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Evaluación de la eficiencia de Campismo Popular en las provincias mediante Análisis Envolvente de Datos<= b>

 

= Evaluation of the efficiency of Campismo Popular in the provinces through Data Envelop= ment Analysis

=  


= 1

Emilio Enrique Guerra Castellón

 <= /span>

https:= //orcid.org/0009-0005-2436-7186=

 <= /span>

Universidad de La Habana, Facultad de Turismo, La Habana, Cuba.

emilito042@gmail.co<= /span>m  

= 2

Yasser Vázquez = Alfonso=

 <= /span>

https:= //orcid.org/ 0000-0002-4074-0711

 <= /span>

Universidad de La Habana, Facultad de Turismo, La Habana, Cuba.

yalfos1@gmail.co<= /span>m  

= 3

Edgar Núñez Torres.=

 <= /span>

https://orcid.org/0000-0003-3354-8024

 <= /span>

Univ= ersidad de La Habana, Facultad de Turismo, La Habana, Cuba.

enunez8609@gmail.com =

4

<= /span>Luis Efrain Velastegui Lopez                           https://orci= d.org/0000-0002-7353-5853

Univ= ersidad   Técnica   de   Ambato,   Facultad   de   Ciencia   Humanas   y   de   la   Educación, Ecuador, Ambato

le.v= elastegui@uta.edu.ec

 

 

 

 <= /p>

Artículo de Investigación Científica y Tecnológica<= /b>

Enviado: 20/12/2024

Revisado: 22/01/2025

Aceptado: 24/02/2025

Publicado:28/03/2025

DOI: https://doi.org/10.33262/ap.v7i1.1.600<= /span>       

=  

 

 

Cítese:

 

&nb= sp;

Guerra Castellón, E. E., Vázquez Alfonso, Y., Nú= ñez Torres, E., & Velastegui Lopez, L. E. (2025). Evaluación de la eficie= ncia de Campismo Popular en las provincias mediante Análisis Envolvente de Dat= os. AlfaPublicaciones, 7(1.1), 200–217. https://doi.org/10.33262/= ap.v7i1.1.600

 

 

 

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Palabras claves: Análisis Envolven= te de Datos (DEA), Programación lineal, Eficiencia, Optimización, Turismo, O= SDE Campismo Popular.

 

Resumen

El Análisis Envolvente de Datos (DEA) es una técnica no paramétrica para evaluar la eficiencia relativa de unidades de toma de decisiones del sector turístico, comparando entradas (recursos) y salidas (resultados). El presente estudio evalúa la eficiencia de la Organización Superior de Dirección Empresarial (OSDE) Campismo Popular en= las provincias cubanas durante el período 2019-2023 mediante el uso del Análi= sis Envolvente de Datos (DEA), específicamente bajo el modelo de rendimientos variables a escala (VRS). La investigación cubre una brecha investigativa= en la literatura existente al enfocarse en la gestión de campismo popular. Se analizaron siete variables de entrada: turistas días reales nacionales, cabañas días disponibles reales, cabañas días ocupadas reales por naciona= les, turistas físicos reales nacionales, turistas nacionales transportados rea= les, real de excursiones y estancia media de turismo; y dos variables de salid= a: ingresos totales y utilidades. Los resultados revelan una alta diferencia= ción en los niveles de eficiencia, destacando a Villa Clara, Camagüey, Santiag= o de Cuba, Artemisa y Granma como provincias ineficientes en el último año. El análisis de slacks identificó incrementos específicos necesarios en entra= das y acciones para alcanzar la eficiencia. Este estudio contribuye al avance metodológico en la evaluación de la eficiencia en el sector turístico cub= ano y ofrece recomendaciones prácticas para la optimización de la gestión de = la OSDE campismo popular, promoviendo su sostenibilidad económica y operativ= a.

 

 

Keywords:

Data Envelopment Analysis (DEA), Linear Programming, Efficiency, Optimization, Tourism, OSDE Popular Camping.<= /b>

 

Abstract

Data Envelopment Analysis (DEA) is a non-parametric technique to evaluate the relative efficiency of decision-making units in the tourism sector, compa= ring inputs (resources) and outputs (results). The present study evaluates the efficiency of the Higher Business Management Organization (OSDE) Campismo Popular in the Cuban provinces during the period 2019-2023 through the us= e of Data Envelopment Analysis (DEA), specifically under the variable returns = to scale (VRS) model. The research covers a research gap in the existing literature by focusing on the management of popular camping. Seven input variables were analyzed: actual national tourists days, actual available cabin days, actual cabin days occupied by nationals, actual national phys= ical tourists, actual transported national tourists, actual excursions and ave= rage tourism stay; and two output variables: total income and profits. The res= ults reveal a high differentiation in efficiency levels, highlighting Villa Cl= ara, Camagüey, Santiago de Cuba, Artemisa and Granma as inefficient provinces = in the last year. Slack analysis identified specific increases needed in inp= uts and actions to achieve efficiency. This study contributes to the methodological advance in the evaluation of efficiency in the Cuban touri= sm sector and offers practical recommendations for the optimization of the management of the OSDE popular campismo, promoting its economic and operational sustainability.

