Search results

The purpose of this study is to propose a sustainable quality assessment approach (model) for the e-learning systems keeping software perspective under consideration. E-learning is becoming mainstream due to its accessibility, state-of-the-art learning, training ease and cost effectiveness. However, the poor quality of e-learning systems is one of the major causes of several failures reported. Moreover, this arena lacks well-defined quality assessment measures. Hence, it is quite difficult to measure the overall quality of an e-learning system effectively.

A pragmatic mixed-model philosophy was adopted for this study. A systematic literature review was performed to identify existing e-learning quality models and frameworks. Semi-structured interviews were conducted with e-learning experts following empirical investigations to identify the crucial quality characteristics of e-learning systems. Various statistical tests like principal component analysis, logistic regression, chi-square and analysis of means were applied to analyze the empirical data. These led to an adequate set of quality indicators that can be used by higher education institutions to assure the quality of e-learning systems.

A sustainable quality assessment model for the information delivery in e-learning systems in software perspective has been proposed by exploring the state-of-the-art quality assessment/evaluation models and frameworks proposed for the e-learning systems. The proposed model can be used to assess and improve the process of information discovery and delivery of e-learning.

The results obtained led to conclude that very limited attention is given to the quality of e-learning tools despite the importance of quality and its effect on e-learning system adoption and promotion. Moreover, the identified models and frameworks do not adequately address quality of e-learning systems from a software perspective.

This paper is focused at studying the current state of research involving the four dimensions of defect management strategy, i.e. software defect analysis, software quality, software reliability and software development cost/effort.

The methodology developed by Kitchenham (2007) is followed in planning, conducting and reporting of the systematic review. Out of 625 research papers, nearly 100 primary studies related to our research domain are considered. The study attempted to find the various techniques, metrics, data sets and performance validation measures used by researchers.

The study revealed the need for integrating the four dimensions of defect management and studying its effect on software performance. This integrated approach can lead to optimal use of resources in software development process.

There are many dimensions in defect management studies. The authors have considered only vital few based on the practical experiences of software engineers. Most of the research work cited in this review used public data repositories to validate their methodology and there is a need to apply these research methods on real datasets from industry to realize the actual potential of these techniques.

The authors believe that this paper provides a comprehensive insight into the various aspects of state-of-the-art research in software defect management. The authors feel that this is the only research article that delves into the four facets namely software defect analysis, software quality, software reliability and software development cost/effort.

Although a large amount of literature about the levels, aims, and relevance of integrated care is present, to realise change in practice knowledge about the implementation and development process of integrated care is also crucial. Instruments such as quality management models can facilitate improvement, but are not frequently used in integrated care practice. The purpose of this paper, therefore, is to present further insight into these models and into the related literature about network and organisational development.

An overview of the recent literature is presented.

The improvement of integrated care is complex and there is no consensus about a set of relevant elements for integrated care. Available quality management models vary in their underlying evidence and do not have integrated care as their central focus or are aimed at specific patient groups such as the chronically ill. The lack of a consistent set of elements and the need for a generic, evidence‐based quality management model for integrated care is important for integrated care improvement. It can be assumed that, as described in the literature about networks and organisations, dynamic stages or phases could be relevant for integrated care. These issues raise important next questions for further research and for facilitating organisational change.

The paper presents a current overview of the available literature about the concept of integrated care, with a special focus on integrated care improvement and its dynamics. It raises the relevant questions and challenges for the further expansion of knowledge about these topics, which will be addressed in a second article in a later issue of Journal of Integrated Care.

This study aims to propose a comprehensive model for assessing the quality of academic programs in higher education institutions (HEIs) by adopting the TQM philosophy.

Based on a sample of 377 responses from higher education academics, experts and professionals in Sweden, partial least squares structural equation modeling (PLS-SEM) technique was used to empirically test the proposed hypotheses and validate the model.

The model reveals that the input-based factors have a stronger impact on the process-based factors; while process-based factors have less impact on the output-based factors. The input-based factors: adoption of international pedagogy standards (IPS), education infrastructure (EDI), and work/study environment (WSE) through the process-based factors quality of students (QOS) and quality of faculty staff (QFS) is found to have a significant impact on output-based factor quality of academic programs (QAP).

This study has been conducted in Sweden. Inclusion of other countries provides opportunities for further analysis by conducting cross-comparison between different cultures in higher education, and including additional stakeholders such as policymakers, parents and students.

This research also contributes to practice by providing an in-depth understanding of the relationships among variables that affect the quality of academic programs in HEIs, and provides insights to internally assess the quality levels of their academic programs

This study contributes to the knowledge by providing a holistic view in which it integrates input, process and output perspectives in a conceptual model to assess the quality of academic programs at the higher education level.

