discussion of determinants of MDM adoption by Malaysia local government is discussed with regards to the technological, organizational, and environmental dimensions. Moreover, moderating effect of population on the relationship between citizen demand and MDM adoption is also elaborated. In evaluating the proposed MDM adoption model in Malaysia local government, the research suggests a set of guidelines and strategy of MDM adoption to the Malaysian public sector. The guidelines and strategy development and validation are discussed in Chapter 6. Finally, Chapter 7 concludes the thesis. It summarizes the findings according to the research objectives, and then it describes the research implications, research limitations followed by recommendations for future research.
25 CHAPTER 2 LITERATURE REVIEW 2.1 Introduction This chapter presents the literature review of the existing research and highlights the knowledge gaps in this research to justify the novelty of this research. First, the chapter begins with a discussion on the key concepts to provide the reader a clear understanding of key concepts of this current research (Section 2.2, page 26). Subsequently, the chapter reviews theories related to IT adoption at the organizational level (Section 2.3, page 42). Then it describes two SLR conducted to identify relevant works within MDM research body and IT adoption in local government context (Section 2.4.1, page 60 and Section 2.4.2, page 67). Next, the chapter highlights the knowledge gap in the existing research (Section 2.5, page 77) to rationalize the need for the research to develop a new model of determinants that influence the MDM adoption by Malaysia local government organizations. And finally, the chapter describes the initial conceptual model by explaining theoretical underpinning of this proposed model (Section 2.6.1, page 82) and a matrix analysis of the SLR is also presented (Section 2.6.2, page 83). Figure 2.1 presents the structure of the literature review.
26 Figure 2.1 Structure of the Literature Review 2.2 Definition of Key Concepts As it is often that the similar terms have been used in multiple studies, the concepts may vary among different research domains and context. To facilitate better understanding of terms used in this research, this section elaborates the key concepts and definitions of IT innovation adoption, master data, MDM, and MDM adoption. 2.2.1 IT Innovation Adoption The IT innovation adoption is a prior stage in which an organisation passes through before implementing a new innovation. According to Rogers (1995), IT innovation adoption refers to “the process through which an individual or other decision-making unit passes from first knowledge of an innovation to forming an attitude towards innovation, to a decision to adopt or reject, to implementation of the new idea, and to confirmation of this decision”. Most of previous research on IT
27 innovation adoption investigated and examined variables influencing IT adoption by the organization, individual and a group of people to accept and use the IT innovations. IT adoption is an extensive research in IS fields of study since the early 1980s. Based on the systematic review conducted by Salahshour, Mehrbakhsh and Dahlan (2017), studies on IT innovation adoption from the year 2006 to 2015 show a significant increase as illustrated in Figure 2.2. This growing trend is due to the emerging IT technologies, applications, and solutions. Figure 2.2 Distribution of IT adoption studies from the year 2006-2015 (Salahshour et al., 2017) Generally, IT adoption stages can be categorized into two: pre-adoption and post-adoption (Lin, 2014). On one hand, pre-adoption stage refers to the initial decision of the organizations to adopt IT innovation. On the other hand, the postadoption stage refers to the willingness of the organization to continue using the IT innovation after the implementation stage (Kamal, 2006). Figure 2.3 distinguishes the layer of the pre-adoption and post-adoption stage of IT innovation adoption in the organization.
28 Figure 2.3 IT innovation adoption stages: Pre-adoption and Post-adoption (Kamal, 2006) According to Hanafizadeh, Keating and Khedmatgozar (2014), studies on IT innovation adoption can be categorized into three main categories, namely descriptive, relational, and comparative studies. First, the descriptive study defines the landscapes and understandings of the technology adopters, challenges of adoption, and the potential characteristics of adoption. Second, the relational study investigates variables that influence the adoption of technology innovation and typically are based on established theories. Third, the comparative study analyses the comparison of major variables among the population, distribution channel, and methods. 2.2.2 Master Data Recently, the volume of data in one organization has increased prominently due to the advanced technology in capturing the data in various formats. Most of the existing data are in structured data formats, which classified into master data, transactional data, metadata, history data, and queue data. Among these categories, master data are the highest priority data that need to be managed, because they contain
29 valuable information about the organization (Nelke et al., 2015). Master data consist of core information of the organization about customers, suppliers, products and the relationships between them (Dreibelbis et al., 2008). Previous researchers defined master data from various different points of view. For example, Wolter and Haselden (2006), argued that master data include four important categories of business data, such as1) people including customers, employees, and salespersons; 2) things including products, parts, stores, and assets; 3) places including locations and geographic divisions; and 4) concepts including contracts, and licenses. They also described master data as commonly related to the transactional data; where master data represent the nouns and transactional data represent the verbs. As such, when a vendor sells a product, the vendor and the product belong to the group of master data, while the act of selling belongs to the group of transactional data. On the other hand, (Dreibelbis et al., 2008) defines master data as an enterprise-critical data which are consumed by multiple business functions, across business units, and between databases and decision support applications. Moreover, according to Otto & Huner (2009), master data contain information about related business entities in the organization, such as customers, products, and suppliers. Contrary to transactional data, master data entities are often unchanged and relatively constant, such as properties of the material. Master data are always being a reference for transactional data, and it can be concluded that there would not be a single transactional data without master data. Table 2.1 summarizes the key attributes of master data in the existing literature. Table 2.1 Key attributes of master data Attributes Wolter, R. & Haselden (2006) Dreibelbis et al. (2008) Otto & Huner (2009) Critical Business Data √ √ √ Reference for transactional data √ - √
30 Attributes Wolter, R. & Haselden (2006) Dreibelbis et al. (2008) Otto & Huner (2009) Rarely changed compare to transactional data - - √ Shared data across different systems and organizational units - √ - Examples Categorized into four (4) categories which are: 1) people; 2) things; 3) places; and 4) concepts Categorized into three (3) categories which are: 1) products; 2) parties; and 3) accounts Examples: customer, products or suppliers In summary, it can be concluded that master data are defined as critical business data in an organization, shared across several different systems or organizational units, serve as a reference for transactional data, and rarely changed. 2.2.3 Master Data Management (MDM) This section discusses the definition of MDM (Section 2.2.3.1, page 30), and Government-to-Citizen and MDM (Section 2.2.3.2, page 32). 2.2.3.1 Definition MDM is identified as one of the core functions of data management in DMBOK as described by DAMA (2009). It refers to the management of consolidation and integration of master data and the relationship between them is known as MDM. MDM is an emerging IS, an area which goes through a hype phenomenon like other IT innovation, such as ERP and Data Warehouses (Scheidl, 2011). The term MDM is
31 described in 2003 by Gartner Group in one of the research articles that analyses SAP newly product known at that time as SAP Master Data Management (MDM). The purpose of the product is to assist the organization in consolidating and harmonizing their master data, which consist of products, locations, customers and suppliers (White & Hope-Ross, 2003). The MDM product is designed to function as a central repository of master data across heterogeneous systems to promote business process integration and inter-enterprise visibility. Since then, the study of MDM continues to grow, both in the industry research, such as Gartner Group, SAP, Oracle, IBM and in the academic research. Shin (2006) defined MDM as a process of creating and maintaining the master data and the relationship between them. On the other hand, Dreibelbis et al. (2008) defined MDM as an approach in which architecture, technology, and business processes are combined to incrementally reduce the amount of duplicated information, and provide information to the consumers throughout an enterprise with authoritative master data. In addition, Smith and McKeen (2008) described MDM is an applicationindependent process which describes, owns and manages core business data entities. It ensures the consistency and the accuracy of these data by providing a single set of guidelines for their management and thereby creates a common view of key company data, which may or may not be held in a common data source. Whilst Cervo and Allen (2011) defined MDM as a merging master data from multiple sources together with the employment of data governance, data stewardship, data quality, metadata management, and master data lifecycle management to ultimately function as the single source of truth for the business. Overall, MDM is not just about the technology. It is about the management of shared master data at the central level, to reduce data redundancy and ensure better data quality, through standardized definitions with a combination of process, governance, and technology. It is functioned as a ‘single reference of truth’ to the consumers by combining and integrating master data from multiple data sources into a central system.
