LibreOffice/6.1.5.2$Linux_X86_64 LibreOffice_project/10$Build-2
0
0
14099
9999
true
$(brandbaseurl)/share/palette%3B$(user)/config/standard.sob
0
$(brandbaseurl)/share/palette%3B$(user)/config/standard.soc
$(brandbaseurl)/share/palette%3B$(user)/config/standard.sod
1270
false
$(brandbaseurl)/share/palette%3B$(user)/config/standard.sog
$(brandbaseurl)/share/palette%3B$(user)/config/standard.soh
false
false
true
true
false
false
true
false
false
false
$(brandbaseurl)/share/palette%3B$(user)/config/standard.soe
false
7
4
false
0
low-resolution
false
false
1
1
true
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 823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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Motivation as key factor in the job performance of decentralized public
officials.
La motivación factor clave en el desempeño laboral de funcionarios
públicos descentralizados.
Autores:
Chamaidán-Calle, Cinthya Karina
UNIVERSIDAD CATÓLICA DE CUENCA
MAESTRÍA EN ADMINISTRACIÓN DE EMPRESAS CON
MENCIÓN EN DIRECCIÓN Y GESTIÓN DE PROYECTOS
Cuenca - Ecuador
cinthya.chamaidan.76@est.ucacue.edu.ec
https://orcid.org/0000-0003-0686-3555
Alvarez-Gavilanes, Juan Edmundo
UNIVERSIDAD CATÓLICA DE CUENCA
MAESTRÍA EN ADMINISTRACIÓN DE EMPRESAS CON
MENCIÓN EN DIRECCIÓN Y GESTIÓN DE PROYECTOS
Cuenca – Ecuador
juan.alvarezg@ucacue.edu.ec
https://orcid.org/0000-0003-0978-3235
Citación/como citar este artículo: Chamaidán, C., y Alvarez, J. (2022). La motivación factor clave en el
desempeño laboral de funcionarios públicos descentralizados. MQRInvestigar, 6(3), 823-844.
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Fechas de recepción: 01-AGO-2022 aceptación: 16-AGO-2022 publicación: 15-SEP-2022
https://orcid.org/0000-0002-8695-5005
http://mqrinvestigar.com/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iVBORw0KGgoAAAANSUhEUgAAAGMAAABfCAIAAABz4e4UAAAUaElEQVR4nO2ce1BU9fvH19K8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iVBORw0KGgoAAAANSUhEUgAAAFgAAABUCAIAAACJNWcYAAARJ0lEQVR4nO2ceVRV1RfHn4UV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iVBORw0KGgoAAAANSUhEUgAAAEgAAABICAIAAADajyQQAAAT9ElEQVR4nN1b+XMjx3We7p4B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iVBORw0KGgoAAAANSUhEUgAAAFYAAABVCAIAAABcoIQOAAAZyUlEQVR4nOVcB3Mkx3We7kk7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Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 824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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Resumen
El presente artículo se enfoca en descubrir los factores que inciden en el desempeño laboral
de los funcionarios públicos del régimen descentralizado. El propósito es correlacionar las
dimensiones que aporten evidencia estadística confiable de asociación bilateral con el
desempeño
laboral.
El
estudio
es
de
tipo
cuantitativo,
exploratorio,
descriptivo
y
correlacional, partiendo de una población finita de 420 funcionarios de un GAD de la
provincia
de
Morona
Santiago,
la
muestra
objeto
de estudio
fue
de
201
sujetos
de
investigación con criterio no probabilístico e intencional mediante la aplicación de encuestas.
El instrumento fue validado por juicio de expertos y su fiabilidad fue medida con el
coeficiente de Alpha de Cronbach de 0,828. Los resultados muestran que la motivación está
correlacionada al desempeño laboral de los funcionarios (0,342) y es estadísticamente
significativa 0.001< 0.05 p-valor. Se concluye que, los planes de capacitaciones tienen que
anclarse en la motivación de los funcionarios.
Palabras claves: motivación, desempeño laboral, productividad, Ecuador.
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 825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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Abstract
The present article focuses on discovering the factors that affect the job performance of
public officials of the decentralized regime. The purpose is to correlate the dimensions that
provide reliable statistical evidence of bilateral association with job performance. The study
is quantitative, exploratory, descriptive and correlational, starting from a finite population of
420 officials of a GAD of the province of the Morona Santiago, the sample under study was
201 research subjects with non-probabilistic and intentional criteria through the survey app.
The instrument was validated by expert judgment and its reliability was measured with
Cronbach´s Alpha coefficient of 0,828. The results show that the motivation is correlated to
the job performance of the officials (0,342) and is statistically significant at 0.001< 0.05 p-
valor. It is concluded that the training plans have to be anchored in the motivation of the
officials.
Keywords: motivation, job, performance, productivity, Ecuador.
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 826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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Introducción
De manera global, es común que las organizaciones recluten en sus procesos de selección,
personal que cumpla con ciertas habilidades técnicas acordes al cargo a desempeñar; sin
embargo, en la actualidad, adicionalmente se necesita que aquel personal posea también
habilidades conocidas como blandas. Se dice que este último conjunto de características
marca un factor diferencial en el desempeño de cada persona, de hecho, estudios de este
nuevo criterio en los últimos años han mostrado que la inteligencia emocional constituye el
factor común de las aptitudes personales y sociales determinantes del éxito profesional
(Goleman, 1998). Dicho desempeño se traduce en la productividad que la organización busca
obtener de su personal y que le permitirá crear entornos más competitivos y sostenibles pues
de acuerdo a Porter (1980), la competitividad puede representar el factor determinante para
el fracaso o éxito de las organizaciones.
A pesar de que el termino productividad parezca de cumplimiento natural e incluso obvio en
las organizaciones, las investigaciones han demostrado que no es así. De acuerdo a
información
desprendida
de
la
página
de
la
Organización
Internacional
del
Trabajo
(ILOSTAT, 2021), la productividad laboral es un indicador que está estrechamente vinculado
al crecimiento económico, la competitividad y el nivel de vida dentro de una economía. Este
indicador es representado a través del volumen total de producción derivado por unidad de
trabajo durante un periodo determinado, es decir, a través de este indicador, se puede obtener
información valiosa para determinar la eficiencia y la calidad del capital humano en los
procesos productivos de entornos económicos o sociales.
Las cifras colocan en la cima de productividad a países como Luxemburgo e Irlanda con un
producto interno bruto de $128,1 y $122,2 respectivamente por hora trabajada versus países
latinoamericanos como México o Ecuador con valores que apenas llegan a $20,6 y $12,4
(ILOSTAT, 2021).
Información de este tipo denota cuan amplia es la brecha productiva entre países, esta
diferencia sin duda está marcada por factores de índole social, tecnológico, político o
ambiental que afectan el desempeño del personal que labora en las organizaciones.
Si bien, esa brecha afecta a organizaciones de todo ámbito, privadas o públicas, existe una
marcada diferencia en estas últimas, sobre todo por las políticas de estado que direccionan o
rigen a las empresas, de allí la importancia de que las organizaciones diseñen e implementen
políticas y prácticas que verdaderamente reconozcan y estimulen a los empleados de la
organización (Bernal et al., 2018).
Por
lo
antes
mencionado,
cabe
preguntarse
¿es
la
motivación
un
factor
asociado
al
desempeño laboral del capital humano de las instituciones públicas, específicamente de un
GAD municipal de la provincia de Morona Santiago? En este sentido, el objetivo del presente
estudio es determinar que la motivación se relaciona de manera directa con el desempeño
laboral que tiene el capital humano de las instituciones públicas.
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 827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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Desempeño Laboral
De manera general se puede definir al desempeño laboral como el conjunto de actitudes,
conductas y procedimientos en los individuos, que derivan en el cumplimiento de las metas
u objetivos laborales encomendados.
Pedraza et al. (2010) define al desempeño laboral como aquellas acciones o comportamientos
observados en los empleados que son relevantes para los objetivos de la organización, este
desempeño puede ser exitoso o no, dependiendo de un conjunto de características que muchas
veces se manifiestan a través de la conducta.
Por su parte, Cajas et al. (s.f.) define al desempeño laboral como un elemento central en la
organización, enfocado en como el trabajador puede alcanzar resultados específicos a través
de acciones que se llevan en un contexto de políticas, procedimientos y condiciones
específicas de la organización.
En el desempeño laboral, la eficiencia es el principal elemento de la organización a cumplir
en
cuanto
a
las
necesidades
que
requieren:
consecución
de
objetivos
institucionales,
acometividad, personal con alto nivel de motivación y capacitación, calidad de vida,
políticas, ética y comportamiento (Castro & Delgado, 2020).
En el éxito de toda organización, el recurso humano juega un papel determinante, en este
sentido, se entiende que su desempeño laboral deberá ser óptimo, sin embargo, en países en
vías de desarrollo, falta mucho por gestionar, en este caso, deberá empezarse por gestionar
las oportunidades de desarrollo personal que los trabajadores tengan dentro de la propia
institución (Jara et al., 2018).
Teorías del Desempeño Laboral
Método Teórico de Campbell
Este método se basa en medir el desempeño laboral de manera integral, en función de
componentes que permitan su medición, con la finalidad de formular estrategias orientadas
al cumplimiento de objetivos (Bautista et al., 2020).
Acorde al autor, la presencia de los siguientes componentes son los que determinan el
desempeño laboral, estos son: dominio de tareas específicas, dominio de tareas anexas o
relacionadas, mantenimiento de la disciplina personal, facilitar el desempeño de los pares y
equipo, supervisión y gestión o administración.
Los Experimentos de Hawthorne
Este apartado hace referencia a una serie de investigaciones realizadas por la Universidad de
Harvard y la fábrica Western Electric Company en la localidad de Hawthorne, en los años
1927 a 1937.
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 828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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX
FBYWGh0lHxobIxwWFiAsICMmJykqKRkfLTAtKDAlKCko/9sAQwEHBwcKCAoTCgoTKBoWGigo
KCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgo/8AAEQgA
SADKAwERAAIRAQMRAf/EAB8AAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYHCAkKC//EALUQAAIB
AwMCBAMFBQQEAAABfQECAwAEEQUSITFBBhNRYQcicRQygZGhCCNCscEVUtHwJDNicoIJChYX
GBkaJSYnKCkqNDU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6g4SFhoeI
iYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2drh4uPk5ebn
6Onq8fLz9PX29/j5+v/EAB8BAAMBAQEBAQEBAQEAAAAAAAABAgMEBQYHCAkKC//EALURAAIB
AgQEAwQHBQQEAAECdwABAgMRBAUhMQYSQVEHYXETIjKBCBRCkaGxwQkjM1LwFWJy0QoWJDTh
JfEXGBkaJicoKSo1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoKDhIWG
h4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uLj5OXm
5+jp6vLz9PX29/j5+v/aAAwDAQACEQMRAD8A1taXxZ4x+JuvE+I9W0jQdMuHs0j0+9kh3FTw
SFIGfek2Jsvp4H1Jj/yP3i//AMGtx/8AF0XC5Zi+H+puCf8AhPvF+B1P9rXH/wAXRcLleXwp
Dbttufil4mib0bWJx/7PRcLjP+Ecsf8AorPiL/wcz/8AxdFwLFv4Kku/+PH4k+Krj/c1ec/+
z0XC4S+AtTQ4Pj7xeD/2Frj/AOLouFyvJ4J1Nf8AmfvF3/g1uP8A4ulcVyBvB+qD/mffF3/g
1uP/AIui4XIz4S1Qf8z74u/8Gtx/8XRcLjf+EU1T/offF3/g1n/+KouFw/4RTVP+h98Xf+DW
f/4qi4XD/hFNU/6H3xd/4NZ//iqLhcP+EU1T/offF3/g1n/+KouFxR4T1Ugn/hPfF2B1P9qz
/wDxdFwuUZtMEDFJ/if4ljcdQ2sT5/8AQ6LsLiwaX9ofZb/E/wASyuey6xP/APF0XYXLp8J6
qME+PfF2D0/4ms//AMXRcLif8Ipqn/Q++Lv/AAaz/wDxVFwuH/CKap/0Pvi7/wAGs/8A8VRc
Lh/wimqf9D74u/8ABrP/APFUXC4f8Ipqn/Q++Lv/AAaz/wDxVFwuH/CKap/0Pvi7/wAGs/8A
8VRcLh/wimqf9D74u/8ABrP/APFUXC5geLW8X+BRB4j0bxXrep2lpzPb6hqE0sbfVWYg00xp
n0v4H1SbXPBXh/VroKtxf6fb3UgXoGeNWOPbJpjPJ/Cy7vE/jn/sLy/zqWSzp5pbews5Lu+k
WK3jGWYnFAHzp8RvjVqOpXsmmeFFaNd2wSIMl6YzI0T4RePPF+L3UFnjiflWdjz+FMZ0t/8A
sz6/BaLJb6gZZT1QdqAOO1HQviL8M2+0slzHao3DglgRQB6z8LvjJZeIjHp2t7be9Pyq5/jN
KwrHq1zDjkcg8gikIz5k60AVHGKAIjQAlABQAEhVZnOEUZJ9BQB4z4u8bax4k8R/8I54KVmc
tsaVR19aaQ0jov8AhRWmaFpH9u/ELxDJErEeZliNrHtTGE3wItNU0dfEHw81+SZOTEAxIcjt
QBi+EfGes+H9fXw544iaKcnEcrd6TQmj1/sCDkEZBpCEoAKACgAoAKAOP+MX/JNdV/3aECPc
vhP/AMks8G/9gWy/9EJVFHn/AIMjD+J/HGTgf2xLk+nNJiZ4l+0J44udT1pfDekSPsjOyTyz
98+lAHrX7PvwasdB0mHWddgSfUp1DKjjIQUxnvcaLGgSNQqqMAAcCgB1AFbUbC11K2e3voI5
4WGCrrkUAfGn7QXwml8E6kviHw8G+wPJu2r1jb/CgD034IeM18XeFhDcP/ploNr5PLUmJna3
CYJpCM+UYNAFdhQA2gAoA5H4tatNongi5ubY4lf92PxoQI6L9l3wLaad4Uh8QXUQbU7pi+4j
kCqKNb9ojxF4Ot9Ej0Pxp9oaK4ZZVWD7wI78UAWv2fvEXhO+8N/2N4N88W1mSxWb73Pc0Acv
+1f4PgvPDcfiO0TbqNk4O4DqtAGb8M9Vl1vwTZXs/wDrcbD+FSSzpqACgAoAKACgDj/jF/yT
XVf92hAj3L4T/wDJLPBv/YFsv/RCVRR5vpEptz8TbhfvRajOw/OkxM8F+BGj/wDCY/Flb6+H
mRRyl3B5zzxTGfQ/x6+K8fw9sIdP01A2oyqAqj+BfWgDxiL4i/Fi106DX57eaTSXYEYU8igD
u/GXx+u9M8H2rCwNvrVyo/dnqB60AcNF8R/ivp1nb67e200ulSMCAFJyKAPoWO5f4gfCq5m1
Gx8iW4t2xGw5Bx1oA+WPgTLcaD8VH0RidrOyt+FAH1HfptmcehqSTKmHNAFR+tADKACgDgfj
qjv8PnMa7ikoYj2oQI9i/Z91SDVfhlpkkDDKDYyjsQBVFHjv7Zfha8uJbHxFGCbOCPyX9iTQ
BofsbeFr2wsb/XpwRa3qCOP3waAPRP2ltVh0z4Y3pmI3SnYinqSaAPNPgtG8fw6sxIpVmYtg
+9SyWdrQAUAFABQAUAcf8Yv+Sa6r/u0IEe5fCf8A5JZ4N/7Atl/6ISqKPM7L/jz+Kn/X/cfz
pMTPL/2PbqCHxVqMMoHmyH5KYyL9rq2ltvibp+ozxM1n5SDOODjGRQB7Po/xN8G6Z8L9Our2
4tpYljVTaDBYH6UAfO/x81e01vxrpniHT7Yro7RxqBtwMjrQB9F2XxN8GaV8NNOur+4triNY
1U2owzA/SgDu/DHiLTfEHhH+09LQR2TRkhcYwMUAfIPgaVJv2jpZI/uGd8fpQB9Pal/r5Pqa
kkyJqAKb9aAGUAFAFXVtPh1XTZ7K4UMkqFRn1oA8f8A+L9V+DXi6bTdVR5NCnkzn+EAntVFH
ufxQfTfix8Ojpvh7WrOO4mdJQsjjoOxoAm8AX2lfCn4a2+meINYtJLm03MyxuMnPYUAeB+M/
FGqfGfxpb29lHJHoVs+Rn7pAPegD2bT7OLT7GG1t1CxxqBgetSST0AFABQAUAFAHH/GL/kmu
q/7tCBHuXwn/AOSWeDf+wLZf+iEqijz3wxb/AGzU/iHaYyJ9UmT8zSYmfOfh3UG+GXxoDThk
tY58OvYgmmM+xfFHhbQviR4bhN7HHNFKgeKQclc0AeV2n7MOhw3yTSX80kStu8s5waAPSfEX
wr8Oaz4XTRWtEihjA2MByD60Aea2H7MGh295HLNqE0satkxnODQB23xP1jR/hl8NZ7a0VIBJ
GYYYwedxGM0AfO37Neh3Gq+J7rxBOpPksTvPcmkxM+kb0s0jHB5PpSEZcwb+6fyoAqurf3T+
VADdrf3T+VABtb+6fyoANrf3T+VAGbr2g2Gv2ht9TtRKuPlJHIoA81k+CjRXjT6VrM9mpOQi
seKLhcdD8FvNvluNX1ia9UHJRieaLjuelaJodhodoLfTLUQr3wOtAjQ2t/dP5UAG1v7p/KgA
2t/dP5UAG1v7p/KgA2t/dP5UAG1v7p/KgDjvjGpHw11XIP3aECPcfhP/AMks8G/9gWy/9EJV
FHzDq3xSu/Bfxj8V6fIA2mS6pIz46gk9aBHQfFvwdafEPw3F4h8NlHuY03MF6txSQHI/Bj40
X/gWf+wvEySSWKNtG770f/1qYz6y0Dxt4f1yzjuLHUrdt4zsLjIoA25tQtIIxJLcxIh6MWGK
AOA+IHxg8NeEbJ3e7ju7jBxFC2TntmgD5G1/WvEfxm8ZooWVrYvtRF+7GvqaAPcdV1XSfg74
JhsUKNqBT7q9WOKQtzxWf47+LHkYq8AUk4G3oKLBYrt8cPFbdZIP++aLBYafjZ4qP/LSD/vm
iwWE/wCF1+Kf+ekP/fNFgsH/AAuvxT/z0h/75osFg/4XX4p/56Q/980WCxo6P8UPHmtStFpN
r9rkUZZYoyxFFgsLqvxN8faReRWmp2htrmXGyOSPBbJxxRYLFfUfi3400y6a21CNLe4UAlJE
wQD0osFh2ofFfxvpywNfwrAs674i8eN6+oosFin/AMLr8U/89If++aLBYP8Ahdfin/npD/3z
RYLB/wALr8U/89If++aLBYP+F1+Kf+ekP/fNFgsH/C6/FP8Az0h/75osFg/4XX4p/wCekP8A
3zRYLGf4h+KviHXtGn0y+eI20wwwC80WCx91/Cf/AJJZ4N/7Atl/6ISmM+EPjr/yWDxd/wBh
CT+dAGh8I/ibeeDb9IZ3MumucMjchRQB7dr3gzwf8U9O/tDRrmG0u2G5nBAJP0pCPLrr4O+O
NEvG/wCEeaWWLPDo2M0wuT3Xgz4vXMCwzm7aMdAZP/rUDLXhn4C6tqVyJ/FN4bQk5beev4ml
cVz0HVPEHhL4SaO9tpAhl1TbjK4O40AfMXjDxNf+KdXlvtRlZixJRCeEHoKYzCoAKACgAoAK
ACgD6C/ZVk8q28XyGdrVVtBm4VdzR+4FADfibcPN4Pa/tJjrfkSqRqtwuySBgQQoHpQBF4Ou
LT4gaBd6zr9ik9/4ci86aTHN2vOFagDybxt4qvfFeqm5uyEgjytvCBxEnZRQBztABQAUAFAB
QAUAFAH6VfCf/klng3/sC2X/AKISgD5F+P3wx8V/8LO1i/03Q9U1S11CZrlJLKzkmVQf4SVB
GaAPOv8AhXHjj/oTfEn/AIK5/wD4mgDT0bwr8SdGnWXT/C/imIqc7Rps+Py20Aeh2XjH412k
CxReGPEO0DHOkz//ABNKwrE//Cd/G/8A6FjxB/4KZ/8A4miwWMfX9b+Mmt27RXXhrxIqnjKa
XOD/AOg0x2PP7nwD4+upDJc+EvFErn+J9MnJ/wDQaAIv+FceOP8AoTfEn/grn/8AiaAD/hXH
jj/oTfEn/grn/wDiaAD/AIVx44/6E3xJ/wCCuf8A+JoAP+FceOP+hN8Sf+Cuf/4mgA/4Vx44
/wChN8Sf+Cuf/wCJoAP+FceOP+hN8Sf+Cuf/AOJoAP8AhXHjj/oTfEn/AIK5/wD4mgDrvh9b
/E/wLPdyaN4N18i6TZKkukzsrD6baAG+N7T4m+L5Yze+Ddft4UXb5FtpM6I3uRt5NAFbwzpP
xL8OaXqthp3hDxEsGpR+VODpU5yP++aAOc/4Vx44/wChN8Sf+Cuf/wCJoAP+FceOP+hN8Sf+
Cuf/AOJoAP8AhXHjj/oTfEn/AIK5/wD4mgA/4Vx44/6E3xJ/4K5//iaAD/hXHjj/AKE3xJ/4
K5//AImgA/4Vx44/6E3xJ/4K5/8A4mgA/wCFceOP+hN8Sf8Agrn/APiaANXwv8IvGmua3b6f
L4d1nTklODc3lhLHEn1YqAKAPvvwVpUuheDdB0i4dXmsLC3tXZehaONVJH4igD//2Q==
De acuerdo a todas las pruebas experimentales desarrolladas, una de las conclusiones a las
que se llegó fue que la producción o el desempeño laboral están relacionados más a los
sentimientos del personal que a las condiciones objetivas de trabajo. El personal objeto de
estudio comentó que sus estados de ánimo, sentimientos y actitud hacia el trabajo habían
mejorado durante el experimento ya que sus supervisores les prestaban mayor atención (Hart,
2012).
Estudios Aplicados al Desempeño Laboral en Contextos Organizacionales
A través de la investigación realizada por Meza (2018), cuyo objetivo era el determinar si
existe una relación significativa entre el clima organizacional y el desempeño laboral de los
empleados de una Universidad, se encontró que existe una correlación positiva entre esas dos
variables, es decir, mientras mejor sea el clima organizacional los empleados tienen un mejor
desempeño laboral.
Conforme el estudio realizado por Bohórquez et al. (2020) el 100% de los trabajadores
indican que sus competencias y conocimientos contribuyen en el desarrollo de actividades
encomendadas. Es decir, existe un alto grado de motivación que influye de manera positiva
en el desempeño laboral del personal.
Motivación
La motivación es un proceso que depende del curso, intensidad y la persistencia de los
esfuerzos de las personas para alcanzar un objetivo determinado (Chiavenato, 2009). En este
sentido,
el
autor
determina
que
la
motivación
está
compuesta
por
3
elementos
interdependientes pero que interactúan entre sí, estos son las necesidades, los impulsos y los
incentivos.
Robbins y Judge (2009), definen a la motivación como todos los procesos que inciden en la
intensidad, dirección y persistencia del esfuerzo que ejecuta todo individuo para alcanzar un
objetivo.
En este contexto, se incluye a la motivación laboral como aquel estimulo personal que
impulsa al trabajador a querer alcanzar sus objetivos o satisfacer sus necesidades, todo esto
mediante el mantenimiento de conductas durante la ejecución de sus labores (Macías &
Vanga, 2021).
Es importante mencionar que, además de la motivación como factor de estímulo para el
personal, se puede complementar el desarrollo del trabajo mediante el eficiente manejo de
facultades en la toma de decisiones organizacional, estas facultades pueden ser el liderazgo,
poder y el desarrollo.
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 829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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Figura 1
Bases para el facultamiento en la toma de decisiones
Fuente: Idalberto Chiavenato (2009). Gestión del Talento Humano
Un estudio realizado a los empleados de las empresas de servicio eléctrico en el estado de
Zulia indicó que la mayoría de los empleados poseen grandes necesidades de logro, es decir,
tienen a estar muy motivados por los retos y situaciones laborales competitivas. Este personal
tiene éxito en trabajos interesantes, estimulantes y complejos (Donawa, 2019).
Teorías de la Motivación: Teoría de la jerarquía de las necesidades
Teoría ampliamente popularizada en el entorno psicológico, a través de ella, Abraham
Maslow determina que todas las necesidades humanas se pueden jerarquizar iniciando por
aquellas necesidades físicas tales como el alimento, aire o agua, seguido de cuatro niveles de
necesidades psicológicas ordenadas de la siguiente manera: seguridad, amor, estima y la
autorrealización. (Maslow, 1991). El autor también complementa lo enunciado, con el
argumento que
las necesidades superiores
son tan
importantes
como las
necesidades
fisiológicas básicas.
