68 search strings were combined using “AND” and “OR” Boolean operators. Using the defined search strings, the retrieval of articles was performed based on the titles, abstracts, and keywords, depending on the advanced search function provided by the database. The search criteria were involved papers written in English and the papers are categorized under journals, proceedings, books, and book chapters. In addition, since the factors inhibiting local governments change over time (Li & Feeney, 2014), the searching process only involved a retrieval of of studies conducted in the latest five years, from 2013 to 2017 (Craighead, Ketchen, Dunn, & Hult, 2011; Izquierdo, Olea, & Abad, 2014). Initially, 185 articles were identified from the selected databases. The search was then extended to Google Scholar database using a forward snowballing method (Kitchenham & Brereton, 2013), which identified additional 30 studies, increasing the total number of studies to 215. This is done to ensure that other studies which were not indexed in the selected databases were considered as well. Throughout the searching process, the metadata of 557 identified studies were captured and recorded in a list in Microsoft Excel. The metadata were: 1) electronic database, 2) title, 3) abstract, 4) year, 5) publication type, and 6) DOI/ISBN/ISSN number. Based on the metadata, deduplication was conducted to remove any redundant articles or studies (He et al., 2010). This is followed by a practical screening process to titles and abstracts select relevant studies. In this process, articles that discussed topics other than IT adoption such as policy, environment, energy, and product innovation adoption were removed. A quality appraisal was then conducted to eliminate studeis that do not meet the standard quality assessment criteria of (Okoli & Schabram, 2010). The quality assessment criteria in this review involved the retrieval of the full-text version of the studies (Can the full-text version of the article be retrieved?) (Rocha et al., 2017), whether the research support the undergoing study context (Does the paper explain the adoption relational factors of IT innovation at organizational level?), and (Does the context involves local government?) (Alam et al., 2015). According to Hanafizadeh, Keating and Khedmatgozar (2014), IT adoption studies can be classified into three categories: relational, descriptive, and comparative studies. This research only considered the relational studies of IT adoption, which aimed to discover adoption determinants or factors. During the quality appraisal, 22 articles were selected for the
69 analysis. This is followed by, data extraction against the nineteen qualified articles to extract the factors influencing IT adoption by local government. Table 2.7 describes 22 articles on the determinants of IT adoption in a local government context found in each article according to technological, organizational, and environmental (TOE) dimensions. Table 2.7 Qualified articles from SLR on IT adoption in local governments Article ID Domain Sample References B1 Social Media United States (Seigler, 2017) B2 Cloud Computing Australia (Ali et al., 2016) B3 e-Government Netherlands (Jans et al., 2016) B4 e-Services United States (Wang & Feeney, 2016) B5 Web Accessibility Standard Netherlands (Velleman, Nahuis, & van der Geest, 2015) B6 Social Media Australia (Sharif et al., 2015) B7 Electronic Health Record United States (McCullough, Zimmerman, Bell, & Rodriguez, 2015) B8 E-Democracy Turkey (Sobaci & Eryigit, 2015) B9 Social Media United States (Anderson, Lewis, & Dedehayir, 2015b) B10 Smart Grid Technologies United States (Dedrick, Venkatesh, Stanton, Zheng, & Ramnarine-Rieks, 2014) B11 e-Services International (Lagrandeur & Moreau, 2014) B12 Social Media United States (Rubin et al., 2014) B13 e-Services United States (Li & Feeney, 2014) B14 e-Services Netherlands (Dijkshoorn, 2013) B15 Enterprise application integration United Kingdom (Kamal, Hackney, & Ali, 2013) B16 Social Media United States (Harris, Mueller, & Snider, 2013) B17 Social Media United States (Reddick & Norris, 2013)
70 Article ID Domain Sample References B18 e-Government United States (Norris & Reddick, 2013) B19 e-Government Pakistan (Kamal, Hackney, & Sarwar, 2013) B20 Data Sharing United States (Welch, Feeney, & Park, 2016) B21 Social Media United States (Gao & Lee, 2017) B22 Cloud Computing China (Liang, Qi, Wei, & Chen, 2017) The segnificant finding of this SLR was that, social media adoption is the most popular IT domain been examined for the past five years within the context of local government (Anderson et al., 2015a; Gao & Lee, 2017; Harris et al., 2013; Reddick & Norris, 2013; Rubin et al., 2014; Seigler, 2017; Sharif et al., 2015). Followed by research on the adoption of e-government (Jans et al., 2016; Kamal, Hackney, & Sarwar, 2013; Norris & Reddick, 2013), e-services (Dijkshoorn, 2013; Lagrandeur & Moreau, 2014; Li & Feeney, 2014), and cloud computing (Ali et al., 2016; Y. Liang et al., 2017), and data sharing (Welch et al., 2016). Further, studies conducted in developed countries such as Australia (Sharif et al., 2015), United States (Gao & Lee, 2017; Harris et al., 2013; Li & Feeney, 2014; Reddick & Norris, 2013; Rubin et al., 2014; Seigler, 2017; Welch et al., 2016), United Kingdom (Kamal, Hackney, & Ali, 2013), Netherlands (Velleman et al., 2015), and China (Liang et al., 2017) are higher in number than studies in conducted developing countries such as Turkey (Sobaci & Eryigit, 2015), and Pakistan (Kamal, Hackney, & Sarwar, 2013). Subsequently, data extraction process was performed against the 22 qualified articles to extract the influencing factors of IT adoption at local government level. Based on the directive content analysis, Table 2.8 shows 33 determinants influencing IT adoption in local government, categorized into technological, organizational and environmental. The results of this SLR show that the majority of determinants of IT adoption in a local government context are organizational in nature (16 determinants), followed by the technological (8 determinants) and environmental (8 determinants).
71 Table 2.8 Determinants of IT adoption in local governments ID Dimensions Factors Article ID F1 Technological Relative Advantage B3, B5, B6, B10, B15, B19, B21, and B22 F2 Technological Complexity B11 F3 Technological Security B2, B11, B12, and B15 F4 Technological Cost B5, B8, B10, B15, B19, and B22 F5 Technological Compatibility B5, B6, and B11 F6 Technological Perceived Risk B4, B6, and B15 F7 Technological Data storage location B2 F8 Technological Availability of multiple providers B2, and B22 F9 Organizational Governance B2, B4, B5, B7, B19, and B22 F10 Organizational Top Management Support B1, B5, B10, B12, B15, B19, and B22 F11 Organizational Technological Competence B1, B2, B5, B4, B7, B10, B11, B12, B18, B15, B19, B21, and B22 F12 Organizational Organization experience B3, B10, B14, B17, and B18 F13 Organizational Organization size B3, B8, and B11 F14 Organizational Organizational structure B1, and B4 F15 Organizational Geographic location B9, B16, and B18 F16 Organizational Type of government B17, and B18 F17 Organizational Region of the country B17, and B18 F18 Organizational Citizen median household income B17, and B18 F19 Organizational Size of population served B12, B7, and B21 F20 Organizational Number of services offered B7 F21 Organizational Poverty indexed factor B7
72 The most prominent result that emerged from the data was that technological competence from the organizational dimension appears is the most influential determinant for IT adoption in local government, followed by policy and regulations, relative advantage, citizen demand, cost of the implementation, sufficient resources, personnel competency and knowledge, governance, organization experience, and others. 2.4.2.1 Technological Technological dimension defines the characteristic of the technology itself which includes the equipment, functionalities, cost and methods to adopt technology (Tornatzky & Fleischer, 1990; Wisdom et al., 2014). This review found that relative advantage was one of the most influential factors of IT adoption by local government from this dimension (B3, B5, B6, B10, B15, B19, B21, and B22). Relative advantage F22 Organizational Population education level B18 F23 Organizational Population racial composition B18 F24 Organizational Adaptability B2, B11, and B22 F25 Organizational Trust B22 F26 Environment Policy and Regulation B2, B3, B5, B6, B10, B12, B19, B20, and B22 F27 Environment Citizen Demand B4, B5, B6, B10, B13, B19, and B22 F28 Environment Political Influence B1, B3, B4, and B19 F29 Environment ICT Infrastructure B2, B11, B15, and B19 F30 Environment Technology Movement B6, and B8 F31 Environment Financial support B2, B11, and B22 F32 Environment Competition B10 F33 Environment Higher authority support B15
73 refers to the degree in which IT innovation could increase the return on investment, reduce operating costs, resolve current problems and receive many benefits. Local government is motivated to adopt a particular IT innovation when they perceive its benefits to their organization. On the other hand, the cost of implementing IT innovation also appeared to be the most influential factor for IT adoption by the local government as shown by previous studies. The financial budget needed for IT innovation implementation is crucial and influences local government IT innovation adoption, such as web accessibility standard (B5), e-democracy (B8), smart grid technologies (B10), enterprise application integration (B15), e-government (B19) and cloud computing (B22). Other factors influencing local government IT innovations adoption include security, compatibility, complexity, perceived risk, data storage location and availability of different providers. Security refers to the degree in which IT innovation could preserve information confidentiality. This review reveals that security factor plays an important role in local government decision of technology adoption (B2, B11, B12, and B15). Compatibility refers to the extent of compatibility between IT innovation and organization’s norms and culture. It is appeared to be significant in the adoption of web accessibility standard (B5), social media (B6) and e-services (B11). The extent of organization struggle to implement IT innovation is also a significant factor when adopting e-services by local government (B11). Perceived risks refer to the extent of probability of damage or loss. This is identified as one of the influencing factors when adopting e-services (B4), social media (B6) and enterprise application integration (B15). Data storage locations and availability of providers are influencing factors of cloud computing adoption by local government (B2 and B22). Data storage location refers to the location where the information is located, whether in-shore cloud or off-shore cloud. The availability of providers refers to the number of vendors or solution providers delivering the IT innovation to the organization.
