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1 – 10 of over 202000Pratima Verma, Vimal Kumar, Ankesh Mittal, Bhawana Rathore, Ajay Jha and Muhammad Sabbir Rahman
This study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis…
Abstract
Purpose
This study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis, speed and significance. Based on these factors, the organization enhances its big data analytics (BDA) performance followed by the selection of data quality dimensions to any organization's success.
Design/methodology/approach
A fuzzy analytic hierarchy process (AHP) based research methodology has been proposed and utilized to assign the criterion weights and to prioritize the identified speed, synthesis and significance (3S) indicators. Further, the PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) technique has been used to measure the data quality dimensions considering 3S as criteria.
Findings
The effective indicators are identified from the past literature and the model confirmed with industry experts to measure these indicators. The results of this fuzzy AHP model show that the synthesis is recognized as the top positioned and most significant indicator followed by speed and significance are developed as the next level. These operational indicators contribute toward BDA and explore with their sub-categories' priority.
Research limitations/implications
The outcomes of this study will facilitate the businesses that are contemplating this technology as a breakthrough, but it is both a challenge and opportunity for developers and experts. Big data has many risks and challenges related to economic, social, operational and political performance. The understanding of data quality dimensions provides insightful guidance to forecast accurate demand, solve a complex problem and make collaboration in supply chain management performance.
Originality/value
Big data is one of the most popular technology concepts in the market today. People live in a world where every facet of life increasingly depends on big data and data science. This study creates awareness about the role of 3S encountered during big data quality by prioritizing using fuzzy AHP and PROMETHEE.
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Daniel P. Lorence and Robert Jameson
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…
Abstract
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.
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Rose Clancy, Ken Bruton, Dominic T.J. O’Sullivan and Aidan J. Cloonan
Quality management practitioners have yet to cease the potential of digitalisation. Furthermore, there is a lack of tools such as frameworks guiding practitioners in the digital…
Abstract
Purpose
Quality management practitioners have yet to cease the potential of digitalisation. Furthermore, there is a lack of tools such as frameworks guiding practitioners in the digital transformation of their organisations. The purpose of this study is to provide a framework to guide quality practitioners with the implementation of digitalisation in their existing practices.
Design/methodology/approach
A review of literature assessed how quality management and digitalisation have been integrated. Findings from the literature review highlighted the success of the integration of Lean manufacturing with digitalisation. A comprehensive list of Lean Six Sigma tools were then reviewed in terms of their effectiveness and relevance for the hybrid digitisation approach to process improvement (HyDAPI) framework.
Findings
The implementation of the proposed HyDAPI framework in an industrial case study led to increased efficiency, reduction of waste, standardised work, mistake proofing and the ability to root cause non-conformance products.
Research limitations/implications
The activities and tools in the HyDAPI framework are not inclusive of all techniques from Lean Six Sigma.
Practical implications
The HyDAPI framework is a flexible guide for quality practitioners to digitalise key information from manufacturing processes. The framework allows organisations to select the appropriate tools as needed. This is required because of the varying and complex nature of organisation processes and the challenge of adapting to the continually evolving Industry 4.0.
Originality/value
This research proposes the HyDAPI framework as a flexible and adaptable approach for quality management practitioners to implement digitalisation. This was developed because of the gap in research regarding the lack of procedures guiding organisations in their digital transition to Industry 4.0.
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Rohit Agrawal, Vishal Ashok Wankhede, Anil Kumar and Sunil Luthra
This work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify associated…
Abstract
Purpose
This work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify associated literature gaps and to provide a future research direction in the field of DDQM in SCs.
Design/methodology/approach
A systematic literature review was done in the field of DDQM in SCs. SCOPUS database was chosen to collect articles in the selected field and then an SLR methodology has been followed to review the selected articles. The bibliometric and network analysis has also been conducted to analyze the contributions of various authors, countries and institutions in the field of DDQM in SCs. Network analysis was done by using VOS viewer package to analyze collaboration among researchers.
Findings
The findings of the study reveal that the adoption of data-driven technologies and quality management tools can help in strategic decision making. The usage of data-driven technologies such as artificial intelligence and machine learning can significantly enhance the performance of SC operations and network.
Originality/value
The paper discusses the importance of data-driven techniques enabling quality in SC management systems. The linkage between the data-driven techniques and quality management for improving the SC performance was also elaborated in the presented study.