En las necesidades fisiológicas, se incluyen características como hambre, sed y demás
necesidades corporales. En cuanto a la seguridad, está el cuidado contra daños físicos o
emocional. Las necesidades sociales hacen referencia a la amistad, afecto y aceptación. En
estima se incluyen factores internos como el respeto a sí mismo y externos como el
reconocimiento, atención y status. Por su parte, las necesidades de autorrealización conllevan
iVBORw0KGgoAAAANSUhEUgAAAuoAAAGACAIAAAB0gtBQAADFhUlEQVR4nOydCTxU6xvH32Ew
MdayDFFRJEsLSiWhsitZS1JJJYm6oVvULalEexLtudKGKHXTbVHSXrf1htK/RbRd6xBZ5n/O
jGWYM7bUUM/343ZnznnP+z7vO5zzm+c553nI79+/RwAAAAAAAN0HMq8NAAAAAAAAaB8gXwAA
AAAA6GaAfAEAAAAAoJsB8gUAAAAAgG4GyBcAAAAAALoZIF8AAAAAAOhmgHxpgpycHK9NAIAu
AaRUAACgKwPypTkMBoPXJgAAjyGRSLw2AQAAoCVAvgAA8BMCnlQAaOCndKaCfAEA4OcEPKkA
gH5eZyrIFwAAAAAAuhkgXwAAAAAA6GaAfAEAAAAAoJsB8gUAAAAAgG4GyBcAAAAAALoZIF8A
AAAAAOhmgHwBAAAAAKCbAfIFAAAAAIBuBsgXAAAAAAC6GSBfAAAAAADoZoB8AQAAAACgmwHy
BQAAAACAbgbIFwAAAAAAuhkgXwAAAAAA6GaAfAEAAAAAoJsB8gUAAAAAgG4GyBcAAAAAALoZ
IF8AAAAAAOhmgHwBAAAAAKCbAfIFAAAAAIBuBsgXgIdUZO93U5t9gvmaqmJkY2M1bb6nuSqV
v7UDP19aaj4uTHlfVoy7KuW7mwkAAAB0MUC+ADxHUs83ZKmh5MerBwL8p5x5dyJjs7k0iddG
AQAAAF0YkC8Az6FIaptMthtIMhB9FPN31IV/cr6YSQuDfgEAHlJbcilo4Lj1+dhLzXU3bv2u
j/9JMspvho4YufwJtpG27GJmiIkYXxv7QQEXMzeYiH2bMU07KXm4fdaUfybG73DVaN1fC/yE
gHwBuhhi1B7kyvybx3ftTzgb/07OwdrOfa6bvjz2m8qgPzkRti5sy12x2dNH55XUH1BDf3lx
f/im7VGPpWb+tnLZHCtVcUb2fgu1IEZYqGvekXBB/4yOnzcB4FeG2lel1+cnGbefl+kPpiJU
9vx2xpO+Kiqfc3JaOopR/fnfS2cvfdBwm64j1mPApMgEXSQ/oEfHzSBxdsL4ePev9w7xO5xA
u/yygHwBeE5F4aNLJxP/+Xj1QGwhdeAYVZF7O6aZ7kCLdxy61/flgWUuE+6+P39wmX7V+RXu
znuVws+kOAlf+8MF+1o4BDuYUXhju4fHelGfE7d9noR420wqvpixygjvtvR5dHCI4pAxzuLw
Ww4AHUK0r4mJxodLl+69mT94kEDVm3uXMgeamOj81bJ8qXx5arXZ7JcBF6diykNAcYSt4jea
QdAJScbk93Xf2C3QvYETO8BzCu9sW+CwDfump+e44sCqgKH5YSGX6drb7Mdp9BMZYG+hvcZn
Y8xNDxWUdOgO1WaJ0xhVJZLU9MmRB8OwYxlfstKPXBa0OWpvpkdTnqTnPzst7WmRkSy2i/5Z
c9u9E7NUBSAOBQAdRHDw6HFPT6688Dh/1iDF/McXLvYwitAT/WtP/f7K/JvHmjpKBa/it9Xf
w/aFjZMOM933bBvyVZ/9OODiswWf5mtMOW1z9OlhZyVS0c21k0eGKh99usXw9fFte+PiE0s0
Ztk7uMyYirtamd3uOhiXSB+2yPs3L+fhpYct1PBOmMEjzkHlybWZ+y1MZjO8j876mrBp9198
Vms3rvIyVIAr3E8MfLgAz6GZ7rv0l/vAuig6dhq6hX21M6D2wH45SeQeIlRM4Lz8WFCAXhYi
UUVpiSZqpDL333+eoJwnU5SPTKnr7dbzT7W4fEGian3lQLsAwLfQQ2245WD6phuPPk2iPLpx
TmBczCil9Lp91YU3tnM4Svf/tuzkDQWfkb6vfRNilxoq9Sw4zmpNUhw+yUblyDmsK3sliVe3
zz6k2niOFLoaPGlOrOG+u7k6aTNs3eagAbeW6uQenj/BN8vjYMqN8g2GbhNyhB7/0WBQDdGg
B5eNYO58nnr5c8DiDbIi8wN858lpZqwykYLQ0k8LyBegi8HXq+8QeXS+nP6lGg+ifymjI+rA
Ef3lpUqUJdGdkvIKBsK+fpWXVLJaU0REqWjobOxEOVKSuYUkINqLL+86L6cAAD8PkpqGRgOD
LtzOzEJnLlaNDRgkLVAvX4qf/HWKw1F62yNyvBRVAPvbFJKQofWi1hbU90RSGDlpLPXIxTO3
P0xQTD92XXy857DesgN2FTB24bs/v1PriZL/+Te3oMe5xGR6X89x+moaPfyPnrQpp6GqJ/W9
FBENetNjeH98Z+VopxkWI6nZz/qJHjyf9epzFQL58vMC8gXoakgOtXU2jgzedSBRb3qfF38e
uU4dFz5RU1TyvZ5h36jYmKNu6tZVp/bEYqczdYQEaXqmjgNOpl597DFU9em2BVP29dt3b89M
Xs8BAH4W+KhqOkaS2y6mJpWl/ac9X6s3X70eqfn0nMhRWsTg1pWg4khTG+rxtGt37g64+Yg6
atsIBT5Gycu/j+yOP3vhyKl7dKyNMkJf/nv3HiEhMWEhEklqkKntIIRqs5/W9VHLbdD+330l
gC4GyBegq8EvOdLn8N+ykZsjTHVz1Rxn7/t7vpuuFAlJuu06WBa8bpvp1GuLnIfbaZ6KwVsL
KNtuOELac+jITO2nonZT9/09z01VGGXzehIA8LNAktYwMpeKPfZnXs6wxWP7C6LbdTv4iR2l
cnzoPbeu8PiR7JHkA9v6PkLjVhj0Eyy4vN7KLFZ129G/C/f+E2gxDr+hrUdPBTmEXpeUVzIY
Bc/+vppZThvWv14TcfHOyrX6BDfw0wHyBeAhFFX34wx3zu1CNH23Ncfd1jTZSCLTxi7chf2w
3i4JPFS3Q1rHaTn2s52trap7KlG/AAC0G5K8nsVIdCT2lebs4QNEUHXDDgliRyliyZf/Mu/f
uttLu0la7Lr40f4TmX0n7dPvJ1Cb/ykvF/t7RVWlOVdPn8piel9EBow1m0BdfvHM1UcKXzZP
mZ1ofvTxHw03sXHxzqKPP25BgK4ByBcAAACgBQR7aw/XRkkFVsNVe5BQacN2MhdHKUPZwjPc
+dXq1X/UfF4VMatJV8z40f4jaNREg74C2FurxftWfAkLdJ/5bK7LNFPqinL6lxrhwXMPnSFv
2LTJwJDPwmvP2UW2SqWH63vg4p2tBfnyywHyBQAAAGgGn5jJujxGXWYVwcELMxgL6/aImWzI
Y2yoe8PNUWrid/S2X/17dlcoSck5rtQ5ruEtVdMpOM4puO6tR1DdC5rh/K3YT8NhNHZ/KtGg
fAPdU/PqW7C/Bn5aQL4AAAAAANDNAPkCAAAAAEA3A+QLAAAAAADdDJAvAAAAAAB0M0C+AAAA
AADQzQD5AgAAAABANwPkCwAAAAAA3QyQLwAAAAAAdDNAvvCC6nsbB+r659S/VTH19Fni7z5O
mfrjiqMyTXgRlhdhR+vM34Hv1C0AAAAAsAPXGF6h4pZw+ZBd7+rP2TeuJO5cNtP1a/IFPz1h
XpsFAAAAAF0fkC+8hUTupTbGfnGf6tdD5h9Ldx9mJvXjHDAAAAAA0E0B+dJZ1NCfHvWf5R91
R3zi2oioABMamdT6QXVQFEcZmxZG3M2mm+kL/C9lw8JFm8/k0JHKpKVhG1bZqZHxiMyNqWGy
qcHn1Q9dPmSnWHdcfuIM+RChFRM+xEWeykEqE1fsjvI1oQkhRnF28lZ/v42ncuhUPY+1G1d5
GSpgnzSD/ijWf4FX1DU61cB9as9yJNfU8nykYr9299YAk96I24ionNM8CtGUiFtW514KWzQ3
MCEHURutraM6P9FbflHlCvfSuNVYg4ENy9hodsNGdH/jQIcbU91lU3ftUo/IO2SFLm3znLvm
FD5W3RT46ZnJ4YF+wYk5iKY3c9nGdXMNsbHw4JbNaRNbWtqfx56L6nmGHwifokHlR1U5KSuX
LApNxg2zWhS2eamdqkh1/qUwT+/AU5mIzZgO/GYAPzN1gWDJUdsuXfYZIsjayHiX4mVuE/VE
Jfxupp9OG06yzF9+e5QAUVcAaDPwp9JJlN+PmrMw6k4hpilOBXr3HJi6z06p7dc6Eq2/rkr+
47wSVPkhIegI9Y97VdOV8o4tGuJ9yHR8iCHeJDEkAPtXRb35oc/TS5anPChKprxL8Z/iEDwo
M9KKen/vTPcXM67mJmlSPl0Ks7X6XfLh3un9ii+HzPctn3e3NE0VZcX5ue1nyZeSjDCH0HL/
86W31GtvbTK33KidudGc24iV/3KaZyLGRzAlgpbBuncjXaPUYouqTUSe77e38DthdsdncBN3
09uskkEHHtSeoOSf8R+3JFj7TKQ15VrYAt/yObjZtbfXmy8I1j4didv3KjFkJX6IOj6Fra7x
6rEvk/B+PdT8Eq3uzKiOWuyeZX+19Lgm5cOlNTOtvCQeJkxXxg/If1wybOf9HbElf/mPWxR0
cnjC9P5Vj04GRUj+kfNlutK7Y3OsvfcZj9+gfXfr0ij1nUVJxiLPD9irbTxhpe8zWKTNHynw
S1F4/cz9V95DVFl/CoVPLhx70toh1Z8fnjpyT8HVfYQk4pcc6nryJBosCc5XAGgrIF86ibIP
Lx4X1r/JfZBbWIuUOnIq4u87cV+is5oKGfHL9+8vlf+iqKyWuWOA8/aYHfNHSDd3AGg52puo
4Pf8KhrbmVKdrz/ZoC+QnJDp9oejpjjWVMbI0WPUxISMt66y+WlxlXMOWqrhjdVtp1krRr9H
uMy49ucHsyhbdSqJHw01tBL3Tn9SZC7OZURC8wjlC7eJFN5PTUgVGW84K/l/BCVhFU3trVSp
JBKSH2XnKOycnrVhgvDVP0vdosyYZg8ysRKxwzYy7ZN03pm6Y7aetBAquYTQ+9upp7B+DWbF
4YVpy9JXbs9322OjiR8lbzRtyqjg1IzXTsq4F6mv6eTxGlR+EnXwmOHo9IcSzCz+PpP2ZTir
9aMgkmx/Dbn8x0VlTHMKb2PGio43mJHMgPq1ADcUTR2H5Z04d+2Fi6oqBaHakgdXjqvPnCeT
dqGloype/73D5/FCB/w3i0TpZ2Db7weZCwA/ByBfOole+jOWWR0OPEPHXg+YvtR6QPu0S2H+
iw80VXkxRKaIVp3dtcAnLuYy88mkefUtTJwcdDm0C4aQmDDLY00SEqYK4iqhFL3+KKXVS4y1
n09EQq72yYfimpJPr9+KaolTGhvjL6oL8t68Kow277mpvkMVt6IvSJzLiGRRLuZxQNCST8xw
wdG1q/x8rcLoiKrnuyc2ZIoqtclRglRhIdaIgsJiQriMKCjKfvUwxlxuW0MbN0xbYPap2DjZ
4NoFQ2z0oqP+y/1WWIR5IKqB5549W8Zik5XU6lXnLOETlZBD7z4UVCFcvvALCfA3nRWJLCFS
dSp6ge+RmLTnzCEWItTTcNGmtcuX+1pgxtL0PDfHbnFWpUDw6FegvYFgQTkrO8NnIRf+yZ+l
2o+ECu6mpvVzXmJ4L71OvhAEc0nnPMfZRGcilCafuPD0yy3mb7YwH9nbanDv94FuKCZzo7UM
dnKmP9w+ZcgxsweXZ4ud5wzF3msaQrWj/YC1AYAuA8iXToLUa2TA/muDkv9+I6JjY23Ur12P
EFW8uZRyXEAnRlmkKjtmvt0Fg30bL+8cTMvCz2itHVtdWVXDfFFT8vl9gaKctJgY6iNTkPu5
BCEpbPPX/3JfIM3x4vyYjKFVlpR/ZTZmVJbTma9IQiJUyb5rz2Yu02fpBkZ5UQkZEQ/LqMo+
3EbziFrW0PMLRYxWXiqJqHh28eCqRV6RFqZbzaTYD6v5WlXDQPjVouxzbqFiH2kxoXJJSYOQ
symB+uKsFmVFZULoeROz6B8/iRgHX/LaU5F54eBaF6+9tg+t+ygW5n4uQwiTR4yvH9+9QArj
pQS4GFueHeNvd27ovo0Xdg6Tytpko/sYO6gs/5OoUXBqyZ4vzy4cWuUSGmlrvNVMtrWPA+j+
dCAQLDp43BSF0OTbb536KZU/Szsi5XxanXKPtY9RThjMjbpztz/2m7aQJTuq37Aak6VHWcyg
+J25/cHKWoH09cWVY9m2nlu0GP9u5gza4ucYthAqAPxigHzpPMgyg23nDG7fMYy6B6dXHJed
e2KUNKno9oOLqJebtoYiJS/1xJkcpNZaDw9ij6XP0rWiVTw5ceCMrEuEhrAEMhon6xB7wkN/
ribl/eWE2GfGS8YqkkRFTJzRosTrniOxxtlnEs6/RdoI8UtqjbIt3pGUNl3XTL7iYfQ0g/MW
t+I8iMeqLcpuo3k1RC1rCq5vMVrcM/7JejP1YVoKVIqgQPPfv1cpx/521bVWqHhy+kCMiEu8
mrBkhaFtaWjSDXddM7mK+xHTph63SExrZl/B9VCjDbLxZzebDRyu1ZtCIQuIqRm5iDgcOO0x
epYmJe/ykcRnLp5j+wihWkJri7Jv30c9HbUHK1Lep544/pB5Mfh0PdRpsezOJ5vN1IdrKVDI
ggJwX8KvQUcCwRKDRo4U3XTj0Se7Xi+vnZQyOjiQ+rpuV+E9wmCushxhRyQpXTsPiUmJt1ZY
Te71LP3YozE+Y/vw8ZcQhGKZX5EaQ6gA8IsB8oVX5MTYK8WwXlL1HBfvTggwkiLxM4w8Iict
mNGbYk81mDN/gF7r/fTWKDtqK2lzh07T8wyPD8I7QcZ+CVFhAba9PVnO6qRV9krY2U1uXMC6
2YsXKQjYIJVJv1nTWK5mkrzV2qR3y/1GCJjn437pfSGu6sLoPuFY/L3aah5hS0FFK9/ItAUO
Pcl05pNHsVGjxZodR5MpOzlN0gZ/NsozcleQsQyJxJi4dlvu8uWqAhZ0NNBq6dpoV3V+9ID9
IJKieWBkxiwHGh8d4U8exW41FKfxB0VHhQTZis5hPXmUtNFWiQ9xkS+yRguDJs3y6i0wmao3
a/4QDVSJdapkFbg6bdasnnz5dU8eGfZs/dMAfgI6EgjmE9MdP53y24V//kd7nIYcV6kLkerl
S9knwmAuIpYvCEnpWFoqTDiTvspo4JXUZ66exoqCiMQtaMsWQgWAXwyQL7yArOP3guFHtIdE
1Z6+K336rvr3Ycx/aVhzbn3J6c+Lit8d27QX8UFOa1Oc1jbrm0wz8jv6wO9o3ftNW+usoRku
PHB74QH2tjrEIxKbxwa58UDClk03ciJsMC/ab/e+ZmaP9TmQ4dPcPra4FUlcY/r229O3N+mK
qukUmuQUymlfw4GKdode2LHaakzbdXtac2ObbgR+FToWCBbTNptOdj19SPgfxpRN2DeA0vod
ItKEwVzuwwsPs5ijPTX5cmr+sTLv0JEypHYEbQHg1wHkCwAAQFM6EgiW0BxjUGGzbr3muhva
4qhRvohrEAZzURW+s6iotJrR/NZgwf5jnVUC16y40WP2ER2p9gRtAeAXAuRLd4Zmd4hhx2sj
OgsyzS7q55kN8MtBlh4+3lly37UpRtrC7HKEX4o4mEtWHm1pvHq2msDthLyIiU26omqZ243y
9aNvsxiGd9X2oC0A/EKAfAEAAOgobOFIkoz1roIGp0tjaJJLMJdfcqT/pVL/undNA8R8qu6p
bKmGuARtm4ZQAeAXA+QLAAAAAADdDJAvAAAAAAB0M0C+8IK6Mm/1b1VMPX2W+LuPU6YSP47A
bP4irPuWc8NLSwagBPbSjzjtmRf93sbGHF8/C1wm1bhckvUNJiIuJf3asYZ4U+fHYc0/BQAA
gO5I97wc/gyouOHXp951aeuWzXT9mnzBT69dyXp5BHupubYhOdTj5BY0WKr1lgBiX66GuyGg
pB8AAEATQL7wFhK5l9oY+8V9ql8PmX8s3X2YmVTXvz6xl5prG5R+Y6AeXdtpXC56/SYo6ddV
+ZE+LS5ezO/DT+nvBH4qQL50Fu0t88YORXGUsWlhxN1supk+tb6ffDyB7O6tASa92Zsy6I9i
/Rd4RV2js1LB4gN9zb+6e7nf+oN38qnGLrY1l2/YnM700yFX5aSsXLIoNDkHUVWsFoVtXmqn
ivBT0g39MNlrwbs0DrFHHAgaU/ITveUD+t/F+2KdOndo3Y0auMeWvdScVY/nyeGBfsGJrOS2
G9fNNaQJoWbF5Pb1OVx/im+0n2rgPrVneV3u0fL/pXBUpGtc0nyq3rSpSu+RCPtCvEvxMner
XP1kn508ttJfH2w3npwy/fQZT836ykb0ppPdZloQ32xhydW5l8IWzQ1MyGFmAd4d5WtC+5g4
w3iRkLP7h8TVpzIbW2LGZJ8O918WjG2kjpq5dt06L0PsI2bGbs6Y+EqnbTvxHM8UvDPcVZta
w9mtYNX/UlYuDAg9gy0dnjt486rJqqzqma8SV08KjMbHslu5dcMy6/6Uxitio3uLPUjUjjVk
FD+NXTHLaweelNndQqm8yRIC3QDGp4fxifcU7d31e4EXEwDYAfnSSXSgzBsbJFp/XZX8x3kl
qORhmENouf/50lvqtbc2mVtu1M7caN7Y8L9rYQt8y+fcLU1Trb293nxBsPbpyFGZ2+YdlQi/
WYWXCjrgYRiHbBBelPHRyaAIyT9yvkxXendsjrX3PuPxG4bjfSSGBuD/02Abn7CxIZGlCtZN
Ss0V39u42D3L/mrpcU3Kh0trZlp5STxMmK6MtyQqJsf4eDlkvm/5PNx+lBXn57afdemt/DeB
oyKdcXVaSP1SoOfH/KyT0cgmS2bo4iBreSZ9lbWzkkD544tHH5n4Wqo2r8rYMNniaxwLG2b4
JNI1Si22qNpE5Pl+ewu/E2Z3fPBrw9v0L4NSbtUmC+WnrBjngH8E1tR/omauyJpxtDRJm/Lp
0hrbOV6SZxKmD2CO8a5Efev92j9LzqwYNzX85OjoSa84u+33KGFHBDUop8pFKe/EnCHr9pka
bjBh6pf7XzWu3ahN7oGP5bZ58JNtdjIt/q60Yw1XDbm70cG3zP9u8S1V9DxuufX+JksIdANq
3vy9LPxxmCXu6wQvJgCwAfKlk+hImTdOGJX/Xvvzg1mUrTqVxI+GGlqJe6c/KTIXr99f+fzq
n6VuUWZq+E2+g0ysROzS/10ufDmmeGysYW8y4qNqjJ9s2vcusy1/n0n7MpzV+lEQSba/hlz+
46IyVieS07anbpqvJ8v+2XNt3MqsHyVvz3fbY6OJ2yNvNG3KqODUjNdOykzfdmMxueq62ruo
PCstrnLOQUum/eq206wVo98zh+/LUZGupvxFelyxzUGHQVQSH1I1nzZFK/o1+9h8YjqWs7Wc
9l946egufS856ZVH0ARFweYW1k1Wpubm+iUcC4sLtML7qQmpIuMNZyX/jxkNe4v9p+g42UpF
DFOf8sY2jtTF6U8KjQXObM802+OoSSWRkMzoaR5DghNuvnYdwJyowWRLzEgBqo7+cPqOD4XV
iKDbyj4TwzOcVfqRSSR5FQ2posdFX7DLEb5nnI21tgQ+lqH5RMpvVx8X2I1rcc3bsYZFT9Mu
Fs8JcVDD56Jq6zxF8dzrFvsGmHTMk8r09p3u50u7v+3YQ6rewsgDa1w1xEmo+vPdg0FeK6Pv
lNb7NalEPtQygsNl0ucr20TTEbJXSpx3+mUE7RDTLXfQ4KHXwGUo5lyktQIJMb4+jDAekur8
4KjPoA8cPlQq0293Y2qYbGrwefVD7IEnQpcnEQSuWZHq/Ethnt6BpzJR4xQaVuktVxcmp2u5
mXkTSRyeS3I7HJ8kBpGb82v+pW2ec9ecwmfaYAzQ7YEPsZPoSJk3NgrzX3ygqcqLFOS9eVUY
bd5zU/0OFTfsOtcgXwrysl89jDGX29ZwoFvBh6KCfOH+4sJ8+FsSv4AQa2QSWUKk6lT0At8j
MWnPmS0X1h1iY+fQVLu01LhlSj69fiup1asuIsEnKiGH3n0oqEL4GZKomBzeXlRLnHWSJAkJ
U+vkBpmzIl1Nyaf3bxvmhQSFxTjq0gkPsnQb5r/v78cWAxL3khckjpDivMrUTbb6E8HCVopN
XHB07So/X6swOqLq+e6JDZmiyhxMTLhuMCFhMUFMatBL0Pu3Uv17ifHXmSshhZ58KKhhThQJ
CjS5vPGJGXJ2KyIhWn5q10LfuPi0HLywpFtDc3ERCutoYXFpYfrjsq9tWPM2riFeLFBYS1y4
cS6t9A3gfIsn9XGV+s4rtbHFZ/ynTA06NTpher+iKxunH+gRfrPKSuTGKjvLVZq6h8e/5vSh
WosTHh5VeLd/w7019V8DSNK6tjMEpp75Z7WVggyp7NmV1Ee2zn9qiVT+w+lDNWF++okhuBNS
hd0TSujyNBHj45gSoWtW++7WpVHqO4uSjEWeH7BX23jCSt9ncJPIJIELk3Kdi2u5wbyakqu7
m3suPb622fG5d7pyJYGbU/fRVtd49diXSXinHmp+iVZ3fAZ3/ZsMgdYA+dJJdKzMWx0Vby6l
HBfQiVGmChVTJfuuPZu5TF+IecJklBeVkFFDak0hEUlJg5CzKYH6LEVTU1ZUWnH/Ia28oLi8
FmGnHkZlWTHr+leeHeNvd27ovo0Xdg6Tytpko/u4BQNab8yorqrkPE5Muo9iYe7nMoSoWJOv
H9+9QArjpZoHcBoRkZCjVZaUsyxkVJbTWa+IKtLxiUhI0b7SyysZCBcHX8tLOMcXUbd0muq/
LWI1LUFzyjUdcY4GDZCERDgXlkTPzxExWnmpJKLi2cWDqxZ5RVqYbh2E7ayprKph/W2UfM4t
kOkjLSaG5BQL3n8uqUH4vdUVH3NzkeZoLrdZ19DzC5t3G66YNH/hOYNVGy/vGEbL2oRdkBqa
F5dVMDADsQ+h+FO5hJxED+6zaO8a9sC6+1pSXsmaS2V5SWvSCMD5Fk+qqbWlhjiJJKIzRpt+
+kNhLUP2aXpc2YTDxorYb7HezLV/PhMVJvChZm2wHk50OJdRSDIj7SbJTjp7bYWZXa8XV47l
2PgY9ONDNUQ+VOaZaIDz9pgd80dIs+tsAncd8xzCATfXbOHt1IRU0fEGM5IZBLfxc7owJwhz
cy03mMcoudTMc8koS1/dZsdnFXaKJHJzYry/nXoK69RgVhyRsUC3BORL59GRMm+MugenVxyX
nXtilLSQpNYo2+IdSWnTdc3kKx5GTzM4b3ErzqOhuaS6oW1paNINd10zuYr7EdOmHrdITLMb
Nanit92HJmp5Dio9s29H8ltkgDUtyr59H/V01B6sSHmfeuL4wyb3oDSHsDFTQBS8eJFXoaNY
+fRsynmEdBuPYJaaE1YzchFxOHDaY/QsTUre5SOJz1w8x/YRQtxOu6LqJs5oUeJ1z5FWtIrs
Mwnn3yJt7HpPVJGOT1TL0JmyJvGy+0hrhYqcSwknHqPhzfsjKRq4uIbYRBXOStiiLtDCN2R+
ooU9aPl4i9HinvFP1pupD9NSoFIEBVh/D69iE/+eNdSaVvHkRGyM7Lh4DQlhNMZF1vvAiemj
52pT3l87EvvaZcnoPiTCidYUXOfotujl7YvVPd00BysKvE9NOp7D9mlcPJ3yyMZbmy87/vCe
CoMYTQmEWozwtGMNJbRMxlEWnb7sOdyaVplz5uSJt5xLCHDwLZ5UoaaeOJYTUbC/MFM01z0+
lp/I4UOtj9U2P5wbfGI6pq4KM+LT31oNTD/2zGyZsSKpJR+qiZODrnSzrgncdYQQdtvTcNGm
tcuX+1qE0RFNz3Nz7BZnVUqT/jldmAVF3FzLDeaRODyXq8Z+arvjE99C4OYUG73oqP9yvxUW
YR6IauC5Z8+WKQOJw2RAtwLkC6/IibFXimG9pOo5Lt6dEGCExz7krdYmvVvuN0LAPB8PSO8L
cVUXRvfrDyIpTly7LXf5clUBCzozshvtqs5PHbAyaVHwKjeZ5RKOi22NLRQT8KayRguDJs3y
6i0wmao3a/4QDUTgPGmAsDGfmKFXrN+iuX16TEGDnX2HadU17sFeam5yUHRUSJCt6BzWk0dJ
G22VsO9vXL81yo0LWDd78SIFARtsdr9Z05jPZBJXpCNJGwfszVzsoYG3tZphrdWbqENZnQmj
UbKYs5FCy6d9EsHCSogo+UamLXDoSaYzA+2xUaPF0EesMU3jv5O2/WzwJ54WRsb7GUvxk5BR
