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This paper aims to investigate the relationship of software company culture and core values and project management methodologies on the demand for corporate real estate (CRE), impacting decisions regarding location, square footage, office design and amenities.

A researcher-administered survey was designed with the assistance of a purposive sample of brokers, architects and interior designers to elicit responses from the CRE officers in software companies at four stages of growth, from small, entrepreneurial startups to large, publicly traded software companies, located in the same metropolitan area of the USA. Quantitative responses are summarized with traditional statistics and data visualizations. Linguistic analysis, including sentiment analysis and keyword relevance, was performed on the unstructured, English text responses.

Differences exist in the office layouts, amenities and locations across the four software company size categories studied. Linguistic analysis of company descriptions of office design, culture and core values, and the relationship between the two, provide another way for brokers, investors and other stakeholders to understand company perspectives and communication idioms related to CRE needs. The research was unable to show any differences in any dependent variable based on software project management methodologies due to sampling limitations.

This study is limited by the sample size of the participating software companies based on access to company leadership. Results are not generalizable.

Architects, investors, brokers and lenders may find value in using this study’s approach to better understand the needs of software technology clients. Specifically, stakeholders may find value in examining the linkage from software company size, culture and core values to CRE office layout, amenities and location.

The qualitative findings suggest that software company culture and core values and company size influence the design of the CRE demanded by software companies. Multivariate data visualization was designed to communicate longitudinal CRE data. Linguistic analysis was used to extract the emotional content and relevance scores from company descriptions of office design, company culture and core values and the reported effect of culture and core values on office design. Findings may be beneficial for stakeholders involved in the design, location and future CRE investments, and they suggest the need for future research on a larger sample.

With the advent of the internet and communication technology, the penetration of e-learning has increased. The digital data being created by the educational and research institutions is also on the ascent. The growing interest in recent years toward big data, educational data mining and learning analytics has motivated the development of new analytical ways and approaches and advancements in learning settings. The need for using big data to handle, analyze this large amount of data is prime. This trend has started attracting the interest of educational institutions which have an important role in the development skills process and the preparation of a new generation of learners. “A real revolution for education,” it is based on this kind of terms that many articles have paid attention to big data for learning. How can analytics techniques and tools be so efficient and become a great prospect for the learning process? Big data analytics, when applied into teaching and learning processes, might help to improvise as well as to develop new paradigms. In this perspective, this paper aims to investigate the most promising applications and issues of big data for the design of the next-generation of massive e-learning. Specifically, it addresses the analytical tools and approaches for enhancing the future of e-learning, pitfalls arising from the usage of large data sets. Globally, this paper focuses on the possible application of big data techniques on learning developments, to show the power of analytics and why integrating big data is so important for the learning context.

Big data has in the recent years been an area of interest among innovative sectors and has become a major priority for many industries, and learning sector cannot escape to this deluge. This paper focuses on the different methods of big data able to be used in learning context to understand the benefits it can bring both to teaching and learning process, and identify its possible impact on the future of this sector in general. This paper investigates the connection between big data and the learning context. This connection can be illustrated by identifying the several main analytics approaches, methods and tools for improving the learning process. This can be clearer by the examination of the different ways and solutions that contribute to making a learning process more agile and dynamic. The methods that were used in this research are mainly of a descriptive and analytical nature, to establish how big data and analytics methods develop the learning process, and understand their contributions and impacts in addressing learning issues. To this end, authors have collected and reviewed existing literature related to big data in education and the technology application in the learning context. Authors then have done the same process with dynamic and operational examples of big data for learning. In this context, the authors noticed that there are jigsaw bits that contained important knowledge on the different parts of the research area. The process concludes by outlining the role and benefit of the related actors and highlighting the several directions relating to the development and implementation of an efficient learning process based on big data analytics.

Big data analytics, its techniques, tools and algorithms are important to improve the learning context. The findings in this paper suggest that the incorporation of an approach based on big data is of crucial importance. This approach can improve the learning process, for this, its implementation must be correctly aligned with educational strategies and learning needs.

This research represents a reference to better understanding the influence and the role of big data in educational dynamic. In addition, it leads to improve existing literature about big data for learning. The limitations of the paper are given by its nature derived from a theoretical perspective, and the discussed ideas can be empirically validated by identifying how big data helps in addressing learning issues.

Over the time, the process that leads to the acquisition of the knowledge uses and receives more technological tools and components; this approach has contributed to the development of information communication and the interactive learning context. Technology applications continue to expand the boundaries of education into an “anytime/anywhere” experience. This technology and its wide use in the learning system produce a vast amount of different kinds of data. These data are still rarely exploited by educational practitioners. Its successful exploitation conducts educational actors to achieve their full potential in a complex and uncertain environment. The general motivation for this research is assisting higher educational institutions to better understand the impact of the big data as a success factor to develop their learning process and achieve their educational strategy and goals. This study contributes to better understand how big data analytics solutions are turned into operational actions and will be particularly valuable to improve learning in educational institutions.

The purpose of this paper is to investigate how scholars in the digital humanities employ information visualization techniques in their research, and how academic librarians should prepare themselves to support this emerging trend.