32 2.2.3.2 Government-to-Citizen (G2C) and MDM The public sector is often claimed to having difficulties in integrated and collaborative working in serving the citizen (Daglio, 2014). Relatively in Malaysian Public Sector, each agency still captures, stores and manages similar information and applications in their silo databases. For instance, citizens are required to key in same profile information repeatedly when they deal with different applications from several government agencies. This situation destructs customer experience when engaging with government services in fulfilling their needs, since it is inefficient and timeconsuming (Horwitz, 2015). To address this issue, the interaction between Government and Citizens is needed to be improved, or Government-to-Citizens (G2C) has to be more friendly, convenient, transparent, and low-cost (Hussein, Mohamed, Ahlan, Mahmud, & Aditiawarman, 2010). Master Data Management came into prominence to support Government-toCitizens (G2C) as a solution for data consolidation and centralization among government organizations. Applying MDM, the master data from multiple organizations, potentially valuable to government organizations can be identified and consolidated in a central repository. This repository is functioned as a ‘single source of truth’ to applications across organizations (Spruit & Pietzka, 2014). Government organizations are perceived to adopt MDM by participating as data providers to the MDM. The government organizations are expected to provide a high-quality master data to the central platform of MDM. By sharing master data of their organizations, it would encourage for data enrichment in the public sector via data integration at the central level. Hence the citizens are not required to key in the information repeatedly when they deal with different applications from several government agencies. Figure 2.4 shows how MDM system relates to the Malaysian Integrated Government Online Services Framework under the Government-to-Citizen model.
33 Figure 2.4 MDM and the Malaysian Integrated Governm
ment Online Services Framework (Government-to-Citizen)
34 MDM is designed to be an integrated system for the multiple data sources as a single unified view (Versini et al., 2013). Even though there are various architectures for MDM implementation (Baghi et al., 2014), typically, MDM system consists of four main modules, namely: 1) data integration module; 2) master data repository; 3) metadata repository; and 4) data quality module (Galhardas, Torres, & Damásio, 2010). Data quality module pre-processes the input data from the sources to the MDM system through data cleaning and data standardization techniques. This module is also performing Entity Resolution Process and Entity Identity Information Management. Data integration module then combines pre-processed data from data quality module to provide a unified view using schema mappings. Next, the master data repository stores the integrated master data processed by the data integration module. The metadata repository manages information about schema mappings between data sources and the master data repository (Lowry, Warner, & Deaubl, 2008; Luh et al., 2008). MDM is considered as an innovation in the public sector to support G2C implementation, which perceived to reduce data duplication, enhance data quality, enable broader data integration, and eliminate redundant integration practices among government organizations. In Malaysian Public Sector, few MDM projects have been established, such as central data repository for the individual profile (MyIdentity), a central data repository for bankruptcy status (e-Insolvency) and central data repository for government public key infrastructure (GPKI). In addition, the development of more central data repositories in Malaysian Public Sector is one of the national agenda in Eleventh Malaysia Plan, 2016-2020 (The Economic Planning Unit, 2016). It is expected to promote data de-duplication, data sharing and data utilization among various government agencies with a central data management platform. And ultimately to improve the efficiency of public service delivered to the customer, and to generate cost-saving to the government. The intention of developing more central data repositories in Malaysian Public Sector is also one of the action plans in Malaysian Public Sector ICT Strategic Plan, 2016-2020, under the strategy of Public Sector Data Management and Coordination in achieving the Strategic Trust of Data Centric Government (MAMPU, 2016b).
35 2.2.4 MDM Adoption This section describes MDM pre-adoption and post-adoption(Section 2.2.4.1, page 35), decision-making role of MDM adoption (Section 2.2.4.1, page 35) and MDM adoption challenges (Section 2.2.4.3, page 37). 2.2.4.1 Pre-adoption and post-adoption of MDM The success of MDM depends on the participation or adoption of multiple organizations as data providers. According to Gartner (2015), it takes only five to ten years to adopt MDM after the technology has been introduced. Low MDM adoption by data provider organizations leads to project failure due to the incomplete MDM repository. Based on Kamal (2006), there are two categories of IT adoption, namely pre-adoption and post-adoption. Figure 2.5 illustrates MDM adoption process at organization level, which includes eight phases of pre-adoption and post-adoption: 1) motivation towards innovation, 2) specific conception about innovation, 3) a formal proposal to the rest of the organization about innovation adoption, 4) actual adoption decision stage, 5) implementation of innovation in the organization, 6) confirmation of innovation idea, 7) user acceptance of the technology and 8) integration of innovative technology with other information system applications. Figure 2.5 Pre-adoption and post-adoption stages of MDM
36 Pre-adoption refers to the initial decision of the organizations to adopt the IT innovation. Pre-adoption begins with motivation stage, when an organization becomes aware of a specific innovation and attempts to acquire knowledge or understanding about the innovation (Kamal, 2009). In MDM context, the motivation phase refers to the motivation of the organizations towards the participation of MDM as data providers. Pre-adoption process is then followed by the conception stage which refers to action plans to be pursued by the organization. This stage is known as the persuasion stage, when an organization forms a favorable or unfavorable attitude towards innovation adoption (Kamal, 2009). The conception stage of MDM is presumably indicated when the organization’s members create an attitude towards MDM adoption, such as reviewing data structure and data quality in the organization. This process is followed by the proposal stage, which refers to the formal proposition for innovation adoption to the members of organization (Kamal, 2009). At this crucial stage, the departments responsible for making decisions to adopt any MDM innovation; need to substantially justify for approval from the organization. In addition, the departments also need to analyze their requirements and capabilities to participate in MDM innovation. And finally, the adoption decision is the vital stage where organizations take the decision to adopt MDM. Karahanna, Straub, and Chervany (1999) stated that the leading stages to the adoption decision as pre-adoption stages where the target behavior is the adoption of innovation, and the stages after the adoption decision are known as post-adoption stages where the target behavior is to continuously use of the innovation, including confirmation of MDM idea, user acceptance and actual use of MDM. 2.2.4.2 Decision-making Role of MDM adoption The decision-making role to adopt IT innovation varies in different organizations, depending on the nature or policy of the organization. The decisionmaking approach can be a top-down, bottom-up, and middle-out approach (Rahim, 2009). The top-down approach is when the decision to adopt is initiated by the top management. The bottom-up approach is when the employees such as IT personnel in the organization propose the implementation of IT innovation to the top management.
