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Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.

Entrepreneurial identity is a complex concept. It has been recognised as a subjective and dynamic socio-cognitive factor which is not stable over time and is part of an iterative formation process. This chapter explores the journey of adopting, implementing and reviewing visual methods, in order to examine entrepreneurial identity, from the researchers’ perspectives. A critical standpoint is offered which explores both the benefits and challenges that presented themselves in the search for rich data.

This chapter explores the Aboriginal artefacts ‘clapsticks’ as a form of cultural data – a means of disseminating cultural knowledge in the galleries, libraries, archives, and museums [GLAM] sector. How might alternative methods of curation animate clapsticks as active objects that deliver effective knowledge transfer? This research aims to explore and extend current industry practices of the curation of clapsticks, within the existing parameters of technology, spatial capacity, financial support, and governance as part of the operation of the GLAM sector. The research problem, therefore, explores the past limitations of colonial framing of cultural institutions that once hindered the revealing, the disseminating, and the ‘awakening’ of the complexities of knowledge intrinsic to Aboriginal cultural artefacts. Informal communication with Aboriginal community members and academics was critical to providing cultural context as well as personal beliefs and aspirations vital to conceptualising the future of cultural representation. This investigation explores how a cultural centre offers a space and an opportunity to facilitate the clapsticks datasets in its capacity as a performance-focussed building rather than solely an exhibition space or keeping place. This potential represents a shift in thinking around the clapsticks being a lens through which the stories of Aboriginal culture can be disseminated.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

Horse racing occupies a significant but separate part of sports gambling provision. The historical importance of horse breeding as well as employment effects have been used to justify the necessity of betting revenue in the horse sector. In the Nordic countries, these arguments are used to organise horse racing separately from other sports betting. In this context, horse racing, and particularly trotting, remains a lucrative sector for horse owners and producers of racing. The current study focuses on the production chains and financial interests behind horse racing in the Nordic countries. We use financial statements and annual reporting of Finnish, Norwegian and Swedish horse betting companies to determine who benefits financially from racing, what kind of production-related interests are involved and whether horse racing produces surplus to societies or merely maintains its own function. The results are discussed in light of the intertwined organisation of horse racing in the Nordics and the role of horses and animal welfare in the production of racing.

Changes and transformations experienced today have created alternative lifestyles in cities. Many life concepts try to find solutions to environmental problems, so much so that this section focuses on smart eco-city concept, which is one of the trending city initiatives of the last century and is expected to become more popular each day. In the chapter, firstly, information about the concepts of eco-city and smart city is given and then what is meant by the concept of smart eco-city is discussed. Next, smart eco-cities have been tried to be explained with application examples. Finally, the chapter emphasizes the importance of ecological cities blended with technology for the sustainability of living spaces and offers implications for future research.

Artifacts are rarely used today to visualize thoughts, insights, and ideas in strategy work. Rather, textual and verbal communication dominates. This is despite artifacts and visual representations holding many advantages as tools to create and make sense of strategy in teamwork. To advance our understanding of the benefits of visual aids in strategy work, I synthesize insights from cognitive psychology, neuroscience, and management research. My analysis exposes distinct neurocognitive advantages concerning attention, emotion, learning, memory, intuition, and creativity from visual sense-building. These advantages increase when sense-building activities are playful and storytelling is used.

This study uses a mixed methods approach to investigate the pedagogical approaches and teaching practices that the English Department at a private Lebanese university implemented to rectify the deteriorating speaking skills of their students during the first few months of the lockdown in Lebanon between March and June 2020. The data consisted of semi-structured interviews with English instructors, the speaking assessment scores, and the English peer support program (PSP) enrollment records. Qualitative analysis of the interviews identified the main challenges that the English instructors faced during their transition from face-to-face (FTF) to distance education. The analysis also identified the intervention strategies that were implemented to overcome these barriers to learning and adapt to and even benefit from computer-mediated communication (CMC) instruction and other technological resources to develop and improve the oral proficiency and fluency of tertiary English as a Foreign Language (EFL) students. The quantitative analysis referred to the average percentage variation of the pre-test and post-test scores of the speaking assessment to track the improvement (or lack thereof) of the students’ speaking skills across four semesters, prior to and after the intervention. A one-way ANOVA test was also conducted for the enrollment records of the English PSP and speaking assessment scores that showed that the program was an effective form of intervention that helped struggling students to at least prevent the further deterioration of their speaking capabilities in English. These findings show that for students to develop and enhance their speaking skills in English in a distance education context and during emergency times, efforts need to be made to reduce the hindering psychological factors and address all technology-related barriers that disrupt the online language learning environment and process.