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Although big data have emerged at the cornerstone of business and management research, past studies have failed to offer explanations and classifications of different levels of capacity and expertise possessed by different countries in utilising big data. The purpose of this paper is to examine the different capacities of governments in utilising big data.

The paper is based on a comprehensive synopsis of the literature on big data and the role of governments in utilising and harnessing big data.

The study provides an array of explanations to account for why some countries are adept at using big data to solve social problems, while others often faltered.

The study offers a range of explanations and suggestions, which include skills upgrading, to help countries improve their capabilities in data collection and data analysis.

In this paper, data collection-data analysis matrix was developed to characterise the role of governments in data collection and analysis.

The development of IT has enabled organizations to collect and store many times more data than they were able to just decades ago. This means that companies are now faced with managing huge amounts of data, which represents new challenges in ensuring high data quality. The purpose of this paper is to identify barriers to obtaining high master data quality.

This paper defines relevant master data quality barriers and investigates their mutual importance through organizing data quality barriers identified in literature into a framework for analysis of data quality. The importance of the different classes of data quality barriers is investigated by a large questionnaire study, including answers from 787 Danish manufacturing companies.

Based on a literature review, the paper identifies 12 master data quality barriers. The relevance and completeness of this classification is investigated by a large questionnaire study, which also clarifies the mutual importance of the defined barriers and the differences in importance in small, medium, and large companies.

The defined classification of data quality barriers provides a point of departure for future research by pointing to relevant areas for investigation of data quality problems. The limitations of the study are that it focuses only on manufacturing companies and master data (i.e. not transaction data).

The classification of data quality barriers can give companies increased awareness of why they experience data quality problems. In addition, the paper suggests giving primary focus to organizational issues rather than perceiving poor data quality as an IT problem.

Compared to extant classifications of data quality barriers, the contribution of this paper represents a more detailed and complete picture of what the barriers are in relation to data quality. Furthermore, the presented classification has been investigated by a large questionnaire study, for which reason it is founded on a more solid empirical basis than existing classifications.

The purpose of this paper is to present new empirical data on leases, energy management, and energy meters in the UK, with a particular focus on small and medium enterprises (SMEs) and other “minor” players. The paper develops a new segmentation model that identifies six different combinations of energy and organizational conditions.

The authors surveyed participants in an online energy management and data analytics service. A 30-question online survey gathered data from 31 respondents on three kinds of infrastructure – legal, organizational, and technical.

SMEs and other minor players are generally “data poor,” lack energy managers, and have legacy meters that are read only annually or quarterly; some rent via leases that inhibit permanent alterations to the premises, including the meter.

The research is exploratory and subject to self-selection bias. Further research is needed into: lease language, governance structures, social practices to facilitate cooperation between tenants and landlords; the scope for energy management positions in small organizations; low-cost “smart-er” meters that can be reversibly retrofitted onto existing energy meters; and the combination of these areas.

Organizations may need to augment a combination of legal, organizational, and technical infrastructures to enable better energy management.

SMEs and other “minor” energy users are important to society and the economy, yet they are often overlooked by government programs. This developing data set can help policymakers include these groups in their programs.

This paper presents a new conceptual framework for future research and new empirical data on understudied groups.

The purpose of this paper is to explore the effect of globalization and credit market imperfections on child labour.

Analysis is based on cross-country regression framework, incorporating 129 developing countries for the period 1970-2010.

The findings indicate that countries that are more open to trade and having higher foreign direct investment inflow have lower incidence of child labour. As child labour in export-related industries is hard to find, trade sanctions may not have a significant effect on child labour. Further study concludes that income of the bottom quartile of the population is the best representation of the income of the poor when studying child labour.

The study uses the data compiled by International Labour Organization (ILO). Though much of the variation in the data is because of the adjustments made by ILO, this is the only comparable cross-country estimates available. Hence in the absence of the cross-country comparable estimates, many empirical studies have used this data set (e.g. Cigno et al., 2002; Dehejia and Gatti, 2002; Rogers and Swinnerton, 2001). This study acknowledges this limitation but again in the absence of any comparable estimates, the assessment is also based on this data set.

Study contributes in the literature by comparing the effect of export and trade and by exploring the effect of an alternate measure of the income, estimated by using Gini coefficient, on child labour. Further studies exploring the effect of globalization did not explore the presence of imperfect credit market, however, this study has explored the effect of credit market imperfections as well.

This study aims to evaluate the quality of transition from child and adolescent services to adult intellectual disability services, using the relevant National Institute for Health and Care Excellence (NICE) standard (QS140). In addition, this study also identifies any differences in transition quality between those young people with intellectual disability with and without autism.

Using routinely collected clinical data, this study identifies demographic and clinical characteristics of, and contextual complexities experienced by, young people in transition between 2017 and 2020. Compliance with the quality standard was assessed by applying dedicated search terms to the records.

The study highlighted poor recording of data with only 22% of 306 eligible cases having sufficient data recorded to determine compliance with the NICE quality standard. Available data indicated poor compliance with the standard. Child and adolescent mental health services, generally, did not record mental health co-morbidities. Compliance with three out of the five quality statements was higher for autistic young people, but this only reached statistical significance for one of those statements (i.e. having a named worker, p = 0.02).

Missing data included basic clinical characteristics such as the level of intellectual disability and the presence of autism. This required adult services to duplicate assessment procedures that potentially delayed clinical outcomes. This study highlights that poor compliance may reflect inaccurate recording that needs addressing through training and introduction of shared protocols.

