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The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

The purpose of this paper is to investigate whether the different approaches to the classification of quality attributes deliver consistent results.

The investigation includes four approaches and enables comparisons to be made from a methodological perspective and from an output perspective. The different approaches are described, analyzed, and discussed in the context of an empirical study that investigates how 430 respondents perceive the performance of an e‐service. The theory of attractive quality rests on a solid theoretical foundation and a methodological approach to classify quality attributes. Recently, various authors have suggested alternative approaches to the traditional five‐level Kano questionnaire – including a three‐level Kano questionnaire, direct classification, and a dual‐importance grid.

The classification of quality attributes are found to be dependent on the approach that is utilized. The development of new ways to classify quality attributes should follow rigid procedures to provide reliable and consistent results.

This is the first attempt to compare alternative approaches to classify quality attributes. For managers, our results provide guidance on what approach to choose based on the strengths and weaknesses with the different approaches.

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 provide empirical evidence regarding the classification of European countries based on accounting quality metrics. The authors investigate whether the grouping of countries based on accounting quality levels differs from other classifications based on accounting practices or country-specific factors identified in previous studies.

The authors run panel data regressions for 2.078 European listed companies using value relevance and earnings smoothing metrics. The authors also apply cluster analysis to classify the countries.

The results suggest that the adoption of a common set of International Financial Reporting Standards (IFRS) did not lead to a similar level of accounting quality of financial information. The authors identified three clusters of countries that are not coincident with previous classifications.

The results show that the adoption of different accounting practices allowed in IFRS does not necessarily influence accounting quality.

The results suggest that the way regulators decided to incorporate IFRS into national accounting systems is one issue that may be relevant in explaining the three clusters.

The paper provides empirical evidence that supports two theoretical assertions. The first is that a classification depends entirely on the characteristics used to represent the countries being classified. The second is that the adoption of a single set of accounting standards does not determine similar accounting practices and does not lead to similar levels of accounting quality.

This study aims to propose a service quality evaluation model for health-care services.

In this study, a service quality evaluation model is proposed based on the service quality measurement (SERVQUAL) scale and machine learning algorithm. Primarily, items that affect the quality of service are determined based on the SERVQUAL scale. Subsequently, a service quality assessment model is generated to manage the resources that are allocated to improve the activities efficiently. Following this phase, a sample of classification model is conducted. Machine learning algorithms are used to establish the classification model.

The proposed evaluation model addresses the following questions: What are the potential impact levels of service quality dimensions on the quality of service practically? What should be prioritization among the service quality dimensions and Which dimensions of service quality should be improved primarily? A real-life case study in a public hospital is carried out to reveal how the proposed model works. The results that have been obtained from the case study show that the proposed model can be conducted easily in practice. It is also found that there is a remarkably high-service gap in the public hospital, in which the case study has been conducted, regarding the general physical conditions and food services.

The primary contribution of this study is threefold. The proposed evaluation model determines the impact levels of service quality dimensions on the service quality in practice. The proposed evaluation model prioritizes service quality dimensions in terms of their significance. The proposed evaluation model finds out the answer to the question of which service quality dimensions should be improved primarily?

Ensuring quality is one of the most significant challenges in microtask crowdsourcing tasks. Aggregation of the collected data from the crowd is one of the important steps to infer the correct answer, but the existing study seems to be limited to the single-step task. This study aims to look at multiple-step classification tasks and understand aggregation in such cases; hence, it is useful for assessing the classification quality.

The authors present a model to capture the information of the workflow, questions and answers for both single- and multiple-question classification tasks. They propose an adapted approach on top of the classic approach so that the model can handle tasks with several multiple-choice questions in general instead of a specific domain or any specific hierarchical classifications. They evaluate their approach with three representative tasks from existing citizen science projects in which they have the gold standard created by experts.

The results show that the approach can provide significant improvements to the overall classification accuracy. The authors’ analysis also demonstrates that all algorithms can achieve higher accuracy for the volunteer- versus paid-generated data sets for the same task. Furthermore, the authors observed interesting patterns in the relationship between the performance of different algorithms and workflow-specific factors including the number of steps and the number of available options in each step.

