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Modelo de gestión de marketing digital para PYMES:= un estudio de ecuaciones estructurales

Digital marketing manageme= nt model for SMEs: a study of structural equations

 

1

   Juan Gabriel Saltos Cruz

https://orcid.o= rg/0000-0002-4398-2564

DIDE-Universidad Técnica de Ambato,Ambato, Ecuador

jg.saltos@uta.e= du.ec

2

   Lucila Alejandra Reyes Terán

https://orcid.o= rg/0000-0001-6425-6636

Ministerio de Educación de Ecuador, Ambato, Ecuador

lucila.reyes@ed= ucacion.gob.ec

3

William Franklin Ortiz Paredes                                  https://orcid.org/0000-0003-1569-3412<= o:p>

Universidad Técnica de Cotopaxi, Ambato, Ecuador

william.ortiz97= 49@utc.edu.ec

4

Edwin Javier Santamaría Freire                                https://orcid.org/0000-0003-180= 3-2572

Universidad Técnica de Ambato, Ambato, Ecu= ador

edwinjsantamari= a@uta.edu.ec

 

 

 

 =

Artículo de Investigación Científica y Tecnológica

Enviado: 10/09/2022

Revisado: 06/10/2022

Aceptado: 09/11/2022

Publicado:21/03/2023

DOI: https://doi.org/= 10.33262/ap.v5i1.344   =  

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Cítese:

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Saltos Cruz, J. G., Reyes Terán, L. A., Ortiz Paredes , W. F., & Santamaría Freire , E. J. (202= 3). Modelo de gestión de marketing digital para PYMES: un estudio de ecuacion= es estructurales. AlfaPublicaciones, 5(1), 163–177. https://doi.org/10.33262/ap.v5i1.344

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Palabras claves:

Marketing digital, medios digitales, sistemas digitales

 

Resumen

Introdu= cción. El marketing digital en térmi= nos postpandemia COVID-19 ha constituido una necesidad evidente para la sobrevivencia de l= as organizaciones. = Objetivo general. Estudiar la validez de constructo de un modelo de marketing digi= tal a partir del comportamiento métrico del criterio de expertos. Metodol= ogía. Este estudio fue cuantitativo= no experimental de corte transversal. El método de recopilación de información fue ejecutada a través de la técnica de encuesta digital dise= ñada en Google Forms. El instrumento está diseñado con anclajes de una escala de Likert de 5 opciones. La intervención se realizó en una muestra= de 250 especialistas.  Los indicadores empleados en el análisis de métricas fueron, Alfa de Cronbach, KMO, Varia= nza total explicada y matriz rotada con método Varimax.  Resultados= . El modelo tiene consistencia interna, adecuación de muestreo, un nivel óptimo de explicación del comportamiento de sus componentes. Las ca= rgas factoriales muestran un aporte importante al modelo. Conclus= iones. El marketing digital está constituido por: redes sociales, publicidad pagada, marketing mobile, posicionamiento orgánico, social media, email marketing y sitios web. Esta investigación es un resultado parc= ial de la ejecución del proyecto “DESARROLLO DE UN MODELO DE MARKETING DIGITAL E= N LA GESTIÓN ESTRATÉGICA DE PYMES DE CALZADO DE LA PROVINCIA DE TUNGURAHUA DES= DE EL CRITERIO DE EXPERTOS" - UTA-CONIN-2022-0282-R. Se hace un extensi= vo un agradecimiento a la Dirección de Investigación y Desarrollo de la Universidad Técnica de Ambato.

 

Ke= ywords: Digital marketing, digital media, digital systems.

<= /span>

 

Abstract

Digital marketing in post-COVID-= 19 terms has been an obvious need for the survival of organizations. = Study the construct validity of a digital marketing m= odel from the metric behavior of expert criteria. This study was quantitative non-experimental cross-sectional. The method of collecting information was executed through the digital survey technique designed in Google Forms. T= he instrument is designed with anchors of a Likert scale of five options. The intervention was performed on a sample of 250 specialists. The indicators used in the analysis of metrics were, Cronbach's alpha, KMO, total varian= ce explained, and matrix rotated with Varimax method. Results<= /span>. The model has internal consistency, sampling adequa= cy, an optimal level of explanation of the behavior of its components. Factor= ial loads show an important contribution to the model. Digital marketing consists of so= cial networks, paid advertising, mobile marketing, organic positioning, social media, email marketing and websites. This research is a partial result of the execu= tion of the project "DEVELOPMENT OF A DIGITAL MARKETING MODEL IN THE STRATEGIC MANAGEMENT OF FOOTWEAR SMES IN THE PROVINCE OF TUNGURAHUA FROM = THE CRITERIA OF EXPERTS" - UTA-CONIN-2022-0282-R. An extended thanks are made to the Directorate of Research and Development of the Technical University of Ambato.

