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(UBE), Dur=C3=A1n, Ecuador</td><td = style=3D"width:20pt; border-top:0.75pt solid #000000; padding:0.65pt 1.35pt= 0pt 0pt; vertical-align:top"> = </td><td style=3D"width:175.95pt; border-top:0.75pt solid #000000; bord= er-right:0.75pt solid #000000; padding:0.65pt 0.98pt 0pt 0pt; vertical-alig= n:top"> </= td></tr><tr style=3D"height:33.6pt"><td style=3D"width:12.32pt; border-righ= t:0.75pt solid #000000; border-left:0.75pt solid #000000; border-bottom:0.7= 5pt solid #000000; padding:0.65pt 0.98pt 0pt 0pt; vertical-align:top"> </td><td style= =3D"width:202.42pt; border-left:0.75pt solid #000000; border-bottom:0.75pt = solid #000000; padding:0.65pt 1.35pt 0pt 0pt; vertical-align:top">Maestr=C3=ADa en= Administraci=C3=B3n y Direcci=C3=B3n de empresas misuarezp@ube.edu.ec <span= style=3D"text-decoration:underline; color:#0070c0">merlysuarez518@gmail.co= m </td><td style=3D"width:20pt; border-bottom:0.75p= t solid #000000; padding:0.65pt 1.35pt 0pt 0pt; vertical-align:top"> </td><td style=3D"width:175.95pt;= border-right:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pad= ding:0.65pt 0.98pt 0pt 0pt; vertical-align:top"><s= pan> </td></tr><tr style=3D"height:17.75pt"><td style=3D"wi= dth:12.32pt; border:0.75pt solid #000000; padding:0.65pt 0.98pt 0pt 0pt; ve= rtical-align:top">2</= span> </td><td style=3D"width:202.42pt; border:0.75pt soli= d #000000; padding:0.65pt 0.98pt 0pt 0pt; vertical-align:top">Glen Freddy Robayo Ca= brera </td><td style=3D"width:19.62pt; border:0.75pt solid #0000= 00; padding:0.65pt 0.98pt 0pt 0pt; vertical-align:top"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAAN= SUhEUgAAABQAAAAUCAYAAACNiR0NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSs= OGwAAA3VJREFUOI21lE1onFUUhp9z7zffN5lJMpkU2kVsbaRNQ6GIJrUau0lExSKCG9FNIYhYRK= rixkKVLtxYRFBQEfyhNFFwW3ChKGL9iSaRUI0KtrRBU5PU2GSS+fn+7nExyTSJKV35Xs7icu/7n= vPec7jCJnhnrKdQ9P0dKm4/avalzhWsMYtqdJIoHDXOXHqkd3xxM66s3Xw8udd3Yes9frN9Io21= 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AAABJRU5ErkJggg=3D=3D" width=3D"20" height=3D"20" alt=3D"" /></td><td s= tyle=3D"width:175.58pt; border:0.75pt solid #000000; padding:0.65pt 0.98pt = 0pt 0pt; vertical-align:top"><= a href=3D"https://orcid.org/0009-0006-9195-7423%20" style=3D"text-decoratio= n:none">https://orcid.org/0009-0006-9195-7423</a> </td></tr><tr style=3D"height:58pt"><td= style=3D"width:12.32pt; border:0.75pt solid #000000; padding:0.65pt 0.98pt= 0pt 0pt; vertical-align:top"> </td><td colspan=3D"3" style=3D"wid= th:401.22pt; border:0.75pt solid #000000; padding:0.65pt 0.98pt 0pt 0pt; ve= rtical-align:top">Universidad Bolivariana de Ecuador (UBE), Dur=C3=A1n, Ecuador. </= span>Universidad de Valencia, Valencia, Espa=C3=B1a. 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29LH/J8Dqdh+XrvC7p27/OMOq8T8aQySCDDDLIIIN/Pv7adf/v0vkRQqTS2ePxOGeeeSb19fV/Y= 5czyCCDDDLIIIMM/ufozDMeOXIkH3/8carG4v8Ufzbslc6/6extySCDDDLIIIMMMvjfRGdx4K/z= /vzdCs/pvJg/11AGGWSQQQYZZJDBPwOdKxb8vfizloxLaHb1cP4aLZ0MMsgggwwyyCCDfyRcO6S= z9MzfmLP15wnPGU9PBhlkkEEGGWTwrwS3WPPfQ3jOIIMMMsgggwwyyCCDDDLIIIMMMsgggwwyyC= CDDDLIIIMMMsgggwwyyCCDDP7fwv8PGbshodW+encAAAAASUVORK5CYII=3D" width=3D"575"= height=3D"174" alt=3D"" /><table style=3D"width:444.7pt; margin-left:0= .5pt; margin-bottom:0pt; border:0.75pt solid #000000; padding:0pt; border-c= ollapse:collapse"><tr style=3D"height:598.1pt"><td style=3D"width:87.32pt; = border-right:0.75pt solid #000000; padding:3.4pt 0pt 0pt 5.03pt; vertical-a= lign:top"> Palabras claves: <= /span>Transformaci=C3= =B3n digital, m= icroempresas, t= ecnolog=C3=ADas de <s= pan>la informaci=C3=B3n, eficiencia = operativa, <spa= n>innovaci=C3=B3n empresarial. </td><td style= =3D"width:9.38pt; border-right:0.75pt solid #000000; border-left:0.75pt sol= id #000000; padding:3.4pt 0pt 0pt 5.03pt; vertical-align:top"> </span= ></td><td style=3D"width:329.92pt; border-left:0.75pt solid #000000; pa= dding:3.4pt 0pt 0pt 5.03pt; vertical-align:top">Resumen Introducci=C3=B3n: <span= >la transformaci=C3=B3n digital se convirti=C3=B3 en un factor determinante= para la competitividad de las microempresas, especialmente en contextos do= nde predominan barreras t=C3=A9cnicas, econ=C3=B3micas y organizativas. En = Ecuador, este sector constituye m=C3=A1s del 90 % de las unidades productiv= as, pero presenta rezagos en adopci=C3=B3n tecnol=C3=B3gica y acceso a ecos= istemas de apoyo, lo que limita su eficiencia operativa y sostenibilidad</s= pan>. Objetivo: analizar la t= ransformaci=C3=B3n digital en microempresas ecuatorianas del sector comerci= o y servicios, identificando barreras, nivel de adopci=C3=B3n tecnol=C3=B3g= ica y estrategias orientadas a mejorar su eficiencia operativa.<span= style=3D"font-weight:bold"> Metodolog=C3=ADa: se desarroll=C3= =B3 un estudio bajo el paradigma pragm=C3=A1tico, con un enfoque mixto, dis= e=C3=B1o no experimental, transeccional y alcance descriptivo-explicativo. = Se aplicaron 200 encuestas estructuradas distribuidas en cinco bloques tem= =C3=A1ticos clave. Los datos cuantitativos fueron procesados mediante estad= =C3=ADstica descriptiva, mientras que los datos cualitativos se analizaron = mediante codificaci=C3=B3n tem=C3=A1tica. Posteriormente, se formularon est= rategias y se validaron a trav=C3=A9s del juicio de cinco expertos, aplican= do el coeficiente de concordancia de Kendall (W). Resultados: los hallazgos evidencian un uso ext= endido de herramientas digitales b=C3=A1sicas (71.5 %), presencia en redes = sociales (73.5 %) y ventas en l=C3=ADnea (61 %). Sin embargo, persisten bar= reras significativas como falta de conocimientos digitales (54 %), altos co= stos tecnol=C3=B3gicos (66 %) y bajo apoyo institucional (22.5 %). La valid= aci=C3=B3n experta mostr=C3=B3 altos niveles de consenso (W > 0.75), des= tacando dimensiones como actitud hacia la digitalizaci=C3=B3n (5/5) y adopc= i=C3=B3n tecnol=C3=B3gica (4.6/5), mientras que apoyo institucional (3.2/5)= y barreras econ=C3=B3micas (3.6/5) recibieron las calificaciones m=C3=A1s = bajas. Conclusi=C3=B3n: l<sp= an>a transformaci=C3=B3n digital impacta positivamente en la eficiencia ope= rativa de las microempresas, pero su efecto pleno depende de superar las li= mitaciones t=C3=A9cnicas, financieras y de articulaci=C3=B3n institucional.= Las estrategias propuestas: capacitaci=C3=B3n modular, mecanismos de finan= ciamiento adaptado, fortalecimiento organizacional y redes de apoyo institu= cional, se consolidan como soluciones viables para fomentar una digitalizac= i=C3=B3n efectiva y sostenible. =C3= =81rea de estudio general: Ciencias empresariales. <spa= n style=3D"font-weight:bold">=C3=81rea =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0 de estudio =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 espe= c=C3=ADfica: =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0 Direcci=C3=B3n de =C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= empresas / transformaci=C3=B3n digital en microempresas. </sp= an>Tipo de estudio: Art=C3=AD= culo original. <= /span></td></tr></table>= <table style=3D"width:444.7pt; margin-left:0.5pt; ma= rgin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr style=3D"height= :569.85pt"><td style=3D"width:91.98pt; border-top:0.75pt solid #000000; bor= der-right:0.75pt solid #5b9bd5; border-left:0.75pt solid #5b9bd5; border-bo= ttom:0.75pt solid #5b9bd5; padding:3.2pt 2.02pt 0pt 0pt; vertical-align:top= ">Keywords: <= /span>Digital transfor= mation, microenterprises, information technology, operational efficiency, </spa= n>business innovation.= </td><td style=3D"width:10.78pt; border-top:0= .75pt solid #000000; border-right:0.75pt solid #5b9bd5; border-left:0.75pt = solid #5b9bd5; border-bottom:0.75pt solid #5b9bd5; padding:3.2pt 2.02pt 0pt= 0pt; vertical-align:top"> </td><td style=3D"width:332.88pt; border-top:0= .75pt solid #000000; border-right:0.75pt solid #5b9bd5; border-left:0.75pt = solid #5b9bd5; border-bottom:0.75pt solid #5b9bd5; padding:3.2pt 2.02pt 0pt= 0pt; vertical-align:top">Abstract Introduction: digital transformation bec= omes a decisive factor for the competitiveness of microenterprises, especia= lly in contexts where technical, economic, and organizational barriers prev= ail. In Ecuador, this sector accounts for more than 90% of productive units= , yet it shows delays in technological adoption and access to support ecosy= stems, which limits its operational efficiency and sustainability. <= span style=3D"font-weight:bold">Objective: To analyze digital = transformation in Ecuadorian microenterprises in the commerce and services = sector, identifying barriers, level of technological adoption, and strategi= es aimed at improving their operational efficiency. Methodology: A study was conducted under the = pragmatic paradigm, with a mixed methods approach, a non-experimental, cros= s-sectional design, and a descriptive-explanatory scope. Two hundred struct= ured surveys were administered, distributed across five key thematic blocks= . Quantitative data were processed using descriptive statistics, while qual= itative data were analyzed using thematic coding. Subsequently, strategies = were formulated and validated through expert judgment by five specialists, = applying Kendall's W coefficient of concordance. Results: The findings show an extensive use of b= asic digital tools (71.5%), presence on social media (73.5%), and online sa= les (61%). However, significant barriers persist, such as lack of digital k= nowledge (54%), high technology costs (66%), and low institutional support = (22.5%). Expert validation revealed high levels of consensus (W > 0.75),= highlighting dimensions such as attitude toward digitalization (5/5) and t= echnological adoption (4.6/5), while institutional support (3.2/5) and econ= omic barriers (3.6/5) received the lowest scores. Conclusion: Digital transformation has a positi= ve impact on the operational efficiency of microenterprises, but its full e= ffect depends on overcoming technical, financial, and institutional coordin= ation limitations. The proposed strategies=E2=80=94modular training, adapte= d financing mechanisms, organizational strengthening, and institutional sup= port networks=E2=80=94are consolidated as viable solutions to foster effect= ive and sustainable digitalization. General field of study: Business sciences. Specific field of study: Business mana= gement / transformation in microenterprises. Type of study: Original article.</td></tr= ></table> <h1 style=3D"margin-right:0pt;= margin-left:17.75pt; margin-bottom:7.8pt; text-align:left; line-height:115= %">1. Introducci=C3=B3n </h1>La cuarta revoluci=C3=B3n ind= ustrial o Industria 4.0, se caracteriza por la integraci=C3=B3n de la </spa= n>Inteligencia Artificial (IA</sp= an>), la anal=C3=ADtica de datos masivos, la rob=C3=B3tica avanzada, = la computaci=C3=B3n en la nube, la tecnolog=C3=ADa blockchain y el Internet de las Cosas (IoT), todas ellas c= on un alto potencial disruptivo sobre los procesos tradicionales (<s= pan style=3D"background-color:#ffff00">Flores et al., 2021). <= /span>A nivel eco= n=C3=B3mico redefini=C3=B3 los patrones de competitividad y eficiencia; en = lo social reconfiguro las formas de interacci=C3=B3n y consumo; y en el =C3= =A1mbito empresarial, obligaron a las organizaciones a replantear sus estra= tegias para mantenerse vigentes. Aquellas que lograron adaptarse a la digit= alizaci=C3=B3n reportan incrementos sustanciales en productividad, eficienc= ia operativa y capacidad de innovaci=C3=B3n, mientras que las que se rezaga= n enfrentan mayores riesgos de exclusi=C3=B3n del mercado (Prieto & Loredo, 2025). El= estudio de Brito<sp= an> et al. (2023) se=C3=B1ala que cerca del 70 % de estas iniciativas no al= canzan sus objetivos debido a la falta de liderazgo digital, la resistencia= cultural dentro de las organizaciones y la carencia de una visi=C3=B3n int= egral que abarque no solo la dimensi=C3=B3n tecnol=C3=B3gica, sino tambi=C3= =A9n la humana y organizacional. Desde una perspectiva internacional, organismos como= la Organizaci=C3=B3n para= la Cooperaci=C3=B3n y el Desarrollo Econ=C3=B3micos (OCDE) y = el Banco Mundial reconocen a la digitalizaci=C3=B3n como un motor esencial = para el cumplimiento de los Objetivos de Desarrollo Sostenible (ODS), particularmente en= lo relacionado con el ODS 9: industria, innovaci=C3=B3n e infraestructura = (Espejo et al., 2023= ). En consecuencia, se concibe a la transformaci=C3=B3n digital no so= lo como un mecanismo para incrementar la competitividad empresarial, sino t= ambi=C3=A9n como una estrategia de desarrollo sostenible y la generaci=C3= =B3n de empleo digno (Dur= =C3=A1n, 2025).<h2 style=3D"margin-right:0pt; margi= n-left:18pt; text-indent:-18pt; text-align:left; line-height:115%">1.1.