Junk food consumer profile and behavior: a case study on the colombian population
People's consumption patterns, alimentary security, and alimentary sovereignty are becoming increasingly relevant as the epidemics of different diseases exhibit comorbidity with overweightness and other junk food-fostered medical conditions. Thus, governments and health-monitoring organizations...
- Autores:
-
Antequera-Jiménez, Anthony
Pineda-Martínez, Oscar
Portnoy, Ivan
Troncoso Palacio, Alexander
Verdeza, Arnaldo
Espinosa, José
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2024
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/10700
- Acceso en línea:
- https://hdl.handle.net/11323/10700
https://repositorio.cuc.edu.co/
- Palabra clave:
- Price elasticity
System simulation
Mon-linear regression
Socioeconomic level
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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Junk food consumer profile and behavior: a case study on the colombian population |
| title |
Junk food consumer profile and behavior: a case study on the colombian population |
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Junk food consumer profile and behavior: a case study on the colombian population Price elasticity System simulation Mon-linear regression Socioeconomic level |
| title_short |
Junk food consumer profile and behavior: a case study on the colombian population |
| title_full |
Junk food consumer profile and behavior: a case study on the colombian population |
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Junk food consumer profile and behavior: a case study on the colombian population |
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Junk food consumer profile and behavior: a case study on the colombian population |
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Junk food consumer profile and behavior: a case study on the colombian population |
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Antequera-Jiménez, Anthony Pineda-Martínez, Oscar Portnoy, Ivan Troncoso Palacio, Alexander Verdeza, Arnaldo Espinosa, José |
| dc.contributor.author.none.fl_str_mv |
Antequera-Jiménez, Anthony Pineda-Martínez, Oscar Portnoy, Ivan Troncoso Palacio, Alexander Verdeza, Arnaldo Espinosa, José |
| dc.subject.proposal.eng.fl_str_mv |
Price elasticity System simulation Mon-linear regression Socioeconomic level |
| topic |
Price elasticity System simulation Mon-linear regression Socioeconomic level |
| description |
People's consumption patterns, alimentary security, and alimentary sovereignty are becoming increasingly relevant as the epidemics of different diseases exhibit comorbidity with overweightness and other junk food-fostered medical conditions. Thus, governments and health-monitoring organizations have proposed and promoted strategies to mitigate the future health consequences of low-nutritional value food intake. In this research, we propose a model-based methodology to assess the effects of discouraging junk food intake (via price rising) on the customers' consumption patterns, separating the population by socioeconomic level and analyzing their price elasticity curves. A simulation model in ARENA® recreates the junk food buying patterns after price rises for a user-defined restaurant/stall setting. Further, non-linear regression models fit the price elasticity curves (discriminated by socioeconomic level). The main contribution of this work is to discriminate the elasticity curves by income level and provide a non-linear approach to fit them. The outcomes indicate that the higher the customers’ income, the less susceptible they are to price changes, i.e., the less elastic the yielded curve is. Finally, future research could focus on assessing the effects of reducing prices on customers' buying behavior and discussing the health-related consequences of the observed outcomes. |
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2024 |
