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Article
Publication date: 10 August 2018

Mohammad Kamel Daradkeh

Visual analytics is increasingly becoming a prominent technology for organizations seeking to gain knowledge and actionable insights from heterogeneous and big data to…

Abstract

Purpose

Visual analytics is increasingly becoming a prominent technology for organizations seeking to gain knowledge and actionable insights from heterogeneous and big data to support decision-making. Whilst a broad range of visual analytics platforms exists, limited research has been conducted to explore the specific factors that influence their adoption in organizations. The purpose of this paper is to develop a framework for visual analytics adoption that synthesizes the factors related to the specific nature and characteristics of visual analytics technology.

Design/methodology/approach

This study applies a directed content analysis approach to online evaluation reviews of visual analytics platforms to identify the salient determinants of visual analytics adoption in organizations from the standpoint of practitioners. The online reviews were gathered from Gartner.com, and included a sample of 1,320 reviews for six widely adopted visual analytics platforms.

Findings

Based on the content analysis of online reviews, 34 factors emerged as key predictors of visual analytics adoption in organizations. These factors were synthesized into a conceptual framework of visual analytics adoption based on the diffusion of innovations theory and technology–organization–environment framework. The findings of this study demonstrated that the decision to adopt visual analytics technologies is not merely based on the technological factors. Various organizational and environmental factors have also significant influences on visual analytics adoption in organizations.

Research limitations/implications

This study extends the previous work on technology adoption by developing an adoption framework that is aligned with the specific nature and characteristics of visual analytics technology and the factors involved to increase the utilization and business value of visual analytics in organizations.

Practical implications

This study highlights several factors that organizations should consider to facilitate the broad adoption of visual analytics technologies among IT and business professionals.

Originality/value

This study is among the first to use the online evaluation reviews to systematically explore the main factors involved in the acceptance and adoption of visual analytics technologies in organizations. Thus, it has potential to provide theoretical foundations for further research in this important and emerging field. The development of an integrative model synthesizing the salient determinants of visual analytics adoption in enterprises should ultimately allow both information systems researchers and practitioners to better understand how and why users form perceptions to accept and engage in the adoption of visual analytics tools and applications.

Details

Information Technology & People, vol. 32 no. 3
Type: Research Article
ISSN: 0959-3845

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Article
Publication date: 27 July 2018

Evangelia Triperina, Georgios Bardis, Cleo Sgouropoulou, Ioannis Xydas, Olivier Terraz and Georgios Miaoulis

The purpose of this paper is to introduce a novel framework for visual-aided ontology-based multidimensional ranking and to demonstrate a case study in the academic domain.

Abstract

Purpose

The purpose of this paper is to introduce a novel framework for visual-aided ontology-based multidimensional ranking and to demonstrate a case study in the academic domain.

Design/methodology/approach

The paper presents a method for adapting semantic web technologies on multiple criteria decision-making algorithms to endow to them dynamic characteristics. It also showcases the enhancement of the decision-making process by visual analytics.

Findings

The semantic enhanced ranking method enables the reproducibility and transparency of ranking results, while the visual representation of this information further benefits decision makers into making well-informed and insightful deductions about the problem.

Research limitations/implications

This approach is suitable for application domains that are ranked on the basis of multiple criteria.

Originality/value

The discussed approach provides a dynamic ranking methodology, instead of focusing only on one application field, or one multiple criteria decision-making method. It proposes a framework that allows integration of multidimensional, domain-specific information and produces complex ranking results in both textual and visual form.

Details

Data Technologies and Applications, vol. 52 no. 3
Type: Research Article
ISSN: 2514-9288

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Article
Publication date: 4 September 2020

Jing Lu, Lisa Cairns and Lucy Smith

A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight…

Abstract

Purpose

A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight generation. The purpose of this study is to propose a process model for data-driven decision-making which provides an overarching methodology covering key stages of the business analytics life cycle. The model is then applied in two small enterprises using real customer/donor data to assist the strategic management of sales and fundraising.