Due to the nature of crowdsourcing, aggregating the collected data is an important process to understand the quality of crowdsourcing results. Different inference algorithms have been studied for simple microtasks consisting of single questions with two or more answers. However, as classification tasks typically contain many questions, the proposed method can be applied to a wide range of tasks including both single- and multiple-question classification tasks.

Indicates that it is 14 years since the Beavis Consultative Committee recommended that the matter of hotel registration and its effectiveness be reviewed within three to five years. Presents definitions of registration, classification and grading together with a recent historical perspective of the developments of statutory registration in the United Kingdom. Examines the reasons for statutory registration and describes existing UK compulsory schemes outside the mainland. Contrasts the lack of progress in the UK, and the degree to which price and tariff controls are in operation, with schemes in the European Community. A brief literature review presents the support for statutory registration and classification. Re‐examines the eight conclusions of the Beavis Consultative Committee. Concludes that it may be pertinent to review the position of statutory hotel registration, classification and grading.

The purpose of this paper is to set out to explore how cancer patients and their carers perceive and evaluate the healthcare experience in order to develop and validate a classification framework for experience quality in healthcare.

The empirical work is centred on the systematic analysis of 200 cancer patient stories published on an independent healthcare feedback web site. Using the critical incident method, the authors captured 1,351 experience quality data items. Three judges independently sorted and classified these data items.

The authors identify and describe 22 main categories and 51 sub-categories that underlie the experience quality concept in healthcare and present them in a classification framework. The framework is informed through the categorisation of direct, indirect, and independent interactions. It also suggests a relationship between experience quality and satisfaction and loyalty behaviours.

This study provides researchers with a foundation for the further development and validation of a measurement scale for experience quality in healthcare.

The framework assists managers and healthcare professionals with the definition, evaluation, and improvement of the quality of the experience of patients and their carers.

The main contributions of this study lie in: first, a comprehensive classification framework for experience quality in healthcare; second, dimensions that extend existing health service quality models; third, dimensions that contextualise the generic concept of customer experience quality to healthcare.

The purpose of this paper is to present a multi‐faceted summary and classification of the existing literature in the field of quality of service for e‐government and outline the main components of a quality model for e‐government services.

Starting with fundamental quality principles the paper examines and analyzes 36 different quality approaches concerning public sector services, e‐services in general and more specifically e‐government services. Based on the dimensions measured by each approach the paper classifies the approaches and concludes on the basic factors needed for the development of a complete quality model of e‐government services.

Based on the classification of literature approaches, the paper provides information about the main components of a quality model that may be used for the continuous monitoring and measuring of public e‐services' quality. The classification forms the basis for answering questions that must be addressed by the quality model, such as: What to assess?; Who will perform the assessment? and How the assessment will be done?

This model can be used by the management of public organizations in order to measure and monitor the quality of e‐services delivered to citizens.

The results of the work presented in this paper form the basis for the development of a quality model for e‐government services.

This is an initial attempt to classify natural roofing slate quality using the new European Standard BS EN 12326 and suggest how such classification may be employed to predict in‐service performance and identify potentially problematic materials.

A wide range of natural roofing slates has been subjected to the new standard testing regime and additional tests carried out. Experience of known in‐service performance and previous test results have also been taken into consideration.

An initial classification of natural roofing slate quality has been proposed with the flexural strength, water absorption, potential for oxidation and carbonate content considered to be the key components.

There is considerable scope for refinement of the proposed classification by investigating the performance of the wide range of other natural roofing slates available and taking the results into consideration. Predicted in‐service performance is based on practical experience and can be considered only a general guide.

By classifying natural roofing slate quality users will be able to make better informed purchasing decisions based on cost versus quality. Slate producers, especially those with higher quality slates, will also be able to market their materials accordingly with less chance of losing out to lower quality, potentially problematic materials that still conform to the new standard.

The concept of a quality classification for natural roofing slate is not new, but this has been omitted during the creation of the new standard. The proposed classification is broader and probably better defined than those in existence elsewhere or previously used within the European Union member states.