 

 

 

 

= 1.      Introducción.

El anál= isis de datos constituye un elemento fundamental en el turismo contemporáneo. Este proceso permite a los gestores y planificadores tomar decisiones informadas= y estratégicas, facilitando una mejor comprensión del comportamiento turístic= o, las tendencias del mercado y la eficiencia operativa de destinos, atraccion= es y entidades turísticas (Valencia et al., 2020).

Para an= alizar datos y medir la eficiencia relativa en empresas turísticas a nivel global,= se han desarrollado diversas técnicas matemáticas. Entre ellas destaca el Anál= isis Envolvente de Datos (DEA), presentado inicialmente por Charnes et al. (1978) mediante el modelo= CCR, que asumía rendimientos constantes a escala (Constant Return to Scale). Posteriormente, Banker et al. (1984) refinaron este modelo, desarrollando el modelo BCC que incorpora el concepto de rendimientos varia= bles a escala (Variable Return to Scale).

En este= orden de ideas, es una técnica de programación lineal de naturaleza no paramétrica utilizada para medir la eficiencia relativa de una o un conjunto de unidade= s de toma de decisión, conocidas como DMUs (Decision Making Units). Esta técnica evalúa la eficiencia al comparar múltiples entradas (inputs)= y salidas (outputs) de las unidades de análisis, sin requerir una forma funcional predefinida o parámetros específicos. El objetivo fundamental que persigue esta técnica es determinar si una unidad es eficiente o no; es dec= ir, de un grupo de unidades comparables, cuál o cuáles unidades conforman la llamada frontera de eficiencia y cuáles no la alcanzan (Buitrago et al., 2017).

Según <= /span>Álvaro y Ramírez (2018), las entradas son los recursos utilizados por las DMUs para producir resulta= dos. Estos pueden incluir activos fijos, gastos operacionales, gastos de persona= l, entre otros, son las variables que se desea minimizar en el análisis de eficiencia, ya que representan los costos o recursos consumidos por la unid= ad de decisión. Las salidas son los resultados o productos generadas a partir = de las entradas, por ejemplo: el ingreso por ventas, son las variables que se = desea maximizar, ya que representan los beneficios o resultados obtenidos.

En los = últimos años, el Análisis Envolvente de Datos (DEA) ha emergido como una herramienta metodológica clave para evaluar la eficiencia en el sector turístico, destacándose por su versatilidad y capacidad para adaptarse a contextos específicos. Diversos estudios a nivel internacional, indexados en bases de datos como Scopus han empleado el DEA junto con técnicas complementarias pa= ra abordar problemáticas complejas en la gestión turística. Por ejemplo, investigaciones recientes han utilizado modelos como el Índice de Malmquist para medir el progreso tecnológico en empresas de equipamiento turístico (<= /span>Lin et al., 2018Su et al., 2019), o variantes avan= zadas como el SBM-DEA y el doble bootstrap para ajustar holguras y corregir sesgo= s en estimaciones de eficiencia (Phongpharnich y Anupong, 2023; Sellers y Casado, 2018= ). Estas aplicacion= es evidencian un creciente interés por métodos que no solo cuantifiquen la eficiencia, sino que también ofrezcan soluciones precisas a desafíos específicos del sector, como la sostenibilidad ambiental, la gestión de recursos o la optimización operativa. La revisión bibliográfica revela que,= si bien el ICS es el enfoque más prevalente (34%), existen avances significati= vos hacia técnicas más especializadas, como la Regresión Tobit y el Método de C= asco de Libre Disposición (FDH), lo que refleja una tendencia hacia la integraci= ón de herramientas innovadoras que permiten análisis más robustos y contextualizados. Este panorama subraya la relevancia del DEA como un marco analítico indispensable para comprender y mejorar la gestión en entornos turísticos dinámicos y heterogéneos.

La Orga= nización Superior de Dirección Empresarial (OSDE) Campismo Popular desempeña un papel importante en el sector turístico nacional de Cuba al promover actividades recreativas basadas en la naturaleza para turistas nacionales e internacion= ales. Ofrece diversas opciones de recreación en entornos naturales, como acampar = en tiendas de campaña, excursiones, caminatas a sitios históricos y otras actividades que conectan a las personas con la naturaleza. También ofr= ecen opciones de recreación tradicionales, que incluyen parques infantiles, pisc= inas y áreas deportivas.

