A contextually adaptive data collaborative using action design research

Autores/as

Palabras clave:

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

Resumen

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.

Biografía del autor/a

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.

Citas

Alemayehu, B., Steffens, P., Gordon, S. & Santos, E. C. S. (2023) South Australian Innovation District (SAID) survey 2022: Preliminary findings, The University of Adelaide in collaboration with the Department of the Premier and Cabinet, Government of South Australia.

Aslett, J., Hamilton, S., Gonzalez, I., Hadwick, D. & Hardy, M. A. (2024) Understanding artificial intelligence in tax and customs administration. Technical Notes and Manuals 2024 (6). International Monetary Fund. https://doi.org/10.5089/9798400290435.005.

Athey, S. (2019) The impact of machine learning on economics. In Agrawal, A., Gans J. & Goldfarb, A. (Eds) The economics of artificial intelligence: An agenda. University of Chicago Press, pp. 507–547.

Australian Bureau of Statistics (2020) Use of integrated South Australian business data for economic analysis, Available at https://www.abs.gov.au/about/data-services/data-integration/use-and-benefits/blade-case-studies#case-study-2-use-of-integrated-south-australian-business-data-for-economic-analyses.

Australian Bureau of Statistics (2024) Australian Defence Industry Account, experimental estimates methodology, Available at https://www.abs.gov.au/statistics/economy/national-accounts/australian-defence-industry-account-experimental-estimates/latest-release.

Bartolomucci, F. & Bartalucci, S. (2024) Designing data collaboratives’ governance dimensions for long-term stability: an empirical analysis, Data & Policy, 6, p. e37. https://doi.org/10.1017/dap.2024.29.

Commonwealth of Australia (2023) Royal Commission into the Robodebt Scheme .Available at https://robodebt.royalcommission.gov.au/system/files/2023-09/rrc-accessible-full-report.PDF.

De Leoz, G. & Petter, S. (2018) Considering the social impacts of artefacts in information system design science research. European Journal of Information Systems, 27 (2), 154-170.

Gavin, E. (2021) How to collaborate effectively to improve data quality and use in revenue administration and official statistics. IMF Working Paper No. 2021/05. Available at https://doi.org/10.5089/9781513582863.061.

Gill, T. G. & Hevner, A. R. (2013) A fitness-utility model for design science research. ACM Transactions on Management Information System, 4 (2), 1-24. https://doi.org/10.1145/2499962.2499963.

Government of South Australia (2021) Economic value of creative businesses in South Australia, Available at https://www.dpc.sa.gov.au/responsibilities/economic-insight-and-evaluation/DIS-creative-industries-final-report.pdf.

Green, E. & Ritchie, F. (2023) The present and future of the Five Safes framework. Journal of Privacy and Confidentiality, 13 (2). https://doi.org/10.29012/jpc.831.

Gregor, S., & Hevner, A. R. (2013) Positioning and presenting design science research for maximum impact. MIS Quarterly, 37 (2), 337-355. https://doi-org.ezproxy.une.edu.au/10.1111/radm.12413.

Guertler, M., Kriz, A. & Sick, N. (2020) Encouraging and enabling action research in innovation management. R & D Management, 50 (3), pp. 380-395.

Han, H., Steffens, P., Mitchell, L. & Gordon, S. (2023) Adelaide’s emerging technology heatmaps. Economic Insight 2023-01. The University of Adelaide in collaboration with the Department of the Premier and Cabinet, Government of South Australia.

Hevner, A., March, S., Park, J. & Ram, S. (2004) Design science in information systems research. MIS Quarterly, 28 (1), 75-105.

Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B. & Ermon, S. (2016) Combining satellite imagery and machine learning to predict poverty. Science, 353 (6301), pp. 790–794. https://doi.org/10.1126/science.aaf7894.

Kalkar, U. & Alarcón, N. G. (2019) Facilitating data flows through data collaboratives: A practical guide to designing valuable, accessible, and responsible data collaboratives, Inter-American Development Bank. Available at: https://publications.iadb.org/publications/english/document/Facilitating-Data-Flows-through-Data-Collaboratives-A-Practical-Guide-to-Designing-Valuable-Accessible-and-Responsible-Data-Collaboratives.pdf.

Kleinberg, J., Ludwig, J., Mullainathan, S. & Obermeyer, Z. (2015) Prediction policy problems. American Economic Review, 105 (5), 491–495. https://doi.org/10.1257/aer.p20151023.

Messier, G., Elliott, S. & Seitz, D. (2025) Understanding housing and homelessness system access by linking administrative data. arXiv preprint arXiv:2505.08743. Available at https://doi.org/10.48550/arXiv.2505.08743.

Meyer, B. & Mittag, N. (2019) Using linked survey and administrative data to better measure income: Implications for poverty, program effectiveness, and holes in the safety net. American Economic Journal: Applied Economics 11 (2), 176- 204. https://doi.org/10.1257/app.20170478.

Mullainathan, S. & Spiess, J. (2017) Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31 (2), 87–106. https://doi.org/10.1257/jep.31.2.87.

Peffers, K., Tuunanen, T. & Niehaves, B. (2018) Design science research genres: Introduction to the special issue on exemplars and criteria for applicable design science research. European Journal of Information Systems, 27 (2), 129-139.

Perry, C. (2013) Efficient and effective research: A toolkit for research students and developing researchers. AIB Publications.

