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Dr Nastaran Hajiheydari

Nastaran

Senior Lecturer (Associate Professor) in Digital Marketing & Analytics; Head of Department Marketing

Email: n.hajiheydari@qmul.ac.uk

Profile

Roles: 

Biography:

Dr Nastaran Hajiheydari is Head of the Department of Marketing and Senior Lecturer (Associate Professor) in Digital Marketing & Analytics at the School of Business and Management, Queen Mary University of London. She is an internationally recognised scholar in digital innovation, computational social science, and data-driven decision-making, with extensive experience spanning academia, industry, and executive leadership.

Before joining QMUL, Nastaran held academic appointments as Tenured Associate Professor at the University of Tehran and Assistant Professor at the University of Sheffield. She began her academic career in 2010, following over seven years of professional experience in IT management, business analysis and digital transformation, where she worked closely with organisations navigating large-scale technological change.

Nastaran’s career uniquely bridges academic leadership and practical business management. In addition to her academic roles, she has led a Strategic Business Unit (SBU) and a start-up, giving her first-hand experience of managing budgets, teams, and organisational processes in complex environments. She has also provided consultancy and executive workshops across sectors including courier services, telecommunications, financial services, manufacturing, and securities exchanges. Her work has supported organisations in areas such as business analytics, digital transformation, and AI-enabled decision-making.

Alongside her academic and industry engagement, Nastaran has contributed to the innovation and investment ecosystem by serving on committees evaluating ICT and digital innovation funding proposals. As Head of Department, she provides strategic leadership across education, research, and citizenship, with a strong commitment to internationalisation, interdisciplinary collaboration, and inclusive excellence.

Teaching

Postgraduate 

  • BUSM098: Research Methods for Marketing
  • BUSM204: Consumer and Digital Cultures
  • BUSM205: Digital Economy, Big Data and Platformisation

Nastaran’s teaching spans business analytics, digital marketing, digital transformation, and platform-based economies, delivered across undergraduate, postgraduate, and MBA programmes. Her teaching is strongly research-led, drawing on the latest theoretical and empirical developments in information systems, AI, and digital innovation.

Her pedagogical approach emphasises:

  • Solving real-world problems through applied projects and industry-relevant cases
  • Hands-on experience using contemporary analytics and visualisation tools
  • Creativity and collaboration, encouraging critical thinking and peer learning in diverse cohorts

She is particularly committed to developing students’ capabilities in both analytical and practical skills, enabling them to succeed in data-driven and digitally mediated roles. Nastaran is a Senior Fellow of the Higher Education Academy (SFHEA).

Research

Research Interests:

Nastaran’s research lies at the intersection of digital transformation, artificial intelligence, and computational social science. Her work focuses on understanding and theorising user, organisational, and societal behaviour in digital environments through large-scale data analysis and advanced computational methods.

Her core research areas include:

  • Computational social science, particularly the analysis of online behaviour on social media, platforms, crowdsourcing, and crowdfunding environments
  • Algorithmic management and digital platforms, examining how AI and algorithms shape work, decision-making, and organisational outcomes
  • Text mining and natural language processing, including classification, clustering, and sentiment analysis
  • Digital transformation and emerging technologies, including AI, big data, IoT, and robotics, with implications for business strategy and public policy

Her research adopts machine learning and data-driven approaches to develop theory and generate insights that inform managerial decision-making, organisational strategy, and policy design.

Publications

Dr Hajiheydari’s work has appeared in Journal of Association for Information Systems, Information Systems Journal, The Journal of Strategic Information Systems, Social Science and Medicine, Technological Forecasting and Social Change, Information Technology & People, Technovation, Journal of Business Research, Computers in Human Behavior, and Information Systems Frontiers, Journal of Information Systems, among more. Her Google Scholar profile contains a detailed list of publications. 

Selected Journal Publications 

Selected Conferences 

  • Hajiheydari, N., & Soltani Delgosha, M. (2025). A Person-Centred View of Algorithmic Management and Gig Workers’ Turnover. In Academy of Management Proceedings(Vol. 2025, No. 1, p. 12736). Valhalla, NY 10595: Academy of Management.
  • Hajiheydari, N. & Delgosha, M. S (2023). Algorithmic management and the experience of non-human manager: An exploratory analysis of crowdworkers' narratives presented in 39th EGOS, July 6–8, 2023, Cagliari, Italy.
  • Hajiheydari, N., & Soltani Delgosha, M. (2022). How Crowdworkers Engage in the Age of Algorithms? An Empirical Study of On-Demand Service Platforms. In Academy of Management Proceedings (Vol. 2022, No. 1, p. 11174). Briarcliff Manor, NY 10510: Academy of Management. 
  • Hajiheydari, N., & Soltani Delgosha, M. (2021). Why People Collaborate in Civic Crowdfunding Platforms: Configurational Analysis of Citizens Profile. In Academy of Management Proceedings (Vol. 2021, No. 1, p. 11960). Briarcliff Manor, NY 10510: Academy of Management.
  • Hajiheydari, N., Delgosha, M. S., (2020). How to Avoid Falling into Death Spiral: Switching and Multihoming Behaviours in Two-Sided Platforms. Academy of Management Proceedings, 2020
  • Delgosha, M. S., Hajiheydari, N., & Saheb, T. (2020). The Configurational Impact of Digital Transformation on Sustainability: a Country-Level Perspective. European Conference of Information systems 2020.
  • Hajiheydari, N., Delgosha, M.S., Talafidaryani, M. (2019). IS Research Theoretical Foundation: Theories Used and the Future Path. International Conference on Research and Practical Issues of Enterprise Information Systems CONFENIS 2019, Prague, Czech Republic,16-17 December
  • Hajiheydari, N., Talafidaryani, M. Khabiri S.H., (2019). Toward a Knowledge Sharing-Aimed Virtual Enterprise, 7th World Conference on Information Systems and Technologies, Galicia, Spain, April 16 - 19.
  • Hajiheydari, N., Talafidaryani, M. Khabiri S.H., (2019). Big data IoT Value Map: How to Generate Value form IoT Data, The 2nd International Conference on Information Management and Processing, Laxenburg, Austria, January 10-12.
  • Hajiheydari, N., Salehi, M. Goudarzi, A. (2018). Optimizing Humanitarian Aids: Formulating Influencer Advertisement in Social Networks, PRO-VE 2018 Cardiff, UK, September 17-19.


Supervision

Areas of Supervision Interest: 

Dr Hajiheydari welcomes PhD applications from motivated and intellectually curious candidates interested in business and societal challenges in the digital era. She is particularly keen to supervise research in:

  • Digital transformation and organisational change
  • Artificial intelligence and algorithmic decision-making
  • Digital platforms, sharing economy, and online communities
  • Computational social science using machine learning and large-scale digital trace data

She values interdisciplinary collaboration and is especially interested in projects situated in healthcare, financial services, and platform-based industries, as well as cross-School and cross-Faculty research initiatives.

Current Doctoral Students:

  • Zihao Chen – Unpacking the Drivers and Social Effects of Corporate Digital Transformation
  • Xinru Niu- Leveraging Generative AI for Sustainable Marketing
  • Yuetong Guo- The Effects of Avatars Realism on Consumer Inference of Manipulative Intent
  • Berlin Asong- Reasoning Calibration in Consumer–AI Interaction
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