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THE LOGICS OF DOUBLE PROOF IN PROOF OF CONCEPT: A DESIGN THEORY-BASED MODEL OF EXPERIMENTATION IN THE UNKNOWN

Published online by Cambridge University Press:  27 July 2021

Caroline Jobin*
Affiliation:
MINES ParisTech, PSL University Les Sismo
Sophie Hooge
Affiliation:
MINES ParisTech, PSL University
Pascal Le Masson
Affiliation:
MINES ParisTech, PSL University
*
Jobin, Caroline, MINES ParisTech, Centre for Management Science, France, caroline.jobin@mines-paristech.fr

Abstract

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The literature on design distinguishes between exploration-based experimentation and validation-based experimentation. This typology relies on an assumption that exploration and validation cannot and should not be performed simultaneously in the same experimentation. By contrast, some practitioners, such as les Sismo, propose that proof of concept might combine these two logics. This raises the question of what design logic might enable this type of combination of exploration and validation. We first use design theory to build an experimentation design framework. This framework highlights a typology of proof logics in experimentation related to proof of the known and proof of the unknown. Second, we show that these proof models are supported by les Sismo's cases and describe a diversity of arrangements of exploration and validation mechanisms: sequential, parallel, and combinational. Through the formalisation of proof of concept as a double proof (proof of the known and proof of the unknown), we show that proof of concept can be more than a tool for the go/no-go decision by gradually validating propositions, questioning the relevance of propositions, and discovering new propositions to be investigated and tested.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