<span= > Justificaci= =C3=B3n e importancia del problema cient=C3=ADfico </h2>En Am=C3=A9rica Latina las </span= >Micro, Peque=C3=B1as y Medianas = Empresas (MIPYMES) representan m=C3=A1s del 99 % de las unidad= es productivas y aportan alrededor del 60 % del empleo formal (<span= style=3D"background-color:#ffff00">Bartels, 2021). Estas orga= nizaciones, por su importancia econ=C3=B3mica y social, constituyen la colu= mna vertebral de las econom=C3=ADas nacionales. Sin embargo, enfrentan m=C3= =BAltiples limitaciones estructurales que reducen su capacidad para adaptar= se a las exigencias de la era digital. Las microempresas se encuentran toda= v=C3=ADa en una etapa incipiente en comparaci=C3=B3n a grandes corporacione= s como la falta de competencias digitales en el personal, la resistencia al= cambio organizacional, las restricciones presupuestarias y la ausencia de = pol=C3=ADticas claras de apoyo a la innovaci=C3=B3n (Banka et al., 2024). En la pandemia del COVID-= 19, diversas microempresas tuvieron que suspender o cerrar sus operaciones = por la imposibilidad de migrar a entornos digitales (Quinallaz, 2022). Esta situaci=C3=B3= n evidenci=C3=B3 que la digitalizaci=C3=B3n no es un lujo opcional, sino un= a necesidad estrat=C3=A9gica para la resiliencia empresarial. <p= style=3D"margin-right:0pt; line-height:115%">En el contexto ecuatori= ano, las microempresas representan m=C3=A1s del 90 % de las unidades produc= tivas registradas y contribuyen de manera significativa a la generaci=C3=B3= n de empleo, en especial en sectores como el comercio, los servicios, la ag= ricultura y la manufactura artesanal (Ortiz-Choez et al., 2024). No obstante, su rezago d= igital limita su competitividad frente a un mercado globalizado donde la in= novaci=C3=B3n tecnol=C3=B3gica es el factor diferenciador. El prop=C3=B3sito de este = estudio es analizar la transformaci=C3=B3n digital en las microempresas ecu= atorianas, mediante un enfoque descriptivo y anal=C3=ADtico de sus procesos= , para la determinaci=C3=B3n de su impacto en la eficiencia operativa y la = competitividad empresarial y as=C3=AD proponer estrategias que promuevan la= transformaci=C3=B3n digital en las microempresas, con base en el an=C3=A1l= isis de barreras y experiencias exitosas. Este argumento da paso a la formu= laci=C3=B3n de la siguiente hip=C3=B3tesis: La implementaci=C3=B3n efectiva de estrat= egias de transformaci=C3=B3n digital en las microempresas ecuatorianas se a= socia significativamente con la mejora de su eficiencia operativa, siempre = que se superen barreras como la insuficiente capacitaci=C3=B3n digital, la = resistencia al cambio organizacional y los costos iniciales de adopci=C3=B3= n tecnol=C3=B3gica. <h2 style=3D"margin-right:0pt; margin-left:1= 8pt; text-indent:-18pt; text-align:left; line-height:115%">1.2. </span= >Antecedentes del pro= blema </h2>En = muchos casos, la falta de adaptaciones tecnol=C3=B3gicas se reduce a una pr= esencia b=C3=A1sica en redes sociales o al uso de aplicaciones de mensajer= =C3=ADa, sin integrar sistemas de Planificaci=C3=B3n de Recursos Empresariales (ERP), pl= ataformas de comercio electr=C3=B3nico o soluciones de ciberseguridad (</sp= an>=C3=81lvarez, 2025= ). Tamayo= (2025) subraya que en Ecuador las microempresas enfrentan debilidade= s estructurales adicionales como la falta de planes de contingencia, la esc= asa capacitaci=C3=B3n del personal, la debilidad financiera y la limitada d= iversificaci=C3=B3n productiva, factores que restringen su capacidad de ada= ptaci=C3=B3n frente a contextos de crisis. A ello se suma la dificultad par= a acceder a financiamiento formal, dado que muchos de estos negocios no cum= plen con requisitos contables o carecen de planes de negocio estructurados = (Ortiz-Choez e= t al., 2024).Otro obst=C3=A1culo cr=C3=ADtico identificado es la= carencia de estrategias de ciberseguridad, lo que expone a las microempres= as a riesgos de p=C3=A9rdida de datos, ataques inform=C3=A1ticos y p=C3=A9r= dida de confianza por parte de los clientes (Largo, 2025). Por su parte Ortiz et al. (2025) destaca que= la insuficiente capacitaci=C3=B3n digital de los microempresarios y de sus= empleados limita no solo la adopci=C3=B3n de herramientas tecnol=C3=B3gica= s, sino tambi=C3=A9n el aprovechamiento de oportunidades en mercados digita= les. A pes= ar de estas limitaciones se document=C3=B3 experiencias exitosas en las que= la digitalizaci=C3=B3n permiti=C3=B3 a las microempresas reducir costos, o= ptimizar la gesti=C3=B3n de clientes y expandir su alcance comercial, espec= ialmente en ecosistemas de innovaci=C3=B3n urbana o en emprendimientos lide= rados por j=C3=B3venes (Ban= ka et al., 2024). Estas evidencias sugieren que, si se dise=C3= =B1an estrategias adecuadas de acompa=C3=B1amiento, capacitaci=C3=B3n y fin= anciamiento, las microempresas ecuatorianas tienen el potencial de aprovech= ar las oportunidades que ofrece la econom=C3=ADa digital. <h1 st= yle=3D"margin-right:0pt; margin-left:17.75pt; margin-bottom:7.8pt; text-ali= gn:left; line-height:115%">2. Metodolog=C3=AD= a </h1>La inve= stigaci=C3=B3n se desarroll=C3=B3 bajo el paradigma pragm=C3=A1tico, que pe= rmiti=C3=B3 abordar la transformaci=C3=B3n digital en microempresas como va= riable independiente y la eficiencia operativa como variable dependiente, p= riorizando la generaci=C3=B3n de soluciones aplicadas a los problemas detec= tados. Se adopt=C3=B3 un enfoque mixto, combinando la aplicaci=C3=B3n de en= cuestas estructuradas y la validaci=C3=B3n mediante juicio de expertos, lo = que permiti=C3=B3 triangular resultados y garantizar mayor rigor cient=C3= =ADfico (Cutanda-L=C3=B3pez= , 2021). El estudio tuvo un alcance descriptivo-explicativo: describi=C3= =B3 el estado actual de la transformaci=C3=B3n digital en microempresas ecu= atorianas del sector comercio y servicios a su vez, explic=C3=B3 las barrer= as que limitan su desarrollo, as=C3=AD como su relaci=C3=B3n con la eficien= cia operativa (Ocampo</span= >, 2023). El dise=C3=B1o fue no experimental, transeccional y de camp= o, ya que no se manipularon variables, sino que estas se observaron en su c= ontexto natural en un =C3=BAnico momento temporal. La poblaci=C3=B3n estuvo conformad= a por microempresas ecuatorianas de los sectores comercio y servicios, cons= iderando su peso en el tejido productivo y en la generaci=C3=B3n de empleo.= Seg=C3=BAn Fern=C3=A1ndez-= Ronquillo & Romero-C=C3=A1rdenas (2015) =E2=80=9Cdefinen a= las microempresas (RIMI) como negocios populares que tienen ingresos de $2= 0.000 a $300.000 (p.14)=E2=80=9D. La muestra se seleccion=C3=B3 mediante un muestreo = no probabil=C3=ADstico por conveniencia, tomando en cuenta la disposici=C3= =B3n de los participantes y la accesibilidad al campo de estudio. Finalment= e, se aplic=C3=B3 una encuesta estructurada a 200 microempresarios, n=C3=BA= mero potencial que permiti=C3=B3 obtener informaci=C3=B3n suficiente para e= l an=C3=A1lisis descriptivo y exploratorio que permiten identificar patrone= s, tendencias y percepciones vinculadas a la transformaci=C3=B3n digital. <= /span><h2 style=3D"margin-right:0pt; text-align:left; line-height:115%"= >2.1.</s= pan>&#= xa0;M=C3=A9tod= os, t=C3=A9cnicas e instrumentos de recolecci=C3=B3n de datos </h2><= p style=3D"margin-right:0pt; line-height:115%">Con base en el enfoque= metodol=C3=B3gico, la ejecuci=C3=B3n pr=C3=A1ctica de la investigaci=C3=B3= n se estructur=C3=B3 en cuatro fases que guiaron el proceso desde la aplica= ci=C3=B3n de los instrumentos hasta la organizaci=C3=B3n e interpretaci=C3= =B3n de los datos. a) &#x= a0; Recolecci=C3=B3n emp=C3=ADrica<= /span>: se aplic=C3=B3 una encuesta estructurada a 200 microempresari= os, que consto de cinco dimensiones relacionadas a: Adopci=C3=B3n digital b= =C3=A1sica/presencia en l=C3=ADnea, barreras t=C3=A9cnicas/econ=C3=B3micas,= disposici=C3=B3n/actitudes hacia la digitalizaci=C3=B3n, capacidades, lide= razgo/toma de decisiones y ecosistema de apoyo e inversi=C3=B3n. Esta etapa= permiti=C3=B3 captar datos tanto cuantitativos como cualitativos en base a= la observaci=C3=B3n sobre el nivel de adopci=C3=B3n tecnol=C3=B3gica, los = procesos internos y las estrategias organizacionales vinculadas a la transf= ormaci=C3=B3n digital, siendo temas claves para el levantamiento de informa= ci=C3=B3n de este estudio en base a los datos cuantitativos. b) &= #xa0; Procesamiento y a= n=C3=A1lisis de datos: los datos cuantitativos fueron analizad= os mediante estad=C3=ADstica descriptiva (frecuencias, porcentajes y medida= s de tendencia central) con el fin de identificar patrones, tendencias y re= laciones entre variables. <= p class=3D"ListParagraph" style=3D"margin-right:0pt; margin-left:36.5pt; ma= rgin-bottom:0pt; text-indent:-18pt; line-height:115%">c)&#= xa0; Formul= aci=C3=B3n de estrategias: esta fase se centr=C3=B3 en la form= ulaci=C3=B3n de estrategias orientadas a fomentar la transformaci=C3=B3n di= gital en las microempresas, tomando como base las barreras estructurales, o= rganizativas y tecnol=C3=B3gicas detectadas durante el an=C3=A1lisis. Estas= propuestas se fundamentan en los hallazgos emp=C3=ADricos obtenidos y busc= an aportar soluciones pr=C3=A1cticas, viables y contextualizadas para mejor= ar la eficiencia operativa mediante una digitalizaci=C3=B3n efectiva, con e= l objetivo de fortalecer la eficiencia operativa de las microempresas media= nte la adopci=C3=B3n digital. d) Validaci=C3=B3n por expertos: la propuesta fue evalu= ada por un panel de cinco especialistas en transformaci=C3=B3n digital y ge= sti=C3=B3n empresarial. Los criterios de selecci=C3=B3n incluyeron formaci= =C3=B3n acad=C3=A9mica de cuarto nivel, experiencia comprobada y participac= i=C3=B3n en proyectos de digitalizaci=C3=B3n. La validaci=C3=B3n se estruct= uro en base a las cinco dimensiones claves del estudio, donde =C3=A9l exper= to valor=C3=B3 la propuesta mediante una escala ordinal, y para medir el gr= ado de consenso se aplic=C3=B3 el Coeficiente de Concordancia de Kendall (W= ) (Ecuaci=C3=B3n 1), t= =C3=A9cnica estad=C3=ADstica no param=C3=A9trica id=C3=B3nea para validar j= uicios colectivos sobre variables (Castillo & Villanueva, 2024).12S<= span style=3D"height:0pt; display:block; position:absolute; z-index:2"><img= src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGYAAAAeCAYAAADermvOA= AAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAhBJREFUaIHtmr2q4kAYht/Z= TrAVCy28AEGCnSAoWNjZ2FhaeA1egZ2FYJPawkqw9QIUrARFEYMIIkYtBBEFIZJ3m1VW3D1kcg6= ccDIPpJifj++Dh0mGmQiShMIzCCEEAPz67kIU/0aJ8ShKjEdRYjyKEuNRlBiPosR4FCXGo0iLMU= 0T1WoVh8MBAFCpVFAoFCCEgBAChmE859ZqNQwGg6+r1k9Qgvl8Tk3T2Ol0aNv2s/9yuRAAAXCxW= Dz7l8sls9ks2+22TBpfIy3mdDoxnU6z0Wi8jf1PDEmORiMC4HA4/HzVPkBaTLPZJABuNpu3sY/E= kGSxWGQ+n+f9fv9c1T7g4cXRN8ayLOi6jkQigUgkIv26TKVS6PV6GI1G0rF+xZGY1WqF2WyGeDy= OP4efUkSjUQDAeDyWjvUrjsQcj0cAQDAYdJUkEAgAAHa7nat4P+JIzGOVuFktAGDbNgCA6urHMY= 7EhEIhAMD1enWV5Ha7AQDC4bCreD/iSEwsFoOmaZhOp29jJGGa5rO93+/fVsZ2uwUAJBKJz9TqL= 5xu43RdJwCu1+uX/nK5/NwqP55KpfIyp1QqMZfL0bKsL9hQ/mykxZzPZ2YyGdbrdalEk8mEANjv= 96WL9CPSYkjSMAwmk0l2u92XI5mP5mezWbZaLdeF+o2HF/F3Q/H9qL9kPI4S41GUGI+ixHgUJUa= hUPwAfgN/h1JHKjQpRgAAAABJRU5ErkJggg=3D=3D" width=3D"102" height=3D"30" alt= =3D"" style=3D"margin-top:6.26pt; margin-left:132.7pt; position:absolute" /= ><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAIsAAAACC= AYAAABopi/vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABZJREFUOI1j= ZGBg+M8wCkbBKBgF1AQAWpoBAV1qK/UAAAAASUVORK5CYII=3D" width=3D"139" height=3D= "2" alt=3D"" />W =3D =F0=9D=91=9A2(=F0=9D=91=9B2 =E2=88=92 =F0=9D=91=9B) =E2=88=92 =F0=9D=91=9A =E2=88=91 =F0=9D=91=87 <h2 style=3D"margin-right:0pt; text-align:left; line-height:= 115%">2.2.<spa= n style=3D"width:18.15pt; text-indent:0pt; display:inline-block"> </sp= an>Aspectos =C3=A9tic= os </h2>La inv= estigaci=C3=B3n cumpli=C3=B3 con los principios =C3=A9ticos de consentimien= to informado, confidencialidad y respeto a la autonom=C3=ADa de los partici= pantes. Antes de la aplicaci=C3=B3n de la aplicaci=C3=B3n de la encuesta di= rigida a los microempresarios, se inform=C3=B3 a los participantes sobre lo= s objetivos del estudio y se garantiz=C3=B3 que su participaci=C3=B3n fuera= voluntaria. Asimismo, se protegi=C3=B3 la identidad de los informantes y s= e asegur=C3=B3 el uso exclusivo de los datos con fines acad=C3=A9micos. El = protocolo fue revisado y aprobado por el comit=C3=A9 cient=C3=ADfico de la = instituci=C3=B3n acad=C3=A9mica donde se desarroll=C3=B3 el programa de mae= str=C3=ADa. <h2 style=3D"margin-right:0pt; ma= rgin-left:17.75pt; margin-bottom:7.9pt; text-align:left; line-height:115%">= 3. Resultados </h2>Los resultados de l= a encuesta aplicada a 200 microempresarios muestran una adopci=C3=B3n digit= al b=C3=A1sica consolidada. El 74 % de los encuestados son mujeres, predomi= nando el sector comercio (57 %) y servicios (20.5 %). El 71.5 % utiliza de = manera constante herramientas digitales, mientras que un 73.5 % promociona = sus productos en redes sociales y el 61 % ya vende mediante plataformas de = comercio electr=C3=B3nico, pero en base a las 5 dimensiones abarcadas en el= levantamiento de informaci=C3=B3n, se identificaron barreras cr=C3=ADticas= en dos dimensiones abordadas que son, barreras t=C3=A9cnicas/ econ=C3=B3mi= cas, identificando como problem=C3=A1tica la falta de conocimiento (54%), d= ificultad de integrar la tecnolog=C3=ADa (45.5%), los costos de herramienta= s digitales (66%) y la falta de presupuestos <spa= n>para capacitaciones (89%), a su vez, la segunda dimensi=C3=B3n se relacio= na con el apoyo institucional e inventarios, identificando falencia bajas e= n la fomentaci=C3=B3n de capacitaciones digitales (39.5%), poco apoyo insti= tucional (32%), falencia de asesor=C3=ADas externas (8%), falta de particip= aci=C3=B3n en programas de digitalizaci=C3=B3n (22%) y la no inversi=C3=B3n= en tecnolog=C3=ADa (33%), estos datos, reflejan que la falta de compromiso= en procesos tecnol=C3=B3gicos es cr=C3=ADtica y se aleja en gran medida de= una estabilidad tecnol=C3=B3gica microempresaria, como se muestra en la </= span>Tabla 1. <h1 = style=3D"margin-right:0pt; margin-bottom:0pt; text-align:center; line-heigh= t:115%">Tabla 1</h1>Resumen de resultados relevan</span= >tes de la ado= pci=C3=B3n digital b=C3=A1sica en microempresas <table style=3D"width:100%; margin-left:0.5pt; mar= gin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr style=3D"height:= 27.95pt"><td style=3D"width:39.18%; border-top:0.75pt solid #000000; border= -bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Dimensiones = </td><td style=3D"width:41.48%; border-top:0.75pt solid #000000;= border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top"= >Ind= icador </td><td style=3D"width:19.34%; border-top:0.75pt solid #= 000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-ali= gn:top">Porcentaje (%) </td></tr><tr style=3D"height:26.15pt"><= td style=3D"width:39.1%; border-top:0.75pt solid #000000; border-bottom:0.7= 5pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Perfil y Adopci=C3=B3n= digital b=C3=A1sica </td><td style=3D"width:41.48%; border-top:= 0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt= ; vertical-align:top">Mujeres empresarias Uso de herramientas digitales <= /p></td><td style=3D"width:18.8%; border-top:0.75pt solid #000000; border-b= ottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">74 <= p style=3D"margin-right:3pt; margin-left:0pt; margin-bottom:0pt; text-inden= t:0pt; text-align:right; line-height:normal; font-size:10pt">71,5 </s= pan></td></tr><tr style=3D"height:13.8pt"><td style=3D"width:39.1%; bor= der-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0= pt 5.4pt; vertical-align:top"> </td><td style=3D"width:41.48%; bord= er-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0p= t 5.4pt; vertical-align:top">Promoci=C3=B3n en redes sociales </td><td s= tyle=3D"width:18.8%; border-top:0.75pt solid #000000; border-bottom:0.75pt = solid #000000; padding:0pt 5.4pt; vertical-align:top">73,5 </td></tr><t= r style=3D"height:13.8pt"><td style=3D"width:39.1%; border-top:0.75pt solid= #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-a= lign:top"><spa= n> </td><td style=3D"width:41.48%; border-top:0.75pt solid = #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-al= ign:top"><span= >Ventas por e-commerce </td><td style=3D"width:18.8%; border-top= :0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4p= t; vertical-align:top">61 </td></tr><tr style=3D"height:13.8pt"><td sty= le=3D"width:39.1%; border-top:0.75pt solid #000000; border-bottom:0.75pt so= lid #000000; padding:0pt 5.4pt; vertical-align:top"> </td><td style= =3D"width:41.48%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:0pt 5.4pt; vertical-align:top">Sector comercio </td>= <td style=3D"width:18.8%; border-top:0.75pt solid #000000; border-bottom:0.