This study adopts a content analysis methodology, which further draws techniques from data mining, natural language processing and information visualization to analyze three peer-reviewed journals published within the last five years and ten online university library research guides in this field.

To successfully support and effectively contribute to the digital humanities, academic librarians should be knowledgeable in more than just visualization concepts and tools. The content analysis results for the digital humanities journals reflect the importance of recognizing the wide variety of applications and purposes of information visualization in digital humanities research.

This study provides useful and actionable insights into how academic librarians can prepare for this emerging technology to support future endeavors in the digital humanities.

Although information visualization has been widely adopted in digital humanities research, it remains unclear how librarians, especially academic librarians who support digital humanities research, should prepare for this emerging technology. This research is the first study to address this research gap through the lens of actual applications of information visualization techniques in digital humanities research, which is compared against university LibGuides for digital humanities research.

A Recent work in information studies re-engages with theories of subject enunciation first developed in the work of twentieth century structuralist and post-structuralist critics. To date this work has not been extended to the analysis of data visualizations. The purpose of this paper is to assert that information visualizations embody specific dynamics of positionality for which linguist Emile Benveniste’s formulation of a speaking and spoken subject provides a critical analytic framework. In particular, enunciative theory can be used to explicitly address the mechanisms of power formation in information graphics “spreadsheets, charts and interfaces” that are frequently seen as mere presentations of quantitative or statistical information. This approach is based on attention to the performative aspects of graphic expression and the ways familiar features such as frontality framing and scale can be read critically.

A theoretical argument that applies literature in enunciation as developed in linguistics, film theory, and psychoanalysis to information visualizations. The paper makes specific analyses of the graphical features of spreadsheets, common charts and graphs, and interfaces to show how they create speaking and spoken subject positions.

The theory of enunciation is useful in understanding the ways information representations, particularly visualizations of data, work to produce power relations.

The topic may seem to draw on theoretical positions that are associated with structural and post-structural theory popular three decades ago, but since the study of enunciation was never applied to information visualizations, the work feels timely. Recent work in information studies has re-engaged with these theoretical issues, but not applied them to charts, graphs, and other visual forms. Information visualizations are so prevalent that any critical insight into their operations feels timely, even urgent.

The concept of information as enunciation in recent work in information studies has not been applied to visualizations. Analysis of the production of subject positions as commonly understood in other fields (linguistics, textual studies, film studies, visual studies, and psychoanalysis across a broad range of cultural studies fields) can be usefully applied to information graphics.

To propose a comprehensive and semi‐automatic method for constructing or updating knowledge organization tools such as thesauri.

The paper proposes a comprehensive methodology for thesaurus construction and maintenance combining shallow NLP with a clustering algorithm and an information visualization interface. The resulting system TermWatch, extracts terms from a text collection, mines semantic relations between them using complementary linguistic approaches and clusters terms using these semantic relations. The clusters are mapped onto a 2D using an integrated visualization tool.

The clusters formed exhibit the different relations necessary to populate a thesaurus or ontology: synonymy, generic/specific and relatedness. The clusters represent, for a given term, its closest neighbours in terms of semantic relations.

This could change the way in which information professionals (librarians and documentalists) undertake knowledge organization tasks. TermWatch can be useful either as a starting point for grasping the conceptual organization of knowledge in a huge text collection without having to read the texts, then actually serving as a suggestive tool for populating different hierarchies of a thesaurus or an ontology because its clusters are based on semantic relations.

This lies in several points: combined use of linguistic relations with an adapted clustering algorithm, which is scalable and can handle sparse data. The paper proposes a comprehensive approach to semantic relations acquisition whereas existing studies often use one or two approaches. The domain knowledge maps produced by the system represents an added advantage over existing approaches to automatic thesaurus construction in that clusters are formed using semantic relations between domain terms. Thus while offering a meaningful synthesis of the information contained in the original corpus through clustering, the results can be used for knowledge organization tasks (thesaurus building and ontology population) The system also constitutes a platform for performing several knowledge‐oriented tasks like science and technology watch, textmining, query refinement.

In this chapter, we explore uses of corpus linguistics within higher education research. Corpus linguistic approaches enable examination of large bodies of language data based on computing power. These bodies of data, or corpora, facilitate investigation of the meaning of words in context. The semiautomated nature of such investigation helps researchers to identify and interpret language patterns that might otherwise be inaccessible through manual analysis. We illustrate potential uses of corpus linguistic approaches through four short case studies by higher education researchers, spanning educational contexts, disciplines and genres. These case studies are underpinned by discussion of the development of corpus linguistics as a field of investigation, including existing open corpora and corpus analysis tools. We give a flavour of how corpus linguistic techniques, in isolation or as part of a wider research approach, can be particularly helpful to higher education researchers who wish to investigate language data and its context.

The purpose of this paper is to demonstrate the possibility for the galleries, libraries, archives and museums sector to employ playful, immersive discovery interfaces for their collections and raise awareness of some of the considerations that go into the decision to use such technology and the creation of the interfaces.