37 The middle-out approach is when the middle managers in the organization propose the implementation of the IT innovation to the top management. MDM adoption by organizations involves the willingness of the organizations or application owner to share their master data to the MDM repository. Data or application owners are typically the organizations or department units responsible for managing master data regarding their business. These master data are required to be shared at MDM central (Baghi et al., 2014). Loshin (2009) has highlighted that data or application owner is a key stakeholder and a ‘gatekeeper’ to MDM success. It is necessary to engage the data or application owner of the organization which normally leads by the senior managers during the adoption stage of MDM, in order to get their approval to share their department or organization master data. In addition, the participation of data or application owner is also very critical at the adoption stage to assist identifying MDM data requirements (Loshin, 2009). 2.2.4.3 MDM Adoption Challenges MDM adoption in Public Sector may expose to different challenges, since it is often involved multiple organizations, such as federal governments, state governments, and statuary bodies. According to Silvola et al. (2011), centralizing master data used across applications and organizationsis a very tough task. Centralized master data is the subset of “the single version of the truth” which means that the master data from the different applications are unified into one format and shared across organizations. Silvola et al. (2011) has characterized MDM challenges into three groups: data, process and IS as shown in Figure 2.6. The challenges of data include the vague definitions of master data and poor data quality among the business units and organizations. The challenges of process refer to the incoherent data management practices and inconsistent data quality practices. While the IS challenge involves the complexity of integration among systems.
38 Figure 2.6 MDM Challenges (Silvola et al., 2011) The problems of data quality occur because different agencies function independently, thus data duplication exists across local, state, and national boundaries. A survey conducted by Price Water House Coopers involving interviews with highranking representatives from 49 companies, 12 countries, and 8 different industries revealed that data quality is one of the challenges of MDM implementation (Orkisz, Wnek, & Joerg, 2010). The close relationship between the data quality and MDM exists to the same extent across all industries. Particularly in the public sector, a study by Spruit and Pietzka (2014) found that the main problems of MDM implementation in the public sector are data outdated, incomplete, incorrect and invalid data. This is due to the lack of data quality standards and guidelines in the sector for reference. In addition, challenges in adopting MDM can occur due to its complexity governance (Spruit & Pietzka, 2014). One of the challenges is the lack of policies in data sharing implementation across organizations, or inconsistent legal regulatory (Ziemba & Kolasa, 2015). Besides, the organization also exposes to the technical, operational and managerial changes during the adoption process (Loshin, 2008). In addition, Spruit and Pietzka (2014) pointed out that MDM implementation is a complex task. This comes from the complexity of a master data repository which is integrated through a service layer with applications throughout all the companies. Companies have different databases which feed into various systems. The relations between more than hundreds of systems are highly complex and not easy to see the source of the data used in one application. Data Process Information Systems
39 2.2.5 MDM Adoption by Malaysia Local Government Previous MDM initiatives have been developed involving Malaysia local government organizations as the main data providers such as BLESS and ePBT. However, the adoption of MDM by Malaysia local government remains at a very low pace. The adoption of MDM refers to the intention of the organizations to participate in the MDM initiatives by sharing their selected master data with the MDM repository. The following section describes each initiative and its adoption rate by the Malaysia local government. 2.2.5.1 Business Licensing Electronic Support System (BLESS) BLESS is an MDM initiative developed by Malaysian Implementation Coordination Unit in 2008, aimed to provide a one-stop center for firms or individuals to facilitate business licenses applications in Malaysia (ICU, 2017). Master data of business licensing (e.g. license name, license requirement, license fee, business type, and license validity) from government licensor agencies are consolidated into BLESS to help applicants to submit their applications and inquiries on a single portal. BLESS provides a processing services for application submissions, approvals, and tracking the issuance of business licenses. BLESS helps the government in future planning and service improvement through an analysis of centralized information in the repository. Figure 2.7 illustrates the BLESS architecture which involved four main layers i.e. the users, BLESS single window, MDM, and data providers. Over 10 years, only 3% of local government organizations have participated in this initiative as data provider (ICU, 2009, 2010, 2011, 2012, 2013, 2014, 2015). It is difficult to achieve the aim of BLESS to function as a one-stop center for firms or individuals applying for business licenses in Malaysia; if the local government organizations are not willing to share and integrate their master data about business licensing with the BLESS. Lower rate adoption by Malaysia local government organizations produced undesirable impact to the BLESS initiative, since most of the business licenses are provided by local government organizations, such as premise and
40 composite license, hawker license, advertisement license, and entertainment license, among others. Figure 2.7 BLESS architecture (Adapted from ICU, 2017) There are more than 30 business licenses provided by Kuala Lumpur City Hall (DBKL, 2018). However, only four licenses are available in BLESS and the rest are currently not available on BLESS. The steady adoption of BLESS by local government organizations had negatively affected the efficiency of government service delivered to the citizens. To apply for the unavailable licenses on BLESS, applicants need to browse different websites or applications of local government organization. 2.2.5.2 Electronic Pihak Berkuasa Tempatan (ePBT) ePBT is an MDM initiative developed by Ministry of Urban Wellbeing, Housing and Local Government in 2007, which merged accounts, taxation, esubmission and complaints services from Malaysia local government (KPKT, 2017b). The ePBT aims to help citizen by providing a single access to the online services across
41 local government organizations in Malaysia. Master data from the participated local government are consolidated into ePBT to make any application, complaint, and inquiry from the citizens available on a single portal. The master data from local government in ePBT repository include accounting data, complaint and feedback, and asset data. The ePBT architecture involves four main layers, i.e. users, ePBT portal, MDM, and data providers, as illustrated in Figure 2.8. Figure 2.8 ePBT MDM architecture (Adapted from KPKT, 2017b) From the year 2007 until now, there are four phases of adoption extension; Pilot (2007-2009), Phase 1 (2008-2010), Phase 2 (2011-2016), and Phase 3 (2007- present). Based on Technology Priority Matrix of Hype Cycle for Enterprise Information Management by Gartner, as shown in Figure 1.2, MDM initiative takes five to ten years to be adopted by the majority. Thus, over more than 10 years of ePBT implementation, less than 40% Local Governments in Malaysia have actually adopted ePBT (KPKT, 2017b). Figure 2.9 describes the trend of ePBT adoption by Malaysia local government. The trend of adopting ePBT is growing very slowly, especially from Phase 2 to Phase 3.