= 75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">57 </td></tr= ><tr style=3D"height:27.6pt"><td style=3D"width:39.1%; border-top:0.75pt so= lid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertica= l-align:top">Barreras T=C3=A9cnicas y econ=C3=B3micas </td><td style= =3D"width:41.48%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:0pt 5.4pt; vertical-align:top">Falta de conocimiento </p= >Dificul= tad para integrar tecnolog=C3=ADa </td><td style=3D"width:18.8%;= border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; paddi= ng:0pt 5.4pt; vertical-align:top">54 45,5 </td></tr><tr style=3D"he= ight:13.8pt"><td style=3D"width:39.1%; border-top:0.75pt solid #000000; bor= der-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top"> </spa= n></td><td style=3D"width:41.48%; border-top:0.75pt solid #000000; bord= er-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Costo de her= ramientas digitales </td><td style=3D"width:18.8%; border-top:0.= 75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; = vertical-align:top">66 </td></tr><tr style=3D"height:13.8pt"><td style= =3D"width:39.1%; border-top:0.75pt solid #000000; border-bottom:0.75pt soli= d #000000; padding:0pt 5.4pt; vertical-align:top"> </td><td style= =3D"width:41.48%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:0pt 5.4pt; vertical-align:top">Sin presupuesto para capacitaci= =C3=B3n </td><td style=3D"width:18.8%; border-top:0.75pt solid #= 000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-ali= gn:top"><span= >89 </td></tr><tr style=3D"height:13.8pt"><td style=3D"width:39.= 1%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pa= dding:0pt 5.4pt; vertical-align:top">Disposici=C3=B3n a la digitalizaci=C3=B3n </sp= an></td><td style=3D"width:41.48%; border-top:0.75pt solid #000000; bor= der-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Inter=C3=A9= s en herramientas digitales </td><td style=3D"width:18.8%; borde= r-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt= 5.4pt; vertical-align:top">93 </td></tr><tr style=3D"height:13.8pt"><t= d style=3D"width:39.1%; border-top:0.75pt solid #000000; border-bottom:0.75= pt solid #000000; padding:0pt 5.4pt; vertical-align:top"> </td><td = style=3D"width:41.48%; border-top:0.75pt solid #000000; border-bottom:0.75p= t solid #000000; padding:0pt 5.4pt; vertical-align:top">Adopci=C3=B3n con apoyo t= =C3=A9cnico </td><td style=3D"width:18.8%; border-top:0.75pt sol= id #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical= -align:top"><= span>90 </td></tr><tr style=3D"height:13.75pt"><td style=3D"widt= h:39.1%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #00000= 0; padding:0pt 5.4pt; vertical-align:top"> </td><td style=3D"width:= 41.48%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000= ; padding:0pt 5.4pt; vertical-align:top">Transformaci=C3=B3n es esencial </p= ></td><td style=3D"width:18.8%; border-top:0.75pt solid #000000; border-bot= tom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">91 <= /td></tr><tr style=3D"height:13.75pt"><td style=3D"width:39.1%; border-top:= 0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt= ; vertical-align:top"> </td><td style=3D"width:41.48%; border-top:0= .75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt;= vertical-align:top">Digitalizaci=C3=B3n como oportunidad </td><td style= =3D"width:18.8%; border-top:0.75pt solid #000000; border-bottom:0.75pt soli= d #000000; padding:0pt 5.4pt; vertical-align:top">94,5 </td></tr><tr st= yle=3D"height:13.8pt"><td style=3D"width:39.1%; border-top:0.75pt solid #00= 0000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align= :top">Fo= rmaci=C3=B3n y liderazgo digital </td><td style=3D"width:41.48%;= border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; paddi= ng:0pt 5.4pt; vertical-align:top">Tiene l=C3=ADder digital </td><td styl= e=3D"width:18.8%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:0pt 5.4pt; vertical-align:top">76 </td></tr><tr sty= le=3D"height:13.8pt"><td style=3D"width:39.1%; border-top:0.75pt solid #000= 000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:= top">&#x= a0;</td><td style=3D"width:41.48%; border-top:0.75pt solid #0000= 00; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:t= op">Toma= decisiones t=C3=A9cnicas </td><td style=3D"width:18.8%; border-= top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5= .4pt; vertical-align:top">60,5 </td></tr><tr style=3D"height:27.6pt"><t= d style=3D"width:39.1%; border-top:0.75pt solid #000000; border-bottom:0.75= pt solid #000000; padding:0pt 5.4pt; vertical-align:top"> </td><td = style=3D"width:41.48%; border-top:0.75pt solid #000000; border-bottom:0.75p= t solid #000000; padding:0pt 5.4pt; vertical-align:top">Sabe resolver problemas tec= nol=C3=B3gicos </td><td style=3D"width:18.8%; border-top:0.75pt = solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; verti= cal-align:top">71,5 </td></tr><tr style=3D"height:13.8pt"><td style=3D"= width:39.1%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0= 00000; padding:0pt 5.4pt; vertical-align:top"> </td><td style=3D"wi= dth:41.48%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #00= 0000; padding:0pt 5.4pt; vertical-align:top">Fomenta capacitaci=C3=B3n digital </sp= an></td><td style=3D"width:18.8%; border-top:0.75pt solid #000000; bord= er-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">39,5 </span= ></td></tr><tr style=3D"height:12.35pt"><td style=3D"width:39.1%; borde= r-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt= 5.4pt; vertical-align:top">Apoyo institucional= e inversi=C3=B3n </td><td style=3D"width:41.48%; border-top:0.7= 5pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; v= ertical-align:top">Percibe apoyo institucional = = 0; </spa= n></td><td style=3D"width:18.8%; border-top:0.75pt solid #000000; borde= r-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">32 </td></tr><tr style=3D"height:12.35pt= "><td style=3D"width:39.1%; border-top:0.75pt solid #000000; border-bottom:= 0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top"> </td>= <td style=3D"width:41.48%; border-top:0.75pt solid #000000; border-bottom:0= .75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Recibi=C3=B3 asesor=C3= =ADa externa </td><td style=3D"width:18.8%; border-top:0.75pt so= lid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertica= l-align:top">8 participan en programas de 22 digitalizaciones </td></t= r><tr style=3D"height:12.35pt"><td style=3D"width:39.1%; border-top:0.75pt = solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; verti= cal-align:top"> </td><td style=3D"width:41.48%; border-top:0.75pt s= olid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertic= al-align:top">= No invirti=C3=B3 en tecnolog=C3=ADa </td><td style=3D"widt= h:18.8%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #00000= 0; padding:0pt 5.4pt; vertical-align:top">33</td></tr></table>Nota: res= ultados de la encuesta a 200 microempresarios. <p= style=3D"margin-right:0pt; line-height:115%">Los resultados obtenido= s evidencian que la mayor=C3=ADa de las microempresas ecuatorianas avanzaro= n principalmente en la adopci=C3=B3n digital b=C3=A1sica, a trav=C3=A9s del= uso de redes sociales, mensajer=C3=ADa instant=C3=A1nea y plataformas de c= omercio electr=C3=B3nico. Esta tendencia coincide con lo se=C3=B1alado por = Banka et al (2024<sp= an>) y =C3=81lvarez<= span> (2025) quienes sostienen que dichas herramientas constituyen el punto= de partida m=C3=A1s accesible para la integraci=C3=B3n digital de las micr= o y peque=C3=B1as empresas en Am=C3=A9rica Latina. En este sentido, los dat= os del presente estudio confirman que, aunque las iniciativas de digitaliza= ci=C3=B3n a=C3=BAn son incipientes, estas contribuyen significativamente a = la ampliaci=C3=B3n de mercados, la optimizaci=C3=B3n de procesos comerciale= s y la mejora de la visibilidad empresarial. <img src=3D"data:image/png;base64,iVBORw0KGgoA= AAANSUhEUgAAAiAAAAABCAYAAADn5hWmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQ= BlSsOGwAAABtJREFUSIntwTEBAAAIwKDZP7S28AKm2gAAHh2F/QEBXUBBtwAAAABJRU5ErkJggg= =3D=3D" width=3D"544" height=3D"1" alt=3D"" />Sin embargo, persisten = barreras t=C3=A9cnicas y econ=C3=B3micas estructurales que condicionan el = =C3=A9xito de la transformaci=C3=B3n digital. M=C3=A1s del 50 % de los micr= oempresarios encuestados report=C3=B3 deficiencias en competencias digitale= s, mientras que un 66 % identific=C3=B3 los altos costos tecnol=C3=B3gicos = como el principal obst=C3=A1culo para adoptar soluciones digitales avanzada= s. Estos hallazgos son consistentes con los estudios de Ortiz-Choez et al (2024) quienes= enfatizan que la falta de formaci=C3=B3n tecnol=C3=B3gica y la limitada ca= pacidad financiera son los factores m=C3=A1s restrictivos en el proceso de = modernizaci=C3=B3n de las microempresas ecuatorianas. La coincidencia entre= estos resultados refuerza la necesidad de pol=C3=ADticas p=C3=BAblicas ori= entadas a fortalecer las capacidades digitales y el acceso a cr=C3=A9dito p= roductivo. <span= >Otro hallazgo relevante se relaciona con la actitud positiva hacia la digi= talizaci=C3=B3n, observada en m=C3=A1s del 90 % de los encuestados, quienes= manifestaron disposici=C3=B3n para capacitarse y adoptar nuevas herramient= as. Este resultado representa un activo intangible de alto valor, ya que la= disposici=C3=B3n al cambio es un elemento clave para la innovaci=C3=B3n or= ganizacional. En l=C3=ADnea con Tamayo (2025) la actitud proactiva de los emprendedores p= uede convertirse en un catalizador para la resiliencia empresarial, siempre= que se acompa=C3=B1e de formaci=C3=B3n y asesoramiento t=C3=A9cnico contex= tualizado. En el bloque de barreras t=C3=A9cnicas y econ=C3=B3m= icas, el 54 % reporta falta de conocimientos digitales, el 66 % se=C3=B1ala= el costo de la tecnolog=C3=ADa como obst=C3=A1culo y apenas un 11 % cuenta= con presupuesto para capacitaci=C3=B3n. Estos datos confirman que la falta= de capital humano especializado y los altos costos de implementaci=C3=B3n = y mantenimiento limitan la digitalizaci=C3=B3n como se muestra en la </span= >Tabla 2. <h1 styl= e=3D"margin-right:0pt; margin-bottom:0pt; text-align:center; line-height:11= 5%">Tabla 2</h1>Principales barreras t=C3=A9cnicas/ econ=C3=B3micas y apoyo institucio= nal e inversi=C3=B3n reportadas por microempresarios<table style= =3D"width:100%; margin-left:0.5pt; margin-bottom:0pt; padding:0pt; border-c= ollapse:collapse"><tr style=3D"height:14.25pt"><td style=3D"width:78.94%; b= order-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding= :1.45pt 5.75pt 0pt 0pt; vertical-align:top">Problemas identificados </td= ><td style=3D"width:21.06%; border-top:0.75pt solid #000000; border-bottom:= 0.75pt solid #000000; padding:1.45pt 5.75pt 0pt 0pt; vertical-align:top"><p= style=3D"margin-right:0pt; margin-left:0pt; margin-bottom:0pt; text-indent= :0pt; text-align:left; line-height:115%; font-size:10pt">Porcentaje% = </td></tr><tr style=3D"height:15.8pt"><td style=3D"width:78.94%;= border-top:0.75pt solid #000000; padding:1.45pt 5.75pt 0pt 0pt; vertical-a= lign:top"><spa= n>Falta de conocimientos en barreras t=C3=A9cnicas y econ=C3=B3micas </span= ></td><td style=3D"width:21.06%; border-top:0.75pt solid #000000; paddi= ng:1.45pt 5.75pt 0pt 0pt; vertical-align:top">54% </td></tr><tr style=3D"h= eight:14.3pt"><td style=3D"width:78.94%; padding:1.45pt 5.75pt 0pt 0pt; ver= tical-align:top">Dificultad para integrar nuevas tecnolog=C3=ADas </td><= td style=3D"width:21.06%; padding:1.45pt 5.75pt 0pt 0pt; vertical-align:top= ">45.5% </= span></td></tr><tr style=3D"height:14pt"><td style=3D"width:78.94%; pad= ding:1.45pt 5.75pt 0pt 0pt; vertical-align:top">Costos elevados = </td><td style=3D"width:21.06%; padding:1.45pt 5.75pt 0pt 0pt; v= ertical-align:top">66% </td></tr><tr style=3D"height:14pt"><td style=3D"wi= dth:78.94%; padding:1.45pt 5.75pt 0pt 0pt; vertical-align:top">D=C3=A9ficit presupu= estario para capacitaciones </td><td style=3D"width:21.06%; padd= ing:1.45pt 5.75pt 0pt 0pt; vertical-align:top">89% </td></tr><tr style=3D"= height:14pt"><td style=3D"width:78.94%; padding:1.45pt 5.75pt 0pt 0pt; vert= ical-align:top">Falta de capacitaciones digitales </td><td style=3D"widt= h:21.06%; padding:1.45pt 5.75pt 0pt 0pt; vertical-align:top">39,5% </td></= tr><tr style=3D"height:13.8pt"><td style=3D"width:78.94%; padding:1.45pt 5.