 =

 

 

 

Introducción

Los efectos generados por = la pandemia COVID-19 impactaron de manera representativa en la estabilidad económica de las empresas. En el mundo, es inconmensurable el número de esfuerzos por enfrentar la recesión económica. Por su parte, la crisis en la salud publica contribuyó al incremento de la tasa de mortalidad. La configuración de las variables mencionadas afectó el flujo de mercancías a través de los canales de distribución o logística. Sin embargo, el marketing digital fue una alternativa de solución a este problema. En este sentido, l= os medios digitales fueron alternativas efectivas al momento de comunicar y distribuir información (Papadopoulos et al., 2020).

El factor clave de éxito d= el marketing digital gira con base en la limitación del contacto directo entre= las personas. Las herramientas que potenciaron la socialización de productos en= los medios de comunicación digital están orientados al pago en línea y al aseguramiento de entrega de productos. Esta sinergia herramental permitió estimular la demanda. Este fenómeno logró enfrentar las caídas de los merca= dos de capital y otros conflictos de índole económica (Saltos et al., 2022).

A partir del surgimiento d= el COVID – 19, la tecnología de información y comunicación se ha constituido en una herramienta imprescindible para la gestión comercial de las organizacio= nes. Estas han generado desarrollo social sustentable, acceso a políticas de participación y un incremento evidente en la producción sostenible. En este sentido, el uso del marketing digital se ha constituido en un motor de la e= conomía empresarial. Desde un punto de vista comercial, la transformación digital ha contribuido con la producción interna del país y el comercio justo (Papadop= oulos et al., 2020).

Una organización que asume= la transformación digital está contribuyendo a su adaptación a los entornos microeconómicos turbulentos. Las organizaciones con capacidad de adaptación desarrollan una capacidad de crecimiento y contracción de acuerdo con el comportamiento del mercado y las variaciones tecnológicas. En este sentido.= Una organización competitiva es una fuente rica en oportunidades laborales, oportunidades de crecimiento y productos sostenibles (Saltos et al., 2022).=

En la última década se ha presentado un incremento en la demanda de servicios de acceso a la web y del uso de dispositivos móviles. Este comportamiento se ha visto evidente en el sector comercial, bancario y educativo. Este fenómeno dio cabida al desarro= llo de algoritmos que modelan el comportamiento de la audiencia. En este sentid= o, se hace evidente el desarrollo de herramientas, instrumentos y modelos que contribuyan a la gestión del marketing digital en los ecosistemas de medios= (Qalati et al., 2021).

La configuración de los elementos de un ecosistema de medios digitales permite el incremento del posicionamiento de marca de organizaciones. En un primer momento de desarro= llo del marketing digital se evidencia que existía una comunicación unidireccio= nal. Este tipo de relación no contribuía al desarrollo comercial, ya que la audiencia solo podía navegar por los recursos presentados por los diseñador= es de contenido. Por lo contrario, el desarrollo de los entornos Web 2.0 permi= tió la interacción simultáneamente de los usuarios con los gestores de las interfases (Castelló-Martínez, 2018).

Según el modelo de Saltos-= Cruz et al. (2022= ), un ecosistema= de medios digitales debe interrelacionar la gestión de la web a través de inductores de audiencia. Estos instrumentos permiten tener acceso a un grup= o de usuarios con perfiles homogéneos al buyer persona. El mecanismo c= all to action registra los datos de la audiencia interesada en los recursos= que se pone a disposición en los medios social y buscadores con criterios SEO y SEM. La interacción de una estrategia de atracción a través de inbound marketing permite la inducción y educación de la audiencia para convert= irle en un lead. Según Colapinto = (2010= ), un ecosistema= de medios digitales generalmente está constituido por: Email / CRM, Website, Social media, Social, Inbound Marketing, Adaptive SEO, Mobile y Paid Search= Advertising.

