Modelling the Level of Adoption of Analytical Tools; An Implementation of Multi-Criteria Evidential Reasoning

Document Type: Research Paper


1 Chapingo Autonomous University (UACh) , Carretera México, Texcoco de Mora, MEX, Mexico

2 Popular Autonomous University of Puebla State. Puebla, Mexico

3 Manchester Business School (MBS)

4 The University of Manchester, Manchester, England


In the future, competitive advantages will be given to organisations that can extract valuable information from massive data and make better decisions. In most cases, this data comes from multiple sources. Therefore, the challenge is to aggregate them into a common framework in order to make them meaningful and useful.This paper will first review the most important multi-criteria decision analysis methods (MCDA) existing in current literature. We will offer a novel, practical and consistent methodology based on a type of MCDA, to aggregate data from two different sources into a common framework. Two datasets that are different in nature but related to the same topic are aggregated to a common scale by implementing a set of transformation rules. This allows us to generate appropriate evidence for assessing and finally prioritising the level of adoption of analytical tools in four types of companies.A numerical example is provided to clarify the form for implementing this methodology. A six-step process is offered as a guideline to assist engineers, researchers or practitioners interested in replicating this methodology in any situation where there is a need to aggregate and transform multiple source data.


Amabile, T. M., Conti, R., Coon, H., Lazenby, J., & Herron, M. (1996). Assessing the work environment for creativity. Academy of Management Journal, Vol. 39(5), pp. 1154-1184.

Anderson-Cook, C. M., Lu, L., Clark, G., DeHart, S. P., Hoerl, R., Jones, B., et al. (2012).Statistical Engineering-Forming the Foundations. Quality Engineering, Vol. 24(2), pp. 110-132. 

Barahona, I., & Riba, A. (2012). Applied Statistics on Business at Spain: A Case of Statistical Engineering. In A. S. Association. (Ed.). In Joint Statistical Meetings [Contributed]. San Diego,
CA:(American Statistical Association.).

Belton, V., & Stewart, T. (2002). Multiple Criteria Decision Analysis: An Integrated Approach: Springer US.

Brans, J.-P., Vincke, P., & Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European journal of operational research, Vol. 24(2), pp. 228-238.

Cohen, J. (1960). A COEFFICIENT OF AGREEMENT FOR NOMINAL SCALES. Educational and Psychological Measurement, Vol. 20(1), pp. 37-46.

Cronbach, L. J. (1951). COEFFICIENT ALPHA AND THE INTERNAL STRUCTURE OF TESTS. Psychometrika, Vol. 16(3), pp. 297-334.

Davenport, T. H., Harris, J. G., & Morison, R. (2010). Analytics at work: smarter decisions, better results. Boston, MA: Harvard Business Press.

Deming, W. E. (2000). Out of the Crisis: MIT press.

Fleiss, J. L. (1971). MEASURING NOMINAL SCALE AGREEMENT AMONG MANY RATERS. Psychological Bulletin, Vol. 76(5), pp. 378-382. 

Gantz, J., & Reinsel, D. (2012). THE DIGITAL UNIVERSE IN 2020: Big Data, Bigger Digital Shadows, and Biggest Grow in the Far East. (EMC Corporation).

Gardner, R. (2004). The Process-focused Organization: A Transition Strategy for Success: ASQ Quality Press.
Hayashi, K. (2000). Multicriteria analysis for agricultural resource management: a critical survey and future perspectives. European Journal of Operational Research, Vol. 122(2), pp. 486-500.

Herrmann, A., Huber, F., & Braunstein, C. (2000). Market-driven product and service design: Bridging the gap between customer needs, quality management, and customer satisfaction. International Journal of Production Economics, Vol. 66(1), pp. 77-96. 

Liu, X.-B., Zhou, M., Yang, J.-B., & Yang, S.-L. (2008). Assessment of strategic R&D projects for car manufacturers based on the evidential reasoning approach. International Journal of Computational Intelligence Systems, Vol. 1(1), pp. 24-49. 

Louviere, J. J., Hensher, D. A., & Swait, J. D. (2000). Stated choice methods: analysis and applications: Cambridge University Press. 

Lynch, C. (2008). Big data: How do your data grow? Nature, Vol. 455(7209), pp. 28-29.

Malczewski, J. (1999). GIS and multicriteria decision analysis: John Wiley & Sons.

Mendoza, G., & Martins, H. (2006). Multi-criteria decision analysis in natural resource management: a critical review of methods and new modelling paradigms. Forest ecology and management, Vol. 230(1), pp. 1-22.

