Course Name: Quantitative methods for systematic literature reviews
Time and place: 22 – 25 September 2026, University of Turku
Learning goal and objectives: This course introduces doctoral students to state-of-the-art quantitative methodologies and techniques for conducting systematic literature reviews. A rigorous literature review forms the foundation of good doctoral research and, when executed systematically, holds strong potential for publication in high-quality journals. In this regard quantitative approaches tailored to literature reviews provide significant value by enabling the systematic and unbiased analysis, mapping, and synthesis of existing bodies of research.
In this course students will learn key quantitative and AI-assisted methods for literature analysis, including bibliometric techniques, correspondence analysis and Latent Dirichlet Allocation (LDA) for topic modeling. The course emphasizes understanding how these techniques work, their appropriate applications and their respective strengths and limitations in the context of business research.
Students will also gain practical competence in using tools such as Bibexcel, Gephi, Python (for text mining and topic modeling) and SPSS to process and analyze data for literature review. Importantly, students will have the opportunity to apply these techniques to their own doctoral literature meta data, thereby acquiring both conceptual understanding and practical experience in executing a well-designed quantitative technique-based literature review.
By the end of the course, students will be able to evaluate methodological choices, conduct data-driven literature reviews and develop theoretically grounded discussions based on insights from data analysis. These competencies will enable them to produce publishable empirical papers and enhance their overall capacity for high-quality and evidence-based doctoral research.
Instruction and examination: TBA
Credits: 6 ECTS
Grading: The course assessment is based on two components: (1) an analysis of a journal article and a group presentation, and (2) an individual written assignment. Both components are graded on a scale of 1–5, where 1 is sufficient and 5 is excellent. To successfully complete the course, students must achieve at least a grade of 1 in both components.
Prerequisites: Participants must be active doctoral students. The course is suitable for both those in the early and late phases of their studies.
Admittance: The course is targeted toward doctoral students in any field of business and management, and related disciplines. The maximum number of students admitted to the course is 25.
To apply to the course, please fill out the form below and send it to Farhan Ahmad (farhan.ahmad@utu.fi) by the 10 August 2026.
Name:
Degree:
Address:
E-mail:
University, faculty and department:
Major:
Officially accepted as a Ph.D. student (when and where?):
Completed methodology studies:
Subject or title of dissertation:
Phase of the dissertation:
Summary of the research objectives, questions and methodologies (approx. 500 words):
Experience of literature review publications:
Your own objectives for participating in the course:
Instructors:
Dr. Farhan Ahmad is a university lecturer in the Department of Information Systems at the Turku School of Economics in Finland. He also serves as an adjunct professor (docent) at Åbo Akademi University and as a doctoral supervisor at Edinburgh Business School in the UK. Previously, he held academic positions at Åbo Akademi University, Edinburgh Business School and Edge Hill University Business School in England. His teaching focuses on knowledge and innovation management, business intelligence, emerging technologies, business models and research methods. His current research projects examine employees’ use of generative AI and large language models (LLMs) in the workplace, as well as the role of emerging technologies in sustainability. Methodologically, he specialises in bibliometrics, fuzzy set qualitative comparative analysis (fsQCA), and topic modelling using latent Dirichlet allocation (LDA), and has expertise in both quantitative and qualitative research methods. His work has been published in leading international journals such as Long Range Planning, Technological Forecasting & Social Change, International Journal of Information Management, Journal of International Management, Journal of Information Science and Information, Technology & People, among others. He also serves as a reviewer for several scholarly journals.
Professor Marina Dabić is a full Professor of Entrepreneurship and International Business at the University of Zagreb, Faculty of Economics and Business, and at the University of Dubrovnik, Croatia, as well as at the University of Ljubljana, School of Economics and Business, Slovenia. From 2013 to 2022, she also served as a part-time Professor at Nottingham Trent University in the United Kingdom. Professor Dabić holds several prominent editorial positions, including Editor-in-Chief of Technology in Society, Associate Editor of Technological Forecasting and Social Change and Strategic Change, and Senior Department Editor of IEEE Transactions on Engineering Management. Recognised among the top 2% of scientists in business and management on the Stanford list since 2022, her research has been widely published in leading international journals such as Journal of International Business Studies, Journal of World Business, Journal of Business Research, Technological Forecasting and Social Change, Small Business Economics, International Journal of Human Resource Management, IEEE Transactions on Engineering Management, Technovation, and Journal of Small Business Management, among others. Her research spans innovation, technology management, entrepreneurship, open innovation, corporate social responsibility and engineering management. She has supervised numerous doctoral theses and served on over 25 PhD committees worldwide. Professor Dabić has also contributed to European research and innovation policy as a panel member for the European Research Council, Horizon 2020, Horizon Europe, FWO Belgium, and Finnish Research panels, and has held leadership roles in several Horizon and Erasmus projects. She additionally serves as an AACSB mentor and EFMD peer reviewer and is Strategic Director for Accreditations at the University of Zagreb, Faculty of Economics and Business.
Dr. Majid Aleem is a university teacher in International Business at the Turku School of Economics, University of Turku. His research focuses on how organizations and leaders adapt to the challenges of digitalization, remote work, and AI integration. He investigates global virtual teamwork, leadership development, and innovation processes in international contexts, bridging strategic HRM and digital management. Dr. Aleem has developed innovative AI-based teaching and research tools such as GVT analytics and the AJG Rank Filter, recognized internationally in AMBA & BGA Innovation Awards. He actively collaborates in multi-disciplinary research projects, including ARCANA (Eurostars) and NordForsk initiatives on AI and flexible work.
Dr. Anu Bask is a Professor of Business Development at the School of Marketing and Communication, University of Vaasa. She also serves as an Adjunct Professor of Sustainable Supply Chain Management at the Turku School of Economics, University of Turku. In addition, she is the Director of Kataja’s Finnish Graduate School of Logistics and Supply Chain Management. Her research interests include modularity in services, sustainable logistics and supply chain management, and circular economy. Her research has been published in reputable journals such as Technological Forecasting and Social Change, Journal of Business and Industrial Marketing, International Journal of Physical Distribution and Logistics Management, and Journal of Cleaner Production, among others.
Course coordinator and contact information: farhan.ahmad@utu.fi, Turku School of Economics; FI-20014 TURUN YLIOPISTO, Turku, Finland