Design/methodology/approach

Data science is a multi-disciplinary subject that aims to discover knowledge and insight from data while providing a bridge to data-driven decision-making across businesses. This paper starts with a review of established frameworks for data science and analytics before linking with process modelling and data-driven decision-making. A consolidated methodology is then described covering the key stages of exploring data, discovering insights and making decisions.

Findings

Representative case studies from a small manufacturing organisation and an independent hospice charity have been used to illustrate the application of the process model. Visual analytics have informed customer sales strategy and donor fundraising strategy through recommendations to the respective senior management teams.

Research limitations/implications

The scope of this research has focused on customer analytics in small to medium-sized enterprise through two case studies. While the aims of these organisations are rather specific, they share a commonality of purpose for their strategic development, which is addressed by this paper.

Originality/value

Data science is shown to be applicable in the business environment through the proposed process model, synthesising micro- and macro-solution methodologies and allowing organisations to follow a structured procedure. Two real-world case studies have been used to highlight the value of the data-driven model in management decision-making.

Details

Journal of Modelling in Management, vol. 16 no. 2
Type: Research Article
ISSN: 1746-5664

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Article
Publication date: 20 October 2021

Sumeer Gul, Shohar Bano and Taseen Shah

Data mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself…

Abstract

Purpose

Data mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself as an emerging field and manifests itself in the form of different techniques such as information mining; big data mining; big data mining and Internet of Things (IoT); and educational data mining. This paper aims to discuss how these technologies and techniques are used to derive information and, eventually, knowledge from data.

Design/methodology/approach

An extensive review of literature on data mining and its allied techniques was carried to ascertain the emerging procedures and techniques in the domain of data mining. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored to discover the extent of literature published on Data Mining and its varied facets. Literature was searched against various keywords such as data mining; information mining; big data; big data and IoT; and educational data mining. Further, the works citing the literature on data mining were also explored to visualize a broad gamut of emerging techniques about this growing field.

Findings

The study validates that knowledge discovery in databases has rendered data mining as an emerging field; the data present in these databases paves the way for data mining techniques and analytics. This paper provides a unique view about the usage of data, and logical patterns derived from it, how new procedures, algorithms and mining techniques are being continuously upgraded for their multipurpose use for the betterment of human life and experiences.

Practical implications

The paper highlights different aspects of data mining, its different technological approaches, and how these emerging data technologies are used to derive logical insights from data and make data more meaningful.

Originality/value

The paper tries to highlight the current trends and facets of data mining.

Details

Digital Library Perspectives, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2059-5816

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Article
Publication date: 18 May 2015

Victoria Uren and Aba-Sah Dadzie

The purpose of this paper is to assess high-dimensional visualisation, combined with pattern matching, as an approach to observing dynamic changes in the ways people tweet…

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Abstract

Purpose

The purpose of this paper is to assess high-dimensional visualisation, combined with pattern matching, as an approach to observing dynamic changes in the ways people tweet about science topics.

Design/methodology/approach

The high-dimensional visualisation approach was applied to three science topics to test its effectiveness for longitudinal analysis of message framing on Twitter over two disjoint periods in time. The paper uses coding frames to drive categorisation and visual analytics of tweets discussing the science topics.

Findings

The findings point to the potential of this mixed methods approach, as it allows sufficiently high sensitivity to recognise and support the analysis of non-trending as well as trending topics on Twitter.

Research limitations/implications

Three topics are studied, these illustrate a range of frames, but results may not be representative of all science topics.

Social implications

Funding bodies increasingly encourage scientists to participate in public engagement. As social media provides an avenue actively utilised for public communication, understanding the nature of the dialog on this medium is important for the scientific community and the public at large.

Originality/value

This study differs from standard approaches to the analysis of micropost data, which tend to focus on large-scale data sets. It provides evidence that this approach enables practical and effective analysis of the content of midsize to large collections of microposts.