= 75pt 0pt 0pt; vertical-align:top">D=C3=A9ficit de apoyo institucional&= #xa0; </td><td style=3D"width:21.06%; padding:1.45pt 5.75pt 0pt = 0pt; vertical-align:top">32% </td></tr><tr style=3D"height:13.8pt"><td sty= le=3D"width:78.94%; padding:1.45pt 5.75pt 0pt 0pt; vertical-align:top">Falencia de = asesor=C3=ADas externas </td><td style=3D"wid= th:21.06%; padding:1.45pt 5.75pt 0pt 0pt; vertical-align:top">8% </td></tr= ><tr style=3D"height:14.1pt"><td style=3D"width:78.94%; padding:1.45pt 5.75= pt 0pt 0pt; vertical-align:top">Falta de participaci=C3=B3n en programas de digital= izaci=C3=B3n </td><td style=3D"width:21.06%; padding:1.45pt 5.75= pt 0pt 0pt; vertical-align:top">22% </td></tr><tr style=3D"height:12.95pt"= ><td style=3D"width:78.94%; border-bottom:0.75pt solid #000000; padding:1.4= 5pt 5.75pt 0pt 0pt; vertical-align:top">No hay inversi=C3=B3n en tecnolog=C3=ADa </= span></td><td style=3D"width:21.06%; border-bottom:0.75pt solid #000000= ; padding:1.45pt 5.75pt 0pt 0pt; vertical-align:top">33% </td></tr></table= >Nota:</sp= an> base en los resultados de la encuesta a 200 microempresarios </sp= an> </spa= n>Los datos m= =C3=A1s cr=C3=ADticos muestran porcentajes altos, evidenciando una problem= =C3=A1tica latente, con respecto a la adopci=C3=B3n de la tecnolog=C3=ADa a= microempresarios, revelando que el 54% muestra una falta de conocimientos = en t=C3=A9rminos de barreras t=C3=A9cnicas y econ=C3=B3micas, el 45.5% pres= enta dificultadas para integrar tecnolog=C3=ADas en sus departamentos empre= sariales, 66% muestra que uno de los problemas m=C3=A1s cr=C3=ADticos se as= emeja a los elevados costos de implementaci=C3=B3n y adquisici=C3=B3n de te= cnolog=C3=ADa, 89% la falta de presupuesto para capacitaciones del personal= , 39.50% falta de capacitaci=C3=B3n digital, 32% el d=C3=A9ficit de apoyo i= nstitucional entre entidades, 8% a la falta de asesor=C3=ADas externas, 22%= falta de participaci=C3=B3n en programas de digitalizaci=C3=B3n y por =C3= =BAltimo el 33% no existen inversiones de tecnolog=C3=ADa en las microempre= sas, tambi=C3=A9n se id=C3=A9ntica que m=C3=A1s de la mitad invirti=C3=B3 m= enos de $100 en procesos de digitalizaci=C3=B3n, lo que refleja una descone= xi=C3=B3n significativa con programas p=C3=BAblicos o privados de apoyo. Es= tos puntos, son claves para la optimizaci=C3=B3n de procesos y sobre todo l= a incorporaci=C3=B3n y adopci=C3=B3n de nuevos inversionistas, para mantene= r la calidad de los procesos. 3.1. Transformaci=C3=B3n digital en eficiencia operativa y competitividad= En t=C3=A9rminos de transformaci=C3=B3n digital se puede consi= derar como un motor que redise=C3=B1a la eficiencia operativa y posicionami= ento de manera estrat=C3=A9gica en las diferentes organizaciones modernas, = por ello en la Tabla 3= se desglosan las distintas dimensiones claves de estos procesos que se vin= culan estrictamente en la adopci=C3=B3n de herramientas tecnol=C3=B3gicas q= ue mantienen resultados tangibles en la productividad y gesti=C3=B3n de los= recursos, partiendo de un an=C3=A1lisis exhaustivo de factores como la aut= omatizaci=C3=B3n de la contabilidad y digitalizaci=C3=B3n de los servicios = al cliente, demostr=C3=A1ndose una disminuci=C3=B3n dr=C3=A1stica en los er= rores manuales o p=C3=A9rdidas de documentos, lo cual, hace que se optimice= n los tiempos de respuesta en la operatividad y atenci=C3=B3n. Estas funcio= nes convergen a una alta competitividad empresarial, que se constituye como= una relaci=C3=B3n de confianza y fidelidad en el mercado, a su vez, mantie= ne una innovaci=C3=B3n constante y precautela la seguridad de la informaci= =C3=B3n de la empresa.<h1 style=3D"margin-right:0pt; margin-bott= om:0pt; text-align:center; line-height:115%">Tabla 3</h1>Operatividad y competitividad</s= pan><table style=3D"width:100.08%; margin-right:auto; margin-left:0.5pt= ; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr style=3D"he= ight:25.7pt"><td style=3D"width:24.18%; border-top:0.75pt solid #000000; bo= rder-bottom:0.75pt solid #000000; padding:1.3pt 4.15pt 0pt 0pt; vertical-al= ign:top"><sp= an>Caracter=C3=ADsticas</td><td style=3D"width:20.5%; border-top= :0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:1.3pt 4.= 15pt 0pt 0pt; vertical-align:top">Causa</td><td colspan=3D"2" style=3D"b= order-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding= :1.3pt 4.15pt 0pt 0pt; vertical-align:top">Impacto operativo</td><td col= span=3D"2" style=3D"border-top:0.75pt solid #000000; border-bottom:0.75pt s= olid #000000; padding:1.3pt 4.15pt 0pt 0pt; vertical-align:top">Competitividad empr= esarial</td><td style=3D"padding:0pt; vertical-align:top"></td><= /tr><tr style=3D"height:64.85pt"><td style=3D"width:24.18%; border-top:0.75= pt solid #000000; border-bottom:0.75pt solid #000000; padding:1.3pt 4.15pt = 0pt 0pt; vertical-align:top">Automatizaci=C3=B3n de procesos. </td><td s= tyle=3D"width:20.5%; border-top:0.75pt solid #000000; border-bottom:0.75pt = solid #000000; padding:1.3pt 4.15pt 0pt 0pt; vertical-align:top">Integraci=C3=B3n d= e software contables. </td><td colspan=3D"2" style=3D"border-top= :0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:1.3pt 4.= 15pt 0pt 0pt; vertical-align:top">Optimiza costos y tiempo operativo, agilizando en= un <= span>70% los procesos administrativos y errores manuales. </td><= td colspan=3D"2" style=3D"border-top:0.75pt solid #000000; border-bottom:0.= 75pt solid #000000; padding:1.3pt 4.15pt 0pt 0pt; vertical-align:top">Mejora la image= n ante proveedores y clientes mejorando los servicios financieros y de aten= ci=C3=B3n. </td><td style=3D"padding:0pt; vertical-align:top"></= td></tr><tr style=3D"height:40.5pt"><td style=3D"width:24.18%; border-top:0= .75pt solid #000000; border-bottom:0.75pt solid #000000; padding:1.3pt 4.15= pt 0pt 0pt; vertical-align:top">Contacto de clientes y gesti=C3=B3n. </t= d><td style=3D"width:20.5%; border-top:0.75pt solid #000000; border-bottom:= 0.75pt solid #000000; padding:1.3pt 4.15pt 0pt 0pt; vertical-align:top">Redes socia= les, WhatsApp Business, p=C3=A1ginas web. </td><td colspan=3D"2"= style=3D"border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0000= 00; padding:1.3pt 4.15pt 0pt 0pt; vertical-align:top">Mejora el tiempo de respuesta m= ejorando la trazabilidad de pedidos con chat autom=C3=A1ticos. <= /td><td colspan=3D"2" style=3D"border-top:0.75pt solid #000000; border-bott= om:0.75pt solid #000000; padding:1.3pt 4.15pt 0pt 0pt; vertical-align:top">= Fidelizaci= =C3=B3n y valor de la marca, fomentando la retenci=C3=B3n de clientes. </sp= an></td><td style=3D"padding:0pt; vertical-align:top"></td></tr><tr sty= le=3D"height:64.8pt"><td style=3D"width:24.18%; border-top:0.75pt solid #00= 0000; border-bottom:0.75pt solid #000000; padding:1.3pt 4.15pt 0pt 0pt; ver= tical-align:top">Visibilidad de marca. </td><td style=3D"width:20.5%; bo= rder-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:= 1.3pt 4.15pt 0pt 0pt; vertical-align:top">Marketing </td><td colspan=3D"2"= style=3D"border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0000= 00; padding:1.3pt 4.15pt 0pt 0pt; vertical-align:top">Reduce la dependencia de loca= l f=C3=ADsico y ayuda al posicionamiento y reconocimiento del local a nivel= nacional. </td><td colspan=3D"2" style=3D"border-top:0.75pt sol= id #000000; border-bottom:0.75pt solid #000000; padding:1.3pt 4.15pt 0pt 0p= t; vertical-align:top">Expansi=C3=B3n de mercado. </td><td style=3D"border= -bottom:0.75pt solid #000000; padding:0pt; vertical-align:top"></td></tr><t= r style=3D"height:36.6pt"><td style=3D"width:24.18%; border-top:0.75pt soli= d #000000; border-bottom:0.75pt solid #000000; padding:0pt; vertical-align:= top">Cultu= ra y talento </td><td colspan=3D"2" style=3D"border-top:0.75pt s= olid #000000; border-bottom:0.75pt solid #000000; padding:0pt; vertical-ali= gn:top">Ca= pacitaci=C3=B3n digital y liderazgo al experimentar nuevas herramientas. </= span></td><td colspan=3D"2" style=3D"border-top:0.75pt solid #000000; b= order-bottom:0.75pt solid #000000; padding:0pt; vertical-align:top">Mejora el desempe= =C3=B1o laboral </td><td colspan=3D"2" style=3D"border-top:0.75p= t solid #000000; border-bottom:0.75pt solid #000000; padding:0pt; vertical-= align:top"><s= pan>Intensiva la innovaci=C3=B3n de manera sostenible. </td></tr= ><tr style=3D"height:36.7pt"><td style=3D"width:24.18%; border-top:0.75pt s= olid #000000; border-bottom:0.75pt solid #000000; padding:0pt; vertical-ali= gn:top">Ge= sti=C3=B3n de riesgo </td><td colspan=3D"2" style=3D"border-top:= 0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt; vert= ical-align:top">= Integrar sistemas de Back-up en la nube. </td><td colspan= =3D"2" style=3D"border-top:0.75pt solid #000000; border-bottom:0.75pt solid= #000000; padding:0pt; vertical-align:top">Asegura la continuidad operacional y re= duce el riesgo de p=C3=A9rdida de informaci=C3=B3n. </td><td col= span=3D"2" style=3D"border-top:0.75pt solid #000000; border-bottom:0.75pt s= olid #000000; padding:0pt; vertical-align:top">Mejora la confianza de los cliente= s e intensifica la credibilidad con la microempresa al mantener est=C3=A1nd= ares de seguridad digital.</td></tr><tr style=3D"height:0pt"><td= style=3D"width:106.3pt"></td><td style=3D"width:90.15pt"></td><td style=3D= "width:1.95pt"></td><td style=3D"width:128.75pt"></td><td style=3D"width:2.= 65pt"></td><td style=3D"width:109.55pt"></td><td style=3D"width:0.35pt"></t= d></tr></table>Nota: tabla operativa y competitividad= en base al levantamiento de informaci=C3=B3n, an=C3=A1lisis y observaci=C3= =B3n de procesos en microempresas. 3.2. Pro= puesta E</s= pan>n contraste a la propuesta reflejada en la Tabla 4 se hace =C3=A9nfasis a una ruta estr= at=C3=A9gica con el prop=C3=B3sito de mitigar brechas tecnol=C3=B3gicas en = los sectores microempresariales, en base a soluciones pr=C3=A1cticas frente= a los desaf=C3=ADos identificados y a un mundo altamente tecnol=C3=B3gico = en la actualidad, por lo cual, la propuesta cuenta con una estructura de oc= ho dimensiones cr=C3=ADticas que abarcan desde las herramientas de bajo cos= to para empezar y que precauteles fortalecimiento del ecosistema institucio= nal como tambi=C3=A9n las acciones a abordar con el uso de software Bajo su= scripci=C3=B3n como lo es (SaaS), programas de vinculaci=C3=B3n acad=C3=A9m= ica y por ende las capacitaciones microLearning que comprometen la estabili= dad financiera de las microempresas, que permita el =C3=A9xito en base a un= a tecnolog=C3=ADa digital y expansi=C3=B3n del negocio.<h1 style= =3D"margin-right:0pt; margin-bottom:0pt; text-align:center; line-height:115= %"> </h1><h1 style=3D"margin-right:0pt; margin-bottom:0pt= ; text-align:center; line-height:115%"> </h1><h1 style=3D= "margin-right:0pt; margin-bottom:0pt; text-align:center; line-height:115%">= </h1><h1 style=3D"margin-right:0pt; margin-bottom:0pt; t= ext-align:center; line-height:115%">Tabla 4</h1>Propuesta estrat=C3=A9gica de implemen= taci=C3=B3n para microempresarios sobre aspectos tecnol=C3=B3gicos</= p><table style=3D"width:98.7%; margin-left:0.5pt; margin-bottom:0pt; paddin= g:0pt; border-collapse:collapse"><tr style=3D"height:13.2pt"><td style=3D"w= idth:17.42%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0= 00000; padding:2.8pt 6.7pt 0pt 0pt; vertical-align:top">Dimensi=C3=B3n </t= d><td style=3D"width:26.56%; border-top:0.75pt solid #000000; border-bottom= :0.75pt solid #000000; padding:2.8pt 6.7pt 0pt 0pt; vertical-align:top">Estrategia </= span></td><td colspan=3D"2" style=3D"border-top:0.75pt solid #000000; b= order-bottom:0.75pt solid #000000; padding:2.8pt 6.7pt 0pt 0pt; vertical-al= ign:top">P= ropuesta </td><td colspan=3D"3" style=3D"border-top:0.75pt solid= #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 6.7pt 0pt 0pt; = vertical-align:top">Acciones Claves </td></tr><tr style=3D"height:149.35pt= "><td style=3D"width:17.42%; border-top:0.75pt solid #000000; border-bottom= :0.75pt solid #000000; padding:2.8pt 6.7pt 0pt 0pt; vertical-align:top">1. Adopci= =C3=B3n tecnol=C3=B3gica. </td><td style=3D"width:26.56%;= border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; paddi= ng:2.8pt 6.7pt 0pt 0pt; vertical-align:top">Adopci=C3=B3n de tecnol=C3=B3gica prog= resiva, enfocado en soluciones digitales accesibles (facturaci=C3=B3n elect= r=C3=B3nica, POS, plataformas de ventas y gesti=C3=B3n) de manera gratuita = o a bajo costo. </td><td colspan=3D"2" style=3D"border-top:0.75p= t solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 6.7pt 0p= t 0pt; vertical-align:top">Kit digital de inicio r=C3=A1pido </spa= n></td><td colspan=3D"3" style=3D"border-top:0.75pt solid #000000; bord= er-bottom:0.75pt solid #000000; padding:2.8pt 6.7pt 0pt 0pt; vertical-align= :bottom"><s= pan>=E2=80=A2</spa= n> = Implementar atenci=C3=B3n automatizada a nuevos clientes, que est=C3=A9n pr= e configuradas en herramientas potenciales como WhatsApp businees y chatbot= . =E2=80=A2 &#= xa0; Adoptar la factura electr=C3=B3nica por medio de he= rramientas gratuitas o de bajo costo. =E2=80=A2 = Uso de la nube con el fin de guardar = y gestionar documentaci=C3=B3n colaborativa. </td></tr><t= r style=3D"height:128.05pt"><td style=3D"width:17.42%; border-top:0.75pt so= lid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 6.7pt 0pt 0p= t; vertical-align:top">2. Barreras t=C3=A9cnicas. </td><td style=3D= "width:26.56%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid = #000000; padding:2.8pt 6.7pt 0pt 0pt; vertical-align:top">Consolidar un programa in= tegral de asistencia t=C3=A9cnica y soporte post implementaci=C3=B3n, que g= arantice la funcionalidad y sostenibilidad de las soluciones digitales adop= tadas. </td><td colspan=3D"2" style=3D"border-top:0.75pt = solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 6.7pt 0pt = 0pt; vertical-align:top">Programa =E2=80=9Cdigital de vinculaci=C3=B3n de j=C3=B3= venes=E2=80=9D </td><td colspan=3D"3" style=3D"border-top:0.75pt= solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 6.7pt 0pt= 0pt; vertical-align:bottom">= =E2=80=A2 = ; Recibir capacitaciones para que incentive el desarroll= o del negocio con menci=C3=B3n en tecnolog=C3=ADa y optimizaci=C3=B3n de pr= ocesos. =E2=80=A2</sp= an> </spa= n>Participar en programas universitarios que le de valor al negocio e= integre la intervenci=C3=B3n de universitarios en tecnolog=C3=ADa. = = ;</td></tr><tr style=3D"height:95.45pt"><td style=3D"width:17.42= %; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pad= ding:2.8pt 6.7pt 0pt 0pt; vertical-align:top">3. Barreras econ=C3=B3micas. <= /p> <= /span></td><td style=3D"width:26.56%; border-top:0.75pt solid #000000; = border-bottom:0.75pt solid #000000; padding:2.8pt 6.7pt 0pt 0pt; vertical-a= lign:top"><spa= n>Dise=C3=B1ar mecanismos de financiamiento adaptados al perfil microempres= arial, combinando cr=C3=A9dito digital, cofinanciamiento p=C3=BAblico-priva= do y leasing tecnol=C3=B3gico. </td><td colspan=3D"2" style=3D"b= order-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding= :2.8pt 6.7pt 0pt 0pt; vertical-align:top">Crear un modelo de suscripci=C3=B3n y econo= m=C3=ADa colaborativa. </td><td colspan=3D"3" style=3D"border-to= p:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 6= .7pt 0pt 0pt; vertical-align:bottom">=E2=80=A2 &#x= a0; Uso de software como servicios (SaaS) con pago= s mensuales que var=C3=ADan entre $15 y$20, para no comprar licencias caras= . = =E2=80=A2</s= pan> <spa= n>Gestionar fondos para proyectos de innovaci=C3=B3n tecnol=C3=B3gica. </sp= an>&#x= a0;</td></tr><tr style=3D"height:76.35pt"><td style=3D"width:17.