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This chapter explores the impact of equality, diversity, and inclusivity (EDI) on internal change agents’ (ICAs) personal and professional development. We have surveyed 117 ICAs that undergo a four-year digital development programme at Edinburgh Business School (EBS). Our survey design draws from expectancy, surprise, sensemaking, and attribution theories to test four hypotheses using Spearman’s rank. We found that diversity features such as gender, age, sector affiliation, work experience, management responsibility, and programme stage do not strongly impact ICAs’ experience of personal and professional development. Surprisingly, some diversity features had a modest or moderate impact on ICAs’ experience of personal and professional development. This disconfirmed our basic assumption about the effectiveness of inclusivity practices in the digital development programme at EBS. We conclude that future research should further investigate the impact of evaluation on ICAs’ personal and professional development and how we can secure it in a digital Business School context.

This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.

This study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.

Through the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.

This study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.

This study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.

This study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.

Assessing whether a book is relevant or suitable for use in teaching materials is not an easy and haphazard matter, various methods and theories have been offered by researchers in studying this matter. Taking a study of the context of textbooks, researchers found the urgency that textbooks are a foundation for education, socialization and transmission of knowledge and its construction. Researchers offer another approach, namely by using praxeology as a study tool so that the goals of the textbooks previously intended are fulfilled.

The researcher uses a qualitative approach through grounded theory. Grounded theory procedures are designed to develop a well-integrated set of concepts that provide a thorough theoretical explanation of the social phenomena under study. A grounded theory must explain as well as describe. It may also implicitly provide some degree of predictability, but only with respect to certain conditions (Corbin and Strauss, 1990). Document analysis in conducting this research study. Document analysis itself examines systematic procedures for reviewing or evaluating documents, both printed and electronic materials.

Two issues regarding gender acquisition have been investigated in L2 Arabic acquisition studies; the order in which L2 Arabic learners acquire certain grammatical features of the gender system and the effect of L1 on the acquisition of some grammatical features from L2 grammatical gender. Arabic has a two-gender system that classifies all nouns, animate and inanimate, as masculine or feminine. Verbs, nouns, adjectives, personal, demonstrative and relative pronouns related to nouns in the syntactic structure of sentences show gender agreement.

In practice, as a book intended for non-speakers, the book is presented using a general view of linguistic theory. In relation to the gender agreement, the presentation of the book begins and is inserted with the concepts of nouns and verbs. Returning to the praxeology context, First, The Know How (Praxis) explains practice (i.e. the tasks performed and the techniques used). Second, To Know Why or Knowledge (logos) which explains and justifies practice from a technological and theoretical point of view. Answering the first concept, the exercise presented in the book is a concept with three clusters explained at the beginning of the discussion. And the second concept, explained with a task design approach which includes word categorization by separating masculine and feminine word forms.

Practically, this research obtains perspectives studied from a textbook, namely the Arabic gender agreement is presented with various examples of noun contexts; textbook authors present book concepts in a particular way with regard to curriculum features and this task design affects student performance, and which approach is more effective for developing student understanding. Empirically, the material is in line with the formulation of competency standards for non-Arabic speakers in Indonesia.

With this computational search, the researcher found a novelty that was considered accurate by taking the praxeology context as a review in the analysis of non-speaking Arabic textbooks, especially in the year 2022 (last data collection in September) there has been no study on this context. So then, the researcher finds other interests in that praxeology can examine more broadly parts of the task of the contents of the book with the approach of relevant linguistic theories.

Based on conservation of resources theory, this study explored the relationship between multitasking and creative work involvement through the mediation of emotional exhaustion, taking regulatory focus as a first-stage moderator.

Based on a three-wave and two-source survey among a sample of 418 employees and 116 leaders, this study conducted multilevel analyses to examine the conceptual model.

The results showed that multitasking was negatively associated with creative work involvement and that emotional exhaustion mediated the relationship between multitasking and creative work involvement. Furthermore, promotion focus and prevention focus moderated the relationship between multitasking and emotional exhaustion and the indirect relationship between multitasking and creative work involvement via emotional exhaustion.

Organizations can promote creative work involvement through interventions that reduce employees' multitasking or emotional exhaustion. In addition, supervisors should be aware of the different responses to multitasking exhibited by employees with different regulatory focuses and could potentially assign multiple tasks to employees with either a high promotion focus or a low prevention focus.

This study contributes to the literature on multitasking and creative work involvement by exploring whether and how multitasking is related to creative work involvement.