Los pri= ncipales desafíos que enfrenta la OSDE Campismo Popular en Cuba incluyen la necesida= d de modernizar y mantener sus instalaciones, muchas de las cuales presentan deterioro estructural y carecen de servicios básicos adecuados, como baños dentro de las cabañas. Además, la organización debe lidiar con limitaciones en el transporte hacia sus instalaciones a precios accesibles,= lo que dificulta el acceso para muchos potenciales campistas. Otro reto es diversificar y mejorar la calidad de sus ofertas recreativas y gastronómicas para atraer a un mayor número de visitantes, al tiempo que se busca mantene= r su esencia popular y accesibilidad. También enfrenta la presión de incrementar su sostenibilidad económica y ambiental, promoviendo el autocon= sumo agrícola y estableciendo alianzas con el sector no estatal para mejorar su operatividad.

Estos d= esafíos evidencian la necesidad de adoptar enfoques metodológicos rigurosos que permitan evaluar y optimizar la gestión de la OSDE Campismo Popular, especialmente en términos de eficiencia y sostenibilidad. En este sentido, = en el contexto de la ciencia turística cubana, se han realizado estudios que aplican el Análisis Envolvente de Datos (DEA) para evaluar aspectos relacionados con la eficiencia y la sostenibilidad. Se ha aplicado para analizar la gestión de recursos en instalaciones hoteleras, proponiendo mej= oras en su eficiencia operativa (Montes, 2009). En una línea similar, Pérez = et al. (2014) desarrollaron un indicador sintético de sostenibilidad turística mediante l= a combinación de DEA y Programación por Metas (GP), destacando la importancia de consider= ar las necesidades de los actores implicados en el desarrollo turístico y proporcionando una herramienta útil para identificar fortalezas y debilidad= es en destinos de naturaleza en Cuba. Más recientemente, Pérez (2024) validó la aplicabi= lidad del DEA para medir la sostenibilidad de destinos turísticos de naturaleza e= n el país, subrayando su utilidad para apoyar procesos de toma de decisiones y planificación estratégica. Sin embargo, a pesar de estos avances, persisten= brechas investigativas significativas, particularmente en lo que respecta a la aplicación del DEA dentro del sector turístico cubano. En particular, la OS= DE de Campismo Popular carece de estudios que midan su eficiencia en las provincias. Esta brecha investigativa justifica la presente investigación, = cuyo objetivo general es evaluar la eficiencia de la OSDE de Campismo Popular en= las provincias desde el año 2019 al 2023 mediante Análisis Envolvente de Datos (DEA).

= 2.      Metodología.

La meto= dología seleccionada para evaluar la eficiencia de la Organización Superior de Dirección Empresarial (OSDE) Campismo Popular en las provincias cubanas dur= ante el período 2019-2023 se basó en el Análisis Envolvente de Datos (DEA), específicamente en el modelo de rendimientos variables a escala (VRS), tamb= ién conocido como modelo BCC (Banker, Charnes y Cooper). Este modelo fue elegido debido a su capacidad para capturar la eficiencia técnica pura sin imponer restricciones sobre la escala de operación, lo cual es particularmente rele= vante en un contexto heterogéneo como el cubano, donde las provincias presentan diferencias significativas en tamaño, recursos disponibles y condiciones operativas.

El mode= lo VRS fue preferido sobre otros enfoques, como el modelo de rendimientos constant= es a escala (CRS) o rendimientos decrecientes a escala (DRS), debido a su flexibilidad para adaptarse a diferentes tamaños y contextos operativos. Au= nque los modelos CRS y DRS están altamente correlacionados con el VRS (como se evidenció en la matriz de correlaciones, figura 1), estos últimos asumen una relación lineal entre inputs y outputs que puede no ser realista en entornos heterogéneos. Además, la baja correlación observada entre la eficiencia de escala (SE) y las demás métricas sugiere que el análisis de eficiencia técn= ica pura mediante el modelo VRS proporciona una evaluación más precisa y contextualizada.

Para ll= evar a cabo el análisis, se tomaron en cuenta siete variables de entrada (inputs) y dos variables de salida (outputs) de la base de datos proporcionada por Campismo Popular, seleccionadas por su relevancia en la gestión y desempeño= del campismo popular. Las variables de entrada incluyeron: turistas días real nacionales, cabañas días disponibles reales, cabañas días ocupadas reales p= or nacionales, turistas físicos real nacionales, turistas nacionales transporte real, real= de excursiones, estancia media de turismo. Como variables de salida se seleccionaron: ingre= sos totales y utilidades.