74 2.4.2.2 Organizational Organizational dimension includes the measures of organization such as size, region and organizational structure (Tornatzky & Fleischer, 1990; Wisdom et al., 2014). This review found that organizational dimension represents the major factor of IT adoption in a local government context. The most prominent factor of this dimension is technological competence. Technological Competence refers to the ICT infrastructure readiness, personnel knowledge, skills, experience and a sufficient number of personnel to implement the technology (Wang & Wang, 2016). Personnel knowledge and innovation culture attitude were found to be the influencing factors of IT adoption in a local government context. Personnel knowledge, which refers to the level of personnel’s knowledge of IT innovation or equivalent technology, is found significant factor in the studies of IT adoption (B1, B2, B5, B7, and B15). Innovation culture attitude which refers to the degree in which IT innovation is accepted by organization’s personnel found to have a positive effect on the adoption of social media (B1 and B6) and smart grid technologies (B10). The existence of IT champion in the organization also plays an important role in influencing the local government to adopt smart grid technologies (B10) and enterprise application integration (B15). Personnel experience refers to the duration of personnel’s experience in operating the IT innovation or equivalent technology. This is also found to be a key factor in the adoption of social media (B1) and web accessibility standard (B5) by local government. Top management support seems to be one of the most influential factors of IT innovation adoption by local government. It refers to the extent in which management of local government organizations support IT innovation adoption. The review reveals that top management support influences the adoption of social media (B1, B12), web accessibility standard (B5), smart grid technologies (B10), enterprise application integration (B15), e-government (B19), and cloud computing (B22). Other influential factors are governance, organization experience, organization size, geographic location, type of government, size of the population, and citizen median household income. Previous studies have concluded that governance is a
75 critical factor for the local government to adopt IT innovation (B2, B4, B5, B7, B19, and B22). Governance refers to the predefined decision-making accountability, roles, responsibilities, and systematic processing in steering the IT innovation. Organization experience refers to the duration in which an organization has implemented the IT innovation or equivalent technology. Organization experience represents the capabilities of an organization to adopt technology based on its existing knowledge and experience, sometimes referred to as absorptive capacity (Vincent Homburg, 2013). This factor appeared relevant in local government context as an influencing factor in adopting e-government (B3 and B18), smart grid technologies (B10), eservices (B14), and social media (B17). Organizations with more experience in implementing equivalent technology have a higher motivation to adopt the same IT innovation compared to non-experienced organizations. Organization size which refers to the number of personnel in the organization also plays an important role in IT innovations adoption. This factor is significantly shown in recent studies in egovernment (B3), e-democracy (B8), and e-services (B11) respectively. Geographic location and type of local government are also identified as key factors in the adoption of IT innovations in a local government context. The location in which local government is found, whether it is in the city or districts influences the adoption of social media (B9 and B16) and e-government (B18). While the status of the local government whether it is a city, district, municipal, or county council influences the adoption of social media (B17), e-government (B18) and data sharing (B20). The size of the population which refers to the number of citizens served by the local government was also identified as one of the top motives of local government intend to adopt IT innovation such as electronic health record (B7) and social media (B12 and B21). Citizen median house-hold income is also one of the key factors in local government context which influences the adoption of social media (B17), and egovernment (B18, B19). The findings of this review also found that structure and region of the local government influence the adoption of social media (B1 and B17), e-services (B4), and e-government (B18). The region of the local government refers to place in which the organization is located, such as West, South, North Central, or Northeast of the
76 country, while the type of local government refers to the authority power within the organization, whether centralized or decentralized. The education of the population which refers to the percentage of graduates or those with professional degrees among the citizens served by the local government is the factor that associates with the adoption of e-government by local government (B18 and B19). The population racial composition which refers to the percentage of each race in the country served by the organization, is also appeared as relevant factor influencing the e-government (B18 and B19). Other factors that are positively influence IT innovations adoption of in local government context are adaptability, a number of services offered, poverty indexed factor, and trust. Adaptability refers to the extent in which an organization is able to respond to the need for change when implementing the IT innovation. Adaptability has a significant effect to the adoption of e-services (B11) and cloud computing (B22). The number of services offered to the citizens and citizen poverty index factors have a positive impact on the adoption of electronic health record (B7). A recent study of IT adoption in local government context reveals that trust is a critical factor for the local government to adopt cloud computing (B22). 2.4.2.3 Environmental Environmental dimension in this review is defined as the conditions in which local government conduct its business including those of their competitors and the government. The identified factors of this review are categorized into: policy and regulation, citizen demand, political influence, ICT infrastructure, technology movement, financial support, competition and higher authority support. The existence of policies and procedures to adopt or implement the IT innovation seems to be the most common factor influencing the local government to adopt IT innovations. This factor is reported in the studies on cloud computing (B2 and B22), e-government (B3), web accessibility standard (B5), social media (B6 and B12), smart grid technologies (B10) and data sharing (B20). On the other hand, citizen demand has an effect on eservices (B4 and B13), web accessibility standard (B5), social media (B6), smart grid
77 technologies (B10) and cloud computing (B22) adoption. Political Influence refers to the degree of political support for the local government to implement the IT innovation. Even though the political influence is an external pressure from third parties, this factor is found to be significant for local government when adopting social media (B1), e-government (B3 and B19) and e-services (B4). ICT Infrastructure, which refers to the extent of availability of ICT infrastructure to support the implementation of IT innovation is crucial factor in the adoption of cloud computing (B2), e-services (B11), enterprise application integration (B15) and e-government (B19). Technology movement which refers to the high proliferation of the IT innovation found to have a significant effect on the adoption of social media and edemocracy (B6 and B8). Financial support which refers to the existence of financial support from external parties (e.g. politicians or federal government) has a significant effect to the adoption of cloud computing (B2, B22), and e-services (B11). Only one study established that the level of competition among the local government plays an important role in the adoption of smart grid technologies (B10). Another study revealed that higher authority support is a critical factor to adopt enterprise application integration in local government (B15). Higher authority support is the level of moral support received from external authorities (e.g. politicians, federal government). 2.5 Knowledge Gaps Knowledge gaps or research shortcomings refers to the deficiencies in past literary works (Creswell, 2014). By performing the review on theories of IT adoption and two SLR as described in the previous section, this research identified three main gaps in the previous related works on MDM, IT adoption in the local government context, and TOE research. Table 2.9 indicates the knowledge gaps as identified in those three research domains.