42 Figure 2.9 Adoption rate of ePBT by Malaysia local government (Adapted from KPKT, 2017b) 2.3 Theories of IT Adoption A theory is defined as a formal and logic statement describes a phenomenon. It can be considered as an expression of the relationship between concepts (factors, variables, or constructs) based on specific boundaries and assumptions (Zikmund, Babin, Carr, & Griffin, 2013). The main function of the theory is to help researchers to describe the phenomenon in a clearer and organized scheme, and to explain why or how the phenomenon occurred (Corley & Gioia, 2011). There are numerous theories used as theoretical underpinnings of most relational studies on IT adoption. According to Salah shour et al., (2017), the most cited theories are Technology–Organization– Environment (TOE) framework, Diffusion of Innovations (DOI), Fit-Viability Model, DeLone and McLean IS Success model (ISS), Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Theory of Planned Behaviour (TPB), Task-Technology Fit model (TTF), and Uses and Gratifications Theory (UGT). Given the list of the theories above, only four theories of IT innovation adoption are related to the organizational level, namely Technology–organization–
43 environment (TOE) framework, Diffusion of Innovations (DOI), Fit-Viability framework and DeLone and McLean IS Success model (ISS). The Remaining six theories, namely Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Theory of Planned Behaviour (TPB), Task-Technology Fit model (TTF) and Uses and Gratifications Theory (UGT) focus on the adoption at individual level. The following sub-sections discuss the principle of IT innovation adoption theories at the organizational level, followed by the discussion on the adoption at individual level. 2.3.1 Technology-Organization-Environment (TOE) framework The existing research suggest that the TOE framework is a prominent approach used by researchers to explain the determinants or factors influencing IT adoption in organizations (Oliveira & Martins, 2011). As shown in Figure 2.10, the TOE framework proposed by Tornatzky and Fleischer (1990) posited three dimensions that influence organization to adopt and implement IT innovation, namely technological, organizational, and environmental. These three dimensions significantly influence IT innovation adoption because they might be the explicit challenges and opportunities for the adoption (Saldanha & Krishnan, 2012). Figure 2.10 Technology-Organization-Environment (TOE) framework by Tornatzky and Fleischer (1990)
44 The technological dimension defines the characteristic of existing and emerging technologies relevant to the organization calling for the change (Baker, 2012; Tornatzky & Fleischer, 1990). This dimension includes the relative advantage, compatibility, and complexity of the innovation and security concern in the organization when adopting the innovation (Ahmadi, Nilashi, Shahmoradi, & Ibrahim, 2017). In addition, earlier researchers relate this dimension to the evaluation of benefits over the cost of adoption (Lin, 2014). The organizational dimension refers to the characteristics and resources of the organization. This includes personnel linking structure, communication process, organization demographics, and the organizational slack (Baker, 2012; Tornatzky & Fleischer, 1990). Linking structure of the personnel within the organization and communication process are commonly formed in the governance of the IT innovation (Krishnan, Teo, & Lymm, 2017; Smallwood, 2014). Organization demographics include organization’s characteristic such as size, type, and structure (Awa & Ojiabo, 2016). Organization slack is often defined as “the cushion of potential resources that allows the organization to successfully adapt the internal pressures for adjustment, or to external pressures for change in technologies” (Lawson, 2001). This entails that organization slack includes the resource (i.e. human, and infrastructure) availabilities and capabilities within the organization. The environmental dimension refers to the context in which the organization conducts its business. This comprises interactions with the government (i.e. through regulation), competitors, and clients (Baker, 2012; Tornatzky & Fleischer, 1990). In addition, a study by conducted Ahmadi et al. (2017) argued external pressure to be an environmental factor, which includes mimetic pressure from competitors, and coercive pressure from the government. The TOE framework has been applied in numerous IT adoption studies. It provides a flexible and useful analytical framework that can be employed to explore the adoption and assimilation of different kinds of IT innovation. Each study has defined its own independent variables that typically classified into technological, organizational, and environmental characteristic. Due to its flexibility and usefulness
45 as an analytical framework, the TOE has been consistently used as underpinning theory in recent studies to investigate IT adoption as presented in Table 2.2. Table 2.2 Recent studies that used TOE framework as an underpinning theory No. Researcher IT innovation Context Independent Variable(s) (T- Technological, O- Organizational, E- Environment) 1. (Ahmadi et al., 2017) Hospital Information System Hospitals in Malaysia T- Relative Advantages, Compatibility, Complexity, Security Concern, O- IS Infrastructure, Top Management Support, Hospital Site, Financial resources E- External pressure (Mimetic pressure competitors, Coercive pressure government), Vendor Support Human-Perceived technical competence of IS staff, Employees IS knowledge 2. (Krishnan et al., 2017) E-participation in egovernment Cross-countries T- ICT Infrastructure O- Governance E- Human Capital 3. (Awa & Ojiabo, 2016) ERP Small Medium Enterprise in Nigeria T- ICT Infrastructures, Technical know-how, Perceived Compatibility, Perceived Values, Security O- Size of the firm, Demographic Composition, Scope of Business Operations, Subjective Norms E- External support, Competitive Pressure, Trading Partners’ Readiness 4. (Wang & Wang, 2016) Knowledge Management Firms in Taiwan T- Perceived benefit, Complexity, Compatibility O- Sufficient resources, Technological Competence, Top management Support, Organizational Culture E- Competitive Pressure
46 No. Researcher IT innovation Context Independent Variable(s) (T- Technological, O- Organizational, E- Environment) 5. (Hsu & Lin, 2015) Cloud service Enterprise in Taiwan T- Relative advantage, Observability, Security O- Financial Costs, Satisfaction with existing IS E- Competition Intensity 6. (MacLennan & Van Belle, 2014) Serviceoriented Architecture Enterprise in South Africa T- Use of multiple standards and platforms, Compatibility, Size, Relative Advantages O- Top Management Support, Effective SOA Governance, Adequate Human and Financial resources, Intraorganizational, Interorganizational E- Vendor Influence, Vendor support for Integration and Development Tools, Industry pressure and IT media 7. (Lin, 2014) Supply Chain Management Firms in Taiwan T- Perceived Benefits, Perceived Costs O- Top Management Support, Absorptive Capacity, E- Trading Partner Influence, Competitive Pressure 8. (Hung, Chang, Lin, & Hsiao, 2014) Website Small Medium Enterprise in China O- Awareness of Corporate Website, Enterprise Resources, Technological resources E- Government e-readiness, Market Force e-readiness, Supporting Industries ereadiness 9. (Lian, Yen, & Wang, 2014) Cloud Computing Hospitals in Taiwan T- Data security, Complexity, Compatibility, Cost O- Relative Advantage, Top Manager’s Support, Adequate resource, Benefit E- Government Policy, Perceived Industry Pressure
47 Referring to Table 2.2, previous studies have demonstrated that TOE framework has been used in explaining the adoption of HIS, e-participation, ERP, knowledge management and cloud service. TOE has been used in various developing countries domains, such as Malaysia (Ahmadi et al., 2017), Taiwan (Wang & Wang, 2016) (Hsu & Lin, 2015) (Lian et al., 2014), and South Africa (MacLennan & Van Belle, 2014). In addition, the TOE framework also been used in an international or cross-countries context to examine e-participation in e-government (Krishnan et al., 2017). 2.3.2 Diffusion of Innovation (DOI) theory DOI theory which is developed by Rogers (1995) is frequently used in IT innovation adoption research. This theory is used to explain IT innovation adoption, in particular how, why, and what is the adoption rate (Rogers, 1995). DOI theory focuses on the spread of innovation over time among the members of social system (Rogers, 1995). The members of the social systems are defined as adopters, which can be individuals, groups, or organizations. The Rogers' concept of diffusion includes the technological variables of adoption determinant, adopter categories, and S-shaped curve. DOI theory proposed that innovation carriages certain technological characteristics which are; relative advantage, compatibility, complexity, trialability, and observability of adopters. According to Baker (2012), DOI theory is in line with the TOE framework in the elaboration of the technological dimension of the TOE framework. With regards to the adopter categories, DOI has categorized adopters into five categories: 1) Innovators - the first category to adopt an innovation. They are usually new, have great monetary flexibility and ready for risks, and have the closest contact with the innovation sources; 2) Early Adopters - the second category of individuals to adopt an innovation which has common characteristics of the innovators; 3) Early Majority – third category to adopt an innovation by changing the scope of time, but it is noteworthy longer than the innovators and early adopters. Early majority usually have contact with the early adopters; 4) Late Majority – the fourth