Details

Aslib Journal of Information Management, vol. 67 no. 3
Type: Research Article
ISSN: 2050-3806

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Article
Publication date: 13 November 2019

Diamantino Torres, Carina Pimentel and Susana Duarte

The purpose of this study intends to make a characterization of a shop floor management (SFM) system in the context of smart manufacturing, through smart technologies and…

Abstract

Purpose

The purpose of this study intends to make a characterization of a shop floor management (SFM) system in the context of smart manufacturing, through smart technologies and digital shop floor (DSF) features.

Design/methodology/approach

To attain the paper objective, a mixed method methodology was used. In the first stage, a theoretical background was carried out, to provide a comprehensive understanding on SFM system in a smart manufacturing perspective. Next, a case study within a survey was developed. The case study was introduced to characterize a SFM system, while the survey was made to understand the level of influence of smart manufacturing technologies and of DSF features on SFM. In total, 17 experts responded to the survey.

Findings

Data analytics is the smart manufacturing technology that influences more the SFM system and its components and the cyber security technology does not influence it at all. The problem solving (PS) is the SFM component more influenced by the smart manufacturing technologies. Also, the use of real-time digital visualization tools is considered the most influential DSF feature for the SFM components and the data security protocols is the least influential one. The four SFM components more influenced by the DSF features are key performance indicator tracking, PS, work standardization and continuous improvement.

Research limitations/implications

The study was applied in one multinational company from the automotive sector.

Originality/value

To the best of the authors’ knowledge, this work is one of the first to try to characterize the SFM system on smart manufacturing considering smart technologies and DSF features.

Details

International Journal of Lean Six Sigma, vol. 11 no. 5
Type: Research Article
ISSN: 2040-4166

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Book part
Publication date: 15 July 2019

David E. Caughlin and Talya N. Bauer

Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data…

Abstract

Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data visualizations have become more accessible and more efficient to generate. In fact, virtually all enterprise resource planning and human resource (HR) information system vendors offer off-the-shelf data visualizations as part of decision-support dashboards as well as stand-alone images and displays for reporting. Plus, advances in programing languages and software such as Tableau, Microsoft Power BI, R, and Python have expanded the possibilities of fully customized graphics. Despite the proliferation of data visualization, relatively little is known about how to design data visualizations for displaying different types of HR data to different user groups, for different purposes, and with the overarching goal of improving the ways in which users comprehend and interpret data visualizations for decision-making purposes. To understand the state of science and practice as they relate to HR data visualizations and data visualizations in general, we review the literature on data visualizations across disciplines and offer an organizing framework that emphasizes the roles data visualization characteristics (e.g., display type, features), user characteristics (e.g., experience, individual differences), tasks, and objectives (e.g., compare values) play in user comprehension, interpretation, and decision-making. Finally, we close by proposing future directions for science and practice.

Details

Research in Personnel and Human Resources Management
Type: Book
ISBN: 978-1-78973-852-0

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Article
Publication date: 20 September 2018

Sharon Bratt

The purpose of this paper is to assess the efficacy of the Institute of Electrical and Electronics Engineers (IEEE) Xplore digital library search engine to return relevant…

Abstract

Purpose

The purpose of this paper is to assess the efficacy of the Institute of Electrical and Electronics Engineers (IEEE) Xplore digital library search engine to return relevant materials on information visualization pedagogy literature and to recommend search strategies to assist the digital library academic readership improve the efficacy of their search tasks. Furthermore, the results are of interest to general readers using similar digital repositories.

Design/methodology/approach

An initial scoping review using EBSCO Discovery services returned the number and accessibility of sources and publications-based various Boolean searches. A revised search strategy focused the search to IEEE publications as the primary source of visualization research. A corpus of keywords were extracted from the 44 relevant articles and analyzed for relevance, keyword trends and contexts of use.

Findings

Keyword analysis results show visualization education research is confounded by several information retrieval issues: relevancy, incomplete taxonomy, non-standard lexicon, diverse disciplines and under-representation. Recommendations include: search strategies, alternative digital collections, a potential opportunity for research in information visualization pedagogy to address this gap in an emerging field and the need for more effective interactive tools to assist with keyword selection.

Research limitations/implications

The study focused on the IEEE publications as the primary source of visualization research.

