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Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments.

This work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study.

The analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata.

The employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments.

This study aims to investigate the adoption of Quality 4.0 (Q4.0) and assess the critical failure factors (CFFs) for its implementation and how its failure is measured.

A qualitative study based on in-depth interviews with quality managers and executives was conducted to establish the CFFs for Q4.0.

The significant CFFs highlighted were resistance to change and a lack of understanding of the concept of Q4.0. There was also a complete lack of access to or availability of training around Q4.0.

The study enhances the body of literature on Q4.0 and is one of the first research studies to provide insight into the CFFs of Q4.0.

Based on the discussions with experts in the area of quality in various large and small organizations, one can understand the types of Q4.0 initiatives and the CFFs of Q4.0. By identifying the CFFs, one can establish the steps for improvements for organizations worldwide if they want to implement Q4.0 in the future on the competitive global stage.

The concept of Q4.0 is at the very nascent stage, and thus, the CFFs have not been found in the extant literature. As a result, the article aids businesses in understanding possible problems that might derail their Q4.0 activities.

Background: Most children in low-income countries complete their elementary education with low competency in essential reading, writing, and arithmetic skills. Besides, about 250 million students are not learning the basics, most of whom have spent at least four years in school, and this failure is coined the global learning crisis. This study aimed to examine educational quality management practices perceived by secondary school teachers.

Methods: The study employed a multilevel mixed-method design. Employing a simple random sampling technique, the researcher selected 251 teachers from 10 secondary schools in the research regions. He collected data through a researcher-designed questionnaire, school standards, and student achievement records from November 2018 to March 2019. He analyzed data from a questionnaire using frequency, percentage, mean, Pearson correlation, and exploratory factor analysis. The document review concerning quality management was analyzed using content analysis to triangulate the quantitative findings.

Results: At the school level, the study revealed the impracticality of laboratories. Besides, incompetent and unmotivated teachers and students ran the education business from the input side. At the same level, principals' management practices on staff development and encouraging parents to support their schools were low. The principals' management practices in the teaching-learning process were also undesirable at the classroom level. Overall, the study revealed incredibly insufficient input, process, and output management in the study context.

Conclusion: Hence, the study concluded that it is difficult to achieve the very objectives of producing creative, critical, and problem-solving individuals through this type of educational provision and its management. Due to this, it is not easy to achieve quality education for all goals.

Many organisations, including professional councils, operate manually to ensure document flow to clients and stakeholders. This results in the loss of valuable documentation such as certificates and the incurring of costs due to the returning of post to the sender. The purpose of this study was to explore digital transformation of document flow at the South African Council for Social Science Professionals.

The methodological approach involved qualitative data collected through interviews, observation and document analysis in response to research questions. The study was a participatory action research project that involved collaboration between researchers and study participants in defining and solving the problem through needs assessment exercise. All three phases of participatory action research were followed, namely, the “look phase”: getting to know stakeholders so that the problem is defined on their terms and the problem definition is reflective of the community context; the “think phase”: interpretation and analysis of what was learned in the “look phase” and the “act phase”: planning, implementing, and evaluating, based on information collected and interpreted in the first two phases.

The study identified various issues relating to poor data quality, high rate of registered postal returns and non-delivery electronic messages that cannot reach all the intended recipients and accumulation of data for decades. In this regard, the study proposes a framework that can be used by SACSSP to update and verify their details on the portal, as well as digital certificates for membership.

Although the proposed framework is tailor-made for the professional council, it is not depended on prescribed technologies due to usage of open standards that can be used by industry and researchers. Therefore, it can be applied in other context where institutions such as universities communicate with many clients via postal or courier services.

The study used participatory action research involving the researchers and the organisation to solve the problem. The study presented a workflow that the council can use to ensure that the documents reach intended recipients. Furthermore, digital transformation of the process will ensure that the registered professionals are able to access their certificates online and can print them when necessary.

Data represents a critical resource that enables construction companies’ success; thus, its management is very important. The purpose of this study is to assess the benefits of construction data risks management (DRM) in the construction industry (CI).

This study adopted a quantitative method and collected data from various South African construction professionals with the aid of an e-questionnaire. These professionals involve electrical engineers, quantity surveyors, architects and mechanical, as well as civil engineers involved under a firm, or organisation within the province of Gauteng, South Africa. Standard deviation, mean item score, non-parametric Kruskal–Wallis H test and exploratory factor analysis were used to analyse the retrieved data.

The findings revealed that DRM enhances project and company data availability, promotes confidentiality and enhances integrity, which are the primary benefits of DRM that enable the success of project delivery.

The research was carried out only in the province of Gauteng due to COVID-19 travel limitations.

The construction companies will have their data permanently in their possession and no interruption will be seen due to data unavailability, which, in turn, will allow long-term and overall pleasant project outcomes.

This study seeks to address the benefits of DRM in the CI to give additional knowledge on risk management within the built environment to promote success in every project.

