Modelo de lealtad a partir de un análisis de ecuaciones estructurales
Para una compañía siempre ha sido importante la relación con sus clientes, por ello en diferentes trabajos se han planteado patrones para describir dicha asociación, en esta búsqueda ha cobrado relevancia el uso de modelos estadísticos que permiten establecer la forma en que interactúan las distinta...
- Autores:
-
Romero, Gil Robert
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2015
- Institución:
- Universidad Santo Tomás
- Repositorio:
- Repositorio Institucional USTA
- Idioma:
- spa
- OAI Identifier:
- oai:repository.usta.edu.co:11634/468
- Acceso en línea:
- https://hdl.handle.net/11634/468
- Palabra clave:
- Statistics
Equations
Linear Models (Statistics)
Statistics
Estadística
Ecuaciones
Modelos lineales (Estadística)
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 2.5 Colombia
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Modelo de lealtad a partir de un análisis de ecuaciones estructurales |
| title |
Modelo de lealtad a partir de un análisis de ecuaciones estructurales |
| spellingShingle |
Modelo de lealtad a partir de un análisis de ecuaciones estructurales Statistics Equations Linear Models (Statistics) Statistics Estadística Ecuaciones Modelos lineales (Estadística) |
| title_short |
Modelo de lealtad a partir de un análisis de ecuaciones estructurales |
| title_full |
Modelo de lealtad a partir de un análisis de ecuaciones estructurales |
| title_fullStr |
Modelo de lealtad a partir de un análisis de ecuaciones estructurales |
| title_full_unstemmed |
Modelo de lealtad a partir de un análisis de ecuaciones estructurales |
| title_sort |
Modelo de lealtad a partir de un análisis de ecuaciones estructurales |
| dc.creator.fl_str_mv |
Romero, Gil Robert |
| dc.contributor.advisor.none.fl_str_mv |
Babativa Márquez, Giovanny |
| dc.contributor.author.none.fl_str_mv |
Romero, Gil Robert |
| dc.subject.keyword.none.fl_str_mv |
Statistics Equations Linear Models (Statistics) Statistics |
| topic |
Statistics Equations Linear Models (Statistics) Statistics Estadística Ecuaciones Modelos lineales (Estadística) |
| dc.subject.proposal.eng.fl_str_mv |
Estadística Ecuaciones Modelos lineales (Estadística) |
| description |
Para una compañía siempre ha sido importante la relación con sus clientes, por ello en diferentes trabajos se han planteado patrones para describir dicha asociación, en esta búsqueda ha cobrado relevancia el uso de modelos estadísticos que permiten establecer la forma en que interactúan las distintas variables que determinan el comportamiento de un cliente. Adicionalmente, también se ha analizado la causalidad y se ha encontrado un ciclo en el comportamiento de compra, así, cuando un cliente es más leal a una marca, mayor es su grado de recomendación y recompra hacia ésta. Así mismo, se ha buscado establecer la relación entre satisfacción y lealtad ya que no necesariamente un alto grado de satisfacción causa lealtad ni tampoco un alto grado de lealtad causa satisfacción. |
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2015 |
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2015-12-01T14:21:59Z 2017-02-13T19:31:02Z |
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2015-12-01T14:21:59Z 2017-02-13T19:31:02Z |
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2015 |
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2017-06-24T16:19:03Z |
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2017-06-24T16:19:03Z |
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Trabajo de grado |
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Romero, Gil Robert. (2015). Modelo de lealtad a partir de un análisis de ecuaciones estructurales. Universidad Santo Tomas. Bogotá |
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https://hdl.handle.net/11634/468 |
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Romero, Gil Robert. (2015). Modelo de lealtad a partir de un análisis de ecuaciones estructurales. Universidad Santo Tomas. Bogotá reponame:Repositorio Institucional Universidad Santo Tomás instname:Universidad Santo Tomás repourl:https://repository.usta.edu.co |
