Determinants of master data management adoption by local government organizations: An empirical study

Haneem, Faizura; Kama, Nazri; Taşkın, Nazım; Pauleen, David J.; Bakar, Nur Azaliah Abu

doi:10.1016/j.ijinfomgt.2018.10.007

Cited by 82 publications

(73 citation statements)

References 80 publications

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“…The systematic review process produced 15 related studies as presented in Table III. Regarding the credibility of the source, eight studies are from indexed journals [1], [14], [21], [59]- [63], four studies are from established conferences [13], [64]- [66], and three studies are from book publications [45], [67], [68]. In the case of present study, four articles were published in 2019 [14], [62], [63], [66], two articles in 2018 [13], [65], four articles in 2017 [1], [59]- [61], two articles in 2016 [21], [64] [24, 86], and three articles in 2015 [45], [67], [68].…”

Section: Resultsmentioning

confidence: 99%

“…In this case, a total of 11 studies were found focusing on an organizational component in improving master data quality. The discussed factors are data governance [14], [21], [68], [45], [59], [61]- [65], [67], teamwork [59], [61], [64], [67], data quality vision and strategy [45], [62], [63], [67], training [59], and change management [64].…”

Section: )mentioning

confidence: 99%

“…Factor Author Organizational Data governance [14], [21], [68], [45], [59], [61]- [65], [67] Organizational Teamwork [59], [61], [64], [67] Organizational Data quality vision and strategy [45], [62], [63], [67] Organizational Training [59] Organizational Change management [64] Managerial Data quality policy and standard [14], [21], [59], [61], [62], [65], [67], [68] Managerial Data quality assessment [21], [45], [59], [62], [64], [65], [68] Managerial Continuous improvement [21], [45], [59], [62], [64]…”

Section: Componentmentioning

confidence: 99%

“…The importance of master data requires it of high quality in supporting the organization to perform roles such as planning and decision making [11] and ensuring compliance with the regulatory and legal provision [12]. While the increasing demand for information system initiatives evidenced that high-quality master data is one of the important elements in the successfulness of the implementation [13], [14]. According to [12] current, accurate, and complete master data is required.…”

Section: Introductionmentioning

confidence: 99%

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Factors Influencing Master Data Quality: A Systematic Review

Ibrahim¹,

Mohamed²,

Safie³

2021

IJACSA

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Master data refers to the data that represents the core business of the organization, shared among different applications, departments, and organizations and most valued as the important asset to the organization. Despite the outward benefit of master data mainly in decision making and organization performance, the quality of master data is at risk. This is due to the critical challenges in managing master data quality the organization may expose. Hence the primary aim of this study is to identify factors influencing master data quality from the lens of total quality management while adopting the systematic literature review method. The study proposed 19 factors that inhibit the quality of master data namely data governance, information system, data quality policy and standard, data quality assessment, integration, continuous improvement, teamwork, data quality vision and strategy, understanding of the systems and data quality, data architecture management, personnel competency, top management support, business driver, legislation, information security management, training, change management, customer focus, and data supplier management that can be categorized to five components which are organizational, managerial, stakeholder, technological, and external. Another important finding is the identification of the differences for factors influencing master data compared to other data domain which are business driver, organizational structure, organizational culture, performance evaluation and rewards, evaluate cost/benefit tradeoffs, physical environment, risk management, storage management, usage of data, internal control, input control, staff participation, middle management's commitment, the role of data quality and data quality manager, audit, and personnel relation. It is expected that the findings of this study will contribute to a deeper understanding of the factors that will lead to an improved master data quality.

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Section: Resultsmentioning

confidence: 99%

Section: )mentioning

confidence: 99%

Section: Componentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Factors Influencing Master Data Quality: A Systematic Review

Ibrahim¹,

Mohamed²,

Safie³

2021

IJACSA

View full text Add to dashboard Cite

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“…The standard steps for assessing a measurement model are convergent validity and discriminant validity. Convergent validity was analysed by calculating the factor loading of the indicators, composite reliability (CR), and average variance extracted (AVE) [58]. The convergent validity results in Table 3 show that the factor loadings for all indicators are higher than 0.708, as suggested by Hair et al [59], with the elimination of three indicators (AC3, BE2, and EO4) from the original 28 indicators.…”

Section: Measurement Modelmentioning

confidence: 96%

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

et al. 2021

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The popularity of big data analytics (BDA) has boosted the interest of organisations into exploiting their large scale data. This technology can become a strategic stimulation for organisations to achieve competitive advantage and sustainable growth. Previous BDA research, however, has focused more on introducing more traits, known as Vs for big data traits, while ignoring the quality of data when examining the application of BDA. Therefore, this study aims to explore the effect of big data traits and data quality dimensions on BDA application. This study has formulated 10 hypotheses that comprised of the relationships of big data traits, accuracy, believability, completeness, timeliness, ease of operation, and BDA application constructs. This study conducted a survey using a questionnaire as a data collection instrument. Then, the partial least squares structural equation modelling technique was used to analyse the hypothesised relationships between the constructs. The findings revealed that big data traits can significantly affect all constructs for data quality dimensions and that the ease of operation construct has a significant effect on BDA application. This study contributes to the literature by bringing new insights to the field of BDA and may serve as a guideline for future researchers and practitioners when studying BDA application.

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Factors influencing village information systems adoption in Indonesia: A qualitative study

Sihotang

Purwandari

Eitiveni

et al. 2023

E J Info Sys Dev Countries

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Like other developing countries, the Indonesian government is pursuing digital transformation to achieve good governance at the central and micro levels. One of the strategies for achieving digital government transformation at the micro level is implementing village information systems (VIS), information systems that village officials manage. Unfortunately, not all villages in Indonesia are thriving in adopting VIS. Therefore, this study aims to answer an overarching puzzle: Why did some village governments successfully adopt VIS while others failed? Using a case‐study approach to VIS adoption in Gunungkidul Regency, Indonesia, this study fills the gap in the literature from the technological‐organizational–environmental perspective that affects e‐government adoption at the village government level. We found four main factors that influence the success or failure of village information systems adoption: (1) VIS interoperability (technology context), (2) the workload of village officers (organizational context), (3) the role of civil society, and (4) the role of a vendor (environmental context). This research enriches the literature by identifying these four factors within the TOE framework, still rarely present in e‐government adoption studies, especially in the context of village governments in developing countries. This research has practical implications for the successful adoption of VIS as a village government effort to gain data sovereignty.

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Determinants of master data management adoption by local government organizations: An empirical study

Cited by 82 publications

References 80 publications

Factors Influencing Master Data Quality: A Systematic Review

Factors Influencing Master Data Quality: A Systematic Review

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

Factors influencing village information systems adoption in Indonesia: A qualitative study

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