Share:


Prioritization of the launch of ICT products and services through linguistic multi-criteria decision-making

    Andr´es Cid-López Affiliation
    ; Miguel J. Hornos Affiliation
    ; Ram´on A. Carrasco-Gónzález Affiliation
    ; Enrique Herrera-Viedma Affiliation

Abstract

The market launch of new products and services is a basic pillar for large and medium-sized companies in the ICT (Information and Communications Technology) sector. Choosing the right moment for it is usually a differentiating factor in terms of competition, since it is a source of competitive advantage. There are several mechanisms and strategies to address this problem from the market perspective. However, the criteria of the different actors involved – managers, sales representatives, experts, etc. – coexist in the corporate sphere and they often differ, causing difficulties in priority setting processes in the launch of a product or service. The assessment of the prioritization of these criteria is usually expressed in natural language, thus adding a great deal of uncertainty. Fuzzy linguistic models have proved to be an efficient tool for managing the intrinsic uncertainty of this type of information. This paper presents a linguistic multi-criteria decision-making model, able to reconcile the different requirements and viewpoints existing in the corporate sector when planning the launch of new products and services. The proposed model is based on the fuzzy 2-tuple linguistic model, aimed at managing linguistic data expressing different corporate criteria, without compromising accuracy in the calculation of said data. In order to illustrate this, a practical case study is presented, in which the model is applied for scheduling the launch prioritization of several new products and services by a telecommunications company, within the deadlines set in its strategic planning.

Keyword : strategic planning, linguistic multi-criteria decision-making, 2-tuple representation, launch of products/services, project prioritization, ICT sector

How to Cite
Cid-López, A., Hornos, M. J., Carrasco-Gónzález, R. A., & Herrera-Viedma, E. (2018). Prioritization of the launch of ICT products and services through linguistic multi-criteria decision-making. Technological and Economic Development of Economy, 24(3), 1231-1257. https://doi.org/10.3846/tede.2018.1423
Published in Issue
May 31, 2018
Abstract Views
977
PDF Downloads
702
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Allen, K. R. (2015). Launching new ventures: An entrepreneurial approach. Mason, OH: Cengage Learning.

Barney, J. B. (2014). Gaining and sustaining competitive advantage. New York, NY: Pearson Higher Ed.

Behzadian, M., Otaghsara, S. K., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051-13069. https://doi.org/10.1016/j.eswa.2012.05.056

Benedetto, C. A. (1999). Identifying the key success factors in new product launch. Journal of Product Innovation Management, 16(6), 530-544. https://doi.org/10.1016/S0737-6782(99)00014-4

Bezdek, J. C. (2013). Pattern recognition with fuzzy objective function algorithms. Logan, UT: Springer Science & Business Media.

Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: toward a next generation of insights. MIS Quarterly, 37(2), 471-482. https://doi.org/10.25300/MISQ/2013/37:2.3

Bryson, J. M. (2011). Strategic planning for public and nonprofit organizations: A guide to strengthening and sustaining organizational achievement. Somerset, NJ: John Wiley & Sons.

Cabrerizo, F. J., Chiclana, F., Al-Hmouz, R., Morfeq, A., Balamash, A. S., & Herrera-Viedma, E. (2015). Fuzzy decision making and consensus: challenges. Journal of Intelligent & Fuzzy Systems, 29(3), 1109-1118. https://doi.org/10.3233/IFS-151719

Cabrerizo, F. J., Herrera-Viedma, E., & Pedrycz, W. (2013). A method based on PSO and granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts. European Journal of Operational Research, 230(3), 624-633. https://doi.org/10.1016/j.ejor.2013.04.046

Cabrerizo, F. J., Ureña, M. R., Pedrycz, W., & Herrera-Viedma, E. (2014). Building consensus in group decision making with an allocation of information granularity. Fuzzy Sets and Systems, 255, 115-127. https://doi.org/10.1016/j.fss.2014.03.016

Calantone, R. J., & Di Benedetto, C. A. (2012). The role of lean launch execution and launch timing on new product performance. Journal of the Academy of Marketing Science, 40(4), 526-538. https://doi.org/10.1007/s11747-011-0258-1

Carlsson, C., & Fuller, R. (2002). Fuzzy reasoning in decision making and optimization. Germany, Heidelberg: Springer-Verlag. https://doi.org/10.1007/978-3-7908-1805-5