78 Table 2.9 Knowledge Gaps Research Area Research Gap MDM There is a surprising lack of studies focusing specifically on MDM adoption (Figure 2.19, page 63). Prior studies on MDM focused more on implementation stage as compared to the adoption stage. None of the studies has built a definitive model of MDM adoption at the organizational level, which investigates the causal relationships between influential determinants and MDM adoption. There has been a lack of quantitative approach in MDM extant research. Most of the MDM studies are conceptual, and qualitative in nature, involving interviews, focus groups, or case studies. IT Adoption in Local Governments Lack of IT adoption literature in local government context explores the adoption of IT innovations in developing countries (Table 2.7, page 69). TOE Framework Lack of TOE research investigating inter-organizational adoption (e.g. MDM) and examining the internal relationship between the variables within a technological, organizational or environmental dimension. Referring to the text analysis of SLR A (Figure 2.19, page 63), there is a dearth of studies in existing research investigating MDM adoption. There is a lack of theoretical framework and empirical research on MDM adoption. Even though there is an increasing interest in MDM research, there are very little studies directly investigate the causal relationship between determinants affecting MDM adoption and proposing a definitive MDM adoption model. This scenario is consistent with the study of e-government initiatives by Rana, Dwivedi, and Williams (2013). They indicated that most of the e-government previous studies initially emphasized on the implementation rather that on the adoption. This is because the adoption is only realized sometime later when the technology implementation was introduced. Most of MDM previous studies investigated its technical implementation (Baghi et al., 2014; Otto et al., 2012; Otto, 2015), implementation approach (Vilminko-Heikkinen & Pekkola, 2013), maturity model (Spruit & Pietzka, 2014), implementation advantages and challenges (Alharbi, 2016; Piedrabuena et al., 2015), but not the MDM adoption itself. Former MDM models focus on MDM implementation phase rather than the adoption phase. As such, former MDM model proposed by Silvola et al. (2011) focused on the MDM implementation problem which outlines three main challenges
79 of managing one master data; data, information systems, and process. Besides, Haug et al. (2013) proposed a model of master data quality barriers that includes twelve barriers in achieving high-quality master data in MDM implementation. VilminkoHeikkinen and Pekkola (2017) in their study presented issues of MDM model and their dependencies throughout the implementation process which consists of fifteen issues. Figure 2.20 MDM challenges and preconditions model (Haug et al., 2013) Figure 2.21 Master data quality barriers (Silvola et al., 2011)
80 Figure 2.22 Master data management issues in the public sector (Vilminko-Heikkinen & Pekkola, 2013) Furthermore, there is lack of quantitative studies in MDM existing research (Haug et al., 2013). Currently, most of the MDM literature is 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). The potential reason for this scenario is because previous studies more focus on the implementation of MDM at a central level which leads to the problem of sampling size requirement if the study intends to use quantitative approach. According to Table 2.7 of SLR B, it indicates that there is a continuous interest to study IT adoption in a local government domain. However, there is lack of literature which explores IT adoption in developing countries (Kamal, Hackney, & Sarwar, 2013; Sobaci & Eryigit, 2015). In addition, the result also shows that many studies on IT adoption have been conducted to investigate the social media, e-government, and
81 e-services domain. Thus, the determinants of the MDM adoption t remain the outstanding area to be explored. According to Technology-Organization-Environment (TOE) framework proposed by Tornatzky and Fleischer (1990), IT innovation is influenced by technological, organizational, and environmental context. The TOE framework provides the primary theoretical foundation for many studies on IT innovation adoption at the organizational level. Particularly in developing countries, the TOE framework has been used to examine factors influencing the adoption of Hospital Information Systems in Malaysia (Ahmadi et al., 2017), ERP within Small-Medium Enterprises in Nigeria (Awa & Ojiabo, 2016), Knowledge Management within firms in Taiwan (Wang & Wang, 2016), Service-oriented Architecture within enterprises in South Africa (MacLennan & Van Belle, 2014), Supply Chain Management within firms in Taiwan (Lin, 2014), website within Small-Medium Enterprises in China (Hung et al., 2014), and cloud computing in enterprises (Hsu & Lin, 2015) and hospitals in Taiwan (Lian et al., 2014). However, although the TOE framework was used extensively in IT adoption studies at the organizational level, there is dearth of research investigating the intention of multiple organizations to adopt a new IT innovation, i.e., inter-organizational adoption. Most of the earlier studies on TOE only focus on the adoption of IT innovation from the perspective of a single organization (Baker, 2012). Baker (2012) in his review on TOE framework suggested that interorganizational adoption, such as MDM is a potential area of TOE research. Additionally, Mohammed, Huda and Maslinda (2014) proposed that future studies are needed to examine the adoption of inter-organizational information sharing in the public sector, which is influenced by technological, organizational, and environmental factors. Inter-organizational adoption studies using TOE framework could provide valuable insights for practitioners promoting IT innovation, involving multiple organizations (Allen, Karanasios, & Norman, 2014). Furthermore, because TOE framework defines only the causal relationship between the constructs under each TOE dimension and IT innovation adoption, further research is needed to examine the internal relationship between the variables within a technological, organizational or environmental dimension.
82 Thus, the rationale to develop a model of determinants influencing the MDM adoption by local government organizations in Malaysia was based on the knowledge gap analyses described above. The gap analyses advocate the development of the proposed model due to the three justifications; 1) MDM adoption is an underexplored topic in MDM literature, 2) there is a lack of literature on IT adoption exploring local government context in developing countries, and 3) there is a lack of TOE research investigating inter-organizational adoption (e.g. MDM) and examining internal relationship between the variables within technological, organizational or environmental dimensions. Therefore, this research attempts to address these gaps by developing a new model of the determinants that influence the MDM adoption by local government. 2.6 Initial Conceptual Model 2.6.1 Theoretical Underpinning The initial conceptual model of this research presents the potential determinants that influence MDM adoption by Malaysia local government. Based on the discussion of IT adoption theories (Section 2.3, page 42), TOE framework, DOI theory, and Fit-Viability are selected as the underpinning conceptual models. The selection of those theories were based on the appropriateness of theories to explain the adoption of IT innovation at organizational level. Since this research attempts to be the first research in exploring the influential determinants of MDM by local government, the flexibility in determining the variables in each technological, organizational, and environmental dimension of TOE framework is justified to be an appropriate foundation for this research. Furthermore, TOE framework is valid, robust enough, and most dominant in studying organizational-level adoption (Awa & Ojiabo, 2016). TOE framework also has a solid theoretical and reliable empirical foundation in understanding IT adoption at the organizational level (Oliveira & Martins, 2011). The application of TOE framework as underpinning theory is consistent with the current studies on e-government adoption (Krishnan, Teo, and Lymm 2017), ERP (Awa & Ojiabo, 2016), and knowledge management (Wang and Wang 2016).
83 Similarly, as DOI theory acknowledges the TOE theory by elaborating variables in the technological dimension, the initial conceptual model also incorporated two variables from DOI theory by Rogers (1995) into the conceptual model. The variables are relative advantages and complexity from the technological dimension. Furthermore, other variables of the DOI theory which are compatibility, trialability, and observability are not suggested by the existing literature due to the nature of MDM implementation, which is complex (Dreibelbis et al., 2008; Smith & McKeen, 2008) and requires a high-cost investment (Alharbi, 2016). In addition, this research also incorporated the Fit-Viability model in elaborating the relationship within the organizational dimension of TOE framework. In existing research, most of the TOE studies only examine the direct relationship between TOE constructs and IT innovation adoption, such as adoption studies of Hospital Information System (Ahmadi et al., 2017), e-Government (Krishnan et al., 2017), and Knowledge Management (Wang & Wang, 2016). Occasionally, TOE studies examine the internal relationship among constructs within the TOE dimension. Hence, based on the Fit-Viability model, the initial conceptual model also outlined a relationship between top management support and other organizational determinants, i.e. data governance and technological competence. It identifies top management support as a cornerstone to the other organizational variables. To sum up, top management support to adopt MDM positively affects data governance and technological competence in local government organizations. 2.6.2 Analysis Matrix of Systematic Literature Reviews Kitchenham and Charters (2007) stated that SLR is an appropriate approach in positioning new research activities aim to develop a new model or framework. Based on the selection of underpinning theories of TOE framework, DOI theory, and FitViability model, the SLR results of MDM adoption (Section 2.4.1, page 60) and IT innovation adoption in local government context (Section 2.4.2, page 67) were further analysed. The determinants from both SLRs were mapped to the TOE framework, DOI theory, and Fit-Viability model as presented in Table 2.10. Table 2.10 summarizes the
84 matrix analysis of the SLR’s result from Table 2.6 and Table 2.8, and the decision to adopt the determinants in the initial conceptual model. Determinants that appeared in both SLR were adopted in the initial conceptual model. Similarly, this decision is consistent with Kamal, Hackney and Sarwar (2013), in which two streams of review (i.e. enterprise application integration, and government context) were conducted. According them, factors that exist in both SLR were adopted in developing an enterprise application integration adoption model for local government.