This purpose of this paper is to assess information technology (IT) disaster recovery plans (DRPs) in publicly listed companies on Abu Dhabi securities exchange (ADX) in the United Arab Emirates. The authors assessed, among other things, DRP preparedness, documentation, employees’ preparedness and awareness and the most significant physical and logical risks that pose the most threads to drive the development of the DRP, etc.

The authors surveyed publicly listed companies on the ADX using a questionnaire adapted from past research papers as well as from audit programs published by the Information Systems Audit and Control Association. The surveys were completed through interviews with middle and senior management familiar with their firm’s IT practices.

The majority of the respondents reported having a DRP, and a significant number of the respondents reported that their top management were extremely committed to their DRP. Employees were generally aware of their role and the existence of the DRP. The greatest risk/threat to their organization’s IT system was logical risk followed closely by power and network connectivity loss as the second highest physical risk. The most highly ranked consequence of an IT disaster was loss of confidence in the organization.

Because this paper only examined publicly listed companies on ADX, the research results may lack generality. Therefore, further research is needed in this area for determining the extent of the deployment of the DRP in the region.

Results of this paper could be used for IT DRP planning bench-marking purposes.

This paper adds value to research by investigating the current IT DRP practices by public companies listed on ADX.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

The purpose of this paper is to analyze the evolution of total quality management (TQM) models, frameworks, and tools and techniques in higher education (HE) over the last thirty years from 1991 till 2020, based on a literature review

30 articles from 52 journals were used to perform this detailed literature review. For the detailed analysis, the focus was only on articles related to TQM in higher education and specifically related to models, frameworks and tools and techniques. The study has investigated the growth of research articles, research streams, research methodologies, models and frameworks in the higher education sector and tools and techniques related to those.

This review addresses the progress and gaps in the application of TQM in HE, including the shift in global research in this area from the USA and Europe to Asia in recent years. The articles have been classified into four research methodologies and two research streams which have been reviewed in detail. The findings include reasons for multiple models/frameworks in HE proposed by researchers over the years and the importance of tools and techniques used in TQM implementation.

This study, which tries to bring a perspective to the main trends in TQM application to higher education wrt models, frameworks, tools and techniques over the last thirty years, is expected to add to the body of knowledge in this area and help future researchers to focus on the relevant areas identified in this paper.

Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.

Combining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.

Computer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.

This paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.

The purpose of this paper is to propose a national framework for promoting business excellence (NFPBE) using a quality management assessment framework (QMAF) with information knowledge communication (IKC) as an enabler.

The NFPBE using the plan–do–check–act (PDCA) cycle is developed, re-enforcing key quality management aspects/areas including national quality awards, QMAF summary and national innovation programmes using a meta-analysis.

The operationalising of the NFPBE is enabled and supported by an auditing tool to assess the degree of mentoring provided by universities/research institutions/consulting firms to participating firms whose progress will be assessed by the QMAF. It was emphasised that the success of the framework requires the buy-in of national governments to support the programme through stimulus incentives such as government assistance with the costs of research and development, and cooperative research ventures between universities and companies.

The framework is conceptualised into a process flowchart, which is a blueprint to advancing business excellence in organisations at a national scale. The study is limited to only the conceptualisation of the framework. Therefore, an extended study of the framework implementation/application is required for revealing implementation guidelines.

The national framework has a propensity to enhancing the business excellence of organisations at a national level. Therefore, quality managers and policy makers could use the framework to understand the quality management shortfalls and consider strategies to achieving business excellence.

This research study proposed a blueprint to advancing quality excellence in organisations at a national level, guided by several quality frameworks.

The purpose of this paper is to overcome evaluative limitations of previous studies to provide a more decisive test of the causal relationships implied in the Baldrige Criteria for Performance Excellence (CPE) using a unique data source.

The authors employ partial least squares path modeling on blinded scoring data from Baldrige Award applicants. In addition, the authors conduct multi-group analysis to examine whether the hypothesized causal model is universal across different industry sectors.

The path analysis provided strong support for the CPE framework in its entirety. However, analysis of sector-specific subsets of the data did not confirm all relationships, suggesting the possibility of industry-dependent performance excellence frameworks and raising new research questions to be explored.

This research offers several pertinent implications for managers who seek to translate the theoretical CPE framework to actionable quality-improvement efforts.

CPE operationalizes many total quality management (TQM) concepts and provides guidelines to TQM programs. This study validates the CPE framework using the most relevant data set to date – the applicant scoring data. The authors are also the first to investigate the cross-industry differences in the relationships between the CPE constructs.