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While few would disagree that high data quality is a precondition for the efficiency of a company, this remains an area to which many companies do not give adequate attention. Thus, this paper aims to identify which are the most important barriers preventing companies from achieving high data quality. By improving awareness of barriers on which to concentrate, companies are put in a better position to achieve high quality data.

First, a literature review of data quality and data quality barriers is carried out. Based on this literature review, the paper identifies a set of overall barriers to ensuring high data quality. The significance of these barriers is investigated by a questionnaire study, which includes responses from 90 Danish companies. Because of the fundamental difference between master data and transaction data, the questionnaire is limited to focusing only on master data.

The results of the survey indicate that a lack of delegation of responsibilities for maintaining master data is the single aspect which has the largest impact on master data quality. Also, the survey shows that the vast majority of the companies believe that poor master data quality does have significant negative effects.

The contributions of this paper represent a step towards an improved understanding of how to increase the level of master data quality in companies. This knowledge may have a positive impact on the data quality in companies. However, since the study presented in this paper appears to be the first of its kind, the conclusions drawn need further investigation by other research studies in the future.

This paper identifies the main barriers for ensuring high master data quality and investigates which of these factors are the most important. By focusing on these barriers, companies will have better chances of increasing their data quality.

The study presented in this paper appears to be the first of its kind, and it represents an important step towards understanding better why companies find it difficult to achieve satisfactory data quality levels.

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 review paper was identifying barriers to the use of telemedicine systems in primary health-care individual level among professionals.

This study used Scopus and PubMed databases for scientific records identification. A systematic review of the literature structured by PRISMA guidelines was conducted on 37 included papers published between 2009 and 2019. A qualitative approach was used to synthesize insights into using telemedicine by primary care professionals.

Three barriers were identified and classified: system quality, data quality and service quality barriers. System complexity in terms of usability, system unreliability, security and privacy concerns, lack of integration and inflexibility of systems-in-use are related to system quality. Data quality barriers are data inaccuracy, data timeliness issues, data conciseness concerns and lack of data uniqueness. Finally, service reliability concerns, lack of technical support and lack of user training have been categorized as service quality barriers.

This review identified and mapped emerging themes of barriers to the use of telemedicine systems. This paper also through a new conceptualization of telemedicine use from perspectives of the primary care professionals contributes to informatics literature and system usage practices.

Existing overviews of barriers for openly sharing and using government data are often conceptual or based on a limited number of cases. Furthermore, it is unclear what categories of barriers are most obstructive for attaining open data objectives. This paper aims to categorize and prioritize barriers for openly sharing and using government data based on many existing Open Government Data Initiatives (OGDIs).

This study analyzes 171 survey responses concerning existing OGDIs worldwide.

The authors found that the most critical OGDI barrier categories concern (in order of most to least critical): functionality and support; inclusiveness; economy, policy and process; data interpretation; data quality and resources; legislation and access; and sustainability. Policymakers should prioritize solving functionality and support barriers and inclusiveness barriers because the authors found that these are the most obstructive in attaining OGDI objectives.

The prioritization of open data barriers calls for three main actions by practitioners to reduce the barrier impact: open data portal developers should develop advanced tools to support data search, analysis, visualization, interpretation and interaction; open data experts and teachers should train potential users, and especially those currently excluded from OGDIs because of a lack of digital skills; and government agencies that provide open data should put user-centered design and the user experience central to better support open data users.

This study contributes to the open data literature by proposing a new, empirically based barrier categorization and prioritization based a large number of existing OGDIs.

This paper reports on a critical literature review, which aimed to identify, understand and qualify barriers that hinder the release of open government data (OGD) in China. Moreover, the purpose of this paper is to develop and propose a theoretical framework, which can be adopted as a basis for empirical investigation in the future, and to articulate mitigating strategies.

This study adopted an inductive qualitative approach, retrieving 42 academic articles from three main Chinese academic databases: CNKI, Wanfang and CQVIP. A thematic analysis approach was employed for the literature analysis.

The literature analysis pointed to 15 barriers to the release of OGD in China. Furthermore, the barriers emerged in the following three main themes: institutional barriers, data integrity and quality barriers, and user participation barriers.

This paper reports on one of the early research efforts into the problems of releasing OGD in China. Although this study focusses on Chinese context and issues, the findings and lessons learnt can be shared across international borders.

The purpose of this study is to examine the barriers to the implementation of big data (BD) in the healthcare supply chain (HSC).

First, the barriers concerning BD adoption in the HSC were found by conducting a detailed literature survey and with the expert's opinion. Then the exploratory factor analysis (EFA) was employed to categorize the barriers. The obtained results are verified using the confirmatory factor analysis (CFA). Structural equation modeling (SEM) analysis gives the path diagram representing the interrelationship between latent variables and observed variables.

The segregation of 13 barriers into three categories, namely “data governance perspective,” “technological and expertise perspective,” and “organizational and social perspective,” is performed using EFA. Three hypotheses are tested, and all are accepted. It can be concluded that the “data governance perspective” is positively related to “technological and expertise perspective” and “organizational and social perspective” factors. Also, the “technological and expertise perspective” is positively related to “organizational and social perspective.”