48 category to adopt an innovation with a high degree of doubtfulness and commonly adopt the innovation after the majority has already adopted the innovation. Late Majority typically have below average social status and very little monetary flexibility; and 5) Laggards – the last category adopters in which individuals in this category have a tendency to stay focus on traditions, lowest monetary flexibility and are the eldest among all the adopters. In addition, DOI theory also explains the adoption rate of IT innovation by evaluating the S-shaped curve. Innovation typically plotted in a normal, bell-shaped curve over time on a frequency basis of adopters (Rogers, 1995). If a cumulative number of adopters is plotted, the result is an S-shaped curve is as shown in Figure 2.11. The S-shaped slowly increases at the beginning when there are a few adopters in each duration, then rises to the maximum until half of the adopters have adopted the innovation, then, the curve decreases gradually as fewer lasting adopters adopt the ideas, processes, and technology innovations. Figure 2.11 S-Shaped curve of DOI by Rogers (1995) Since the early applications of DOI theory to IS research, the theory has been used and adapted in multiple ways. Recent studies that adapted DOI as an underpinning theory are described in Table 2.3. Percentage of adoption (%)
49 Table 2.3 Recent studies that used DOI theory as an underpinning theory No. Researcher IT innovation Context DOI Adaption 1. (Salum & Rozan, 2017) ERP Enterprises DOI technological variables - Relative Advantage, and Complexity 2. (Meske, Stieglitz, Brockmann, & Ross, 2017) Bring Your Own Device Enterprises in German DOI adopter categories - Innovators, Early Adopters, Early Majority, Late Majority, and Laggards 3. (Abualrob & Kang, 2016) E-commerce Companies in Palestine DOI technological variable - Perceived Complexity External variables - Government Instability, Occupation Restrictions, and Logistics Obstacles 4. (Annabi & Muller, 2016) Massive open online courses (MOOCs) International Branch Campuses in United Arab Emirates DOI phases– Awareness, Interest, Evaluation, Trial, and Adoption 5. (Gowanit, Thawesaengsk ulthai, Sophatsathit, & Chaiyawat, 2016) Mobile claim management Mobile claim motor insurance in Thailand DOI technological variables - Relative Advantages, and Information and guidance offered on mobile devices (external) 6. (Penjor & Zander, 2016) Virtual learning environment Colleges of Royal University of Bhutan DOI technological variables - Relative Advantage, Compatibility, Complexity, Trialability, and Observability 7. (Alomari, 2016) E-voting Citizen in Jordan DOI technological variables - Relative Advantage, Compatibility, and Complexity 8. (Alraja, Hammami, & Alhousary, 2015) E-government services Citizen in Dhofar region in south Oman DOI technological variable - Relative Advantage Previous studies in Table 2.3 demonstrated that the DOI has been used in explaining the adoption of ERP, Bring your own device, e-commerce, and e-
50 government services. Most of the studies adapted DOI technological characteristics of relative advantage, compatibility, complexity, trialability, and observability (Alomari, 2016; Alraja et al., 2015; Gowanit et al., 2016; Penjor & Zander, 2016; Salum & Rozan, 2017). Annabi and Muller (2016) adapted DOI phases which are awareness, interest, evaluation, trial, and adoption to investigate the adoption of Massive open online courses (MOOCs). While Meske et al. (2017) adapted DOI adopter categories such as innovators, early adopters, early majority, late majority, and laggards to study ‘Bring Your Own Device’ adoption. Although DOI theory has solid theoretical foundations and consistent empirical support in the literature of IT innovation adoption, its applicability to explain the adoption at organizational level has received substantial criticism from the earlier researchers (Lundblad, 2003). This is due to the fact that DOI ignores the influence of organizational and environmental factors in investigating IT adoption (Lee & Cheung, 2004). Hence, it is suggested that DOI theory alone is less suitable to explain IT adoption at organizational level. 2.3.3 Fit-Viability Model Fit-Viability Model (FVM) is a theory of innovation adoption at the organizational level. FVM was initially proposed by Tjan (2001) aimed to evaluate the adoption of e-commerce initiative. It was formulated with two main dimensions that influence the IT innovation adoption, namely Fit and Viability. Earlier work by Liang and Wei (2004) expanded FVM by integrating the theory of Task-Technology Fit (Goodhue, Thompson, & Goodhue, 2013). The new framework suggested that the fitness of the innovation is influenced by the individual task and technology characteristic, while the viability of the organization is influenced by capital needs or economic, organizational and society norms. Figure 2.12 illustrates the Fit-Viability framework proposed by (Liang & Wei, 2004).
51 Figure 2.12 Fit-Viability framework by Liang and Wei (2004) The first dimension in the framework is ‘Fit’. Fit refers to the extent of the IT innovation capabilities to meet the organizational culture and nature. The fit is higher when the organization task is performed and technology characteristic is suitable for the organization (Salum & Rozan, 2017). The second dimension in the framework is ‘Viability’. Viability refers to the organization readiness in terms of human resource requirement and IT infrastructure to adopt the innovation (Wang & Wang, 2016). Viability is influenced by economic, IT infrastructure, and organization factors. Economic factor includes special investment, uncertainty, and frequency of usage., The organization factor refers to top management support and process re-engineering. And societal factor refers to the general environment in which the innovation is implemented (Liang, Huang, & Yeh, 2007). The FVM has been used in IT innovation adoption studies in various research domain, such as in mobile commerce (Liang et al., 2007), ERP (Salum & Rozan, 2017), and cloud computing (Mohammed, Ibrahim, & Ithnin, 2016). 2.3.4 DeLone and McLean IS Success (ISS) Model The main objective of DeLone and McLean IS Success model (ISS) was to describe a comprehensive understanding of the achievement of an information system implementation by studying the relationships among six critical constructs of success; information quality, system quality, service quality, system use/usage intentions, user satisfaction, and net system benefits. Based on both, the process and causal
52 consideration, these six constructs were proposed to be interrelated among each other, rather than independent. Figure 2.13 shows the relationship of the constructs in DeLone and McLean IS Success (ISS) Model. Figure 2.13 IS Success Model (DeLone & McLean, 1992) Despite the ISS model is widely known to measure the implementation of an information system, it might be more appropriate for IT innovation implementation, or post-adoption stage rather than the pre-adoption stage. The main purpose of the ISS model was to evaluate the success of IS implementation by measuring user satisfaction and intention to continue using the system using multidimensional and interdependent constructs. 2.3.5 Theory of Reasoned Action (TRA) TRA is originally a theory of psychology introduced to study user behaviour (Ajzen & Fishbein, 1980). TRA's principle aim is to distinguish the users to execute certain behaviours after consciously considering the consequences of such behaviour (Ajzen & Fishbein, 1980). This situation indicates that users can rationally think and use existing information systematically to make decisions and take action.
