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Design with Simon's Inner and Outer Environments: Theoretical Foundations for Design Science Research Methods for Digital Science

Published: 12 March 2024 Publication History

Abstract

Design science research has traditionally been applied to complex real-world problems to produce an artifact to address such problems. Although design science research efforts have been applied traditionally to business or related problems, there is a large set of problems in the area of digital science that also require important, digital artifacts. The digitalization of science has resulted in the need to develop essential, specialized, devices and software before it is feasible for scientists to carry out their work. This research examines digital science to identify its challenges and demonstrate how it can be possible to progress digital science with design science research, thereby establishing digital science as an important area of transdisciplinary inquiry. These areas of research are examined for their synergies and explained by positioning artifact development challenges with respect to Simon's inner and outer environments and the interface between them.

References

[1]
I. Aaen. 2008. Essence: Facilitating software innovation. Eur. J. Inf. Syst. 17, 5 (2008), 543–553.
[2]
R. Baskerville, M. Kaul, and V. C. Storey. 2015. Genres of inquiry in design-science research. MIS Quart. 39, 3 (2015), 541–564.
[3]
R. L. Baskerville, M. D. Myers, and Y. Yoo. 2020. Digital First: The ontological reversal and new challenges for information systems research. MIS Quart. 44, 2 (2020), 509–523.
[4]
R. L. Baskerville and J. Pries-Heje. 2019. Projectability in design science research. J. Inf. Technol. Theory Appl. 20, 1 (2019), 3.
[5]
C. L. Borgman and P. E. Bourne. 2021. Why it takes a village to manage and share data. arXiv:2109.01694. Retrieved from https://arxiv.org/abs/2109.01694
[6]
K. R. Coombes, J. Wang, and K. A. Baggerly. 2007. Microarrays: Retracing steps. Nat. Med. 13, 11 (2007), 1276.
[7]
M. de Bayser et al. 2022. DevOps and Microservices in Scientific System development: Experience on a multi-year industry research project. In Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing.
[8]
P. J. Denning. 2013. The science in computer science. Commun. ACM 56, 5 (2013), 35–38.
[9]
In Proceedings of the 17th International Conference on Design Science Research in Information Systems and Techology (DESRIST ’22).
[10]
D. Dougherty and D. D. Dunne. 2012. Digital science and knowledge boundaries in complex innovation. Org. Sci. 23, 5 (2012), 1467–1484.
[11]
M. N. Fienen and M. Bakker. 2016. HESS Opinions: Repeatable research: What hydrologists can learn from the duke cancer research scandal. Hydrol. Earth Syst. Sci. 20, 9 (2016), 3739–3743.
[12]
W. Hasselbring et al. 2020. Open source research software. Computer 53, 8 (2020), 84–88.
[13]
T. Herndon, M. Ash, and R. Pollin. 2014. Does high public debt consistently stifle economic growth? A critique of reinhart and rogoff. Cambr. J. Econ. 38, 2 (2014), 257–279.
[14]
K. Hinsen. 2015. Technical debt in computational science. Comput. Sci. Eng. 17, 6 (2015), 103–107.
[15]
L. Hwang et al. 2017. Software and the Scientist: Coding and citation practices in geodynamics. Earth Space Sci. 4, 11 (2017), 670–680.
[16]
A. Johanson and W. Hasselbring. 2018. Software engineering for computational science: Past, present, future. Comput. Sci. Eng. 20, 2 (2018), 90–109.
[17]
R. Jung, S. Gundlach, and W. Hasselbring. 2022. Thematic domain analysis for ocean modeling. Environ. Model. Softw. 150, C (2022), 105323.
[18]
D. Kelly. 2015. Scientific software development viewed as knowledge acquisition: Towards understanding the development of risk-averse scientific software. J. Syst. Softw. 109 (2015), 50–61.
[19]
D. Kelly, D. Hook, and R. Sanders. 2009. Five recommended practices for computational scientists who write software. Comput. Sci. Eng. 11, 5 (2009), 48–53.
[20]
D. Kelly, S. Smith, and N. Meng. 2011. Software engineering for scientists. Comput. Sci. Eng. 13, 05 (2011), 7–11.
[21]
H. Larsen. 2016. The crisis of public service broadcasting reconsidered: Commercialization and digitalization in Scandinavia. In The Crisis of Journalism Reconsidered: Democratic Culture, Professional Codes, Digital Future. Cambridge University Press, Cambridge, UK, 43–58.
[22]
