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Marketing and Consumer Studies (MACS) Research Centre Assembly with Dr Anna Minà

Dr Anna Mina
Public lectures | Seminars | Workshops

The Marketing and Consumer Studies Research Centre (MACS) cordially invites you to our fourth Research Assembly, delivered on 8th May 2024, 2 pm - 4 pm BST by Dr Anna Minà (via MS Teams from Italy). Details of the speaker and the talk can be found below:

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Event details

Title: Machine Learning Toolkit for Selecting Studies and Topics in Systematic Literature Reviews

Speaker: Dr Anna Minà

Biography: Anna Minà is an Associate Professor of Management at the University of Rome LUMSA, Palermo Campus. Previously, she was a Postdoctoral Research Fellow in Strategic Management, respectively, at Sapienza University of Rome and the University of Catania, and Assistant Professor of Management at University of Enna. She has been Visiting Scholar at NYU’s Stern School of Management and at ISB-Indian School of Business. She gained her PhD in Business Economics and Management from the University of Catania and her MSc and BSc Degrees in Economics & Management from the University of Palermo. In the case of both degrees, she was awarded magna cum laude for the remarkable results achieved with the final thesis. Her research interests look at: (a) competitive and cooperative interactions among firms, with a specific emphasis on interfirm networks and ecosystems; (b) the foundations of coopetition strategy, and the role of uncertainties, unfairness and attribution errors in coopetition; and (c) the antecedents and consequences of corporate social irresponsibility. Her work has appeared or is forthcoming in outlets such as the Academy of Management Perspectives, Long Range Planning, Strategic Management Review, and Journal of Business and Industrial Marketing.

Abstract: Scholars conduct systematic literature reviews (SLR) to summarize what we know and discern what we should know about a specific theme. Machine learning (ML) can support researchers in conducting systematic literature reviews. We present an ML approach - based on Network Analysis and Natural Language Processing (NLP) - that allows extracting textual features to categorize papers. The method consists of an algorithm that allows: (a) to select relevant studies on a specific theme; (b) to discern the main topics around the theme. We offer two applications of our toolkit. Specifically, we select relevant studies and discern the main topics around cobranding and coopetition. We juxtapose ML results with previous systematic literature reviews and show that ML may boost the rigour of SLR (in terms of transparency, completeness, saturation, and universalism).

If you have any questions, please contact us via macs@ntu.ac.uk.

Virtual Event https://www.ntu.ac.uk/about-us/events/events/2024/5/marketing-and-consumer-studies-macs-research-centre-assembly-with-anna-mina
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