Carrasco, R. A., Muñoz-Leiva, F., Sánchez-Fernández, J., & Liébana-Cabanillas, F. J. (2012). A model for the integration of e-financial services questionnaires with SERVQUAL scales under fuzzy linguistic modelling. Expert Systems with Applications, 39(14), 11535-11547. https://doi.org/10.1016/j.eswa.2012.03.055

Carrasco, R. A., Sánchez-Fernández, J., Muñoz-Leiva, F., Blasco, M. F., & Herrera-Viedma, E. (2015). Evaluation of the hotels e-services quality under the user’s experience. Soft Computing, 21(4), 995-1011. https://doi.org/10.1007/s00500-015-1832-0

Carrasco, R. A., Villar, P., Hornos, M. J., & Herrera-Viedma, E. (2011). A linguistic multi-criteria decision making model applied to the integration of education questionnaires. International Journal of Computational Intelligence Systems, 4(5), 946-959. https://doi.org/10.1080/18756891.2011.9727844

Cid-López, A., Hornos, M. J., Carrasco, R. A., & Herrera-Viedma, E. (2015a). A hybrid model for decision-making in the information and communications technology sector. Technological and Economy Development of Economy, 21(5), 731-748. https://doi.org/10.3846/20294913.2015.1056281

Cid-López, A., Hornos, M. J., Carrasco, R. A., & Herrera-Viedma, E. (2015b). SQUAL: A fuzzy linguistic multi-criteria model to assess the quality of service in the ICT sector from the user perspective. Applied Soft Computing, 37, 897-910. https://doi.org/10.1016/j.asoc.2015.09.019

Cid-López, A., Hornos, M. J., Carrasco, R. A., & Herrera-Viedma, E. (2016). Applying a linguistic multi-criteria decision-making model to the analysis of ICT suppliers’ offers. Expert Systems with Applications, 57, 127-138. https://doi.org/10.1016/j.eswa.2016.03.025

Cid-López, A., Hornos, M. J.; Carrasco, R. A.; & Herrera-Viedma, E., Chiclana, F. (2017). Linguistic multi-criteria decision-making model with output variable expressive richness. Expert Systems with Applications, 83, 350-362. https://doi.org/10.1016/j.eswa.2017.04.049

Debruyne, M., Moenaert, R., Griffin, A., Hart, S., Hultink, E. J., & Robben, H. (2002). The impact of new product launch strategies on competitive reaction in industrial markets. Journal of Product Innovation Management, 19(2), 159-170. https://doi.org/10.1016/S0737-6782(01)00135-7

Dong, Y., Li, C. C., & Herrera, F. (2016). Connecting the linguistic hierarchy and the numerical scale for the 2-tuple linguistic model and its use to deal with hesitant unbalanced linguistic information. Information Sciences, 367, 259-278. https://doi.org/10.1016/j.ins.2016.06.003

Dong, Y., Zhang, H., & Herrera-Viedma, E. (2016). Integrating experts’ weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors. Decision Support Systems, 84, 1-15. https://doi.org/10.1016/j.dss.2016.01.002

Figueira, J., Greco, S., & Ehrgott, M. (2005). Multiple criteria decision analysis: State of the art surveys. New York, NY: Springer Science + Business Media.

Gal, T., Stewart, T., & Hanne, T. (2013). Multicriteria decision making: Advances in MCDM models, algorithms, theory, and applications. New York, NY: Springer Science + Business Media.

Goetsch, D. L., & Davis, S. B. (2014). Quality management for organizational excellence. Upper Saddle River, NJ: Pearson Higher Education.