UEJwSMAUUU9WAD5io30/LhMVVLTi6LZXxcJIw1kz+gnY0/TmOA/RE2j8NIYJPl05ku9UJhow
LTwp2FyGjKpbnknb15AsPc5n7+ylHgoUG+y35bfxWvAIbFv4Jk9q875wt1+dExFV/C/j3DNR
gxGcPtQy7Eqf166OhbWs56iGJp9Pyb9Q4L3EEPu1aZ/DlUHkriOEqFtGWf4nUaPg1JI9X55d
OLTKJTTS1nirmSz7YRwuTKpQeYuuZeZBHJ5L84f2bXd8IlT1LIbDzcmgf/wkYhx8yWtPReaF
g2tdvPbamm7oDikqgFYA+cIL8DJvDD/ifUI0w4UHbi88wL5Np7GcG5k21udAhg/77pI7Jw5m
D1p3vVa/Fyq5umr0weGK2J8mP1Vj2q7b05pVeaP5XWYrDNcAcWNE7m2yPP7F8vqtW+v+36TU
HNJ0Ck1yCuWc4AvCt2Sakd/RB35H6/ZsquuTsCIdmWa46Gj2ovq2DeOzwfhw7++bAz22jyQ4
GVF1mkyWaGGbVcKrR1jfKzp+374m2/ipgxxDUxwJJtowRmMBcM5uhTSmb789fXv92y2s/+EW
Mn8Popp3+qKxQf0rtt+Btq+hgqFfbLZfbH3T5jMFiOmIJ5UQptuPvhF3IlpRrkcvdS9elmmu
TeBD9ezLvZOvRUVl1c1P1iLqY820AzcsvdFv1hFtMXxLuxyutUTuOkIIu/10PdRpsezOJ5vN
1IdrKVDIggLN/wA5XJhSkhUtupZxOD2XgmIabXd8YsYSuTkLrocabZCNP7vZbOBwrd4UCrmN
Pi6giwPypfsjqj7OuHbWBGkf3NmNu2TCrNrxzHa3hpF/K/GYpOtZ1ukbALocJHmzoJinixcx
nYgTV8RHTZDhEyTwoaIvxMfz9xntrr16trrAlYS8fX3YOxbUmjB71PrZ9MnWw1henHY5XIld
nkQQdUtSsgpcnTZrVk++/Lonjwx7NjuMyIXZomsZh8BzKS5FbrPjE6FeYzjdnCRF88DIjFkO
ND48mmS/NnarIdEtPkC3A+RL94ck3vSb/S8ESd5uf4Fdp3apaHfoRef2CPzMELrKEJlmF1Xn
iUMiytYrkq1XNDmI04faxFnIfnivkctTSus9oE2cmnwD3VPz2O5D5eJDZXcQskGiErrriOwh
7LbpRk6IXJituJYJrGKZ0g7HJyJ0c/6yZ8ifG5AvAAAAAAB0M0C+AAAAAJ0IuDCBHwHIl+7D
jywOBwAAAABdGJAvAAAAAAB0M0C+AAAAAADQzQD5whO4lS7jrEBGeHj7y5LlJ86QDxFaMeFD
XOSpHFRfC00IMYqfxq6Y5bXjDr2hQFoVXjruhn6Y7LXgXRqH8iImoivNa7Pxl2Qnb/X324iN
RdXzWLtxlZehAplryTqiynDVuRyF2ThKGgFA14dZOdA/p/4tddTMdWFh3qOlf4nUBcw/+bri
812EtptUnZ/oLW+fFX73tJ8O9YfYBnQyIF94QUkGl9JlnBXICC/qpR0qS/Y8vWR5yoOiZMq7
FP8pDsGDMiMtKde2OviW+d8tvqVafWv9VEt8oxHeNjEUr6GGNBD6fLV5bbYRHtV7Z7q/mHE1
N0mT8ulSmK3V75IPMVOZg3DUnFOu4qwMF6x7N7J5YTaooQZ0V1TcElglFT8+iAmeOjs8YdxQ
z0HfkqsXaKD688NTR+4puLqPkOzknvklh7qeTCjv1wfqB3RXQL78eBiV/3IrXUZQgYyoh46V
JdNytDdRwavEKRrbmVKdrz/ZMFr46oUPbkG2amIkxBhqMlbcDttoiBsiOW176qb5eK3mz5ea
12YrSF+ZkOn2h6OmOPYNU8bI0WPUxISMt67KcvggnDXnCCvDoWaF2QCg+0OWGeLs7Lhq1o3M
/0C+dBIVr//e4fN4oUPnnyNIdSWogG4LyJcfT01BHrfSZZwVyAjpWFkyITFhQeYLkpAwVRCX
EUVF2fmFMeY9G2vHzcO0BW6IjZ0Drl0QUW22UZ9ef5TS6lWX6JZPREKu9smH4hrElC+cNecI
KsPxcRRmC5miCv5boIvQENsVr4uWtj3HPFlAiJ9VNpwgQExG1Z/vHgzyWhl9p1TFalHY5qV2
qlQGPTM5PNAvOJFVNWzjurmGNKFqLqFkwsaIuNtHsf4LvKKu0RtivuSyZnFhOxrr/P82cYZx
gNaxTD8dMiukEtD/LvYGj2fbnDaxpaX9eey5qJ5n+IHwKRpUDifpq8TVkwKjT2UiFbuVWzcs
s+5b2NADmRW23qHFDNBwNYkg4swiL8VznE10JkJp8okLT7/cYv5my0DdG1PDZFODz6sfurR3
6EOOGDqhSf0pRCFsMr7IL8KY68BlYYEuDciXHw+zilsrpctapINlyaqZNdQQXgX38/sCRTlp
sR7lktS+ITcyA/VZf6mMsuISIf4mhhDUZouz7yNTkPu5BCEprMHX/3JfIM3x4lxCP4SV4TgL
s1mYbjWTascaAsB3o/x+1JyFUXcKsWvvqUDvngNT99m1rQoHo+TlX8nJxcN/HyhOGCC2Il/Z
OP1Aj/CbVVYiN1bZWa7S1D1s/ilqsXuW/dXS45qUD5fWzLTykniYMF0J744zlFxxn6hxvyLO
bse/DlvgWz7nbmmaau3t9eYLgrVPR1ozaws0xoXbQv7jkmE77++ILfnLf9yioJPDE6YPaJ5v
//5XjWs3apN75KesGOe2efCTTSOJu/rvGjeTOCPOdWPIW0fdudu/8V6W6jf4BELwCaiooxKC
GLoJhcikbXY9OUPYIYaNthUTLqwylBbo2oB8+fEwq7i1UrqsRTpYluxB7LH0WbpWeA21A2dk
XSI0hHtWGI4qDj2T5j7UTO7Lw4i5BsdNbqW5Nj2KszablIbROFmH2BMe+nM1Ke8vJ8Q+M14y
VpGEqohsJawMx1mYTQB+EYGuQtmHF48L69/kPsgtrEVKLd6YxVY9HvWdtO+MjTy58iZngLjQ
WCA9rmzCYWNF7E9Tb+baP5+JCpc9St6e77bHRhP3asgbTZsyKjg147UTU75whJKJGzvK5nJ0
W/n86p+lblFmanjLQSZWInbpWRush+O9NsaF20Jf08njNaj8JOrgMcPR6Q8ltQg1v6aPs7HW
lsDONfKG5hMpv119XEAsX1owiTPi3JJuGOC8PWbH/BHS5K+fszlj6BRCk+zGcQlhs+DyKSj3
AwdMlwauGjyAJN9q6bIW6WBZst4aZUdtJW3u0Gl6nuHxQUZS2Il1YmBS7io/VYo5napi5bMv
eqo6f1NDCGqzyUrx+yVEhQXY9vZkPXmUtMpeCfs7J5QvhJXhCAqzQc1FoKvQS3/GMqvDgWfw
EqgDpi+1HtDaTeUNt+5+uLNriVn4iZsOy/oTBIjpJej9W8H+wkyfa92NF/mJr99KavUSYTXi
E5WQQ+8+FLD+lDhCySWfiBpXlHwi6Db71cMYc7nGsLBbURnrRWNcuC3wCwnwt+J5EhehsFoI
i0sL0x+XfSVuVpDH1STOiHNLmDg56ErjBxDF0LmZRJbhCGGzQbywVQjkS9cG5AtPaK10GVsF
skbYisN1qCyZnP68qPjdsU37VDD02XPbZ09TQy6z1VBrXgeOifggp7UpTmub9s+l5hxxZTiC
wmwA0CUg9RoZsP/aoOS/34jo2Fgb9WvzTbhkWV1rM0Of1Ff/VWsQBIhJtTepkl/p5ZUM7Lxb
8b+Mc89EDQyl+ygW5n7GruNUrNHXj+9eIIXxUgKogKh/McLGgkKfObodISIpaRByNiVQn1WM
uqasqAy7FOe1PgdGdRUX/cGN4rIK7M8em2V58ady5n0/RU26q6t7LdRhk7hAFEPnYhKFKITN
BvHCCnTYNODHAPIFAACgKWSZwbZzBrf/OFJPBTXa6zcfqggDxPOwjfSNiZfdR1pRrkcvdS9e
lmkx3MhFxOHAaY/RszQpeZePJD5z8RzbR4hYvgirETUWlhTi6NZc29C2NDTphruumVzF/Yhp
U49bJKZ59uVidQ/sCl/wNCevephixb9nT15DaFA75nzxdMojG29tvuz4w3sqDGI0pUSeSNEK
XrzIq9BRrHx6NuU8QrpYM0n19pjUlKKi0mpG8xuoCWPoxCaJFd3mDGGzQbyw4Hrp6oB8+TUg
dOcAANC5UOXVtPKP/Zu/TJ8gQEwimQXFPF28SEPABk8dGR81QYYkZBwUHRUSZCs6h/XMS9JG
WyU+VE3YOUmGsDGS5+iWT3Di2m25y5erCljQmU/lRLuq86MvXIzuabhog5+ndx8BZzTA0ddI
oX1THib4dOVIvlOZaMC08KRgcxlBsqFXrN+iuX16TEGDnX2HadUZr9gekxrooTza0nj1bDWB
2wl4Ck02iGLoXEwS4icIYbPBbWGBrg3IFwAAgI7CFtLF4RvonvqClaOEIECMRJStVyRbr2Df
RKJqOoUmOYVy9EoUSiZsTNgtmTbW50CGT5PhqU3jwo0mkGnjlic/W17/fmvd/9mnRlhEmtmh
H/4qqsnYvU2Wx7/g6K41k9gizo3wS470v1TqX/eOxrYsJHHCGDqxSQQhbEbZC/pXqqAg06nD
ZWGBLg3IFwAAAOCXouZTymJlmyS9P2JG9IJ0390VkC8AAADALwW/tPX2UkYLDzoA3QCQLzyC
UdxQ9RAv87Z23Tovw3Yk92RRkXl08Zw5Uddkwy/GojUjo6zqMl1+d7pgqTYAAADgFwLkC0+o
/nh+/aSAj/N3PzhsLFtwLfaP2TNnoIQkn2HtKpRSk3U+LEpxW1aZu6rg54fLEgbJ9gE/KAAA
APALAPKFF9S+SNkcr/hb0nwTFQGEqGNcFv3+fPPzvP8Yw4Tb439hVH8tQWISooLY59hrsKld
Bx70BADgW2BWJ/LPqX9LHTVzXViY92jpdjpSf0qq2YoK8doW4CcEfqs6i/aUefvw75XzmnZb
lOvzIokNdt90iNVJ9ulw/2XBpzLZI0pE9ds0vpxZrGyzg46QvfyVeaeT5mR61kdzCIqTUfAu
HG5MdZdN3bVLPYI96ENQRw3db1OpNhZVOSkrlywKTc5B1PpycSLV+ZfCPL0DT2WixtpsDauB
14dbJOTs/iFxNV5TraGaHUGJO7xmXGOFtsuHJpIuhS2aG5iQw8zVuzvKF+u3zSumTSUxqv6X
wlHj7Wv+pW2ec9fgUbxGYwCg7TRk3f34ICZ46uzwhHFDoeI0AHxv4ETdSbSnzFt13ssMJDdJ
UpCgk5krsmYcLU3Spny6tMZ2jpfkmYTpA5j7OOq3WW8vvKtU/+Wm4l5mfSeVBMXJTPBz6avE
kJV4A3X2IYnqqJmjNpVqw2FUPjoZFCH5R86X6Urvjs2x9t5nPH6D9t2tS6PUdxYlGYs8P2Cv
tvGElb7PYBH2w96mfxmUcqs2WQivqeaAV7OzplznLHGHG9JYoa2m5Opu1yi12KJqE5Hn++0t
/E6Y3fH42o4VU64kqPGm+2ira7x67MskvFMPNb9Eqzs+gyEGB3QAsswQZ2fHVbNuZP4H8gUA
vjcgXzqJdpd544RRdu/M9kyzPY6aVBIJyYye5jEkOOHma9cBivhejvptiEtSSH6i4mTMc6mk
887UHbP1pNkOJKyjZi7eplJtrNH6TNqX4azWj4JIsv015PIf19UxKbydmpAqOt5gRjLDnfMo
RcfJVipieE01YxtH6uL0J4UThDlL3BXhhjRWaGOUXML6vY/1KzLecFby/9yxFUtf3a4V6zOR
s8YbxvvbqaewTg1mxREZC/xqtMeT2gyygBA/M3E+kTeRjKo/3z0Y5LUy+k5pvauSyqBnJocH
+gUnshKmbVw315AmxHQfnh49TyIj+lQO1cBz5xo3lOi7YMcdeoM7syY/0Vt+UeUK99K41Qk5
bG7ONvtT+Yi8pF/a47sldGcSrhWhSziXw5nacGqqbufs2M2zQhz+VH6iRWYGt4jczO32KAO8
AeRLJ9GeMm/8Moq66NrHwq+oSUi4Bi+9JtW/lxjrULKohBR68qGgBjEvxhz127hBFuVSnEzF
xsmmiXZB3OqoibepVBsOiSwhUnUqeoHvkZi058zDFzKTeG5au3y5r0UYHTtZeG6O3eLc7KQm
KCZcZ4eQsJggJiPoBUWcJe6+4IY0VmgjiRkuOLp2lZ+vFdYvVc93T+yqse1bMaIab2KjFx31
X+63wiLMA2HXiT17tkwZSGl94sDPS3s8qU1glLz8Kzm5ePjvA8VRSQanN9GKfGXj9AM9wm9W
WYncWGVnuUpT97D5p6jF7ln2V0uPa1I+XFoz08pL4mHCdGbF6fzKodsf1B7H3Yc2dg9Xnkgp
3CKcc3jByJD9JjqB+lS8yduskkEHHtSeoOSf8R+3JFj7TKQ1pc3+VMmrnF7Sgc/a47stJXBn
mvQiWBkCl3Cw7t3I5s7UZn7PdsyOzbySDA5/6oxqokVWRoTL0r+qQx5l4McD8qWTaE+ZN5Ks
2qhR/567+Wr2oEHM218+pW9cubd0UtA4OcWC959LapAU9ldc8TE3F2mOlmqfD4fRSnGyZhDX
UXve5uHKs2P87c4N3bfxws5hUlmbbHQf416k/E+iRsGpJXu+PLtwaJVLaKSt8VYzWfbDaiqr
ali/fCWfcwtk+khThco5S9yRURPba+j5hSJGKy+VRFQ8u3hw1SKvSPOH9u1ZMaIabwz6x08i
xsGXvPZUZF44uNbFa6+t6Qazdi468FPRbk9qToy9Ukzd676T9p2xkSdX3uT0JhYaC6THlU04
bKyI6Wq9mWv/fCYqXPYoeXu+2x4bTdz9KW80bcqo4NSM105M+WLiNFGDSiLj7kN0R2viCBky
P1LR0JJ6+zivlFlcECFFU3srVdz1KD/KzlHYOT1rwwThNvtTJRGnl7S6Pb5bru5MDghdwqiZ
M5WDdsyOzTzcSdvUn1qWvpJokZXxbzkEbuaOeZSBHw/Il86j7WXeBDUcl1lH/LZxl8ISlzHS
Hy/t3bD6HHnbwj6aAi6y3gdOTB89V5vy/tqR2NcuS0b3IaHadhhRW5TdYnGyZhDWUfNo+3BF
2bfvo56O2oMVKe9TTxx/yPxy9ul6qNNi2Z1PNpupD9dSoJAFBZqf/F/FJv49a6g1reLJidgY
2XHxGlKSFQQl7poaUlNwfYvR4p7xT9abqQ/TUqBSBAXFNMa0Y8UIa7wVXA812iAbf3az2cDh
Wr0pFHJbvVzAz0p7PKlMGm7d/XBn1xKz8BM3HZb1z+P0JtJL0Pu3gv2FmQKd0s/Ath/25T/x
9VtJrV513+P5RCXk0LsPBVVtNVWQyuoNeyUsJoRfaAuK2uxPJfSStsd3S+jOJITAJczH4UwN
maJK7ejs2Mzj9KeO/US8yLh84VyWDnqUgR8PyBeeIECz+iOpPCxggb4vMxgcsC1ukZu6IFkt
KCE4JGCKqCfrOZqIjfb9+FC75At/r5aLkzWDuI7agzYPJ2u0MGjSLK/eApOperPmD9FAlVif
SlaBq9NmzerJl18XJzbs2ewwmsZ/J2372dzJp+otjIz3M5bCziAEJe7QffaDBBWtfCPTFjj0
JNOZwfLYqNHiUuR2rBhRjTeSonlgZMYsBxoffvq1Xxu71VAMarX92rTHk9oEsqyutZmhT+qr
/6o1RDi9iaTam1TJr/TySgZ23q34X8a5Z6IGhtJ9FAtzP5cxvSmMrx/fvUAK46UEiCtOc1Lz
taoG6w0bouxzbqFiH2kxofK2+lMJvKRG4f3OtsN3S+TOJG5I4BLmdKZamG41k/rm2RH4Ux9a
Ey8yMR30KAM/HpAvPIIkPshpbYrT2qZb+amDHENTHJsVDuNWv41tO1vlM4LiZKhp9bUmdhDV
UWtbqTbWS41pu25Paz5a042cCOt7Rcfv29dkmxBBiTv2ieNl1ZpOjWVKe1aMsMZb040A0B5P
alNIPRXUaK/ffKiS1CLwJs7DNtI3Jl52H2lFuR691L14WabFcCMXEYcDpz1Gz9Kk5F0+kvjM
xXNsH6G2ypdXKcf+dtW1Vqh4cvpAjIhLvJqwZEWb/amcXlLUPt8toTuTgBqibjmdqQLNr0Yd
mx2nP1VMjXiRuThpO+ZRBn48IF8AAAA6Caq8mlb+sX/zl+kTeBNJJLOgmKeLF2kI2CCViSvi
oybIkISMg6KjQoJsReewHopJ2mirxIeq2zgcTabs5DRJm2t0PLnRriBjGRKJ0VZ/KoGXVLZX
RXt8t0TuTCIIXcIEzlSxzpgdgT9VnMZPtMhc5EsHPcrAjwfkC/CDIXTnAED3hNzUr8k30D31
BevGTgJvIhJRtl6RbL2CfROJqukUmuTU1H/IzX3INhxT4QgbzIv2293EjdkOfyqV00vaLt8t
SZzQnUk0C8JuCZypTejI7DitYs2LYJGbfnZcl6WNHmXgxwPyBQAAAACAbgbIFwAAgG4HmWYX
xfhp3Zg/9+yAzgHkCwAAAAAA3QyQL7ygWZVaFVNPnyX+7uOUuZVFbKkf58dhlw/Z0fAc2/Yo
IS/CTvph/UbFzrabHfq9jTb1RSJ/FhgfLy2bEiK07PCK8ZARHAAAoCsD8oVXsFJd9a7+nH3j
SuLOZTNdvyZf8NPraJ03fsmhridPosGS3/A4H+PTw/jEe4r27vpEab9/MDwwhvH136SdH2fs
2A7VTAAAALo6IF94C4ncS22M/eI+1a+HzD+W7j6so+nqSXVJPBFq8zOXHNS8+XtZ+OMwyy6R
EJsHxpAENeYm7P9x4wE/A808qQ0ZeDuF/MQZ8vYZ4Xcz/XS61pm60e/7XV287YD5ObwIy4uw
o3WtpQK+H/BJdxbfUKUWURRHGZsWRtzNppvpC3CWZiXjf5k3pobJpgafVz9EfMpo/OuVxt6V
v0r+Y1LAiVM5SGViwNYtv1kriyBG8dPYFbO8dtyhN9Z3rW7oOWDXHVZH9kqJ806/jLKSIKgl
W9tQRJeqN22q0nvUpGZZ9ccUv4FuX/c+2WYnL4AQ/eH2KUNSbJ6emTtIoH4pCEq5UvCwV0D/
ujM0fr7eoXVq9guX6dH0BmOse7WnYCxhCd+6xqPNJTKwZcEar9/mhmJ8l0XdKW26GqwzIH+b
S+kCQEclS6suRsmhHidPOvdrb+164MdQ/fnhqSP3FFzdR0jy2pRfE5AvnUSHq9QyIdH666rk
P84rQZUfOCu+GuJNEkMCsH9VuOS1bEb+/SLdaw/2JFPepfhPcQvReLLPlnptq4Nvmf/d4luq
1bfWT7UMHpQZaS3F1rPb0bVagYH136hK7nHUkg0d8jCkvoguen7MzzoZjWQflCxjaOct6xGf
njvZuR+p/NnfR3Om+pqqN2gXxKgkKOVqSGC+vG1U4d3+jV/viu+1o2CszD8tNK4cdfXBngS8
8dRRDwMupvwvUvj5gQVTffePuRU4UrDRgjaX0gWADtOqi5HSb0ydTxXoglS8/nuHz+OFDl3C
X/0rAvKlk2h3lVoucCvNigY4b4/ZMX+EdJucOorjHEy18RuBexvaGFEcrj/eaCR+9cIHtyBb
NTESYgw1GStud/3JBktD9p7R/Y2BDT1w1pItL3+aHldsc9BhEJXEh1TNp03Rin7ddFixwZNm
q+juv7zKsY/cvbP7XtlsndBEw3Er5doKXKryEheMLXvPvbGKjZMZvh1vLHtay9JQRoiE+g7W
6vXkcX4xQtKNQ7a5lC7wE/ItnlQmBJ5Ower8S2Ge3oGnMuvSts4sX602kc3FaNGj3rWJ54rd
vTXApDe5MUbTm8gdWIbfPn9DP0z2WvAujUPscZPq3Ethi+YGJuRQDTyX6r1e8Y/J3dN+OoWJ
M4wDtI4xHZ3VjV5PfgK/LPtkCIZmv24wPt7dvcrLb9cdev1e/pccTlYq07V5xsRXOm3bied4
Ft2d4a7aVOxk9PnW7iA/v+hr9HpnM4Vg9arwmZ7u50u7v+3YQ7xQ2oE1rhriXD6Vck4HNqVh
QRDBHJm2nR49TyIj+hS+YjvXuKFE3wWYAQMbfgEY9Eex/gu8oq7RWR9fwMB73uNtorE1SZNP
XHj65WYz+l8cH9DX/EvbPOeuOYVbUv+Ztu83CWgJWMxOot1VaptSmP/iA01VXgyRKVwqvpo4
Oei2TbtgCIqLsCrF8QmLSwkXviirLC7Lzi+MMe/ZWKp1Xr0wqu+5yU0znLVka0o+vX8r3F9c
mFXREK8ByzGumLal7QT/xHOPx6glniYv2DGmya08hKVc20BJewrGttS47bS5lC7w8/FtnlSE
aksIPJ36T7YujVLfWZRkLPL8gL3axhNW8U1cjCVXV9a7NmtvbTK33KidudG6sYAhoTuQgu9J
DMWdp0iDzYCqvFPrHP5UjnlXYS1TeGOrtyn2R94+a61lGidMNHSjz7Sm4PKO6Vup4VkVVtRb
q0Z7rBo6ZH9/TierCfOhhHcl6lvv1/5ZcmbFuKnhJ0fvnd6v+PJGv609lmZVpVFvrBttGTZU
d6fFa057jPCjH1ep77xSG1t8xn/K1KBToxOmKxMWV638l8OBHax7N9I1Si22qNpE5Pl+ewu/
E2Z3fAY3PUXnVw7d/qD2OG6bjd3DlSdSCrcI5xxeMDJkv4lOoH7NtbAFvuVz7pamqdbeXm++
IFj7dGTUnbv9G56+LLl3iGOVdB9tdY1Xj32ZhI/qoeaXaMUxKvAtgHzpJDpcpRan4s2llOMC
OjHKIlXZMe2o+MqVr8VllQzMKFRbXlxQTpOSEKFUSVL7htzIDNRniQ5GWXGJED/K49IBQS1Z
koiEFK2uXi52bvtaXsJZ4YQkoG46e+qmkIhw7YQ+C65pN10FolKuTWFUVxGUTRHjUpWXkHY1
5kabS+kCPyHt9qTmxNgrxdS9Ngq/G2tA4Okcju0rvJ2akCo63mBGMoMZb2j8wsCo/Pfanx/M
omzVqSR+NNTQStw7/UmRdaNKIHQHMuWL5LTtqZvm68myncqLM2/dpjpuMZbH/tbl9O0njfNv
4Y700n+J/LImjXXXW/ZEFj9Nu1jmuAEbi4R0ZkZufiZG5VMgcLIyTwUGky0HUUkCVB394fQd
HwqrkGxWWlyN4+FR8mR+pOca+edzMeEyInsM8QLTptaWGuIkkojOGG366Q/Yp4II5Qs3B3bh
fWz1RcYbzkr+H1G0x8RpogaVRMZtQ3e0Jo6QwUxS0dCSevs4rxRVvrv6Z6lblJka7tMdZGIl
YpeetcF6ONvh3Fbp/e3UU9ioBrPiGBBj6mxAvnQeHalSy6h7cHrFcdm5J0ZJk4put6fiK1fe
Xow//2hyP230LP7QyYpJ4ZqivaoNRxWHnklzH2om9+VhxFyD4ya30jxVmx/4taioDDupkglq
yfKLahk6U9bg9XKtFSpyLiWceIyGc4xMUjR2sZxvs+3drIRwdZGm+whLufLhqqjgxYu8Ch3F
yqdnU84jpNvMGOH2FIxtV2NutLWULvAz0m5ParNbd98mbuf0dEpNXLRp7fLlvhZhdETT89wc
u8WZLQRTU5D35lVhtHnPTY19NlEJ3N2BNnYOTbQLxpei90WCWsKsLyokioh4S8aX5BH6ZRvl
S8ueyLJPrz/WjyXSb4w1fqtOdTUXJ6ugQDP/Me4rFdISZt51Vnejz9tEInvwKQg1P5oYsiiH
A5tPzHDB0bWr/HytsNWn6vnuiQ2ZokptQ1/1FORlv3oYYy7XaJRbs8A30SqJjV501H+53wqL
MA9ENfDcs2fLlIGUdowKtALIF17B9o2Nque4eHdCgJEUiZ9BUJq1A9CGSdxdOWTpqRw0wHlN
UugEGZIAmhiYlLvKT5ViTqeqWPnsi56qzo9q2A/i7zPaXXv1bHWBKwl5B8dx1pIlSRsH7M1c
7MGsl2s1w1qrN9HQZBkdQxt0Vcp5FK35uYaolCt+ZvGK9Vs0t0+PKWiws+8wLU5jDtkRVuUl
ViQkmXY05kZbS+kCPyPf5EnFEBTh9HQKfsl/JmoUnFqy58uzC4dWuYRG2hpvHdc4pJAIVbLv
2rOZy/RZUV9GeVEJdnL+WLe/fe5AEek+MgW5n0sQkuLm0cQ3f+VqrRCbYGtlaPzwryXllfi1
pOx/6ZefiWkq3wts2cnaiJCIpGRlSTnTkor/pZ97LmYwiNCetnuhq7IPcziwa+j5hSJGKy+V
RFQ8u3hw1SKvSAvTrWZSrXfGbqdByNmUQH2WFKwpKyrDzGt0XhOtEoP+8ZOIcfAlrz0VmRcO
rnXx2mtruqGjqTEAAkC+8AJmJVY/oj0kwoqvNLbys837ecHWJesl9iqP2XlTjzFZwdBnz22f
PRyGNIzda+TylNLlde9oRLVkaYaLjmYvOlq/dSuBTdUf7109PXBS8kgZjm9K/MSlXMm9TZbH
v6gft75TKrsx7SoY277GiKrjd7lx5epetVJKF/jJ6YgntQEpLU5P52Wzx6FOi2V3Ptlspj5c
S4FCFhRgXcZYLkZ+Sa1RtsU7ktKm65rJVzyMnmZw3uJWXKN3tH3uQInBE8xkJx1Pmj9yliqD