53 TRA consists of three main constructs: behavioural intention, attitude toward behaviour and subjective norm. Within the constructs, TRA focuses on behavioural intention as individual’s voluntary behaviour or basic motivation towards the next 'actual behaviour' of the user. Behavioural intention depends on the attitude toward behaviour and subjective norm constraints to respond to certain behaviours. Attitude toward behaviour refers to user's positive or negative assessment of the object, action or event (Fishbein & Ajzen, 1975); and can be expected to be clearly based on the user's trust, knowledge, or experience (Ajzen & Fishbein, 1980). In other words, attitude toward behaviour are related to the user's assessment of behaviour whether it is good or not. For example, if a user has a good perception of an event, then positive attitudes may affect the user's behavioural intention. Meanwhile, subjective norm refers to social influences on users regarding certain behaviours (Fishbein & Ajzen, 1975). This social influence consists of a number of human groups, such as society, family and friends. Figure 2.14 shows the relationship between constructs ofTRA. Figure 2.14 Theory of Reasoned Action (TRA) Model (Ajzen & Fishbein, 1980) TRA is well-known in social psychology as it seeks to provide a clear understanding on the effect of positive or negative judgments and human societies on consumer behaviour. However, TRA has limitations, because the decision to conduct a behaviour is influenced by the behaviour that has reason only, which the user knows the consequences of the action taken (Conner & Armitage, 1998). This situation indicates that TRA cannot explain the behaviour that has no reason, which the user does not know the good or bad effect of the action. Attitude Toward Behaviour Subjective Norm Behavioural Intention Actual Behaviour
54 2.3.6 Technology Acceptance Model (TAM) TAM is broadly used to explicate an information system adoption at the individual level. TAM was first proposed by Davis (1989) to describe the individual’s acceptance within the organization after an information system was introduced. The development of TAM is based on the previous TRA constructors but excluded subjective norm due to the uncertainty of theoretical and psychometric status to describe the constructs (Davis, 1989). Davis added new constructs of perceived usefulness and perceived ease to use in TAM to examine attitudes towards use and behavioural intention. Perceived usefulness refers to users believe about the use technology to enhance job performance. While perceived ease to use refers to users believe about the use of technology to reduce workload (Davis, 1989). The relationship between the new constructs in TAM can be presented in Figure 2.4. Figure 2.15 Technology Acceptance Model (TAM) (Davis, 1989) Figure 2.15 shows the direct and indirect relationship of perceived usefulness with behavioural intention; and the relationship of perceived ease of use with attitude towards use. However, Venkatesh and Davis (1996) shows that the final model of TAM abolishes the 'attitude towards use' construct because: (i) attitude towards use cannot fully function as intermediary between perceived usefulness and perceived ease of use with behavioural intention; and (ii) weak relationships between actual usage and attitude towards use. On the other hand, the perceived usefulness has a significant impact on the behavioural intention. TAM is a strong and parsimony model because Perceived Ease of Use Behavioral Intention Actual Usage Perceived Usefulness Attitude Towards Use External Variables
55 can be applied and replicated across time boundaries, situations, populations and technologies (Venkatesh, 2000). 2.3.7 Unified Theory of Acceptance and Use of Technology (UTAUT) UTAUT was designed and proposed by Venkatesh, Morris, Davis, and Davis (2003) to provide theoretical acceptance and dissemination theory. UTAUT's theoretical development is based on the revision, comparison and integration of eight previous theories: TRA, TPB, TAM, Motivation Model, TAM-TPB, Model of PC Utilization, DOI and Social Cognitive Theory. This eight-theory integration is largely due to the majority of constructs used in TRA, TPB, TAM, MM, C-TAM-TPB, MPCU, DOI and SCT are overlapping and almost identical. Therefore, Venkatesh et al. (2003) combines these constructs to form a new unified theory. This unified theory has four main constructs: performance expectation, effort expectation, social influence and facilitating condition. These components have relationships with behavioural intention and use behaviour. UTAUT also assumes that the main construct is influenced by gender, age, experience and voluntariness of use to affect the predicted constructs (“behavioural intention” and “use behaviour”). Figure 2.16 shows the relationship between the main constructs, moderator constructs and predicted constructs in UTAUT. A systematic study done by Williams, Rana, Dwivedi, and Lal (2011) showed that most studies did not fully utilize all the constructs in UTAUT because not all of these constructs were used to examine the acceptance of consumers towards a new technology. Complementary to this, another studies done by Escobar-Rodr’iguez and Carvajal-Trujillo (2014) and Kijsanayotin, Pannarunothai and Speedie (2009) applied some of the constructs of UTAUT and combines these constructs with constructs from other theories.
56 Figure 2.16 UTAUT Model (Venkatesh et al., 2003) 2.3.8 Theory of Planned Behaviour (TPB) The TPB was introduced by Ajzen (1991) which is a continuation of the TRA theory. TPB has an additional construct of “perceived behavioural control” to address the situation of users who have no reasoning plan on behaviour. This perceived behavioural control construct is defined as the perception of the user's pleasure or difficulty in doing something (Ajzen, 1991); or perceptions of internal and external constraints on behaviour towards behaviour. Figure 2.17 shows the relationship of this new construct in TPB. The focus of TPB is on behavioural intention or intention construct. According to Ajzen (1991), the intention is considered to attract the motivational factor which in turn can affect the behaviour of the user. The higher the user intention for a behaviour is; the higher likelihood of the behaviour being performed. In TPB, this intention is influenced by attitude toward behaviour, subjective norm and perceived behavioural control responses, while the behaviour is influenced by the behavioural intention and perceived behavioural control. However, the behavioural intention constraints have great impact on behaviour rather than behavioural control impression, especially when
57 it involves the situation that the user has a complete reason or control over the behaviour as in TRA (Ajzen, 1991). Figure 2.17 Theory of Planned Behaviour (TPB) Model (Ajzen, 1991) On the other hand, if the full control of the behaviour does not occur, the relationship between perceived behavioural control and behaviour is significant and does not necessarily require behavioural intention as an intermediate construct (Madden, Ellen, & Ajzen, 1992). This condition shows that TRA is appropriate if the user has a high level of behaviour control; otherwise, the TPB is inappropriate if the user control level is low. 2.3.9 Task-Technology Fit model (TTF) One of the most important purposes of research in information systems field is to better understand the impact of technology on individual performance. Tasktechnology fit is a key model but often overlooked. TTF is fundamental in understanding the impact of information systems technology on individual