Govindan, K., & Jepsen, M. B. (2016). ELECTRE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 250(1), 1-29. https://doi.org/10.1016/j.ejor.2015.07.019

Herrera, F., & Martínez, L. (2000a). A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems, 8(6), 746-752. https://doi.org/10.1109/91.890332

Herrera, F., & Martínez, L. (2000b). An approach for combining linguistic and numerical information based on the 2-tuple fuzzy linguistic representation model in decision-making. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 8(5), 539-562. https://doi.org/10.1142/S0218488500000381

Herrera, F., & Martínez, L. (2001). The 2-tuple linguistic computational model: Advantages of its linguistic description, accuracy and consistency. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 9(supp01), 33-48. https://doi.org/10.1142/S0218488501000971

Herrera, F., Alonso, S., Chiclana, F., & Herrera-Viedma, E. (2009). Computing with words in decision making: foundations, trends and prospects. Fuzzy Optimization and Decision Making, 8(4), 337-364. https://doi.org/10.1007/s10700-009-9065-2

Herrera, F., Herrera-Viedma, E., & Martínez, L. (2008). A fuzzy linguistic methodology to deal with unbalanced linguistic term sets. IEEE Transactions on Fuzzy Systems, 16(2), 354-370. https://doi.org/10.1109/TFUZZ.2007.896353

Howard, A. F. (1991). A critical look at multiple criteria decision making techniques with reference to forestry applications. Canadian Journal of Forest Research, 21(11), 1649-1659. https://doi.org/10.1139/x91-228

IBM (n. d.). SPSS Modeler. Retrieved from https://www.ibm.com/products/spss-modeler

Kacprzyk, J., & Fedrizzi, M. (1990). Multiperson decision making models using fuzzy sets and possibility theory. The Netherlands, Dordrecht: Kluwer Academic Publishers. https://doi.org/10.1007/978-94-009-2109-2

Kacprzyk, J., & Zadrozny, S. A. (2001). Computing with words in decision making through individual and collective linguistic choice rules. International Journal of Uncertainty, Fuzziness and KnowledgeBased Systems, 9(supp01), 89-102. https://doi.org/10.1142/S0218488501001010

Kahn, K. B., Barczak, G., Nicholas, J., Ledwith, A., & Perks, H. (2012). An examination of new product development best practice. Journal of Product Innovation Management, 29(2), 180-192. https://doi.org/10.1111/j.1540-5885.2011.00888.x

Kao, C., & Liu, S. T. (2001). Fractional programming approach to fuzzy weighted average. Fuzzy Sets and Systems, 120(3), 435-444. https://doi.org/10.1016/S0165-0114(99)00137-2

Kapferer, J. N. (2012). The new strategic brand management: Advanced insights and strategic thinking. London, UK: Kogan Page.

Kaplan, R. S., & Norton, D. P. (2001). The strategy focused organization: How balanced scorecard companies thrive in the new business environment. Boston, MA: Harvard Business Press.

Kolios, A., Mytilinou, V., Lozano-Minguez, E., & Salonitis, K. (2016). A comparative study of multiplecriteria decision-making methods under stochastic inputs. Energies, 9(7), 566. https://doi.org/10.3390/en9070566

Kou, G., Lu, Y., Peng, Y., & Shi, Y. (2012). Evaluation of classification algorithms using MCDM and rank correlation. International Journal of Information Technology & Decision Making, 11(01), 197-225. https://doi.org/10.1142/S0219622012500095

Li, C. C., Dong, Y., Herrera, F., Herrera-Viedma, E., & Martínez, L. (2017). Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching. Information Fusion, 33, 29-40. https://doi.org/10.1016/j.inffus.2016.04.005

Liao, H., & Xu, Z. (2015). Approaches to manage hesitant fuzzy linguistic information based on the cosine distance and similarity measures for HFLTSs and their application in qualitative decision making. Expert Systems with Applications, 42(12), 5328-5336. https://doi.org/10.1016/j.eswa.2015.02.017

Liou, J., & Tzeng, G. H. (2012). Comments on “Multiple criteria decision making (MCDM) methods in economics: an overview”. Technological and Economic Development of Economy, 18(4), 672-695. https://doi.org/10.3846/20294913.2012.753489

Liu, W., Dong, Y., Chiclana, F., Cabrerizo, F. J., & Herrera-Viedma, E. (2017). Group decision-making based on heterogeneous preference relations with self-confidence. Fuzzy Optimization and Decision Making, 16, 429-447. https://doi.org/10.1007/s10700-016-9254-8

Ma, Y. X., Wang, J., Wang, J. Q., & Chen, X. H. (2016). Two-tuple linguistic aggregation operators based on subjective sensation and objective numerical scales for multi-criteria group decision-making problems. Scientia Iranica. Transactions E: Industrial Engineering, 23(3), 1399-1417. https://doi.org/10.24200/sci.2016.3906