Practical implications

A repository of visualization education research that is easily findable and relevant benefits both faculty using information visualization in their teaching and academics whose work must be disseminated to the broadest audience. Strategic keyword selection, interactive keyword tools or more robust thesaurus will enable IEEE Xplore digital library users to optimize their interaction with the system. Furthermore, results suggest a need for more research in information visualization pedagogy.

Originality/value

This is the only study to uniquely assess the efficacy of the IEEE Xplore digital library database system to retrieve relevant visualization education literature based on keyword search.

Details

Journal of Applied Research in Higher Education, vol. 10 no. 4
Type: Research Article
ISSN: 2050-7003

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Article
Publication date: 17 July 2019

Upeksha Hansini Madanayake and Charles Egbu

The purpose of this paper is to identify the gaps and potential future research avenues in the big data research specifically in the construction industry.

Abstract

Purpose

The purpose of this paper is to identify the gaps and potential future research avenues in the big data research specifically in the construction industry.

Design/methodology/approach

The paper adopts systematic literature review (SLR) approach to observe and understand trends and extant patterns/themes in the big data analytics (BDA) research area particularly in construction-specific literature.

Findings

A significant rise in construction big data research is identified with an increasing trend in number of yearly articles. The main themes discussed were big data as a concept, big data analytical methods/techniques, big data opportunities – challenges and big data application. The paper emphasises “the implication of big data in to overall sustainability” as a gap that needs to be addressed. These implications are categorised as social, economic and environmental aspects.

Research limitations/implications

The SLR is carried out for construction technology and management research for the time period of 2007–2017 in Scopus and emerald databases only.

Practical implications

The paper enables practitioners to explore the key themes discussed around big data research as well as the practical applicability of big data techniques. The advances in existing big data research inform practitioners the current social, economic and environmental implications of big data which would ultimately help them to incorporate into their strategies to pursue competitive advantage. Identification of knowledge gaps helps keep the academic research move forward for a continuously evolving body of knowledge. The suggested new research avenues will inform future researchers for potential trending and untouched areas for research.

Social implications

Identification of knowledge gaps helps keep the academic research move forward for continuous improvement while learning. The continuously evolving body of knowledge is an asset to the society in terms of revealing the truth about emerging technologies.

Originality/value

There is currently no comprehensive review that addresses social, economic and environmental implications of big data in construction literature. Through this paper, these gaps are identified and filled in an understandable way. This paper establishes these gaps as key issues to consider for the continuous future improvement of big data research in the context of the construction industry.

Details

Built Environment Project and Asset Management, vol. 9 no. 4
Type: Research Article
ISSN: 2044-124X

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Article
Publication date: 3 August 2021

Luis Hernan Contreras Pinochet, Guilherme de Camargo Belli Amorim, Durval Lucas Júnior and Cesar Alexandre de Souza

The article's objective is to analyze the consequent factors of Big Data Analytics Capability for firms in the competitive scenario, using different analytical models.

Abstract

Purpose

The article's objective is to analyze the consequent factors of Big Data Analytics Capability for firms in the competitive scenario, using different analytical models.

Design/methodology/approach

The research had a quantitative approach, using a survey of data from firms located in the state of São Paulo – Brazil. Structural Equation Modeling (SEM) was used to validate the model.

Findings

The results reveal that all hypotheses were accepted. Business value was the construct that had the most explanatory power in the model. It is necessary to invest more in analytical tools, as well as people trained in the analysis of these models, in addition to a change of mindset, which will dictate the bias of the firm's strategic decision-making. The Big Data analysis is evident from firms' growing investments, particularly those that operate in complex and fast-paced environments.

Practical implications

The proposed theoretical model makes it possible to verify firms' analytical structure and whether they are better positioned to analyze customer data and information in real-time, generate insights and implement solutions to maintain and improve their market position.

Originality/value

The contribution of this article is to present a proposal to expand the research models in the literature that analyzed the direct and indirect relationship between “Big Data Analytics Capability” and “Product Innovation Performance”.

Details

Journal of Enterprise Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0398

Keywords

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