Maturity Models of Quality Management System in High-Tech Industry: a Systematic Literature Review

Shrayner, Yulia S.; Vladimir, Yashchenko V.

doi:10.1109/eiconrus.2019.8656993

Cited by 4 publications

(7 citation statements)

References 16 publications

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“…Previous Studies TIQM-CMM [25] Goal, Policy, Process, Structure, Training Fidler and Lavbic [26] , Schäffer et al [27] Gartner [23] Vision, Strategy, Governance, Organization, Process, Enabling Infrastructure, Metrics M. Miftahul Akbar [28] IQM-CMM [24] Measurement and Analysis (MA), Organizational Process Performance (OPP), and Quantitative Project Management (QPM) Schäffer et al [27] , Shrayner and Vladimir [29] Loshin [8] Data Quality Management Expectation, Dimension of Data Quality, Policies, Procedures, Governance, Standard, Technology, Performance Management Sabtiana et al [9] , Wibisono et al [10] , Wilantika and Wibowo [11] , Rahmawati and Ruldeviyani [14] , Indriany et al [15] , Gunawan and Ruldeviyani [30] , Setiadi et al [31] FIGURE 1 The research flow.…”

Section: Model Dqmm Componentsmentioning

confidence: 99%

Data Quality Management Strategy To Improve Remote Sensing Data Quality: A Case Study On National Remote Sensing Data Bank

Payani

Mayanda

Adiresta

et al. 2023

JTS

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National Remote Sensing Data Bank (Bank Data Penginderaan Jauh Nasional; BDPJN) is the operational implementer in the field of remote sensing to meet the needs of national data on remote sensing images. Remote sensing images are essential in the Indonesian government for various needs, such as in regional (spatial planning, city boundaries), disaster (forest fires, floods, landslides, earthquakes, volcanic eruptions), plantation, natural resources, and environment sectors. Quality management of BDPJN's satellite images becomes challenging due to the increasing number of products owned and users annually. For this reason, a data quality management strategy is needed to guarantee and improve the quality of BDPJN data. To develop such a strategy, an assessment of the maturity of BDPJN's data quality management was conducted from the aspect of data processing by implementing Loshin's Data Quality Management Maturity Model (DQM3) to find out the characteristics that were lacking. The results were then mapped based on Data Quality Management (DQM) activities in DAMA-DMBOK as a recommendation for data quality management strategies. This study applies quantitative research where data collection was done by distributing questionnaires to 24 respondents who are data stewards of a medium, high, and very high-resolution mosaic images. Based on the assessment, BDPJN is in the maturity level of Defined to Managed. The recommendations are 21 DQM activities that can be carried out to improve BDPJN data quality.

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Section: Model Dqmm Componentsmentioning

confidence: 99%

Data Quality Management Strategy To Improve Remote Sensing Data Quality: A Case Study On National Remote Sensing Data Bank

Payani

Mayanda

Adiresta

et al. 2023

JTS

View full text Add to dashboard Cite

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“…It helps indexing machine to determine which articles met general requirements automatically, as mentioned in Pautasso's rule 13 . Those inclusion-exclusion criteria were performed well as initial step of SLR in related studies [7][8][9][10] .…”

Section: Inclusion-exclusion Criteria Developmentmentioning

confidence: 99%

“…Lack of validation became repeated issue on MM development as said by Tarhan et.al 8 . Some prior research had tried to identify how MMs were validated using comprehensive methods, such as in Shrayner 9 , Aljowder 10 , and Santos-Neto 2 . Unfortunately, their descriptions were relatively normative.…”

Section: Introductionmentioning

confidence: 99%

Towards a Comprehensive Exploration and Mapping of Maturity Models in Digital Business

Gandhi

Sucahyo

2020

DESIDOC Jl. Lib. Info. Technol.

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Many Maturity Models (MM) in digital business were difficult to be implemented and adopted in real world due to lack of validation. In order to solve the theoretical gap, this study conducted Systematic Literature Review (SLR) to generate comprehensive exploration and mapping of MM. Out of 179 articles captured using Scopus, 28 articles were eligible. Using Kitchenham et.al’s SLR phases, this study classified type, schemes, and technique on MM creation. Nine research method attributes were employed: referred methodologies, hierarchical type, maturity status, cascading scheme, leveling, criteria sources, classification scheme, implementation technique, and evaluation/ verification/validation technique. Moreover, this study provided rationalisation on each alternative so that acceptance of MM can be increased.

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“…Research question is main baseline when the SLR runs. It actualises the expected goals as produced in the results [12][13][14][15] . Without clear research question, SLR processes lose control and cannot fulfill expected goals since the results are not relevant to occurred problems.…”

Section: Research Questions Postulatementioning

confidence: 99%

Exploring the Soft System Methodology in Development of Knowledge Management Conceptual Model a Sytematic Literature Review

Sensuse

Gandhi

Sucahyo

2019

DESIDOC Jl. Lib. Info. Technol.

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Soft System Methodology (SSM) is common guidance to conceptualise Knowledge Management (KM) model since its benefit to handle complex situations. This study highlights SSM implementation on KM conceptual model using Systematic Literature Review (SLR) to produce insights about current implementation and promising chances in the future. This study also concerns on KM conceptual models as created without certain features in many case studies. Many various interpretation can arise to understand how system should work. This situation affected more spending time to validate KM conceptual model with stakeholders. Using SLR, this study criticises the importance of features as provided in eligible articles. Out of 144 articles from 2009 until 2019 as delivered from Scopus query, this study selected 12 articles about SSM on KM. It classified them into several criteria: published year, sector, and related KM life cycle. By mapping them to solve research questions, this study promoted actors, activities, and sequential order of activities as recommended features. By implementing them, KM conceptual model creation can be faster and accepted by stakeholders.

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Maturity Models of Quality Management System in High-Tech Industry: a Systematic Literature Review

Cited by 4 publications

References 16 publications

Data Quality Management Strategy To Improve Remote Sensing Data Quality: A Case Study On National Remote Sensing Data Bank

Data Quality Management Strategy To Improve Remote Sensing Data Quality: A Case Study On National Remote Sensing Data Bank

Towards a Comprehensive Exploration and Mapping of Maturity Models in Digital Business

Exploring the Soft System Methodology in Development of Knowledge Management Conceptual Model a Sytematic Literature Review

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