= 42%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; p= adding:0pt; vertical-align:top">4. Actitud hacia la digitalizaci=C3=B3n. <= /p> <= /span></td><td colspan=3D"2" style=3D"border-top:0.75pt solid #000000; = border-bottom:0.75pt solid #000000; padding:0pt; vertical-align:top">Consolidar = una cultura digital empresarial, basada en liderazgo adaptativo, gesti=C3= =B3n del cambio y formaci=C3=B3n motivacional en innovaci=C3=B3n. </= p> </= span></td><td style=3D"width:18.58%; border-top:0.75pt solid #000000; b= order-bottom:0.75pt solid #000000; padding:0pt; vertical-align:top">Taller de Digital= izaci=C3=B3n tecnol=C3=B3gica </td><td style=3D"width:0.86%; bor= der-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0= pt; vertical-align:top">=E2=80=A2 =E2=80=A2 </td><td style=3D"width:3= 1.76%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000;= padding:0pt; vertical-align:top">Acudir a campa=C3=B1as de sensibilizaci=C3=B3n so= bre beneficios digitales. Crear comunidades de pr=C3=A1ctica y aprendizaje= entre microempresarios.=E2=80=A2Desarrollar programas de liderazgo digita= l con enfoque motivacional. </td><td style=3D"border-top:= 0.75pt solid #000000; padding:0pt; vertical-align:top"></td></tr><tr style= =3D"height:77.55pt"><td style=3D"width:17.42%; border-top:0.75pt solid #000= 000; border-bottom:0.75pt solid #000000; padding:0pt; vertical-align:top"><= p style=3D"margin-right:0pt; margin-left:0pt; margin-bottom:0pt; text-inden= t:0pt; text-align:left; line-height:115%; font-size:10pt">5. Capacida= des organizacionales/ Talento humano. </td><td colspan=3D= "2" style=3D"border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0= 00000; padding:0pt; vertical-align:top">Implementar un modelo de formaci=C3=B3n m= odular y certificaci=C3=B3n en competencias digitales para propietarios y p= ersonal operativo. </td><td style=3D"width:18.58%; border-top:0.= 75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt; vertic= al-align:top"><s= pan>Incorporar el MicroLearning </td><td style=3D"width:0.86%= ; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padd= ing:0pt; vertical-align:top">=E2=80=A2 =E2=80=A2 </td><td style=3D"wi= dth:31.76%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #00= 0000; padding:0pt; vertical-align:top">Tomar m=C3=B3dulos de capacitaci=C3=B3n en T= IC, marketing digital y ciberseguridad. Contratar personal especializado e= n el uso de herramientas tecnol=C3=B3gicas. </td><td style=3D"pa= dding:0pt; vertical-align:top"></td></tr><tr style=3D"height:63.25pt"><td s= tyle=3D"width:17.42%; border-top:0.75pt solid #000000; border-bottom:0.75pt= solid #000000; padding:0pt; vertical-align:top">6. Mejora de procesos. </= td><td colspan=3D"2" style=3D"border-top:0.75pt solid #000000; border-botto= m:0.75pt solid #000000; padding:0pt; vertical-align:top">Promover la automatizaci= =C3=B3n y digitalizaci=C3=B3n de los procesos internos, fortaleciendo la ef= iciencia administrativa y la gesti=C3=B3n de clientes. </td><td = style=3D"width:18.58%; border-top:0.75pt solid #000000; border-bottom:0.75p= t solid #000000; padding:0pt; vertical-align:top">Digitalizaci=C3=B3n de puntos cr= =C3=ADticos </td><td style=3D"width:0.86%; bo= rder-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:= 0pt; vertical-align:top">=E2=80=A2 </td><td style=3D"width:31.76= %; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pad= ding:0pt; vertical-align:top">Incentivar la adopci=C3=B3n de ERP y CRM adaptados al s= egmento micro. </td><td style=3D"padding:0pt; vertical-align:top= "></td></tr><tr style=3D"height:90.35pt"><td style=3D"width:17.42%; border-= top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt; = vertical-align:top">7. Ventaja competitiva. </td><td colspan=3D"2" style= =3D"border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pa= dding:0pt; vertical-align:top">Desarrollar un modelo de posicionamiento digital y c= omercializaci=C3=B3n online que incremente la competitividad y la proyecci= =C3=B3n de marca. </td><td style=3D"width:18.58%; border-top:0.7= 5pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt; vertica= l-align:top"><= span>Estrategia de geolocalizaci=C3=B3n n comercial entre microempresarios = </td><td style=3D"width:0.86%; border-top:0.75pt solid #000000; = border-bottom:0.75pt solid #000000; padding:0pt; vertical-align:top">=E2=80=A2 </s= pan>= =E2=80=A2 </td><td style=3D"width:31.76%; border-top:0.75pt soli= d #000000; border-bottom:0.75pt solid #000000; padding:0pt; vertical-align:= top">Cre= ar estrategias de marketing digital personalizadas para la microempresa, qu= e ayude a expandirse. Promover la inserci=C3=B3n en plataformas de e-comme= rce y marketplaces. =E2=80=A2 Implementar la geolocalizaci=C3=B3n en redes= o p=C3=A1ginas web del negocio.</td><td style=3D"padding:0pt; v= ertical-align:top"></td></tr><tr style=3D"height:89.6pt"><td style=3D"width= :17.42%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #00000= 0; padding:0pt; vertical-align:top">8. Apoyo institucional / Ecosistema <p= style=3D"margin-right:0pt; margin-left:0pt; margin-bottom:0pt; text-indent= :0pt; text-align:left; line-height:115%; font-size:10pt">digital. </s= pan></td><td colspan=3D"2" style=3D"border-top:0.75pt solid #000000; bo= rder-bottom:0.75pt solid #000000; padding:0pt; vertical-align:top">Fortalecer el ec= osistema interinstitucional de apoyo a la digitalizaci=C3=B3n, articulando = esfuerzos entre Estado, academia y sector privado. </td><= td style=3D"width:18.58%; border-top:0.75pt solid #000000; border-bottom:0.= 75pt solid #000000; padding:0pt; vertical-align:top">Red de alianza local = </td><td style=3D"width:0.86%; border-top:0.75pt solid #000000; border-bott= om:0.75pt solid #000000; padding:0pt; vertical-align:top">=E2=80=A2 </td><= td style=3D"width:31.76%; border-top:0.75pt solid #000000; border-bottom:0.= 75pt solid #000000; padding:0pt; vertical-align:top">Establecer convenios universitar= ios de Transformaci=C3=B3n digital para MIPYMES que ayuden a autocapacitars= e y obtener nociones estrat=C3=A9gicas por parte de la nueva generaci=C3=B3= n profesional. =E2=80=A2Participar en proyectos de innovacion con financia= miento compatido entre microempresarios</td><td style=3D"padding= :0pt; vertical-align:top"></td></tr><tr style=3D"height:0pt"><td style=3D"w= idth:75.5pt"></td><td style=3D"width:115.2pt"></td><td style=3D"width:19.5p= t"></td><td style=3D"width:80.55pt"></td><td style=3D"width:3.75pt"></td><t= d style=3D"width:137.75pt"></td><td style=3D"width:1.4pt"></td></tr></table= >Nota: </= span>en base a los datos obtenidos en la Tabla 2 El an=C3=A1lisis integrado de las encuestas y la validac= i=C3=B3n experta muestra que las microempresas ecuatorianas poseen un poten= cial significativo de transformaci=C3=B3n digital, sustentado en su alta di= sposici=C3=B3n al cambio (5.0) y fuerte adopci=C3=B3n tecnol=C3=B3gica inic= ial (4.6). <h2 style=3D"margin-right:0pt; line-height:115%"><spa= n style=3D"font-weight:normal; font-style:italic">3.3. Validaci=C3=B3n por expertos= </h2>La propuesta metodol=C3=B3gica fue evaluada por un panel de c= inco especialistas con trayectoria en transformaci=C3=B3n digital, gesti=C3= =B3n de microempresas y pol=C3=ADticas p=C3=BAblicas, seleccionados con bas= e en formaci=C3=B3n de cuarto nivel y experiencia profesional comprobada. <= /span>La valor= aci=C3=B3n se efectu=C3=B3 mediante una escala ordinal del 1 al 5, donde: <= /span>1 =3D De= ficiente, 2 =3D Regular, 3 =3D Bueno, 4 =3D Muy Bueno, 5 =3D Excelente. </s= pan>Se evaluaron cuatro criterios: pertinencia, claridad, coherencia y = relevancia como se detalla en la Ta= bla 5. <h1 style=3D"margin-right:0pt; margin-bottom= :0pt; text-align:center; line-height:115%">Tabla 5</h1>Escala de valoraci=C3=B3n y cri= terios de evaluaci=C3=B3n<table style=3D"width:100%; margin-left= :0.5pt; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr style= =3D"height:13.1pt"><td style=3D"width:10.02%; border-top:0.75pt solid #0000= 00; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.3pt; ver= tical-align:top">Valor </td><td style=3D"width:26.88%; border-top:0.75pt= solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0p= t 5.3pt; vertical-align:top">Criterio cualitativo </td><td style=3D"widt= h:63.1%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #00000= 0; padding:2.8pt 2.65pt 0pt 5.3pt; vertical-align:top">Descripci=C3=B3n </= td></tr><tr style=3D"height:13.2pt"><td style=3D"width:10.02%; border-top:0= .75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.3pt; vertical-align:top"><p= style=3D"margin-right:2.65pt; margin-left:0pt; margin-bottom:0pt; text-ind= ent:0pt; text-align:center; line-height:115%; font-size:10pt">1 </spa= n></td><td style=3D"width:26.88%; border-top:0.75pt solid #000000; padd= ing:2.8pt 2.65pt 0pt 5.3pt; vertical-align:top">Deficiente </td><td styl= e=3D"width:63.1%; border-top:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt= 5.3pt; vertical-align:top">El indicador no cumple con el criterio propuesto. = </td></tr><tr style=3D"height:13.2pt"><td style=3D"width:10.02%; paddin= g:2.8pt 2.65pt 0pt 5.3pt; vertical-align:top">2 </td><td style=3D"wid= th:26.88%; padding:2.8pt 2.65pt 0pt 5.3pt; vertical-align:top">Regular <= /td><td style=3D"width:63.1%; padding:2.8pt 2.65pt 0pt 5.3pt; vertical-alig= n:top">Cum= ple parcialmente; requiere ajustes importantes. </td></tr><tr st= yle=3D"height:13.1pt"><td style=3D"width:10.02%; padding:2.8pt 2.65pt 0pt 5= .3pt; vertical-align:top">3 </td><td style=3D"width:26.88%; padding:2= .8pt 2.65pt 0pt 5.3pt; vertical-align:top">Bueno </td><td style=3D"width= :63.1%; padding:2.8pt 2.65pt 0pt 5.3pt; vertical-align:top">Cumple adecuadamente; adm= ite mejoras menores. </td></tr><tr style=3D"height:13.2pt"><td s= tyle=3D"width:10.02%; padding:2.8pt 2.65pt 0pt 5.3pt; vertical-align:top"><= p style=3D"margin-right:2.65pt; margin-left:0pt; margin-bottom:0pt; text-in= dent:0pt; text-align:center; line-height:115%; font-size:10pt">4 </sp= an></td><td style=3D"width:26.88%; padding:2.8pt 2.65pt 0pt 5.3pt; vert= ical-align:top">Muy Bueno </td><td style=3D"width:63.1%; padding:2.8pt 2= .65pt 0pt 5.3pt; vertical-align:top">Cumple adecuadamente </td></tr><tr st= yle=3D"height:13.1pt"><td style=3D"width:10.02%; border-bottom:0.75pt solid= #000000; padding:2.8pt 2.65pt 0pt 5.3pt; vertical-align:top">5 </td>= <td style=3D"width:26.88%; border-bottom:0.75pt solid #000000; padding:2.8p= t 2.65pt 0pt 5.3pt; vertical-align:top">Excelente </td><td style=3D"widt= h:63.1%; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.3pt= ; vertical-align:top">Cumple plenamente con el criterio evaluado. </td></t= r></table>Fu= ente: Ocampo</= span> (2023). La validaci=C3=B3n de la propuesta por parte de cinco expertos ap= ort=C3=B3 solidez y fiabilidad a los resultados emp=C3=ADricos. Las valorac= iones m=C3=A1s altas se concentraron en las dimensiones de adopci=C3=B3n te= cnol=C3=B3gica (4.6/5), actitud hacia la digitalizaci=C3=B3n (5.0/5) y vent= aja competitiva (4.6/5), lo que demuestra un consenso t=C3=A9cnico sobre el= potencial transformador de la digitalizaci=C3=B3n para incrementar la efic= iencia operativa y la competitividad empresarial. Estas coincidencias reafi= rman lo planteado por Priet= o & Loredo (2025) y Brito et al. (2023) quienes argumentan que la incorp= oraci=C3=B3n estrat=C3=A9gica de tecnolog=C3=ADas digitales incrementa la a= daptabilidad organizacional y la sostenibilidad econ=C3=B3mica. = En contraste, las val= oraciones m=C3=A1s bajas correspondieron a las dimensiones barreras econ=C3= =B3micas (3.6/5) y apoyo institucional / ecosistema digital (3.2/5), eviden= ciando la debilidad del entorno estructural en el que operan las microempre= sas. Esta situaci=C3=B3n se alinea con Espejo et al. (2023) quienes destacan que la limit= ada articulaci=C3=B3n de las pol=C3=ADticas de innovaci=C3=B3n con las nece= sidades reales del sector productivo constituye uno de los principales desa= f=C3=ADos de la digitalizaci=C3=B3n en Am=C3=A9rica Latina. La falta de aco= mpa=C3=B1amiento institucional y la escasez de incentivos econ=C3=B3micos c= ontin=C3=BAan restringiendo el alcance de los procesos de transformaci=C3= =B3n digital en el pa=C3=ADs como se detalla en la Tabla 6. Tabla 6Matriz consolidada de validac= i=C3=B3n por dimensiones<table style=3D"width:93.86%; margin-lef= t:0.5pt; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr styl= e=3D"height:38.9pt"><td style=3D"width:22.78%; border-top:0.75pt solid #000= 000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; ve= rtical-align:top">Dimensi=C3=B3n/Criterio </td><td style=3D"width:14.04%; b= order-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding= :2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Pertinencia </td><td style=3D"= width:11.5%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0= 00000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Claridad </td><t= d style=3D"width:14.08%; border-top:0.75pt solid #000000; border-bottom:0.7= 5pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Coherencia </sp= an></td><td style=3D"width:13.52%; border-top:0.75pt solid #000000; bor= der-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-a= lign:top">R= elevancia </td><td style=3D"width:11.9%; border-top:0.75pt solid= #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4p= t; vertical-align:top">Promedio total (1=E2=80=935) </td><td style=3D"wid= th:12.2%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0000= 00; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Nivel </td></t= r><tr style=3D"height:25.8pt"><td style=3D"width:22.78%; border-top:0.75pt = solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt= 5.4pt; vertical-align:top">Adopci=C3=B3n tecnol=C3=B3gica </td><td style= =3D"width:14.04%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">4.0 </= td><td style=3D"width:11.5%; border-top:0.75pt solid #000000; border-bottom= :0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">= 4.8 </= span></td><td style=3D"width:14.08%; border-top:0.75pt solid #000000; b= order-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical= -align:top"><sp= an>4.4 </td><td style=3D"width:13.52%; border-top:0.75pt solid #= 000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt;= vertical-align:top">4.8 </td><td style=3D"width:11.9%; border-top:0.75= pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt = 0pt 5.4pt; vertical-align:top">4.6 </td><td style=3D"width:12.2%; borde= r-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8= pt 2.65pt 0pt 5.4pt; vertical-align:top">Muy alto </td></tr><tr style=3D"= height:13.1pt"><td style=3D"width:22.78%; border-top:0.75pt solid #000000; = border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertica= l-align:top"><spa= n>Barreras t=C3=A9cnicas </td><td style=3D"width:14.04%; border-= top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt= 2.65pt 0pt 5.4pt; vertical-align:top">3.6 </td><td style=3D"width:11.