Critical Success Factors of Knowledge Management Implementation in BPKP

Amelia, Reza; Endrastaty, Azizah; Sensuse, Dana Indra

doi:10.1109/icisit54091.2022.9872767

Cited by 2 publications

(3 citation statements)

References 25 publications

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“…So, it is a viable tool for learning from highly experienced practitioners in the least amount of time. The Delphi technique has been considered appropriate to validate the usability enhancement model because it allows the experts to judge and evaluate the proposed model, guides them to identify the issues in the proposed solution, and achieves consensus concerning problem resolution [8,[30][31][32]. These factors and properties of the Delphi technique make it an appropriate and suitable method for the present research work according to its format and nature.…”

Section: Model Validationmentioning

confidence: 99%

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Usability enhancement model for unstructured text in big data

Adnan,

Akbar,

Wang

2023

J Big Data

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The task of insights extraction from unstructured text poses significant challenges for big data analytics because it contains subjective intentions, different contextual perspectives, and information about the surrounding real world. The technical and conceptual complexities of unstructured text degrade its usability for analytics. Unlike structured data, the existing literature lacks solutions to address the usability of unstructured text big data. A usability enhancement model has been developed to address this research gap, incorporating various usability dimensions, determinants, and rules as key components. This paper adopted Delphi technique to validate the usability enhancement model to ensure its correctness, confidentiality, and reliability. The primary goal of model validation is to assess the external validity and suitability of the model through domain experts and professionals. Therefore, the subject matter experts of industry and academia from different countries were invited to this Delphi, which provides more reliable and extensive opinions. A multistep iterative process of Knowledge Resource Nomination Worksheet (KRNW) has been adopted for expert identification and selection. Average Percent of Majority Opinions (APMO) method has been used to produce the cut-off rate to determine the consensus achievement. The consensus was not achieved after the first round of Delphi, whereas APMO cut-off rate was 70.9%. The model has been improved based on the opinions of 10 subject matter experts. After second round, the analysis has shown majority agreement for the revised model and consensus achievement for all improvements that validate the improved usability enhancement model. The final proposed model provides a systematic and structured approach to enhance the usability of unstructured text big data. The outcome of the research is significant for researchers and data analysts.

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Section: Model Validationmentioning

confidence: 99%

“…Panel size Panel group [8] 3 Homogeneous [30] 5 Homogeneous [31] 6 Homogeneous [32] 11 Heterogeneous…”

Section: Studymentioning

confidence: 99%

Usability enhancement model for unstructured text in big data

Adnan,

Akbar,

Wang

2023

J Big Data

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“…8, No. 1, 2024: 49-57 50 advancement [1]. Effective execution of Information Administration activities within the education division may be a multifaceted challenge that includes different organizational, mechanical, and human variables [2].…”

Section: Introductionmentioning

confidence: 99%

Application of SmartPLS in Analyzing Critical Success Factors for Implementing Knowledge Management in the Education Sector

Jacqueline,

Putri Ayu Senjaya,

Firli

et al. 2024

ATM

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This research explores the critical success factors influencing the implementation of knowledge management in the education sector, employing Structural Equation Modeling (SEM) with SmartPLS as the analytical tool. The study addresses the intricate challenges inherent in knowledge management within educational environments, aiming to provide a nuanced understanding of the factors contributing to successful implementation The conceptual framework identifies key factors, including HRM-Driven Knowledge Sharing, IT Integration in Teaching Practices, Organizational Culture Alignment, Perceived Effectiveness of Teacher Training, and Perceived Top Management Support. The chosen methodology, utilizing SmartPLS, is justified for its suitability in handling challenges associated with small sample sizes and non-normal data distributions. The data analysis reveals the generally robust reliability of constructs but also underscores potential concerns related to discriminant validity among certain variables. The research contributes significantly to the understanding of critical success factors for knowledge management in education. However, the conclusion suggests the need for refinement in the conceptual model to enhance the validity and applicability of the findings. In conclusion, this research offers a comprehensive perspective on the complexities of implementing knowledge management in the education sector. The findings pave the way for potential enhancements to the conceptual model, aiming to elevate the effectiveness of knowledge management practices within educational contexts.

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Critical Success Factors of Knowledge Management Implementation in BPKP

Cited by 2 publications

References 25 publications

Usability enhancement model for unstructured text in big data

Usability enhancement model for unstructured text in big data

Application of SmartPLS in Analyzing Critical Success Factors for Implementing Knowledge Management in the Education Sector

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