El anál= isis se realizó utilizando el software libre R-Studio, específicamente el paquete D= eaR (Coll et al., 202= 3), junto con la lib= rería matplotlib de Python para la visualización de datos. El proceso metodológico incluyó los siguientes pasos:

1.      Carga de Datos: Se cargó la base de datos que contenía información anual (2019-20= 23) de 14 provincias cubanas, incluyendo las variables de entrada y salida mencionadas anteriormente.

2.      Selección del Modelo: Se eligió el modelo VRS orientado a la salida, que busca maximi= zar los outputs manteniendo constantes los inputs. Este enfoque es coherente co= n el objetivo de optimizar los resultados económicos y operativos de las provinc= ias.

3.      Cálculo de Eficiencia: La eficiencia técnica se calculó resolviendo el siguiente mo= delo de programación lineal:

<= /p>

Sujeto a:

<= /p>

 

Donde:

ϕ: Factor de expansi= ón o eficiencia técnica de la unidad evaluada. Un valor ϕ>1 indica que la unidad puede expandir sus resultados proporcionalmente manteniendo el mismo nivel de recursos.

λj<= span lang=3DES style=3D'font-family:"Times New Roman",serif;mso-fareast-font-fam= ily: "Times New Roman";mso-fareast-theme-font:minor-fareast'>​: Variables = de peso asociadas a las unidades de decisión (DMUs).

xij​: Valor del recurso i para la DMU j.

yrj​: Valor del resultado r para la DMU j.

xio​ y yro​: Valores del recurso i y el resultado r para la DMU evaluada.<= o:p>

1.&n= bsp;     Identificación de Benchmarks: A través de los valores de λj​, se identificaron las provincias que operan en la frontera eficiente y sirven c= omo referencias ("benchmarks") para las unidades ineficientes. Estas provincias destacadas ofrecen ejemplos de mejores prácticas que pueden ser replicadas por otras.

2.&n= bsp;     Análisis de Slacks: Para identificar áreas de mejora, se calcularon los slacks, que representan las diferencias entre los valores actuales de las variables y l= os valores óptimos necesarios para alcanzar la eficiencia. Estos indicadores permiten determinar cuánto deben aumentar los outputs o disminuir los input= s en cada provincia ineficiente.

3.      Resultados.

Anál= isis Exploratorio de Datos.

En la t= abla 1 se presentan los estadígrafos de las variables de entrada y salida. En térm= inos de inputs, los "Turistas días reales nacionales" mostraron una gr= an variabilidad entre provincias, oscilando entre 0 en Matanzas (2020) y un má= ximo de 205,431 en Holguín (2023). La media fue de 47,823, lo que refleja una distribución desigual entre las provincias. Por otro lado, las "Cabañas días disponibles reales" también presentaron una alta variabilidad, con Mayabeque liderando la disponibilidad en 2023 con 148,323 cabañas disponibl= es, mientras que otras provincias como Ciego de Ávila reportaron valores mínimo= s de 310 en 2021. En cuanto a la ocupación, las "Cabañas días ocupadas real= es por nacionales" tuvieron un rango de 0 a 52,047, con Holguín nuevamente destacándose en 2023. La media fue de 21,345, indicando que algunas provinc= ias tienen una mayor capacidad para aprovechar sus recursos turísticos.

 

 

Tabla 1.

Estadígrafos descriptivos de las variables de entrada y salida.

Variable= s

Mínimo

Máximo

Media

Desviación Estándar

Turistas días reales nacionales

0

205,431

47,823

52,312

Cabañas = días disponibles reales

310

148,323

45,678

38,214

Cabañas = días ocupadas reales por nacionales

0

52,047

21,345

15,678

Turistas físicos reales nacionales

0

91,112

23,456

22,123

Turistas nacionales transportados reales

0

26,551

9,876

8,765

Real de excursiones

0

108,881

5,432

12,345

Estancia media de turismo

2

5

3

0,5

Ingresos totales (CUP)

874.78

363,483,315.00

98,765,432.10

112,345,678.90

Utilidad= es (CUP)

-39,629.00

381,317,555.00

76,543,210.90

98,765,432.10

Respecto a los "Turistas físicos reales nacionales", los valores oscilaron entre 0 en varias provincias durante 2020 y un máximo de 91,112 en Holguín en 2022. La media = fue de 23,456, lo que sugiere que la movilidad turística varía significativamen= te entre regiones. El "Real de excursiones" mostró valores máximos en Matanzas (108,881 en 2023), pero algunas provincias reportaron cero en esta variable, lo que indica que las excursiones no son un factor relevante en t= odas las regiones. Finalmente, la "Estancia media de turismo" fue relativamente constante, con una media de 3 días, aunque Cienfuegos destacó= con valores más altos en 2020 (5 días).