= 5%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pa= dding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">3.6 </td><td style= =3D"width:14.08%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">3.6 </td>= <td style=3D"width:13.52%; border-top:0.75pt solid #000000; border-bottom:0= .75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top"><p= style=3D"margin-right:2.9pt; margin-left:0pt; margin-bottom:0pt; text-inde= nt:0pt; text-align:center; line-height:115%; font-size:9pt">3.8 </spa= n></td><td style=3D"width:11.9%; border-top:0.75pt solid #000000; borde= r-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-ali= gn:top"><span= >4.2 </td><td style=3D"width:12.2%; border-top:0.75pt solid #000= 000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; ve= rtical-align:top">Alto </td></tr><tr style=3D"height:25.7pt"><td style= =3D"width:22.78%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Barreras econ=C3=B3mic= as </td><td style=3D"width:14.04%; border-top:0.75pt solid #0000= 00; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; ver= tical-align:top">3.4 </td><td style=3D"width:11.5%; border-top:0.75pt = solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt= 5.4pt; vertical-align:top">3.6 </td><td style=3D"width:14.08%; border= -top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8p= t 2.65pt 0pt 5.4pt; vertical-align:top">3.6 </td><td style=3D"width:13.52= %; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pad= ding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">3.6 </td><td style=3D"= width:11.9%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0= 00000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">3.6 </td><td= style=3D"width:12.2%; border-top:0.75pt solid #000000; border-bottom:0.75p= t solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Alto = </td></tr><tr style=3D"height:25.8pt"><td style=3D"width:22.78%; border= -top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8p= t 2.65pt 0pt 5.4pt; vertical-align:top">Actitud hacia la digitalizaci=C3=B3n </= p></td><td style=3D"width:14.04%; border-top:0.75pt solid #000000; border-b= ottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:= top">4= .8 </td><td style=3D"width:11.5%; border-top:0.75pt solid #00000= 0; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vert= ical-align:top">5.0 </td><td style=3D"width:14.08%; border-top:0.75pt = solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt= 5.4pt; vertical-align:top">5.0 </td><td style=3D"width:13.52%; border-to= p:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2= .65pt 0pt 5.4pt; vertical-align:top">5.0 </td><td style=3D"width:11.9%;= border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; paddi= ng:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">5.0 </td><td style=3D"wi= dth:12.2%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000= 000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Muy alto </td></= tr><tr style=3D"height:38.9pt"><td style=3D"width:22.78%; border-top:0.75pt= solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0p= t 5.4pt; vertical-align:top">Capacidades organizacionales / Talento humano = </td><td style=3D"width:14.04%; border-top:0.75pt solid #000000; border-bot= tom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:to= p">4.2= </td><td style=3D"width:11.5%; border-top:0.75pt solid #000000;= border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertic= al-align:top">4.2 </td><td style=3D"width:14.08%; border-top:0.75pt so= lid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5= .4pt; vertical-align:top">4.2 </td><td style=3D"width:13.52%; border-top:= 0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.6= 5pt 0pt 5.4pt; vertical-align:top">4.4 </td><td style=3D"width:11.9%; b= order-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding= :2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">4.2 </td><td style=3D"widt= h:12.2%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #00000= 0; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Alto </td></tr>= <tr style=3D"height:13.25pt"><td style=3D"width:22.78%; border-top:0.75pt s= olid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt = 5.4pt; vertical-align:top">Mejora de procesos </td><td style=3D"width:14.04= %; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pad= ding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">4.0 </td><td style=3D= "width:11.5%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #= 000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">4.2 </td><= td style=3D"width:14.08%; border-top:0.75pt solid #000000; border-bottom:0.= 75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">4.2 <= /p></td><td style=3D"width:13.52%; border-top:0.75pt solid #000000; border-= bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align= :top">4= .2 </td><td style=3D"width:11.9%; border-top:0.75pt solid #00000= 0; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vert= ical-align:top">4.4 </td><td style=3D"width:12.2%; border-top:0.75pt so= lid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5= .4pt; vertical-align:top">Alto </td></tr><tr style=3D"height:13.45pt">= <td style=3D"width:22.78%; border-top:0.75pt solid #000000; border-bottom:0= .75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top"><p= style=3D"margin-right:0pt; margin-left:0pt; margin-bottom:0pt; text-indent= :0pt; text-align:left; line-height:115%; font-size:9pt">Ventaja compe= titiva </td><td style=3D"width:14.04%; border-top:0.75pt solid #= 000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt;= vertical-align:top">4.4 </td><td style=3D"width:11.5%; border-top:0.7= 5pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt= 0pt 5.4pt; vertical-align:top">4.6 </td><td style=3D"width:14.08%; bo= rder-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:= 2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">4.6 </td><td style=3D"width:1= 3.52%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000;= padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">4.6 </td><td style= =3D"width:11.9%; border-top:0.75pt solid #000000; border-bottom:0.75pt soli= d #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">4.6 </td= ><td style=3D"width:12.2%; border-top:0.75pt solid #000000; border-bottom:0= .75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top"><p= style=3D"margin-right:0pt; margin-left:2.5pt; margin-bottom:0pt; text-inde= nt:0pt; text-align:left; line-height:115%; font-size:9pt">Muy alto </= span></td></tr><tr style=3D"height:25.9pt"><td style=3D"width:22.78%; b= order-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding= :2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Apoyo institucional / Ecosistema digital </td><= td style=3D"width:14.04%; border-top:0.75pt solid #000000; border-bottom:0.= 75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">3.2 </spa= n></td><td style=3D"width:11.5%; border-top:0.75pt solid #000000; borde= r-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-ali= gn:top"><spa= n>3.2 </td><td style=3D"width:14.08%; border-top:0.75pt solid #0= 00000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; = vertical-align:top">3.2 </td><td style=3D"width:13.52%; border-top:0.75pt= solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0p= t 5.4pt; vertical-align:top">3.2 </td><td style=3D"width:11.9%; border-= top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt= 2.65pt 0pt 5.4pt; vertical-align:top">3.2 </td><td style=3D"width:12.2= %; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pad= ding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Moderado </td></tr></table= >Nota:<= span> los promedios se calcularon a partir de las valoraciones individuales= de los cinco expertos, conforme a los criterios definidos. <img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGIAAAAgCAYAAAD= pNa1pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAArBJREFUaIHtmj1L62= AYhu8cuogOLtLBDv4ARULEISCkoOAPcHd0detQByl0qtWii0IXHToJHXVVtApipaIIIqUoVQsGR= OoHVHKf4ZwGQ46Y1HJ8Sd8LMuR5b8gTrrz5VkgSkh9DURQFAH79dCOSP0gRgiBFCIIUIQhShCBI= EYIgRQiCFCEIvkXc3t4iFouhVqsBAF5eXpBOpzE6OoqJiQnk83k7m0wmsb+/375ugwx9cHFxQVV= Vubm5ScuySJLxeJwAHMvOzg5J8urqitFolLlczs9mOgrfIh4fHzk2NsZMJmPXKpUKc7kcX19feX= Z2xqGhIQLg3NycnSkWiwTAw8PD9u5BQPAtYmVlhQB4c3Nj1+r1uiOzvLxMAFxYWHDUp6amODk5y= ff392+2HTyaHjxdIxqNBlZXVzE8PIz+/n673t3d7chZlgUAMAzDUdd1Hdvb2ygWi14215F4ElEu= l3F+fo7BwUH8fVn4TwqFAmZnZ6FpmqMeiUQAAKVS6RutBpuQl5BpmgCAnp6eTzPHx8cwTRNra2u= usa6uLgDA3d1dKz12BJ5mRHMWfDYb6vU6UqkUstksent7XePNUxblp49P8SSir68PAPD8/OwaI4= mlpSXMz89jYGAAlmVha2sLDw8Pdubt7Q0AEA6H29FzMPFyZW80GlRVlaqqusbW19ddzxG6rjsyi= 4uLBMCDg4N23GgEiqYHTzMiFAphZmYGJycnuL6+tuuFQgHT09OuvK7rjvWjoyOMj49jZGSklWOl= M/Bq7unpiYZhuJ4RvuL09JQAuLe35/to6QR8iyDJy8tLaprGfD5vv+L4Kh+NRrmxsdFyo0GnJRE= kWa1WGYvFeH9//2U2kUhwd3e3pQY7haYH5eOK5P8jf6cRDClCEKQIQZAiBEGKEAQpQhCkCInkI7= 8BoaaGNkcjxHEAAAAASUVORK5CYII=3D" width=3D"98" height=3D"32" alt=3D"" style= =3D"margin-top:26.76pt; margin-left:382.04pt; position:absolute" /><= span>Dado a la suma de los rangos obtenemos un total de =E2=88=91 Ri =3D 75= , al desfragmentar esta expresi=C3=B3n con la Ecuaci=C3=B3n 2.<img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEMA= AAASCAYAAAAAGGMhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAxJJREF= UWIXV2EuoVWUUB/DfPnd789ULpQhBTAyxDEIRjAiKRiGIBJLUxEkGNVAJwWrUQCc1cqADUUSoSI= mCTBs1F5GKGmgPJF83NHxF3bz37LMdrL05h9M5Z3vvvlduf9jsw/ft71vrW4//Wt9J1MMw1mIlc= iS4jWO4hXV4pJh7AGfwFVo15U4LGjXX51iPxzEPD2MjMqR4E4sxF01cLNbMSKQ117dwAqdERLyI= +3ADc8TBP8HZ4nfTDDbGUM31Oc4JQzyE97Efl0UKbcJsPC8Mf94MTRHqG6NEA89hAY6INGkKjri= IP/Fu8e2PZrBB6qIhUmIvVgsSJQxdPsP4EEcx/x7olBRPNycmhT4pZnU8D1KfM0oBLwgC/UWbEx= 7FK4IzyjT6HeNTIHMQGsLgq/ADrnfMDWEDFnaMzRYFYOtUCJ+Hz0QZLT2RYDm+E6X0U3yBRdqRM= x1oCCd8jJ+xomt+Dk5iRKTvBcFj+5mayLgt+OCSdlTk+BUvaXthBH+bvmqSirL+jCjxDb0N/y9e= F1Hc7BiTurteI9f/EE1RUbrnMxGiZZhWGaHM88nqkQvC/hJv4IkB313DH9rGQBjjaRE+g3BL9Ap= Zj7mk690Pif4HSfAk7q/YYww/Fe9uZPje3fUziThzs9irhTzFTizV/zC5IKLtIsw7Nxw28fI8ps= sjxR7bhGP6RWqOK9gsPNvrsFXRlxfy3xbXhFn4FodwNcVbqg80htGusWG8h2cr1nYiwx5806V4h= ncK5QY5pSWidLK8M44dxT6jWIbdIqW2pPin+HCQElkPBXJBmmcnoEwLN/vMjQoyHqRHaZDJIhNN= X5myF3AcrxGcsR2PVWxyDvsKZUuM44CJX/Za/mvYRITuEoO55wY+wl8TlNkpp1OHcfym4MxUlLw= qj1zqM57pTaqTUXLE4IqSCyPUiYylWCM64Zagh+XiuiAVHWJVJchqKlGFFj5XXV7LStEPQ6LTXS= n+P1khUqHsbxZjV/HtSTyFl3FQheD/GxLRdL0qqtJ8XMVpfCCMskjckZYJIk7wNQ7jyh1ZCM8NJ= BPWQwAAAABJRU5ErkJggg=3D=3D" width=3D"67" height=3D"18" alt=3D"" style=3D"m= argin-right:9pt; margin-left:9pt; float:left; position:relative" />= =E2=88=91Ri siguiente formula R =3D <img src=3D"data:image/png;base6= 4,iVBORw0KGgoAAAANSUhEUgAAABAAAAACCAYAAABR7VzxAAAABHNCSVQICAgIfAhkiAAAAAlwS= FlzAAAOxAAADsQBlSsOGwAAABVJREFUCJljZGBg+M9APmCkQC8EAAB+yAECR0E4BQAAAABJRU5E= rkJggg=3D=3D" width=3D"16" height=3D"2" alt=3D"" /> =F0=9D=91=82=F0=9D=91=8F=F0=9D=91=A1=F0=9D= =91=92=F0=9D=91=9B=F0=9D=91=92=F0=9D=91=9A=F0=9D=91=9C=F0=9D=91=A0<s= pan style=3D"width:29.02pt; text-indent:0pt; display:inline-block"> </= span> <= /span>y al calcular la suma de cuadrados de desviaci= =C3=B3n n =3D5S con la formula S=3D =E2=88=91 = (Ri =E2=80=93 R)2, se obtiene un resultado de S= =3D212 el cual ayuda a ser plasmado para poder realizar el C=C3=A1lculo de W-Kendall (</= span>Ecuaci=C3=B3n 3). </span= >= <img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGY= AAAAeCAYAAADermvOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAqdJRE= FUaIHt2s8rPHEcx/HnfvuWxFXaUpz9aNvkyBokysHBxb/g5qSUoxxIioOLhNq9KLnKwcEWDliR2= qR2hSQlsfnVvr6H79dk2u3bDFum3c+j5rCfz3v7vLdXM/OZaQOShOEbgUAgAPDrpxsx8jPB+JQJ= xqdMMD5lgvEpE4xPmWB8ygTjU56Dubq6YmRkhJubGwAkEYvF6OjooK2tjVgsZteOj48Tj8cL120= pkQenp6cKh8NaXV1VNpuVJC0uLgpwHLu7u5Kks7MzWZalaDTqZZmS5jmY+/t7tba2amZmxh67vb= 3VysqKMpmM0um0+vr6BGhhYcGu2d/fF6CdnZ3C/oIi5TmY2dlZAbq4uLDH3t/fHTUTExMCtL297= RgfGBhQT09PTr2Ry1Mwr6+vamhoUCgUsi9hn2UyGW1sbKi2tlaTk5M589PT0wK0t7f3/c6L3Ecu= rm7+5+fnnJyc0NjYyL+Xnw5zc3N0d3eTSqWIRqMcHh465mtqagBIJBJuljNwGczd3R0AlZWVeee= HhobY3NwkEolwcHDA8PCwY768vByA6+vr7/RaUn67Kfo4S/KdLQAVFRV0dnZSX19PJBJha2uLl5= cXysrKAMhms8DfrbXhjqtgqqqqAHh6evpvXTAYpLe3l6amJjsUgOfnZwCqq6u/2mfJcXUpq6urI= xwOc3x87BhfWlrCsizW19d5fHwknU6TSCQYGxtz1F1eXgIQCoUK1HYJcLtbmJ+fF6BUKmWPxeNx= 9ff3KxgMqqWlRaOjo0omkznfHRwcVFdXl97e3gqycylmnoN5eHhQe3u7pqamPC10dHSU99nGyM9= zMJKUTCbV3NystbW1vM8z+eoty9Ly8vKXGy01H7kEPn8wfp75l4zPmWB8ygTjUyYYnzLBGIZRBP= 4AXxXm5pGMwQ8AAAAASUVORK5CYII=3D" width=3D"102" height=3D"30" alt=3D"" styl= e=3D"margin-top:1.61pt; margin-left:376.33pt; position:absolute" /><= span>W =3D <img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgA= AADMAAAAcCAYAAADMW4fJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAABM= pJREFUWIXt2FmM3WMUAPDfvbMYpTra0o2iNEqslcausUQVoQlPUhIillQoYok1NITUFhKJIFFUx= BI8SCTKS60NorUv0fBQiqRqa6edzng458v85+bOIiOxxEn+uffbznf25asZGmoYh7mYhtvQhRac= gCOwA+q4Gx+iN8+24HgchfFoxT1Ylevb4DAcixuxMfEckucmYDQew6vYPBih9WEwsicuwynYo0J= oDftjCa7Bb7ggGahC2XMtfsKFydTWWIjZOLCBpsOxAtfjLdyCsUPQqnWoDViDu3C6kGKBLbi9Mv= 