zaPZQ0JOouBpTl71MMWKf8+evIbQIGJr0zwbHTatDI0fTg88fdlzuBXlZvTcoOLwaEsCJysX
JNUNbUsDE697jjSnXN8/1700PHPMMAJ7XNu8+DVF2ZwO7JqC61uMFveMf7LeTH2YlgKVIijQ
visf087QpBvuumZyFfcjpk09bpGY5tmXuT5FpdUMGuEqFVwPNdogG392s9nA4Vq9KRRy29xH
QFsB+QJ0Koy8a4kXFVx364gRxqUB4KdHQJ7T00mmKgWuTps1qydfft2TR4Y9ET9iczFarU16
t9xvhIB5Pv68zL4QV3XhRu9o+9yBfGIGi+K3rZilKz6bPnDiPJ06jybqabhog5+ndx8BZzTA
0ddIgau17A6CVoYWkLf8Lebh0kUKFBvWvMx1RftxOlm5QFK0DFr/cLG/Au7RtV8bv9VcpgeZ
yJ77bV18/l4EDmxBRSvfyLQFDj3JdOaTR7FRo8Xa2mGdnRPXbstdvlxVwILOfLAo2hVfJOXR
lsarZ6sJ3E7IW0vgrlY0D4zMmOVA48OjSfZrY7cawlmxUwH5AnQqJCW7/Y+hUizwq9DEq9ew
kcDT2cz1yKSJvxMZLjxwe+EB9v2V5SVfBXoI8iMSsTuw0XHYDPb2+YkzonewtpJp45YnP2vu
6EQE1hJ31Ti0YuOsKf2tVydYr2Y7hNDJyu7opdkdqi8nTVG2Xp3c5GjC1WObKXExajZHMpED
u9lGrsc2sa2Je5s21udAhk+TjwdJjvS/VOpf94boA2q6dEAnA/IFAACg6/EpxVPZ5rDe2jMj
ZHhtCgB0RUC+AAAAdD2kraNKGVHf3g+7OwEAfiJAvvCCZmXeVEw9fZb4u49TprbzpnS8QlAA
6sQScT+AzrK56xWNAwAAAH4YIF94BStXRO+6vC/LZrp+Tb7gp9e+ZzTxim5b0OB2PADYQVqt
Ldd2fpjNAAAAwM8LyBfeQiL3Uhtjv7hP9esh84+luw9rX1aAH1bRrdXacm0HqtABAAAA3wzI
l87iW8q8URRHGZsWRtzNppvpC3DWGyPjsaYbU8NkU4PPqx9iC5ewBVDaUHUsN3GG8SIhZ/cP
iatPZbKVEKuhZ58O918WjG2kjpq5dt06L0PM+OqGQQN23WENV1dbzkqCoITb/Y0DbU6b2NLS
/jz2XFTPM/xA+BQNKj9BOTe8ZQtV6NgWrSqHs/Ab8foRtBRpXiGvySfylutScJTNQ80WfyKJ
o/YbmfsCctaoIyqAB6Xdui/NA8Hmi9ZuWe/czuSqLUVUmbUV7VECe0XGDhnK2Q+j5MZ68yVv
Fuzd6DKI2u6MJHS8jGJdxZ+WR+46cd4229yMLjQFoBE4SXYS31bmjUTrr6uS/zivBFV+4Kg3
FsKsfJIYgldmU2kxXVXLVcfwFm/TvwxKuVWbLJSfsmKcA7MsHPWfqJkrsmYcLU3Spny6tMZ2
jpfkmYTpAxDboG5H12oFBtb/9ZbcIy7hlv+4ZNjO+ztiS/7yH7co6OTwhOnKRc3LuQ09bN9g
LWEVuobgFKPyEUHhN6JsDYQtte82r5Cn7zO4SREDgqWgXA/jKJtn3mTxa0qu7m5e+83jK/cF
5KhRp1xJMGso7da9qS8aUP3hzq4lZvP3mk9oPbkq4/M/8Uf+VXSdoi/J32JElV9yqOvJk2iw
5Df+QnD2U/48fs/tqTtiO6Jduj1N1h/onoB86STaXeaNC9zqjaEBzttjdswf0WLR6RarjjFR
dJxspSKGdSFvbONIXZz+pNBY4Mz2TLM9jppUEgnJjJ7mMSQ44eZr1wGK7IOi+xsDG0bhVpys
r+nk8RpUfhJ18Jjh6PSHklqicm4IFbTWT/1K9CEo/EaYbIqwJeKskNcUzqWYIExQNs9cnH3x
GSWXmtV+Y5Slr+a+gBw16qC0208MWVbX1sbU52DW2wozKZGW29a8vrjM50WYgzP+pqWIKonS
z6Azwq2c/Qiruu9P+vaOuydN1h/onoB86STaXeatKYX5Lz7QVOXFEJnCUW+MhYmTg26L2qVN
CIrVFXJDQsJigti1k16C3r+V6t9LjGUvWVRCCj35UFCDFNkHrW7SB5cSbvxCAvxN7SMs5/am
tX5YkMgSIlwKvzWDsGVPQ84KeZQm1nEuRUERUdk8cbZ1QCSO2m+rxn5qYQE5atRBabduwDcE
gqu/VjD/X/W/0xwhwjI8cnFDnxmP/YfV3F7+yrzTF6PM8xtiE4zPt3YH+flFX6M3iR2/CMuL
sEOnZsiHCK2Y8CEu8lQOqo9dChGHWTnitspPN9X1QyMz6JnJ4YF+wYmsumAb1801pAlxCXcS
rkw+VW/aVKX3iCnSGPRHsf4LvKKu0QkCtU0gHBdV53IEZIUaV7Rp9PagaSHHWJyxV7lPid7y
Af3vZvrpkFmxuR1ad0/76dR1+SHFR9VmB71h/a0lOEP2FEbx09gVs7x23KHT9NwtlMpZc+3I
FIDvBMiXTuKbyrxVvLmUclxAJ0ZZpCo7hqPeWGdSU1lVw/rUSz7nFsj0kRYTQ3KKBe8/l9Qg
3N1d8TE3F2mObsnz3Y7qcZzl3EZYaLSxn/LsGP+2FX4jaskoy//EUSHPTLbFpaAKlROVzWvy
CXDWfjN/aN+eBYTSbl2fjgaCGfTnZw4cuUgb6tOv6tFuzsAoU5EmhuJxSDTvaIJioP1blphA
1fn1XXy8vNFva4+lWVVp1BvrRluGDdXdZd9kkOfpJctTHhQlU96l+E9xCB6UGWklThA8HVvN
Ebc91L+hk+L7UYvds+yvlh7XpHy4tGamlZfEw4TpSvguznBn42WYUZAWUh9dRc+P+Vkno5HY
5v+uhS3wLZ9ztzRNtfb2evMFwdqnI60ViFaMcNxpva5GNg/INo+dNkRvP10L820+lmEWR+zV
q8VMf2RZ6+2Fd5UaxByqvMsRsl815O5GB98y/7vFt1TR87jl1vuZc+34FIDOB+RL59GRMm+M
ugenVxyXnXtilDSp6DZnvbHO5FVs4t+zhlrTKp6ciI2RHRevISGMxrjIeh84MX30XG3K+2tH
Yl+7LBndh8RZoZlVWw6R21E9jrOc25lG+dJKP0XZbS38Rtjy03XiCnktLIWUZAVB2TyPJgdx
1n4TFNNo4wJynzWUdutStDsQzFY9Hi9t42Uo1qOIIETIlC+S07anbpqvJ4vuscVjGyjPSour
cTw8Sp7Mj/RcI/98LibcrEqOlqO9iQqeIErR2M6U6nz9yQYLQ4LgafELzrhtgw+17FHy9ny3
PTaaeD/yRtOmjApOzXjtxJQvBOHO+sMY5U/T44ptDjpgDfiQqvm0KVrRrxGqfH71z1K3KDM1
vLdBJlYidulZG6wVCOK8xOM6TELNArKc1Edva26tXcIxFn7jXbPYa4MebBsEIfuip2kXi+eE
OKjh8WVVW+cpiudef9MUgM4H5AuvYDvlUfUcF+9OCDCSIvEzCOqNdSY0jf9O2vazwX2/CyPj
/Yyl+EnIKCghOCRgiqgn68GZiI32/bBTZpOrL3+fxtpyB8e1uXocZzk3eRJ6X7ezlVJwskYL
21j4jaglScmKs0Je60tBVDavSbE4gtpv4lLk1hewAaJZQ2m3rkW7A8H1t+42wuAaGLWxc9CT
xU671ZzdYJR8ev1WSEtYEH9df0NM9Wv2FkJirL2IJCRMFcQvtAyyNGfwlCBuW32PfRRJrV51
d+fwiUrIoXcfCqqY7zjDnQ3UlHx6/1a4v3idohIUFmN2X5CX/ephjLnctoaGblxuUyMet4Yj
IBsypfkzhvXR209EY4lZccRe+6N2QRblCNnjCyisJV7nRcfjy984BaDzAfnCC5glwvyI9pCo
RPXGaGwVxZr386LxZStVx95i/wnre0XH79vXpBd+6iDH0BTHUAIbG8xqUluO1nIJN6Rod+hF
fUE2jnJuqNFmwip0jVYRFn4jmjtxS4IKeU0gWgohGmfZPJ0mi9/8A2IZ0OoCsn0ihLOG0m5d
iG8KBDNpR4C1KUIikpKVJeVf8dcV/0s/91zMwESjSYtqZswT4WLi8/sCRTlpsUqiMCtB3HZC
Qx9i0n0UC3M/YxoDu8Qyvn589wIpjJcSaLyrnhg+EQkp2ld6eSUD4RLna3lJZb3NBiFnUwL1
xVmGlRWVEd/3QTwuH0dA1sJ0qxlxXkuisQTpH581j72u1WY7iFFd1WKBbkZV9mGOkH0PCTmJ
+gVk1c7spCkAnQfIFwAAvi/5+flfvnxpvR2THj160Gjty8rR+XQkEMxGWwOsJUWlXxF7NhdJ
dUPb0sDE654jzSnX9891Lw3PbCZfHsQeS5+la4XHPA+ckXWJ0BAuvk4QPCWI25o2LKqwmpGL
iMOB0x6jZ2lS8i4fSXzm4jm2j1Cr8kVUy9CZsibxsvtIa4WKnEsJJx6j4XU2hybdcNc1k6u4
HzFt6nGLxDRPTYIOiMclF5xoFpAV4HpZIhrrsuVTjtgrP660Cl68yKvQUax8ejblPEK6XNdf
qCibM2QvoWUyjrIIX0BrWmXOmZMn3uJz7YQpAJ0HLPKvA5tT5FcHlqLTKGRSXl7+5g3+TNnz
589LSkqwFxkZGQ8ePGC1MTc3V1FRaWOHOTk5586da3i7YMEC1gsdHfy5kV5MJJl03iQ6m1YC
ozj8yvruxttnq2lfSbh8aGL9VpKiZdD6h4v9FQRs8DBi/FZzGTLTbdpAb42yo7aSNvjjMJ7h
8UFGUiREFGYliNuihw2jyBgHRUeFBNmKzmE9PpO00VaJj0s8iw2StHHA3szFHhq4dVYzrLV6
s2yeuHZb7vLlqgIWdOZjVtGu6sThNuJxBRkcAVnC/AjcxiKLKHLEXslkQ69Yv0Vz+/SYggY7
+w7TanH9D5pyhuzJ0uN89s5e6sFcQKvfxmvROmkKQOcB8gUAgDZRUVGRl5eHyRRMrNy7d6+o
qOjw4cNDhgwZPXo0qpcXI0eOxOQF9sLb2/vbFQZrROxFgzxKTU1F9dqIpYrU1NRkZGSUlJTk
5eUplB/+vDlbELMRkjhhiFDH73JjPFbSYPmlN/Xx2MbYK0XZenUyR7iVdRh+P6qc/ryo+N2x
7PuJg6eccVu2aCaJqukUmuTUNN7JNQDdiBDNcNHR7EVH699vrWs81udAhs8BjuaN/b5oYVzC
gCyxVYhEJhhLnCD2Su5tsjz+Rf361hvK9hE0XX+CkD1SMPSLzfarX+r6dAodmALwnQD5AgAA
MYWFhe/evcN0AyZWWIphwYIFCgoKcnJykydPFhYW3rRpU+u9fAOYHFFWVma91tTE4xGWlpYN
e1kxqczMzJcvX6anp+/cuZOlpTAhhamZ72oYAAA8B+QLAACNZGdnv3r1CjEjPti/LDWAiRUP
Dw/e35LSFJY9DfpmxYoVDXrr5MmT2Jbhw4fb2toOHDgQkz6qqqq8tLVTIPaIAMAvCsgXAPjV
yc3N/ffff+/evRsYGDhp0qTx48djGw8cONDV9EqrsO6JwcSKpaXlzp07z5079+zZs3/++eev
v/7au3evv78/NrVBgwb17t2b15YCAPCtgHwBgF+UBw8eXLt27cKFC3l5eW5ubmPHjv3vv/+k
pPDnPRcuXNjttAsn2FxGM8Feb9iwgSVlIiMjWfM1MDBQU1Pr0aMHr80EAKAjgHwBgF+IL1++
3L9//8qVK4GBgR4eHhYWFhEREb+CN6JBynh7e7O8TTt37ty7d+/atWsx3TZs2DDQMQDQvQD5
AgC/BA8ePDh79ixLtTg6OjY4Wn5BejMxNTVluWQwMWdgYMDSMSxXDQAAXR+QLwDwM1NQUJCS
khIRESEvL+/l5fUrqxZOGlwynp6ed+/ePXjw4OLFi93c3GxtbX8FjxQAdGtAvgDAz0lGRkZy
cnJaWhp2PU5MTITrcQtgOsaUSW5u7qVLl+zs7AYPHjxz5kxwxgBAlwXkCwD8VHz58uWvv/6K
icHrgfr7+69evRru6mg7mMhzY4KJP5Yz5vfff7ewsIA1BICuBsgXAPhJKCgoSEtLCw0NNTIy
CgsL+xkynfAOVlApOzv7/PnzwsLCO3bscHFxgbgbAHQdQL4AQLfny5cv+/btW7hwIXaVPXfu
HFxlOwtVJphwiYuL69mzJ4gYAOg6gHwBgG4MK1QUGhrq5uYGt+V+J7BV9fb2bhAxhw4dcnR0
hHASAPAWkC8A0F05f/58UFCQkZER3Jn7A2gQMVFRUWPHjv3999/t7CCFPwDwDJAvAND9yM7O
Dg8P//TpU2xsLNzj8iPBRMzy5csdHByw9Y+JiYF7jACAV4B8AYDuxJcvX06cOBEREQHf/nkI
Jln27Nlz/vx5V1dXW1vbxYsXQywJAH4wIF8AoNuQnZ0dEBCAXTvh/tyugKmp6ZgxY7Zs2TJ2
7Njdu3cPGTKE1xYBwC8EyBcA6B7ExMRERESEhIRgV01e2wLU0aNHj+XLl1taWs6dO9fNzW32
7NnghgGAHwPIFwDo6hQUFCxduhR7AU6XrsmQIUOuXLnyxx9/+Pj4YP/CbdQA8AMA+QIAXZoH
Dx5g3+y9vb2xL/e8tgXgSo8ePcLCwhITE+3s7LZs2QLVBgDgewPyBQC6LqxHo+G+iu4Cpl00
NTVdXV1BbgLA9wbkS3NIJBKvTQCAJgwdOpTXJgBtRVVVNTExcfXq1SUlJZiI4bU5APDTAvKl
Ce/fv+e1CQCAs3///qdPn65du5ZCofDaFqB99O7de/v27T4+PnPmzMFewM28APA9APkCAF2L
ioqK6OjoN2/egHb5RrqCJ3Xv3r28NgEAfk5AvgBA1yIuLg60y7fTRTyp4EUDgO8EyBcA6ELA
1e4nw93dfcmSJdHR0b6+vry2BQB+KkC+AEBX4fjx46Bdfj6wDzQwMBATppiU4bUtAPDzAPIF
ALoEt2/fxq5wBw4cAO3yk4F9oJiCsbW11dTUHD58OK/NAYCfBJAvAMB78vPzV65cGRkZSaPR
eG0L0PlgCgYTpkOHDv3nn3/gIwaATgHkCwDwno0bN/r4+CgrK/PaEOB7gamWo0ePLlu2LCoq
ChxsAPDtgHwBAB5z9uxZ7F9LS0teGwJ8X4yMjNLT00+dOuXk5MRrWwCg2wPyBQB4SWFhobu7
+z///MNrQ4Afgbe399SpU3V1dcHTBgDfCMgXAOAlERER+/fvh/shfhEkJSV9fHwOHz68YsUK
XtsCAN0bkC8AwDNevnyZkZHh7+/Pa0OAH4elpeXx48dv374NTyEBwLcA8gUAeAb2LRz7Ls7l
Rs7Kl3Heo347zbZF2zv+aJCBVIeHq30Z5zLqt2fe8deCDEQ73Atbf6XXQg0ctqNO67ATYa3e
629cse+El5dXZGQkyBcA+BZAvgAAb8jPz2+D60ViyJzfF46UYRbvEZRVFu6kwRnVBdnXLqZ/
VnNy0BbraCckirL5hv1DkKxyOx+k+VqQffNi6mc1j8naPXhelqhTlqJ9YMIFky9PnjzR1NT8
MSMCwM8HyBcA4A1//fUXd9dLA0IS6gYWlv35Onnwr2/ObZyCOyfsvqETkoD8MHP59h9X++bc
yoW/PXOOn8nAOvkGAzqFTlmKdjNz5syLFy+CfAGADgPyBQB4QEVFxfHjx48cOdLO4xjVH27H
7Y1NOpVBH2huaWM/1XaYLPnrh3vJhw4eSzhbpj3X3XPmJB3pt3Eu9rg+uPa7AboeYuAQgXyw
16PqOnmfFmIzJ+IR9irCYVCE0ebrcVOVPnF0y5eDd1I1ZbPNl4Rdx3N7Tw1Y7Sx/89DGPSdz
B8wIXLnARlWMwRaNopb978qR3Tv2HH8gOWnJEq8Z4/uLMF7gPdS6R0+tStn15yW+8ctW+c3U
yAnF7PmADb7dYcAxo80JcS6KnzD7j5y5kJIvY21qNdXVUUeuyYmJQX995Wjkjv2HHvR0XuLr
M8NYRaSaGRuqWhFtUZZyIPqSgPWy5ctn6suSCcVQ5Ydb8XtjE0+dLR04xdrGzsm2sf+CayFT
HJovxb0Th46euZD+UWbMeKspMxx1sBVmDlf+22bj0oT9e3L7zA0IcJN/uGfj7kO5qgsDf19k
M1CERPjRcBVn+vr6U6ZMmTdvHuSAAYCOAfIFAHjAixcvNDQ0JCUlW2tYWfTs2l9ns5mXQdH+
o/u93rw0IH7Q5tRzOtf/sPRei/odWCBxdqlT0Itp2w+f+bLD1tvpldDVCI0W+5QZufDgGblA
q6C3c/ZHeusr9CR9uMzR7aKhzLYZVx7aB65YL7dlTnDA7H/svRatWK+IvV68fsDgfQ6KDV0y
Cu7tXTF7O2XJniMLMnfPmb64MP7AcpZc+t/ljP+8563oJbx0TZCfzMDTCxeePyW3cE5Qlu3+
ZC99aYmSu3u9nPeheet2nFd8fXTdfKcHH49t99GVqr/y1xTdO/Tb7IPUJevPLcjeMmfejMKD
KctZd408PZ0xJmDe77LCIUuD1skMPLDcoBenXmB8vrp5xpJ4/S2pD7SvL5zpvQT1+2uhTl3Q
SkKv2VKU3Nnp5bkduezacbTP64Tl8+fe+hi1z2cws/HNUw+twlcE07YsCw7wvG3vFbgiuA/2
enGE2uBNDkqFBGuoI87tM8BUy7Jlyx49egR3wABAxwD5AgA84Pbt2zY2Nm1oWPRgz++z97Be
22y+HuGyIe39BoTfNvtekYqu38x6Oa78zLkyxRljdFTUKAuiDppVyJCqGS32ySck3lNSmIy/
EOslKyWCkMiE5t1+qmXJF1lDG5vRQ0RoZnrRqQ+0JlgZDJHqjb++n/22rLZRvlS8vHbyBjLd
PMl4OK3vC73gFX9df+E7qhe+6+vwic4muiIvs5Wox9JevCnkN+grJYwHw4TEekpLiRTfunQ+
o2xQiNUYNSXhflbGgzYH7Tpxb5rOhHolQs+5djEDjYmeZDiE1hcb+re/bmf56krjuyQNJk5m
dt0PHbv9+E0RAxHIF1KvCRsymZNDBe/7S6Jzj7Pyv+ooCxEtRQ/6rfQTGeWDQsyN1PoJ9jO3
GLQ3aNfpO9M0VPDGKuY25iOHiMiaDQlOfT54gpnBEEkF/HV25tsy1EeOYA11xFuI+o0cOfLG
jRsgXwCgY4B8AQAecPXqVQsLizY0lGWGVxrufakpe301Pjb57ysXLjz6gO1VRxWF7z8iJCja
Q5BEklA1MlfFWtW+aKc5WLcZTbslQqhHDyHiy3FtfvbNzLKyzHk6J+fVbbr/8mNtr9ZHrv0v
5/4rhIYL98DORSRyD2FMTBW9/lTSoERqP2XdzEFl1+fpxNV3nZnz8at02yeHx54SY1MuXjmZ
+qgMe9+He9OqjzmZL5CQkTAFPzOSKcIiQqjo/aeSapXmLZkL3mRL29aQjQEDBqxcudLX17ft
UwEAoAGQLwDwo6moqDh37tzBgwfbeyCj6PrWabP2qyxPPL5u15NNBg7HEKJIyskglFv65SuD
UfT8ys0XFTJDjNv3FDNRt+2DJCQiKYJkXfYne+v0YG0SoPbky7/Z6pF8UkqasijtS/mXakwT
VH8pL0Mi/YcpyzTIJJKgiCQFyc7bnzy/PuKDdS30ofWuWVQXZeyZNiVeJST6eNbmJ6FTHSJa
aCwgrdRPFqWXlVcwrakoL6tE/QeqyAi2OkwH1lBSUlJOTi4/Px+SFgJABwD5AgA/mry8vAUL
FnTkyLKC3PwypMKoov8v/fx17Du+OqIqjzQ2FFmXfuHGv7SKKM/FZ42jr5qOVtJSQmnXU1Nv
8H89cw73BRB2V/j80f2HUoNUxDi7bR8kGS3ziZonL996OE1dLeugx7yz/Tcf3jOlxeel6C8f
3XkgNVBF03zS6AObDx49O8Sh96v4pDsiY/4wGyjS2LX0EHNj5ZP3bjz8rKmWs8fjt7j+Ief3
TG7zA0u1Zf99yEdIBVXRX908fy6Hi/elbin6a453Hx2//uDxU0Oq+75KPHhH2PAPYw0Rvo+t
jkPw0bSOiorKf//9B/IFADoAyBcA+NG8efNGQUGhAweS5I0Xb/Grjohc4JvjYWdkJrK3oLxS
QMN1x2H+iF27Jk7iM5m1KW6OuQKfoKzH8pCSbXuWhpYsnWQzWnZL82iSoNK4GSsnvd20Kaym
0D9sGWe3FdWoPU/ECPSzCtoUHXss0cc+kzrGfsuuGY4DBFAOcWM+xXHe8ycF79oUuqPQf83y
CR6Rx6UPRu1zNstXsXHZcnyG42AJNnXSo4/Vkl3Rx44n/macKWppv/74jEkqAuhlWy0TlB8/
d8tvXyLWL/Z9Pt3OfqzIBpanR4h4KZaPXxC5W+bgvt3O5jkqk+du2T3TcSiV9LVV+UL00TBd
OC0epaOjg/0ywOPTANABQL4AwI+mvLxcTk6utVZCyi573rs03UYSU53ot2eiH+vdnGl1L2T1
Z67BftgakmVHemzAfphv5nosZW0VdTna0KOsgVf0Oa+GA4i6dTn6sL51f+LX7B32HDzJF/tZ
x2YFl6PwwT2jz3s2tJPVcVq6x2kpt5UgSw+e5I39rGXbxr44BAvVZPWkJgbsmhjAej1/2uJm
LUnkZkshqzt1Kfazi0tvXIcm/GhahU6nt7ElAADsgHwBAADgDQMHDjx8+DCvrQCAbgnIFwAA
AAAAuhkgXwAAAAAA6GaAfAEAAOANnz9/FhP7QXUiAeAnA+QLAAAAb8Dky4ABA3htBQB0S0C+
AMCPplevXjdu3OC1FVwovYaXeFTffD3ORbntda7bfRSrViLyjj8aZCBF3BuzzKSBaGcX2+5K
0Ol0KpXKaysAoFsC8gUAfjSYfHnz5g2vreAtwsoWAfuHIVllYYKdFGWLDdhOOWVKm7PTdU+y
srKmTZvGaysAoFsC8gUAfjTKysqHDx/etGkTrw3hIRR5nfHy3HYKyOuYc935M5GRkeHt7c1r
KwCgWwLyBQB4APad+8mTJ10m3WpNWfbZneE7ox9JukzXfF9Zvxkvdng0csf+Qw96Oi/x9Zlh
rCLC36Gj+Ko/3Dtx6M8TCel0bedZnjMcdSTfxHmP+u01HjzS/4rtizoQdzqHZjNvgf8CS9Ue
/4tzsf/tmTMzeFT14V7yoSNnLqTky1ibWk11ddSRI9e+wBvUukdPrUrZ9eclvvHLVvnN1KeR
8bqJ6Ucio/Yceibp7LnEx3W8iiip+n3z/pvMgmfk5+fLyclJSkry2hAA6JaAfAEAHjBixIh/
//23i8gXxn9XNsxcdHhAwOHjZj0yIuYXsTbXFN079Nvsg9Ql688tyN4yZ96MwoMpy8c05PNv
x1EBsmeXzv/thfX+wwfKdyzwdvogfHW1Vl031f9d27tw8ROn+KQ/0ClPh5AtaoMjbRpMqym6
u9fLeR+at27HecXXR9fNd3rw8dh2n2HMnf+7nPGf97wVvYSXrgnykxmY4jca3dv722/bqR57
znlkblk2fUZJfMoi9Qcc/U9S6gpBqaysLENDQ15bAQDdFZAvAMADxowZs2zZMicnJ14bgnAB
cf/y8Ze9TJdajOijRJKytdwQy6zKTM+5djEDjYmeZDiE1tdML/q3v25n+Y4eQeVr51G3HpmI
Hjn3VmLGqOEqmgIL1u83q5Ql1daPXltRXPABfXx29272GJuY1wulhPhQbUOJppKsS+czygaF
WI1RUxLuZ2U8aHPQrhP3pg3th+/8Onyis4muyMtsJeqxtBdvCqp0Cm+ezBAwjbY2HiLb12xI
8G/Xr2fN7MfZf9fg9OnTs2bN4rUVANBdAfkCADyAVWT45cuXysrKvLalpuTT+yIkrtCT2sQn
Ufsp62YOKrs+TyduXt2mzJyPVSOoQu086snDf2hvEBIS7SFE4hdVHWOpim2srK+5KCg/fn7E
71u37FjoEopEhszesG2pfcOjxLX/5dx/hdBw4R5kvDhRD2ERhIpefyph9COaSGV+1uNM9Dpz
3vCTdWPL3s8pXTKJo381MZ57X/Lz858+fdpF3G8A0B0B+QIAvMHJySktLa0LyBd+MWk5CfS8
9MtXBvau8ksp6y4WkqCIJAXJztufPF+nB+tyL0DtKdiBowTfJ55HeNNKRk3t8+sZLyplh+g1
3PFBEhlgsShiwpyVL28dC56zbunW4SMjNOr28UkpacqiNFaNaMHqL+VlSKT/MGUZYgcKn5CI
qAjSdNkf6a0jzupbgCrFL0Ju3n/kJAVe65e//vrL3d2dx0YAQHcG5AsA8AYTE5O+fftOnjyZ
1zdv8osrD9IX2RMfm+KoblJ9+lh8EXMzSXqIubHyyXs3Hn7WVMvZ4/FbXP+Q83umqHTgqB3D
JxnKB6WnZfwrWRa17Lezw6Ovjqifc3lOXJDpbwUL49c7yUhLCogoqilINWoLcU3zSaMPbD54
9OwQh96v4pPuiIz5w2ygCPpMNBEBmSFjJyqfvXzj2TRNlaw9v8+LU9p8fs2IWyu4988bKioq
jh8/fuTIER7bAQDdGZAvAMAbKBTKunXrTp48yetv4SQBFbsNhys3h+92dr83d+ogS9mUE/j2