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Aish, A.M., Joreskog, K.G., Apanel model for political efficacy and responsiveness: an application of LISREL 7 with weighted least squares Qual Quant. 24, 1990. Anderson,JamesC.Gerbing,DavidW.,StructuralequationmodelinginpracticePsychological Bulletin, 1988. Anderson, James C. Gerbing, David W., Monte Carlo evaluations of goodness of fit indices for structural equation models Sage Periodical Press, 1992. Anderson, James C. Gerbing, David W., The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis Psychometrica, 1984. Anderson, James C. Gerbing, David W., The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis Psychometrica, 1984. Batista F. Joan M., Coenders G. Germá, Modelos de Ecuaciones Estructurales. Editorial La Muralla, 2000. Bentler, P. M. Bonett, D. G., Significance test and goodness of fit in the analysis of covariance structures Psychological Bulletin, 1980. Bollen,KennethA.,StructuralequationswithlatentvariablesPsychologicalBulletin, 1980. Browne,M.W.,Asymptotically distribution-free methods for the analysis of covariance structures British Journal of Mathematical and Statistical Psychology, 1984 Byrne Barbara, Structural equation modeling with AMOS Routledge, 2009. Chan Wai, Comparing indirect effects in SEM-A sequential model fitting method using covariance equivalent specifications Structural Equation Modeling: A Multidisciplinary Journal, 2007. FoxJ., Effect analysis in structural equation models II-Calculation of specific indirect effects Sociological Methods & Research, 1985 Gelin, M.N.,Beasley, T.M., y Zumbo,B.D., Whatistheimpactonscalereliabilityand exploratory factor analysis of a Pearson correlation matrix when some respondents are not able to follow the rating scale?. Annual meeting of the American Educational Research Association (AERA), 2003. Gelin, M.N.,Beasley, T.M., y Zumbo,B.D., Whatistheimpactonscalereliabilityand exploratory factor analysis of a Pearson correlation matrix when some respondents are not able to follow the rating scale?. Annual meeting of the American Educational Research Association (AERA), 2003. Germà Coenders , Albert Satorra & Willem E. Saris, Alternative approaches to structural modeling of ordinal data: A Monte Carlo study. Structural Equation Modeling: AMultidisciplinary Journal, 4:4, 1997 Hancock Gregopry & Mueller Ralph, Structural Equation Modeling a second course IAP, 2006. Heuchenne, Christian, A sufficient rule for identification in structural equation modeling icluding the null B and recursive rules as extreme cases Structural Equation Modeling: A Multidisciplinary Journal, 2009. HolgerBrandt,AugustinKelava&AndreasKlein,ASimulationStudyComparingRecent Approaches for the Estimation of Nonlinear Effects in SEM Under the Condition of Nonnormality Structural Equation Modeling: A Multidisciplinary Journal, 2014 Hoyle,RickH.,HandbookofStructuralEquationModeling.TheGuilfordPress,2012. Field, A., Discovering Statistics using SPSS for Windows. Sage publications, 2000. KennethA.Bollen&WalterR.Davis,TwoRulesofIdentificationforStructuralEquation Models. Structural Equation Modeling: A Multidisciplinary Journal, 2009. Jöreskog, K.G., Sörbom, D., LISREL 7: A Guide to the Program andApplications SPSS Publications, 1989. KennethA.Bollen&WalterR.Davis,TwoRulesofIdentificationforStructuralEquation Model Structural Equation Modeling: A Multidisciplinary Journal, 2009 Kline Rex B., Principles and practices of Structural Equation Modeling The Guilford Press, 2004. Loehlin, John C., Latent Variable Models Laurence Erlbaum Associates Publishers, 2004. Lyhagen,J., Ornstein, P., A Rank Based estimator of the Polychoric Correlation Uppsala University, 2011. Marcoulides George A., Schumacker Randall E., New developments and techniques in structural equation modeling Lawrence Erlbaum Associates, 2001 Muthén,B.,Ageneralstructuralequationmodelwithdichotomous,orderedcategorical, and continuous latent variable indicators Psychometrika 49, 1984. Olsson, Ulf Henning, Maximum likelihood estimation of the polychoric correlation coefficient. Psychometrika, Volume 44, 1979. Olsson, Ulf Henning,FossTron,TroyeSigurdV.