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Qasim Ali Nisar, Nadia Nasir, Samia Jamshed, Shumaila Naz, Mubashar Ali and Shahzad Ali
This study is undertaken to examine the antecedents and role of big data decision-making capabilities toward decision-making quality and environmental performance among the…
Abstract
Purpose
This study is undertaken to examine the antecedents and role of big data decision-making capabilities toward decision-making quality and environmental performance among the Chinese public and private hospitals. It also examined the moderating effect of big data governance that was almost ignored in previous studies.
Design/methodology/approach
The target population consisted of managerial employees (IT experts and executives) in hospitals. Data collected using a survey questionnaire from 752 respondents (374 respondents from public hospitals and 378 respondents from private hospitals) was subjected to PLS-SEM for analysis.
Findings
Findings revealed that data management challenges (leadership focus, talent management, technology and organizational culture for big data) are significant antecedents for big data decision-making capabilities in both public and private hospitals. Moreover, it was also found that big data decision-making capabilities played a key role to improve the decision-making quality (effectiveness and efficiency), which positively contribute toward environmental performance in public and private hospitals of China. Public hospitals are playing greater attention to big data management for the sake of quality decision-making and environmental performance than private hospitals.
Practical implications
This study provides guidelines required by hospitals to strengthen their big data capabilities to improve decision-making quality and environmental performance.
Originality/value
The proposed model provides an insight look at the dynamic capabilities theory in the domain of big data management to tackle the environmental issues in hospitals. The current study is the novel addition in the literature, and it identifies that big data capabilities are envisioned to be a game-changer player in effective decision-making and to improve the environmental performance in health sector.
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Rose Clancy, Dominic O'Sullivan and Ken Bruton
Data-driven quality management systems, brought about by the implementation of digitisation and digital technologies, is an integral part of improving supply chain management…
Abstract
Purpose
Data-driven quality management systems, brought about by the implementation of digitisation and digital technologies, is an integral part of improving supply chain management performance. The purpose of this study is to determine a methodology to aid the implementation of digital technologies and digitisation of the supply chain to enable data-driven quality management and the reduction of waste from manufacturing processes.
Design/methodology/approach
Methodologies from both the quality management and data science disciplines were implemented together to test their effectiveness in digitalising a manufacturing process to improve supply chain management performance. The hybrid digitisation approach to process improvement (HyDAPI) methodology was developed using findings from the industrial use case.
Findings
Upon assessment of the existing methodologies, Six Sigma and CRISP-DM were found to be the most suitable process improvement and data mining methodologies, respectively. The case study revealed gaps in the implementation of both the Six Sigma and CRISP-DM methodologies in relation to digitisation of the manufacturing process.
Practical implications
Valuable practical learnings borne out of the implementation of these methodologies were used to develop the HyDAPI methodology. This methodology offers a pragmatic step by step approach for industrial practitioners to digitally transform their traditional manufacturing processes to enable data-driven quality management and improved supply chain management performance.
Originality/value
This study proposes the HyDAPI methodology that utilises key elements of the Six Sigma DMAIC and the CRISP-DM methodologies along with additions proposed by the author, to aid with the digitisation of manufacturing processes leading to data-driven quality management of operations within the supply chain.
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Helinä Melkas and Vesa Harmaakorpi
The purpose of this article is to investigate data, information and knowledge in regional innovation networks. Emphasis has been put recently on regional innovation systems, where…
Abstract
Purpose
The purpose of this article is to investigate data, information and knowledge in regional innovation networks. Emphasis has been put recently on regional innovation systems, where various actors are involved in innovative processes. The article responds to the need to study matters related to knowledge management and information quality in such environments.
Design/methodology/approach
Regional innovation networks and data, information and knowledge as well as research on them are discussed at a theoretical level. An existing innovation network of the Lahti region, Finland, was utilised as a pilot environment when building the knowledge management framework that is introduced. The framework is based on established knowledge management literature and practice.
Findings
The results confirm that the aspects of data, information and knowledge need to be addressed systematically in regional innovation networks. They are intertwined with knowledge management and network management. The knowledge management framework introduced incorporates, apart from information quality considerations, future‐oriented self‐transcending knowledge as well as knowledge vision and knowledge assets. Considerations of absorptive capacity and information brokerage in the regional knowledge environment are emphasised.
Research limitations/implications
The limitations of the framework will be assessed in future studies. This will also improve understanding of practical implications. Research implications are related to data, information and knowledge quality – as well as absorptive capacity between the two subsystems of the regional innovation system.
Originality/value
The article combines in a novel way research fields that have previously barely been combined – information quality, knowledge management and regional innovation networks. It provides new insights into a societally important theme and shows possible avenues of further research.