85 Table 2.10 Analysis Matrix o Related Theories Determinants A 1 A 2 A 3 A 4 A 5 A 6 A 7 A 8 A 9 A 10 A 11 A 12 A 13 A 14 A 15 A 16 A 17 A 18 B 1 B 2 B 3 (Duff, 2005) (Dreibelbis et al., 2008) (Smith & McKeen, 2008) (Luh et al., 2008) (Loshin, 2009) (Otto & Schmidt, 2010) (Galhardas et al., 2010) (Cleven & Wortmann, 2010) (Silvola et al., 2011) (Otto, 2012) (Otto et al., 2012) (Vilminko-Heikkinen & Pekkola, 2013) (Bonnet, 2013) (Haug et al., 2013) (Baghi et al., 2014) (Spruit & Pietzka, 2014) (Piedrabuena et al., 2015) (Alharbi, 2016) Seigler 2017 (Ali, Soar, and Yong 2016) (Jansetal 2016) TOE(T), DOI Relative Advantage • • • • • • • • • • TOE(T), DOI Complexity • • • • • • TOE(T) Security • • • TOE(T) Cost • • TOE(T) Compatibility TOE(T) Perceived Risk TOE(T) Data storage location • TOE(T) Availability of multiple providers • TOE(T) Data Quality • • • • • • • TOE(O), FVM Governance • • • • • • • • • • TOE(O), FVM Top Management Support • • • TOE(O), FVM Technological Competence • • • • • • •
of Systematic Literature Reviews B 4 B 5 B 6 B 7 B 8 B 9 B 10 B 11 B 12 B 13 B 14 B 15 B 16 B 17 B 18 B 19 B 20 B 21 B 22 Frequency SLR A SLR B Decision (Jans et al., 2016) (Wang and Feeney 2016) (Velleman, Nahuis, and van der Geest 2015) (Sharif, Troshani, and Davidson 2015) (McCullough et al., 2015) (Sobaci & Eryigit, 2015) (Anderson et al., 2015b) (Dedrick et al., 2014) (Lagrandeur and Moreau 2014) (Rubin et al., 2014) (Li and Feeney 2014) (Dijkshoorn 2013) (Kamal, Hackney, and Ali 2013) (Harris, Mueller, and Snider 2013) (Reddick and Norris 2013) (Norris and Reddick 2013) (Kamal, Hackney, and Sarwar 2013) (Welch et al., 2016) (Gao & Lee, 2017) (Liang et al., 2017) • • • • • 15 ● ● Adapt • 7 ● ● Adapt • • • 6 ● ● Adapt • • • • • 7 ● ● Adapt • • • 3 ● • • • 3 ● 1 ● 1 ● 7 • • • • • 15 ● ● Adapt • • • • • • 9 ● ● Adapt • • • • • • • • • 16 ● ● Adapt
86 Related Theories Determinants A 1 A 2 A 3 A 4 A 5 A 6 A 7 A 8 A 9 A 10 A 11 A 12 A 13 A 14 A 15 A 16 A 17 A 18 B 1 B 2 B3 (Duff, 2005) (Dreibelbis et al., 2008) (Smith & McKeen, 2008) (Luh et al., 2008) (Loshin, 2009) (Otto & Schmidt, 2010) (Galhardas et al., 2010) (Cleven & Wortmann, 2010) (Silvola et al., 2011) (Otto, 2012) (Otto et al., 2012) (Vilminko-Heikkinen & Pekkola, 2013) (Bonnet, 2013) (Haug et al., 2013) (Baghi et al., 2014) (Spruit & Pietzka, 2014) (Piedrabuena et al., 2015) (Alharbi, 2016) Seigler 2017 (Ali, Soar, and Yong 2016) (Jansetal 2016) TOE(O), FVM Organization experience TOE(O), FVM Organization size • TOE(O), FVM Organizational structure • TOE(O) Geographic location TOE(O) Type of government TOE(O) Region of the country TOE(O) Citizen median house -hold income TOE(O) Size of population served TOE(O) Number of services offered TOE(O) Poverty indexed factor TOE(O) Population education level
B 4 B 5 B 6 B 7 B 8 B 9 B 10 B 11 B 12 B 13 B 14 B 15 B 16 B 17 B 18 B 19 B 20 B 21 B 22 Frequency SLR A SLR B Decision (Jans et al., 2016) (Wang and Feeney 2016) (Velleman, Nahuis, and van der Geest 2015) (Sharif, Troshani, and Davidson 2015) (McCullough et al., 2015) (Sobaci & Eryigit, 2015) (Anderson et al., 2015b) (Dedrick et al., 2014) (Lagrandeur and Moreau 2014) (Rubin et al., 2014) (Li and Feeney 2014) (Dijkshoorn 2013) (Kamal, Hackney, and Ali 2013) (Harris, Mueller, and Snider 2013) (Reddick and Norris 2013) (Norris and Reddick 2013) (Kamal, Hackney, and Sarwar 2013) (Welch et al., 2016) (Gao & Lee, 2017) (Liang et al., 2017) • • • • 4 ● • • • 4 ● • 2 ● • • • 3 ● • • • 3 ● • • 2 ● • • • 3 ● • • • 3 ● • 1 ● • 1 ● • • 2 ●
87 Related Theories Determinants A 1 A 2 A 3 A 4 A 5 A 6 A 7 A 8 A 9 A 10 A 11 A 12 A 13 A 14 A 15 A 16 A 17 A 18 B 1 B 2 B 3 (Duff, 2005) (Dreibelbis et al., 2008) (Smith & McKeen, 2008) (Luh et al., 2008) (Loshin, 2009) (Otto & Schmidt, 2010) (Galhardas et al., 2010) (Cleven & Wortmann, 2010) (Silvola et al., 2011) (Otto, 2012) (Otto et al., 2012) (Vilminko-Heikkinen & Pekkola, 2013) (Bonnet, 2013) (Haug et al., 2013) (Baghi et al., 2014) (Spruit & Pietzka, 2014) (Piedrabuena et al., 2015) (Alharbi, 2016) Seigler 2017 (Ali, Soar, and Yong 2016) (Jansetal 2016) TOE(O) Population racial composition TOE(O) Adaptability TOE(O) Trust TOE(E) Policy and Regulation • • • • • • TOE(E) Citizen demand • • TOE(E) Political Influence • • TOE(E) ICT Infrastructure • TOE(E) Technology Movement TOE(E) Financial support • TOE(E) Competition TOE(E) Higher authority support Note: TOE (T) – Technological dimension of TOE framework TOE (O) – Organizational dimension of TOE framework TOE (E) – Environmental dimension of TOE framework DOI (T) – Diffusion of Innovations FVM – Fit-Viability Model
B 4 B 5 B 6 B 7 B 8 B 9 B 10 B 11 B 12 B 13 B 14 B 15 B 16 B 17 B 18 B 19 B 20 B 21 B 22 Frequency SLR A SLR B Decision (Jans et al., 2016) (Wang and Feeney 2016) (Velleman, Nahuis, and van der Geest 2015) (Sharif, Troshani, and Davidson 2015) (McCullough et al., 2015) (Sobaci & Eryigit, 2015) (Anderson et al., 2015b) (Dedrick et al., 2014) (Lagrandeur and Moreau 2014) (Rubin et al., 2014) (Li and Feeney 2014) (Dijkshoorn 2013) (Kamal, Hackney, and Ali 2013) (Harris, Mueller, and Snider 2013) (Reddick and Norris 2013) (Norris and Reddick 2013) (Kamal, Hackney, and Sarwar 2013) (Welch et al., 2016) (Gao & Lee, 2017) (Liang et al., 2017) • • 2 ● • • 1 ● • 1 • • • • • • 12 ● ● Adapt • • • • • • 8 ● ● Adapt • • 4 ● • • • 4 ● • • 2 ● • • 3 ● • 1 ● • 1 ● Total determinants adapted in initial conceptual model 9
88 Figure 2.23 outlines initial conceptual model driven by the analysis matrix from both SLR of MDM and IT innovation adoption in local government. Building on TOE framework, DOI theory and Fit-Viability model, nine determinants were chosen to be adopted in the initial conceptual model. Four determinants are from technological dimension (i.e. relative advantage, complexity, cost, and data security). From these technological determinants, relative advantage and complexity are the variables from the DOI theory. Three determinants are from organizational dimension (i.e. governance, top management support, and technological competence). According to Fit-Viability model, top management support is considered as an organizational factor that influence the viability or readiness of the organization, such as data governance and technological competence in adopting MDM. And two determinants from environmental dimension that potentially influence the MDM adoption which are policy and regulation, and citizen demand. Additionally, based on the Fit-Viability model, this research also proposed to examine the internal relationships within the organizational dimension. It is revealed top management support in previous studies is a critical factor in facilitating IT adoption such as ERP (Liang, Saraf, Hu, & Xue, 2007), offering technical assistance to address hardware and software challenges, encouraging activities through engagement, and allocating sufficient financial resources to the project (Dong, Neufeld, & Higgins, 2009). This research considers top management support as a cornerstone to data governance and technological competence in local government organizations. Top management support ensures the efficiency of data governance through their participation and responsibility in the governance committee. More importantly, further support from top management increases the technological competence of personnel through budget allocation for technical training and change management programs. Significantly, the relationship between top management support and other organizational determinants in this research appears to be a new addition to the existing studies on TOE research.