&HowellRoyD.,ThePerformanceof ML,GLS,andWLSEstimationinStructuralEquationModelingUnderConditionsof Misspecification and Nonnormality Structural Equation Modeling: A Multidisciplinary Journal, 2009 Ory David, Mokhtarian Patricia, The impact of non-normality,sample size and estimation technique on goodness-of-t measures in structural equation modeling: evidence from ten empirical models of travel behavior Springer Science+Business Media, 2009. Pui-Wa Lei, Evaluating estimation methods for ordinal data in structural equation modeling Springer Science + Business Media, 2007. Pui-Wa Lei, Qiong Wu, Introduction to structural equation modeling-Issues and practical considerations Pennsylvania State University, 2007 RaudenbushS.W., SampsonR.,Assessing direct and indirect effects in multilevel designs with latent variables Sociological Methods & Research, 1999. Raykov Tenko &Marcoulides George, A first course in Structural Equation Modeling Lawrence ErlbaumAssociates, 2004. Revelle, W. & Rocklin, T., Very Simple Structure: an Alternative Procedure for Estimating the Optimal NumberofInterpretable Factors. Multivariate Behavioral Research, 1979. Rietveld, T. &VanHout,R.,Statistical Techniques for the StudyofLanguageandLanguage Behaviour. De Gruiter Mouton, 1993. RigdonEdwardE.,Calculatingdegreesoffreedomforastructuralequationmodeling Lawrence ErlbaumAssociates, 1994. Rosseel Yves, lavaan: an R package for structural equation modeling and more Version 0.3-1 (BETA) Ghent University, 2010. Rosseel Yves, The lavaan tutorial Ghent University, 2013. Satorra Albert, Robustness issues in structural equation modeling Kluwer Academic Publishers, 1990. Saurina C., Evaluación de un modelo de medida de la calidad en el sector servicios. Estadística Española, 1997. ¸Sim¸sek Gülhayat G. & Noyan Fatma, Structural equation modeling with ordinal variables: a large sample case study Yildiz Technical University, 2012. SkrondalAnders,Rabe-HeskethSophia,Generalizedlatentvariablemodeling Chapman&Hall/CRC,2004. Sörbom,Dag, Model Modification Uppsala University, 1989. Streiner D., Starting at the beginning: an introduction to coefficient alpha and internal consistency. Journal of personality assessment, 2003 Xitao Fan, BruceThompson&LinWang,Effectsofsamplesize,estimationmethods, andmodelspecificationonstructuralequationmodelingfitindexesStructuralEquation Modeling: A Multidisciplinary Journal, 1999. |
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Babativa Márquez, GiovannyRomero, Gil Robert2015-12-01T14:21:59Z2017-02-13T19:31:02Z2017-06-24T16:19:03Z2015-12-01T14:21:59Z2017-02-13T19:31:02Z2017-06-24T16:19:03Z2015Romero, Gil Robert. (2015). Modelo de lealtad a partir de un análisis de ecuaciones estructurales. Universidad Santo Tomas. Bogotáhttps://hdl.handle.net/11634/468reponame:Repositorio Institucional Universidad Santo Tomásinstname:Universidad Santo Tomásrepourl:https://repository.usta.edu.coPara una compañía siempre ha sido importante la relación con sus clientes, por ello en diferentes trabajos se han planteado patrones para describir dicha asociación, en esta búsqueda ha cobrado relevancia el uso de modelos estadísticos que permiten establecer la forma en que interactúan las distintas variables que determinan el comportamiento de un cliente. Adicionalmente, también se ha analizado la causalidad y se ha encontrado un ciclo en el comportamiento de compra, así, cuando un cliente es más leal a una marca, mayor es su grado de recomendación y recompra hacia ésta. Así mismo, se ha buscado establecer la relación entre satisfacción y lealtad ya que no necesariamente un alto grado de satisfacción causa lealtad ni tampoco un alto grado de lealtad causa satisfacción.For a company, the relationship with its customers has always been important; therefore, different studies have proposed patterns to describe this association. In this search, the use of statistical models that allow establishing the way in which the different variables that determine the behavior of a customer interact has gained relevance. In addition, causality has also been analyzed and a cycle in purchasing behavior has been found; thus, the more loyal a customer is to a brand, the greater the degree of recommendation and repurchase towards it. Likewise, we have sought to establish the relationship between satisfaction and loyalty, since a high degree of satisfaction does not necessarily cause loyalty, nor