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Ruihan Zhao, Liang Luo, Pengzhong Li and Jinguang Wang
Quality management systems are commonly applied to meet the increasingly stringent requirements for product quality in discrete manufacturing industries. However, traditional…
Abstract
Purpose
Quality management systems are commonly applied to meet the increasingly stringent requirements for product quality in discrete manufacturing industries. However, traditional experience-driven quality management methods are incapable of handling heterogeneous data from multiple sources, leading to information islands. This study aims to present a quality management key performance indicator visualization (QM-KPIVIS) system to enable integrated quality control and ultimately ensure product quality.
Design/methodology/approach
Based on multiple heterogeneous data, an integrated approach is proposed to quantify explicitly the relationship between Internet of Things data and product quality. Specifically, this study identifies the tracing path of quality problems based on multiple heterogeneous quality information tree. In addition, a hierarchical analysis approach is adopted to calculate the key performance indicators of quality influencing factors in the quality control process.
Findings
Proposed QM-KPIVIS system consists of data visualization, quality problem processing, quality optimization and user rights management modules, which perform in a well-coordinated manner. An empirical study was also conducted to validate the effectiveness of proposed system.
Originality/value
To the best of the authors’ knowledge, this study is the first attempt to use industrial Internet of Things and multisource heterogeneous data for integrated product quality management. Proposed approach is more user-friendly and intuitive compared to traditional empirically driven quality management methods and has been initially applied in the manufacturing industry.
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Franziska Franke and Martin R.W. Hiebl
Existing research on the relationship between big data and organizational decision quality is still few and far between, and what does exist often assumes direct effects of big…
Abstract
Purpose
Existing research on the relationship between big data and organizational decision quality is still few and far between, and what does exist often assumes direct effects of big data on decision quality. More recent research indicates that such direct effects may be too simplistic, and in particular, an organization’s overall human skills are often not considered sufficiently. Inspired by the knowledge-based view, we therefore propose that interactions between three aspects of big data usage and management accountants’ data analytics skills may be key to reaching high-quality decisions. The purpose of this study is to test these predictions based on a survey of US firms.
Design/methodology/approach
The authors draw on survey data from 140 US firms. This survey has been conducted via MTurk in 2020.
Findings
The results of the study show that the quality of big data sources is associated with higher perceived levels of decision quality. However, according to the results, the breadth of big data sources and a data-driven culture only improve decision quality if management accountants’ data analytics skills are highly developed. These results point to the important, but so far unexamined role of an organization’s management accountants and their skills for translating big data into high-quality decisions.
Practical implications
The present study highlights the importance of an organization’s human skills in creating value out of big data. In particular, the findings imply that management accountants may need to increasingly draw on data analytics skills to make the most out of big data for their employers.
Originality/value
This study is among the first, to the best of the authors’ knowledge, to provide empirical proof of the relevance of an organization’s management accountants and their data analytics skills for reaching desirable firm-level outcomes. In addition, this study thus adds to the further advancement of the knowledge-based view by providing evidence that in contemporary big-data environments, interactions between tacit and explicit knowledge seem crucial for driving desirable firm-level outcomes.
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The purpose of this paper is to share the experience of the document discovery process, during the implementation of an asset management system for a rail company. This system…
Abstract
Purpose
The purpose of this paper is to share the experience of the document discovery process, during the implementation of an asset management system for a rail company. This system will deliver comprehensive enterprise asset management information from a single source, with information provided to mobile devices, for use by field workers. This case study presents the challenges encountered in the search, retrieval and management of documentation for use on a daily basis for civil standard maintenance tasks.
Design/methodology/approach
Evidence gathered for this paper was a result of direct and participant observation over a period of 18 months from 2014 to 2016. As a member of the project team, certain privileges were accorded to the researcher who was placed in a unique position to act as the main research instrument, able to collect data on the systems used as well as the everyday practices on information capture and document production.
Findings
Document quality and standards can be overlooked or deemed as not crucial; the value, significance and importance of documentation are lost when no one takes ownership; the understanding and application of standards, quality management and governance can have a direct bearing on the effective management and control of documents and subsequent records produced.
Research limitations/implications
Research is limited, as this is a single case study.
Practical implications
By highlighting the challenges faced and the resolutions used, this paper hopes to offer a level of practical guidance with the detection process for maintenance tasks for the civil assets discipline for a rail network.
Originality/value
This case study contributes to the understanding of quality management and the role it plays in document management and in turn the search and retrieval process. It provides evidence that documents must be systematically managed and controlled to limit risk both internally and externally.
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