Con base en lo expresado se hace evidente el problema de esta investigación ¿Qué elementos de un ecosis= tema de medios digitales son los aptos para la gestión de un modelo? Para respon= der a esta interrogante científica, esta investigación se propone como objetivo general: Estudiar la validez de constructo de un modelo marketing digital a partir del comportamiento métrico del criterio de expertos.

En la actualidad, las interfases digitales web han constituido una herramienta estratégica de des= arrollo comercial. La capacidad de adaptación, capacidad de predictibilidad, el uso amigable son características imprescindibles al momento del uso de páginas = web. Gracias a este tipo de recursos las empresas mantienen mayor interacción co= n el usuario no solo en el campo de la comunicación, sino también en el comercio electrónico. La capacidad de adaptación que presentan las empresas incremen= ta su sostenibilidad comercial (Zhang et al., 2008).

No es posible interpretar = el término " web" como un recurso de atracción de audiencia sin lleg= ar a su contextualización. Cuando la audiencia se convierte en un lead incrementa la probabilidad de concretar su relación comercial con la organización. Cuando se genera la recompra de recursos web el usuario regul= ar se convierte en un elemento crucial. La repetición de sus búsquedas es capt= ada por los algoritmos de clasificación de contenido. Este mecanismo hace que l= os resultados de búsqueda resalten el recurso en estudio (Zhang et al., 2008).=

Existen dos métodos de posicionar un contenido en los buscadores web. Por una parte, el posicionamiento SEO que constituye en un esfuerzo generado por la empresa p= ara tener un lugar en los resultados de búsqueda. Por otra parte, la estrategia= de publicidad pagada Paid search advertising, que es un acuerdo contrac= tual entre la organización gestora del recurso y la empresa que alberga y gestio= na la plataforma de búsqueda (Kumar & Kumar, 2019).

En términos de posicionami= ento de búsqueda las palabras claves usadas para la programación de recursos son= una estrategia fundamental. en este sentido, este uso genera mayores visitas con criterios específicos. Desde un punto de vista de búsquedas pagadas, las palabras claves son contratadas en procesos de remate o puja. En estos procesos, los gestores de contenido deben plantear una estrategia para no incrementar su radio de audiencia. La finalidad de las estrategias de conte= nidos con base en palabras claves crea un embudo de filtración de consumidores de recursos que incrementa la posibilidad de cumplimiento de objetivos del marketing digital (Kumar & Kuma= r, 2019).

Cuando se trata del social como medio digital, se habla de una plataforma con un alto grado de convocatoria. Las aplicaciones de interacción más relevantes de los últimos= 3 años son: YouTube, Twitter, LinkedIn, Instagram y Facebook. En cuant= o a la red Facebook, se puede mencionar su estrategia de fusión con las redes más importantes en el mundo (WhatsApp, Line, WeChat y otras). = Esta decisión generó el mayor monopolio digital de la última década (Capatina et al., 2018).

Facebook nace como un proy= ecto de comunicación al interior de una institución de educación superior. En el= año 2004 este canal digital se lanzó a la red mundial Web. El factor clave de e= sta red radica en el algoritmo de personalización de contenido que gestionan en cada perfil de usuario. En el año 2018 se generó una alta migración de usua= rios de esta plataforma debido a una controversia legal por los derechos de auto= r de esta plataforma (Vidal et al., 2013).

Uno de los canales que presentan mayor tasa de crecimiento a partir del 2018 es la plataforma T= ikTok (llamada Douyin en la República Popular China). Esta red social es la más utilizada por millennials y centennials. Su factor de crecimiento obedece a la combinación de funcionalidades de otras redes sociales. Además, las posibilidades de edición de videos simplificados con guías para su gest= ión han generado un crecimiento sostenible de usuarios (Capatina et al., 2018).=

En la actualidad, la plataforma de transmisión de mensajería digital WhatsApp ocupa el pr= imer lugar de su categoría. Existe un incremento de usuarios y grupos que se incorporan al Social Network. Las principales finalidades de los usuarios q= ue crean grupos de interacción se orientan al intercambio de contenido como audiovisuales, imágenes, datos y otros. En esta evolución tecnológica se ha incorporado el canal de intercambio de mensajes de voz (Vidal et al., 2013)= .