Mardani, A., Jusoh, A., & Zavadskas, E. K. (2015). Fuzzy multiple criteria decision-making techniques and applications –Two decades review from 1994 to 2014. Expert Systems with Applications, 42(8), 4126-4148. https://doi.org/10.1016/j.eswa.2015.01.003

Martínez, L. (2007). Sensory evaluation based on linguistic decision analysis. International Journal of Approximate Reasoning, 44(2), 148-164. https://doi.org/10.1016/j.ijar.2006.07.006

Martínez-Cruz, C., Porcel, C., Bernabé-Moreno, J., & Herrera-Viedma, E. (2015). A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Information Sciences, 311, 102-118. https://doi.org/10.1016/j.ins.2015.03.013

Massanet, S., Riera, J. V., Torrens, J., & Herrera-Viedma, E. (2014). A new linguistic computational model based on discrete fuzzy numbers for computing with words. Information Sciences, 258, 277-290. https://doi.org/10.1016/j.ins.2013.06.055

Mendel, J. M. (2007a). Computing with words and its relationships with fuzzistics. Information Sciences, 177(4), 988-1006. https://doi.org/10.1016/j.ins.2006.06.008

Mendel, J. M. (2007b). Computing with words: Zadeh, Turing, Popper and Occam. IEEE Computational Intelligence Magazine, 2(4), 10-17. https://doi.org/10.1109/MCI.2007.9066897

Meyr, H., Wagner, M., & Rohde, J. (2015). Structure of advanced planning systems: Supply chain management and advanced planning. Germany, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-55309-7_5

Morente-Molinera, J. A., Mezei, J., Carlsson, C., & Herrera-Viedma, E. (2017). Improving supervised learning classification methods using multi-granular linguistic modelling and fuzzy entropy. IEEE Transactions on Fuzzy Systems, 25, 1078-1089.

Morente-Molinera, J. A., Pérez, I. J., Ureña, M. R., & Herrera-Viedma, E. (2015). On multi-granular fuzzy linguistic modeling in group decision making problems: A systematic review and future trends. Knowledge-Based Systems, 74, 49-60. https://doi.org/10.1016/j.knosys.2014.11.001

Peng, H. G., & Wang, J. Q. (2017). Cloud decision model for selecting sustainable energy crop based on linguistic intuitionistic information. International Journal of Systems Science, 48(15), 3316-3333. https://doi.org/10.1080/00207721.2017.1367433

Peng, H. G., Wang, J. Q., & Cheng, P. F. (2018). A linguistic intuitionistic multi-criteria decision-making method based on the Frank Heronian mean operator and its application in evaluating coal mine safety. International Journal of Machine Learning and Cybernetics 9(6), 1053-1068. https://doi.org/10.1007/s13042-016-0630-z

Pérez, I. J., Cabrerizo, F. J. & Herrera-Viedma, E. (2010). A mobile decision support system for dynamic group decision making problems. IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems and Humans, 40, 1244-1256. https://doi.org/10.1109/TSMCA.2010.2046732

Pérez, I. J., Cabrerizo, F. J., Alonso, S., & Herrera-Viedma, E. (2014). A new consensus model for group decision making problems with non-homogeneous experts. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(4), 494-498. https://doi.org/10.1109/TSMC.2013.2259155

Pérez-Asurmendi, P., & Chiclana, F. (2014). Linguistic majorities with difference in support. Applied Soft Computing, 18, 196-208. https://doi.org/10.1016/j.asoc.2014.01.010

Shih, H. S., & Wu, W. P. (2015). A formulation of DM’s risk attitude in ELECTRE III. In 81st Meeting of the European Working Group-Multiple Criteria Decision Aiding (No. mcda81). Retrieved from http://spirit.tku.edu.tw:8080/phd/upload/603620070/%B5o%AA%ED%BD%D7%A4%E5%A5%FE%A4%E5.pdf

Thor, J., Ding, S. H., & Kamaruddin, S. (2013). Comparison of multi criteria decision making methods from the maintenance alternative selection perspective. The International Journal of Engineering and Science, 2(6), 27-34.

