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Ranking Absorption Practices of Knowledge for Collaborative Innovation: Which is the Ideal Multi Criteria Decision Method

Published online by Cambridge University Press:  26 July 2019

Elizabeth Gendreau
Affiliation:
Clemson University;
Lamiae Benhayou-Sadafiyine
Affiliation:
Universite Internationale de Rabat;
Marie-Anne Le Dain
Affiliation:
Grenoble Institute of Technology
Joshua Summers*
Affiliation:
Clemson University;
*
Contact: Summers, Joshua, Clemson University, Mechanical Engineering, United States of America, jsummer@clemson.edu

Abstract

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This paper focuses on evolving an absorptive capacity (ACAP) assessment tool designed to help firms understand their ACAP maturity in processing external knowledge. ACAP maturity is evaluated based on a firm's capacity and willingness to do relevant ACAP practices. Although an earlier version of the ACAP tool was able to evaluate maturity and highlight immature practice, it could not determine how critical these practices were for improvement action. Thus, a means of eliciting the importance of practices and aggregating it with their ACAP maturity evaluations is needed. This paper provides summaries of the subjective weight elicitation methods and aggregation techniques which were identified from the domain of multi-criteria decision making. Criteria for comparing these methods are defined and analyzed to determine the most appropriate methods for the current application. The SRF method for subjective weight elicitation, aggregated with the maturity evaluations through weight sum models, is deemed the most appropriate for the current application. During testing with users, the SRF procedure was found to suffer from various usability concerns which will be investigated in future work.