58 performance (Goodhue & Thompson, 1995). According, the success of an information system should be related to the fit between task and technology, whereby success has been related to individual performance and to group performance (Goodhue & Thompson, 1995). Goodhue & Thompson (1995) defined TTF as the “degree to which a technology assists an individual to perform their portfolio of tasks”. TTF is the matching of the capabilities of the technology and the demands of the task. Hence, it reflects the ability of information technology to support a task. TTF model has four key constructs, Task Characteristics, Technology Characteristics, which together affect the third construct, i.e., Task-Technology Fit. Task-Technology Fit construct in turn affects the outcome other variables, i.e., Performance or Utilization. TTF is an established theoretical framework in information systems research that enables the investigation of issues of fit of technology to tasks as well as performance. One significant focus of TTF is on individuals to assess and explain information systems success and its impact on individual performance. TTF relationships can form associations between tasks and technology use from a number of perspectives: improved performance; altered user perceptions, or increased user utilization (Goodhue & Thompson, 1995). The original TTF model is shown in Figure 2.18 as presented by (Goodhue & Thompson, 1995). Figure 2.18 Task-Technology Fit Model (Goodhue & Thompson, 1995)
59 TTF model supports the argument that when there is a fit between user task characteristics and characteristics of the IS, the utilization of the system and user performance are high (Kositanurit, Ngwenyama, & Osei-Bryson, 2006). In addition, this model suggests that technology adoption depends relatively on how well the new technology fits the requirements of a particular task. A technology is adopted if it is a good fit with the task it supports. The TTF model has been applied successfully in various research to predict acceptance of system adoption for various information systems (Klopping & McKinney, 2004). 2.3.10 Uses and Gratifications Theory (UGT) Uses and Gratification Theory (UGT) seeks to understand why people seek out the media and for what they use it (Ruggiero, 2000). The primary findings were motivated to categorize audience motivation for watching political programs during the 1964 election in United Kingdom (Katz, Blumler, & Gurevitch, 1973). UGT differs from other media effect theories. It assumes that individuals have power over their media usage, rather than positioning individuals as passive consumers of media. UGT explores how individuals deliberately seek out media to fulfil certain needs or goals such as entertainment, relaxation, or socializing. 2.4 Related Works on the Determinants of MDM Adoption in Local Governments To identify the determinants affecting the MDM adoption by local government organizations in Malaysia, this research performed two SLR. Both SLR was designed based on a systematic review methodology for IS research that is proposed by Okoli and Schabram (2010). The explanation of the SLR approach is presented in Section 3.4.2, page 99. First, the SLR was conducted on the literature in MDM research domain (Section 2.4.1, page 60). The results from the first SLR revealed that most of the MDM literature has focused only on the technical solution of implementation such as data quality, business intelligence, and data integration. Therefore, there is a dearth
60 of existing research focusing specifically on the adoption domain. Previous studies on MDM have also suffered from lack of a strong theoretical framework of MDM adoption. Hence, further SLR was conducted to support the result of the first SLR. Since the context of this research is local government, the second SLR was conducted on studies of IT innovation adoption in local government domain (Section 2.4.2, page 67). The results from the second SLR show that several studies have identified a set of determinants that influence the adoption of IT innovations such as e-government, eservices, cloud computing and social media. The results of both SLR were synthesized as a theoretical foundation for the proposed conceptual model of this research. This approach is consistent with Kamal, Hackney and Sarwar (2013) study, when two streams of review was conducted (i.e. enterprise application integration, and government context) in developing an enterprise application integration for local government. 2.4.1 Systematic Literature Review on MDM adoption determinants The first SLR (SLR A) was conducted on MDM literature domain to answer the review questions: 1) What is the status of the existing literature on MDM adoption? 2) What are the determinants that influence organization’s MDM adoption?. Initially, seven databases were used for this review: 1) ACM Digital Library; 2) Emerald; 3) IEEE; 4) Science Direct; 5) Scopus; 6) Springer Link; and 7) Web of Science. Then, the review also included Google Scholar to find more related studies on MDM. The selection of databases was based on MDM experts’ recommendation (Kitchenham & Charters, 2007) and the subscription of the databases at the university library (Salleh, Mendes, & Grundy, 2011). Consequently, three stages of formulating the search keywords suggested by Kitchenham and Charters (2007) were applied. This includes identification of alternative spellings and synonyms for major terms, identification of keywords in relevant papers or books, and the use of the Boolean OR operator to incorporate alternative spellings and synonyms. The keyword search strings were “master data”, “management”, “Master Data Management”, and “MDM”. The search strings were
61 then joined using “AND” and “OR” Boolean operators. The keyword search strings were then used as input to each database to retrieve studies based on the titles, abstracts, contents, and keywords, depending on the advanced search facility. The review only selected articles written in English and categorized under journals, proceedings, books, book chapters, and industry research. The searching process involved a retrieval of studies up to July 2016. Initially, 547 articles were identified from all seven academic databases, followed by searching from Google Scholar if the study is not indexed in the chosen academic databases. Ten (10) additional studies were found during the Google Scholar search process. Throughout the searching process, the metadata of all 557 identified studies were captured and recorded in a list in Microsoft Excel. The recording list comprised six (6) columns: 1) Electronic Database; 2) Title of the study; 3) Abstract; 4) Year; 5) study Type; and 6) DOI/ISBN/ISSN Number. Then, deduplication was performed to remove the duplicated copies of the identified studies exist across electronic databases (He, Li, & Zhang, 2010). During this process, 32 duplications were identified and eventually deleted. This reduced the list 525 unique studies only. To answer the first review question; What are the status of MDM adoption research existing MDM literature, text analysis was performed against the titles and abstracts of 525 unique studies to examine the common research topics of MDM. To ensure reliability, the text analysis filtered out the exact phrase of ‘master data management’ and stop words from titles and abstracts of the literature. Stop words in data analysis usually refer to the most common words in a language (e.g. ‘the’, ‘or’, ‘and’) (Rajaraman & Ullman, 2011). Table 2.4 indicates the most frequent phrases used in the literature titles and abstracts. These frequent phrases represent the most research topic in MDM existing literature. Most of the MDM literature focused on the technical implementation solution such as master data, data quality, business intelligence, and data integration.
62 Table 2.4 Frequent phrases in MDM literature title and abstract Phrases used in Title and Abstract Frequency master data 148 data quality 70 business intelligence 57 business process 47 data integration 41 big data 34 data governance 29 information governance 29 data management 28 product data 28 information systems 26 information management 25 business processes 21 data sources 21 best practices 19 data model 18 information quality 18 research highlights 18 customer data 17 case study 16 Furthermore, the text analysis was performed to identify studies on MDM adoption by searching the title and abstract of the study which contain the phrase of ‘adopt’ or ‘accept’ or ‘influence’ or ‘determinant’. Nevertheless, there have been small body of studies focusing particularly on MDM adoption. The analysis result identified only 11 articles that may be related to the MDM adoption. However, none of the studies has built a definitive model of MDM adoption at the organizational level, which investigates the causal relationships between determinants of MDM adoption. Figure 2.19 illustrates a visualization of knowledge gap of MDM adoption in the existing literature, based on the text analysis performed thereon. This shows that MDM adoption is an underexplored topic in MDM literature.
