International Consensus on Data and Information Quality for Better Quality Decision Making in Higher Education Institutions

Syafar, Faisal; Husain, Halimah; Sabara, Edy

doi:10.1504/ijpqm.2021.10039191

Cited by 3 publications

(4 citation statements)

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“…Motivated by prior research underscoring asset management inefficiencies due to subpar asset information quality in these institutions, it aims to devise information management strategies from their diverse building types, applicable to commercial buildings (Curvelo Magdaniel et al. , 2019; Syafar et al. , 2020).…”

Section: Introductionmentioning

confidence: 99%

“…This study adopts a multi-case study approach, investigating higher education institutions across the UK, Ireland, Germany, and Northern Ireland. Motivated by prior research underscoring asset management inefficiencies due to subpar asset information quality in these institutions, it aims to devise information management strategies from their diverse building types, applicable to commercial buildings (Curvelo Magdaniel et al, 2019;Syafar et al, 2020). Semi-structured interviews with asset management professionals yielded nuanced insights into their perspective, completed by onsite observations for validations.…”

Section: Introductionmentioning

confidence: 99%

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Can blockchain prevent the deterioration of building handover information quality for higher education institutions?

Chang,

Jaskula,

Papadonikolaki

et al. 2024

BEPAM

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PurposeThis research investigates the distinct characteristics of blockchain technology to safeguard against the deterioration of handover information quality in the post-construction phase. The significance of effective management of handover information is highlighted by global building failures, such as the Grenfell Tower fire in London, UK. Despite existing technological interventions, there remains a paucity of understanding regarding the factors contributing to the decline in the quality of handover information during the post-construction phase.Design/methodology/approachThis study employed a multi-case studies approach across five higher education institutions. It involved conducting semi-structured interviews with 52 asset management professionals, uncovering the underlying reasons for the decline in handover information quality. Building on these insights, the study performed a mapping exercise to align these identified factors with blockchain technology features and information quality dimensions, aiming to evaluate blockchain’s potential in managing quality handover information.FindingsThe study findings suggest that blockchain technology offers advantages but has limitations in addressing all the identified quality issues of managing handover information. Due to the lack of an automated process and file-based information exchange, updating handover information still requires an error-prone manual process, leading to potential information loss. Additionally, no solutions are available for encoding drawings for updates and validation.Originality/valueThis study proposes a framework integrating blockchain to enhance the information management process and improve handover information quality.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Can blockchain prevent the deterioration of building handover information quality for higher education institutions?

Chang,

Jaskula,

Papadonikolaki

et al. 2024

BEPAM

View full text Add to dashboard Cite

show abstract

“…In opinion mining, there are two types of sentiment classification techniques: machine learning and lexicon-based techniques [9], [10], [11], [12]. This study focuses on Machine Learning approaches in which classification data mining, which is part of Machine Learning techniques [13], [14], [15] which is used for research [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Integrating Information Gain methods for Feature Selection in Distance Education Sentiment Analysis during Covid-19

et al. 2023

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The disparities in access to public assistances between rural and urban areas are analyzed in this study, corresponding to the intensity in which the constructs are related to expected quality, perceived quality and perceived value as influential factors in citizen satisfaction and loyalty. In Guayaquil, a survey is carried out at the household level, 428 valid questionnaires are obtained in the rural area of Tenguel and 521 Valid questionnaires in the urban area of Tarqui, applyingto the American Customer Satisfaction Index (ACSI). The research used a Structural Equation model (SEM) to evaluate the hypotheses raised, if they observe significant differences in citizen perception between inhabitants of urban and rural areas about the quality of community services, this as a determining cause in the level of citizen satisfaction and loyalty to choose their municipal authorities. The multigroup analysis allowed to identify inequalities in the observation of the quality of municipal or local communityassistances between rural and urban areas; the findings are considered to local public administrators for the design of public policy aimed at improving levels of citizen satisfaction and loyalty.Sentiment analysis is a way to automatically understand and process text data to figure out how someone feels about an opinion sentence. If there are too many reviews, it will take a lot of time and they will start to be biased. Sentiment classification tries to solve this problem by putting user reviews into groups based on whether they are positive, negative, or neutral. The dataset comes from Drone Emprit Academic. It is made up of tweets with the words "online learning method" in them, with as many as 4887 data crawled from them. Information Gain and adaboost on the C4.5 (FS+C4.5) method are used in the feature selection method. We use feature options to get rid of bias and improve accuracy. The results of the experiments will be compared to other algorithms like C4.5 and random forest. Based on the results, the accuracy of the two standard decision tree models (C4.5 and random forest) went up from 48.21% and 50.35% to 94.47 %. The value of how accurate it was went up by 44 percent. The FS+C4.5 model, on the other hand, has an RMSE of 0.204 and a correlation of 0.944. So, adding the feature selection technique to the sentiment analysis of bold learning education can make the C4.5 algorithm even more accurate.

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“…Knowledge management and business performance have been connected in a number of knowledge management reviews articles [25]- [30]. Agriculture has started to use the idea of knowledge management, but it is still in its early stages.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Android Application as Knowledge Management for Agriculture Farmers

Santoso¹,

Fauzi²,

Hendrarini³

et al. 2023

ujar

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The research has fresh ideas through activities to explore farmer knowledge management to find out the application of Indo-Good Agricultural Practices as a reflection of the behavior of producing fruits to obtain Prima Certification, and is developed into a knowledge management system with an Android-based application. The purpose of the study was to analyze the level of knowledge management of farmers in implementing Indo-Good Agricultural Practices to support the sustainability of Prima certified fruit production in East Java. Purposive sampling was used to select up to 15 fruit farmers for a total of 45 samples. With true or false assertions, the Guttman Scale measured knowledge creation, sharing, and implementation. Then, each dimension's proportion of knowledge was calculated. Mango Farmers in Pasuruan Regency have good knowledge of knowledge creating, knowledge sharing, and knowledge implementation. Different results are seen among Kediri Regency pineapple growers, who had the lowest level of knowledge sharing understanding since Prima certified producers find it difficult to understand good agriculture practise criteria. The highest level of knowledge formed by starfruit farmers is the Knowledge Sharing dimension, it can be concluded that there is a strong social relationship when a farmer is aware of Indo-Good Agricultural and willing to share knowledge or mutual welfare and reduce harmful mistakes.

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International Consensus on Data and Information Quality for Better Quality Decision Making in Higher Education Institutions

Cited by 3 publications

References 0 publications

Can blockchain prevent the deterioration of building handover information quality for higher education institutions?

Can blockchain prevent the deterioration of building handover information quality for higher education institutions?

Integrating Information Gain methods for Feature Selection in Distance Education Sentiment Analysis during Covid-19

Implementation of Android Application as Knowledge Management for Agriculture Farmers

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