En un contexto generaciona= l y etario, las plataformas con mayor número de usuarios millennials y centennials son Instagram y YouTube para la categoría ges= tión de videos unidireccionales. Al hablar de gestores de videos interactivos la plataforma TikTok cubre el mayor número de usuarios de las generacio= nes antes descritas. Este comportamiento se debe a que este canal heredó las funcionalidades de Snapchat en cuanto al uso de inteligencia artificial (Ca= patina et al., 2018).

En cuanto al uso de móvile= s y teléfonos inteligentes los canales de marketing móvil aprovecharon de un ni= vel de conectividad más eficiente para la atracción de usuarios. Este medio dig= ital permite a los gestores de recursos estar en contacto con el usuario con may= or facilidad en relación con sus antecesores digitales (computadoras portátile= s y de escritorio). Hoy en día constituye el medio más usado por las empresas p= ara la atracción de leds y la consecución de objetivos de comunicación <= w:Sdt Citation=3D"t" ID=3D"-1939129245">(Dutt & Ferrara, 2018).

Con el surgimiento de la g= ran burbuja tecnológica del año 2011 se observa un incremento de inversión en comunicación por parte de las grandes marcas en el mundo. Las ventajas del = uso de marketing móvil están condicionadas por el uso de canales directos de comunicación, además de la geolocalización que permite una segmentación efectiva de contenido. Entre las ventajas más representativas de este medio digital está el uso de aplicaciones móviles adaptables a múltiples sistemas operativos de los teléfonos móviles. Cabe destacar que una amenaza evidente= es la posibilidad de bloqueos y reportes negativos por parte del usuario cuand= o un contenido no es aceptado (Capatina et al., 2018).

Las funcionalidades que vuelven atractivo a este medio se orientan a la permisión de transmisiones = de publicidad a través de las aplicaciones programadas. Además, esta interacci= ón permite acortar el proceso de compra gracias a las herramientas de geolocalización, las de control logístico y de pago en línea. En este senti= do, el marketing móvil constituye un sistema de gran importancia para las organizaciones. Este medio digital permite una aproximación de usuarios al perfil del buyer persona. El diseño interactivo y simultaneo real de= las características de la audiencia como medios para clasificar a los consumido= res de contenido hacen a estos canales más amigables. Esta sinergia relacional permite que el usuario sienta satisfacción en su relación con el teléfono m= óvil (Dutt & Ferrara, 2018).

El medio digital caracteri= zado por la indagación de recursos web pone a los motores de búsqueda en un alto nivel de importancia. Las estrategias dirigidas al posicionamiento de recur= sos o contenido en las listas de recuperación generan posicionamiento orgánico.= En estos canales se prioriza la calidad de contenido ya que este criterio hace= al recurso más amigable y con mayor frecuencia de consumo. Esta repetición de aperturas muestra al contenido como valioso en los criterios de selección de los robots de los motores de búsqueda (Isoraite, 2019)<= !--[if supportFields]>.

Dentro de la tabla clasificatoria de buscadores, el líder en número de usuarios es Google. Esta plataforma de búsqueda es pionera en el uso de algoritmos de personalizació= n de contenidos sobre la base de perfiles de búsqueda.  Uno de los factores claves de este motor= de búsqueda es el mecanismo de identificación de autores. Este permite que los usuarios de contenido sean redireccionados al dueño de la propiedad intelec= tual de dicho recurso. Google ofrece una gama importante de herramientas para los usuarios, como: correo electrónico, almacenamiento en la nube. Gestor documental en línea y otros (Nagy et al., 2018).

El conjunto de algoritmos programados está orientado a la personalización de búsquedas de contenido. = Este flujo de programación permite la optimización de tiempo en la localización = de recursos. Para este fin el motor de búsqueda arma una lista con los enlaces= de los recursos clasificados y ordenados según su relevancia. Sin embargo, esta plataforma usa la estrategia de sindicación de contenido para ajustar más l= os criterios y precisión de búsqueda (Nagy et al., 2018).