63 Figure 2.19 Knowledge gap of MDM adoption in the existing literature Subsequently, to answer the second review question, what are determinants that influence organization MDM adoption? quality appraisal was applied using practical screening against 525 studies. Practical screening refers to the activity of screening the titles and abstracts of studies to check the relevancy of the studies (Okoli & Schabram, 2010). Therefore, the quality appraisal was conducted to eliminate studies that do not meet the standard quality assessment (Okoli & Schabram, 2010). The quality assessment criteria involved the retrieval of full-text version of the studies (Can the full version of the article be retrieved?) (Rocha et al., 2017), whether the research support the undergoing study’s context (Is the main focus of the article is on MDM?) (Alam, Ahmad, Akhunzada, Nasir, & Khan, 2015), and description of factors that influence organizations MDM adoption (Does the article describe any factor that influences organizations MDM adoption?) Based on quality appraisal, eighteen qualified studies were selected for the analysis. Data extraction from these eighteen articles was then conducted to extract the determinants that affect organizations to adopt MDM. Table 2.5 lists the eighteen qualified articles on the determinants of MDM adoption. Most of the qualified MDM articles are conceptual (Alharbi, 2016; Bonnet, 2013; Dreibelbis et al., 2008; Duff, 2005; Loshin, 2009; Luh et al., 2008), and qualitative in nature, involving interviews (Baghi et al., 2014), focus groups (Otto et al., 2012; Smith & McKeen, 2008), or case studies (Cleven & Wortmann, 2010; Otto, 2012; Otto & Schmidt, 2010; Silvola et al., 2011; Spruit & Pietzka, 2014; Vilminko-Heikkinen & Pekkola, 2013). Remarkably, only one article takes a quantitative approach to conduct a survey (Haug et al., 2013).
64 In addition, none of the articles focused on examining the factors or determinants that influence MDM adoption. Table 2.5 Qualified articles from SLR on MDM adoption determinants Article ID Year Type References A1 2005 Conference (Duff, 2005) A2 2008 Book (Dreibelbis et al., 2008) A3 2008 Journal (Smith & McKeen, 2008) A4 2008 Journal (Luh et al., 2008) A5 2009 Book Chapter (Loshin, 2009) A6 2010 Conference (Otto & Schmidt, 2010) A7 2010 Conference (Galhardas et al., 2010) A8 2010 Conference (Cleven & Wortmann, 2010) A9 2011 Journal (Silvola et al., 2011) A10 2012 Journal (Otto, 2012) A11 2012 Journal (Otto et al., 2012) A12 2013 Conference (Vilminko-Heikkinen & Pekkola, 2013) A13 2013 Book (Bonnet, 2013) A14 2013 Journal (Haug et al., 2013) A15 2014 Conference (Baghi et al., 2014) A16 2014 Journal (Spruit & Pietzka, 2014) A17 2015 Conference (Piedrabuena et al., 2015) A18 2016 Journal (Alharbi, 2016) Data extraction process was performed against the 18 qualified articles to extract the factors that influence MDM adoption by the organizations. Data extraction was based on directive content analysis technique. Table 2.6 summarizes the data extraction result.
65 Table 2.6 Determinants of MDM adoption Determinant ID Dimensions Factors Article ID G1 Technological Relative Advantage A2, A3, A5, A10, A11, A12, A13, A15, A17, A18 G2 Technological Cost A2, A18 G3 Technological Security & Privacy A2, A17 G4 Technological Complexity A2, A3, A5, A9, A12 G5 Technological Data Quality A4, A7, A8, A9, A12, A13, A14 G6 Organizational Data Governance A1, A2, A3, A5, A6, A9, A12, A13, A14, A18 G7 Organizational Technological Competence A13, A16 G8 Organizational Top Management Support A9, A12 G9 Environmental Policy and Regulation A2, A11, A14 G10 Environmental Citizen Demand A2, A11 The review presents ten main determinants affecting MDM adoption. These factors can be categorized into three main dimensions; technological, organizational and environmental. The most striking result to emerge from the findings is that relative advantage, data governance, data quality, and complexity are the significant driving determinants of MDM adoption. This is followed by other important factors, such as policy and regulation, sufficient resources, customer influence, top management support, personnel competency, security and privacy, cost of MDM implementation, and organization structure. 2.4.1.1 Technological Technological dimension describes the characteristic of innovation adoption which includes the equipment, functionalities, cost, and methods to adopt technology (Tornatzky & Fleischer, 1990; Wisdom, Chor, Hoagwood, & Horwitz, 2014). Table 2.6 shows five factors in this dimension which have influenced the MDM adoption
66 which are: relative advantages (A2, A3, A5, A10, A11, A12, A13, A15, A17, and A18), cost (A2, and A18), security and privacy (A2, and A17), complexity (A2, A3, A5, A9, and A12), and data quality (A4, A7, A8, A9, A12, A13, and A14). Relative advantage refers to the degree in which MDM innovation could increase the return on investment (ROI), reduce operating costs, resolve current problems and receive a lot of benefits. Cost is the financial budget of MDM innovation implementing. Security and privacy is the degree in which MDM innovation could preserve their information confidentiality. Complexity refers to the degree in which the organization finds difficulty to implement MDM innovation. Data Quality is the degree of completeness, uniqueness, timeliness, validity, accuracy, and consistency of master data at the organization. 2.4.1.2 Organizational Organizational dimension includes the measures of the organization such as size and structure (Tornatzky & Fleischer, 1990; Wisdom et al., 2014). Table 2.6 presents three factors in this dimensions which are data governance (A1, A2, A3, A5, A6, A9, A12, A13, A14, A18), technological competence (A13, A16), and top management support (A9, A12). Data Governance refers to the comprehension level of predefined roles and responsibility in navigating the MDM innovation. Technological competence refers to the characteristic of technical and business personnel’ skills to operate MDM innovation. Characteristic of personnel or human within the organization to adopt the technology is one of the organizational dimensions (Ahmadi et al., 2017; Wisdom et al., 2014). Top management support refers to the degree in which the management supports MDM innovation adoption. 2.4.1.3 Environmental Environmental dimension is defined as the condition of fields in which the organization conducts its business. This includes internal and external pressure (Tornatzky & Fleischer, 1990; Wisdom et al., 2014). Table 2.6 shows two factors in
67 this dimension which are policy and regulation (A2, A11, and A14), and citizen demand (A2, and A11). Policy and Regulation refer to the existence of rules and procedure to adopt or implement the MDM innovation. Citizen Demand refers to the extent of customer demand and acceptance of the MDM innovation. 2.4.2 Systematic Literature Review on IT Adoption in Local Governments Since the first SLR revealed that there have been limited studies on MDM investigating the causal relationship between determinants affecting MDM adoption, and proposing MDM adoption models, a further review was conducted to support the first SLR. The second SLR (SLR B) was conducted by reviewing studies on IT innovation adoption in the local government context. IT innovations in local government have navigated their organizations to improve their productivity and efficiency. Besides offering substantial benefits to organizations, IT innovations require a strategic approach during the adoption phase before IT is being implemented. There are three dimensions of determinants which affect IT adoption in local government: technological, organizational and environmental (Tornatzky & Fleischer, 1990). The aim of this SLR was to identify determinants of IT adoption by local government. The proposed review question for this SLR was; what are determinants that influence IT adoption in a local government context? Four databases were selected: 1) Scopus, 2) Emerald, 3) Springer Link, and 4) Web of Science. The title, abstract and index terms were used to conduct the search for journals, proceedings, books, and book chapters. The number of selected databases is consistent with the review conducted by Shahrokni and Feldt (2013) which involved four databases and the selection of databases was based on the librarians’ and IS experts’ recommendations (Kitchenham & Charters, 2007). In addition, since IT adoption is frequently categorized as IS field of study, the selection of databases is indexed all top IS journals databases (AIS, 2018) . This review also applied Kitchenham and Charters (2007) approach to construct the search keywords. The initial search strings were “adopt”, “adoption”, “local”, “government”, “authorities”, “city councils”, and “municipal”. Next, the