Mendoza, G., & Prabhu, R. (2002). Enhancing participatory planning of community-managed forest using problem structuring models and approaches: experiences from a case study: Working Paper GAM–2003–1. Dep. Natural Resour. Environ. Sci., University of Illinois, Champaign. 

Mousseau, V., & Slowinski, R. (1998). Inferring an ELECTRE TRI model from assignment examples. Journal of global optimization, Vol. 12(2), pp. 157-174. 

Mousseau, V., Slowinski, R., & Zielniewicz, P. (2000).A user-oriented implementation of the ELECTRE-TRI method integrating preference elicitation support. Computers & operations research, Vol. 27(7), pp. 757-777.

Pohekar, S., & Ramachandran, M. (2004).Application of multi-criteria decision making to sustainable energy planning—a review. Renewable and Sustainable Energy Reviews, Vol. 8(4), pp. 365-381. 

Powell, T. C., & DentMicallef, A. (1997). Information technology as competitive advantage: The role of human, business, and technology resources. Strategic Management Journal, Vol. 18(5), pp. 375-405. 

Predictive analytics (2014). Retrieved 01 of July of 2014, 2014, from

Pukkala, T. (2002). Multi-objective forest planning: Kluwer academic publishers.

Reynolds, T. J., & Gutman, J. (1984). Laddering: Extending the Repertory Grid Methodology to Construct Attribute-Consequence-Value Hierarchies. In in Personal Values and Consumer. (Books).

Reynolds, T. J., & Gutman, J. (1988). LADDERING THEORY, METHOD, ANALYSIS, AND INTERPRETATION. Journal of Advertising Research, Vol. 28(1), pp. 11-31. 

Rousseau, D. M. (2006).Is there such a thing as "evidence-based management"? Academy of Management Review, Vol. 31(2), pp. 256-269. 

Roy, B. (1968). Classementetchoix en présence de points de vue multiples. RAIRO-Operations Research-RechercheOpérationnelle, Vol. 2(V1), pp. 57-75. 

Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process. European journal of operational research, Vol. 48(1), pp. 9-26. 

Saaty, T. L. (1991). Rank and the Controversy About the Axioms of Utility Theory--A Comparison of AHP and MAUT. Paper presented at the Proceedings of the 2nd International Symposium of The Analytic Hierarchy Process. 

Satty, T. L. (1980).The analytic hierarchy process. (New York: McGraw-Hill New York).

Scott, A. J. (2012). Moneyball: Message for Managers [Marketing paper]. Pennsylvania:(University of Pennsylvania ScholarlyCommons ). 

Shrout, P. E., & Fleiss, J. L. (1979). INTRACLASS CORRELATIONS - USES IN ASSESSING RATER RELIABILITY. Psychological Bulletin, Vol. 86(2), pp. 420-428. 

Schwartz, S. H. (1994). Are there universal aspects in the structure and contents of human values?. Journal of social issues, Vol. 50(4), pp. 19-45. 

Tallon, P. P., Kraemer, K. L., & Gurbaxani, V. (2000). Executives' perceptions of the business value of information technology: A process-oriented approach. Journal of Management Information Systems, Vol. 16(4), pp. 145-173. 

Tzeng, G.-H., & Huang, J.-J. (2011). Multiple attribute decision making: methods and applications: CRC Press. 

Xu, D.-L., McCarthy, G., & Yang, J.-B. (2006). Intelligent decision system and its application in business innovation self assessment. Decision Support Systems, Vol. 42(2), pp. 664-673.  

Yang, J., & Xu, D. (2005).The IDS multi-criteria assessor software. Intelligent Decision System, Cheshire, UK. 

Yang, J.-B., & Singh, M. G. (1994). An evidential reasoning approach for multiple-attribute decision making with uncertainty. Systems, Man and Cybernetics, IEEE Transactions on, Vol. 24(1), pp. 1-18.

Yang, J. B. (2001). Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties. European Journal of Operational Research, Vol. 131(1), pp. 31-61. 

Yang, J. B., Xu, D. L., Xie, X., & Maddulapalli, A. K. (2011). Multicriteria evidential reasoning decision modelling and analysis-prioritizing voices of customer. Journal of the Operational Research Society, Vol. 62(9), pp. 1638-1654. 

Yoon, K. P., & Hwang, C.-L. (1995). Multiple attribute decision making: an introduction (Vol. 104): Sage Publications.