Analysing qualitative data from virtual worlds: using images and text mining

There is an increasing interest within both organisational and social contexts in virtual worlds and virtual reality platforms. Virtual worlds are highly graphical systems in which avatars interact with each other, and almost every event and conversation is logged and recorded. This presents new challenges for qualitative researchers in information systems. This paper addresses the challenges of analyzing the huge amounts of qualitative data that can be obtained from virtual worlds (both images and text). It addresses how images might be used in qualitative studies of virtual worlds, and proposes a new way to analyze textual data using a qualitative software tool called Leximancer. This paper illustrates these methods using a study of a social movement in a virtual world

Beyond ‘the Beamer, the boat and the bach’? A content analysis-based case study of New Zealand innovative firms

In this paper we will use case studies to seek to understand the dynamic innovation processes at the level of the firm and to explain the apparent 'enigma' between New Zealand's recent innovation performance and economic growth. A text-mining tool, Leximancer, (version 4) was used to analyse the case results, based on content analysis. The case studies reveal that innovation in New Zealand firms can be best described as 'internalised', and the four key factors that affect innovation in New Zealand firms are ‘Product’, ‘Market’, ‘People’ and ‘Money’. New Zealand may be an ideal place for promoting local entrepreneurship, however, many market/technology opportunities cannot be realized in such a small and isolated economy, hence the poor economic performance

Identity development in career-changing beginning teachers : a qualitative study of professional scientists becoming school teachers

This qualitative study provides a critical case to analyse the identity development of professionals who already have a strong sense of identity as scientists and have decided to relinquish their professional careers to become teachers. The study followed a group of professionals who undertook a one-year teacher education course and were assigned to secondary and middle-years schools on graduation. Their experiences were examined through the lens of self-determination theory, which posits that autonomy, confidence and relationships are important in achieving job satisfaction. The findings indicated that those teachers who were able to achieve this sense of autonomy and confidence, and had established strong relationships with colleagues generated a positive professional identity as a teacher. The failure to establish supportive relationships was a decisive event that challenged their capacity to develop a strong sense of identity as a teacher

Cross-check for completeness: exploring a novel use of Leximancer in a Grounded Theory study

This paper investigates the potential for Leximancer software to actively support the Grounded Theory (GT) analyst in assessing the ‘completeness’ of their study. The case study takes an existing GT study and retrospectively analyzes the data with Leximancer. The Leximancer output showed encouraging similarities to the main themes emerging from the GT analysis; but not sufficiently at the selective coding level to justifiably claim a definitive cross-check for overall theoretical saturation. Whilst Leximancer is not found to be a substitute for the ‘hard labor’ of GT coding and theory development, it can provide a very useful, efficient and relatively impartial cross-check of completeness/saturation in the open (and possibly axial) coding stage(s) of a GT study. This automated post-analysis check of GT coding is a novel use of a CAQDAS package.<br/

Building an analysis of new venture startup with Leximancer

Leximancer is a valuable qualitative analysis tool. Analysing qualitative data requires expertise on the part of the researcher. Leximancer is a computer assisted text analysis program that examines the actual language of research participants. The software generates visual concept matrix maps that display principal concepts and themes and relationships among them. Leximancer provides a system for discovering the underlying core associations in a body of text while reducing any bias that may occur with grounded thematic coding. A study examining the startup of entrepreneurial ventures combined Leximancer and thematic analyses. The dual analysis verified major concepts and themes generated in Leximancer, thus increasing the quality of the study by providing coherence, credibility and confirmability of findings

Tackling social media data analysis: Comparing and contrasting QSR NVivo and Leximancer

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications The paper provides practical recommendations for analysts considering which programs to use when exploring big, text-based, online data. Originality/value 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

What are the main dimensions of the visitor grand prix experience, based on the narratives shared online

