Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

A hybrid proposal for cross-sectoral analysis of knowledge management

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, Belmont 358

    MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Corchado E, MacDonald D, Fyfe C (2004) Maximum and minimum likelihood hebbian learning for exploratory projection pursuit. Data Min Knowl Discov 8:203–225

    Article  MathSciNet  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • Du Plessis M (2007) The role of knowledge management in innovation. J Knowl Manag 11:20–29

    Article  Google Scholar 

  • Durst S, Edvardsson IR (2012) Knowledge management in SMEs: a literature review. J Knowl Manag 16:879–903

    Article  Google Scholar 

  • 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

    Article  MATH  Google Scholar 

  • Herrero Á, Corchado E, Gastaldo P, Zunino R (2009) Neural projection techniques for the visual inspection of network traffic. Neurocomputing 72:3649–3658

    Article  Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24:417–444

    Article  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Levy M (2011) Knowledge retention: minimizing organizational business loss. J Knowl Manag 15:582–600

    Article  Google Scholar 

  • Maier R, Remus U (2002) Defining process-oriented knowledge management strategies. Knowl Process Manag 9:103–118

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Maurer H, Tochtermann K (2002) On a new powerful model for knowledge management and its applications. J Univ Comput Sci 8:85–96

    Google Scholar 

  • Nielsen BB, Michailova S (2007) Knowledge Management systems in multinational corporations: typology and transitional dynamics. Long Range Plan 40:314–340

    Article  Google Scholar 

  • 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

    Article  MATH  Google Scholar 

  • 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

    Google Scholar 

  • Rollett H (2003) Knowledge management: processes and technologies. Kluwer, Dordrecht

  • Ruggles RL (2009) Knowledge management tools. Butterworth-Heinemann, Oxford

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21:660–674

    Article  MathSciNet  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Sun P (2010) Five critical knowledge management organizational themes. J Knowl Manag 14:507–523. doi:10.1108/13673271011059491

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Wang Z (2004) Knowledge systems engineering: a new discipline of knowledge management and enabling. Int J Knowl Syst Sci 1:9–16

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, University of Burgos, Avenida de Cantabria, s/n, 09006, Burgos, Spain

    Álvaro Herrero, Lourdes Sáiz-Bárcena & Miguel A. Manzanedo

  2. Departamento de Informática y Automática, Universidad de Salamanca, Plaza de la Merced, s/n, 37008, Salamanca, Spain

    Emilio Corchado

Authors
  1. Álvaro Herrero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lourdes Sáiz-Bárcena
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miguel A. Manzanedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Emilio Corchado
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lourdes Sáiz-Bárcena.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by A. Herrero.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-016-2293-9

Keywords

Search

Navigation