5BSLoKPVjcsGecEMgmPIxO7F3Z0533FfgRv+f8oDCUZnrxcyLcok8rZa3MTRPmtiTnmu3ZDcfgk= Zzrxrf5W8Ur13uE9ubiBqwfipnhaGYwqGFnXIpF+KwJYTVMruz5uMmeZjAOV+IVvO4v0Ewj1BrG= E4RJPII3c64l99Xzd0fch8eTqGb3NuLtwEWJ8wnB/JC0NjprM2jDDOEz07FSRJ1uEYF2wAYRgc4= Qkjwbu+ML4fgTRYA4OPe8rM/0jsMcEeE2Je5zME/46364SgSCQU1tOJrpFFFnO2Hjl+DQPNsuwv= RsEYHW5Jmtcq0upLwh9xyBtbmnhjNxND7B+TgphTc57zpM+Nn6xPE//A9/N9QwFdv+3YSMEHrwa= Q0Pidroz4bpfwr04lfsU8NMEV4bY/2/CbqxrCS3/wL0DKWNwmxd2GWP5qVIbYD56npJ0AXPSKDQ= 3e/OlspitQQpm0eJQm++yNZfiiRZoC6Cx0RRkDbDURfVwHycLBLg2kZChiC8Eef2Ipn3S6SFmfG= iVJkosu8sfC16mXl4Duclcysql8zN9U+TyOnYKfHsLZqpjaLA/BLf4Aq8JMqb4TAyA1MS5/SkeW= Pee7gog/o1beditai7VuJp0eG1iTK8E0txYuXMZNybDNYxBstFabIqv4Pz8m1EeTMLD+aZ4UAr3= hbd6yq8K8qfetK2UDSN/dzlPFE0TspvrL7qd0weOk3UWUVii0RBWAJIJ57HAYljoqjRir8cgptE= tzqcArcw82SemSSq9A59JrcfnpLCKYSUhmpMLvwubHoULhf+sFyou5bzc/C5PmcujVibsOmaCJk= 17IqzRPPWJjRVS2K3avK16Qsq3TnuzPHm/O3F96KfmlI4h3cSySzhB6txK/YSFfLoXPsI9wvT2x= 6/VKS4ES/mmXHiQeJy4XsLhF8uEBq7TiS6k8RjR2NU/V6YcBeWiTeCaaJdWChcobTeG0Se/KIws= wofCE2txjNCvSuFeZXIs0n/KFQloks0aUVIu4h24WLxMNFSkWjR/DoRFBqZWSe0vAWP5lwH9sWp= SWuxpnqhqTUvOUhEm/X6q3Gz5s87v4l3gfJiUnxrUuIpZ9vz0q4mOOA1vNFkvtzfmUL5KMdd+r8= xdAiLWisZaReSOyjHc/E+XhiAEcKOxyYD7+VFU3FnXraHaKwWixeZgXJKr74kWv3K/t1xhzDJmS= I6PoDvcn2GSB9L0V2iwo7CSUfnxpIcB0ts43FzCmKd0PKuwhm78ZXoFkeS7duFYCYkPV8Jf+oVG= rlavCssQ2/VVqv/h5Oda6IVPhLPivzSa4BSYwTQjK7x4k2iSzx4DGTG/174Ay9kMICbxBFiAAAA= AElFTkSuQmCC" width=3D"51" height=3D"28" alt=3D"" /> = ; W =3D <img src=3D"data:image/png;base64,iVBORw0= KGgoAAAANSUhEUgAAADoAAAAdCAYAAAD7En+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxA= AADsQBlSsOGwAABO1JREFUWIXt2FmMnXMUAPDfvXfa6cy0hqmiVdXYUktQe0ss9WBNLAmxlIoHg= ijCgwQREUo0jSVBPFFbNRFJrUEjxBIPfUAtVaWo1r7V0jKLh3M+93NNzeiMdoKTfLnf/f+/c/5n= /59zKgYOTdgW26EZy7AEPdgc++c3MBr3oDP/VxJvE7yOrhLdZuyHN/FN0ltvqA4EOWFn3IVfMQI= PYpvcm4RzcCCmYji6c6+CNszDJQ28VHEqHhCKGDA09f1Jn/AJLsJbaMG52AMfCYYfxf1CwE51y4= zAVXhZWLpSojkhaSwzOMYYFCJfY7EQoCas9nzudWNrzMDxQhHyu72xAm/kd5XkZzhuEcppK62XF= fG3YVC0lUyMw724Ft8Lwd8UFlsi3PghDMOuuEC4bU/i74tWzMT8xKmIWD1qEHldb6hgJO4UAtQa= 9qq51oJVgvGr8HY+q/AFnsMoPCPCYCm+xXLMbqC7UWATzMVp+b4vjhZCzhDJaoRIKm8I12zKtVY= R0/OEmzYJRTRjU7yGg4QXDMh1ByMZ7YTdMRmX59rs/N0Ss7AIe+V7Vz6dQujxQpBW/Kh+9WyPn0= RIlJPYesGAtJTQii1K/7tFgvpBuNtoYb3V4j4sQ00ICl8lTgEdwpU7sdIQELQ3Oo1MVXpZa8Ttb= f+v9v6H3qCiHuiDZd2hBj1EMjoL0w2Be+ofgm680iRKtRcMgXvqH4JuLC2XV/9q1/3PwGBYsUxj= KGivV36KuKyWnsZatSaSVk1k6DasLe034WBx2f+kXt9WSweV12oNzBRrxTMSv/RTqEbcjuTtWHy= WdH7PukTLdEQKMgFP4JFkaA52SIQW0V/OSbzNRbexSL3qGYcTRaUzTySDdpwsqqB2/CzKxK+wC6= 7Lsws4TjTyfcERuFiUlMW0Yk+8gyuxAC+qN/uuTOKtojh/X4xHhotuYrIo2EeJgltq8DacpO4FU= 3G36EdnltZ3FIV/Wwr6pKiLKzggBd0s6Y/U/5CajusTb1TSLzynHc9iLPW7cxYeE2ZfU3qol28t= QsuFpseKkclT6rOe94SGX2pgdhnOFkX7L0l7VWm/O5VSLZ1bnL2up4zbXMLtybXVWIh9UCtctys= /bBGTgEuFW1XwIW5Ija0WrdfnGJPMFvEq14f545CrYKYQZjcRzw/l3o/CqvthovCSO3JvD3++33= tEkV/0seeLzmhrXI3HhTF68KpoGZ8pt2kjcZNw1adLzJ5TYvIaEXfTsBW+VPL/PqCKQ3AeLlO33= GIcmXTGC3dbIJLJdNH5lKFL5JBPk8+FiTsN9+FwMdkglDhayaLNuFB0+fNFPP6cBMYIDXbl74TE= 6RTJrLd4anSvqhilzMCZSWuMUFRHMrRGeMTaPH9FKqQ3+oV7jhVTiDXJWy1xy+d2U3eLM3B6Ik1= JoRelIHOFGzcJa9wqrNCMY0QWLqzfLCYKF4jEsFi46Rg8LNxtEk4Qk4iFee5p+EBYdiJuV3e/dT= 1wc9JZjlNE2Nwm8kBVJMdOvFwIelgKOUzE6QpR/36bQk4W8TJfuE0xJThdZNjvkk67cPWVKeAkM= f9pEzG+Rrhil0hY7yZuh7iLf8WNeW5/io+VYooxBR/jCuGJhBFnpoI/6QetdUJFXC23imtoqNTK= RZEzTX1kOuCOZYlw10Pzfe1ffr1hoCYKid3EHbt647KzgeE3x9gwUfNz4BcAAAAASUVORK5CYII= =3D" width=3D"58" height=3D"29" alt=3D"" /> W =3D <img src=3D"= data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABsAAAAaCAYAAABGiCfwAAAABHNCS= VQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAz1JREFUSIm91ktonVUQB/Dfd3tzk6Zp= 4gP7UEt81Ed8I4IBXxvFhQvRagRRVGhRizsXgsVFBQVR6UJcKCoooiIUig9ExI0ovooRYquibVE= pSqzVaJsY097rYuZwv0bpqunAx/2+O2fOzPzPnPlPJaSFMzCMDr7Gj6kbwWpUue4kbNKVJi7GFL= 5Du6YbxCX4FPsb+ecabMDPGMVmnIwGbs/nsjTcW9usymBexVh+F2ngPryGY0pU0vNn+AHTuBsr0= nkDr+NtHMinHvlafJPrirNFuAYXZsaN4h12YkdCOIJfEpJOwnJ2BnBFzaaJB/AJZmvZNDCEm/FI= 7lGhUQwLJKflojX4AwfxfGb9Je7EMxn55fgLb9ayWYtjsREvYi6drcL5dUdn4nNRKPODKBFfj1+= xDC9jO77Fb4nGBK7EV6LIdugWzrVlw0E8J6pqIOE6J983ivPry8gn8r2FXizFWwlZH3pStxgXiC= M6FT2lQMZEFV4qzqiNezKrYTyZGYyIyvy7lvnShKo3YZ+rIbI8kViFXaV6js8syncbk7lpC8fl7= 17sc6gsTlg72J0OiyxL/TT21O9F/V0az9d1/L8cTl8dxm7hpBIHWvlvZkdaOk1xd25zaLkfcUfY= 2hQN9wNxKRdK2thZLuxCw3j0C+SoSoGuTg+VwLij1rF170vRzbdT0xWbIh20S7taLbp9CycK2ng= FM6Id3StoA54WnaISPfS6tJvAS6Jd9WEdVqJfMMAXxfMY1ovWMoqfBCX04HE8iiV4EO+mo5X4CK= dkQOOy4Qp22JSO1otGPVBS3YxnBQsvwX7BZ0O4Ch+KPjkumGFFOvldUMuM4Lxb09m6/J7FFpyOo= eLsYG58E+7HLbnJQP7/Z+I+mesHE/pZ3fPbLeaWJi4Szbcj+GwGrfoh9mf03+Nh0bEL3RRZlBsc= 0B0F6rp/8n2+roFOcbY8I3kDDwnKP0vAOaU7yp2QAU1iVy0TYsT7WBTItvyuBH3NYbo4uwOPiXO= 4AXtysylRlWNiXLhRMPc+QfnTuEuMecN4J7N7Im3OFUPRe+J8wXl4QRTAFjGCFTboFRU5LmbL/h= o0o6I638fVaSPXbBBF8pQgX/8ChA/Lm9SdJdsAAAAASUVORK5CYII=3D" width=3D"27" heig= ht=3D"26" alt=3D"" />=3D 0.848 R//Al realizar esta fragmentaci=C3=B3n de la f= =C3=B3rmula de Kendall, obtenemos un resultado de W=3D0.84, que equivale a = un nivel alto de concordancia entre los cinco expertos que evaluaron las pr= opuestas, cabe mencionar que al obtener valores superiores a 0,7 se los con= sidera aceptables para una validaci=C3=B3n de criterios, evidenciando la co= nsistencia de las valoraciones y la validez metodol=C3=B3gica del instrumen= to. Seg=C3=BAn la escala de Juna (2025) este valor corresponde a una concordancia sustanc= ial, lo que respalda la confiabilidad de los resultados y la aplicabilidad = pr=C3=A1ctica de las estrategias evaluadas. El an=C3=A1lisis evidencia un alto consen= so (coeficiente de Kendall W > 0.84), con valoraciones promedio de 4.6 e= n adopci=C3=B3n tecnol=C3=B3gica, 5.0 en actitud hacia la digitalizaci=C3= =B3n, 4.4 en mejora de procesos y 4.6 en ventaja competitiva. En contraste,= las dimensiones de apoyo institucional/ecosistema digital (3.2) y barreras= econ=C3=B3micas (3.6) recibieron las valoraciones m=C3=A1s bajas.</= p><sp= an>Finalmente, las estrategias propuestas que incluyen programas de capacit= aci=C3=B3n modular, mecanismos de financiamiento adaptado a la escala micro= empresarial, talleres de gesti=C3=B3n del cambio organizacional, manuales d= e procesos digitales estandarizados y redes de articulaci=C3=B3n interinsti= tucional se consolidan como una respuesta pr=C3=A1ctica, viable y contextua= lizada frente a las brechas identificadas. Su validaci=C3=B3n t=C3=A9cnica = y su anclaje emp=C3=ADrico las posicionan como una ruta integral para promo= ver la digitalizaci=C3=B3n sostenible de las microempresas ecuatorianas, fo= rtaleciendo su capacidad de innovaci=C3=B3n, resiliencia y eficiencia opera= tiva. <h2 style=3D"margin-right:0pt; margin-left:17.75pt; margin= -bottom:0.85pt; text-align:left; line-height:115%">4. Conclusiones </h2>La investigaci=C3=B3n permiti=C3=B3 evid= enciar que la transformaci=C3=B3n digital constituye un factor decisivo par= a la sostenibilidad y competitividad de las microempresas ecuatorianas. El = an=C3=A1lisis emp=C3=ADrico realizado demostr=C3=B3 que, si bien existe un = nivel significativo de adopci=C3=B3n digital b=C3=A1sica y una amplia dispo= sici=C3=B3n hacia la innovaci=C3=B3n tecnol=C3=B3gica, las limitaciones est= ructurales, econ=C3=B3micas y organizativas siguen representando un obst=C3= =A1culo relevante para avanzar hacia procesos de mayor complejidad y valor = agregado. En correspondencia con el objetivo general, se logr= =C3=B3 analizar la relaci=C3=B3n entre la transformaci=C3=B3n digital y la = eficiencia operativa, mostrando que la primera incide positivamente en la s= egunda cuando se articulan estrategias de capacitaci=C3=B3n, financiamiento= y acompa=C3=B1amiento t=C3=A9cnico. Asimismo, se cumplieron los objetivos = espec=C3=ADficos: se identificaron las principales barreras que frenan la d= igitalizaci=C3=B3n, se evalu=C3=B3 el grado de implementaci=C3=B3n de tecno= log=C3=ADas en procesos clave y se formularon estrategias contextualizadas = que aportan soluciones viables y aplicables al entorno de las microempresas= . El principal aporte cient=C3=ADfico del estudio radica en la = integraci=C3=B3n de un enfoque mixto que combina datos cuantitativos y cual= itativos, junto con la validaci=C3=B3n experta, lo cual permiti=C3=B3 const= ruir una propuesta metodol=C3=B3gicamente robusta y adaptada al contexto lo= cal. A nivel pr=C3=A1ctico, se ofrecen lineamientos estrat=C3=A9gicos que p= ueden servir de base para dise=C3=B1ar pol=C3=ADticas p=C3=BAblicas, progra= mas de apoyo institucional y modelos de gesti=C3=B3n empresarial orientados= a la digitalizaci=C3=B3n inclusiva de las microempresas. La di= gitalizaci=C3=B3n representa una oportunidad hist=C3=B3rica para redefinir = el modelo productivo de las micro y peque=C3=B1as empresas ecuatorianas. Si= se consolida mediante una pol=C3=ADtica integral que combine formaci=C3=B3= n, financiamiento y acompa=C3=B1amiento institucional, la transformaci=C3= =B3n digital puede convertirse en el principal motor de innovaci=C3=B3n, co= mpetitividad y equidad empresarial en el siglo XXI. <h2 style=3D= "margin-right:0pt; margin-left:17.75pt; margin-bottom:0.85pt; text-align:le= ft; line-height:115%">5. Conflicto de interes= es </h2>Los autores decla= ran que no existe conflicto de intereses en relaci=C3=B3n con el art=C3=ADc= ulo presentado. <ol start=3D"6" style=3D"margin:0pt; padding-lef= t:0pt"><li style=3D"margin-left:31.25pt; margin-bottom:0.85pt; text-indent:= 0pt; text-align:left; line-height:115%; padding-left:4pt; font-weight:bold;= color:#767171">Declaraci=C3=B3n de contribuci=C3=B3n de los autores = </li></ol>Todos autores contribuyeron significativamente en la elab= oraci=C3=B3n del art=C3=ADculo. <ol start=3D"7" style=3D"margin:= 0pt; padding-left:0pt"><li style=3D"margin-left:31.25pt; margin-bottom:0.85= pt; text-indent:0pt; text-align:left; line-height:115%; padding-left:4pt; f= ont-weight:bold; color:#767171">Costos de financiamiento </li></ol>La presente investigaci=C3= =B3n fue financiada en su totalidad con fondos propios de los autores. </sp= an><h2 style=3D"margin-right:0pt; margin-left:17.75pt; margin-bottom:0.= 85pt; text-align:left; line-height:115%">8. R= eferencias Bibliogr=C3=A1ficas </h2> <div s= tyle=3D"margin-left:24pt">Prieto Albornoz, I. V., & Loredo Mendoza, A. (2025). Fact= ores que inciden en la productividad empresarial en entornos digitales. </s= pan>Perspectiva Revista Cient=C3=ADfica d= e Econom=C3=ADa, 1</s= pan>(1), 16=E2=80=9352. <a href=3D"https://doi.org/10.56469/PR= CE.V1I1.1925" style=3D"text-decoration:none">http= s://doi.org/10.56469/PRCE.V1I1.1925</a> </div><div = style=3D"margin-left:24pt">Ocampo Alvarado, A. M. (2023). Efectos de la transformaci=C3= =B3n digital en el sector contable y financiero en Ecuador. Ciencia y Educaci=C3=B3n, <sp= an style=3D"font-style:italic">4(11), 42=E2=80=9352. <a= href=3D"https://doi.org/10.5281/ZENODO.10212374" style=3D"text-decoration:= none">https://doi.org/10.5281/ZENODO.10212374</sp= an></a> </div><div style=3D"margin-left:24pt"><sp= an style=3D"background-color:#ffff00">=C3=81lvarez Alp=C3=ADzar<span= >, O. C. (2025). Estrategia para e= l mejoramiento de la gesti=C3=B3n y la competitividad empresarial mediante = el uso de TIC: PYMEs de servicios odontol=C3=B3gicos en Tres R=C3=ADos, Car= tago [Tesis de posgrado, Ciudad Universitaria Rodrigo Facio, S= an Jos=C3=A9, Costa Rica]. <a href=3D"https://hdl.handle.net/10669/1= 02752" style=3D"text-decoration:none">https://hdl= .handle.net/10669/102752</a> </div><div style=3D"ma= rgin-left:24pt">Banka, M.= S., Andrade, R. D. de, Tien, N. H., Trai, D. V., & Tinh, = N. H. (2024). La cooperaci=C3=B3n de corporaciones p=C3=BAblicas con empres= as privadas de nueva creaci=C3=B3n en todas las etapas de su ciclo de vida.= Brazilian Journal of Development<= /span>, 10(6), = e70679. <a href=3D"https://doi.org/10.34117/bjdv10n6-051" style=3D"t= ext-decoration:none">https://doi.org/10.34117/bjd= v10n6-051</a></div><div style=3D"margin-left:24pt"><spa= n style=3D"background-color:#ffff00">Bartels Ortega, T. (2021)= . El rol de la Agencia Universitar= ia para la Gesti=C3=B3n del Emprendimiento (AUGE) en la innovaci=C3=B3n e i= nternacionalizaci=C3=B3n de las micro, peque=C3=B1as y medianas empresas (M= ipymes). Pa=C3=ADs: Costa Rica [Tesis de posgrado, Universidad= de Chile, Santiago de Chile, Chile]. <a href=3D"https://repositorio= .uchile.cl/handle/2250/184282" style=3D"text-decoration:none">https://repositorio.uchile.cl/handle/2250/184282</a><= span> Brito Bautista , S. M., Naranjo Navas, N. N., Remache Alarc=C3=B3n, = B. S., & Quishpe Alarc=C3=B3n, S. A. (2023). El Liderazgo y la Comunica= ci=C3=B3n Digital. Dos cauces de un mismo fin. Ciencia Latina Revista Cient= =C3=ADfica Multidisciplinar, 7(2), 649= 1-6507. <a href=3D"https://doi.org/10.37811/cl_rcm.v7i2.5801" style= =3D"text-decoration:none">https://doi.org/10.37811/cl_rcm.v7i2.5801</a></p= ></div><div style=3D"margin-left:24pt">Castillo Inche, C. H., & Villanueva Quispe, = E. V. (2024). Transformaci=C3=B3n digital de los noticieros televisivos y s= u impacto social en Cerro de Pasco, 2023 [Tesis de posgrado, Universidad Na= cional Daniel Alcides Carri=C3=B3n =E2=80=93 UNDAC, Capital Minera de Cerro= de Pasco, Per=C3=BA]. <a href=3D"https://scholar.google.com/scholar= ?hl=3Des&as_sdt=3D0%2C5&q=3DTransformaci%C3%B3n+digital+de+los+noti= cieros+televisivos+y+su+impacto+social+en+Cerro+de+Pasco&btnG=3D" style= =3D"text-decoration:none">https://scholar.google.