References

Åhlström, P., & Karlsson, C. (2016), “Longitudinal field studies”, In: Karlsson, C. (Ed.), Research methods for operations management, 2nd edition, Routledge, Abingdon-on-Thames, United Kingdom, pp. 214248. https://doi.org/10.4324/9781315671420Google Scholar
Bendavid, Y., & Cassivi, L. (2012), “A ‘living laboratory’ environment for exploring innovative RFID-enabled supply chain management models”, International Journal of Product Development, Vol. 17 No. 1-2, pp. 94118. https://doi.org/10.1504/IJPD.2012.051150CrossRefGoogle Scholar
Ben Mahmoud-Jouini, S., & Midler, C. (2020), “Unpacking the notion of prototype archetypes in the early phase of an innovation process”, Creativity and Innovation Management, Vol. 29 No. 1, pp. 4971. https://doi.org/10.1111/caim.12358CrossRefGoogle Scholar
Blomkvist, J., & Holmlid, S. (2011), “Existing prototyping perspectives: considerations for service design”, Proceedings of the 4th Nordic Design Research Conference, Helsinki, Finland, May 29-31, 2011, pp. 3140.Google Scholar
Cooper, R. G., & Sommer, A. F. (2016), “The agile–stage-gate hybrid model: a promising new approach and a new research opportunity”, Journal of Product Innovation Management, Vol. 33 No. 5, pp. 513526. https://doi.org/10.1111/jpim.12314CrossRefGoogle Scholar
Cross, N. (2008), Engineering design methods, 4th edition, Wiley, Chichester, United Kingdom.Google Scholar
Dagnelie, P. (2000), “La planification des expériences : choix des traitements et dispositif expérimental”, Journal de la Société française de statistique, Vol. 141 No. 1-2, pp. 529.Google Scholar
Eisenhardt, K. M., & Graebner, M. E. (2007), “Theory building from cases: Opportunities and challenges”, Academy of Management Journal, Vol. 50 No. 1, pp. 2532. https://doi.org/10.5465/amj.2007.24160888CrossRefGoogle Scholar
Emmert-Streib, F., and Dehmer, M. (2019), “Understanding statistical hypothesis testing: the logic of statistical inference”, Machine Learning and Knowledge Extraction, Vol. 1 No. 3, pp. 945961. https://doi.org/10.3390/make1030054CrossRefGoogle Scholar
Gillier, T., & Lenfle, S. (2019), “Experimenting in the unknown: lessons from the Manhattan project”, European Management Review, Vol. 16 No. 2, pp. 449469. https://doi.org/10.1111/emre.12187CrossRefGoogle Scholar
Guilford, J. P. (1957), “Creative abilities in the arts”, Psychological Review, Vol. 64 No. 2, pp. 110118. https://doi.org/10.1037/h0048280CrossRefGoogle ScholarPubMed
Hacking, I. (1983), Representing and intervening: introductory topics in the philosophy of natural science, 25th edition, Cambridge University Press, New York, United Stated of America.10.1017/CBO9780511814563CrossRefGoogle Scholar
Hatchuel, A., and David, A. (2008), “Collaborating for management research, from action research to intervention research in management”, In: Shani, A. B., Mohrman, S. A., Pasmore, W.A., Stymne, B., & Adler, N. (Eds.), Handbook of collaborative management research, SAGE Publications, Thousand Oaks, pp. 143162. http://doi.org/10.4135/9781412976671CrossRefGoogle Scholar
Hatchuel, A., & Weil, B. (2009), “CK design theory: an advanced formulation”, Research in Engineering Design, Vol. 19 No. 4, pp. 181192. http://doi.org/10.1007/s00163-008-0043-4CrossRefGoogle Scholar
Hatchuel, A., Le Masson, P., Reich, Y., & Subrahmanian, E. (2018), “Design theory: a foundation of a new paradigm for design science and engineering”, Research in Engineering Design, Vol. 29 No. 1, pp. 521. http://doi.org/10.1007/s00163-017-0275-2CrossRefGoogle Scholar
Hatchuel, A., Le Masson, P., Reich, Y., & Weil, B. (2011), “A systematic approach of design theories using generativeness and robustness”, Proceedings of the 18th International Conference on Engineering Design, Lyngby/Copenhagen, Denmark, 15-19 August, 2011, pp. 8797.Google Scholar
Hatchuel, A., Reich, Y., Le Masson, P., Weil, B., & Kazakçi, A. (2013), “Beyond models and decisions: situating design through generative functions”, Proceedings of the 19th International Conference on Engineering Design, Seoul, Korea, 19-22 August, 2013, pp. 233242.Google Scholar
Jensen, M. B., Elverum, C. W., & Steinert, M. (2017), “Eliciting unknown unknowns with prototypes: introducing prototrials and prototrial-driven cultures”, Design Studies, Vol. 49 No. 1, pp. 131. https://doi.org/10.1016/j.destud.2016.12.002CrossRefGoogle Scholar
Jobin, C., Le Masson, P., & Hooge, S. (2020), “What does the proof-of-concept (POC) really prove? A historical perspective and a cross-domain analytical study”, XXIXème conférence de l'Association Internationale de Management Stratégique, Online, June 1012, 2020.Google Scholar
Lakatos, I. (1977), The methodology of scientific research programmes, Cambridge University Press, Cambridge, United Kingdom.Google Scholar
Le Châtelier, H. (1887), “Du mécanisme de la découverte scientifique”, In: Letté, M. (2004), Henry Le Chatelier (1850-1936) ou la science appliquée à l'industrie, Presses universitaires de Rennes, Rennes, France. https://doi.org/10.1016/10.4000/rh19.1171Google Scholar
Le Masson, P., Weil, B., & Hatchuel, A. (2017), Design theory, Springer International Publishing AG, New York, United States of America. https://doi.org/10.1007/978-3-319-50277-9CrossRefGoogle Scholar
Löfqvist, L. G. (2009), “Design processes and novelty in small companies: a multiple case study”, Proceedings of the 17th International Conference on Engineering Design, Palo Alto, United States of America, August 24-27, 2009.Google Scholar
March, J. G. (1991), “Exploration and exploitation in organizational learning”, Organization Science, Vol. 2 No. 1, pp. 7187. https://doi.org/10.1287/orsc.2.1.71CrossRefGoogle Scholar
Mees, C. E. K., and Leermakers, J. A. (1950), The organization of industrial scientific research, 2nd edition, McGraw-Hill, New York, United States of America.Google Scholar
Neyman, J., & Pearson, E. S. (1933), “On the problem of the most efficient tests of statistical hypotheses”, Philosophical Transactions of the Royal Society of London, Vol. 231 No. 694-706, pp. 289337.Google Scholar
Nicolaÿ, A., & Lenfle, S. (2019), “Experimenting and prototyping the design of complex services”, European Review of Service Economics and Management, Vol. 2019-2 No. 8, pp. 5590.Google Scholar
Perrin, J. (1948), La nouvelle espérance, Presses Universitaires de France, Paris, France.Google Scholar
Radaelli, G., Guerci, M., Cirella, S., & Shani, A. B. (2012), “Intervention research as management research in practice: learning from a case in the fashion design industry”, British Journal of Management, Vol. 25 No. 2, pp. 335351. https://doi.org/10.1111/j.1467-8551.2012.00844.xCrossRefGoogle Scholar
Simpson, J. (1978), “What weather modification needs—a scientist's view”, Journal of Applied Meteorology, Vol. 17 No. 6, pp. 858866.10.1175/1520-0450(1978)017<0858:WWMNSV>2.0.CO;22.0.CO;2>CrossRefGoogle Scholar
Thomke, S. H. (2003), Experimentation matters: unlocking the potential of new technologies for innovation, Harvard Business Press, Brighton, United States of America.Google Scholar