Hn2sluyKPnY88TfjTFFL+/XHZ0xSESB15ChRwT47dqKI3TsnzkYmTmFx7lYKgvV5b4SVJ/0W
Vbw3asn4UKQ3wzd6n9vQHuh/9aPwS+h6RB6XPhi1z9ksX8XGZcvxGY6DJUi1hPKFJNDHImgX
Kfb4SR/jLKrl5C3Hpzuq9OSX4+yfx1y6dMnJyYnXshUAujcgXwCAZ0yePFldXb0LOGCEZPVn
bkiYuYH1btZvO1gv+KQHT/LGftZ+81Fk2ZEea7Cfxi3KLnveu7BeSk2YH4r9sDXv73L0oUvD
KDpOS/c4LWUfmY+9Aftr1HPwRF/sh31skf4c/fOSwsLC7du3g+sFAL4RkC8AwDMw1bJ///6I
iIgVK1bw2hbgBxETEwOuFwD4dkC+AAAvMTExOX78eFdKYQd8R16+fPnXX38lJSXx2hAA6PaA
fAEAXkKhUFauXOnl5XXkyBH4Rv5zU1FRERwcvHHjRuxD57UtANDtAfkCADxGWVnZ3d0dQkg/
PdHR0UOHDgU3GwB0CiBfAID3TJw4MTAw8Pjx410jDy/Q+aSlpf3zzz9RUVG8NgQAfhJAvgAA
76FQKEFBQVOnTu3bt+/w4cN5bQ7QyTx58iQ0NPTAgQMQNgKAzgLkCwB0CSQlJbHL29ChQ69f
v94FUvECnUZ+fr6fn19wcDCrUgQAAJ0CyBcA6Cpgl7dTp055eXlhOgYudd9IcXHxkydPeG0F
TmhoqK6ublVVVUZGBq9tAX5FNDU1xcXFeW1F5wPyhRg5OTlemwD8ugwdOpTXJjTh/fv3vDah
3WDa5ebNm/r6+jy0ITMzMzAw8NChQ1QqlYdmAL8y2F8B9u/o0aN5bUjnA/KFKwwGg9cmAL8o
2Nf0xYsXJyYm9u7dm9e2IBKJ1yWCOgqmXezs7Hg1em5ubmho6LVr137KKwcA8ByQLwDQ5cAu
eFu2bFFUVISLXzclOzvb1dUV+xDh4wOA7wTIFwDoimCXvX/++Wfu3LkhISGmpqa8NgdoBxkZ
GQYGBiA9AeC7AvIFALooQ4YMSUxM9Pb2xr7Kz549u0cPnldKBlonIiIiJibm7du3XSHwBwA/
MSBfAKDrgl0Cjxw58scff/j4+GD/whWxK1NQULB0KV4Y+8qVK6A1AeB7A/IFALo02IUwLCws
MTHRzs4OAkldFtbd1t7e3m5ubry2BQB+CUC+AEA3ANMumpqaAQEBFy5c+P3336WkpHhtEVDH
ly9ftmzZkpSUFBsbq6qqymtzAOBXAeQLAHQPsEvjkSNHTpw4YW5uDm6YLgLL6WJrawsBIwD4
wYB8AYBuA3aBdHNz09fXDwgIwHSMv78/fN3nFbm5udu3b8/Ozt69e/eQIUN4bQ4A/HKAfAGA
bgYmWZKSkhITE11dXTE14+LiArGkH8mXL18w7ThjxoyEhITVq1eD0wUAeALIFwDoltjZ2RkZ
GcXFxZmbm//+++8WFhZwHf3eYMLlr7/+Cg0NtbW1/e+//0A1AgAPAfkCAN0V7PLp7e2NXUq3
b9+OXVNBxHw/GoQLJhnhFl0A6AqAfAGA7k3v3r3DwsKys7P37t0LIqbTAeECAF0TkC8A8DOA
XVZZIub8+fPCwsI7duyAe2K+kYKCgri4uJiYGBAuANAFAfkCAD8Pqkww4cK6Jwa77k6aNAkq
77SXBw8enD17NjAwEFOB586dAxUIAF0QkC8A8LPBuidm9uzZ9+/fDw8PZ2WDNTExgZoDLVNQ
UJCSkpKYmIi99vLyKi8vhxgcAHRZQL4AwM8JdukdzSQ3NzcpKcnOzm7w4MGOjo66urrgTmAH
Uy137949ceLEw4cP3dzcwsLCIE4EAF0fkC8A8JPTu3dvbyasmIiZmZmHhwfomAbVsnfv3rVr
186cOXPYsGHgbgGA7gLIFwD4VRjCZPHixffv3//nn3+CgoLk5eXt7Oz09fV/HX9Ddnb2zZs3
09PTG1TL9u3bQbUAQLcD5AsA/Fo0BJVY/phHjx6Fh4ezruW6urqDBg36+W6Ryc3N/ffff+/e
vZuUlMRSbAsWLNizZw+v7QIAoOOAfAGAXxeWP8bNzW3Dhg3Pnj37559/IiMjk5OT/f399fX1
NTU1FRUVeW1jR6ioqMjLy3vz5s3NmzfT0tIwyTJ+/PixY8d6enr+yvEyAPiZAPkCAAD+sFKD
S6agoAC78D969Gjv3r3h4eHY3m3btg0YMEBJSUlBQUFSUpLXxhJQyCQzM/P58+eYCDt37tyk
SZNUVVWxf3///XeQLADw8wHyBQCAJkgxYVVRDgsLI5FII0eOfPXq1cmTJzMyMh48eDBt2jRM
ymCCphcTSSY/zDyWUvnMBBMrJSUlO3fuxKzFtJeamhpmqo2NzcGDBzFTscaQ8wYAflZAvgDf
ndrs/RZqs8+b7sv6y12Vr25byaWggePWo4CLmRtMxBrbfr601HxcmPK+rBh3VQpvzAU4GM6E
9ZoVl2Gphxs3bmDqgaVpWAICa6CgoCAnJ4e9EBYWxlQOez/KysotjJKfn//ly5eGt2/evCkv
L8de0On0rKws7AX7QGJiYph+wsQKpp/8/f0pFPhtAYBfC5AvAE8g9RgwKTJBF8kPgEc+uheY
UFBm0mw7yymC2GTHy5cv09PTGxoUFRUdPny4hZ7Nzc1VVFQa3jbIIBkZGV1dXeyFt7d31wxd
AQDw4wH5AvAEkoDiCNtueVcoQExDCKkFF8umTZt+oEUAAPzMgHwBeAMrovSYGTwSpT85EbYu
bMtdsdnTR+eV1Depob+8uD980/aox1Izf1u5bI6VqjgDPyqIERbqmnckXNA/Y63aw3079x6N
TyzVmDXZ0cXdfnjpYTxQ1TAMK2JFKv73xPYNuxKeSk+cN/zdH/4Pp188t8GkZ3X+zZhdBxLP
XnovZ2JpN2u+mz6NTOLJagAAAADtAuQLwGsY+edXuDvvVQo/k+IkfO0Pl3yEhuCbC29s9/BY
L+pz4rbPkxBvm0nFFzNWGeEHlD6PDg5RHDLGWfi/c+GTPJMN953OHXFjhv68OajfraVOh/Ms
qlD5ixMrZvke+dBThEKqyj+z2tb5pHr4/mM2X2Lme+YjGWb/GWHTXNYj97hD55Rfxi50cb72
Pi5hmYEkCBgAAIAuD8gXgMcwPt1LOnSHarPEaYyqEklq+uTIg2H45i9Z6UcuC9octTfToylP
0vOfnZb2tMhIFttF/6y57d6JWaoCmNDQL2BsxXsp+agmSk+++ix32UhVmkjVy+OBEadzBsw/
GmKjhD6mnPn7ueSkzW5jVGQq7Gy1gy+/R6i29MnFPy/TtbfZmmn0Fxxga6u93XdjfIaHvrUM
/FEAAAB0deBMDfCY2qKPLwuRqKK0RBO3R2Xuv/88QTlPpigfmcLaQrv1/FMtLl+QqFpfOVy7
IAb9xd+xB+JTUo+cuUfH3msh5sb7UYt/P/Bca9Hp3+2VhVHtm48v/0MUYWEKH/YL34MqzOr/
/fOnmYhiSqXgfwNkCpVKQYV5H4uqEcgXAACALg+cqQEewychoyyJ7pSUVzCQGKosL6lkbaaI
iFLR0NkJsUtHsh42IQmI9uLLu954JOPj5ZAZZjsVtl0+Vxj3KHDgOKbX5n36xhXLT9UYrw1b
aaWE/36TRGWUe6LrHz5h0kSs+gu9nHmwkFzf/jR0gU6vqEZIsLoCe4EGagyQE/zB0weArk1F
9n43tdknWG+oOrazJk+e7GJr1E/sR0dZazP3W5jMfjzzYmZQ7/iZarNfBuB3sPX6wVYAXQeQ
L8CPovDRpZOJT5jnPD55PYv6PKgkSWU9w75RsTFH3dStq07tiX2CkDqmKGh6po4DTqZefewx
VPXptgVT9vXbd2/PzCY9ln168wEhBVRVlHPh7Kl8hLRqy24eWLD6L/oAD6cRAi/v30NIrM/g
fiNsLQdEHf/zz7NKBv/F7LqCkBpmguhQS2/j2MBdh47pVfV/EbfrOnVCuPlgUb4fvCoA0B2Q
1PMNWWooU/42bX/QjB2H5sWkbJyuSuW1VcAvDcgX4EdxZ9t8h22sl7SAi09n128XUHfbdbAs
eN0206nXFjkPt9M8FcPcrGy74Qhpz6EjM7WfitpN3ff3PDdVYZTN1iFJySoodEV1WOD0Bc8W
Tpo2se+Kz6XFxSX/Ybue750/fi9zpGUXM0NMTJcnHeuxKszP98XcWQ5jUMhH/GjJ0QGHj8lF
7thmOjxLbeqifce83Ib/8G+UANAtoEhqm0y2G8iHbM0GUseab/DdYm0WaS0Dfy8A7wD5Anx3
+FTdUxnunNsbN9LGLtyF/bDeLAk8VLdZWsdpOfazne2YJl3xUwc5BB93CGa98/UKYv4/j7G+
6ThV+eknz76RnRJ9YaQ8/UZYEqKqqfUWxgQMmTbSfQ32E/ftcwSAXwOyzJhJrpo7g5KvP9lg
aSJKf3n+UPiGHVF3pGf+sXyZp7kqlQ/PRxC5I3LrEexbweKA5QGOmlRSZX76n9v2xsUnlmjM
sndwmTFVX56PPQkCn9G0J9uWMbyPuBbsDhcKyghU/zd+1/6Es/Hv5Bys7dznuunLc71WVefd
jNm9PzEl/r2Cg6W9+3xnfZrQj1wRgFeAfAF+egTkhuoqXdi0wznAPkdUZ6JT+BFPpwHCvLYK
ALonFEmavCh68uLV+y+FT6M9HCJF/4i4HfAsxHHKpM9JGesH3QnznX3L9uLT5+jPBeNmb9HQ
3+VE+Tt40pxYw313c3XSZti6zUEDbv3OLEJRnwTBXkrwCULP9weFKA4b4/D11o5pdjvR4h2H
7vV9eWCZy4S7788fXDaCyBjG5xthc03X1y6O23dP+dWBhQsnXPtwPuG3kZL8P3ZRAB4A8gX4
+SFRNZ1WH8B+eG0IAPwsUEVFKGVZl85eRiZHnSbo9VaZNGrz7ORrT5f1KfpQgD4/vXHl+TjH
2C/LpSl4gMl6VwGD6V39/E6tJ0r+59/cSqZ8qU+CwJ+1/zS2c8Tye3vcVb+kr7TFcxrYj9Po
JzLA3kJ7jc/GmJsew/sTmFH6719/XqFrr7M30+4nqGJvq73GNyYmw0XfWgHiWj89IF8AAACA
tsIoepf9phT17d9H6r9/r2YhetqUPvvrkhugp88/0JxWbQpZuTbUzTII0fQ8ww+ET9EQKXv5
95Hd8WcvHDnFzHDQWFaiLglCLetNn75yFFT75vmtHIQMqD3IeIS3hwgVocKXH4sYBPKl9v2L
W5l0ZCrSA8+XzUqL8N/Lj6UM7MjvvxQAbwH5AgAAALSRirdppxMz0cClw1WFKa97CiPa4oR0
v5HCLLUgKCotTCVPCjxq5bsl89r+FY5Bf6w1Gr5DOsbKLFZ129G/C/f+E2jBzHDAHb5efYfI
o/Pl9C/VmFiq/lJGR9SBI/rLET0UyCfXdwgNnaeXfalmIEFWWgSVEQOk4QHCXwGQLwAAAEDL
VLDyHjAfnN75fMD8o3NHSvEhvUmmA47cvHr3w1CN7G2OHvvUtt2L07/mYDX745yLR13kaFIC
SFGjj3jZ67xchFRRVWnO1dOnsti9L0RIDrV1No4M3nUgUW96nxd/HrlOHRc+UVMUfSRoK6pt
620VGXjkwDHt6f1f/7nrCnXCmomDJb7LGgBdDJAvAAAAQMsU3tm2AM97oGLstvhg+hyH0Yr4
ze/K9n8cOXroUNwc7adidi47/57vrCpMlg+LKNiyfW6f9ch4+vLTuzxHyIhoLt634ktYoPvM
Z3NdpplSVzA9Kz24jcUvOdLn8N+ykZsjTHVz1Rxn7/t7vpuuFKmWSL6Qeo0M2P233K7N26bq
ZvV2XLTzby9nXTG4b/eXAOQLAAAAwA2KqvtxorwHTMiyOs4B2A97cgOqqpXfLuyHfZOmU3Cc
U12GA+TBynCA2JIg8A10T81jG0SIpu+25rjbGvax2Nuwm0SW13dfc9y9SVvgVwDkCwAAAAAA
3QyQLwAAAAAAdDNAvgAAAAAA0M0A+cJTqu9tHKjrn1P/lmrgGbkz3FWb2jxlwdvEGcb2KCzv
kB2trb2+CMuLsKO15/PFD3N+HHYZGyQ/0VveHiU074F+b6ON7uOFbTQDAAAAAL4TIF94jopb
AqYYFBn0V+n7g2x9Y2ytNphJNbtzXmqox5aTSFPyB5nELznU9eRJNBgSbwMAAABdEpAvXQUS
te+YyTamvlGP//fFTKpZJXqRfmNs+v1AWyj9DGx/4HgAAAAA0C5AvnQ6NfSnR/1n+UfdEZ+4
NiIqwIRGbnP26uqvFUic2oOvLkxzQz9M9lrwLo1DeaF9Dk9mRW2k8cDQGRNf6bRtJ56zBZsY
9Eex/gu8oq7RqQbuU3uWIzlmj+X/S9mwcNHmMzl0pDJpadiGVXZqZLyHG1PDZFODz6sfwh0/
BIY0hp9I9dPJp+pNm6r0HokwWzCKn8aumOW14w6dqjJxxe4oXxOaUHWTni/tHfpw5cKA0DOZ
CA20Wrp286rJKlk79Ib4/lM3CNPtNJHvauQqv8C9d+iKxm4GNTH5NndP++lQGfTM5PBAv+DE
HETTm7ls47q5hlBFFgAAAKgH5EtnU34/as7CqDuFCOWfCvTuOTB1n51SW/QLg55zPi7x4sgx
K/r2QKgM35QYGoD/T4Oj7bsS9a33a/8sObNi3NTwk6P3Tu9XfDlkvm/5vLulaaooK87PbT9L
vlT+mxB0hPrHvarpSnnHFg3xPmQ6PsSQ2XUI3rWKeqtWFaSFOISW+58vvaWOnh/zs05GI7HN
tSXXtjr4lvnfLb6lWn1r/VTL4EGZkdZSTXoueZSwI4IalFPlopR3Ys6QdftMDTeY+Nxn+GDi
7GmUh/5muUm6UgVXQ+ftkgzPqrCmVTzZ7W0Yg2zwTorvRy12z7K/Wnpck/Lh0pqZVl4SDxOm
K0MmcAAAAIAJyJfOpuzDi8eF9W9yH+QW1iKlFm8hyYmxV4qpe63pHLVSrUe92pGctj1103w9
WTKi32tyiMFky0FUkgBVR384fceHwiokm5UWVznnoKUaFRtK3XaatWL0e7whf9+J+xKd1VTI
iF++f3+p/BdFZazaaAOct8fsmD9CuhXPEKP8aXpcsc1BB2w4PqRqPm2KVvRrhJd5vXrhg1uQ
rZoYCTGGmowVt7v+ZIOlYZOev37ODs9wVulHJpHkVTSkih4XfWH1WfE0brn/C9dD6ycqfbm6
86/iiWGG8kIICWlYWpuiKOYaPkrenu+2x0YTn4680bQpo4JTM147KfcDBwwAAACAA/Kls+ml
P2OZ1eHAM3hd1QHTl1oPaO3217pbdxGD/ib9QICtT9igpOAxzOu0jZ0Drl04ERRoJjtKPr1+
K6olTmG+IQkJUwVZ28miolVndy3wiYu5zHy8aV79ASZODrqtaReMmpJP798K9xcXZvk9BIXF
WAKiJC87vzDGvOe2hpbz6oVRQ8+CEqLlp3Yt9I2LT8uh49NkNax4fHB52DPXsB0T+5JRbtH7
ImEtcWGW3WQBocbpSGr1YoWpEJ+ohBx696GgCoF8AQAAAJiAfOls8Boc+68NSv77jYiOjbVR
P+E2H0hVMrR1stkWcOvVyjFq7RtUREKOVllS/pX5hlFZTme9qso+PN/ugsG+jZd3DqZlbRmo
+6J93SI+EQkp2ld6eSUD4Yrka3lJJXO7oIgktW/IjcxAfZagYJQVlwjxozy2Q6uexcxfeM5g
1cbLO4bRsjYNdH6Mb6U/PbjO/5n1oR2WSniHPSTkJMo/FZcjJIa7ZcqKWceKSfdRLMz9XIYQ
Fdv89eO7F0hhvJRAO40HgO9Ps9wHKqaePkv83ccpU3/4U3uNiQ8UO5g64WegfTkmgG7Nr/bL
/UMgywy2nTO4vUcxSv536fjR0z0mOMq1+8wnqm7ijBYlXvccaUWryD6TcP4t0kaopij7wUXU
y01bQ5GSl3riTA5qpypCfKJahs6UNYmX3UdaK1TkXEo48RgNx7ZLaRmOKg49k+Y+1Ezuy8OI
uQbHTW6leaqyH1r08vbF6p5umoMVBd6nJh3PQepNwkYsR5GE5hiDCrf9hxw0PNWLz0TtT0bI
ANssrGbkIuJw4LTH6FmalLzLRxKfuXiO7QOuF6BrwnKg9q7+nH3jSuLOZTNdvyZf8NNr8xcX
oPrzw1NH7im4uo/45twQPzjHBMBLQL7wHPZ7XwZOXBux0lyexLp1t+2Q5MYFrJu9eJGCgA1S
mfSbNY35zYO/l5FH5KQFM3pT7KkGc+YP0Gu/cSRp44C9mYs9NPCOrWZYa/VmbhaQnxiYlLvK
T5ViTqeqWPnsi56qzo9q2I/sNWZhpOGsGf0E7Gl6c5yH6AlUoprCrPun6PeQvTLzDpeB805f
jDL3T1q9ftXU/sslbBZP0bFAzJt8SDLGQdFRIUG2onNYTx4lbbRVgvt2gS4NidxLbYz94j7V
r4fMP5buPowjexPAjYrXf+/webzQgVthyHbwg3NMALwE5AtPIev4vWD4Eeyg6vhdZhC+xY+o
30yzO8Swq+uJZuR39IHf0bo9m7ay/q89fVf69IbSr2Gso9h6aG7MCza76js2XHQ0e1F9x6iu
Y7KCoc+e2z57OGZT/4YkrjF9++3pDZVotzD/jWIwotiOqC25uevgvxrr7hTpS6KS9HWjqcqK
0vh9OySqplNoklMokZ0A8H35htwHiKI4yti0MOJuNt1Mn9qQdACp2K/dvTXApDe5OvdS2KK5
gQk5qCHjQA1ndgMKHv5xuDHVXTZ11y71iDc+L43Zkh0cNC1rc1oBgtQJzTpnj7MwPt/aHeTn
F32NXt9YiDCFAd6DzenR5hIZJ07liOp5rt/mhmJ8l0XdKa2fFD+euXtR5Qr30rjV2GQHNqxk
Y4qHuo0D73mPt4nORChNPnHh6ZdbrHo8bdoAO6oMTyRxup8v7f62Yw+pegsjD6xx1RAnoerP
dw8Gea2Mxsa1WhS2eamdKmrMDF6Vk7JyyaLQZHyp6/aKVOdfCvP0DjyVidhM+tbfF4BHgHwB
eAifqOYYY7RggpQXfqczdsbcF2KlKMhrq4Bfm47mPmBBovXXVcl/nFeCSh6G1ScdqL21ydxy
o3ZmmOGTSNcotdiiahOR5/vtLfxOmN2ZV8WZ3cAEjzy9SgxZifdYl96gISVB4f2opQRpBQit
IUqdQNQ5E8bHyxv9tvZYmlWVRr2xbrRl2FDd0P7HiVIY4K3zH1eOuvpgT0LJX/7jpo56GHAx
5X+Rws8PLJjqu3/MrUCmq/dtVsmgAw9qT1Dyz/iPWxKsfSbSmnItbIFv+Rw8xUPt7fXmC4K1
T0dG3bnbv6EayX/pKzkaWIvjvT2uUt95pTa2+Iz/lKlBp0YnTO9XdGXj9AM9wm9WWYncWGVn
uUpT97BVw2QqH50MipD8I+fLdKV3x+ZYe+8zHr9B++7WpVHqO4uSjEWeH7BX23jCSt9nsEib
P1ugawHyBeAlJGpT/xAA8Jx25z4ghFH577U/P5hF2apTSfxoqKGVuHf6kyI8uUDh/dSEVJHx
hrOS/4dHS6o/E2Q3YN44I+m8M3XHbD1pPCFkY0qCymsr1YnSChCkn+SSOqFp542Ny7PS4moc
D4+SJ/MjPdfIP5+L1T5KIExhgI+lYuNkhm+nDh4zXPa0lqWhjBAJ9R2s1evJ4/y6e/AVTe2t
VKkkEpIfZeco7JyetWGC8NU/S92izJgpHgaZWInYYRuthzfaUPmcawNTa0sNcRJJRGeMNv30
h8JahuzT9LiyCYeNFckkpDdz7Z/PRNnvN+LvM2lfhrNaPwoiyfbXkMt/XMQKyRfexpZfdLzB
jGRGJwSrAB4C8gUAAICNduc+aEph/osPNFV5kYK8N68Ko817bqrfoeJWVCk2ccHRtav8fK3C
6Iiq57snNmSKKrfsBpg+sGGTF/UpCT5xSStAKF+4pk5o1jkTPGGBkJYw0/1J6TfGth/KT9zc
9rE4EaQKC7H8VnjOBVxAFBRlv3oYYy7XmHLBrajJjX4FeVwbCDXLF8FM6yDYnzVEfZ0T+uu6
vSSyhEjVqegFvkdi0p4z+1mIUE/DRZvWLl/ua4EtP03Pc3PsFmdVCgSPuisgXwAAANjocO4D
nIo3l1KOC+jEKFOFiqmSfdeezVymz7qEM8qLSkj0/BwRo5WXSiIqnl08uGqRV6S5sVde+7Ib
tCetQPtSJwiJSErW51+o+F/6uediQ8S/KYVBzdeqGlbOhbLPuYWKfaTFhMolJQ1CzqYE6ouz
WpQVlWEaqjHlAm5Diw0aIQmJUCXr0jpg9maceyZqYKFSv7c8O8bf7tzQfRsv7BwmlbXJRvcx
nuAh/5OoUXBqyZ4vzy4cWuUSGmlrvNVMtq3TAboYIF8AAACa0pHcB4y6B6dXHJede2KUtJCk
1ijb4h1JadN1zeQrHkZPMzhvceug5eMtRot7xj9Zb6Y+TEuBShHkK2lvdgNuaQVqOZu2M3WC
pLqhbWkgnn/BnHJ9/1z30vBM/zaPRcSrlGN/u+paK1Q8OX0gRsQlXk1YsgIbIjTphruumVzF
/YhpU49bJKZ59sUbFxWVVjNoTBuIGzSHH19h+kY8rYMV5Xr0UvfiZZmN8qUo+/Z91NNRe7Ai
5X3qieMPmXf5fLoe6rRYdueTzWbqw7UUKGRBAXg6rBsD8oXXMIqzz+zfsnlX1OXnCA0w9pz/
22J3K1VxDodmNX4nvz1K6CqpqOiNd/jz2hQA4ClsuQ+oeo6LdycEGElhf8DyVmuT3i33GyFg
ns+6Ld1VXUJEyTcybYFDTzKd+eRRbNTY/qJy7ctuwC2tAIGkaGfqBJKiZdD6h4v9mfkX7NfG
bzWXUeBv61hE0GTKTk6TtMHryHpG7goyliGRGBPXbstdvlxVwILOrOQa7aqOKQjl0ZbGq2er
CdzGzm9EDb4Q2ytvFhTzdPEiZlqHiSvioybIkKre1u2UNVoYNGmWV2+ByVS9WfOHaKBK7AAl
q8DVabNm9eTLr3vyyLBn22YCdEW6woXwV6Yq/8xq66nPJu+MenpcX6n8UUrk6qmTPsRfCTGT
afbR8EsOdT15Eg2WbN/XBcbnf+KP/KvoOkW/nQcCANA6XHMfYAjRDBceuL3wAPs2gnvVibIb
oMYsBqhZSgJuaQWIEx+03jkbJIqy9epk69VNDG5lLIQU7Q69sKtvXZ/ioRoTCEjYYF603+59
TYYg08b6HMjwabIoSHKk/6VS//p3nA3YE0mQaXZR9fkiRJStVyRbr2BrKdTYUmPartvTmk+9
6UagWwPyhafUZCVviO/1R8IfbqwcnfpT1oQX3bcJPjrF2GdI0weISfX3prVzhNcXl/m8CHNw
7hyDAQAAAKALAPKl02lPzquP2TeuaUyJGNR4c6CAsqHd4PnnHr9bOETx/saBbLmq9vU5Vl/H
hESUDouZSMrElpb257Hnonqe4QfCHWXS/JRtdtARspe/gqe4NfvCkceJyiyP0jgKXjyyDqKE
V4SzaHd6KLwuySIhZ/cPiauxBg1TaFy6ZvNiS7G1b/iz5lm/hBhEmbWY8zpj4iudtu3Ec9x3
vTPcVZtKYlT9L2XlwoDQM7hhVkvXbl41WZXyNf/SNs+5a07hM20wBgCADsPuIwGA7wKcpTub
9uS8qs57mYH6TJJhf3xRUFJGDj35UFDDejixIVcVGyUZHOmwNlpLITyRVMmwnfd3xOKJpBYF
nRyeMH174V2letHDX3lvE0ceJ2YKK8JRCBNeiXGm7u9geqi36V8GpdyqTRbKT1kxzoE5Bcp1
LvNqSLFVU3yVI+uXT98HUQSZtZTwA96VqG+9X/tnyZkV46aGnxy9d7py5aOEHRHUoJwqF6W8
E3OGrNtnarhB99FW13j12JdJeKcean6JVnd8BkOoDQAAoAsD8qWz6ZycVw3U56oik6rv1W0i
TIdljefD6ms6ebwGlZ+EJ5JCpz+UNLvBjjCPk3DTUdhaEyW8IpAvHUwPpeg42UpFDL/B0djG
kbo4/UnhBGFu82pIsSVQcumf5lm/ytKTiTJrMeWLwWTLQVSSAFVHfzh9x4fCKoSE+kwMz3BW
6UcmkeRVNKSKHhex7gp8fzv1FNapwaw4yGUFAADQ9QH50tm0J+cVv5SsJrqY+7ESNT5MVJ73
8iXSNZapO6w+V1Uj1UTpsFjXYH4hAX7ukSrCPE7cRmkh4VVbum09PZSgmHCd00lIWEwQkxH0
giJu82pMsSVmyJH1S5RLFi/mIALNI3eCEqLlp3Yt9I2LT8uh40PgnY5edNR/ud8KizAPRDXw
3LNny5SBxGEyAAAAoGsA8qWzaU/OK5KClsmozKQr2XMHD627XpZnXU58buoxWIHE7eFEZrKm
5umwsM/xY2uWEeVx4gqjzQmvOpgeqqayqob1+1fyObdApo80Vai81XnV0PMLm2b9sjBdyyWL
VwGRsVXPYuYvPGewauPlHcNoWZsGOuPG0j9+EjEOvuS1pyLzwsG1Ll57bU03QLlgoE3kJ86Q
t49h26ASfjfTT6ebnliZd4yxYs0/dgb4wM6Pwy4fspP8ThkZvvPUIJEED+imf2Vdm7bnvBLU
