Mini Track on The Fourth Research Methodological Tradition: Big Data, Analytics and Data Science
Mini Track Chair: Ben Kei Daniel, University of Otago, New Zealand
Contemporary digital environments are increasingly generating various forms of data traces (structured, semi-structured and unstructured). Such data are the outcome of human-to-human, human-to-machine and machine-to-machine interactions in social media environments, online business enterprise systems (social, financial and administrative), smartphones and sensors. The application of artificial intelligence techniques (AI) and machine learning approaches to these new forms of data offer researchers enormous opportunities for understanding complex human and social systems. However, in order to fully leverage the opportunities afforded by Big Data and analytics, it is necessary for the design of research methods programmes to reflect the changing nature of these data and incorporate techniques of Data Science.
Further, the utilisation of Big Data and analytics enable researchers and decision-makers to harvest and extract useful knowledge to enhance the quality of decision-making, the use of personal data primarily in predictive modelling raises concerns around data ownership, privacy, ethics, and informed consent. In this mini-track we invite work on theoretical and empirical work on research methods and techniques in the analysis of Big Data and analytics. Topics will include but not limited to:
- Curriculum design and delivery of Data Science
- Perspectives, application and challenges of working with Big Data and Analytics
- Learning analytics (LA) and business analytics (BI)
- Techniques for visualisation of data (e.g. dashboards, infographics, etc.)
- Modelling and predictive models
- Human ethics, data literacy, privacy, security, and morality in the use of human data
- Regulatory and data compliance issues with Big Data
- Developing data governance models, challenges and opportunities
- Examples of the application of the following machine learning methods in business, education and health:
- Social network Analysis
- Sentiment Analysis
- Random Forest
- Naive Bayes
- Decision Trees
- Support Vector Machines (SVM)
- Neural Networks
Mini Track on 2020 Hindsight: Evaluating the Contribution of Qualitative Research Methods
Mini Track Chair: Dr Serge Basini, Technological University Dublin, Ireland
It has been stated that some forms of qualitative methodology are easy to do badly and difficult to do well (e.g. Interpretative phenomenological analysis - IPA). So how does qualitative research establish contribution and utility? This may be an especially relevant question where such research output might be described as possessing theoretical validity rather than empirical validity.
Since an appreciable corpus of published qualitative research exists within business domains, it is both reasonable and desirable to evaluate this work, as well as to look forward to advance quality investigations.
Papers within this track should clearly reflect and elaborate on the commitment to rigor of the qualitative methods applied. They must also specify the underlying strength of the data and transparency in the analytical processes.
Although this mini-track welcomes reports on research projects utilizing innovative qualitative research methods, the interest lies more in comments and research into the use, evaluation and understanding of qualitative research methods. Topics include but are not limited to:
- Developing Rigor and Evolving guidelines for Quality in qualitative research
- Defending the value of Qualitative Research
- Organisational Context elaboration and meaning in Qualitative research
- The challenge of Dissemination & Publication of Qualitative research
- Organisational application of Qualitative research findings
- Developing commitment to qualitative research