89 Figure 2.23 Initial C
Conceptual Model
90 2.7 Chapter Summary Throughout the literature review, key concepts, relevant theories and related works of MDM and IT adoption by local governments have been discussed thoroughly. Based on the review, this research demonstrated the knowledge gap in MDM that need to be addressed. The knowledge gaps justify the rationale of the research to develop a new model of determinants that influence the MDM adoption by Malaysia local government organizations. Based on the TOE framework, DOI theory, and Fit-Viability model as the underpinning theories and the analysis of SLR results, the initial conceptual model was proposed. The following Chapter 3 describes the research methodology and Chapter 4 continues discussing the development of a conceptual model which is based on the initial conceptual model that has been proposed.
91 CHAPTER 3 RESEARCH METHODOLOGY 3.1 Introduction This chapter discusses the research methodology which refers to the overall process involved in this research to fulfil this research objectives and obtain the expected outcomes in each phase. This chapter begins with a discussion of research design (Section 3.2, page 91). Subsequently, it elaborates research task, techniques, tools and outcomes of six phases involved in this research: Phase 1: Research Problem and Knowledge Gaps Definition (Section 3.3, page 96); Phase 2: Theoretical Foundation (Section 3.4, page 98); Phase 3: Conceptual Model Development (Section 3.5, page 101); Phase 4: Instrument Development and Data Collection (Section 3.6, page 106); Phase 5: Model Validation (Section 3.7, page 128); and Phase 6: Model Evaluation and Discussion (Section 3.8, page 136). 3.2 Research Design This research applies a quantitative approach using a survey with Partial Least Squares-Structural Equation Modelling (PLS-SEM) to validate the proposed model. The quantitative approach is a structured method of making a generalization to the whole population (e.g. country or region) by examining the relationship between variables (Creswell, 2014). The generalization in quantitative approach is very important in this research because the success of MDM depends on the participation or adoption by the whole population of selected data provider organizations. A roadmap of this research was designed based on the generic PLS-SEM research design proposed by Urbach and Ahlemann (2010). The purpose of a research roadmap is to use it as a guidance throughout the research process by structuring the research tasks into significant phases by listing its objectives, tasks and expected outcomes
92 (Papazoglou, Traverso, Dustdar, Leymann, & Krämer, 2006). The roadmap of this research has six phases; 1) research problem and knowledge gaps definition, 2) theoretical foundation, 3) conceptual model development, 4) instrument development and data collection; 5) model validation, and 6) model evaluation and discussion. Figure 3.1 defines the phases, inter-related tasks and research objectives of each phase. Phase 1 and Phase 2 were conducted to achieve Research Objective 1 (RO1) – “To identify the potential determinants that influence the MDM adoption by Malaysia local government”. Phase 3 was performed to achieve Research Objective 2 (RO2) – “To develop a new MDM adoption model in Malaysia local government”. Phase 4 and Phase 5 were performed to achieve Research Objective 3 (RO3) – “To validate the developed MDM adoption model in Malaysia local government through a survey with local government organizations”. Phase 6 was conducted to achieve Research Objective 4 (RO4) – “To evaluate the developed MDM adoption model in Malaysia local government by developing a set of guidelines and strategy for MDM adoption in Malaysia local government”.
93 Figure 3.1 R
esearch roadmap
94 Phase 1 is the beginning stage of the research which involves the definition of problem and knowledge gaps. These steps are very essential to justify the rationale for conducting the current research. These steps also lead to the formulation of research questions and research objectives. Therefore, this phase is important because it determines the appropriateness of the research approach (Saunders, Lewis, & Thornhill, 2008). Thus, PLS-SEM is applied to answer the following questions: 1) Are the positivism philosophy acceptable, and are the characteristics suitable?; 2) Is the phenomenon sufficiently understood so that the construction of a structural equation model is promising?; 3) Will it be possible to collect data for the required quality?; and 4) Is there enough PLS-SEM competency in the research team?. All these questions were answered positively, hence it is worth selecting PLS-SEM as a research design for this research (Urbach & Ahlemann 2010). Phase 2 is a continuum of Phase 1 in which the research continues to review existing studies according to the key concepts, IT adoption theories, and determinants influencing MDM adoption in local government context. This phase is a very crucial stage since PLS-SEM requires a strong theoretical foundation in developing a new causal relationship model. Without a strong theoretical foundation, the relationships between variables in the model will not be proved by the data and this does not reflect the actual causal relationship, but it is accidental correlations. Two SLR in both MDM and IT innovation adoption in local government context were conducted. Chapter 2 discusses the literature review and highlights the knowledge gaps in the existing research to justify the novelty of this research. The findings of this phase were incorporated in the development of the conceptual model in the next phase. Phase 3 is the stage in which the conceptual model was developed by designing a new model of MDM adoption by local government, which was based on the TOE framework, DOI theory and Fit-Viability model as underpinning theories. The determinants or variables of the model were identified based on the SLR. The initial version of the conceptual model was verified using expert verifications and then the hypotheses and operational definitions were developed. Chapter 4 presents the process of conceptual model development. It discusses the expert verifications on the initial conceptual model, research hypotheses, and operational definition.