Tong, R. M., & Bonissone, P. P. (1980). A linguistic approach to decision making with fuzzy sets. IEEE Transactions on Systems, Man, and Cybernetics, 10(11), 716-723. https://doi.org/10.1109/TSMC.1980.4308391

Triantaphyllou, E. (2013). Multi-criteria decision making methods: A comparative study. Logan, UT: Springer Science & Business Media.

Tscheikner-Gratl, F., Egger, P., Rauch, W., & Kleidorfer, M. (2017). Comparison of multi-criteria decision support methods for integrated rehabilitation prioritization. Water, 9(2), 68. https://doi.org/10.3390/w9020068

Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: Methods and applications. Boca Raton, FL: CRC Press.

Wang, J., Wang, J. Q., Tian, Z. P., & Zhao, D. Y. (2017). A multihesitant fuzzy linguistic multicriteria decision-making approach for logistics outsourcing with incomplete weight information. International Transactions in Operational Research 25(3), 831-856. https://doi.org/10.1111/itor.12448

Wang, J. Q., Peng, J. J., Zhang, H. Y., Liu, T., & Chen, X. H. (2015). An uncertain linguistic multi-criteria group decision-making method based on a cloud model. Group Decision and Negotiation, 24(1), 171-192. https://doi.org/10.1007/s10726-014-9385-7

Winter, S., & Sundqvist, S. (2009). IMC strategies in new high technology product launches. Marketing Intelligence & Planning, 27(2), 191-215. https://doi.org/10.1108/02634500910944986

Wu, J., Chiclana, F., & Herrera-Viedma, E. (2015). Trust based consensus model for social network in an incomplete linguistic information context. Applied Soft Computing, 35, 827-839. https://doi.org/10.1016/j.asoc.2015.02.023

Xu, Z. (2015). Uncertain multi-attribute decision making: Methods and applications. Germany, Heidelberg: Springer. https://doi.org/10.1007/978-3-662-45640-8

Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Transactions on Systems Man and Cybernetics, 18(1), 183-190. https://doi.org/10.1109/21.87068

Yager, R. R. (1993). Families of OWA Operators. Fuzzy Sets and Systems, 59(2), 125-148. https://doi.org/10.1016/0165-0114(93)90194-M

Yager, R. R. (1994a). Interpreting linguistically quantified propositions. International Journal of Intelligent Systems, 9(6), 541-569. https://doi.org/10.1002/int.4550090604

Yager, R. R. (1994b). On weighted median aggregation. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2(01), 101-113. https://doi.org/10.1142/S0218488594000092

Yager, R. R. (2007). Aggregation of ordinal information. Fuzzy Optimization and Decision Making, 6(3), 199-219. https://doi.org/10.1007/s10700-007-9008-8

Yager, R.R., & Filev, D. P. (1999). Induced ordered weighted averaging operators. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 29(2), 141-150. https://doi.org/10.1109/3477.752789

Yu, S. M., Wang, J., & Wang, J. Q. (2016). An extended TODIM approach with intuitionistic linguistic numbers. International Transactions in Operational Research 25(3), 781-805. https://doi.org/10.1111/itor.12363

Yu, S. M., Wang, J., Wang, J. Q., & Li, L. (2017). A multi-criteria decision-making model for hotel selection with linguistic distribution assessments. Applied Soft Computing 67, 741-755. https://doi.org/10.1016/j.asoc.2017.08.009

Zadeh, L. A. (1983). A computational approach to fuzzy quantifiers in natural languages. Computers & Mathematics with Applications, 9(1), 149-184. https://doi.org/10.1016/0898-1221(83)90013-5

Zadeh, L. A. (1996). Fuzzy logic = computing with words. IEEE Transactions on Fuzzy Systems 4(2), 103-111. https://doi.org/10.1109/91.493904

Zavadskas, E. K., & Turskis, Z. (2011). Multiple criteria decision making (MCDM) methods in economics: an overview. Technological and Economic Development of Economy, 17(2), 397-427. https://doi.org/10.3846/20294913.2011.593291

Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy, 20(1), 165-179. https://doi.org/10.3846/20294913.2014.892037

Zhang, H., Dong, Y., & Herrera-Viedma, E. (2017). Consensus building for the heterogeneous largescale GDM with the individual concerns and satisfactions. IEEE Transactions on Fuzzy Systems, 26(2), 884-898. https://doi.org/10.1109/TFUZZ.2017.2697403