Un recurso que diferencia = al marketing tradicional del marketing digital es la capacidad de medir el ren= dimiento o desempeño de las acciones estratégicas. Las métricas en un ecosistema de medios digitales constituyen un tablero de control para la toma de decision= es de los gestores de contenido. En este sentido, todos los medios digitales poseen canales que administran métricas de interacción de la audiencia con referencia al contenido. Los embudos de conversión que se pueden configurar= con las métricas permiten un mejor desarrollo de la estrategia de atracción de audiencia. Las métricas más importantes en el marketing digital son el e= ngagement que mide la interacción de los usuarios con respecto a los recursos o contenido. El analytic marketing en la actualidad constituye = un conjunto de métricas orientadas a una mejor gestión de recursos digitales <= w:Sdt Citation=3D"t" ID=3D"-1671177969">(Chan,= 2008).

La evolución del marketing digital ha dispuesto el uso obligatorio de analítica web para estudiar las interacciones de usuarios en las plataformas digitales. La gestión de marca= ha reemplazado a la gestión de imagen organizacional. La configuración de la imagen de marca, la arquitectura de marca, la notoriedad de marca son las nuevas herramientas para análisis de posición estratégica de las organizaciones. En esta interacción de elementos el Analytics marketing = contribuye al aumento del rendimiento comercial de las empresas (Dutt & Ferra= ra, 2018).

El marketing digital en combinación con el marketing tradicional puede configurar estrategias para = una cobertura total del espacio de actuación de las organizaciones. El uso de métricas digitales e indicadores de gestión permiten una aproximación más r= eal a la realidad del mercado. Es importante precisar que en términos empresari= ales se debe diseñar una estrategia de captación de consumidores de diferentes características generacionales. En efecto, en ciertos sectores industriales prevalecen usuarios de la generación X que no son adeptos del mundo digital= y se consideran analfabetos digitales. A diferencia de un millennial q= ue es un nativo digital (Chan, 2008).

Metodología

La población objeto de est= udio está constituida por 302 especialistas del sector empresarial y académico q= ue fueron seleccionados de bases de datos proporcionados por las cámaras de comercio de Quito, Cuenca, Guayaquil y Ambato, y de las instituciones de educación superior de la Zona 3. Para el cálculo de la muestra se seleccion= ó el método de reducción de poblaciones finitas. Para esta operación se determin= ó el valor partiendo de un nivel de confianza del 95% y un margen de error del 3= %. El resultado final del cálculo de la muestra determinó que para cumplir con representatividad se deberá seleccionar al azar 170 especialistas.

Para asegurar los criterios éticos de esta investigación se usan cláusulas de anonimato y uso consentid= o de la información. Esta aclaratoria se la realiza al iniciar cada sesión de encuesta, además de enunciarlo al inicio del instrumento. Esta investigació= n no debe abordar ningún otro aspecto de afectación bioética ni social ya que es= un estudio de construcción teórica si uso de experimentación animal ni intervención social directa.

El instrumento está constituido por las dimensiones e ítems del modelo de Saltos-Cruz et al. (2022), este modelo e= stá construido a partir de nueve elementos, a saber: Email / CRM, Website, Social media, Social, Inbound Marketing, Adaptive SEO, Mobile y Paid Search= Advertising. Las preguntas se estructuraron con escalas de Likert con anclajes que descr= iben la concordancia de criterios de inclusión del ítem a la dimensión. Se toma = como valor inicial “totalmente en desacuerdo” con un valor de 1 hasta “Totalment= e de acuerdo” con un valor de 5.

Para la aplicación del mét= odo empírico se usó la técnica de encuesta digital con un instrumento diseñado = en la aplicación informática Google Forms que se envió en un enlace al correo electrónico de cada especialista. Cuando el plazo de entrega se cump= lió se procedió a depurar la base de datos para eliminar los datos perdidos y l= os datos atípicos. Finalmente, la base de datos se sometió a los procedimiento= s de cálculo de fiabilidad y validez de constructo con el uso del programa estadístico SPSS Statistics V23 (Byrne, 2009).