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Anthony Antequera-Jiménez, Oscar Pineda-Martinez, Ivan Portnoy, Alexander Troncoso-Palacio, Arnaldo Verdeza, Jose Espinosa, Junk Food Consumer Profile and Behavior: A Case Study on the Colombian Population, Procedia Computer Science, Volume 231, 2024, Pages 532-538, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2023.12.246 |
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https://hdl.handle.net/11323/10700 |
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10.1016/j.procs.2023.12.246 |
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Corporación Universidad de la Costa |
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https://repositorio.cuc.edu.co/ |
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Anthony Antequera-Jiménez, Oscar Pineda-Martinez, Ivan Portnoy, Alexander Troncoso-Palacio, Arnaldo Verdeza, Jose Espinosa, Junk Food Consumer Profile and Behavior: A Case Study on the Colombian Population, Procedia Computer Science, Volume 231, 2024, Pages 532-538, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2023.12.246 1877-0509 10.1016/j.procs.2023.12.246 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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[1] I. V. Simakova, V. N. Strizhevskaya, R. L. Perkel, and G. Y. Rakhmanova, Global Production and Consumption of Fast Food and Instant Concentrates. IGI Global, 2022. [2] E. Isganaitis and R. H. Lustig, “Fast food, central nervous system insulin resistance, and obesity,” Arterioscler. Thromb. Vasc. Biol., vol. 25, no. 12, pp. 2451–2462, 2005. [3] A. Kardashian, J. L. Dodge, and N. A. Terrault, “Racial and ethnic differences in diet quality and food insecurity among adults with fatty liver and significant fibrosis: a US population-based study,” Aliment. Pharmacol. \& Ther., vol. 56, no. 9, pp. 1383–1393, 2022. [4] R. Fantasia, “Fast food in France,” Theory Soc., pp. 201–243, 1995. [5] D. Kim and I. Kawachi, “Food taxation and pricing strategies to ‘thin out’ the obesity epidemic,” Am. J. Prev. Med., vol. 30, no. 5, pp. 430– 437, 2006. [6] A. M. Daniel and I. O. Esther, “Electronic taxation and tax compliance among some selected fast food restaurants in Lagos State, Nigeria (Tax Payers Perspective),” Eur. J. Account. Audit. Financ. Res, vol. 7, pp. 52–80, 2019. [7] R. C. Hill, W. E. Griffiths, and G. C. Lim, Principles of econometrics. John Wiley \& Sons, 2018. [8] D. Bitar, “Las comidas rápidas son las que más consumen los colombianos,” Rev. P\&M, 2016. [9] L. M. Powell, M. C. Auld, F. J. Chaloupka, P. M. O’Malley, and L. D. Johnston, “Access to fast food and food prices: relationship with fruit and vegetable consumption and overweight among adolescents,” in The economics of obesity, vol. 17, Emerald Group Publishing Limited, 2006, pp. 23–48. [10] G. Sacks, J. L. Veerman, M. Moodie, and B. Swinburn, “‘Traffic-light’nutrition labelling and ‘junk-food’tax: a modelled comparison of costeffectiveness for obesity prevention,” Int. J. Obes., vol. 35, no. 7, pp. 1001–1009, 2011. [11] N. Mathieu-Bolh, “Hand-to-mouth consumption and calorie consciousness: Consequences for junk-food taxation,” Public Financ. Rev., vol. 49, no. 2, pp. 167–220, 2021. [12] J. C. Caro, S. W. Ng, L. S. Taillie, and B. M. Popkin, “Designing a tax to discourage unhealthy food and beverage purchases: The case of Chile,” Food Policy, vol. 71, pp. 86–100, 2017. [13] T. Blakely et al., “The effect of food taxes and subsidies on population health and health costs: a modelling study,” Lancet Public Heal., vol. 5, no. 7, pp. e404--e413, 2020. [14] M. A. Joyner, S. Kim, and A. N. Gearhardt, “Investigating an incentive-sensitization model of eating behavior: impact of a simulated fast-food laboratory,” Clin. Psychol. Sci., vol. 5, no. 6, pp. 1014–1026, 2017. [15] S. Chhabra, “Determining the optimal price point: using Van Westendorp’s price sensitivity meter,” in Managing in recovering markets, 2015, pp. 257–270. [16] O. Roll, L.-H. Achterberg, and K.