Abstract
At present time, although many theoretical formulations have been successfully proposed, there is a lack of ICT-based tools to support practical deployment of knowledge management (KM) in real settings. To bridge this gap, a hybrid artificial intelligence system is proposed in present study, aimed at gaining deeper knowledge about KM practices in four different economic sectors. By means of soft computing, companies are diagnosed according to their status regarding KM and subsequent explanations about crucial KM practices and perspectives are generated. Interesting conclusions are then derived from these explanations, allowing KM managers to optimise their decisions and obtain better results. Experimental results of real-life data from Spanish companies associated with different economic sectors validate the proposed combination of techniques.
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Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, Belmont 358
Budnik M, Krawczyk B (2013) On optimal settings of classification tree ensembles for medical decision support. Health Inform J 19:3–15. doi:10.1177/1460458212446096
Corchado E, Fyfe C (2003) Connectionist techniques for the identification and suppression of interfering underlying factors. Int J Pattern Recognit Artif Intell 17:1447–1466
Corchado E, MacDonald D, Fyfe C (2004) Maximum and minimum likelihood hebbian learning for exploratory projection pursuit. Data Min Knowl Discov 8:203–225
Corchado E, Wu X, Oja E, Herrero Á, Baruque B (eds) (2009) Hybrid artificial intelligence systems, vol 5572. Lecture Notes in Artificial Intelligence. Springer, Berlin/Heidelberg
Chang CM, Hsu MH, Yen CH (2012) Factors affecting knowledge management success: the fit perspective. J Knowl Manag 16:847–861. doi:10.1108/13673271211276155
Chang LY, Chien JT (2013) Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model. Saf Sci 51:17–22. doi:10.1016/j.ssci.2012.06.017
Chen L, Shadbolt NR, Tao F, Goble C, Puleston C, Cox SJ (2005) Semantics-assisted problem solving on the semantic grid. Comput Intell 21:157–176. doi:10.1111/j.0824-7935.2005.00269.x
Du Plessis M (2007) The role of knowledge management in innovation. J Knowl Manag 11:20–29
Durst S, Edvardsson IR (2012) Knowledge management in SMEs: a literature review. J Knowl Manag 16:879–903
Dustdar S (NO) Collaborative Knowledge Flow—Improving process-awareness and traceability of work activities. In: 4th International conference on practical aspects of knowledge management (2002) LNCS. Springer, NO
Friedman JH, Tukey JW (1974) A projection pursuit algorithm for exploratory data-analysis. IEEE Trans Comput 23:881–890
Herrero Á, Corchado E, Gastaldo P, Zunino R (2009) Neural projection techniques for the visual inspection of network traffic. Neurocomputing 72:3649–3658
Herrero Á, Corchado E, Sáiz-Bárcena L, Manzanedo MÁ (2015) Analysis of knowledge management in industrial sectors by means of neural models. In: Herrero Á, Sedano J, Baruque B, Quintián H, Corchado E (eds) 10th International conference on soft computing models in industrial and environmental applications, vol 368. Advances in intelligent systems and computing. Springer International Publishing, pp 65–75. doi:10.1007/978-3-319-19719-7_6
Herrero Á, Corchado E, Sáiz L, Abraham A (2010) DIPKIP: a connectionist knowledge management system to identify knowledge deficits in practical cases. Comput Intell 26:26–56
Hossain MM, Piantanakulchai M (2013) Groundwater arsenic contamination risk prediction using GIS and classification tree method. Eng Geol 156:37–45. doi:10.1016/j.enggeo.2013.01.007
Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24:417–444
Hyperwave Knowledge Management Software. http://www.hyperwave.com. Accessed 16 Oct 2008
IBM Lotus, Knowledge Management Software. http://www-01.ibm.com/software/lotus/. Accessed 16 Oct 2008
Jung S, Qin X, Oh C (2016) Improving strategic policies for pedestrian safety enhancement using classification tree modeling. Transp Res Part A Policy Pract 85:53–64. doi:10.1016/j.tra.2016.01.002
Kan S, Guo F, Li S (2016) An approach to evaluating the knowledge management performance with interval-valued intuitionistic uncertain linguistic information. J Intell Fuzzy Syst 30:1557–1565
Kang M, Hau YS (2014) Multi-level analysis of knowledge transfer: a knowledge recipient’s perspective. J Knowl Manag 18:758–776. doi:10.1108/JKM-12-2013-0511
Khedhaouria A, Jamal A (2015) Sourcing knowledge for innovation: knowledge reuse and creation in project teams. J Knowl Manag 19:932–948. doi:10.1108/JKM-01-2015-0039