Rahman, M., Ambaw, D. & Santos, E.C.S. (2023a) Business investor migration visa evaluation report. Government of South Australia.

Rahman, M., Ambaw, D. & Santos, E.C.S. (2023b) Export acceleration grant evaluation report. Government of South Australia.

Rahman, M., Ambaw, D. & Santos, E.C.S. (2023c) SA energy efficiency program evaluation report. Government of South Australia.

Return To Work SA. (2019, October) Insurer statistics FY 2019. Available at: https://www.rtwsa.com/about-us/return-to-work-scheme/scheme-statistics.

Return to Work SA. (2021) Register for Cover. Available at https://www.rtwsa.com/insurance/insurance-with-us/register.

Revans, R. W. (2011) ABC of action learning. Gower Publishing Limited.

Rivas, L. & Crowley, J. (2018) Using administrative data to enhance policymaking in developing countries: Tax data and the national accounts. IMF Working Paper No. 18/175. Available at: https://doi.org/10.5089/9781484371701.001.

Rujier, E. (2021) Designing and implementing data collaboratives: A governance perspective. Government Information Quarterly 38 (4). https://doi.org/10.1016/j.giq.2021.101612.

Sansone, D. & Zhu, A. (2021) Using machine learning to create an early warning system for welfare recipients. IZA Discussion Paper No. 14377, Available at SSRN https://ssrn.com/abstract=3851053.

Santos, E.C.S. (2023) Creating a framework for conducting evidence-based policy for a sub-national economy using linked administrative data. [Doctoral dissertation, University of New England]. Available from https://hdl.handle.net/1959.11/55586.

Santos, E.C.S., Yarashov, S., Huk, E. & Dang, T., (2025) Knowledge and technology-intensive industries of South Australia: A study using administrative data. Department of the Premier and Cabinet, Government of South Australia.

Sein, M. K., Henfridsson, O., Purao, S., Rossi, M. & Lindgren, R. (2011) Action design research, MIS Quarterly, 35 (1), 37-56.

Susha, I., Janssen, M. & Verhulst, S. (2017a) Data collaboratives as a new frontier of cross-sector partnerships in the age of open data: Taxonomy development. In Bui, T. X. & Sprague, R. (Eds.) Proceedings of the 50th Annual Hawaii International Conference on System Sciences (HICSS 2017), University of Hawaii, vol. 2017-January, pp. 2691–2700. https://doi.org/10.24251/HICSS.2017.325.

Susha, I., Janssen, M. & Verhulst, S., (2017b) Data collaboratives as “bazaars”? Transforming Government: People, Process and Policy, 11 (1), 157–172. Available at https://www.sciencedirect.com/science/article/pii/S1750616617000142.

University of South Australia (2022) SA-NT DataLink: Supporting health, social and economic research, education and policy in South Australia and Northern Territory. Available at: https://www.santdatalink.org.au/.

Van Aken, J., Chandrasekaran, A. & Halman, J. (2016). Conducting and publishing design science research: Inaugural essay of the design science department of the Journal of Operations Management. Journal of Operations Management, 47-48 (1), 1-8.

Varian, H.R., 2014. Big data: New tricks for econometrics. Journal of Economic Perspectives, 28 (2), 3–28. https://doi.org/10.1257/jep.28.2.3.

Verhulst, S. (2023) Data collaboratives: Enabling a healthy data economy through partnerships, Center for the Governance of Change (CGC), IE University, Madrid.

Vernhulst, S., Young, A. & Srinivasan, P. (2017) Data collaboratives: Exchanging data to create public value across Latin America and the Caribbean. Open Knowledge, Inter-American Development Bank. Available at: https://blogs.iadb.org/conocimiento-abierto/en/data-collaboratives-exchanging-data-to-create-public-value-across-latin-america-and-the-caribbean/.

Waterman, H., Marshall, M., Noble, J., Davies, H., Walshe, K., Sheaff, R. & Elwyn, G. (2007) The role of action research in the investigation and diffusion of innovations in health care: The PRIDE project. Qualitative Health Research, 17 (3), 373-381.

Young, J., Borschmann, R., Camacho, X., Knight, J., Kouyoumdjian, F., Janjua, N., Atkinson, J. & Kinner, S. (2018) Linked data and inclusion health: Harmonised international data linkage to identify determinants of health inequalities. International Journal of Population Data Science, 3 (4). https://doi.org/10.23889/ijpds.v3i4.597.

Yunren, N. & Santos, E.C.S. (2023) Impact of household and neighbourhood income disparities on early childhood language and cognitive skills in SA. Economic Insight 2023-02. Department of the Premier and Cabinet, Government of South Australia.

Yunren, N. & Santos, E.C.S. (2024) Do workers in South Australia’s innovation districts earn more? Department of the Premier and Cabinet, Government of South Australia.

Zheng, S., Trott, A., Srinivasa, S., Naik, N., Gruesbeck, M., Parkes, D. & Socher, R. 2020 The AI economist: Improving equality and productivity with AI-driven tax policies. arXiv preprint arXiv:2004.13332. Available at: https://arxiv.org/abs/2004.13332.

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Publicado

2025-12-29

Cómo citar

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. Recuperado a partir de https://alarj.alarassociation.org/index.php/alarj/article/view/465

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

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