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) 2019

References

Baldwin, J.R. and Gorecki, P. (1998), The Dynamics of Industrial Competition: A North American Perspective, Cambridge University Press.Google Scholar
Barron, F.H. and Barrett, B.E. (1996), “Decision Quality Using Ranked Attribute Weights”, Management Science, Vol. 42 No. 11, pp. 15151523.10.1287/mnsc.42.11.1515Google Scholar
Benhayoun Sadafiyine, L., Le Dain, M.-A. and Dominguez-Péry, C. (2017a), “Designing a maturity grid to measure the knowledge absorptive capacity of a SME integrated in an innovation collaborative network”, The 24th International Innovation and Product Development Management Conference, Reykjavik, Iceland, available at: https://hal.archives-ouvertes.fr/hal-01480035.Google Scholar
Benhayoun Sadafiyine, L., Dominguez-Péry, C. and Le Dain, M.-A. (2017b), “Towards an operationalization of knowledge absorptive capacity for a collaborative innovation network”, European Academy of Management Conference EURAM.Google Scholar
Beynon, M. (2002), “An analysis of distributions of priority values from alternative comparison scales within AHP”, European Journal of Operational Research, Vol. 140 No. 1, pp. 104117.Google Scholar
Blomqvist, K. and Levy, J. (2006), “Collaboration capability a focal concept in knowledge creation and collaborative innovation in networks”, International Journal of Management Concepts and Philosophy, Vol. 2 No. 1, p. 31.10.1504/IJMCP.2006.009645Google Scholar
Chang, P.T. and Lee, E.S. (1995), “The estimation of normalized fuzzy weights”, Computers and Mathematics with Applications, Vol. 29 No. 5, pp. 2142.10.1016/0898-1221(94)00246-HGoogle Scholar
Choo, E.U., Schoner, B. and Wedley, W.C. (1999), “Interpretation of criteria weights in multicriteria decision making”, Computers and Industrial Engineering, Vol. 37 No. 3, pp. 527541.10.1016/S0360-8352(00)00019-XGoogle Scholar
Doyle, J.R., Green, R.H. and Bottomley, P.A. (1997), “Judging Relative Importance: Direct Rating and Point Allocation Are Not Equivalent”, Organizational Behavior and Human Decision Processes, Vol. 70 No. 1, pp. 6572.Google Scholar
Dubois, D. and Prade, H. (1980), “Fuzzy Sets and Systems: Theory and Applications”, edited by Ames, W. Mathematics in Science and Engineering, Vol. 144, Academic Press, Inc.Google Scholar
Eckenrode, R. (2018), “Weighting Multiple Criteria”, Management Science, Vol. 12 No. 3, pp. 180192.Google Scholar
Edwards, W. (1977), “How to Use Multiattribute Utility Measurement for Social Decisionmaking”, in Bell, D.E., Keeney, R.L. and Raiffa, H. (Eds.), Conflicting Objectives in Decisions, John Wiley & Sons, Bath, pp. 247276.Google Scholar
Edwards, W. and Barron, F.H. (1994), “Smarts and smarter: Improved simple methods for multiattribute utility measurement”, Organizational Behavior and Human Decision Processes.Google Scholar
Figueira, J. and Roy, B. (2002), “Determining the weights of criteria in the ELECTRE type methods with a revised Simos’ procedure”, European Journal of Operational Research, Vol. 139 No. 2, pp. 317326.Google Scholar
Gendreau, E. (2017), A Multi-Criteria Multi-Actor Approach to Measure the Knowledge Absorptive Capacity within a Collaborative Innovation Network, Grenoble Insititut Polytechnique.Google Scholar
Gendreau, E. (2018), An Investigation into the Usability of an Innovation Management Assessment Tool, Clemson University.Google Scholar
Gendreau, E.J., O'Shields, S.T. and Summers, J.D. (2017), “Developing a Method for Classifying Design Enablers”, IDETC/CIE, ASME, Cleveland, Ohio.Google Scholar
Hajkowicz, S.A., McDonald, G.T. and Smith, P.N. (2000), “An evaluation of multiple objective decision support weighting techniques in natural resource management”, Journal of Environmental Planning and Management, Vol. 43 No. 4, pp. 505518.Google Scholar
Ho, W., He, T., Lee, C.K.M. and Emrouznead, A. (n.d.), “Strategic logistics outsourcing: An integrated QFD and fuzzy AHP approach”.Google Scholar
Honda, T., Yang, M.C., Dong, A. and Ji, H. (2010), “A comparison of formal methods for evaluating the language of preference in engineering design”, Proceedings of the 2010 ASME IDETC International Design Engineering Technical Conferences & Information in Engineering Conference, IDETC/CIE2010, Vol. 5, pp. 297306.10.1115/DETC2010-29045Google Scholar
Krug, S. (2010), Rocket Surgery Made Easy: The Do-It-Yourself Guide to Finding and Fixing Usability Problems, edited by Davis, N., New Riders, Berkeley, CA.Google Scholar
Malczewski, J. (1999), GIS and Multicriteria Decision Analysis, John Wiley & Sons.Google Scholar
Mardani, A., Jusoh, A. and Zavadskas, E.K. (2015), “Fuzzy multiple criteria decision-making techniques and applications - Two decades review from 1994 to 2014”, Expert Systems with Applications, Elsevier Ltd, Vol. 42 No. 8, pp. 41264148.Google Scholar
Saaty, T.L. (2008), “Decision making with the analytic hierarchy process”, International Journal of Services Sciences, Vol. 1 No. 1, p. 83.10.1504/IJSSCI.2008.017590Google Scholar
San Cristóbal Mateo, J.R. (2012), Multi Criteria Analysis in the Renewable Energy Industry, available at: https://doi.org/10.1007/978-1-4471-2346-0_4Google Scholar
Simos, J. (1990), Évaluer l'impact Sur l'environnement : Une Approache Originale Par l'analyse Multicritère et La Négociation, Presses polytechnique et universitaires romandes, Lausanne, Suisse.Google Scholar
Todorova, G. and Durisin, B. (2016), “Absorptive Capacity : Valuing a Reconceptualization”, Vol. 32 No. 3, pp. 774786.Google Scholar
Triantaphyllou, E. (2000), “Multi-criteria decision making methods: a comparative study”, p. 290.10.1007/978-1-4757-3157-6Google Scholar
Von Winterfeldt, D. and Edwards, W. (1993), “Decision analysis and behavioral research”, Cambridge Univ. Press, Cambridge, MA (USA).Google Scholar
Zahra, S.A. and George, G. (2002), “Absorptive Capacity : A Review, Reconceptualization, and Extension”, The Academy of Management Review, Vol. 27 No. 2, pp. 185203.Google Scholar
Zardari, N.H., Ahmed, K., Shirazi, S.M. and Yusop, Z.B. (2015), “Weighting Methods and Their Effects on Multi-Criteria Decision Making Model Outcomes in Water Resources Management.”, Book, available at: https://doi.org/10.1007/978-3-319-12586-2 ISSN.Google Scholar
Zhang, D., Yu, P.L. and Wang, P.Z. (1992), “State-dependent weights in multicriteria value functions”, Journal of Optimization Theory and Applications, Vol. 74 No. 1, pp. 121.10.1007/BF00939890Google Scholar