-G. Herbert, “Innovative approaches to analyzing the Price Sensitivity Meter: Results of an international comparative study,” Laurea Publ. A• 72, p. 181, 2010. [17] K. A. Meyer et al., “Sociodemographic differences in fast food price sensitivity,” JAMA Intern. Med., vol. 174, no. 3, pp. 434–442, 2014. [18] D. Reynolds, I. Rahman, and W. Balinbin, “Econometric modeling of the US restaurant industry,” Int. J. Hosp. Manag., vol. 34, pp. 317–323, 2013. [19] M. D. Jekanowski, “An econometric analysis of the demand for fast food across metropolitan areas with an emphasis on the role of availability,” Purdue University, 1998. [20] A. K. Kharwat, “Computer simulation: an important tool in the fast-food industry,” 1991. [21] W. Swart and L. Donno, “Simulation modeling improves operations, planning, and productivity of fast food restaurants,” Interfaces (Providence)., vol. 11, no. 6, pp. 35–47, 1981. [22] M. A. Wiering and others, “Multi-agent reinforcement learning for traffic light control,” in Machine Learning: Proceedings of the Seventeenth International Conference (ICML’2000), 2000, pp. 1151–1158. [23] A. Tobón, “El capital en el siglo XXI, por Thomas Piketty,” Lect. Econ., no. 83, pp. 262–272, 2015. [24] J. Rochon, M. Gondan, and M. Kieser, “To test or not to test: Preliminary assessment of normality when comparing two independent samples,” BMC Med. Res. Methodol., vol. 12, no. 1, pp. 1–11, 2012. [25] J. Reiss, Practice ahead of theory: Instrumental variables, natural experiments and inductivism in econometrics. Citeseer, 2004. |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© 2024 The Authors. Published by Elsevier B.V.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Antequera-Jiménez, AnthonyPineda-Martínez, OscarPortnoy, IvanTroncoso Palacio, AlexanderVerdeza, ArnaldoEspinosa, José2024-02-09T16:58:16Z2024-02-09T16:58:16Z2024Anthony Antequera-Jiménez, Oscar Pineda-Martinez, Ivan Portnoy, Alexander Troncoso-Palacio, Arnaldo Verdeza, Jose Espinosa, Junk Food Consumer Profile and Behavior: A Case Study on the Colombian Population, Procedia Computer Science, Volume 231, 2024, Pages 532-538, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2023.12.2461877-0509https://hdl.handle.net/11323/1070010.1016/j.procs.2023.12.246Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/People's consumption patterns, alimentary security, and alimentary sovereignty are becoming increasingly relevant as the epidemics of different diseases exhibit comorbidity with overweightness and other junk food-fostered medical conditions. Thus, governments and health-monitoring organizations have proposed and promoted strategies to mitigate the future health consequences of low-nutritional value food intake. In this research, we propose a model-based methodology to assess the effects of discouraging junk food intake (via price rising) on the customers' consumption patterns, separating the population by socioeconomic level and analyzing their price elasticity curves. A simulation model in ARENA® recreates the junk food buying patterns after price rises for a user-defined restaurant/stall setting. Further, non-linear regression models fit the price elasticity curves (discriminated by socioeconomic level). The main contribution of this work is to discriminate the elasticity curves by income level and provide a non-linear approach to fit them. The outcomes indicate that the higher the customers’ income, the less susceptible they are to price changes, i.e., the less elastic the yielded curve is. Finally, future research could focus on assessing the effects of reducing prices on customers' buying behavior and discussing the health-related consequences of the observed outcomes.7 páginasapplication/pdfengElsevier BVNetherlandshttps://www.sciencedirect.com/science/article/pii/S1877050923022585?via%3DihubJunk food consumer profile and behavior: a case study on the colombian populationArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Procedia Computer Science[1] I. V. Simakova, V. N. Strizhevskaya, R. L. Perkel, and G. Y. Rakhmanova, Global Production and Consumption of Fast Food and Instant Concentrates. IGI Global, 2022.[2] E. Isganaitis and R. H. Lustig, “Fast food, central nervous system insulin resistance, and obesity,” Arterioscler. Thromb. Vasc. Biol., vol. 25, no. 12, pp. 2451–2462, 2005.