Lerro A, Iacobone FA, Schiuma G (2012) Knowledge assets assessment strategies: organizational value, processes, approaches and evaluation architectures. J Knowl Manag 16:563–575. doi:10.1108/13673271211246149
Levy M (2011) Knowledge retention: minimizing organizational business loss. J Knowl Manag 15:582–600
Maier R, Remus U (2002) Defining process-oriented knowledge management strategies. Knowl Process Manag 9:103–118
Mather AL, Johnson RL (2015) Event-based prediction of stream turbidity using a combined cluster analysis and classification tree approach. J Hydrol 530:751–761. doi:10.1016/j.jhydrol.2015.10.032
Maurer H, Tochtermann K (2002) On a new powerful model for knowledge management and its applications. J Univ Comput Sci 8:85–96
Nielsen BB, Michailova S (2007) Knowledge Management systems in multinational corporations: typology and transitional dynamics. Long Range Plan 40:314–340
OPENTEXT Livelink, Knowledge Management Software. http://www.opentext.com. Accessed 16 Oct 2008
Pearson K (1901) On lines and planes of closest fit to systems of points in space. Philos Mag 2:559–572
Ramachandiram K, Pazhanivelan S (2015) Determination of nitrogen and water stress with hyper spectral reflectance on maize using classification tree analysis. J Agrometeorol 17:213–218
Rollett H (2003) Knowledge management: processes and technologies. Kluwer, Dordrecht
Ruggles RL (2009) Knowledge management tools. Butterworth-Heinemann, Oxford
Sadeghi R, Zarkami R, Sabetraftar K, Van Damme P (2013) Application of genetic algorithm and greedy stepwise to select input variables in classification tree models for the prediction of habitat requirements of Azolla filiculoides (Lam.) in Anzali Wetland. Iran Ecol Model 251:44–53. doi:10.1016/j.ecolmodel.2012.12.010
Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21:660–674
Sáiz-Bárcena L, Herrero Á, Manzanedo del Campo MÁ, Martínez RDO (2015) Easing knowledge management in the power sector by means of a neuro-genetic system. Int J Bio-Inspired Comput 7:170–175
Sáiz L, Manzanedo MÁ, del Olmo R, Alcalde R (2010) Proposal of a knowledge management model for power industry. An empirical application. Dir Organ Rev Ing Organ 42:31–37
Seung HS, Socci ND, Lee D (1998) The rectified Gaussian distribution advances in neural information. Process Syst 10:350–356
Shu-Mei T (2008) The effects of information technology on knowledge management systems. Expert Syst Appl 35:150–160. doi:10.1016/j.eswa.2007.06.011
Singh RM, Gupta M (2014) Knowledge management in teams: empirical integration and development of a scale. J Knowl Manag 18:777–794. doi:10.1108/JKM-11-2013-0450
Sreerama KM (1998) Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min Knowl Discov 2:345–389. doi:10.1023/a:1009744630224
Sun P (2010) Five critical knowledge management organizational themes. J Knowl Manag 14:507–523. doi:10.1108/13673271011059491
Tan LP, Wong KY (2015) Linkage between knowledge management and manufacturing performance: a structural equation modeling approach. J Knowl Manag 19:814–835. doi:10.1108/JKM-11-2014-0487
The MathWorks I, Natick, Massachusetts, United States. MATLAB (2016)
Trappey AJC, Trappey CV, Chiang T-A, Huang Y-H (2013) Ontology-based neural network for patent knowledge management in design collaboration. Int J Prod Res 51:1992–2005. doi:10.1080/00207543.2012.701775
Wang J, Ding D, Liu O, Li M (2016) A synthetic method for knowledge management performance evaluation based on triangular fuzzy number and group support systems. Appl Soft Comput 39:11–20. doi:10.1016/j.asoc.2015.09.041
Wang Z (2004) Knowledge systems engineering: a new discipline of knowledge management and enabling. Int J Knowl Syst Sci 1:9–16
Woitsch R, Karagiannis D (2002) Process-oriented knowledge management systems based on KM-services: the PROMOTE approach. Int J Intell Syst Acc Finance Manag 11:253–267
Xu J, Houssin R, Caillaud E, Gardoni M (2010) Macro process of knowledge management for continuous innovation. J Knowl Manag 14:573–591. doi:10.1108/13673271011059536
Yao Y, Wang Y, Xing L, Xu H (2015) An optimization method of technological processes to complex products using knowledge-based genetic algorithm. J Knowl Manag 19:82–94. doi:10.1108/JKM-11-2014-0454
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Herrero, Á., Sáiz-Bárcena, L., Manzanedo, M.A. et al. A hybrid proposal for cross-sectoral analysis of knowledge management. Soft Comput 20, 4271–4285 (2016). https://doi.org/10.1007/s00500-016-2293-9
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DOI: https://doi.org/10.1007/s00500-016-2293-9