To the best of the authors’ knowledge, this is the first study to examine the transition process between children’s and adults’ intellectual disability health services using NICE quality standard 140.

The purpose of this study was to understand the research data management practices of chemistry researchers in the five specialized federal universities of technology in Nigeria. Appropriate research data management practice ensures that research data are available for reuse by secondary users, and research findings can be verified and replicated within the scientific community. A poor research data management practice can lead to irrecoverable data loss, unavailability of data to support research findings and lack of trust in the research process.

An exploratory research technique involving semi-structured, oral and face-to-face interview is used to gather data on research data management practices of chemistry researchers in Nigeria. Interview questions were divided into four major sections covering chemistry researchers’ understanding of research data, experience with data loss, data storage method and backup techniques, data protection, data preservation and availability of data management plan. Braun and Clarke thematic analysis approach was adapted, and the Provalis Qualitative Data Miner (version 5) software was used for generating themes and subthemes from the coding framework and for presenting the findings.

Findings revealed that chemistry researchers in Nigeria have a good understanding of the concept of research data and its importance to research findings. Chemistry researchers have had several experiences of irrecoverable loss of data because of poor choice of storage devices, back-up methods and weak data protection systems. Even though the library was agreed as the most preferred place for long-term data preservation, there is the issue of trust and fear of loss of ownership of data to unauthorized persons or party. No formal data management plan is used while conducting their scientific research.

The research focused on research data management practices of chemistry researchers in the five specialized federal universities of technology in Nigeria. Although the findings of the study are similar to perceptions and practices of researchers around the world, it cannot be used as a basis for generalization across other scientific disciplines.

This study concluded that chemistry researchers need further orientation and continuous education on the importance and benefits of appropriate research data management practice. The library should also roll out research data management programs to guide researchers and improve their confidence throughout the research process.

Appropriate research data management practice not only ensures that the underlying research data are true and available for reuse and re-validation, but it also encourages data sharing among researchers. Data sharing will help to ensure better collaboration among researchers and increased visibility of the datasets and data owners through the use of standard data citations and acknowledgements.

This is a qualitative and in-depth study of research data management practices and perceptions among researchers in a particular scientific field of study.

In literature, there is not agreement on the relevant data quality dimensions in an enterprise resource planning (ERP) system context. The purpose of this paper is to provide some clarification of this topic, by answering two important questions: What are the most relevant dimensions for assessing ERP data quality? What are the causal relationships between these data quality dimensions?

Based on a discussion of existing literature on data quality, a classification model of ERP system data quality is proposed and the relationships between the defined categories of data quality dimensions are defined. The validity of the classification model and the relationships between categories of data quality dimensions are investigated in three case studies.

The three case studies confirm that the classification model captures the most important aspects of describing ERP data quality and that the defined causalities between categories of data quality dimensions correspond with practice.

Besides being relevant in an ERP system context, the contribution of this paper may also be applicable for the evaluation of data quality in other types of information systems.

The defined classification model of ERP system data quality may support companies in improving their ERP data quality, thereby achieving greater benefits from their ERP systems.

A clarification of the most important data quality aspects in an ERP context is provided. Furthermore, some of the most important causalities between categories of data quality are defined.

South Africa is a developing country with an education system that remains in crisis, despite three decades of democracy. The vestiges of South Africa's oppressive past continues to plague a system where repeated efforts at top-down transformation and curriculum renewal have failed to create the change required (Roodt, 2018). Extensive country-wide research attests to persistent inequalities linked to poverty, unemployment, and poor educational outcomes, effectively trapping disadvantaged communities in downward spirals (World Bank, 2018). As in most other countries, evidence-informed practice (EIP) has been widely discussed and advocated for in South Africa, with the matric (school leavers') results resurging the conversation annually. Unfortunately, as is the case in many developing countries, it is well documented that the actual implementation of EIP is not as widespread as desired.

This chapter reviews and analyzes the use of EIP in South Africa through an exploration of the various spaces where EIP is reported to occur within the broader education landscape. Examples of teacher and school level EIP innovations, led by a wide variety of actors within the system, are evident – this despite the pervasive lack of resources, support, and effective leadership within the formal education system. Through reflecting on these ‘pockets of hope,’ which were found to exist not only within, but also outside and alongside the system, we hope to gather insights and initiate debate on how the uptake of EIP might be better informed and facilitated within the broader South African public education system.

The purpose of this paper is to present a novel GREY‒ASMAA model for reliability growth evaluation in the large civil aircraft test flight phase.

As limited data are collected during the large civil aircraft test flight phase, which are not enough to meet the requirements of the ASMAA model for reliability growth, four basic GM(1, 1) models, even grey model, original difference grey model, even difference grey model and discrete grey model, are presented. Then both forward and backward grey models GM(1,1) are built to forecast and obtain virtual test data on left and right sides. Then the ASMAA model for reliability growth evaluation can be built based on original and virtual test data.

Aiming at the background of poor information data during the large civil aircraft test flight phase, first, a novel GREY‒ASMAA model, which was combined by the grey model GM(1,1) with the ASMAA model, has been put forward in this paper.

The GREY‒ASMAA model for reliability growth evaluation can be used to solve the problem of reliability growth evaluation with poor information data during the large civil aircraft test flight phase, and it has been used in reliability evaluation of C919 at the test flight stage.

This paper presents two new definitions of forward grey model GM(1,1) and backward grey model GM(1,1), as well as a novel GREY‒ASMAA model for reliability growth evaluation of large civil aircraft during test flight phase.