Heidi Hietala

Dr. Heidi Hietala is a postdoctoral researcher in the Empirical Software Engineering in Software, Systems and Services (M3S) research unit. Her research focuses on the collaborative dynamics of digital transformation, particularly in digital service ecosystems. She has studied complex systems such as municipal wastewater data management and large-scale digital public service ecosystems, contributing insights into the mechanisms, antecedents, and outcomes of collective ambidexterity in such contexts. Hietala’s doctoral thesis focused on exploring how collective ambidexterity can be achieved through collaboration among public, private, and third-sector stakeholders, balancing innovation and efficiency in complex service environments.

Her current work includes research on how regulation impacts innovation, with a particular focus on the EU's AI Act and its influence on AI innovation in the public sector and healthcare, the European software-defined vehicle (SDV) ecosystem, and cognitive biases in software development, emphasizing collaborative challenges where diverse and potentially conflicting values, goals, and perspectives must be reconciled. These research streams increasingly engage with ethical considerations, particularly regarding the balance between technological advancement, regulatory constraints, and stakeholder alignment.

With a strong industry background in enterprise systems and digital transformation, she brings a unique perspective that bridges practical challenges with academic research, offering insights into the governance and collaboration necessary for effective digital service development. She has published in journals such as Information and Software Technology, Government Information Quarterly, and Communications of the Association for Information Systems (CAIS). She has also published her work at conferences, including the European Conference on Information Systems (ECIS) and the International Conference on Information Systems (ICIS), where her paper was recognised as Best of Track, a Best Paper nominee, and a Best Kauffman Paper nominee.