95 In Phase 4, a survey instrument was developed for data collection based on the proposed conceptual model. In the survey, all constructs and the measurement items were adapted from the earlier research and were revised based on the content validity by experts to suit the context of this ongoing research. The survey instrument underwent the translation process and face validity to provide a quality basis for the translated version to be used for collecting data. Prior to actual data collection, a pilot test was conducted with 30 participants from local government organizations in Malaysia. Subsequently, the actual data collection was then conducted by distributing a survey to 465 potential respondents from local government organizations in Malaysia (Johor State Government 2017, KPKT Selected Statistics 2015). The survey was addressed to the head of departments of IT, business licensing, and town planning since this research only focuses on the master data related to the business domain of local government. These three departments were selected due to the master data they managed and the potential of master data to be shared with the MDM. The data collection obtained 224 valid responses to be analysed in the next model validation stage. In Phase 5, the conceptual model was validated by the valid responses of the data collection. The data analysis started with the initial data preparation by conducting response rate analysis, data cleaning, non-response bias test, common method variance test, and normality test. The responses were then analysed using descriptive and PLSSEM analysis including measurement and structural model analysis. Phase 6 is a final stage in which the data analysis results were interpreted and discussed to understand the MDM adoption determinants in Malaysia local government. Through the understanding of MDM adoption determinants in Malaysia local government, this stage also produced a set of guidelines and strategy of MDM adoption to the Malaysian public sector. To evaluate the developed MDM adoption model in Malaysia local government, this research proposed a set of MDM adoption guidelines and strategy for the Malaysian Public Sector. The model evaluation using a set of guidelines is consistent with a study by (Rahim, 2009) in understanding open source software appropriation process in the Malaysian Public Sector. Proposing guidelines and strategy approach, the slowness of MDM adoption issues could be
96 addressed which will impact the public service delivery effectiveness by improving data sharing and data integration among public sector organizations. Finally, research implications, limitation, and recommendation for future work are proposed and the overall conclusion is also provided. 3.3 Phase 1: Research Problem and Knowledge Gaps Definition Defining research problem and knowledge gaps are essential steps in the research. These definitions are very important because they give solid basis for a clear understanding of research results validity (Urbach & Ahlemann 2010). This research started with defining the research problem to guide the formulation of research questions and objectives, followed by the discussion of knowledge gaps in existing literature to justify the relevance of the research. Table 3.1 illustrates the tasks, techniques, tools, and outcomes of Phase 1. Table 3.1 Phase 1 – Research Problem and Knowledge Gaps definition Research Phase Tasks Techniques Tools Deliverables Phase 1: Research Problem and Knowledge Gaps Definition Defining Research Problem Literature Review Mendeley i. Problem Statement (Section 1.4, page 15) ii. Research Questions (Section 1.5, page 16) iii. Research Objectives (Section 1.6, page 17) Defining Knowledge Gaps SLR Text Analysis Microsoft Excel R-Studio (for text analysis) i. Knowledge Gaps (Section 2.5, page 77) ii. Text Analysis Result (Figure 2.19, page 63)
97 3.3.1 Defining Research Problem Defining a research problem is the most important step in any research activities (Urbach & Ahlemann 2010). The problem of MDM adoption by local government organizations in Malaysia was proven based on the adoption report of MDM initiatives (i.e. BLESS and ePBT) and by reviewing existing literature. According to O’Kane et al., (2014), despite the outward benefits of the MDM, the adoption rate by the organizations remains slow. Particularly in Malaysian public sector and local government context, after ten years of operating BLESS and ePBT, very few local governments have participated in these initiatives at only 3% and 40%, respectively. The adoption rate of local government’ participation in BLESS was extracted from the Implementation Coordination Unit annual reports from the year 2009 to 2015 (ICU, 2009, 2010, 2011, 2012, 2013, 2014, 2015). While the adoption rate of ePBT was extracted from the ePBT status report by KPKT (2017b). In addition, the adoption rate of local government’ participation in both initiatives was reconfirmed with the respective officer of the initiatives via email (Appendix B). From the research problem, three research questions were formulated along with four research objectives. 3.3.2 Defining Knowledge Gaps Knowledge gaps or research shortcomings refers to the deficiencies of past literary works (Creswell, 2014). Defining knowledge gaps is very crucial in justifying the rationale of the research being conducted. Knowledge gaps in this research were derived by performing two SLR as described in Section 2.4, page 59. This research identified four main knowledge gaps in earlier related works (Table 2.9, page 78). First, there is a surprising lack of studies on MDM literature focusing on MDM adoption. None of the studies has built a definitive model of MDM adoption at the organizational level, to investigate the causal relationships between determinants of MDM adoption. Second, there is a lack of quantitative studies in MDM existing research. Most of the MDM studies are conceptual (Alharbi, 2016; Bonnet, 2013; Dreibelbis et al., 2008; Duff, 2005; Loshin, 2009; Luh et al., 2008), and qualitative in nature and applied interviews, focus groups, or case studies. Third, there is a lack of
98 IT adoption studies which explore the adoption of IT innovations in local government context in developing countries. Fourth, none of the literature on IT adoption within the local government context investigate the adoption of MDM. Based on the identified knowledge gaps, this research attempts to address the gap by developing a new model of the determinants that influence the MDM adoption by local government organizations in Malaysia. 3.4 Phase 2: Theoretical Foundation A comprehensive review of literature was performed to better understand the theoretical foundation of the subject areas. First, the key concepts of the research and theoretical underpinnings were reviewed. Then, studies on MDM adoption and IT innovation adoption in the context of local government have been reviewed to develop the conceptual framework of the research. Table 3.2 describes the tasks, techniques, tools, and outcomes of Phase 2. Table 3.2 Phase 2 – Theoretical Foundation Research Phase Tasks Techniques Tools Deliverables Phase 2: Theoretical Foundation Defining key concepts and related theories Literature Review Mendeley i. Definition of key concepts (Section 2.2, page 26) ii. Theories of IT adoption (Section 2.3, page 42) Reviewing related works SLR Microsoft Word SLR on MDM adoption (Table 2.6, page 65) SLR on IT adoption in Local Governments (Table 2.8, page 71)
99 3.4.1 Defining Key Concepts and Related Theories Understanding the key concepts is very important at the early stage of any research. Because it will guide the use of keywords to search for related works from the databases. The key concepts were reviewed involving the IT innovation adoption, master data, MDM, MDM adoption by Malaysia local government, and challenges in adopting MDM (Section 2.2, page 26). Related theories on IT innovation adoption at the organizational and individual level were reviewed (Section 2.3, page 42). Based on the review, TOE framework, DOI theory, and Fit-Viability model were selected as an underpinning theory in developing a conceptual model. This is due to their flexibility and reliability as analytical framework to be employed when empirically studying the adoption of different types of IT innovation. 3.4.2 Reviewing Related Works Reviewing previous studies related to the MDM adoption provides a strong theoretical foundation to the conceptual model. In fulfilling this task, this research applied the SLR approach for IS field of study as proposed by Okoli and Schabram (2010). SLR is a structured way of conducting a review of existing research produced by earlier researchers from academic and industry community (Fink, 2013). Generally, researchers conduct SLR for a variety of reasons such as to analyse the progress of a specific research domain, to propose future research, to review the application of one theoretical or methodological approach or to develop a model or framework (Joseph, Ng, Koh, & Ang, 2007; Kitchenham & Charters, 2007). This research conducted two sequences of SLR. This review approach is consistent with Kamal, Hackney and Sarwar (2013) study. They conducted two streams of the review (i.e. enterprise application integration and IT adoption in government) in developing an enterprise application integration adoption model for local government. The first SLR (SLR A) was conducted on the literature in MDM research domain (Section 2.4.1, page 60). The SLR A shows a surprising dearth of studies focusing on the adoption domain. Previous studies of MDM have suffered from
100 lack of a strong theoretical framework on MDM adoption. Hence, further SLR was conducted to support the results of SLR A. Since the context of this research is local government, the second SLR (SLR B) was conducted on the studies of IT innovation adoption in local government context (Section 2.4.2, page 67).This research conducted SLR to synthesize the current state of MDM, define the knowledge gaps, and to identify related works in proposing potential determinants of MDM adoption by local government. As shown in Figure 3.2, four SLR steps have been applied in each SLR as follows: 1) research questions; 2) search strategy design; 3) study selection; and 4) analysis of findings. Figure 3.2 SLR steps applied in this research The review questions were formulated based on the review aim. Search strategies design which included the selection of databases, type of publication (e.g. journals, proceedings, books, and book chapters), article language, and keywords. The search process was performed using the search strategies and extended to Google Scholar database using forward snowballing method as proposed by Kitchenham and Brereton (2013). This is done to ensure that other articles which not indexed in the selected databases were covered as well. The forward snowballing method also ensures that latest related articles are able to continuously being reviewed to support this research. In addition, the deduplication process was performed to eliminate any duplicated copies of the articles (He et al., 2010). A practical screening process was conducted by screening the titles and abstracts of the articles to select only relevant articles. The quality assessment was conducted against the selected articles by
101 evaluating which articles are sufficient quality to be included in the review synthesis (Okoli & Schabram, 2010). And finally, data extraction from the qualified articles was conducted to produce the analysis of findings of the SLR. Directed content analysis was applied for data extraction as described by Hsieh & Shannon (2005). The directed content analysis starts with defining the theory or existing research as the basis for developing the initial coding groups. Further codes are developed, and existing codes are refined during content analysis. Technological, organizational, and environmental context definitions were used in both SLR as a foundation theory in defining initial coding groups. Using Microsoft word, the directed content analysis results of both SLR are tabulated in Table 2.6, page 65 and Table 2.8, page 71. Jointly, the results of both SLR were synthesized as a theoretical foundation for the conceptual model of this research. 3.5 Phase 3: Conceptual Model Development A conceptual model of this research consists of the potential determinants that influence MDM adoption by Malaysia local government. The conceptual model was based on the TOE framework, DOI theory, and Fit-Viability model as underpinning theories. Because this research attempts to be considered as the first research which explores the influential determinants of MDM by local government, the flexibility in determining the variables in each dimension of TOE framework is validated to be an appropriate foundation for this research. Building upon the matrix analysis of two SLR and verifications, a conceptual model of a new MDM adoption for Malaysia local government was proposed. Table 3.3 illustrates the tasks, techniques, tools, and deliverables of Phase 3.