In literature, very few studies have been performed on finding the barriers to BD adoption in the HSC. The systematic methodology and statistical verification applied in this study empowers the healthcare organizations and policymakers in further decision-making.

This paper is first of its kind to adopt an approach to classify barriers to BD implementation in the HSC into three distinct perspectives.

The purpose of this paper is to empirically validate how the effectiveness of the most influential barriers to Six Sigma implementation may vary in relation to dimensions of organizational factors in a developing country.

An empirical survey, using 500 self‐administered questionnaires, was conducted. Data about 47 Six Sigma barriers and specific organizational parameters from 132 usable questionnaires, with a response rate of 26.4 per cent, were collected and analyzed by means of statistical data analysis package.

The results highlight the key role of soft impediments, i.e. knowledge and support, and hard impediments, i.e. professionals and finance, as the most influential barriers to Six Sigma implementation. The analysis clearly shows that only specific barriers are significantly influencing Six Sigma implementation in relation to dimensions of organizational factors.

Decision makers and quality managers should not waste their resources on overcoming all Six Sigma barriers. High attention should be given to the most obstructing barriers in relation to organizational context. Before implementing Six Sigma projects, managers are advised to activate and boost the level of Six Sigma knowledge and support by means of knowledge management functions such as Six Sigma knowledge acquisition, sharing, storing, revealing, etc. among organizational members.

The paper is one of the first studies which examines a large number of Six Sigma barriers and their effectiveness in relation to dimensions of organizational factors in a developing country.

The purpose of this paper is to identify the latent constructs of various barriers affecting Six Sigma implementation in Indian industries.

Literature review resulted in 15 frequently reported barriers in Six Sigma implementation. An empirical survey of 168 Six Sigma practitioners including green belts, black belts (BB), and master BB from 40 Indian companies was conducted with the help of a structured questionnaire. The responses were analyzed using exploratory factor analysis which resulted into five constructs.

The study proposes five constructs, namely “role of top management,” “cultural change,” “expected attitude,” “availability of resources,” and “level of quality maturity.” The focused approach by organizations to overcome barriers in Six Sigma can be oriented using these constructs.

Six Sigma implementation needs elimination of barriers in projects. Top management support in planning and resource allocation supplemented by favorable employee attitude in bringing cultural change can develop quality maturity to implement Six Sigma successfully.

This study fills the gap in the literature by studying critical success factors, critical failure factors, and barriers together. This study is one of its kinds in the Indian context which captures the views of Six Sigma certified professionals from the organizations which are implementing Six Sigma.

The growing acceptance of evidence‐based decision support systems in healthcare organizations has resulted in recognition of data quality improvement as a key area of both strategic and operational management. Information managers are faced with their emerging role in establishing quality management standards for information collection and application in the day‐to‐day delivery of health care. In the USA, rigid data‐based practice and performance standards and regulations related to information management have met with some resistance from providers. In the emerging information‐intensive healthcare environment, managers are beginning to understand the importance of formal, continuous data quality assessment in health services delivery and quality management. Variation in data quality management practice poses quality problems in such an environment, since it precludes comparative assessments across larger markets or areas, a critical component of evidence‐based quality assessments. In this study a national survey of health information managers was employed to provide a benchmark of the degree of such variation, examining how quality management practices vary across area indicators. Findings here suggest that managers continue to employ paper‐based quality assessment audits, despite nationwide mandates to adopt system‐based measures using aggregate data analysis and automated quality intervention. The level of adoption of automated quality management methods in this study varied significantly across practice characteristics and areas, suggesting the existence of data quality barriers to cross‐market comparative assessment. Implications for healthcare service delivery in an evidence‐based environment are further examined and discussed.

This paper aims to identify the basis of the technological anxiety phenomenon by defining the differences and similarities in terms of barriers of the implementation of Industry 4.0 technologies across industrial processing sector.

This paper presents a qualitative, exploratory research, and the authors apply the cross-case study method. The study is based on interviews with representatives of 11 medium-sized and large companies from industrial processing sector; specifically, the authors focus on three industries: automotive, food and furniture.

The research showed that there are similarities as well as differences in terms of identified barriers between individual industries. Taking into account the various dimensions of technological anxiety, similarities are visible, in particular, in the case of Internal processes and infrastructure and human resources, while in the other two dimensions, i.e. strategic planning and standards and security, differences between the sectors were noted.

The developed list of barriers can be a starting point for middle and senior managers of manufacturing companies to understand the sources of technological anxiety. The planning and introducing preventive and protective tools during Industry 4.0 implementation may reduce the occurrence of technological anxiety and thus ensure a smoother adoption of technologies 4.0, while respecting the organizational culture.

This work contributes to in-depth understanding of multifaced technological anxiety phenomenon. This paper classifies dimensions of existing barriers, increases the awareness on the difficulties during transformation process and, thus enables the improvement of the use of company’s internal potential.