A contextually adaptive data collaborative using action design research

Auteurs-es

Mots-clés :

action design research, data, innovation, information system, socio-technical system

Résumé

This paper explores attributes of a successful data innovation project, which challenges the conventional focus on technical feasibility by highlighting the paramount importance of data intelligibility, relevance, and utility in a socio-technical system. The South Australian (SA) data linkage project in the Business Longitudinal Analytical Data Environment (BLADE) is the first of its kind in Australia where a State government has linked business-related administrative data with Commonwealth data. The data linkage has created a new data asset that opens new opportunities for users in producing policy insights. This study sought to show the impact of this innovation using action design research. An important lesson learnt was that collaboration with partners and stakeholders, particularly the end-users is key to fostering a socio-technical system of co-evolution that mutually informs the other process to avoid adverse unintended consequences. This is even more important with the emergence of artificial intelligence and machine learning.

Bibliographies de l'auteur-e

Emmanuel Candido Soriente Santos, Department of the Premier and Cabinet, Government of South Australia

Emmanuel is a public policy and international development adviser with over two decades of experience. He is the lead researcher and principal author of this study. He introduced action learning to the Philippine Civil Service Commission when he designed and developed a leadership program as part of an international development assistance project funded by the Australian Department of Foreign Affairs and Trade (DFAT). He led in facilitating the program with its maiden cohort in 2014/15. The Philippine government continues to benefit from its adoption to this day. Dr Santos’ doctoral thesis in innovation incorporated action learning and action research.

Renato Andrin Villano, University of New England Business School

Rene Villano is a Professor of Economics at the UNE Business School, University of New England, Australia. A Distinguished Fellow of the Australian Agricultural and Resource Economics Society and a Senior Fellow of the Higher Education Academy, he has over 20 years of academic and research leadership in agricultural economics, applied econometrics, and development. He has participated in major multi-disciplinary research for development projects across Asia and Africa. Professor Villano has supervised over 80 graduate students and actively published in regional and international journals and contributes to several editorial and professional boards.

Jonathan Moss, University of New England Business School

Jonathan is an applied economist with a strong interest in integrating large spatio-temporal datasets with bioeconomic models to provide practical solutions to sustainability challenges in the agricultural and natural resource sectors. Recently he has been working in cross-disciplinary and cross-institutional teams on the development of policies to increase farmer resilience under low carbon futures and the modelling and management of agricultural systems for sustainable production. He co-supervised the lead researcher in the study.

Benjamin Wilson, Department of the Premier and Cabinet, Government of South Australia

Ben is a senior civil servant with more than two decades of experience working in the public sector both at the Federal and state government. With a background in economics, Ben has provided advice in the areas of social, economic, infrastructure, regulatory, and fiscal policy. His contribution to the paper was to act as the direct supervisor to the lead researcher in the workplace, and as industry advisor in the design and implementation of the study.

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Publié-e

2025-12-29

Comment citer

Santos, E. C. S., Villano, R. A. ., Moss, J., & Wilson, B. (2025). A contextually adaptive data collaborative using action design research. Action Learning and Action Research Journal, 31(2), 12–46. Consulté à l’adresse https://alarj.alarassociation.org/index.php/alarj/article/view/465

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Vol. 31 No. 2 (2025): ALARj Vol 31 No 2 December 2025

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Articles

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