This is a case study approach using the methodology of research through design. The paper introduces two examples of immersive interfaces to archival data created by the authors, using these as a springboard for discussing the different kinds of embodied experiences that users have with different kinds of immersion, for example, the exploration of the archive on a flat screen, a data “cave” or arena, or virtual reality.

The role of such interfaces in communicating with the audience of an archive is considered, for example, in allowing users to detect structure in data, particularly in understanding the role of geographic or other spatial elements in a collection, and in shifting the locus of knowledge production from individual to community. It is argued that these different experiences draw on different metaphors in terms of users’ prior experience with more well-known technologies, for example, “a performance” vs “a tool” vs “a background to a conversation”.

The two example interfaces discussed here are original creations by the authors of this paper. They are the first uses of mixed reality for interfacing with the archives in question. One is the first mixed reality interface to an audio archive. The discussion has implications for the future of interfaces to galleries, archives, libraries and museums more generally.

The purpose of this paper is to develop a visualization method for texts produced by a company's competitors, partners, or customers. This method can be used for competitive intelligence purposes, and in particular, for spotting changes over time in a company's communication.

Based on the judgments of expert readers, topics considered to be important in the quarterly reports of two telecommunication companies were turned into so‐called collocational networks, using a statistical method originating in linguistics.

The paper shows that when collocational topic networks are produced out of a sequence of quarterly reports, they provide a visualization of how the presentation of these topics changes from one‐quarter to the next.

The statistical method used in this paper does not handle low‐frequency topics, that nevertheless might be of great importance, very well. The method could be developed further so that users' judgments of importance are given more weight.

The method developed in this paper can be turned into a data visualization tool for intelligence practitioners.

Textual data are often overlooked in competitive intelligence, as it is more difficult to visualize and present than quantitative data, such as financial ratios. The method presented here is an easy to grasp “white box” approach to visualizing textual data, as it combines the judgments of subject matter experts with a statistical method.

The purpose of this study is to identify research perspectives/clusters in the field of urban tourism (city tourism) in narrow sense and tourism cities (cities and tourism) in the broader sense to examine the complex relationship through the optics of science mapping. This paper believes that the existing qualitative assessments of this field can be experimentally verified and visualized.

First, the key conceptual dilemmas of research perspectives in urban tourism are highlighted. Based on the Web of Science (WOS) Core Collection and the VOSviewer (computer program for visualizing bibliometric networks), the data will be analyzed. Clustering is used to evaluate information retrieval (inclusivity or selectivity of the search query), publication patterns (journal articles), author keywords, terminology and to identify the respective cities and author collaborations between countries.

Terminological specificities and their contextuality (authors’ preferences) are elaborated, as the topic is studied by authors from different disciplinary fields. Compared to other specific tourisms, urban tourism includes geographic terms (variations of city names) and terms with different connotations (travelers, visitors). Recent Spanish (also Portuguese) linguistic/geographic contexts are noticeable and a strong presence of WOS Emerging Sources Citation Index papers. Research perspectives are represented in the network of clusters of connected terms. If the search is based on a narrower sense of strict urban tourism, then tourism-business topics predominate. If tourism and cities are less closely linked, socio-cultural and environmental-spatial perspectives emerge, as does tourism/cities vulnerability (climate change and health issues).

The construction of a search syntax for the purpose of retrieval is always marked by compromises, given different terminological usages. A narrow search query will miss many relevant documents. On the other hand, if the query is too general, it returns less relevant documents. To this end, this paper tested queries on three different levels of inclusivity or selectivity. More consistent use of terms would benefit authors in the field of urban tourism when searching for references (information retrieval) and, as a consequence, would allow better integration of the field.

This study provides a practical method for evaluating cities and tourism in combining the expertise of an information scientist and a sociologist. It points out numerous caveats in information retrieval. It offers an overview of publishing just prior to the outbreak of Covid-19, thus providing an opportunity for further comparative studies.

This study is the first to examine urban tourism using such a method and can serve as a complement to the existing systematization of qualitative approaches. The findings are consistent with numerous qualitative assessments of weak the research interconnection between the specifics of cities and tourism in terms of broader socio-spatial processes. However, the study suggests that such research linkage is increasing, which is noticeable in relation to issues of social sustainability (e.g. overtourism, Airbnb and touristification).

We analyze the structure and the dynamics of a field, drawing on data from organizational public behavior in the digital sphere. Organizational self-representations afford rich insights into how organizations position themselves with regard to their peers, both in terms of web page language and hyperlink affiliations. Our empirical example is the lively and important discussion of the social impact of nonprofit organizations. We follow how it has evolved from 2011 to 2018 and with what consequences. We begin with portraits of the discursive movements of powerful, individual organizations, where we observe extensive changes. These portraits show how influential organizations alter their public faces. We then analyze discourse at the field level, which is surprisingly stable even though individual organizations change their discursive and relational positions frequently. Finally, we turn to groups of organizations with similar positions and highlight their ability to integrate vocabularies of other groups. Here we observe that a lingua franca increases integration at the field level, while affording distinction with individual organizations’ positioning. We conclude with a discussion of complementary research avenues that can advance the relational and linguistic view we present in this paper.