102 Table 3.3 Phase 3 – Conceptual Model Development Research Phase Tasks Techniques Tools Deliverables Phase 3: Conceptual Model Development Constructing an initial conceptual model Analysis Matrix Microsoft Word i. Analysis Matrix of SLR A and SLR B (Table 2.10, page 85) ii. Initial Conceptual Model (Figure 2.23, page 89) Verifying and proposing a conceptual model Expert Reviews using questionnaire Microsoft Excel iii. List of experts for conceptual model verification (Table 3.4, page 104) iv. Expert reviews result for model verification (Table 4.1, page 143) v. Conceptual Model (Figure 4.5, page 153) vi. Theoretical Foundation of the Conceptual Model (Table 4.2, page 147) Developing hypotheses and defining operational terms Literature Review Microsoft Word i. Research Hypotheses (Section 4.3, page 154) ii. Operational Definition of the measurement terms (Table 4.3, page 162) 3.5.1 Constructing an Initial Conceptual Model The variables within technological, organizational, and environmental in the conceptual model were constructed based on the analysis matrix between the two SLR (Table 2.10, page 85). Determinants which occurred in both SLR A and SLR B were decided to be adapted in the initial conceptual model. This decision approach is adopted from Kamal, Hackney and Sarwar (2013), where they performed two streams of review of enterprise application integration and government context. The factors which exist in both streams were incorporated in developing an enterprise application integration adoption model for local government.
103 Nine determinants which existed in SLR A and SLR B were decided to be incorporated in the initial conceptual model (Figure 2.23, page 89). Four determinants were from technological dimension i.e. relative advantage, complexity, security, and cost, three determinants were from organizational dimension i.e. governance, top management support, and technological competency, and two determinants from environmental dimension i.e. policy and regulation, and citizen demand. The initial conceptual model also proposes the direct relationship between top management support and, data governance and technological competence. 3.5.2 Verifying and Proposing a Conceptual Model According to Ghobadi and Daneshgar (2010), it is important to have expert verifications for a consensus-building of a conceptual model. Although the initial conceptual model was proposed using SLR which is known as a structured and rigorous review approach, there is an argument on the quality appraisal stage of SLR (Okoli & Schabram, 2010). Moreover, since the data collection for this research involves local government organizations in Malaysia, the expert verifications should be conducted to verify whether the proposed determinants are suitable or applicable in Malaysia context or not. The expert verifications in this research involved five experts from public universities, local government, and central agency. The required number of experts is according to Thangaratinam and Redman (2005), which recommend a minimum of four experts should be involved to obtain reliable consensus of opinions. The experts were selected based on their expertise, roles in the agency, and experiences in MDM and IS as indicated in Table 3.4. Two experts are top management in selected local government organizations in Malaysia and two experts are academicians and researchers of IS in selected public universities of Malaysia. One expert is from a central government agency which has experience in MDM initiatives to improve government administrative efficiency.
104 Table 3.4 Experts involvement for initial conceptual model verification Expert Expertise Agency Roles Experience ID 30 years of experience in Technology Management Modeling and Instrument Lecturer and Researcher, Professor in Business School Malaysia Public University A E1 IS 20 years of experience in Information Science Lecturer and Researcher, Associates Professor in Faculty of Information Science & Technology Malaysia Public University B E2 IS 23 years of experience in Public Sector Public Sector ICT Expert (System Development) Central Government Agency E3 MDM 25 years of experience in Local Government Senior Principal Assistant Director, ICT Division Local Government Organization A Local Government E4 30 years of experience in Local Government Local Mayor Government Organization B Local Government E5 The invitations to the experts were sent through emails with a cover letter attachment. The cover letter explained the model verification purpose and the expert’s role in the verification process. After the experts agree to take part, the questionnaire of the model verification was emailed to them as shown in Appendix C. The questionnaire also provides the instructions to the experts explaining the expert’s role and ranking approach. Two experts (E1 and E2) provided their feedback via email and three experts (E3, E4, and E5) requested for a face-to-face session with the researcher. The experts were requested to rank the relevancy of the proposed nine determinants in the initial conceptual model according to their point of view, based on the existing scenario on MDM adoption by local government organizations in Malaysia. The ranking sheet used a scale of 1 to 5 (strongly disagree to strongly agree) (Allahyari, Rangi, Khosravi, & Zayeri, 2011; Allen & Seaman, 2007). This ranking
105 process is important to verify whether the determinants are appropriate to be included in the conceptual model for Malaysia local government context. In addition, the experts were also allowed to suggest any additional determinants of the conceptual model with the justification about the decisions that they have made. The consensus among the experts was measured using Interquartile Range. The IQR is a rigorous technique of determining consensus and it is frequently applied in evaluating experts’ opinion, especially in Delphi method (De Vet, Brug, De Nooijer, Dijkstra, & De Vries, 2004). It measures the dispersion for the median and comprises the middle 50 per cent of the observations (Sekaran, 2016). The IQR score which represents the consensus from the experts were calculated for each determinant as a justification for whether to retain or drop the determinant from the conceptual model. Based on Gracht (2012), the determinants with IQR of 1 or less were retained while the determinants with an IQR of more than 1 were dropped. In addition, the new determinants suggested by the experts were added to the conceptual model after crosschecking with the literature from the SLR. The final decision on the conceptual model was based on the IQR calculation as presented in Figure 2.23 (page 89). Based on matrix analysis of two SLR and suggestions from the experts, the conceptual model of a new MDM adoption for Malaysia local government was outlined (Figure 4.5, page 153). Overall, the final decision for the conceptual model adopted nine determinants affecting MDM adoption by local government organizations in Malaysia, which are: four technological determinants (relative advantage, complexity, quality of master data, and data security), three organizational determinants (data governance, top management support, and technological competence), and two environmental determinants (government policy, and citizen demand). As suggested by the expert, the moderation effect of citizen population density on the relationship between citizen demand and MDM adoption was also introduced into the conceptual model. All suggestions from the experts were crosschecked with the literature to strengthen the theoretical foundation of the conceptual model as shown in Table 4.2 (page 147).
106 3.5.3 Developing Hypotheses and Defining Operational Terms Phase 3 of this research outlines the development of the hypotheses. Hypotheses development is the next logical step after theory formulation of the conceptual model (Sekaran, 2016). The hypothesis is a statement predicting a relationship between variables of the conceptual model. There are twelve hypotheses in this research to explain the MDM adoption by local government (Section 4.3, page 154). Subsequently, the operational definitions for the terms in the conceptual model were defined (Table 4.3, page 162). 3.6 Phase 4: Instrument Development and Data Collection A questionnaire was developed as an instrument for data collection. The development of the instrument follows the established scale development process by DeVellis (2016). It started with determining the constructs, specifying the measurements items, determining the scale for measurement, content validity, translation and face validity, and pilot testing. Then, the questionnaire was distributed to 465 local government department units in Malaysia. Table 3.5 describes the tasks, techniques, tools, and deliverables of Phase 4. Table 3.5 Phase 4 – Instrument Development and Data Collection Research Phase Tasks Techniques Tools Deliverables Phase 4: Instrument Development and Data Collection i. Determining Constructs ii. Specifying Measurement Items Content Validity SPSS i. Cover letter of content validity invitation (Appendix D) ii. Content validity survey form (Appendix E) iii. Content validity confirmation from experts (Appendix F)
107 Research Phase Tasks Techniques Tools Deliverables iii. Determining Scale for Measurement iv. Content Validity with Experts v. Translation and Face Validity vi. Pilot Testing iv. Content Validity Ratio analysis (Appendix G) Back to back translation Microsoft Word i. Invitation email for the instrument translation (Appendix H) ii. Translation confirmation from expert (Appendix I) Face Validity Microsoft Word Survey Form – Malay Version (Appendix J) Reliability SPSS Reliability analysis (Table 3.10) i. Defining Sampling Size ii. Administering Survey Stratified random sampling G Power Sampling size calculation (Figure 3.4) Mailed questionnaires and selfadministered questionnaires Google Form Cover Letter of Survey (Appendix K) 3.6.1 Developing a Survey Instrument In developing the survey instrument, this research uses guidelines in scale development by DeVellis (2016). There are six steps involved in this process which are: 1) determine constructs to be measured, 2) specify measurement items, 3) determine the scale for measurement items, 4) content validity with experts, 5) translation and face validity, and 6) pilot test. The following sub-sections detail the analysis procedures.