does a high degree of loyalty cause satisfaction.Pregradoapplication/pdfspaUniversidad Santo TomásPregrado EstadísticaFacultad de EstadísticaAtribución-NoComercial-SinDerivadas 2.5 Colombiahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Modelo de lealtad a partir de un análisis de ecuaciones estructuralesStatisticsEquationsLinear Models (Statistics)StatisticsEstadísticaEcuacionesModelos lineales (Estadística)Trabajo de gradoinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesisCRAI-USTA BogotáAish, A.M., Joreskog, K.G., Apanel model for political efficacy and responsiveness: an application of LISREL 7 with weighted least squares Qual Quant. 24, 1990.Anderson,JamesC.Gerbing,DavidW.,StructuralequationmodelinginpracticePsychological Bulletin, 1988.Anderson, James C. Gerbing, David W., Monte Carlo evaluations of goodness of fit indices for structural equation models Sage Periodical Press, 1992.Anderson, James C. Gerbing, David W., The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis Psychometrica, 1984.Anderson, James C. Gerbing, David W., The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis Psychometrica, 1984.Batista F. Joan M., Coenders G. Germá, Modelos de Ecuaciones Estructurales. Editorial La Muralla, 2000.Bentler, P. M. Bonett, D. G., Significance test and goodness of fit in the analysis of covariance structures Psychological Bulletin, 1980.Bollen,KennethA.,StructuralequationswithlatentvariablesPsychologicalBulletin, 1980.Browne,M.W.,Asymptotically distribution-free methods for the analysis of covariance structures British Journal of Mathematical and Statistical Psychology, 1984Byrne Barbara, Structural equation modeling with AMOS Routledge, 2009.Chan Wai, Comparing indirect effects in SEM-A sequential model fitting method using covariance equivalent specifications Structural Equation Modeling: A Multidisciplinary Journal, 2007.FoxJ., Effect analysis in structural equation models II-Calculation of specific indirect effects Sociological Methods & Research, 1985Gelin, M.N.,Beasley, T.M., y Zumbo,B.D., Whatistheimpactonscalereliabilityand exploratory factor analysis of a Pearson correlation matrix when some respondents are not able to follow the rating scale?. Annual meeting of the American Educational Research Association (AERA), 2003.Gelin, M.N.,Beasley, T.M., y Zumbo,B.D., Whatistheimpactonscalereliabilityand exploratory factor analysis of a Pearson correlation matrix when some respondents are not able to follow the rating scale?. Annual meeting of the American Educational Research Association (AERA), 2003.Germà Coenders , Albert Satorra & Willem E. Saris, Alternative approaches to structural modeling of ordinal data: A Monte Carlo study. Structural Equation Modeling: AMultidisciplinary Journal, 4:4, 1997Hancock Gregopry & Mueller Ralph, Structural Equation Modeling a second course IAP, 2006.Heuchenne, Christian, A sufficient rule for identification in structural equation modeling icluding the null B and recursive rules as extreme cases Structural Equation Modeling: A Multidisciplinary Journal, 2009.HolgerBrandt,AugustinKelava&AndreasKlein,ASimulationStudyComparingRecent Approaches for the Estimation of Nonlinear Effects in SEM Under the Condition of Nonnormality Structural Equation Modeling: A Multidisciplinary Journal, 2014Hoyle,RickH.,HandbookofStructuralEquationModeling.TheGuilfordPress,2012.Field, A., Discovering Statistics using SPSS for Windows. Sage publications, 2000.KennethA.Bollen&WalterR.Davis,TwoRulesofIdentificationforStructuralEquation Models. Structural Equation Modeling: A Multidisciplinary Journal, 2009.Jöreskog, K.G., Sörbom, D., LISREL 7: A Guide to the Program andApplications SPSS Publications, 1989.KennethA.Bollen&WalterR.Davis,TwoRulesofIdentificationforStructuralEquation Model Structural Equation Modeling: A Multidisciplinary Journal, 2009Kline Rex B., Principles and practices of Structural Equation Modeling The Guilford Press, 2004.Loehlin, John C., Latent Variable Models Laurence Erlbaum Associates Publishers, 2004.Lyhagen,J., Ornstein, P., A Rank Based estimator of the Polychoric Correlation Uppsala University, 2011.Marcoulides George A., Schumacker Randall E., New developments and techniques in structural equation modeling Lawrence Erlbaum Associates, 2001Muthén,B.,Ageneralstructuralequationmodelwithdichotomous,orderedcategorical, and continuous latent variable indicators Psychometrika 49, 1984.Olsson, Ulf Henning, Maximum likelihood estimation of the polychoric correlation coefficient. Psychometrika, Volume 44, 1979.Olsson, Ulf Henning,FossTron,TroyeSigurdV.