B. Lawlor and P. Walsh. 2015. Engineering bioinformatics: Building reliability, performance and productivity into bioinformatics software. Bioengineered 6, 4 (2015), 193–203.
[23]
B. S. Lawlor. 2021. The Role of Software Engineering in Bioinformatics. Department of Computer Science, Munster Technological University, Cork, Ireland.
[24]
R. Lukyanenko, A. Wiggins, and H. K. Rosser. 2020. Citizen science: An information quality research frontier. Inf. Syst. Front. 22, 4 (2020), 961–983.
[25]
Z. Merali. 2010. Computational science: Error, why scientific programming does not compute. Nature 467, 7317 (2010), 775–777.
[26]
Ó. Pastor et al. 2021. Using conceptual modeling to improve genome data management. Brief. Bioinf. 22, 1 (2021), 45–54.
[27]
K. Peffers et al. 2007. A design science research methodology for information systems research. J. Manage. Inf. Syst. 24, 3 (2007), 45–77.
[28]
N. Prat, I. Comyn-Wattiau, and J. Akoka. 2015. A taxonomy of evaluation methods for information systems artifacts. J. Manage. Inf. Syst. 32, 3 (2015), 229–267.
[29]
U. Rüde et al. 2018. Research and education in computational science and engineering. SIAM Rev. 60, 3 (2018), 707–754.
[30]
P. Ruiz et al. 2017. Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors. Chemosphere 178 (2017), 99–109.
[31]
R. Sharma et al. 2022. Self-admitted technical debt in R: Detection and causes. Autom. Softw. Eng. 29, 2 (2022), 1–41.
[32]
H. A. Simon. 1969. The Science of the Artificial. MIT Press, Cambridge, MA.
[33]
H. A. Simon. 1996. The Sciences of the Artificial. MIT Press, Cambridge, MA.
[34]
D. A. Soergel. 2014. Rampant software errors may undermine scientific results. F1000Res 3 (2014), 303.
[35]
B. S. Steel, D. Lach, and E. P. Weber. 2017. New strategies for wicked problems: Science and solutions in the 21st century. Oregon State University Press.
[36]
T. Storer. 2017. Bridging the chasm: A survey of software engineering practice in scientific programming. ACM Comput. Surv. 50, 4 (2017), 1–32.
[37]
T. Storer. 2017. Bridging the chasm: A survey of software engineering practice in scientific programming. ACM Comput. Surv. 50, 4 (2017), 32.
[38]
V. Storey and R. Baskerville. 2022. Computational science: A field of inquiry for design science research. In Proceedings of the 55th Hawaii International Conference on System Sciences. 2022.
[39]
V. C. Storey and R. L. Baskerville. 2021. The digital science field of design science research. In The Next Wave of Sociotechnical Design: Proceedings of the 16th International Conference on Design Science Research in Information Systems and Technology (DESRIST ’21). Springer.
[40]
K. Szkuta and D. Osimo. 2016. Rebooting Science? Implications of science 2.0 main trends for scientific method and research institutions. Foresight 18, 3 (2016), 204–223.
[41]
M. A. Thomas, Y. Li, and A. S. Lee. 2022. Generalizing the information systems artifact. Inf. Syst. Res. 33, 4 (2022), 1452–1466.
[42]
H. Tu, R. Agrawal, and T. Menzies. 2020. The changing nature of computational science software. In Proceedings of the 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE ’20).
[43]
J. Venable, J. Pries-Heje, and R. Baskerville. 2016. FEDS: A framework for evaluation in design science research. Eur. J. Inf. Syst. 25, 1 (2016), 77–89.
[44]
F. Wickson, A. L. Carew, and A. W. Russell, Transdisciplinary research: Characteristics, quandaries and quality. Futures 38, 9 (2006), 1046–1059.

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  1. Design with Simon's Inner and Outer Environments: Theoretical Foundations for Design Science Research Methods for Digital Science

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    Published In

    cover image ACM Transactions on Management Information Systems
    ACM Transactions on Management Information Systems  Volume 15, Issue 1
    March 2024
    135 pages
    EISSN:2158-6578
    DOI:10.1145/3613505
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 12 March 2024
    Online AM: 16 January 2024
    Accepted: 19 November 2023
    Revised: 17 October 2023
    Received: 17 April 2023
    Published in TMIS Volume 15, Issue 1

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    Author Tags

    1. Design science digital science
    2. computational science
    3. inner environment
    4. outer environment
    5. digitalization of science
    6. transdisciplinary design science research
    7. exhaust artifact
    8. Simon's boundaries

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