With the controversial Abu Dhabi Grand Prix (GP) in 2021 and the popular Netflix documentary series Drive to Survive, Formula 1 (F1) gained unexpected interest. This phenomenon led to the need to investigate the concept of visitor experience, in particular the dimensions of tourist experience. The large audience of F1 tourist creates the necessity to understand the visitors’ experiences. As a result, the dissertation concentrates on the crucial role of identifying the main dimensions that are shaped by the GP visitors based on narratives shared online and if the dimensions of the experience vary for satisfied and dissatisfied visitors. For the methodology part, a mixed content analysis was applied by including 10 GP races. The results of this study were performed by the software Leximancer that identified 13 themes which are race, circuit, day, F1, stands, experience, event, food, organization, train, visit, hour, and tour. According to the results, the most significant dimensions for the visitors that take part in a GP are the race, circuit, and day. Regarding the second research question the study went beyond prior research and discovered if satisfied and dissatisfied visitors convey significant differences about their visitor GP experiences. Visitors expressed their satisfaction through the themes experience, weekend, race, circuit, and tour whereas negative reviews were associated with queue, food, and day. Consequently, these findings contribute to the literature on tourist experiences in the Formula 1 GP by contributing to greater comprehension of the visitors’ subjective perspectives.Com o polêmico Grande Prêmio de Abu Dhabi (GP) em 2021 e a popular série de documentários da Netflix Drive to Survive, a Fórmula 1 (F1) ganhou um interesse inesperado. Este fenómeno levou à necessidade de investigar o conceito de experiência do visitante, em particular as dimensões da experiência turística. O grande público de turistas de F1 cria a necessidade de entender as experiências dos visitantes. Como resultado, a dissertação concentra-se no papel crucial de identificar as principais dimensões que são moldadas pelos visitantes do GP com base em narrativas partilhadas online e se as dimensões da experiência variam para visitantes satisfeitos e insatisfeitos. Para a parte metodológica, uma análise de conteúdo misto foi aplicada incluindo 10 corridas de GP. Os resultados deste estudo foram realizados pelo software Leximancer que identificou 13 temas que são corrida, circuito, dia, F1, estandes, experiência, evento, alimentação, organização, trem, visita, hora e passeio. De acordo com os resultados, as dimensões mais significativas para os visitantes que participam de um GP são a corrida, o circuito e o dia. Em relação à segunda questão de pesquisa, o estudo foi além da pesquisa anterior e descobriu se os visitantes satisfeitos e insatisfeitos transmitem diferenças significativas sobre as experiências de GP dos visitantes. Os visitantes expressaram sua satisfação por meio dos temas experiência, fim de semana, corrida, circuito e passeio, enquanto as críticas negativas foram associadas a fila, comida e dia. Consequentemente, esses achados contribuem para a literatura sobre experiências turísticas no GP de Fórmula 1 ao contribuir para uma maior compreensão das perspectivas subjetivas dos visitantes

Using language technologies to support individual formative feedback

In modern educational environments for group learning it is often challenging for tutors to provide timely individual formative feedback to learners. Taking the case of undergraduate Medicine, we have found that formative feedback is generally provided to learners on an ad-hoc basis, usually at the group, rather than individual, level. Consequently, conceptual issues for individuals often remain undetected until summative assessment. In many subject domains, learners will typically produce written materials to record their study activities. One way for tutors to diagnose conceptual development issues for an individual learner would be to analyse the contents of the learning materials they produce, which would be a significant undertaking. CONSPECT is one of six core web-based services of the Language Technologies for Lifelong Learning (LTfLL) project. This European Union Framework 7-funded project seeks to make use of Language Technologies to provide semi-automated analysis of the large quantities of text generated by learners through the course of their learning. CONSPECT aims to provide formative feedback and monitoring of learners’ conceptual development. It uses a Natural Language Processing method, based on Latent Semantic Analysis, to compare learner materials to reference models generated from reference or learning materials. This paper provides a summary of the service development alongside results from validation of Version 1.0 of the service