El paso final para la vali= dación de constructo fue el diseño de un diagrama de senderos que se estudió en el programa informativo de modelación de ecuaciones estructurales SPSS Amos V2= 4 que se aplicó según el procedimiento de Lévy & Varela (2006). En este senti= do, se configuraron las variables observables que estructuran las variables latent= es del estudio. Para el análisis de fiabilidad se operó el indicador Alpha de Cronbach. Para el estudio de validez de constructo se calcularon los indica= dores KMO (adecuación de muestreo), Varianza total explicada, prueba de bondad de ajuste del modelo, Prueba de esfericidad de Bartlett y matriz con rotación ortogonal Varimax. Al configurar el diagrama de senderos y correr el modelo se obtienen los indicadores de bondad de ajuste. Estos valores indic= an la existencia o no de criterios de validez de constructo.

Resultados

El análisis de consistencia interna por dimensión muestra un Alfa de Cronbach que varía entre 0,865 y 0= ,976 para social media y email marketing que cumple con el requerimiento del = Gold estándar. Estos valores concluyen que existe un grado aceptable de confiabilidad de la escala.

En el análisis de validez = de constructo se presentan los valores de Kaiser-Meyer-Olkin que fluctúan entre 0,78 y 0,98 para adaptative SEO y email marketing respectivamente. E= stos valores demuestran un grado alto de adecuación de muestreo (tabla 1). Otro indicador que corrobora la última afirmación es la prueba de esfericidad de= Bartlett que presenta una significancia estadística menor a 0,005, valor que valida = la adecuación de muestreo expresado por el CMO. En cuanto al estudio de aporte= del modelo sobre el comportamiento de sus componentes el indicador, varianza to= tal explicada muestra un porcentaje de explicación que varían desde 63,34% hast= a un 69,37%, que según la teoría matemática es un valor aceptable.

&nbs= p;

Tabla 1

<= i>Propiedades métricas de = la escala de Marketing digital

Análisis de fiabilidad y validez por dimensió= n

Kaiser-Meyer-Olkin.

Varianza total explicada

Alfa de Cronbach

Prueba de bondad de ajuste (sig.)

Gl.

Sig.

N de elementos

Social

0,89

68,45%

0,874

0,000

254

0,000

5

Paid Search Advertising

0,87

69,23%

0,893

0,000

145

0,000

5

Mobile

0,89

63,34%

0,947

0,000

254

0,000

5

Adaptive SEO

0,78

69,37%

0,919

0,000

75

0,000

5

Inbound Marketing

0,79

68,74%

0,873

0,060

235

0,056

5

Social media

0,89

65,98%

0,865

0,000

275

0,000

5

Email / CRM

0,98

63,56%

0,976

0,000

222

0,000

5

Website

0,89

67,76%

0,928

0,000

298

0,000

5

En el análisis= de componentes se usó la matriz rotada con un método Varimax (tabla 2), donde se puede observar 8 elementos del modelo con cargas factoriales superiores a 0,7 a excepción del ítem Case studies en el factor i= nbound marketing.

Tabla 2

<= i>Matriz rotada de marketi= ng digital

Factores de Marketing Digital

Ítems

1

2

3

4

5

6

7

8

Social

Google +<= o:p>

 

 

 

 

 

 

 

,790

Facebook<= o:p>

 

 

 

 

 

 

 

,702

Twitter

 

 

 

 

 

 

 

,830

LinkedIn<= o:p>

 

 

 

 

 

 

 

,793

YouTube

 

 

 

 

 

 

 

,702

Paid Search Advertising

Campaign management

,937

 

 

 

 

 

 

 

Geofencing

,848

 

 

 

 

 

 

 

Remarketing

,908

 

 

 

 

 

 

 

Call Tracking

,899

 

 

 

 

 

 

 

Pay-Per-Click

,938

 

 

 

 

 

 

 

Mobile

QR Codes<= o:p>

 

,778

 

 

 

 

 

 

Local Sea= rch

 

,951

 

 

 

 

 

 

Check-ins=

 

,929

 

 

 

 

 

 

Mobile Ap= p

 

,924

 

 

 

 

 

 

SMS<= /o:p>

 

,852

 

 

 

 

 

 

Adaptive SEO

Content Syndication

 

 

,816

 

 

 

 

 

Forums

 

 