cFw2OWLq8lUyf3hZ60gV3EuJXL2OMifJpj/33Ar8ROmw4jxVWximpKj0K6IR5nHiRm2bE151
MD3Uq9jEv2cNtaZVPDkRGyM7Ll5DSrKi1XnVFFxvlvVLgMwtixehfCl6eftidU83zcGKAu9T
k47nMI0tuB5qtEE2/uxms4HDtXpTKGQOnw0AtIBR+N3TfjrUNrSs/vzw1JF7Cq7uIyS/u1VA
J/KdPjj4ffgmQL7wFgGa1R8pf+5av82tz1RW2rqFR/bOGCPd0udCIkiHJYxqiBvzK+u7G2+f
raZ9JeHCTs48Tlxpe8KrDqaHomn8d9K2n82dfKrewsh4P2MpfhJqdV6CilbNsn6NFiMJEWbx
qiY0tteYhZGGs2b0E7Cn6c1xHqIngBuraB4YmTHLgcaHR5Ps18ZuNSS6xQcAvpmK13/v8Hm8
0AHur+pmfKcPDn4fvgmQL7yGJK5q+/sB7IdjT7NcVWxvyQTpsLglkpI0WH7pzfK67X0J8jg1
HaXRLiphwqsGqN+YHkpY3ys6fl+TjFaEab6azIu4QjVhFq8mq0ezO1T/EKfG9O23p2+v37GF
aCPwi9Oe3AdcIHpuv1fa/HE20ZkIpcknLjz9cg3tkK3uDf0w2WvBuzQO5UVMFv031n+BV9Q1
ekOuAXSfIxvCFA0qP1GqAi4ZEBjvUrzM3SpXP9lnJ49N4uuD7caTU6afPuOp2VC4iEF/VDcu
1cB9as9yJNd0EdizGOReap62oIYgvULTZAdvfF4asxl20LSMM80Bl1Ukyt3QLJNCY7CmzYke
sKllnw73XxZ8KhNRR81cu26dlyGNjLjneshL8WT/4Dab0f/iSL7A1j2j+GnsilleO+7QG5M7
tOH3YYt1L3p28lZ/v42ncuhUPY+1G1d5GSoQrTm35frlAPkCAADARntyH7QIx3P7UXfu9m+4
Q4KOP0qYGIqnLEAaCP13LWyBb/mcu6VpqrW315svCNY+HWmOiLIh9K8iSFXAJQMCiWbo4iBr
eSZ9lbWzkkD544tHH5n4Wqo2Fl1kfLwcMt+3fB4+LsqK83Pbz5IvBNkZwgyfcKQtmFdFkF4B
N6Uh2QFrmAbDCu9HLeVMc6BMuH6EuRsIOufemNCNin2+M1dkzThamqRN+XRpje0cL8kzCdMl
r3LN9SBv3eSDK7l3iCP5gkmDfqktubbVwbfM/27xLdXqW+unWgYPyoxk5n9o5feBUX5v70z3
FzOu5iZpYnaF2Vr9LvkwetIrzlQRkNWhDpAvwI+HPcs4AHQx2p37IM1fV7Q+472KW0JD7kfO
5/Y5kJy2PXXTfD1ZcuXNtX+WukWZqeHP/w8ysRKxS8/aYC5OmA2hPRkQ+MR0LGdrOe2/8NLR
XfpectIrj6AJimwJvcuz0uIq5xy0ZI6rbjvNWjH6PeKSnQFPYtAsbUH1Z4L0CkxT6pMd4I6H
RsMqr61UJ0hzoKyICCDM3dC081YaE+WpKrt3Znum2R5HTSqJhGRGT/MYEpxw87WrBWot10M9
hMkXGuRL6b9XL3xwC7JVEyMhxlCTseJ2159ssDRs/feh8F5yQqbbH46aeME7GSNHj1ETEzJy
JypwrDlQD8gXAAAANtqT+4AJt1t3OZ/b58DGzgHTLtiLgrzsVw9jzOW2Nexyw0SJOFE2hHZm
QBAeZOk2zH/f348tBiTuJS9IHCHFvr/k0+u3olrirKsvSUiYypQ2hNkZKsUmcqQtUOWWXqEx
2UETwz5xSXNAKF+45m5o1nnLjZtRU/Lp/Vup/r3E+OsOk5BipgltPddDPUTJF9gWNC87vzDG
vGfjBzkPF1LMA1v8fSj79PqjlFYvMdY7PhEJudonH0pFpnKueVtuEv8lAPkCAADARntyH3Qa
QiKSkgYhZ1MC9cWZ72vKisqE0HOipu3KgIAhom7pNNV/W8RqWoLmlGs64k13SsjRKkvKvzLf
MCrL6cxXhNkZSPT8nKZpC8yNvfLall6hHjEuaQ4IaHvuhnY15heTllMseP+5pAbhyREqPubm
Is3RUnyt53qogyj5AhuCIpLUviE3MgP1WfKKUVZcIsSP8lpZGOyTkO4jU5D7uQQhPNL09b/c
F0hzPPULZ6qIrWZSrXb2awDyBQAAoCltz33QAYqKSqsZzZOPSKob2paGJt1w1zWTq7gfMW3q
cYvENA/i49uTAQGHpGjg4hpiE1U4K2GLukBTB4CouokzWpR43XOkFa0i+0zC+bdIm0t2hoOW
j5ulLeAraWt6hXq4pTkgyBLR9twN7WpMEtYY4yLrfeDE9NFztSnvrx2Jfe2yZHQf0qcTreV6
qPvgCJMvNCKlZTiqOPRMmvtQM7kvDyPmGhw3uZXWYl6Lut8HcQ2jcbIOsSc89OdqUt5fToh9
ZrxkrHzh9UXNU0W00NUvBiwFT2Hej+6fU/9WxdTTZ4m/+zhlKjd3NaMi+9hi19+i7qiF31iB
XMdFeXLPkYUn1ApACexVGLs1XBJDMfOGZbQpVxheKtIehUFqKaBTYb/3BSG3BOwXTJq4ZQ/l
0ZbGq2erCdxOyAvtw76HpDhx7bbc5ctVBSzozOdZol3V+dEDok6IUhW0DElWZ8JolCzmbKTQ
PHpBkhsXsG724kUKAjZIZdJv1jTWnwZRdgYJEaVmaQvG9heVa1t6hYbhZAjTHBDJl7bnbmhf
Y5KUUVBCcEjAFFFP1pNHERvt+/GRalvM9cD+wa3lTL7AhoD8xMCk3FV+qhRzOlXFymdf9FTs
g+SS14K92wg7Y7+EqLAA296erCePklbZK4nyS3GkimhpHX4tQL7wHNa9fr2rP2ffuJK4c9lM
16/JF/z0uDisy7POHYqihWRVzVLl//xw+8lBCtxvKpQc6nFyCxr8XRyNjM//xB/5V9F1ir4k
r++Cx6d50rkft3VgTwwlNdRjy0mkCRmigE4DfyafcYhzO5fn9iVH+l8qrZc6DakHcEhk2lif
Axk+TdIGEGdDoBKmKuCSAQGH8eHe3zcHemwfSZBLGhvXyO/oA7+jde83bWX9nyg7A0HaAsL0
Ck2SHTRLAEGY5oA9P0JjRgbi3A1NOmfrtuVED03MoA5yDE1xbGoCP7WlXA/8TT44ouQLjWaT
FQx99tz22cNtdK6/D0h8kNPaFKe1TUYmShUBsAD50kUgkXupjbFf3Kf69ZD5x9Ldh3FJWs+o
/lqBJCRE8VvApAdb2rbk36b0G2Pb7zuZW/P64jKfF2EOzt+p/3bQyjTZE0OJ9Btj871WBAC6
Koz8W4nHJF3PasMXd+BnAuRLp/MtOa8oiqOMTQsj7mbTzfSpHGmj+M6xZ0/KnpY5ZhqzSogi
UU4kbWpNQxkRReKUVogVvbI5PdpcIuPEqRxRPc/129xQjO+yqDuljSmSmmdhWqB1b5myzQ46
QvbyV+advhhl9oUjiZYI9xxQ1R9T/Aa6fd37ZJudvABC9IfbpwxJsXl6Zu6ghqg8o5ggfRNr
16vE1ZMCo7FuVexWbt2wzLo/pbFaimJjAq66QQfe8x7fPFEYHn6ayI/bgGIyN1rLkOtsOGb2
4LKXyqt25bMCgG4ASd5ufwFkKgB+NkC+dDbflvOKROuvq5L/OK8ElTzkSBu1sUn2pOp7mU0O
5ciJpNSsb46UVnXkP64cdfXBngQ8L9bUUQ8DLqb8L1L4+YEFU333j7kVOKKaIAvT9sK7SvX1
z/gr723iSKKlfZdrDiiyjKGdt6xHfHruZOd+pPJnfx/NmeprynZHIaP8Pmf6pr11ma3uf9W4
dqM2uUd+yopxbpsHYxpIpmEiRIm/OBOFMW2QHmUxg+J35vYHK2sF0tcXV45l23pu0aq+v7l9
+awAAAAA3gDypbNpd84rQhiEaaOsTbhkwcbhyInUXL5wprRioWLjZIZnkcLzYsme1rI0lBEi
ob6DtXo9eZxfjEpfc83CVAdhEi3UQg4oscGTZqvo7r+8yrGP3L2z+17ZbJ3ArvAI0ze9dVVm
JgMdZ2OtLYFtlzc0n0j57erjArtx9cdVPr/KmfjLejjhYpGkdO08JCYl3lphNbnXs/Rjj8b4
jFX6cu/wt+WzAgAAAH4QIF86m3bnvGpKYf6LDzRVeRGitFHsuR05aTVHFlFKq9ZpIQsTC8Ik
Wi3ngBLTtrSd4J947vEYtcTT5AU7xjS50YcwfVNxDSuXubhIXTfC4tLC9MdlXxuPI078xQ0p
HUtLhQln0lcZDbyS+szV01iRv+TuN+azAgAAAH4QIF86m2/KeVXx5lLKcQGdGGWqUDFn2ijs
w/raWg+dTqtZmIiSaDFazgFFElA3nT11U0hEuHZCnwXXtJuuEWH6JvE6gVNcVsHAOsCGLf5U
LiEn0aPxOOLEX9zyRZGEh1nM0Z6afDk1/1iZd+hIGRJ/2TfmswJ+QZrlPmi486yZvm1nFgNm
r6zgbHtO0a2P0pB5QdjtR6dUqM5P9Ja3RwmNk6opubnJ3O3NgpR1Lqpi8IUAaC8gX74DHcl5
xah7cHrFcdm5J0ZJCxGljYrzFG+9o86mhSxMJUWlXxGNMInWp+st54AiKRq7WM632fZuVkK4
erP7SAjTNymSELNEyMXTKY9svLX5suMP76kwiNGUQOh13XGEib88++K7CBOFCfYf66wSuGbF
jR6zj+hIfVM+K+CXpq7OEYP+Kn1/kK1vjK3VhuZPDn7PLAbtGYUt88KPvv2cX3Ko68mTaHBD
qoWqrPgNj6ee2AHaBegYIF94Tk6MvVIM6yVVz3Hx7oQAI7woCUHaKGGE6D/cPOIsTEhZ3914
+2w17SsJF3ZyJtEiKbWYAwrhEk/H0AZdlXIeRWt+6uKXIkjfJIRY8mWY4NOVI/lOZaIB08KT
gs1lyKi6/jjixF+Ia6IwRNUytxvl60ffZjFMGDeiQ/msAKAOErXvmMk2pr5Rj//3xUyqaWGa
75nFoD2jsGde+MGQKP0MmlgnMMj95J8/3Azg5wHkC09hJjPyI94nRJA2ClHZkiM1pm/ikhOJ
S4PmBhDkxWo6EEEWJiRpsPzSm+V1b/oSJNFqKQcUwh+fvnf19MBJySNlCM6jJKL0TSyTmIsV
xb65srzkq0APQX4uib9aSBSG+FTdU5vcisvf/nxWwM/HN+Q+wNWBOLUHX12S6Bv6YbLXgndp
HHoz86Wxa0tpDkio8bF/qoH71J7lrJu9UPn/UjYsXLT5TA4d/xoTtmGVnRq5pTwIztxH6ZU2
vz7zQoz5tgfxC1XeJocH+gUnshLgblw315AmxOzE4cZUd9nUXbvUI974vDTWPT16nkRG9Kkc
rJ+da9xQou+CHXfoDUkEUNX/UlYuDAg9g2cWwL42bF41WZVCYny+tTvIzy/6Gr2J2ayIGIme
zZmhgMRtZQCAEJAvAC9g5F1LvKjguluHoKJ9e/iU4qlsc1hv7ZkRMq03BoC20NHcBwx6zvm4
xIsjx6zo2wMh5l3jiaF4sgKkwdGWI81Bv+LLIfN9y+fhj/2jrDg/t/0s+VL5b0LQEeof96qm
K+UdWzTE+5Dp+BDmc3/c8iC0OEqTPALF9zYuds+yv1p6XJPy4dKamVZeEg8TWAkKXiWGrMT/
X9d7fuXQ7Q9qj+P92Ng9XHkipXCLcM7hBSND9pvoBOrzPUrYEUENyqlyUco7MWfIun2mhhuM
ay9v9NvaY2lWVRr1xrrRlmFDdXfZs68wQYaCAcQ2Kws1nxYAMAH5AvACkpLd/sedkEhL2jqq
lBHVejsAaDPtzn3AFv9Fms5RK9V61KsdyWnbUzfN15MlV9/b2OQQjjQHsllpcZVzDloyH/tX
t51mrRj9Hm/I33fivkRnNRUy4pfv318q/0X9c3/c8iC0OEqTaT5K3p7vtscGT5qA5I2mTRkV
nJrx2kmZ6cqRdN6ZumO2nrQQ03ATp4kaVBIZ7wfd0Zo4QobMj1Q0tKTePs4rRUimz8TwDGeV
fmQSSV5FQ6rocdEXVP4qLa7G8fAoeaylnmvkn8/FhBu+qDDK7p0hylAwQJHYZpAvADEgXwAA
ANhod+6Dult3EYP+Jv1AgK1P2KCk4DHMi66NnQOmXQgO4UhzUPLp9VtRLXFWZgSSkDBVkLWd
LCpadXbXAp+4mMvMx5vm1R/QljwILSZTwEeU1OpVd+M8n6iEHHr3oaAK4SJCxcbJBtMuLU+7
YRQJ0fJTuxb6xsWn5dDx1ajrXEhLmDmJ+jtyqutusq8p+USYoYA5cusJIACgDpAvAAAAbHQ4
9wGJqmRo62SzLeDWq5Vj1No3qIiEHK2ypPz/7d15IJT5Hwfw7+SaGESuIcqxSq6EtlyhgxgR
SgdaUtGBNrSVSm1tQulE95IO64iwm7ZSq/v6JRU5ql1FqnU1rHt+nhnDMM9gSml2P69/ZOZ5
vs/3+6D5zHy/z/thJCPQGuupjH81F570cbxocjQi+4Au+VnkGAOcuxV+JDHpkYpVr97XIURq
P2LT29fFSGGqpADX7TTnx/msPG8SEpG9bzz52c4xLnmIkWLAHE7Di5zzRWImlswpND4x3IQC
uJ4PcAnKl69B018JPppzb8xOZpllZ1mFx3nH0pSFFk4ojD6T/fWjL2bsmHfnFm+NFPC4j8k+
aK8Bal9c/uVM+tBps+W4fi0W1bB0Qf4pN7wn2ZIbCjOTL5QiHYRaqwsfXkJS7jqaisSyrMTM
EsRlVdQL4dHm80Wcj6d7GXtoEcuyT6fkz/eePFIItfW9azfVz+9cahnurqWrKPAmK/WXEvqC
GXqKwXpsONbEG8eWeH4IL+gsXzglFHB9ZPAfB+XLV6Dt+cVjl2VVWxOTbobMUuLiDklIUs8r
8izSkvh8fftaDNRIW97nnjt9X8HV89v/wEkDXwbr2hfsepyN1vIExDnwGRdBbkrQT4tW+SsI
2CFV++8pZHqZzidl7hVlv3zhCKITyWSxzzeGA9hrgoxF8MGYrcEOoosZVx6lRjgoDUFcFxFS
piujzDwWKgs4kQ0Xu4wzFGjEGle0Cd6euyqQPhynbUm7sYyDUuaRcRMKuD8y+I+D8mXQ0Zry
fj96YdKqo8oxi5Ivhth7qvdyZ4AeRJRN7T5/msTXYKBG2vDn7/t881Y6w52LwIDgmH3AEj2A
+hNzgPjJ5gFnHgac6Xhm527GVx236By3HrEE5L5zEDgdhbVXBJLWnNDUOaEcG+mlHdbNNN32
3nHby9wokvGFqELZnEbZzNoyS1N4CQWcjwUAHihfBhy3oRG1eb//emP67OOOo/4KPn7mcrGb
uhZz/rn+Zdom+6DEcyVIdWbQ7sjvKSoiLd0iH875Pl/eeQNq1rQGlkkW+pRNurIf+cGehFyS
4cqo4z+6aooTsHzxfdr30gP0SYypmSDthIIAfb7O8AlGRFvQZL7zAWPcUVxBBKX9/ROi5u6d
Oy7B6mH2MtWX7MkNqKX8cpj3ivXnCjoS3tiH/zIz0mNn9M83qN8siIjd+f0kWUL7GeMYAsE+
Uqu3e53H+Z1nNrc0uWz/LNFi9viKPnIvUlamP4+kSFGfxG/wWLbvLpWkOnPDoRg/SzIqv7zH
e8mP57CYDadth3YHWY6APxIAAPjawP/MA43b0Ij6p+djn0xfFak2TMJyjkzYieynnpq6gow9
yh9UG1x7eDiN+DojcK77Vs3HRx3p8SacIh96pDWwyGvWOHC1Lb4mM3DuvOBzxh3pDuz+vha2
3K9+MRY+0XZnu/XyLTrpUUYzFhIDMu9U2FIUCE3FVxMKHbwjtVse7MJJbpD4Y/eaGI0D1akW
IkXHnUZHJNpO9NXtfk+AvCbVA7/WHmvMXjtrSkim3W+e6g29hECwj1RE1/c3mi9CDY9iXBx3
yU83kK57sBsnvoJ+v+3ecy/aanN2O/vVBd6rua3ecnv7PJstYwtChXe7JmnEP0+1FCk65jU6
IMX2rq8uLCoEAICvDJQvA4270IjWypyzBx7r/KA/Yggi6phNIu359fc8d119xs2NFKc4T9fB
UhlGmNmZE51v5EXYT8Ee74x8qOuRJtGZ1tDzONMpNpriBIKIvqkONb2iitMkc2PRHyc+uMdY
0cMnxlraijjmPNtBMXD0GmafcnuD7Syp/JyER6a+k5X+uX8SL7lhRnsbVXeykrNEp5osTKPh
zdB09KRFY4I2Sqr80NZ7CASnkVKf/PxTYD4ldp+NUuPdI3jxFfTypdfcC/Th6R8XK9yDHUaL
ERBNz3KyuOONx+ssEHpzJ+tclshUE49TuCMAAAAw6KB8GWhchUbQ3txI/a1cd6HxGBFs8Z6e
0RQUH3v+6TL9SfSLNQXFRRj3mx4iLC4pXFVc18iYGeYU+cA5rUGof2EKlWWFL3PjrOX2dD7i
Xl2HJPVtbBSmZeaEmI+5mpXv6m2hyFd7Dze5YbiZ/85t69b5zQijIrKh9674SBd1YvcD9+xJ
7yEQuCOlNTw5tS6w2DV2+0wlIirnEF9BP4G9Dru2rLC8Ks56eNdol1bzG/mfCVwXsGFGmBd2
9+DDhyPnjun/WiQAAABfBpQvA42b0Aha6bVT8Y8RNdCAGNj1aPyF+yu/NcX2a6qhFywE1FZf
U1lPlhwm8mkR+xz70drc2Ir9A0trMNn6a8b6iYyPf1rrquuEEIF//IzFOvPSsrPKE+pWhE6S
IfDV4SY3DKkrfydqviWr9vA/+RdjQ+aHRjlY7LaS7fXY3IdANOT9vC4s3zVs38xR2K8vp/iK
yj6HLSgiQRq19WbB+omMio9WV1PT9PdfIhZbLi873FBw8edt85cdcZjOdvdgAFjRagrTdgcG
RGDrpVgWbw1yr7DFbUEouffkBRwtXXcm+vKvDi3lKSvknVByP4/+sWME/w5QvnwG/Q2NaHxx
NSNdwCf59R5HecZq3dbKyyHGU9Iz7i03xe5rUnop6cKjWco6KD8p9myDfbiW6MCVL1hM1psn
xRUt+kMbnlw8e+ElMuhIawhNvelpYCXX8GD/gnm/zEi54q2FBNUmu6iu/3HDzaGLTutLck5u
eJcYOmeV7IHHu6w0JmgrEPkFBfp84ec2BIJl2ojxCsEpvqKX8qW6+kMLjcwvqW1mVBOaecVT
z0run9z9S0x+sbwZJxFqvkM26dddVmMmaI8gEvkhAxT0quXthe32QW99Dj08aSFbeS1+06Lv
FqLkVN/x/U67GzC09/9LOv1U0XXuRAk+JKHndTYS6Up+8V58Cj4JPdezZ5GuRP/eMHzcGGnv
cpNS7is6eU6U+ogugq8HlC+Dpyk/I+bSiCUnzMmdSZd8kiZOPkYHQhJvrTYjI0QeP+zexnFr
zpWgb1x+TA2dJkNALQN1dDFj/3gP7yXjBOa2N+5mrk2/UImgOHPbnlfr1qkLzKDSbx570FWD
/h8JSdva0cgvgLpnxnhh7OUcP7mB0Ga7fvMVD4/hQ8o7rjwyG95nR7gMgah6dvMetajEaSR9
yoeEXUBkixdfweFEDVUxtrHYvGi0wB3sHd7M9amvQgLUidZUkqqt79GD8zRHofVR1z2cyUOw
9HOnbfG7zT7xppLg362tOGNXkuL3qT6Wqu1/xiTT+f4/FO0qKvubxvhD+aJa/7y01rc4zNkF
+4YZ1c9TCERlEy56/XFjbP3r97XheWE2sLCN10H5MngEx/leL/PFebCS8WBAcRk9T+IY6/Pd
ohFYsyW6pzWwYM2f4Cc7xjDDFITIlkFpxUHMzaI6vpIn+x6/7nu8ZytD1D2zui1k5cNLbuAj
aS6IvrMgGuHi1BPcpjiO1DG2mBbbo2Wc+ApOGRISkwIvf+icqlMw8z18x/cw647dQyzAfxA3
2QcVT69e0HKMVGG+BRHT9dzJ+PWkUQvYr+enz83YpRtbD7ueeK5E1NB7+x53FOe3NubuB+al
+3zYBIp/4wbPD6c2J5d0BRCg5hcZG1cGhWZiqQTtby12hcxSJ9ZhsQg3J4bJZgVF/4/RAyf5
q0vTL8VYlzNjuyWY21zbEq0ZyzovQ6thCw7Av88R7f3tQ8EBAQevUVXt14TtCHEcTWSZMiMZ
em2LCFlmpsDPKaaBQzgCor29dyhkWUD0XWrHiFSe7GROXTXQm1JYOuz+wXMF7U0d2OOI4jYs
j2nvAzPRgCWanNYz8aH9jNXhdEYmx0fF7mD7mxMnpZSl6c9jZgzF7RjgBVC+AAAAC26yD1rK
nl9HcvYSgmzP1DyIwbmenx5YUJ7XaPTHw8PJtb8FTplnlBt0KeNFlHDR8eXz/I6Z3l5PT9Yt
fVY79vjDtkRieWbglNVbdDKjKKKPkvftJwWXNM9XKktcPO6no9PNdljSl5WnhNLfhSw9k6y4
3qm0Y9lKS3m37nRso8nyUFvtNbbggCgKzkwM7W12RMDuoWueNV8h3fzJ2CZMzyDK7t2R7zyL
F/7xKlWL+O5ymIPtDxK5RzryGNhjGvDDEVSqs/e57SaFP2uwJd0OMfYK0dOLVet+6DyC3h+3
2pKxpuyMCjZeSqiKIpUcXzXJ74Tl7R8mdp11vMQHijhuZ2Kq7ql13o+l/m4EXmoDfOLKE6B8
AQAAFtxlH3Bq5FEa3vX8Kh33c7bCHifpmk6QTde2MZMRIqBRutpSj/PKaxi7K053slXHogTk
jRxnC7tg+QXGI2eGX3dRVeYnEORVNSWr86r/QYhevkgs2Ju108dQFt2PWM+pP8xtWP7HxwsO
2GFjxr5v/bMrp1pnnzSS5+dDhq5RJ4rEhGvupyUXuG+arSXeXtjJmM/2MpqZfL3UVUUO275n
TAOncIThr65cqpu9w0K+ffj630Xtyhcj9Zz0tZs1EzsE1hRKV5tpJs9PQKq6GpKPi8tqWpE0
czP8xIcJeJ1hbZ1jaoMyfP7CC6B8AQAAFtxkH/DJKBqga2+rmlCPK2VqOVzP389LZARJwozM
BCQoLCZUnlddhwSlRevPRa/0O5V0pQRbmeXeubGdozO9LultYRxzG9Yu4gQH1OEtOcPGIqQt
TP+so2O5SWnKn28ltaXEOoYnMkyu7XFFe0FBL1/6G45Q9+7Pt4LawvT5KhFlU4oydt1Tv05P
T/iJDwivM/3pGFKGF0ZeAD8lAABgwU32AUF2tJHR0/O3Xi4aO5a+/OVdTsTGIx8cQoM4XM/f
T61Nza00hL3w1r1/VaU4UlqsOT/OZ+V5k5CI7H3jyc92jnHJ+9Rx4gQH1ArxoTK2DbE8hcba
+ibs3w0vcs4XiZmMkx4pU/nqfS1C2GRT09+vipHWVHEOdR6ncATB9xKkptr6Rux1qO5FTna+
2LfTPm4o+IkPOEPpX8c+rhPgS4PyBQAAuutv9kF7DaA5ey1l//cR0Qqr55tKv718ZMfm8/x7
VkoJS+Ffz9/Puyq/zEj43dWAotDwOP14nMj8pNHC1XfuXGoZ7q6lqyjwJiv1lxKcW4PQ1VZ/
YPsoCB9OcMDtK97q7BvS8xTWp9zwnmRNvHFsieeH8ILJRuZTZJ3jE70mLtEivslOjs+3WD1Z
kYCa2ffmHI4gKWRmRF2fnu09wZZ46+CS4JrwzOlkvAb6hJ/4MIrzDk3V1XUtnFMbAE+A8mVQ
YbFLTnEsD6iG3ysI0O/3T4W+zr/jDj6fS98xVizr/z9bLz5Fc2nCxgS19fSbUwIwsATItptS
68OClk/0w27zaR+055S/u4YAgWCBdz1/f8sXskzd2QUSdteo2N1Go4MtZAjIdGWUmcdCZQEn
suFil3GGAo1sO/GpTPS02LtotM7V5OzYmX33XJ4tOECDD7Wyb0hQtAnenrsqUEHADksTSNpt
LSPEbxGQHBMW5DDCm3HlUWqIk5IQwi9fOIUjEORtvo/LXeOvQLRjXC5kLY9y+3d+2HqIl/jw
D/7GfCONPXU2L9IQuJpcFmuPm9oAeAKUL4POPLzjts9ggNGqbu78zjPwXAHJYlf2uS+fIgb+
GwjiY+dsy5izrefDJJzr+bsHHCg6xhZ3XNDfFQ1Av2RI2GTpwYBDR1n3FO9+SX8k4wtLGAEi
SJisu/zXOmbjnQdi3aZ7Z3CCA7oHFnQ0TFShbE6jbO571NyFIyCiGmVzcrd2u47OqSnWHnad
TH6cxAdOLUhNWpfxgXmaEG7HAC+A8gX8W9FaGoZTjj4MkMKPsgAAAMC7oHwZcNxkXuGhvc1Y
NmYtijsfRVEgIFpT7n6LcVkuD8/4jq3I2LjaPzStBJFUbf3Ddq1xZJ2mxuah9ml3fJBTmrLQ
Ikg7AZuHai5h3+vTY6xYtiQbes5QqkeMSyxwwqPQ68th/kvWJ2MdYLbJOWOq15QqbIxbhTZM
qzgVda4EdfUQr9st93dqGGRa+klf2ZNYhH0CfyDcVYdE4BAmBsDXotsnDQAATqB8GWjcZF7h
IkgbOCwUmJf5v822CjKEuvyrWY8cXE5oizT+72zwfolNJf+4Kb1OWExZcdRi6o4JfTVGa3zE
Ya9PibFCrZXZEcwtUdGpdZRjaBL2OHt4VFqocLRrzOj46hZLkaJjTjMCEq3u+o64wTFjqteU