Romanian women migrant entrepreneurs (RWMEs) are amongst the largest EU migrant communities in the UK and make significant socioeconomic contributions to both their host and origin nations, but academic research and policy discussions have ignored them. Intersectionality raises complex contextual issues that require comprehensive examination and inclusive policies and programmes. This study is aimed at exploring how Romanian women migrant entrepreneurs experience their transnational intersectional journeys of belonging, as they create, negotiate and enact their intersectional identities of the country of origin, gender and being entrepreneurs in the UK and Romania.

This Interpretative Phenomenological Analysis (IPA) draws on draws upon Crenshaw's (1991) intersectional and Social Identity theories (Tajfel and Turner, 1979) to investigate how nine interviewed RWMEs have experienced their transnational journeys of acculturative belonging in the UK and Romania.

The study findings show how RWMEs undo and negotiate their intersecting identities to adhere to socio-cultural standards in both their host and native nations. In the UK, they feel empowered as women entrepreneurs, but in patriarchal Romania, their entrepreneurial identity is revoked, contradicting the prescribed socio-cultural roles.

This study responds to the call regarding inequalities in entrepreneurship opportunities (Vershinina et al., 2022). By focussing on the understudied community of RWMEs and exploring new intersectional and transnational contextual insights, it contributes to the literature and practice of migrant entrepreneurship. These empirical findings are essential for the development of evidence-based, disaggregated entrepreneurship programmes and policies.

This study responds to the call regarding inequalities in entrepreneurship opportunities (Vershinina et al., 2022). By focussing on the understudied community of RWMEs and exploring new intersectional and transnational contextual insights, it contributes to the literature and practice of migrant entrepreneurship. These empirical findings are essential for the development of evidence-based, disaggregated entrepreneurship programmes and policies.

The purpose of this paper is to elaborate the nature of everyday life as a context of information behaviour by examining how researchers have approached this issue. To this end, particular attention is directed to how they have characterized everyday life as a constellation of work-related and non-work constituents.

Evolutionary concept analysis was conducted by focussing on 40 studies on the topic. It is examined how the conceptualizations of everyday life and the relationships between work-related and non-work constituents have been evolved since the 1990s. The analysis is based on the comparison of the similarities and differences between the characterizations of the above constituents.

Early conceptualizations of everyday life as a context of information behaviour were largely based on Savolainen's model for everyday life information seeking. Later studies have proposed a more holistic approach to everyday life in times when the boundaries between work-related and free-time activities have become blurred, due to the growing use of networked information technologies and telecommuting. Since the late 1990s, the understanding about the nature of everyday life as a context of information behaviour has become more nuanced; thanks to a more detailed identification of the overlaps of work-related and non-work constituents.

As the study is based on a sample of studies examining the relationships of work-related and non-work constituents, the findings cannot be generalized to concern the contextual nature of everyday life as a whole.

The study pioneers by offering an in-depth analysis of the nature of everyday life as a context of information behaviour.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

This study aims to identify user perception of different qualities of optical character recognition (OCR) in texts. The purpose of this paper is to study the effect of different quality OCR on users' subjective perception through an interactive information retrieval task with a collection of one digitized historical Finnish newspaper.

This study is based on the simulated work task model used in interactive information retrieval. Thirty-two users made searches to an article collection of Finnish newspaper Uusi Suometar 1869–1918 which consists of ca. 1.45 million autosegmented articles. The article search database had two versions of each article with different quality OCR. Each user performed six pre-formulated and six self-formulated short queries and evaluated subjectively the top 10 results using a graded relevance scale of 0–3. Users were not informed about the OCR quality differences of the otherwise identical articles.

The main result of the study is that improved OCR quality affects subjective user perception of historical newspaper articles positively: higher relevance scores are given to better-quality texts.

To the best of the authors’ knowledge, this simulated interactive work task experiment is the first one showing empirically that users' subjective relevance assessments are affected by a change in the quality of an optically read text.

There is an increasing interest in the use of knowledge graphs to represent real-world knowledge and a common need to manage imprecise knowledge in many real-world applications. This paper aims to study approaches to solve flexible queries over knowledge graphs.

By introducing fuzzy logic in the query answering process, the authors are able to obtain a novel algorithm to solve flexible queries over knowledge graphs. This approach is implemented in the FUzzy Knowledge Graphs system, a software tool with an intuitive user-graphical interface.

This approach makes it possible to reuse semantic web standards (RDF, SPARQL and OWL 2) and builds a fuzzy layer on top of them. The application to a use case shows that the system can aggregate information in different ways by selecting different fusion operators and adapting to different user needs.

This approach is more general than similar previous works in the literature and provides a specific way to represent the flexible restrictions (using fuzzy OWL 2 datatypes).

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.