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Article
Publication date: 4 April 2016

Mahdi Zahedi Nooghabi and Akram Fathian Dastgerdi

One of the most important categories in linked open data (LOD) quality models is “data accessibility.” The purpose of this paper is to propose some metrics and indicators…

Abstract

Purpose

One of the most important categories in linked open data (LOD) quality models is “data accessibility.” The purpose of this paper is to propose some metrics and indicators for assessing data accessibility in LOD and the semantic web context.

Design/methodology/approach

In this paper, at first the authors consider some data quality and LOD quality models to review proposed subcategories for data accessibility dimension in related texts. Then, based on goal question metric (GQM) approach, the authors specify the project goals, main issues and some questions. Finally, the authors propose some metrics for assessing the data accessibility in the context of the semantic web.

Findings

Based on GQM approach, the authors determined three main issues for data accessibility, including data availability, data performance, and data security policy. Then the authors created four main questions related to these issues. As a conclusion, the authors proposed 27 metrics for measuring these questions.

Originality/value

Nowadays, one of the main challenges regarding data quality is the lack of agreement on widespread quality metrics and practical instruments for evaluating quality. Accessibility is an important aspect of data quality. However, few researches have been done to provide metrics and indicators for assessing data accessibility in the context of the semantic web. So, in this research, the authors consider the data accessibility dimension and propose a comparatively comprehensive set of metrics.

Details

Program, vol. 50 no. 2
Type: Research Article
ISSN: 0033-0337

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Article
Publication date: 3 August 2021

Pratima 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…

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.

Details

The TQM Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1754-2731

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Article
Publication date: 21 May 2021

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…

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.

Details

The TQM Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1754-2731

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Book part
Publication date: 2 November 2009

Gerd Sammer

More than ever before, public transit must compete in the transport market. This competition is, on the one hand, against steadily increasing car traffic; and on the other…

Abstract

More than ever before, public transit must compete in the transport market. This competition is, on the one hand, against steadily increasing car traffic; and on the other hand, between public transit operators. This, in turn, leads to new demands regarding the type, content and quality of data needed for planning and management. Frequently, traditional travel behaviour surveys do not provide sufficiently accurate and detailed information about public transit demand. To plan public transit, frequently a precise description of all trip stages, including the first and the last mile, is necessary. To achieve this, an adaptation of the traditional survey methods is necessary. In many countries, public transit associations have been established to integrate services offered by individual public transit operators with the help of through-ticketing and a coordination of lines and timetables into what looks, to the user, like a single system. To distribute revenue among the operators involved, detailed surveys of passengers are needed. Measuring the quality of public transit service and surveying customer satisfaction are new tasks. Such data are the basis for quality assurance and are essential for gaining and keeping customers of the public transit system. New technologies such as the Global Positioning System, automated passenger counts and Smart Card Payment Systems offer new possibilities to collect data more efficiently and cost-effectively. This article covers essential aspects of surveys and the collection of data that are crucial for the planning and management of public transit; it points to state-of-the-art methods and offers potential solutions.

Details

Transport Survey Methods
Type: Book
ISBN: 978-1-84-855844-1

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Book part
Publication date: 8 July 2013

Sarah H. Theimer

Quality, an abstract concept, requires concrete definition in order to be actionable. This chapter moves the quality discussion from the theoretical to the workplace…

Abstract

Purpose

Quality, an abstract concept, requires concrete definition in order to be actionable. This chapter moves the quality discussion from the theoretical to the workplace, building steps needed to manage quality issues.

Methodology

The chapter reviews general data studies, web quality studies, and metadata quality studies to identify and define dimensions of data quality and quantitative measures for each concept. The chapter reviews preferred communication methods which make findings meaningful to administrators.

Practical implications

The chapter describes how quality dimensions are practically applied. It suggests criteria necessary to identify high priority populations, and resources in core subject areas or formats, as quality does not have to be completely uniform. The author emphasizes examining the information environment, documenting practice, and developing measurement standards. The author stresses that quality procedures must rapidly evolve to reflect local expectations, the local information environment, technology capabilities, and national standards.

Originality/value

This chapter combines theory with practical application. It stresses the importance of metadata and recognizes quality as a cyclical process which balances the necessity of national standards, the needs of the user, and the work realities of the metadata staff. This chapter identifies decision points, outlines future action, and explains communication options.

Details

New Directions in Information Organization
Type: Book
ISBN: 978-1-78190-559-3

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Book part
Publication date: 9 August 2017

Kathleen McDonald, Sandra Fisher and Catherine E. Connelly

As e-HRM systems move into the ‘smart’ technology realm, expectations and capabilities for both the automational and informational features of e-HRM systems are…

Abstract

Purpose

As e-HRM systems move into the ‘smart’ technology realm, expectations and capabilities for both the automational and informational features of e-HRM systems are increasing. This chapter uses the well-established DeLone and McLean (D&M) model from the information systems literature to analyze how a smart workforce management system can create value for an organization.

Methodology/approach

The chapter is based on an exploratory case study conducted with a North American industrial products firm. We review three systems-level predictors of success from the D&M model (system quality, information quality, and service quality) and evaluate the company’s systems on these attributes.

Findings

The company’s e-HRM systems fall short on the information quality dimension, which limits potential for overall system success related to smart workforce management.