KmyLopzadRkPq9OIrzMC5zpjPbQhcuz261qN3Q/aTtRmbpgyL/ys8RF6fhZemBhMdwMAAE+B
8mWgcZ15dSXQQJQZYq/qjt09VWGSo72s/a/XNlg5ShVfTSix8zVRHoJaR9ofve4yWpmICLJq
mnL0KIi+8XHa61NirFDNkyuXahZvdca2ROoOLnMVz/+JcMOjCtZZIVT1ICs5S2SqmUfaC+y+
A423OGZM9Z5ShdGe7WSpiu2oaOE4neTS3kNjYY7dNpllM5ZEECDpT5xA3VdR1cwxTEwZPoAB
AABeAuXLQOMm84oOZ+mumP50V4WFSTmltmNyEvKt1looEhCBf5hI87mDy/1Ox10pwjZyX9mP
3nDe61NirBCWNyWsLd6xGlZIWIyR3o0THvWB32z5mW0hAX62YVREMvQ7HL91rijnjKm+CYkx
4rMQQUiYJFheXF1XXc2x24I9I6s4hYlB+QIAADwFypeBxk3mFUfC2pTF6qFpFzLKL1auWG0m
0/5jqi+MC3Q8r3c04uKB8ZLPdtoZ9JJaRWttbmR8VsHNXv2PsUJDh8kNY+ZNIdRYX0tPtMIL
j6ptKn8tYr7xcu3+hvxLP4f4L4uaMX3jx2VMMbQ0NjOG1lr7/k2lopy02ND6/na7vTD8tDAx
ANix3LzwX3fbvy8RzQDAx4Hy5TPof+YVRyIak6101u9Yc1PZ47QOPZm7uvDOAzR8to6uIvFN
VuIvuT1Tq0SGyZHfPCmuaNEf2vDk4tkLL5FBP/bqpt8xVmiYtuUUoj+WN0UhN5Zknk0sRdjc
D0541Ok4iSjzVcOTHm+30hivrUAiCgrwS6hxmTHF6mF8Qo6HgS254XHi8UzZ+fs1hYc39Lfb
7XXhaPwwMQA+UsvbC9vtg976HHp40kK28lr8pkXfLUTJqb7j4Up9AD4rKF8GHevaF4Tck+lv
dAiC2tMWGW1fRJ1FGc/4iELWfGWwvceyEQKzSIYePuM0UY/UKjFj/3gP7yXjBOaib1zczLXJ
/dqrm37HWCF+6Sm+Rxat8aLnTdl+P7XjaDjhUeNGIb+oK8udh/NT6VcexccYixEEuciY6mmE
Zt0ZBwk77Ion7/CkYHNJAh/qb7fbeyiDHyYGwMdpK87YlaT4faqPpaoAQiTT+f4/FO0qKvub
Nl743/H5CwBfKyhfBhXZMZZGi8V9asgYz6wyz67v+UiaC6LvLIhmfh/GaKArl0mIbBmUVhzE
fD6K8aWvvbrrb4wVfcuA+MKA+I5vdzIfxgmP0nGLznGL7r43FxlTLB3A8rzkJi6NSToUz7pn
393GznNHc/hhYgB04Sb7oOLp1QtajpEqzAlIMV3PnfS/aGr3eII90yuT6G2Wo84gAFT/ImPH
Sv9dmfS43jVhO0IcRxOxDGvnm/M8ZbOiozX2M2dtWt5mBIxxR3EFERRsKpmau3fuuASrh9kr
dJry2KIKHkSMsUu3dCBfOZFQJNpe5R8Pn6tJ4kO0t/cOhSwLiL5Lxd4z7AqZpU5sY46UtVed
wy8nGS6Yp/SGGYrQNUeGxexGhCwzU+Dv2Vt7UfYGW16xRyd85p8g+E+A8gUAAFhwk33QUvb8
OpKzlxDEf7oznqDmWphzaH3ghQ+3Ndpu77S2idBpL0TEnyYHnyZtut/splSW4D9uRez0qVst
sTmnlylbN2L7dU318ksbzVhIDMi8U2FLUSA0FV9NKHTwjtQWrLy2lS2DwLp9+/K82vEHHuyL
r/0tcIp/8NkJyW4q1dn73HaTwp812JJuhxh7hejpnZxRyt4rW/6rW5kPoqKEAEoaPRSBVv/g
yHeexQv/eJWqRXx3OczB9geJ3CNuSt17W3udrcEws8dRbNEJunBXRPDpoHwBvIPlQxQAPheu
sw8464gnkGm9tX11hVWMgwaJwIf0zGzFV+Q8rqaYjZp5NMVltCo/4pNXU5PELqNrQ/QlMxIu
B7L2LTKU7vqUgiBp4Og1zD7l9gbbWVL5OQmPTH0njxzS+AAng8BaHKFR02dN1STxEUi6phNQ
ekVtGz3soG72Dgt5IQLS/y5qV76YSOPTayd69qrKQiDnVI3dz85jSYQhSN16wVztg1goQtX9
tOQC902zsVwDJGM+28toZvL1Ulcl1t4KNt7aztZgNRZh0CM6AYCBAOULAACw4Cb7gE9G0QBd
e1vF4SbPHfEELe/K/npZddB6OHOSFam6V/+D+GVEm3+NXu57Ki67BHtwaeezdnPsWGsXOkl9
GxuFaZk5IeZjrmblu3pbKAqiN7gZBO3lC5+QAF/3T4ywsANBbWF6oyLKphRl1FKewt4rai16
UyqsJi7MWBEmKCwm1Lm7pLaUGGPDISLD5NoeVzCymDp721KJM8xGsZls0QnqcIs3MACgfAEA
ABbcZB8QZEcbGT09f+vlorFj6ctf3uVEbDzywSE0xJh1KyERksSobb8WrJ0oRC8qaPXVtXzN
hSd9HC+aHI3IPqBLfhY5xqAY9xCdjQiPn7FYZ15adlZ5Qt2K0EkyBNyogjohVIS3OxaLwAw7
qHuRk50vZvAtTq+GDLkvSW6i1jfSELbip6m+lrHaX0R6pEzlq/e17WVU+3dNf78qRlpTxflQ
TV/DJFDLS3pGJ+y2wsvyBoA7UL4MKmzhm0FgCfNbVWv/bZHbXcYQv3xPsNsJBSEs81fxyx+8
T/TzVByG3Z6pvaMWTijs6wiiKO3ZGVrlre0L3P9yy4hwUSfB/D7P6n/2gaDm7LWU/d9HRCus
nm8q/fbykR2bz/PvWSlFYA0e4pPQNnKo2Zd6xc3ASr4h9+ACkwszbp9wev7wEpJy19FUJJZl
JWaWoNF9HUttsovq+h833By66LQ+vQDAiSpIueKFuzMWi0Bdj4Ud2BJvHVwSXBOeaT2evVen
lmqbuRB/TMn2nERRaCi5nJyYRw9FENc0nyLrHJ/oNXGJFvFNdnJ8vsXqyYqEbuUL7jB/tsmL
7Bmd0J8TC0Bf4Bdp0DFuFKCIWiruRq+28jliPW2HleQXf+WT0PM6G4l0v/43RZJ6XpFnkZbE
gDdMe5eblHJf0clzotTHdobWXJS6445dYjzULv8dAmTbTan1YUHLJ/rRLyAK2nPK311DAHXL
kSbI225Lfb0u4FsB63LsIqOjW101REWUvKLsly8cQXQimSz2+caw72ORtK0djfwCqHtmdFyW
jRNVoMGHHuL2U97m+7jcNf70sAPsGiVr+SH87L0SJhAsgo4UrPLSFLBDqrYLKdoj6LvzSVoE
JMeEBTmM8GZceZQa4qQkhFq6HQNvmMNElNiiEz7qRAPQA5QvXw1+WQMHu+m+Pz8rbbCSFPnS
Rycqmzoof+mDfgwRZVO7z9LR1r9+XxueF2bDzdLCHp0hCKh7nk0d6I6BrxxBfOycbRlztnV/
lDUIAGG5BmYrj99Z2T0roHumACPVAOkHFHOcRRqi7plFY/0NJeBlELC2oOgYW9yx3J2oRtmc
TNnMuiVer7AH/c8U+p9hfr+7l2Hy9+htP4YJwACB8mXAcRMa0UNLUwP2hXo/gsKSGLF/lmhx
Wvj6gC0pjKS1iJ+WmJGF6NM9W4U2TKs4FXWuBHUGKtCoBTgb94xnmClw7+fgZRsP3v2gausf
tmuNo8qziDEueWH0z4FwAx4YORbpyn7kB3sSckmGK6OO/+iqKU5oLsnYuNo/NA0LdWA0pU6i
z/XcnBcmm7XlgkYsy4RUf1Ii8BpkOUddKebS2FHSjZcOu37wXAnJxPvAj+4oxW/5vrvUMZ1n
nkZ91DMVg78OZyAyOT4qdgepCDkppSxNfx5jSWUP5EAt73ucNHXUFanO1UkbyN83AAD4L4Ly
ZaBxExrBikYtyjx++hJZz1d5KPZ9Z2IEqnkQs8rzmdMfH37RIlZc/vE722XDcpPdVLBni3Jq
12U8rE4jvs4InOu8ZWxBlGkpx41Z4hkqr0a4HR8afqvZVuRmiKNNiJZBrFpnR/ADHuhNoLxm
jQNX2+JrMgPnzgs+Z5zsqvDobPB+iU0l/7gpvU5YTFlx1GLqDnp0BUrZig1Ale0uBb2nRERM
K+XUIK7yRr29D9t+qc3cMMXOMXdjYkZVpHDJyeWTth6z1F8/sfVaGFsqBkUcbyBuMVX31DoL
uMZ7sWyBHBYtbCftpO1HnTQ3Fcj5BQCATwPly0DjOjSiJM5JKY7xT1WnbfHLzMSG5KLOxAhZ
/rqcjXvL3Q/baWHLKeTNF8w12pJ1/c85Ktj6Xu3ZTpaq2OOKFo7TSS43Hm8kXcHdmP7xR2c8
Q13O5lN1005aKPITkOF3207kiwp3TWJzCHhQkcOenE6x0RQnEET0TXWo6RXY2EbaH73uMlqZ
iAiyappy5XnYvaPp1cY3Lnvj9vl8K8324VOvKRFV1jqcGsRlOWemJonAT9KfOAHd1Z75rQw/
H1LV1JYszSv7gBpf46RiUCbgDqQbPvZAjtb64hy2k9aJu5OGoHwBAIBPA+XLQOMmNIKOuXS3
C7YrMzECodp3f5ZKaEt1rIYZIjpMDr2uqGxG8u3fCYkJM+I+CULCJMH2V9nXFfgbY813xTPU
vntTKqgmTL+4kahsgi16abnPPDqngAf6K7GQQPdqhMA/TKT53MHlfqfjrtAv13RfyXzKco6z
AXvt0ldKRAPnBrlXiZuKgfAG0h2/KFsgRyvOSUPUPzt24OqkAQAA+FRQvgw0bkIj+kVMeqRi
1av37S+7JIRoTW9fFyOFqZKMW6y0NDYzbk3YWvv+TaWinLQSmfPGXV3E4hk6oh1Qw4vr5/NF
TaZ1Pssp4AFXfWFcoON5vaMRFw+Ml3y2084gr/8Dw02JaCyMW/GxDbLBT8VgvaIVFw0vkAPv
pM1QZe7C1UkDAADwqaB8+Qz6HxrRH8KjzeeLOB9P9zL20CKWZZ9OyZ/vPXmkEKpof+5hfEKO
h4EtueFx4vFM2fn7NaXHNONu3G1yhB7PQI3Aoh1siTcOrvGsWVswvTNFhVPAQzNe56oL7zxA
w2fr6CoS32Ql/pKL2Ja6cIabEhE+4uMbZIObiuE9ivMOTdXVdS2orbqQPZAD76R1lS9cnTQA
AACfCsqXrx5BxiL4YMzWYAfRxYyLiVIjHJQ6Fk+M0Kw74yBhd5dKNvQOTwo2lyTw4W/cfW0H
Qd4qOO7JKn96tMPMDUkx02TQY+aTHAIe8F+JZc1XBtt7LBshMItk6OEzThM1cjMynJQIFfQJ
DbIdADcV4x/8jflGGnvqbF6kIXA1uewATiAHzkkjNJcyd+bmpAEAAPhUUL4Mqp6pCQw9EiMQ
gaQ1JzR1Tij7/nITl8YkHYpHfW7c80AiKpQNaZQNLI+wbNB3jgU/2TGm4+aJmgui7yzAia6g
IRz9SYnoo0GWbrAehfVujixHwUvF4DAQJDVpXcaHdR1P4AVy8LOdNKGuprg6aQAAAD4NlC8A
AAAA4DFQvvAs1s8bAAAAgP8SKF8AAAAAwGOgfAEAAAAAj4HyBQAAAAA8BsoXAAAAAPAYKF8A
AAAAwGOgfAEAAAAAj4HyBQAAAAA8BsoXAAAAAPAYKF8AAAAAwGOgfAEAAAAAj4HyBQAAAAA8
BsoXAAAAAPAYKF8AAAAAwGOgfAEAAAAAj4HyBQAAAAA8BsoXAAAAAPAYKF8AAAAAwGOgfAEA
AAAAj4HyBQAAAAA8BsoXAMC/U1lZWWFh4WD3AoDB1P5XIC8vP9i9+CygfAEA/AvJysreunXr
yJEjg90RAAaZnp7eYHfhs4DyhSMCgTDYXQAAfCQ1NbXVq1cPdi8AAJ8LlC/43rx5M9hdAAAA
AAA+KF8AAAAAwGOgfAEAAAAAj4HyBQAAAAA8BsoXAAAAAPAYKF8AAAAAwGOgfAEAAAAAj4Hy
BQAAAAA8BsoXAAAAAPAYKF8AAAAAwGOgfAEAAAAAj4HyBQAAAAA8BsoXAAAAAPAYKF8AAAAA
wGP+D/1ZL27ZbZ3/AAAAAElFTkSuQmCC
el desarrollo de potencial propio y la autorrealización. (Robbins & Judge, 2009).
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 830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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Acorde esta teoría, conforme se satisface cada necesidad, la siguiente se convierte en la
dominante y aunque la teoría anuncia que, ninguna necesidad llega a satisfacerse por
completo, aquella que se satisface en su mayoría, deja de motivar.
Teoría de las Necesidades Adquiridas
La teoría de las necesidades adquiridas procede del autor David. C McClelland, quien
identificó tres tipos de necesidades básicas de motivación: la necesidad de poder, de
asociación y de logro. (McClelland, 1961).
Necesidad de Poder: Las personas con esta necesidad se interesan por ejercer influencia y
control, bajo estas características, los individuos ocupan posiciones de liderazgo, son
exigentes y empeñosos. A través de esta necesidad se busca tener impacto, estar al mando,
influenciar a los demás y encontrarse en situaciones competitivas.
Necesidad de Asociación: Se enfocan principalmente en ser aceptados o incluidos en los
grupos sociales. Los individuos se esfuerzan por ser aceptados y optan por situaciones de
cooperación a las de competición. (Schadeck et al., 2015).
Necesidad de Logro: Son personas a las que les gustan los retos y sienten el deseo de
desempeñar con excelencia tareas desafiantes. (Jones & George, 2010).
Teorías X, Y
Teoría desarrollada por Douglas McGregor a través de su libro el Lado Humano de las
Empresas, quien propone dos visiones diferentes de los seres humanos. En la teoría “X” los
gerentes creen que a los empleados les disgusta el trabajo y por ello deben ser dirigidos para
realizarlo, en cambio, con la teoría “Y”, los gerentes suponen que los empleados consideran
el trabajo como algo natural.
A través de esta teoría, se tienen dos estilos de liderazgo, el participativo o “Y” y el
autocrático o estilo “X”, según Douglas, cada uno de estos estilos tendrán características que
repercutirán en el desempeño y motivación de sus colaboradores.
Concluyendo lo citado anteriormente, en la teoría “X”, los trabajadores prefieren ser
dirigidos, no quieren tener responsabilidades, este tipo de personas trabajan por dinero y
temen a la penalización. Por su parte. los trabajadores de la teoría “Y”, son mucho más
responsables y no ven al trabajo como un castigo. (Schadeck el al., 2015).
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 831
/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX
FBYWGh0lHxobIxwWFiAsICMmJykqKRkfLTAtKDAlKCko/9sAQwEHBwcKCAoTCgoTKBoWGigo
KCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgo/8AAEQgA
VwBYAwERAAIRAQMRAf/EAB8AAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYHCAkKC//EALUQAAIB
AwMCBAMFBQQEAAABfQECAwAEEQUSITFBBhNRYQcicRQygZGhCCNCscEVUtHwJDNicoIJChYX
GBkaJSYnKCkqNDU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6g4SFhoeI
iYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2drh4uPk5ebn
6Onq8fLz9PX29/j5+v/EAB8BAAMBAQEBAQEBAQEAAAAAAAABAgMEBQYHCAkKC//EALURAAIB
AgQEAwQHBQQEAAECdwABAgMRBAUhMQYSQVEHYXETIjKBCBRCkaGxwQkjM1LwFWJy0QoWJDTh
JfEXGBkaJicoKSo1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoKDhIWG
h4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uLj5OXm
5+jp6vLz9PX29/j5+v/aAAwDAQACEQMRAD8A779oD4rat8O7vT4tLs0nS4Us7OuQOtAGZ8Fv
j1H4u1FtN19I7e+lYC3WMfeoA9+oA8/+L3xIsfAGjNJMynUJUJt426MaAPF/h7+0R4h8SeLt
O0ufTYfIuZAjtGuSo9aAPqigAoAKACgAoAKAPNP2gtPtbn4aatPLZrPcRx/u225ZT7UAfCfh
PWbvwn4jstXigPnWzb0WQEZNAH6L+BtdPiDwnp2qzmNZLiISOqn7p9KAPi39pDxveeLPGMmm
ywr5emyGOJk53D/JoA9F/Yz0uCV9ZlvbHM8RUxySJ06dM0AfVvOfalqAUwCgApNsApgFAGH4
2a6Tw3dmwtorq4C/LFJ91vrQB8f/ABk8NeK9bsF1S80WysreyUlltyMt+AoAqfDH4jRaJ8Of
EemalqUyX8q7bNS3KcdqAOW+F/h7xFr+vf2xptsl49q4eT7SeJPz60AfZHwjm1h0vTrek2em
4xt8jHzfWgDF+I3xmtPD+piw0ny7meMkTZ6LXLWxChoj3ctyaWKXNPRHNaR8f5G1GBdTt4kt
WbDsvUCsoYy71PQxXDipwbg7s960bVLTWNPhvbCVZIJV3KQa7k01dHytSnKnJxluXaZAUAFA
HN/EX7N/wiN/9tupLSDb800f3l+lAHhXgG18K3fii0hHiXUdR3ZH2abOx/rQB5l8YvDnhO0+
J6W9i1xHZyy/6WI0OIvpQB7H400XwfpPhfQ1tdTutJt2T5ZLcEGXjvigCx4LmsbfwP4gk8P6
1e6lKqE7ps5U47VnVdoto68DGM68YyPF/COknxB4vS1vUlxcP88jA5+teSoupKzP0OrWhg6D
lDodj8Zfh7Z+DbW2Nh5k/ndeM4rSth1S2OPLM4ePuqitY7j9l+5upbS+hmM3kRgCNXzhenSu
nCSbTueFxDShGScT3qu0+aCgAoAwvHH2j/hGbz7HaR3k+35YZPut9aAPI/Ao8Rf8JNa/bfCW
nWUGTumjA3LQBF4vfxQviG+Wz8G2N1AX+SV1G6QetAG944Guf8I/o32Tw1Z3c+395DKBiLjo
KAJ/h8dZXQ9VN/4fs7CXHyRxYxJx3pS2NKWk0UfC02uv4giW68P2VvBu5kTGRWEVrsepWmuT
42zqfig2oxwQf2bpsF8e4lxxV1Ec2DlZ72PN9W8X+NPCWjSahYeHbVbZBun8sAgCimrDxklL
rc4Jf2qNa/i0uD8MVozzzU8PftQ3FzrNpDqunpBZO+JZAR8oqOR9xn1BomrWWt6bBfabMs1v
Mu5WB7VoI5P433txYfDXWLixuPs9ykeUcHBH0oA+Ofhh8S9YsfG+mXGva5dPpqSZmV3yCKAP
s680r/hLNQ0TxBpmrTQ2cI3rGnSUZzzQB8/ftS/El/7WsbHwxq00E9qStysRxzzQBofsy69q
OueHfEf9v6vK8SD5XdvmXjtSlsaUr86sdR4HXST4nhFt4jvbmUNkRPnBrnha+56tfm9nrE7L
xm2n+L1uIbLWZbSTTsmfyzitJ2aOTCuVOVrXucgxtIfhT4lj03VptTPlNuD8kcUqTT2ZePjN
Nc0bHg3wF+FNr8RW1IajcTWotcEEAjOcf41secb/AMX/ANn6fwrpMeo+H5ZL23jBa4LfwD1o
A6z9i/U726k1i1ubmSS3hjURRseE6dKAOm/ad8K+L/E02nw+GFlks9hE8anAJ560AfPf/CjP
Hf8A0CG/P/61AH278MdMudH8C6RYXybLiCEK6+hoA+R/iT8G/GeqeONXvrHTDLbTzF0bOMig
BfAfw0+I3hrWYZ0sHS03ZmiLfK49CMVMtjWjZT1Pe/CI1b/hIozceGbS1i6GVAMiso3vsd1b
k5NJXOUs7jUPBHxZvLTULRJdO1yTAJ54NQ5OMrPqdKpQrUOeDs4nYaF4XvfD0/iWe1sopIZg
TBAT8rexrSnDlbOXGYlVqcV1R5x4o8ReOdM8K6kLHw7aaVFJH+9mhIBA/CtjzTK8T/G21t/h
JY6Lp8xvdSuYPKu2kbJXPU0AWv2JlP23XmPA2L179KAPrGgAoAKACgDE8aR383hm+j0lit60
ZEZHXNRO/LobUHFVE57HzF8PvEviC2+JUOmazqNwMSFWjkPFclOcua0j6PFYejKhz00X9V1C
fUPjpbxanOTBDN+5DH5fwrNzbq2OyGHhHL7panqpttVt18Sya3fPBpjpmKZGzsHtXfG9z5Gq
48qS3PA/GX9hr4avTa+N727n2fLbuT+89qswOcufhPbwad4SuWvcHWnCupOPLzQB9lfD7wVp
fgrRILHTIk3qu15sfM/1oA6mgAoAKACgAoA4Hxt8NNN8R38N/Awsr2MkmWMct9ayqUlLU78L
j5UFZ6o534h/CWPVdFtp9NmZNXs1yso4MhFZzo+7pudWFzNqpap8LPOvhx4t1/T/ABL/AMIx
4nhkvrS5fyZBIM7azoSknaR3ZpRoVKaqUjL+Jui2sfxe8PafJoMFnpfnhUZP+Woz3rtPmDY/
bCVNH07w4umD7MYnPliPjbjPSgD2b4IXlzf/AAz0a4vpWmuHjyzt1NAHd0AFABQAUAFABQAU
AYWsWehaYs2s6hbQRmH53nK8j3pcq3NPazty30Pl3xhqmlax8YvD1/outtqUUtyG8ktkRc9K
Zmes/tM+BYvFXhFtSknMcmlxmRV/vf5zQBc/Zv8AE2l6t4AsdNspw93Zx4mT+7QB61QAUAFA
BQAUAFABQBi+MtBj8TeGr7SJnMcd0hQsO1AHivhP9myw8P8AiGx1RdWkla1kEgQjqaAPbfFu
ip4i8O3ulSuY0uYyhYdqAOG+Dnwmt/hrNfyW189z9qABDDpj/wDVQB//2Q==
/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Estudios aplicados a la motivación
De acuerdo a estudios realizados por Bohórquez et al. (2020) el 25% de los trabajadores
mencionan estar ni satisfecho ni insatisfecho en relación a poder cubrir sus necesidades
fisiológicas a través del trabajo que desempeñan.