[3] A. Kardashian, J. L. Dodge, and N. A. Terrault, “Racial and ethnic differences in diet quality and food insecurity among adults with fatty liver and significant fibrosis: a US population-based study,” Aliment. Pharmacol. \& Ther., vol. 56, no. 9, pp. 1383–1393, 2022.[4] R. Fantasia, “Fast food in France,” Theory Soc., pp. 201–243, 1995.[5] D. Kim and I. Kawachi, “Food taxation and pricing strategies to ‘thin out’ the obesity epidemic,” Am. J. Prev. Med., vol. 30, no. 5, pp. 430– 437, 2006.[6] A. M. Daniel and I. O. Esther, “Electronic taxation and tax compliance among some selected fast food restaurants in Lagos State, Nigeria (Tax Payers Perspective),” Eur. J. Account. Audit. Financ. Res, vol. 7, pp. 52–80, 2019.[7] R. C. Hill, W. E. Griffiths, and G. C. Lim, Principles of econometrics. John Wiley \& Sons, 2018.[8] D. Bitar, “Las comidas rápidas son las que más consumen los colombianos,” Rev. P\&M, 2016.[9] L. M. Powell, M. C. Auld, F. J. Chaloupka, P. M. O’Malley, and L. D. Johnston, “Access to fast food and food prices: relationship with fruit and vegetable consumption and overweight among adolescents,” in The economics of obesity, vol. 17, Emerald Group Publishing Limited, 2006, pp. 23–48.[10] G. Sacks, J. L. Veerman, M. Moodie, and B. Swinburn, “‘Traffic-light’nutrition labelling and ‘junk-food’tax: a modelled comparison of costeffectiveness for obesity prevention,” Int. J. Obes., vol. 35, no. 7, pp. 1001–1009, 2011.[11] N. Mathieu-Bolh, “Hand-to-mouth consumption and calorie consciousness: Consequences for junk-food taxation,” Public Financ. Rev., vol. 49, no. 2, pp. 167–220, 2021.[12] J. C. Caro, S. W. Ng, L. S. Taillie, and B. M. Popkin, “Designing a tax to discourage unhealthy food and beverage purchases: The case of Chile,” Food Policy, vol. 71, pp. 86–100, 2017.[13] T. Blakely et al., “The effect of food taxes and subsidies on population health and health costs: a modelling study,” Lancet Public Heal., vol. 5, no. 7, pp. e404--e413, 2020.[14] M. A. Joyner, S. Kim, and A. N. Gearhardt, “Investigating an incentive-sensitization model of eating behavior: impact of a simulated fast-food laboratory,” Clin. Psychol. Sci., vol. 5, no. 6, pp. 1014–1026, 2017.[15] S. Chhabra, “Determining the optimal price point: using Van Westendorp’s price sensitivity meter,” in Managing in recovering markets, 2015, pp. 257–270.[16] O. Roll, L.-H. Achterberg, and K.-G. Herbert, “Innovative approaches to analyzing the Price Sensitivity Meter: Results of an international comparative study,” Laurea Publ. A• 72, p. 181, 2010.[17] K. A. Meyer et al., “Sociodemographic differences in fast food price sensitivity,” JAMA Intern. Med., vol. 174, no. 3, pp. 434–442, 2014.[18] D. Reynolds, I. Rahman, and W. Balinbin, “Econometric modeling of the US restaurant industry,” Int. J. Hosp. Manag., vol. 34, pp. 317–323, 2013.[19] M. D. Jekanowski, “An econometric analysis of the demand for fast food across metropolitan areas with an emphasis on the role of availability,” Purdue University, 1998.[20] A. K. Kharwat, “Computer simulation: an important tool in the fast-food industry,” 1991.[21] W. Swart and L. Donno, “Simulation modeling improves operations, planning, and productivity of fast food restaurants,” Interfaces (Providence)., vol. 11, no. 6, pp. 35–47, 1981.[22] M. A. Wiering and others, “Multi-agent reinforcement learning for traffic light control,” in Machine Learning: Proceedings of the Seventeenth International Conference (ICML’2000), 2000, pp. 1151–1158.[23] A. Tobón, “El capital en el siglo XXI, por Thomas Piketty,” Lect. Econ., no. 83, pp. 262–272, 2015.[24] J. Rochon, M. Gondan, and M. Kieser, “To test or not to test: Preliminary assessment of normality when comparing two independent samples,” BMC Med. Res. Methodol., vol. 12, no. 1, pp. 1–11, 2012.[25] J. 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Published by Elsevier B.V.open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
 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