,795

 

 

 

 

 

Citations=

 

 

,928

 

 

 

 

 

Industry Directories

 

 

,858

 

 

 

 

 

Motores de búsqueda

 

 

,872

 

 

 

 

 

Tabla 2

<= i>Matriz rotada de marketi= ng digital (continuación)

Factores de Marketing Digital

Ítems

1

2

3

4

5

6

7

8

Inbound Marketing

Press rel= ease

 

 

 

 

 

 

,864

 

Case stud= ies

 

 

 

 

 

 

,663

 

Articles<= o:p>

 

 

 

 

 

 

,996

 

eBooks

 

 

 

 

 

 

,861

 

How to gu= ides

 

 

 

 

 

 

,869

 

Social media

# Hashtag= registration

 

 

 

 

,856

 

 

 

Social bo= okmarking

 

 

 

 

,848

 

 

 

Social Selling Networking

 

 

 

 

,963

 

 

 

KPI<= /o:p>

 

 

 

 

,829

 

 

 

SM.5=

 

 

 

 

,715

 

 

 

Email / CRM

Communica= tion

 

 

 

 

 

,727

 

 

E-Newslet= ter

 

 

 

 

 

,858

 

 

Lead Mana= gement

 

 

 

 

 

,840

 

 

Segmentat= ion

 

 

 

 

 

,971

 

 

List buil= ding

 

 

 

 

 

,840

 

 

Website

Blog=

 

 

 

,959

 

 

 

 

Keyword Analysis

 

 

 

,885

 

 

 

 

Visitor tracking

 

 

 

,848

 

 

 

 

Landing P= age

 

 

 

,850

 

 

 

 

Lead conv= ersion

 

 

 

,858

 

 

 

 

Una vez estudi= ada la fiabilidad y validez del constructo se analiza las ecuaciones estructura= les generadas del comportamiento del modelo (figura 1). En este sentido, se analizan los indicadores de bondad de ajuste del modelo. El indicador CFI de bondad de ajuste del modelo presenta un valor de 0,920 que constituye un va= lor aceptable. El valor aportado por la prueba de error aproximado cuadrático presenta un valor de 0,078, en combinación con el indicador de entropía de = la información AIC de 1666,016 muestran un modelo aceptable.=

&nbs= p;

&nbs= p;

&nbs= p;

&nbs= p;

&nbs= p;

&nbs= p;

Figura 1<= o:p>

<= i>Modelo de configuración = del ecosistema de medios digitales

 

Conclusiones

·      =    Existe un alto nivel de coincidencia entre el modelo teórico analiza= do y la realidad empírica medido entre los especialistas del estudio que afirmar= on que un ecosistema de medios digitales está constituido por: Email / CRM,= Website, Social media, Social, Inbound Marketing, Adaptive SEO, Mobile y Paid Search= Advertising

·         El análisis de consistencia interna por dimensión presenta un indica= dor de consistencia interna mínima de 0,865 lo que permite concluir que la esca= la es fiable.

·         En el análisis de validez de constructo presenta un KMO mínimo de 0,= 78, la prueba de esfericidad de Bartlett presenta una significancia estadística menor a 0,005 y la varianza total explicada mínima muestra un porcentaje de 63,34% hasta un 69,37% lo que permite concluir que la escala = es válida desde su constructo.

·         El análisis del diagrama de senderos presenta una bondad de ajuste d= el modelo en el indicador CFI de bondad de ajuste de 0,920, un indicador de er= ror medio cuadrático de 0,078 y un indicador de entropía de la información AIC = de 1666,016 que permite concluir que existe valides nomológica y general del modelo.

Conflicto de intereses<= /o:p>

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

 =

Referencias Bibliográficas

Byrne, B. (2009). Structural equation modelli= ng with AMOS: Basic concepts, applications, and programming. New York: Routledge/Taylor and Francis.