Por su parte, Peña y Perero (2018) en el artículo Motivación Laboral, elemento fundamental
en el éxito de la organización, acuerdan que la motivación laboral constituye un factor de
suma importancia para que las organizaciones puedan desarrollar su máximo potencial
productivo, en este sentido, un jefe debe contar con herramientas que logren una adecuada
motivación de su personal.
Así mismo, Donawa (2019) concluye que la capacidad de producción de las organizaciones
depende en gran medida de los esfuerzos que realice el equipo humano en función de sus
necesidades, estimulando para ello la motivación laboral hacia su desempeño y el incremento
de la productividad en las organizaciones.
Los autores Peña y Villón (2018) determinan que existen ciertos factores que intervienen en
el proceso de motivación del personal en sus trabajos, estos factores son: una relación laboral
donde exista la satisfacción, motivación y desempeño, el predominio de las expectativas y
compensaciones, un clima laboral positivo, resultados de satisfacción laboral, factores que
indicen en el desempeño y finalmente, la administración de personal.
Tabla 2
Relación Estructura Marco Teórico
Referencia
Y
X1
(Pedraza et al., 2010)
x
(Cajas et al., s.f.)
x
(Castro & Delgado, 2020)
x
(Jara et al., 2018)
x
(Bautista et al.,2020)
x
(Hart, 2012).
x
(Meza, 2018)
x
(Bohórquez et al., 2020)
x
(Chiavenato, 2009).
x
(Robbins & Judge, 2009)
x
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 832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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
(Macías & Vanga, 2021)
x
(Donawa, 2019)
x
(Maslow, 1991)
x
(McClelland, 1961)
x
(Schadeck et al., 2015)
x
(Peña & Villón, 2018)
x
Fuente. Elaboración propia, adaptada a partir del marco teórico con las variables de
investigación independiente (x1) y la variable dependiente (y)
Material y métodos
El abordaje metodológico se cimentó en el paradigma positivista y el método científico. El
estudio de enfoque cuantitativo definió supuestos y midió los hallazgos desde la inmersión
en campo (Sáenz & Tamez, 2014), (Lincoln & Guba, 2000). La investigación exploró los
estudios teóricos y determinó el constructo teórico de causa y efecto. El muestreo que se
aplicó fue no probabilístico e intencional pues la selección de los sujetos de estudio no fue al
azar (Bologna, 2018). El muestreo no probabilístico o dirigido se fundamenta en un proceso
de selección de datos no formal (Hernández et al., 2010). Este tipo de muestreo se lo realizó
por conveniencia pues elige deliberadamente el sitio, el objeto y al sujeto de estudio para la
indagación (Baca, 2016).
A fin de dar respuesta a los objetivos planteados se empleó la investigación exploratoria,
descriptiva y correlacional. Exploratoria ya que el estudio estuvo enfocado en el fenómeno
de investigación en un contexto específico de análisis (Hernández et al., 2010). Descriptivo
pues pretendió recoger información de manera independiente o conjunta sobre los conceptos
o variables de estudio (Hernández et al., 2010) y finalmente correccional pues midió la fuerza
con que la variable independiente se encuentra relacionada a la variable dependiente (Abreu,
2012). Adicionalmente, el estudio es transversal pues analizó el fenómeno en el momento en
cómo se presentó en su contexto de origen.
El enfoque metodológico adaptado es mixto basado en un análisis cuantitativo y cualitativo
con énfasis en la recolección de datos cuantitativos pues como lo menciona Del Canto y Silva
(2013) este último se soporta en la indagación a través de elementos cognitivos y datos
numéricos
extraídos
de
la
realidad,
los
cuales
son
procesados
estadísticamente
para
demostrar teorías; este abordaje se efectuará mediante la recopilación de información de los
sujetos de estudio.
Para la recolección de datos se aplicó la técnica de encuesta y el instrumento de cuestionario.
La preparación del instrumento de medición se basó en el análisis e investigación de la
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 833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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
literatura de las variables de estudio. Para ello, se revisó artículos científicos de bases de
datos como Scopus, Scielo, Redalyc, Google Académico (Arribas, 2004).
La estructura del documento constó de dos partes: en la primera se midieron los factores por
escala de Likert de 5 opciones siendo 1 totalmente en desacuerdo, 2 en desacuerdo, 3 ni de
acuerdo ni en desacuerdo, 4 de acuerdo y 5 totalmente de acuerdo. Los ítems o variables
observables se obtuvieron de la experiencia del investigador; y, en la segunda se midieron
las variables de control para caracterizar el perfil del sujeto de investigación (Mendoza &
Garza, 2009), (Dillman, 2000).
El instrumento se sometió a validación de contenido por consenso de expertos. Se seleccionó
3 jueces a quienes se les envió un formulario con los ítems y la definición del concepto de
cada factor de investigación. Los jueces tienen 4 opciones. 1 irrelevante, 2 poco relevante, 3
relevante y, 4 muy relevante. Se obtuvo el promedio de las calificaciones. Los ítems mayores
al promedio de 3 se quedan en el instrumento y las variables observables iguales o menores
a 3 se eliminaron con lo que se obtuvo el instrumento de medición para prueba piloto (Ander-
Egg, 2003). En el instrumento inicial se contaba con 32 ítems, sin embargo, con la validación
de contenido, el número de ítems se redujo a 27.
La investigación partió de una población finita de 420 sujetos de investigación. Dichos
sujetos fueron los funcionarios pertenecientes a un gobierno autónomo descentralizado de la
provincia de Morona Santiago. El marco muestral fue el distributivo del personal a diciembre
del año 2021, proporcionado por el departamento de talento humano de dicha institución. El
tamaño de la muestra correspondió al 95% de nivel de confianza y el 5% de error con 2
desviaciones estándar, la fórmula aconsejada fue: (Rositas, 2014).
n= Npq/((N-1)(e/z)
〖
^2
〗
+pq)
De donde:
Z= intervalo de confianza del 95% con un valor crítico de Z=1.96
p= probabilidad de éxito del 50%
q= (1-p) probabilidad de fracaso del 50%
N= Población finita
e= error del muestreo aceptable del 5%
n= 201
Fiabilidad del instrumento de medición con prueba piloto
La fiabilidad de la encuesta según Mendoza y Garza (2009) es la intensidad de repitencia con
que un instrumento es aplicado al mismo sujeto de estudio con iguales resultados, lo que
permite mejorar la encuesta en cuanto al formato, redacción y comprensión de los ítems
(Hernández et al., 2010).
Se aplicó el Alpha de Cronbach, procedimiento que implica un análisis de fiabilidad
orientado a medir la confiabilidad que tienen los ítems (George & Mallery, 2003). Nunnally
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 834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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
(1967) indica que en etapas iniciales un coeficiente de fiabilidad de 0,6 o 0,5 puede ser
suficiente en estudios aplicados en ciencias sociales. En cuanto al presente estudio, este
coeficiente mejoró en la medida en que se aplicó la encuesta general. La prueba piloto
permitió medir la confiabilidad del instrumento y se aplicó a 33 sujetos de estudio a través
de la herramienta en línea denominada Google Forms.
A continuación, los resultados una vez aplicados el Alpha de Cronbach mediante un software
estadístico.
Tabla 2
Estadísticas Totales de Variable independiente
Alfa de Cronbach
0,812
Número de elementos
14
Variable independiente -
x1
Motivación
Media de escala
si el elemento se
ha suprimido
Varianza de
escala si el
elemento se
ha
suprimido
Correlación
total
de elementos
corregida
Alfa de
Cronbach si el
elemento se ha
suprimido
PREGUNTA 1
43,3
63,405
0,502
0,797
PREGUNTA 2
44,06
63,559
0,462
0,799
PREGUNTA 3
44,42
65,189
0,318
0,809
PREGUNTA 4
44,27
63,205
0,451
0,799
PREGUNTA 5
43,88
61,797
0,572
0,791
PREGUNTA 6
43,82
63,028
0,549
0,794
PREGUNTA 7
43,67
62,417
0,421
0,802
PREGUNTA 8
43,55
64,006
0,33
0,809
PREGUNTA 9
44,27
67,955
0,132
0,824
PREGUNTA 10
43,79
59,61
0,523
0,793
PREGUNTA 11
42,85
66,508
0,44
0,803
PREGUNTA 13
44,18
60,903
0,52
0,794
PREGUNTA 14
44,21
59,735
0,565
0,79
PREGUNTA 15
43,88
61,172
0,444
0,8
Fuente: elaboración propia
Tabla 3
Estadísticas Totales de Variable dependiente
Alfa de Cronbach
0,844
Número de elementos
11
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 835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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Variable dependiente - y
Desempeño Laboral
Media de escala
si el elemento se
ha suprimido
Varianza de
escala si el
elemento se ha
suprimido
Correlación
total
de elementos
corregida
Alfa de
Cronbach si el
elemento se ha
suprimido
PREGUNTA 16
43,52
20,695
0,38
0,842
PREGUNTA 17
43,39
20,496
0,487
0,835
PREGUNTA 18
43,82
17,966
0,588
0,827
PREGUNTA 19
43,64
19,239
0,614
0,824
PREGUNTA 20
43,64
20,426
0,494
0,834
PREGUNTA 21
43,64
18,864
0,685
0,819
PREGUNTA 22
44,03
19,218
0,403
0,846
PREGUNTA 23
43,55
19,443
0,692
0,821
PREGUNTA 24
43,79
19,61
0,54
0,83
PREGUNTA 26
43,7
19,405
0,547
0,829
PREGUNTA 27
43,61
18,996
0,488
0,836
Fuente: elaboración propia
Tabla 4
Resumen estadístico confiabilidad de instrumento con prueba de validez de contenido
Variables
independientes
Ítems con
validez de
contenido
Alpha de
Cronbach
(prueba
piloto)
X1 - Motivación
14
0,812
Variable dependiente
Y - Desempeño Laboral
11
0,844
Alpha de Cronbach de
la escala general
25
0,828
Fuente: elaboración propia
Con la prueba de validez de contenido se tuvo 27 ítems y a través de la fiabilidad del
instrumento mediante Alpha de Cronbach se definieron 25 ítems, con ello, se afinó el
instrumento para la encuesta general o definitiva.
Tabla 5
Preguntas del Instrumento de medición final con validación de contenido y fiabilidad
mediante Alpha de Cronbach
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 836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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Pregunta
No.
Descripción pregunta
1
Su trabajo le brinda posibilidades para el crecimiento económico y
profesional.
2
Los funcionarios de un departamento son frecuentemente reconocidos.
3
El desempeño creativo de los colaboradores recibe una recompensa por
innovación.
4
Se estimula la capacitación de los funcionarios.
5
Sus
aportes
e
ideas
son
tomados
en
cuenta
y
valorados
por
su
supervisor.
6
Su puesto de trabajo le brinda seguridad y estabilidad laboral.
7
El tamaño de su espacio físico es el adecuado para el desempeño de sus
actividades.
8
Su espacio físico tiene la ventilación e iluminación adecuada.
9
El nivel de ruido de su espacio de trabajo interfiere significativamente
en el desarrollo de sus funciones.
10
Cuenta su área de trabajo con los equipos e insumos necesarios para el
desarrollo normal de sus funciones.
11
Esta totalmente comprometido con su trabajo.
12
Siente que la Institución se preocupa por su bienestar.
13
Considera que su remuneración es la adecuada en relación a las
actividades que desempeña.
14
Puede verse aquí trabajando en los próximos 5 años.
15
Domina las funciones de su puesto de trabajo.
16
Cumple a tiempo las actividades y trabajos que le designan.
17
Realiza su trabajo de acuerdo a los lineamientos establecidos en el
manual de funciones.
18
Mantiene una actitud positiva ante los cambios.
19
Asume funciones con proactividad.
20
Tiene la capacidad de actuar de manera eficiente bajo situaciones de
presión.
21
Participa con entusiasmo en reuniones de trabajo.
22
Hace buen uso del equipo y materiales de trabajo.
23
Participa con entusiasmo en las capacitaciones planificadas.
24
Tiene la capacidad de resolver cualquier problema que se le presente.
25
Tiene la capacidad de trabajar en equipo en pro de alcanzar los objetivos
organizacionales.
Fuente: elaboración propia
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 837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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Caracterización del perfil de los sujetos de investigación
Las figuras representadas a continuación resumen las principales variables de control del
estudio resultado, como parte de los datos obtenidos a través de las encuestas realizadas a la
muestra de estudio.
De acuerdo a la información recopilada, el 49% de los encuestados son casados, seguido
del 28% de personas que se encuentran en el segmento de solteros. El porcentaje restante
representan personas divorciadas, separadas o en unión libre, (véase la figura 1).
Figura 1
Estado Civil
Fuente: elaboración propia
Los resultados reflejaron que el 84% de los encuestados tienen un nivel de escolaridad
universitaria mientras que el 15% tienen culminada la formación secundaria (véase figura 2).
Soltero
28%
Casado
49%
Otro
23%
Estado Civil
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 838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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Figura 2
Nivel de Escolaridad
Fuente: elaboración propia
En cuanto a la información de edad, el porcentaje mayor de los encuestados tienen un rango
de 31 a 40 años de edad (véase figura 3).
Figura 3
Edad
Fuente: elaboración propia
Primaria
1%
Secundaria
15%
Universidad
84%
Nivel de Escolaridad
Menos de 20 años
1%
De 21 a 30 años
22%
De 31 a 40 años
44%
De 41 a 50 años
21%
Mas de
51 años
12%
Edad
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 839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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Finalmente, el 39% de encuestados poseen un nivel de ingresos superior a los $1001
mensuales, seguido por un 25% cuyos ingresos van de $501 a $750 mensuales, (véase figura
4).
Figura 4
Nivel de Ingresos
Fuente: elaboración propia
Resultados
A continuación, se presentan los resultados de la investigación con base en las salidas del
SPSS (2022) y que se enmarcan en las hipótesis correlacionales:
H1: La motivación es un factor asociado al desempeño laboral de los funcionarios públicos
descentralizados.
Para medir la fiabilidad del instrumento, se aplicaron las encuestas con validez de contenido
y
fiabilidad
de
Alpha
de
Cronbach
a
104
funcionarios
públicos
descentralizados
pertenecientes a un GAD de la provincia de Morona Santiago. En cuanto al tamaño de
muestra mínimo, no existe un solo criterio sobre el tamaño muestral mínimo exigido, existe
un consenso en que el tamaño de la muestra debe aumentar en función que aumenta la
complejidad del modelo. Sin embargo, se han establecido los llamados mínimos muestrales
para atender las particularidades de la investigación de campo. Tamaño mínimo de muestra
100 sujetos de investigación cuando las condiciones traten de modelos de cinco o menos
variables de investigación (Guerra & Ponce, 2014).
De 425 -
500
12%
De 501 - 750
25%
De 751 - 1000
24%
Más de 1001
39%
Nivel de ingresos en $
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 840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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Se aplicó la correlación de bilateral de Pearson con coeficientes significativos, para ello se
calculó las variables considerando sus promedios, a continuación, los resultados obtenidos.
La variable Motivación está fuertemente correlacionada con la variable Desempeño Laboral
(0,342**) y es estadísticamente significativa porque Sig. Bilateral es 0,001 que es menor al
p-valor de 0,05.
Tabla 6
Correlaciones Bivariadas de la Motivación en el Desempeño Laboral de funciones públicos
descentralizados
Desempeño
Laboral
Motivación
Desempeño
Laboral
Correlación de
Pearson
1
,342**
Sig. (bilateral)
< ,001
N
104
104
Motivación
Correlación de
Pearson
0,342
1
Sig. (bilateral)
< ,001
N
104
104
Fuente: elaboración propia
Discusión
En la presente sección se expone un diálogo entre los resultados de la investigación con otros
estudios sobre el problema analizado en diferentes contextos. Mediante el estudio realizado
por Ruiz et al. (2021) sobre la motivación y el buen desempeño laboral de los trabajadores
de la gestión pública, los resultados evidenciaron una relación significativa a nivel de 0,000,
así mismo, esta relación se encuentra dentro de un rango de calificación positivo pues el
coeficiente de correlación mostró un valor de 0,671**.
Por otra parte, a través del estudio efectuado por González et al. (2020) se determinó que la
relación entre el clima organizacional y el desempeño laboral de funcionarios públicos de
una entidad del estado es estadísticamente significativa y tiene una correlación positiva de
0,959** lo que dialoga con la correlación del contexto Morona Santiago - Ecuador que es de
0,342** y con los resultados de Bohórquez et al. (2020) sobre la motivación y el desempeño
laboral, en donde se determinó que existe un adecuado grado de motivación, el cual influiría
positivamente en el desempeño laboral de los trabajadores de un GAD Municipal. Por tanto,
se demuestra que a medida que incrementa la motivación en una institución, aumenta también
de forma directa el desempeño laboral de sus funcionarios.
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 841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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Conclusiones
La investigación sobre la relación que tiene la motivación en el desempeño laboral de los
funcionarios públicos descentralizados de un GAD municipal de la provincia de Morona
Santiago, satisface la pregunta y el objetivo de investigación pues se determinó que las dos
variables si están correlacionadas y mantienen una relación estadísticamente significativa,
evidenciando que la motivación incide directa y fuertemente en el desempeño de los
funcionarios.
Complementando lo anterior, se concluye que factores motivaciones como posibilidades de
crecimiento
y
desarrollo
profesional,
reconocimiento,
estabilidad
y
seguridad
laboral,
disponibilidad de recursos físicos, remuneración adecuada, entre otros, tienen una asociación
estadísticamente significativa con el desempeño y el accionar de los funcionarios para
alcanzar los objetivos institucionales.
Adicionalmente, la investigación si mueve el marco teórico del fenómeno estudiado en la
medida que, desde el contexto específico de estudio se comprobó que la variable de
investigación,
es
decir,
la
motivación,
si
está
asociada
al
desempeño
laboral
de
los
funcionarios públicos descentralizados, resultados similares que se corroboraron con estudios
científicos enfocados en la misma rama de investigación.
Finalmente, el estudio refleja los factores que inciden directamente en una alta motivación
en el personal y por tanto su desempeño, ante esto, los entes del estado deben formular
políticas administrativas que promuevan en su personal mayor estabilidad, planes de
capacitación, puestos de trabajo idóneos con herramientas, insumos y recursos adecuados
para el desempeño de funciones, un programa de incentivos y de crecimiento profesional y
de manera general estrategias que permitan mayor competitividad en el personal; todas estas
mejoras se evidenciarán de manera directa en progresos de los servicios que se brindan a la
ciudadanía, esto en pro del desarrollo económico, social y cultural de los cantones del país.
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 842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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Referencias bibliográficas
Abreu, J. (2012). La pregunta de investigación: alma del método científico. Monterrey:
UANL.
Ander-Egg, E. (2003). Métodos y técnicas de investigación social IV. Técnicas para la
recogida de datos e información. Argentina: Lumen.
Arribas, C. (2004). Diseño y Validación de Cuestionarios. Matronas Profesión, 23-29.
Baca Urbina, G. (2016). Evaluación de Proyectos. México D.F.: McGraw Hill Education.
Bautista, R., Cienfuegos, R., & Aguilar, E. (2020). El desempeño laboral desde una
perspectiva teórica. Revista de Investigación Valor Agregado, 109-121.
Bernal Gonzáles, I., Lucio Gómez, D., & Pedraza Melo, N. (2018). Liderazgo y sus efectos
en los resultados de una empresa manufacturera. Revista Venezonala de Gerencia.
Bohórquez, E., Pérez, M., Caiche, W., & Benavides Rodriguez, A. (2020). La Motivación y
el Desempeño Laboral: El Capital Humano como factor clave en la organización.
Universidad y Sociedad, 385-390.
Bologna, E. (2018). Métodos Estádisticos de Investigación. Córdoba: Editorial Brujas.
Cajas, M., Vasquez, G., Chorinos, N., Hernandez, G., Sandoval, L., Lozada, B., & Anzola,
A. (s.f.). Administración de Recursos Humanos. Sangolquí: Universidad de las
Fuerzas Armadas ESPE.
Castro, K., & Delgado, J. (2020). Gestión de talento humano en el desempeño laboral,
Proyecto Especial Huallaga Central y Bajo Mayo 2022. Ciencia Latina Revista
Cientifica Multidisciplinar, 684 - 703.
Chiavenato, I. (2009). Comportamiento Organizacional. La dinámica del éxito en las
organizaciones. México D.F.: McGRAW-HILL/INTERAMERICANA EDITORES
, S.A. de C.V.
Chiavenato,
I.
(2009).
Gestión
del
Talento
Humano.
México
D.F.:
McGRAW-
HILL/INTERAMERICANA EDITORES, S.A. DE C.V.
Del Canto, E., & Silva Silva, A. (2013). Metodología Cuantitativa: Abordaje desde la
complementariedad en Ciencias Sociales. Revista de Ciencias Sociales, 25-34.
Dillman
,
D.
(2000).
Procedures
for
conducting
government-sponsored
establishment
surveys:
Comparisons
of
the
total
design
method
(TDM),
a
traditional
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 843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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
costcompensation model, and tailored design. In Proceedings of American Statistical
Association, Second International Confere. 343-352.
Donawa Torres, Z. (2019). Necesidades adquiridas que impulsan la motivación laboral en
los empleados de las empresas de servicio eléctrico en el estado Zulia de Venezuela.
NOVUM, revista de Ciencias Sociales Aplicadas, 58-73.
George, D., & Mallery, P. (2003). SPSS for Windows Step by Step: A Simple Guide and
Reference. Boston: Allyn & Bacon.
Guerra, S., & Ponce, R. (2014). Análisis Multivariante: Modelización de Ecuaciones
Estructurales. Métodos y Técnicas Cualitativas y Cuantitativas Aplicables a la
Investigación en Ciencias Sociales. Monterrey, México: Editorial Trillas.
Hart, C. W. (2012). Los experimentos de Hawthorne*. Revista Cubana de Salud Pública,
156-167.
Hernández Sampieri, R., Fernández Collado, C., & Baptista Lucio, P. (2010). Metodología
de la Investigación. México D.F.: McGRAW-HILL.
Jara Martinez, A., Asmat Vega, N., Alberca Pintado, N., & Medina Guzmán, J. (2018).
Gestión del talento humano como factor de mejoramiento de la gestión pública y
desempeño laboral. Revista Venezolana de Gerencia, 740-760.
Jones, G., & George, J. (2010). Administración Contemporánea. México D.F.: McGRAW-
HILL/INTERAMERICANA EDITORES, S.A. DE C.V.
Lincoln, Y., & Guba, E. (2000). Paradigmatic Controversies, Contradictions, and Emerging
Confluences. Handbook of Qualitative Research, 163-188.
Macías García, E., & Vanga Arvelo, M. (2021). Clima organizacional y motivación laboral
como insumos para planes de mejora institucional. Revista Venezolana de Gerencia,
548-567.
Maslow, A. (1991). Motivación y Personalidad. Madrid: Ediciones Diaz de Santos. S.A.
McClelland, D. (1961). The Achieving Society. Princeton, N.J., Van Nostrand.
Mendoza, J., & Garza, J. (2009). La medición en el proceso de investigación científica:
Evaluación de validez de contenido y confiabilidad . Innovaciones de Negocios , 17-
32.
Meza Cruz, E. (2018). Clima Organizacional y Desempeño Laboral en empleados de la
Universidad Linda Vista, en Chiapas.
Nunnally, J. (1967). Psychometric Methods. New York: McGraw- Hill Book Co.
Vol.6 No.3 (2022): Journal Scientific
Investigar ISSN: 2588–0659
https://doi.org/10.56048/MQR20225.6.3.2022.823-844
Vol.6-N° 03, 2022, pp. 823-844
Journal Scientific
MQRinvestigar 844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/9j/7gAOQWRvYmUAZAAAAAAB/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgX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==
Pedraza, E., Amaya, G., & Conde, M. (2010). Desempeño Laboral y estabilidad del personal
administrativo contratado de la Facultad de Medicina de la Universidad de Zulia.
Revista de Ciencias Sociales (RCS), 493 -505.
Peña Rivas, H., & Villón Perero, S. (2018). Motivación Laboral. Elemento Fundamental en
el Éxito Organizacional. Revista Scientific, 177-192.
Robbins, S., & Judge, T. (2009). Comportamiento Organizacional. Naucalpán de Juárez:
Pearson Educación de México, S.A. de C.V.
Rositas Martínez, J. (2014). Los tamaños de las muestras en encuestas de las ciencias sociales
y su repercusión en la generación del conocimiento. Innovaciones de Negocios, 235-
268.
Sáenz López, K.,
& Tamez González, G. (2014). Métodos
y técnicas cualitativas y
cuantitativas aplicables a la investigación en ciencias sociales. México D.F.: Tirant
Humanidades.
Schadeck Schadeck, M., Neves, J., Przycznski, R., Tybusch, T., & Rodrigues, L. (2015).
Comportamiento motivacional de los colaboradores como hecho relevante en el
desempeño empresarial: Concesionarias de vehículos de la ciudad de Santo Angelo.
Ciencias Administrativas, 53-64.
Conflicto de intereses:
Los autores declaran que no existe conflicto de interés posible.
Financiamiento:
No existió asistencia financiera de partes externas al presente artículo.
Agradecimiento:
A la Jefatura de Posgrados de la Universidad Católica de Cuenca por permitir el desarrollo
y fomento de la investigación.
Nota:
El artículo no es producto de una publicación anterior, tesis, proyecto, etc.