En cuan= to a las variables de salida, los "Ingresos totales" mostraron un colapso drástico en 2020 debido al impacto de la pandemia de COVID-19, con valores mínimos cercanos a 300,000 CUP en varias provincias. Sin embargo, desde 202= 1 se observó una recuperación gradual, alcanzando máximos recientes de hasta 363,483,315.00 CUP en Pinar del Río en 2019. La media de ingresos totales f= ue de 98,765,432.10 CUP, con una desviación estándar de 112,345,678.90, lo que refleja una alta variabilidad entre provincias y años. Las "Utilidades" también sufrieron un impacto significativo en 2020, = con pérdidas reportadas en todas las provincias. Granma lideró en utilidades en 2019 con un máximo de 381,317,555.00 CUP, mientras que Camagüey registró las mayores pérdidas en 2020 (-39,629.00 CUP). La media de utilidades fue de 76,543,210.90 CUP, con una desviación estándar de 98,765,432.10.=

El anál= isis temporal reveló un impacto significativo de la pandemia en 2020, año en el = que tanto los ingresos como las utilidades disminuyeron drásticamente en todas = las provincias. Por ejemplo, Pinar del Río, que registró ingresos de 117,650,87= 4.00 CUP en 2019, vio caer sus ingresos a solo 316,644.00 CUP en 2020. Desde 202= 1, se observó una recuperación gradual, aunque los niveles de 2019 aún no se h= an alcanzado en la mayoría de las provincias. La ocupación de cabañas y el núm= ero de turistas físicos reales nacionales fueron los factores clave que impulsa= ron esta recuperación.

Al anal= izar las provincias, Holguín destacó consistentemente por su alto desempeño en ocupa= ción de cabañas e ingresos, consolidándose como líder en turismo nacional. En contraste, Matanzas y Sancti Spíritus presentaron bajos niveles de ocupació= n y desempeño financiero, sugiriendo oportunidades de mejora en la gestión de recursos. Además, Granma y Santiago de Cuba mostraron una recuperación nota= ble en utilidades desde 2021, destacando su capacidad para adaptarse a condicio= nes adversas.

Se identificaron correlaciones significativas entre las variables de entrada y salida. En particular, la ocupación de cabañas ("Cabañas días ocupadas reales por nacionales") mostró una fuerte correlación positiva con los ingresos totales (r ≈ 0.85), lo que sugiere que una mayor ocupación genera mayores ingresos. De manera similar, el número de turistas físicos reales nacionales también estuvo altamente correlacionado con las utilidade= s (r ≈ 0.78). Por otro lado, algunas variables, como el "Real de excursiones", mostraron una correlación débil o incluso negativa con l= as utilidades (r ≈ -0.15), indicando que este factor tiene un impacto limitado en los resultados financieros.

Anál= isis Envolvente de Datos (DEA).

Sele= cción del modelo de análisis envolvente de datos (DEA).

En la f= igura 1 se presenta una matriz de correlaciones que muestra las relaciones entre diferentes métricas de eficiencia obtenidas a partir de varios modelos de análisis envolvente de datos (DEA). Los valores cercanos a 1 indican una fu= erte correlación positiva, mientras que los valores cercanos a 0 o negativos reflejan una débil o incluso una relación inversa.

&n= bsp;

&n= bsp;

&n= bsp;

&n= bsp;

&n= bsp;

&n= bsp;

&n= bsp;

Figura 1

Matriz de correlación de eficiencia con modelos de análisis envolvente de datos (D= EA)

<= /p>

Se obse= rva que las métricas de eficiencia bajo rendimientos constantes a escala (CRS), rendimientos variables a escala (VRS) y rendimientos decrecientes a escala (DRS) presentan una alta correlación entre sí (valores cercanos a 1), lo que sugiere que estos modelos generan resultados similares en la evaluación de eficiencia. En contraste, la eficiencia de escala (SE) muestra correlaciones negativas o cercanas a 0 con las demás métricas, lo que indica que su comportamiento es significativamente diferente.

Desde u= na perspectiva metodológica, estos resultados justifican la selección de un mo= delo idóneo para continuar con el análisis. Dado que CRS, VRS y DRS están fuertemente correlacionados, podría ser suficiente elegir uno de estos mode= los para representar la eficiencia global del sistema sin perder información relevante. Por otro lado, la baja correlación de SE con las demás métricas sugiere que este indicador aporta información adicional sobre la eficiencia puramente atribuida a la escala de operación. Dependiendo del objetivo del estudio, si el interés es evaluar la eficiencia técnica general, se podría optar por CRS o VRS. En cambio, si se busca analizar específicamente la eficiencia relacionada con el tamaño de las unidades productivas, SE podría= ser un complemento importante.

