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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

Keywords

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

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

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

Keywords

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

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

Keywords

Article
Publication date: 1 February 2022

Ramadas Thekkoote

Quality 4.0 (Q4.0) is related to quality management in the era of Industry 4.0 (I4.0). In particular, it concentrates on digital techniques used to improve organizational…

Abstract

Purpose

Quality 4.0 (Q4.0) is related to quality management in the era of Industry 4.0 (I4.0). In particular, it concentrates on digital techniques used to improve organizational capabilities and ensure the delivery of the best quality products and services to its customer. The aim of this research to examine the vital elements for the Q4.0 implementation.

Design/methodology/approach

A review of the literature was carried out to analyze past studies in this emerging research field.

Findings

This research identified ten factors that contribute to the successful implementation of Q4.0. The key factors are (1) data, (2) analytics, (3) connectivity, (4) collaboration, (5) development of APP, (6) scalability, (7) compliance, (8) organization culture, (9) leadership and (10) training for Q4.0.

Originality/value

As a result of the research, a new understanding of factors of successful implementation of Q4.0 in the digital transformation era can assist firms in developing new ways to implement Q4.0.

Details

International Journal of Quality & Reliability Management, vol. 39 no. 6
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 27 August 2021

Youngseek Kim

This study examined how the qualities of both data and documents of existing datasets can contribute to researchers' satisfaction of data reuse, and how it affects their…

Abstract

Purpose

This study examined how the qualities of both data and documents of existing datasets can contribute to researchers' satisfaction of data reuse, and how it affects their data reuse intentions mediated by attitudinal and normative beliefs of data reuse.

Design/methodology/approach

A combined theoretical framework integrating IS (Information Systems) Success Model and the Theory of Planned Behavior (TPB) was utilized to develop the research model of researchers' data reuse, which was evaluated using structural equation modeling based on 820 survey responses from STEM disciplines in the US.

Findings

This study found that both data and document qualities significantly contribute to researchers' satisfaction of data reuse. Then, their satisfaction significantly increases perceived usefulness and subjective norm of data reuse, and it decreases perceived risk associated with data reuse. Finally, both perceived usefulness and subjective norm significantly increases their data reuse intentions.

Research limitations/implications

The combined theoretical framework integrating IS success model and TPB provides a new theoretical lens in understanding researchers' data reuse behaviors affected by the qualities of both data and documents.

Practical implications

The findings of this study provided several practical implications in promoting and facilitating researchers' data reuse behaviors by improving data and document qualities of existing datasets.

Originality/value

This is one of the initial studies focusing on the roles of data and document qualities in researchers' data reuse, and it provides a systematic view of how data and document qualities influence researchers' data reuse mediated by their satisfaction of data reuse and attitudinal and normative beliefs.

Details

Journal of Documentation, vol. 78 no. 3
Type: Research Article
ISSN: 0022-0418

Keywords

Article
Publication date: 18 August 2021

G. Shankaranarayanan and Bin Zhu

Data quality metadata (DQM) is a set of quality measurements associated with the data. Prior research in data quality has shown that DQM improves decision performance. The…

Abstract

Purpose

Data quality metadata (DQM) is a set of quality measurements associated with the data. Prior research in data quality has shown that DQM improves decision performance. The same research has also shown that DQM overloads the cognitive capacity of decision-makers. Visualization is a proven technique to reduce cognitive overload in decision-making. This paper aims to describe a prototype decision support system with a visual interface and examine its efficacy in reducing cognitive overload in the context of decision-making with DQM.

Design/methodology/approach

The authors describe the salient features of the prototype and following the design science paradigm, this paper evaluates its usefulness using an experimental setting.

Findings

The authors find that the interface not only reduced perceived mental demand but also improved decision performance despite added task complexity due to the presence of DQM.

Research limitations/implications

A drawback of this study is the sample size. With a sample size of 51, the power of the model to draw conclusions is weakened.

Practical implications

In today’s decision environments, decision-makers deal with extraordinary volumes of data the quality of which is unknown or not determinable with any certainty. The interface and its evaluation offer insights into the design of decision support systems that reduce the complexity of the data and facilitate the integration of DQM into the decision tasks.

Originality/value

To the best of my knowledge, this is the only research to build and evaluate a decision-support prototype for structured decision-making with DQM.

Details

Journal of Systems and Information Technology, vol. 23 no. 2
Type: Research Article
ISSN: 1328-7265

Keywords

Article
Publication date: 10 August 2021

Dan Wu, Hao Xu, Wang Yongyi and Huining Zhu

Currently, countries worldwide are struggling with the virus COVID-19 and the severe outbreak it brings. To better benefit from open government health data in the fight…

Abstract

Purpose

Currently, countries worldwide are struggling with the virus COVID-19 and the severe outbreak it brings. To better benefit from open government health data in the fight against this pandemic, this study developed a framework for assessing open government health data at the dataset level, providing a tool to evaluate current open government health data's quality and usability COVID-19.

Design/methodology/approach

Based on the review of the existing quality evaluation methods of open government data, the evaluation metrics and their weights were determined by 15 experts in health through the Delphi method and analytic hierarchy process. The authors tested the framework's applicability using open government health data related to COVID-19 in the US, EU and China.

Findings

The results of the test capture the quality difference of the current open government health data. At present, the open government health data in the US, EU and China lacks the necessary metadata. Besides, the number, richness of content and timeliness of open datasets need to be improved.

Originality/value

Unlike the existing open government data quality measurement, this study proposes a more targeted open government data quality evaluation framework that measures open government health data quality on a range of data quality dimensions with a fine-grained measurement approach. This provides a tool for accurate assessment of public health data for correct decision-making and assessment during a pandemic.

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