108 3.6.1.1 Determining Constructs of Measurement In this first step, it is very critical to identify the constructs which needed to be measured. From the conceptual model, there are twelve constructs to be measured based on the independent, dependent and moderator variables which are a relative advantage, complexity, quality of master data, data security, data governance, top management support, technology competency, government policy, citizen demand, and citizen population density. The operational definition of each construct is as presented in Table 4.3, page 162. 3.6.1.2 Specifying Measurement Items for Each Construct After identifying the constructs, a set of measurement items or indicators were specified to measure each construct. The survey was developed by adapting measurement from several researchers. To ensure the relevance as suggested by Yassin, Salim and Ashaari (2013), the measurements were modified to suit the study context i.e. MDM and Malaysia local government context. The selection of the measurement was based on the most cited and reliable measurement from the existing studies, and the best suited measurement in MDM context (Creswell, 2014). The technological dimension consists of four independent variables: relative advantage, complexity, data quality, and data security. First, the relative advantage was measured by five items according to Premkumar and Roberts (1999), and Vilminko-Heikkinen and Pekkola (2013) which assess the advantages of MDM adoption. Second, the complexity was measured by four items according to Premkumar and Roberts (1999) which to assess the organization’s difficulty in adopting the MDM. Third, the quality of master data was measured using six items from the DAMA UK Working Group (2013), to assess the degree of quality of master data at the organization. Fourth, the data security was measured by five items – three items of them were adopted from Soliman & Janz (2004), one item regarding physical security of the data centre of MDM as adopted from Hristidis, Chen, Li, Luis, and
109 Deng (2010), and one item regarding digital signature of the data exchange transaction adopted from Smallwood (2014). The organizational dimension consists of three independent variables: data governance, top management support, and technological competency. Data governance was measured by using five items adopted from Hung et al. (2014), and one item adopted from Smallwood (2014). Top management support was measured by using four items adopted from Premkumar and Roberts (1999). Technological competence was assessed by using six items adopted from Lin (2006;) and one item adopted from Wang and Wang (2016). The environmental dimension includes two independent variables: government policy and citizen demand. Five measurement items for government policy were adapted from different measurements by various researchers including among others (Awa & Ojiabo, 2016; Kuan & Chau, 2001; Lian et al., 2014; Pan & Jang, 2008). Since each measurement assessed different content items based on their study’s context, this research measured government policy which is appropriate to the context of Malaysian public sector by assessing the establishment of policy in supporting data sharing, data quality management, laws to protect the organization’s interest, MDM planning in the 11th Malaysia Plan, and data security. Citizen demand was measured based on Wang and Feeney's study (2016) and Liang et al. (2017) by expanding the measurement to assess citizen demand for the integrated services, abilities to use online services, and citizen trust in silo management of services. The dependent variable of the conceptual model is MDM adoption. MDM adoption in this research refers to the decision of the organizations to participate as MDM data providers by sharing their master data with the MDM. Six MDM adoption measurement items were adapted from Awa and Ojiabo (2016). The measurements involved the intention of organizations to adopt MDM to improve their service delivered to the citizens, data quality management, operational efficiencies, and cost reduction, inter-agency data exchange, integration operations across agencies, and the reduction of data duplication across agencies.
110 For the moderator variable, citizen population density is measured by three levels as low, medium, or high (McCullough et al., 2015; Rubin et al., 2014). Depending on the number of the citizen served by each local government, this research has classified a citizen population density of Malaysia local government into ‘low’ if less than 100,000 people, ‘medium’ if 100,000 to 3000,000 people, and ‘high’ if more than 3000,000 people served by the local government. Overall, there are 52 measurement items used to measure eleven constructs of the model. At least four measurement items were specified for each construct except citizen population density which was measured by a single item due to the demographics profile. 3.6.1.3 Determining the Scale for Measurement Items After the measurement items for each construct have been specified, the format of scale to measure the items were determined. This research applied ‘Likert Scale’ format to measure the items in the survey instrument. In this type of scale, a declarative statement is presented followed by response options that indicate varying degrees of agreement or endorsement of the statement (DeVellis 2016). A five-point Likert scale “1=strongly disagree, 2=disagree, 3=agree (but not important), 4=agree, 5=strongly agree” was used for all measurement items, which is same as to the original adapted measurements. 3.6.1.4 Content Validity After specifying measurement items for each construct and defining the scale for each measurement item, content validity test was conducted by experts to validate the instrument. Content validity aimed to evaluate the relevance of the constructs and items (Haynes, Richard, & Kubany, 1995). It is to ensure that the measurement items represent the constructs and that each individual item measures what it is intended to
111 measure (Ali, Tretiakov, & Whiddett, 2014). The content validity steps in this research followed the guidelines proposed by Ali et al. (2014) as shown in Figure 3.3. Figure 3.3 Content validity steps (Ali et al., 2014) Eleven experts were chosen for the content validity test based on their experience, skills, and role related to MDM (Baker, Lovell, & Harris, 2006), IS modeling, and survey instrument development. The required number of experts is based on Lynn (1986) which recommends a minimum of three, and Waltz, Strickland, and Lenz (1988) which suggest a range of two to twenty. As suggested by Baker et al. (2006), the selection of experts was based on their experience, skills, and role related to the MDM, IS modeling and survey instrument development, and PLS-SEM analysis. During this process, the experts provide their agreement on the measurement items for each construct and performing language editing of the instrument. Some of them also proposed sorting the measurement items for each construct based on their importance. Table 3.6 introduces the eleven experts that involved in the content validity test of the instrument development. Table 3.6 Experts for content validity Expert Fields Agency Designation Experiences ID 30 years of experience in Technology Management Modeling and Instrument Lecturer, Professor in Business School Universiti Sains Malaysia IS Modelling/ Instrument Development /Statistical (PLSSEM) E1 15 years of experience in IS Lecturer, Associates Professor in Universiti Universiti Teknologi Malaysia IS Modelling/ Instrument Development E2
112 Expert Fields Agency Designation Experiences ID Teknologi Malaysia 20 years of experience in IS Lecturer, Associates Professor in Faculty of Information Science & Technology The National University of Malaysia IS Modelling/ Instrument Development E3 15 years of experience in Statistical Lecturer in Mathematic Department Institute of Teacher Education, Ipoh Statistical (PLSSEM) E4 25 years of experience in Public Sector Public Sector ICT Expert (System Development) MDM MAMPU implementation E5 15 years of experience in Public Sector Deputy Director of Finance Division MDM MAMPU implementation E6 13 years of experience in Public Sector Public Sector ICT Expert (Database) MDM MAMPU implementation E7 25 years of experience in Public Sector Deputy Under Secretary Office of Information Management Division MDM Ministry of Finance implementation E8 25 years of experience in Local Governments Senior Principal Assistant Director, ICT Division Putrajaya Corporation E9 Local Governments 15 years of experience in Local Governments Senior Assistant Director, ICT Division Putrajaya Corporation E10 Local Governments 30 years of experience in Local Governments Iskandar Puteri City Mayor Council E11 Local Governments The invitations to the experts were sent through email with a cover letter attachment as presented in Appendix D. The cover letter explains the content validity attention, content validity purpose, and the expert’s role in the content validity process.