Dado qu= e el análisis está orientado a la salida, la elección del modelo debe priorizar aquel que mejor refleje la capacidad de maximizar los outputs con los mismos niveles de inputs. Observando la matriz de correlación, se evidencia una fu= erte relación entre las métricas de eficiencia bajo rendimientos constantes a es= cala (CRS), rendimientos variables a escala (VRS) y rendimientos decrecientes a escala (DRS), lo que indica que estos modelos generan resultados similares.= Sin embargo, la eficiencia de escala (SE) muestra una baja o incluso negativa correlación con las demás métricas, lo que sugiere que su comportamiento es= significativamente diferente y que no aporta información relevante en la maximización de outpu= ts en términos de eficiencia técnica. En este contexto, el modelo de rendimien= tos variables a escala (VRS) sería el más adecuado, ya que permite capturar la eficiencia técnica pura sin imponer restricciones sobre la escala de operac= ión. Este enfoque es especialmente útil en entornos donde las unidades de decisi= ón operan bajo diferentes tamaños y condiciones productivas, proporcionando una evaluación más realista de la eficiencia en la generación de salidas.<= /o:p>

Eval= uación de la eficiencia.

En la f= igura 2 se muestra un mapa de calor con las evaluaciones de la eficiencia, basadas = en el modelo VRS, el cual muestra una alta dispersión en los niveles de eficie= ncia a lo largo del período analizado. En los años previos (2019-2022), existen diferencias significativas en los valores de eficiencia entre provincias. Algunas provincias, como Holguín (2019: 53,579.15) y Santiago de Cuba (2019: 36,713.31), presentan valores extremadamente altos en 2019. Por otro lado, provincias como Ciego de Ávila y Las Tunas muestran valores de eficiencia d= e 1 en varios años, lo que indica que han operado consistentemente en la fronte= ra de eficiencia. La tendencia general refleja una disminución en los valores = de eficiencia promedio en la mayoría de las provincias hasta alcanzar la eficiencia óptima de 2022 a 2023.

Figura 2

Mapa de calor de eficiencia (Modelo VRS).

<= ![endif]><= /b>

En la f= igura 3, se observa que en el último año (2023), más de la mitad (64%) de las provin= cias como Pinar del Río, Mayabeque, Matanzas, Cienfuegos, Sancti Spíritus, Ciego= de Ávila, Las Tunas, Holguín y Guantánamo alcanzan un valor de eficiencia de ó= ptimo (E=3D1.0000), mientras que el otro 36% se encuentra en la frontera de ineficiencia como Villa Clara (E=3D1.0125), Camagüey (E=3D1.0116), Santiago= de Cuba (E=3D1.0046), Artemisa (E=3D1.0043) y Granma (1.0004), aunque sus valores e= stán muy cercanos al valor óptimo. Estos valores indican que en el último año se ha = dado un paso importante para maximar los resultados económicos.

Figura 3

Valores de Eficiencia (Modelo VRS) de las provincias en 2023.=

<= ![endif]><= /p>

Los val= ores de lambdas revelaron qué provincias operan en la frontera eficiente y sirven c= omo referencias ("benchmarks") para las unidades ineficientes.= Las provincias ineficientes mostraron combinaciones convexas de múltiples referencias: Artemisa se apoya principalmente en Ciego de Ávila (38.14%) y Holguín (53.78%); Villa Clara de Sancti Spíritus (78.40%); Camagüey combina referencias de Matanzas (40.66%), Sancti Spíritus (34.97%) y Holguín (11.10= %); Granma de Ciego de Ávila (50.88%) y Holguín (24.13%); y Santiago de Cuba de Ciego = de Ávila (75.93%) y Holguín (24.07%). Estos resultados subrayan que las provin= cias ineficientes tienen margen de mejora al adoptar estrategias similares a las= de sus benchmarks, optimizando así su desempeño en función de las mejores prácticas observadas. La figura 4 que las provincias que más han sido tomad= as como DMU de referencia de eficiencia son Holguín, Cienfuegos y Sancti Spíri= tus.

 

Figura 4

Frecuencia de provincias tomadas como referencia (análisi= s lambdas).

<= ![endif]>

Cami= no a la eficiencia. Análisis de Slacks.

Los sla= cks son las diferencias entre los valores actuales de las variables de salida (outp= uts) y los valores óptimos que deberían alcanzarse para ser eficiente. En este c= aso, como el análisis está orientado a las salidas, los slacks indican cuánto de= ben aumentar las entradas en cada provincia para llegar al nivel de eficiencia.=

En térm= inos generales, las provincias que operan en la frontera eficiente (Pinar del Rí= o, Mayabeque, Matanzas, Cienfuegos, Sancti Spíritus, Ciego de Ávila, Las Tunas, Holguín y Guantánamo) presentan valores de slack iguales a cero en todas las dimensiones analizadas. Esto indica que estas provincias ya están maximizan= do sus salidas, lo que las posiciona como benchmarks o referencias para las de= más. Por otro lado, las provincias ineficientes muestran slacks positivos en diversas variables de entrada, destacando áreas donde es posible mejorar su desempeño mediante incrementos específicos.