German (corporate-) real estate management departments have been facing the challenge of poor data quality for years. This holds them back from generating efficiency potentials via the use of new methods from the field of digitalisation and from coping with the increasing requirements from the ESG context. The purpose of this paper is to explore why German (corporate-) real estate managements (do not) share data with their real estate service providers to address the data quality challenge and identify possible solutions.

To answer the research question, the reasoned action approach, an established theory from psychology for predicting human behaviour, is used. The relationships between the constructs are determined using linear regression. The study participants are almost exclusively from Germany.

The organisational milieu (perceived behavioural control) has a significant impact on the behaviour of sharing data with real estate service providers. Especially the change of contractual arrangements (data-driven contracts) seems to be crucial for the improvement of information logistics.

To the best of the author’s knowledge, for the first time, the reasoned action approach is used within the German real estate industry to predict organisational behaviour in the context of digitalisation.

This study aims to identify user perception of different qualities of optical character recognition (OCR) in texts. The purpose of this paper is to study the effect of different quality OCR on users' subjective perception through an interactive information retrieval task with a collection of one digitized historical Finnish newspaper.

This study is based on the simulated work task model used in interactive information retrieval. Thirty-two users made searches to an article collection of Finnish newspaper Uusi Suometar 1869–1918 which consists of ca. 1.45 million autosegmented articles. The article search database had two versions of each article with different quality OCR. Each user performed six pre-formulated and six self-formulated short queries and evaluated subjectively the top 10 results using a graded relevance scale of 0–3. Users were not informed about the OCR quality differences of the otherwise identical articles.

The main result of the study is that improved OCR quality affects subjective user perception of historical newspaper articles positively: higher relevance scores are given to better-quality texts.

To the best of the authors’ knowledge, this simulated interactive work task experiment is the first one showing empirically that users' subjective relevance assessments are affected by a change in the quality of an optically read text.

The purpose of this paper is to explore the concept of data assets and how companies can assetize their data. Using the literature review methodology, the paper first summarizes the conceptual controversies over data assets in the existing literature. Subsequently, the paper defines the concept of data assets. Finally, keywords from the existing research literature are presented visually and a foundational framework for achieving data assetization is proposed.

This paper uses a systematic literature review approach to discuss the conceptual evolution and strategic imperatives of data assets. To establish a robust research methodology, this paper takes into account two main aspects. First, it conducts a comprehensive review of the existing literature on digital technology and data assets, which enables the derivation of an evolutionary path of data assets and the development of a clear and concise definition of the concept. Second, the paper uses Citespace, a widely used software for literature review, to examine the research framework of enterprise data assetization.

The paper offers pivotal insights into the realm of data assets. It highlights the changing perceptions of data assets with digital progression and addresses debates on data asset categorization, value attributes and ownership. The study introduces a definitive concept of data assets as electronically recorded data resources with real or potential value under legal parameters. Moreover, it delineates strategic imperatives for harnessing data assets, presenting a practical framework that charts the stages of “resource readiness, capacity building, and data application”, guiding businesses in optimizing their data throughout its lifecycle.

This paper comprehensively explores the issue of data assets, clarifying controversial concepts and categorizations and bridging gaps in the existing literature. The paper introduces a clear conceptualization of data assets, bridging the gap between academia and practice. In addition, the study proposes a strategic framework for data assetization. This study not only helps to promote a unified understanding among academics and professionals but also helps businesses to understand the process of data assetization.

This paper aims to critically examine the accounting and information systems literature to understand the changes that are occurring in the management accounting profession. The changes the authors are interested in are linked to technology-driven innovations in managerial decision-making and in organizational structures. In addition, the paper highlights research gaps and opportunities for future research.

The authors adopted a grounded theory literature review method (Wolfswinkel et al., 2013) to achieve the study’s aims.

The authors identified four research themes that describe the changes in the management accounting profession due to technology-driven innovations: structured vs unstructured data, human vs algorithm-driven decision-making, delineated vs blurred functional boundaries and hierarchical vs platform-based organizations. The authors also identified tensions mentioned in the literature for each research theme.

Previous studies display a rather narrow focus on the role of digital technologies in accounting work and new competences that management accountants require in the digital era. By contrast, the authors focus on the broader technology-driven shifts in organizational processes and structures, which vastly change how accounting information is collected, processed and analyzed internally to support managerial decision-making. Hence, the paper focuses on how management accountants can adapt and evolve as their organizations transition toward a digital environment.

Benford's Law is an empirical observation about the frequency of digits in a variety of naturally occurring data sets. Auditors and forensic scientists have used Benford's Law to detect erroneous data in accounting and legal usage. One well-known limitation is that Benford's Law fails when data have clear minimum and maximum values. Many kinds of education data, including assessment scores, typically include hard maximums and therefore do not meet the parametric assumptions of Benford's Law. This paper implements a transformation procedure which allows for assessment data to be compared to Benford's Law. As a case study, a data quality assessment of oral language scores from the Early Childhood Longitudinal Study, Kindergarten (ECLS-K) study is used and higher risk data segments detected. The same method could be used to evaluate other concerns, such as test fraud, or other bounded datasets.