Search results

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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”.

This paper aims to offer insights into the ways two computer-aided qualitative data analysis software (CAQDAS) applications (QSR NVivo and Leximancer) can be used to analyze big, text-based, online data taken from consumer-to-consumer (C2C) social media communication.

This study used QSR NVivo and Leximancer, to explore 200 discussion threads containing 1,796 posts from forums on an online open community and an online brand community that involved online brand advocacy (OBA). The functionality, in particular, the strengths and weaknesses of both programs are discussed. Examples of the types of analyses each program can undertake and the visual output available are also presented.

This research found that, while both programs had strengths and weaknesses when working with big, text-based, online data, they complemented each other. Each contributed a different visual and evidence-based perspective; providing a more comprehensive and insightful view of the characteristics unique to OBA.

Qualitative market researchers are offered insights into the advantages and disadvantages of using two different software packages for research projects involving big social media data. The “visual-first” analysis, obtained from both programs can help researchers make sense of such data, particularly in exploratory research.

The paper provides practical recommendations for analysts considering which programs to use when exploring big, text-based, online data.

This paper answered a call to action for further research and demonstration of analytical programs of big, online data from social media C2C communication and makes strong suggestions about the need to examine such data in a number of ways.

This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.

The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.

This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.

The usability aspect of the construction operations building information exchange (COBie) datasheet has been largely overlooked. Users find it difficult to find relevant data inside COBie and understand the dependencies of information. This research study is a part of a more comprehensive research study to identify the usability issues associated with COBie and propose solutions to deal with them. This paper aims to discuss the challenges associated with the visualization aspect of COBie and proposes a solution to mitigate them.

This paper is based on design thinking and waterfall methodology. While the design thinking methodology is used to explore the issues associated with the visualization aspect of COBie, the waterfall methodology is used to develop a working prototype of the visualizer for the COBie datasheet using a spreadsheet format.

The paper demonstrates that the property graph model based on a node-link diagram can be effectively used to represent the COBie datasheet. This will help in storing data in a visually connected manner and looking at links more dynamically. Moreover, converting and storing data into an appropriate database will help reach data directly rather than navigate multiple workbooks. This database can also help get the history of data inside the COBie datasheet as it develops throughout the project.

This research proposes a novel approach to visualize the COBie datasheet interactively using the property graph model, a type of node-link diagram. Using the property graph model will help users see data in a connected way, which is currently missing in the spreadsheet representation of COBie data. Moreover, this research also highlights that storing historical changes in COBie data can help understand how data has evolved throughout the construction. Additionally, structured storage of data in relationship format can help users to access the end of connected data directly through the efficient search.

The purpose of the chapter was to examine the impact of customer care chatbots on customer satisfaction levels in the mobile telephony industry in Zimbabwe, with a special focus on the company Econet Wireless. This chapter shows the conceptual framework used. An online questionnaire was administered to a sample of 100 Econet Wireless subscribers who were selected using probability stratified random sampling from Zimbabwe’s 10 provinces. The research data were collected and analysed for correlation, and a multiple regression analysis was carried out to identify the relationship between customer satisfaction and the three customer service improvements brought in by the introduction of customer service chatbots. The study discovered that there is a positive relationship between customer satisfaction levels and each of the three customer service improvements brought in by customer service chatbots, namely customer service convenience, speed of response, and omnichannel strategies. This study thereby proves that the introduction of customer service chatbots in the mobile telephony industry in Zimbabwe can lead to an improvement in customer satisfaction levels. However, addressing service quality only as a determinant of customer satisfaction in isolation is not sufficient to fully improve customer satisfaction levels. Therefore, organisations that seek to improve their customer satisfaction should consider strategies that address all determinants of customer satisfaction, namely price, product quality, service quality, situational factors, and personal factors. This study contributes to the body of knowledge, particularly regarding the use of artificial intelligence (AI) for customer service in developing economies.

This paper aims to explore the role of records management in supporting the effective use of information visualisation and visual analytics (VA) to meet the challenges associated with the analysis of Big Data.

This exploratory research entailed conducting and analysing interviews with a convenience sample of visual analysts and VA tool developers, affiliated with a major VA institute, to gain a deeper understanding of data-related issues that constrain or prevent effective visual analysis of large data sets or the use of VA tools, and analysing key emergent themes related to data challenges to map them to records management controls that may be used to address them.

The authors identify key data-related issues that constrain or prevent effective visual analysis of large data sets or the use of VA tools, and identify records management controls that may be used to address these data-related issues.

This paper discusses a relatively new field, VA, which has emerged in response to meeting the challenge of analysing big, open data. It contributes a small exploratory research study aimed at helping records professionals understand the data challenges faced by visual analysts and, by extension, data scientists for the analysis of large and heterogeneous data sets. It further aims to help records professionals identify how records management controls may be used to address data issues in the context of VA.