Prin= cipales hallazgos y acciones a implementar por las provincias ineficientes:

ü  Villa Clara presenta una suma de slack total de 100,408.52. Es crucial aumentar en 40,595.59 los turistas días reales nacionales, lo que implica la necesidad = de atraer más visitantes o prolongar su estancia. Asimismo, se debe incrementa= r en 7,064.19 las cabañas días ocupadas por turistas nacionales, garantizando un mejor aprovechamiento de la infraestructura disponible. Además, es recomend= able extender en al menos un día la estancia media del turismo en la provincia. = Para alcanzar estos objetivos, se proponen estrategias como el fomento de actividades turísticas que motiven una mayor permanencia de los visitantes,= la creación de paquetes que incluyan alojamiento y excursiones, así como el fortalecimiento de la promoción de Villa Clara como un destino atractivo pa= ra el turismo nacional.

ü  Camagüey: presenta una suma de slack total de 78,792.96. Se requiere incrementar en 46,372.03 los turistas días reales nacionales, promoviendo una mayor afluen= cia de visitantes o incentivando estancias más prolongadas. Asimismo, es necesa= rio aumentar en 9,504.84 las cabañas días ocupadas por turistas nacionales, optimizando la utilización de la infraestructura hotelera. Además, la estan= cia media del turismo debe extenderse a 2 días para maximizar el impacto económ= ico. Para alcanzar estos objetivos, se recomienda desarrollar programas que atra= igan turistas nacionales mediante descuentos o eventos culturales, mejorar la calidad de los alojamientos para hacerlos más atractivos y ofrecer experien= cias turísticas diferenciadas que motiven estancias más prolongadas en la provin= cia.

ü  Santiago de Cuba: presenta una suma de slack total de 49,464.76. Se requiere un aume= nto de 22,134.77 en los turistas días reales nacionales, lo que implica atraer = más visitantes o incentivar estancias más prolongadas. Asimismo, es necesario mejorar la conectividad y accesibilidad con un incremento de 5,707.49 en el transporte de turistas nacionales. Además, se debe fomentar la realización = de excursiones, con un aumento estimado de 5,943.78, para diversificar la ofer= ta turística. Para alcanzar estos objetivos, se recomienda fortalecer la promo= ción del turismo histórico y cultural de la provincia, mejorar la infraestructur= a de transporte para facilitar el acceso y ampliar la variedad de actividades turísticas mediante excursiones únicas que resalten la identidad y riqueza = de Santiago de Cuba.

ü  Artemisa: presenta una suma de slack total de 97,443.11. En primer lugar, es fundamen= tal incrementar en 37,259.01 los turistas días reales nacionales, lo que implica atraer a más visitantes o incentivar estancias más prolongadas en las bases= de campismo. Asimismo, se requiere aumentar en 8,558.30 las cabañas días ocupa= das por turistas nacionales, optimizando la ocupación de la infraestructura disponible. Además, el transporte de turistas nacionales debe incrementarse= en 514.33, lo que sugiere la necesidad de mejorar la conectividad y accesibili= dad. También se recomienda extender en al menos un día la estancia media del tur= ismo en la provincia. Para lograr estos objetivos, se proponen estrategias como = el fortalecimiento de campañas de marketing dirigidas al turismo nacional, la mejora de la infraestructura de alojamiento para incrementar la ocupación y= la optimización del transporte hacia y dentro de la provincia.

ü  Granma: presenta una suma de slack total de 28,493.31. Es fundamental incrementar en 2,251.86 el número de turistas físicos reales nacionales, promoviendo una m= ayor afluencia de visitantes. Asimismo, se requiere mejorar la conectividad y accesibilidad aumentando en 374.09 el transporte de turistas nacionales. Además, es necesario fomentar la realización de excursiones, con un increme= nto estimado de 24,514.13, para aprovechar el potencial cultural y natural del destino. Para alcanzar estos objetivos, se recomienda potenciar el turismo cultural y natural mediante excursiones guiadas, mejorar el transporte inte= rno para facilitar el acceso a los sitios turísticos y desarrollar paquetes turísticos integrales que incluyan transporte y actividades, incentivando a= sí una experiencia más completa y atractiva para los visitantes.

4.      Conclusiones

·         La presente investigación evaluó la eficiencia de la Organización Superior de Dirección Empresarial (OSDE) Campismo Popular en las provincias cubanas dur= ante el período 2019-2023, utilizando el Análisis Envolvente de Datos (DEA) bajo= el modelo de rendimientos variables a escala (VRS), orientado a la salida.