Capatina, A., Micu, A., Micu, A., Bouzaabia, R., & Bouzaabia, O. (2018). Country-based comparison of accommodation bra= nds in social media: An fsQCA approach. Journal of Business Research, 89(1), 253-242. https://doi.org/10.1016/j.jbusres.2017.11.017<= /p>

Castelló-Martínez, A. (2018). Las estrategias de medios sociales y el Imboun Marketing. I Congreso Internacional de Comunicación y Sociedad Digital= (pp. 1 - 25). Virtual: Dialnet. https://dialnet.unirioja.es/servlet/artic= ulo?codigo=3D4247809

Chan, H. (2008). Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer. Expert Systems with Applications, 2754–2762. Doi:10.1016/j.eswa.2007.05.043.

Colapinto, C. (2010). Moving to a multichannel a= nd multiplatform company in the emerging and digital media ecosystem: The Ca= se of Mediaset Group. The International Journal on Media Management, 12(1), 59 - 75. https://doi.org/10.1080/14241277.2010.510459

Dutt, R., & Ferrara, E. (2018). “Senator, We Sell Ads”: Analysis of the 2016 Russian Facebook Ads Campaign. Interna= tional Conference on Intelligent Information Technologies. 941, pp. 151–168. Singapore: Springer. https://www.doi.org/10.1007/978-981-13-3582-2_12

Isoraite, M. (2019). Remarketing Features. In= ternational Journal of Trend in Scientific Research and Development (IJTSRD), 3(6= ), 6. https://www.ijtsrd.com/papers/ijtsrd28031.pdf

Kumar, G., & Kumar, A. (2019). A work on dig= ital marketing processes at digitally inspired India. International Journal= of Recent Technology and Engineering, 8(2), 212 - 214. https://doi.org/10.35940/ijrte.B1351.0882S819

Lévy, J., & Varela, J. (2006). Modelizaci= ón con estructuras de covarianzas en ciencias sociales. Temas esenciales, avanzados y aportaciones especiales. Barcelona: Gesbiblo.<= /span>

Nagy, J., Oláh, J., Erdei, E., Máté, D., & P= opp, J. (2018). The Role and Impact of Industry 4.0 and the Internet of Things on the Business Strateg= y of the Value Chain—The Case of Hungary. Sustainability, 10(10), 1-25. https://doi.org/10.3390/su10103491

Papadopoulos, T., Baltas, K., & Balta, M. (2020). The use of digital technologies by small and medium enterprises during COVID-19: Implications for theory and practice. International Journal of Informa= tion Management, 55(1), 1-4. https://doi.org/10.1016/j.ijinfomgt.2020.1021= 92

Qalati, S., Li, W., Ahme, N., Mirani, M., & Khan, A. (2021). Examini= ng the Factors Affecting SME Performance: The Mediating Role of Social Media Adoption. Sustainabili= ty, 13(1), 1-24. https://dx.doi.org/10.3390/su13010075=

Saltos, G., Peñaherrera, S., Herrera, J., Naranj= o, F., & Araque, W. (2022). Digital Media Ecosystem: A Core Component Analysis According to Expert Judgment. = Lecture Notes in Networks and Systems, 407(1), 1-13. https://doi.org/10.1007/= 978-3-030-96147-3_2

Saltos-Cruz, G., Peñaherrera-Zambrano, S., Herrera-Herrera, J., Naranjo-Holguín, F., & Araque-Jaramillo, W. (202= 2). Digital Media Ecosystem: A Core Component Analysis According to Expert Judgment. Lecture Notes in Networks and Systems, 407(1), 1 - 13. https://doi.org/10.1007/978-3-030-96147-3_2

Vidal, M., Vialart, M., & Hernández, L. (201= 3). Redes sociales. Educación Médica Superior, 1-15. ISSN 0864-21= 41.

Zhang, G.-Q., Zhang, G.-Q., Yang, Q.-F., Cheng, S.-Q., & Zhou, T. (2008). Evolution of the Internet and its cores. New Journal of Physics, 10(1), 1-12. https://doi.org/10.1088/1367-2630/10/12/123027

3Deditorial1.png<= ![endif]>

 

 

 

 

 

 

 

El artículo que se publica es de exclusiva responsabilidad de los autores y no necesariamente reflejan el pensamiento de la Revi= sta Alfa Publicaciones.

 

3Deditorial1.png

El artículo queda en propiedad de la revista y, por tanto, su publicación parcial y/o total en otro medio tiene que ser autorizado por el director de la Revista Alfa Publicaciones.

 

3D"logo_catalogo3b.jpg"

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