Research limitations/implications

The e-HRM literature focuses on individual-level factors of system success, while the D&M model uses more macro factors. Blending these may help researchers and practitioners develop a more complete view of e-HRM systems. Conclusions from this chapter are limited due to the use of a single, exploratory case study.

Practical implications

Companies must pay attention to all three predictors of system quality when developing smart workforce management systems. In particular, implementation of a data governance program could help companies improve information quality of their systems.

Originality/value

This chapter adds to the literature on smart workforce management by using a model from the information systems literature and a practical example to explore how such a system could add value.

Details

Electronic HRM in the Smart Era
Type: Book
ISBN: 978-1-78714-315-9

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Book part
Publication date: 11 June 2009

Anca E. Cretu and Roderick J. Brodie

Companies in all industries are searching for new sources of competitive advantage since the competition in their marketplace is becoming increasingly intensive. The…

Abstract

Companies in all industries are searching for new sources of competitive advantage since the competition in their marketplace is becoming increasingly intensive. The resource-based view of the firm explains the sources of sustainable competitive advantages. From a resource-based view perspective, relational based assets (i.e., the assets resulting from firm contacts in the marketplace) enable competitive advantage. The relational based assets examined in this work are brand image and corporate reputation, as components of brand equity, and customer value. This paper explores how they create value. Despite the relatively large amount of literature describing the benefits of firms in having strong brand equity and delivering customer value, no research validated the linkage of brand equity components, brand image, and corporate reputation, simultaneously in the customer value–customer loyalty chain. This work presents a model of testing these relationships in consumer goods, in a business-to-business context. The results demonstrate the differential roles of brand image and corporate reputation on perceived quality, customer value, and customer loyalty. Brand image influences the perception of quality of the products and the additional services, whereas corporate reputation actions beyond brand image, estimating the customer value and customer loyalty. The effects of corporate reputation are also validated on different samples. The results demonstrate the importance of managing brand equity facets, brand image, and corporate reputation since their differential impacts on perceived quality, customer value, and customer loyalty. The results also demonstrate that companies should not limit to invest only in brand image. Maintaining and enhancing corporate reputation can have a stronger impact on customer value and customer loyalty, and can create differential competitive advantage.

Details

Business-To-Business Brand Management: Theory, Research and Executivecase Study Exercises
Type: Book
ISBN: 978-1-84855-671-3

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Article
Publication date: 14 January 2021

Fereshte Shabani-Naeeni and R. Ghasemy Yaghin

In the data-driven era, the quality of the data exchanged between suppliers and buyer can enhance the buyer’s ability to appropriately cope with the risks and…

Abstract

Purpose

In the data-driven era, the quality of the data exchanged between suppliers and buyer can enhance the buyer’s ability to appropriately cope with the risks and uncertainties associated with raw material purchasing. This paper aims to address the issue of supplier selection and purchasing planning considering the quality of data by benefiting from suppliers’ synergistic effects.

Design/methodology/approach

An approach is proposed to measure data visibility’s total value using a multi-stage algorithm. A multi-objective mathematical optimization model is then developed to determine the optimal integrated purchasing plan in a multi-product setting under risk. The model contemplates three essential objective functions, i.e. maximizing total data quality and quantity level, minimizing purchasing risks and minimizing total costs.

Findings

With emerging competitive areas, in the presence of industry 4.0, internet of things and big data, high data quality can improve the process of supply chain decision-making. This paper supports the managers for the procurement planning of modern organizations under risk and thus provides an in-depth understanding for the enterprises having the readiness for industry 4.0 transformation.

Originality/value

Various data quality attributes are comprehensively subjected to deeper analysis. An applicable procedure is proposed to determine the total value of data quality and quantity required for supplier selection. Besides, a novel multi-objective optimization model is developed to determine the purchasing plan under risk.

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Book part
Publication date: 7 October 2015

Azizah Ahmad

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive…

Abstract

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

Details

Sustaining Competitive Advantage Via Business Intelligence, Knowledge Management, and System Dynamics
Type: Book
ISBN: 978-1-78441-764-2

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Article
Publication date: 10 March 2021

Manfred Vielberth, Ludwig Englbrecht and Günther Pernul

In the past, people were usually seen as the weakest link in the IT security chain. However, this view has changed in recent years and people are no longer seen only as a…

Abstract

Purpose

In the past, people were usually seen as the weakest link in the IT security chain. However, this view has changed in recent years and people are no longer seen only as a problem, but also as part of the solution. In research, this change is reflected in the fact that people are enabled to report security incidents that they have detected. During this reporting process, however, it is important to ensure that the reports are submitted with the highest possible data quality. This paper aims to provide a process-driven quality improvement approach for human-as-a-security-sensor information.

Design/methodology/approach

This work builds upon existing approaches for structured reporting of security incidents. In the first step, relevant data quality dimensions and influencing factors are defined. Based on this, an approach for quality improvement is proposed. To demonstrate the feasibility of the approach, it is prototypically implemented and evaluated using an exemplary use case.

Findings

In this paper, a process-driven approach is proposed, which allows improving the data quality by analyzing the similarity of incidents. It is shown that this approach is feasible and leads to better data quality with real-world data.

Originality/value

The originality of the approach lies in the fact that data quality is already improved during the reporting of an incident. In addition, approaches from other areas, such as recommender systems, are applied innovatively to the area of the human-as-a-security-sensor.

Details

Information & Computer Security, vol. 29 no. 2
Type: Research Article
ISSN: 2056-4961

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