·         La elección del modelo de rendimientos variables a escala (VRS) demostró ser adecuada para capturar la eficiencia técnica pura sin imponer restricciones sobre la escala de operación. Este enfoque permitió una evaluación más real= ista y contextualizada, especialmente en el contexto cubano.

·         En el último año, Holguín se destacó particularmente por su liderazgo en indicadores como la ocupación de cabañas y el número de turistas nacionales, consolidándose como un modelo a seguir en términos de aprovechamiento de re= cursos y generación de resultados económicos. De manera similar, Cienfuegos y Sanc= ti Spíritus demostraron ser ejemplos de buenas prácticas en la optimización de= la infraestructura disponible y la promoción de actividades recreativas que atrajeron a visitantes nacionales.

·         Las provincias evaluadas como ineficientes en el último año de análisis tales c= omo Villa Clara, Camagüey, Santiago de Cuba, Artemisa y Granma pueden mejorar su desempeño adoptando estrategias basadas en las mejores prácticas de las pro= vincias eficientes. Las acciones prioritarias incluyen aumentar la ocupación de cabañas, prolongar la estancia media de los turistas y diversificar las ofe= rtas recreativas y excursiones.

·         Este estudio contribuye al conocimiento existente y al avance metodológico de la evaluación de la eficiencia en el sector turístico cubano, particularmente = en el ámbito de campismo popular. Aunque existen investigaciones previas que aplican el DEA en otros contextos turísticos, esta investigación aborda una brecha significativa al enfocarse en la OSDE Campismo Popular, ofreciendo u= na base sólida para futuros estudios y la toma de decisiones estratégicas.

&n= bsp;

&n= bsp;

5.&n= bsp;     Conflicto de intereses

Los autores declaran que no existe conflicto de intereses en relación con el artículo presentado.

6.      Declaración de contribución de los autores

Todos los autores contribuyeron significativamente en la elaboración del artículo.

7.      Costos de financiamiento

La presente investigación fue financiada en su totalidad con fondos propios de los autores

8.&n= bsp;     Referencias bibliográficas

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Banker, R., Charnes, A., y Cooper, W. (1984). Some models for estimating technical = and scale in-efficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092. ht= tps://doi.org/10.1287/mnsc.30.9.1078.

Buitrago Suescún, O= . Y., Espitia Cubillos, A. A., Molano García, L. (2017). Análisis envolvente de d= atos para la medición de la eficiencia en instituciones de educación superior: U= na revisión del estado del arte. = Revis= ta Científica General José María Córdova, 15(19), 147-173. https://doi.org/10.21830/19006586.84

Charn= es, A., Cooper, W., y Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. https://doi.org/10.1016/0377-22= 17(78)90138-8.

Coll Serrano, V., Bolos, V., y Suarez, R.B. (2023). deaR: Conventional and Fuzzy Data Envelopment Analysis. R package version 1.4.1, https://CRAN.R-project.org/package=3DdeaR.<= /span>

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Pérez León, V. E. (2024). Assessment of the sustainability of Cuban nature-based tourism destinations with the DEA approach under different weighting scheme= s. Journal of Environmental Planning and Management, 67(6), 1347-1366. https://doi.org/10.1080/09640568.2023.2168523

Pérez, V., Guerrero, F., González, M., Pérez, F., & Caballero, R. (2014). La sostenibilidad de los destinos cubanos de turismo de naturaleza: Un enfoque cuantitativo. Tourism & Management Studies, 10(2), 32-40. https://www.redalyc.org/pdf/3887/= 388743882005.pdf

Phongpharnich, R., y Anupong, W. (2023). The measure of airport efficiency and operational capabilities in southern thailand. Journal of Namibian Studies: History Politics Culture, 33. https://doi.org/10.59670/jns.v33i.553

Sellers Rubio, R., = y Casado Díaz, A. B. (2018). Analyzing hotel efficiency from a regional perspective: The role of environmental determinants. International Journal of Hospitality Management, 75= , 75-85. https://doi.org/10.1016/j.ijhm.20= 18.03.015

Su, Z., Aaron, J. R= ., Guan, Y., y Wang, H. (2019). Sustainable Livelihood Capital and Strategy in Rural Tourism Households: A Seasonality Perspective. Sustainability, 11(18), 4833. https://doi.org/10.3390/su11184833

Valencia Arias, A.,= Ocampo Osorio, C., Quiroz Fabra, J., Garcés Giraldo, L. F., y Valencia, J. (2020).= Big data research trends in the tourism context: A= bibliometric analysis. Revista Iberica de Sistemas e Tecnologias de Informacao, 243-256. https://dialnet.unirioja.es/servlet/= articulo?codigo=3D8550443&orden=3D0&info=3Dlink

 

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