&HowellRoyD.,ThePerformanceof ML,GLS,andWLSEstimationinStructuralEquationModelingUnderConditionsof Misspecification and Nonnormality Structural Equation Modeling: A Multidisciplinary Journal, 2009Ory David, Mokhtarian Patricia, The impact of non-normality,sample size and estimation technique on goodness-of-t measures in structural equation modeling: evidence from ten empirical models of travel behavior Springer Science+Business Media, 2009.Pui-Wa Lei, Evaluating estimation methods for ordinal data in structural equation modeling Springer Science + Business Media, 2007.Pui-Wa Lei, Qiong Wu, Introduction to structural equation modeling-Issues and practical considerations Pennsylvania State University, 2007RaudenbushS.W., SampsonR.,Assessing direct and indirect effects in multilevel designs with latent variables Sociological Methods & Research, 1999.Raykov Tenko &Marcoulides George, A first course in Structural Equation Modeling Lawrence ErlbaumAssociates, 2004.Revelle, W. & Rocklin, T., Very Simple Structure: an Alternative Procedure for Estimating the Optimal NumberofInterpretable Factors. Multivariate Behavioral Research, 1979.Rietveld, T. &VanHout,R.,Statistical Techniques for the StudyofLanguageandLanguage Behaviour. De Gruiter Mouton, 1993.RigdonEdwardE.,Calculatingdegreesoffreedomforastructuralequationmodeling Lawrence ErlbaumAssociates, 1994.Rosseel Yves, lavaan: an R package for structural equation modeling and more Version 0.3-1 (BETA) Ghent University, 2010.Rosseel Yves, The lavaan tutorial Ghent University, 2013.Satorra Albert, Robustness issues in structural equation modeling Kluwer Academic Publishers, 1990.Saurina C., Evaluación de un modelo de medida de la calidad en el sector servicios. Estadística Española, 1997.¸Sim¸sek Gülhayat G. & Noyan Fatma, Structural equation modeling with ordinal variables: a large sample case study Yildiz Technical University, 2012.SkrondalAnders,Rabe-HeskethSophia,Generalizedlatentvariablemodeling Chapman&Hall/CRC,2004.Sörbom,Dag, Model Modification Uppsala University, 1989.Streiner D., Starting at the beginning: an introduction to coefficient alpha and internal consistency. Journal of personality assessment, 2003Xitao Fan, BruceThompson&LinWang,Effectsofsamplesize,estimationmethods, andmodelspecificationonstructuralequationmodelingfitindexesStructuralEquation Modeling: A Multidisciplinary Journal, 1999.ORIGINAL2015RobertRomero.pdfapplication/pdf2320841https://repository.usta.edu.co/bitstream/11634/468/1/2015RobertRomero.pdfda0cfbd539e000923f39bf430018da45MD51open accesscartadefacultad.pdfcartadefacultad.pdfapplication/pdf200946https://repository.usta.edu.co/bitstream/11634/468/3/cartadefacultad.pdf2ff31fbba1695d78cb4c7cdae089f9f8MD53metadata only accesscartaderechosdeautor.pdfcartaderechosdeautor.pdfapplication/pdf235542https://repository.usta.edu.co/bitstream/11634/468/4/cartaderechosdeautor.pdf008a5bab565e460f5cde9336a6bf459eMD54metadata only accessLICENSElicense.txttext/plain1748https://repository.usta.edu.co/bitstream/11634/468/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52open accessTHUMBNAIL2015RobertRomero.pdf.jpg2015RobertRomero.pdf.jpgIM Thumbnailimage/jpeg5759https://repository.usta.edu.co/bitstream/11634/468/5/2015RobertRomero.pdf.jpgb50dc901ab1c351261612d7eba82fb4dMD55open accesscartadefacultad.pdf.jpgcartadefacultad.pdf.jpgIM Thumbnailimage/jpeg6822https://repository.usta.edu.co/bitstream/11634/468/6/cartadefacultad.pdf.jpg4be92237079761bd748365f1ff412c08MD56open accesscartaderechosdeautor.pdf.jpgcartaderechosdeautor.pdf.jpgIM Thumbnailimage/jpeg7430https://repository.usta.edu.co/bitstream/11634/468/7/cartaderechosdeautor.pdf.jpgb6b553ea431250eab058f42ad2d6dd51MD57open access11634/468oai:repository.usta.edu.co:11634/4682023-02-25 03:05:15.665open accessRepositorio Universidad Santo Tomásrepositorio@usantotomas.edu.coTk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo= |