= com/scholar?hl=3Des&as_sdt=3D0%2C5&q=3DTransformaci%C3%B3n+digital+= de+los+noticieros+televisivos+y+su+impacto+social+en+Cerro+de+Pasco&btn= G=3D</a> </div><div style=3D"marg= in-left:24pt">Cutanda-L= =C3=B3pez, M. T. (2021). M=C3=A9todo mixto de investigaci=C3= =B3n: pertinencia y dificultades en el estudio programas de reenganche. </s= pan>Caribe=C3=B1a de Ciencias Sociales</s= pan>, 31=E2=80=9348. <a href=3D"https://doi.org/10.51896/CARIB= E/MGUI5478" style=3D"text-decoration:none">https:= //doi.org/10.51896/CARIBE/MGUI5478</a> </div><div s= tyle=3D"margin-left:24pt">Dur=C3=A1n Mero, R. A. (2025). Transformaci=C3=B3n digital en= las pymes ecuatorianas: desaf=C3=ADos y oportunidades. Revista Cient=C3=ADfica Multidisciplinaria en Cienci= as Sociales y Humanidades Eucken, 1(1), 40-58. <a href=3D"https://revistaeuc= ken.com/indes/index.php/home/article/view/transformacion-digital-pymes" sty= le=3D"text-decoration:none">https://revistaeucken= .com/indes/index.php/home/article/view/transformacion-digital-pymes<= /a></div><div style=3D"margin-left:24pt">Espejo Villar, L. B., L=C3=A1zaro Herrero,= L., & =C3=81lvarez L=C3=B3pez, G. (2023). Digitalizaci=C3=B3n educativ= a y aprendizaje m=C3=B3vil: tendencias en las narrativas pol=C3=ADticas de = los organismos internacionales. Fo= ro de Educaci=C3=B3n, 21(2), 45=E2=80=9366. <a href=3D"= https://dialnet.unirioja.es/servlet/articulo?codigo=3D9262557" style=3D"tex= t-decoration:none">https://dialnet.unirioja.es/se= rvlet/articulo?codigo=3D9262557</a> </div><div styl= e=3D"margin-left:24pt">Fe= rn=C3=A1ndez-Ronquillo, M., & Romero-C=C3=A1rdenas, E. (20= 15). Estructuraci=C3=B3n de la microempresa establecida en el sector comerc= ial de la ciudad de Milagro, Ecuador. </span= >Revista Ciencia Unemi, </sp= an>8(14), 21-29. <a h= ref=3D"https://www.redalyc.org/pdf/5826/582663828004.pdf" style=3D"text-dec= oration:none">https://www.redalyc.org/pdf/5826/58= 2663828004.pdf</a> </div><div style=3D"margin-left:= 24pt">Flores= Arocutipa, J. P., Manrique Nugent, M. A. L., Serna Silva, G. J., & Ayb= ar Bellido, I. E. (2021). Liderazgo en tiempos de 4ta revoluci=C3=B3n indus= trial. Revista Venezolana de Geren= cia. 26(96), 1096-1107. <a href=3D"https://doi.org/10.5= 2080/rvgluz.26.96.7" style=3D"text-decoration:none">https://doi.org/10.52080/rvgluz.26.96.7</a> </di= v><div style=3D"margin-left:24pt">Juna Pozo, L. P. (2025). Importancia de la investigac= i=C3=B3n cient=C3=ADfica en los profesionales quite=C3=B1os y el gusto en s= u profesi=C3=B3n analizado por la prueba de concordancia Kappa de Cohen. </= span>Ciencia Latina Revista Cient=C3=ADfi= ca Multidisciplinar, = 9(2), 8017=E2=80=938029. <a href=3D"https://doi.org/10.= 37811/CL_RCM.V9I2.17522" style=3D"text-decoration:none">https://doi.org/10.37811/CL_RCM.V9I2.17522</a> <= /p></div><div style=3D"margin-left:24pt">Largo Piedrahita, J. A. (2025). Estrategias de ciberseguridad para la protecci= =C3=B3n de la infraestructura cibern=C3=A9tica de las (PYMES), mediante el = estudio de vectores de ataque para fortalecer la seguridad inform=C3=A1tica= [Tesis de posgrado, Universidad Nacional Abierta y a Distanci= a =E2=80=93 UNAD, Bogot=C3=A1, Colombia]. <a href=3D"http://reposito= ry.unad.edu.co/handle/10596/75474" style=3D"text-decoration:none">http://repository.unad.edu.co/handle/10596/75474</a= ></div><div style=3D"margin-left:24pt">Ortiz-Choez, G., Constantine-Castro, J., Mar= tillo-Mieles, O., & Silva-Idrovo, R. (2024). Las PYMES en el Ecuador y = su participaci=C3=B3n en el PIB. D= igital Publisher CEIT, 9(2), 736=E2=80=93743. <a href=3D"https://doi.org/10.= 33386/593DP.2024.2.2273" style=3D"text-decoration:none">https://doi.org/10.33386/593DP.2024.2.2273</a> <= /p></div><div style=3D"margin-left:24pt">Ortiz Valverde, K., Quir=C3=B3s Fonseca, F., G= onz=C3=A1lez Villalobos, P., & Esquivel Montero, N. (2025). <spa= n style=3D"font-style:italic">Desarrollo de las capacidades empresariales d= e las pymes del sector industrial en el cant=C3=B3n de P=C3=A9rez Zeled=C3= =B3n, per=C3=ADodo 2024-2025 [Tesis de pregrado, Universidad N= acional, Heredia, Costa Rica]. <a href=3D"https://hdl.handle.net/110= 56/33459" target=3D"_blank" style=3D"text-decoration:none">https://hdl.handle.net/11056/33459</a></div><div style= =3D"margin-left:24pt">Qui= nallaz Arango, M. Z. (2022). Caracterizaci=C3=B3n de los efectos producidos por la problem=C3=A1= tica socio econ=C3=B3mica surgida durante la pandemia del COVID 19 en la co= muna 8 del municipio de Armenia en los a=C3=B1os 2020 y 2021 [= Tesis de pregrado, Escuela Superior de Administraci=C3=B3n P=C3=BAblica CET= AP, Armenia, Colombia]. <a href=3D"https://repositoriocdim.esap.edu.= co/items/82c04ef0-da4b-4aa2-ad8d-2034da1db755" style=3D"text-decoration:non= e">https://repositoriocdim.esap.edu.co/items/82c0= 4ef0-da4b-4aa2-ad8d-2034da1db755</a></div><div style=3D"margin-l= eft:24pt">Tamayo<s= pan> Freire, J. A. (2025). Estudio= de los factores determinantes para la creaci=C3=B3n de emprendimientos y/o= microempresas en el sector tur=C3=ADstico de conocimiento de la naturaleza= en el cant=C3=B3n Tulc=C3=A1n de la provincia del Carchi [Tes= is de maestr=C3=ADa, Universidad Andina Sim=C3=B3n Bol=C3=ADvar =E2=80=93 S= ede Ecuador, Quito, Ecuador]. <a href=3D"http://repositorio.uasb.edu= .ec/handle/10644/10498" style=3D"text-decoration:none">http://repositorio.uasb.edu.ec/handle/10644/10498</a> </= span></div> <s= pan> <p= style=3D"text-align:left"> <div style=3D"clear:bo= th"><img src=3D"data:image/png;b= ase64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAACCAYAAACZgbYnAAAABHNCSVQICAgIfAhkiAAAA= AlwSFlzAAAOxAAADsQBlSsOGwAAABJJREFUCJljYGBg+M/AwMDAAAAGBAEA+2Da0AAAAABJRU5E= rkJggg=3D=3D" width=3D"1" height=3D"2" alt=3D"" style=3D"margin-right:9pt; = margin-left:9pt; float:left; position:relative" /> &= #xa0; Nombre 1 = </div></div></body></html>

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(UBE), Dur=C3=A1n, Ecuador</td><td = style=3D"width:20pt; border-top:0.75pt solid #000000; padding:0.65pt 1.35pt= 0pt 0pt; vertical-align:top"> = </td><td style=3D"width:175.95pt; border-top:0.75pt solid #000000; bord= er-right:0.75pt solid #000000; padding:0.65pt 0.98pt 0pt 0pt; vertical-alig= n:top"> </= td></tr><tr style=3D"height:33.6pt"><td style=3D"width:12.32pt; border-righ= t:0.75pt solid #000000; border-left:0.75pt solid #000000; border-bottom:0.7= 5pt solid #000000; padding:0.65pt 0.98pt 0pt 0pt; vertical-align:top"> </td><td style= =3D"width:202.42pt; border-left:0.75pt solid #000000; border-bottom:0.75pt = solid #000000; padding:0.65pt 1.35pt 0pt 0pt; vertical-align:top">Maestr=C3=ADa en= Administraci=C3=B3n y Direcci=C3=B3n de empresas misuarezp@ube.edu.ec <span= style=3D"text-decoration:underline; color:#0070c0">merlysuarez518@gmail.co= m </td><td style=3D"width:20pt; border-bottom:0.75p= t solid #000000; padding:0.65pt 1.35pt 0pt 0pt; vertical-align:top"> </td><td style=3D"width:175.95pt;= border-right:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pad= ding:0.65pt 0.98pt 0pt 0pt; vertical-align:top"><s= pan> </td></tr><tr style=3D"height:17.75pt"><td style=3D"wi= dth:12.32pt; border:0.75pt solid #000000; padding:0.65pt 0.98pt 0pt 0pt; ve= rtical-align:top">2</= span> </td><td style=3D"width:202.42pt; border:0.75pt soli= d #000000; padding:0.65pt 0.98pt 0pt 0pt; vertical-align:top">Glen Freddy Robayo Ca= brera </td><td style=3D"width:19.62pt; border:0.75pt solid #0000= 00; padding:0.65pt 0.98pt 0pt 0pt; vertical-align:top"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAAN= SUhEUgAAABQAAAAUCAYAAACNiR0NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSs= OGwAAA3VJREFUOI21lE1onFUUhp9z7zffN5lJMpkU2kVsbaRNQ6GIJrUau0lExSKCG9FNIYhYRK= rixkKVLtxYRFBQEfyhNFFwW3ChKGL9iSaRUI0KtrRBU5PU2GSS+fn+7nExyTSJKV35Xs7icu/7n= vPec7jCJnhnrKdQ9P0dKm4/avalzhWsMYtqdJIoHDXOXHqkd3xxM66s3Xw8udd3Yes9frN9Io21= 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29LH/J8Dqdh+XrvC7p27/OMOq8T8aQySCDDDLIIIN/Pv7adf/v0vkRQqTS2ePxOGeeeSb19fV/Y= 5czyCCDDDLIIIMM/ufozDMeOXIkH3/8carG4v8Ufzbslc6/6extySCDDDLIIIMMMvjfRGdx4K/z= /vzdCs/pvJg/11AGGWSQQQYZZJDBPwOdKxb8vfizloxLaHb1cP4aLZ0MMsgggwwyyCCDfyRcO6S= z9MzfmLP15wnPGU9PBhlkkEEGGWTwrwS3WPPfQ3jOIIMMMsgggwwyyCCDDDLIIIMMMsgggwwyyC= CDDDLIIIMMMsgggwwyyCCDDP7fwv8PGbshodW+encAAAAASUVORK5CYII=3D" width=3D"575"= height=3D"174" alt=3D"" /><table style=3D"width:444.7pt; margin-left:0= .5pt; margin-bottom:0pt; border:0.75pt solid #000000; padding:0pt; border-c= ollapse:collapse"><tr style=3D"height:598.1pt"><td style=3D"width:87.32pt; = border-right:0.75pt solid #000000; padding:3.4pt 0pt 0pt 5.03pt; vertical-a= lign:top"> Palabras claves: <= /span>Transformaci=C3= =B3n digital, m= icroempresas, t= ecnolog=C3=ADas de <s= pan>la informaci=C3=B3n, eficiencia = operativa, <spa= n>innovaci=C3=B3n empresarial. </td><td style= =3D"width:9.38pt; border-right:0.75pt solid #000000; border-left:0.75pt sol= id #000000; padding:3.4pt 0pt 0pt 5.03pt; vertical-align:top"> </span= ></td><td style=3D"width:329.92pt; border-left:0.75pt solid #000000; pa= dding:3.4pt 0pt 0pt 5.03pt; vertical-align:top">Resumen Introducci=C3=B3n: <span= >la transformaci=C3=B3n digital se convirti=C3=B3 en un factor determinante= para la competitividad de las microempresas, especialmente en contextos do= nde predominan barreras t=C3=A9cnicas, econ=C3=B3micas y organizativas. En = Ecuador, este sector constituye m=C3=A1s del 90 % de las unidades productiv= as, pero presenta rezagos en adopci=C3=B3n tecnol=C3=B3gica y acceso a ecos= istemas de apoyo, lo que limita su eficiencia operativa y sostenibilidad</s= pan>. Objetivo: analizar la t= ransformaci=C3=B3n digital en microempresas ecuatorianas del sector comerci= o y servicios, identificando barreras, nivel de adopci=C3=B3n tecnol=C3=B3g= ica y estrategias orientadas a mejorar su eficiencia operativa.<span= style=3D"font-weight:bold"> Metodolog=C3=ADa: se desarroll=C3= =B3 un estudio bajo el paradigma pragm=C3=A1tico, con un enfoque mixto, dis= e=C3=B1o no experimental, transeccional y alcance descriptivo-explicativo. = Se aplicaron 200 encuestas estructuradas distribuidas en cinco bloques tem= =C3=A1ticos clave. Los datos cuantitativos fueron procesados mediante estad= =C3=ADstica descriptiva, mientras que los datos cualitativos se analizaron = mediante codificaci=C3=B3n tem=C3=A1tica. Posteriormente, se formularon est= rategias y se validaron a trav=C3=A9s del juicio de cinco expertos, aplican= do el coeficiente de concordancia de Kendall (W). Resultados: los hallazgos evidencian un uso ext= endido de herramientas digitales b=C3=A1sicas (71.5 %), presencia en redes = sociales (73.5 %) y ventas en l=C3=ADnea (61 %). Sin embargo, persisten bar= reras significativas como falta de conocimientos digitales (54 %), altos co= stos tecnol=C3=B3gicos (66 %) y bajo apoyo institucional (22.5 %). La valid= aci=C3=B3n experta mostr=C3=B3 altos niveles de consenso (W > 0.75), des= tacando dimensiones como actitud hacia la digitalizaci=C3=B3n (5/5) y adopc= i=C3=B3n tecnol=C3=B3gica (4.6/5), mientras que apoyo institucional (3.2/5)= y barreras econ=C3=B3micas (3.6/5) recibieron las calificaciones m=C3=A1s = bajas. Conclusi=C3=B3n: l<sp= an>a transformaci=C3=B3n digital impacta positivamente en la eficiencia ope= rativa de las microempresas, pero su efecto pleno depende de superar las li= mitaciones t=C3=A9cnicas, financieras y de articulaci=C3=B3n institucional.= Las estrategias propuestas: capacitaci=C3=B3n modular, mecanismos de finan= ciamiento adaptado, fortalecimiento organizacional y redes de apoyo institu= cional, se consolidan como soluciones viables para fomentar una digitalizac= i=C3=B3n efectiva y sostenible. =C3= =81rea de estudio general: Ciencias empresariales. <spa= n style=3D"font-weight:bold">=C3=81rea =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0 de estudio =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 espe= c=C3=ADfica: =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0 Direcci=C3=B3n de =C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= empresas / transformaci=C3=B3n digital en microempresas. </sp= an>Tipo de estudio: Art=C3=AD= culo original. <= /span></td></tr></table>= <table style=3D"width:444.7pt; margin-left:0.5pt; ma= rgin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr style=3D"height= :569.85pt"><td style=3D"width:91.98pt; border-top:0.75pt solid #000000; bor= der-right:0.75pt solid #5b9bd5; border-left:0.75pt solid #5b9bd5; border-bo= ttom:0.75pt solid #5b9bd5; padding:3.2pt 2.02pt 0pt 0pt; vertical-align:top= ">Keywords: <= /span>Digital transfor= mation, microenterprises, information technology, operational efficiency, </spa= n>business innovation.= </td><td style=3D"width:10.78pt; border-top:0= .75pt solid #000000; border-right:0.75pt solid #5b9bd5; border-left:0.75pt = solid #5b9bd5; border-bottom:0.75pt solid #5b9bd5; padding:3.2pt 2.02pt 0pt= 0pt; vertical-align:top"> </td><td style=3D"width:332.88pt; border-top:0= .75pt solid #000000; border-right:0.75pt solid #5b9bd5; border-left:0.75pt = solid #5b9bd5; border-bottom:0.75pt solid #5b9bd5; padding:3.2pt 2.02pt 0pt= 0pt; vertical-align:top">Abstract Introduction: digital transformation bec= omes a decisive factor for the competitiveness of microenterprises, especia= lly in contexts where technical, economic, and organizational barriers prev= ail. In Ecuador, this sector accounts for more than 90% of productive units= , yet it shows delays in technological adoption and access to support ecosy= stems, which limits its operational efficiency and sustainability. <= span style=3D"font-weight:bold">Objective: To analyze digital = transformation in Ecuadorian microenterprises in the commerce and services = sector, identifying barriers, level of technological adoption, and strategi= es aimed at improving their operational efficiency. Methodology: A study was conducted under the = pragmatic paradigm, with a mixed methods approach, a non-experimental, cros= s-sectional design, and a descriptive-explanatory scope. Two hundred struct= ured surveys were administered, distributed across five key thematic blocks= . Quantitative data were processed using descriptive statistics, while qual= itative data were analyzed using thematic coding. Subsequently, strategies = were formulated and validated through expert judgment by five specialists, = applying Kendall's W coefficient of concordance. Results: The findings show an extensive use of b= asic digital tools (71.5%), presence on social media (73.5%), and online sa= les (61%). However, significant barriers persist, such as lack of digital k= nowledge (54%), high technology costs (66%), and low institutional support = (22.5%). Expert validation revealed high levels of consensus (W > 0.75),= highlighting dimensions such as attitude toward digitalization (5/5) and t= echnological adoption (4.6/5), while institutional support (3.2/5) and econ= omic barriers (3.6/5) received the lowest scores. Conclusion: Digital transformation has a positi= ve impact on the operational efficiency of microenterprises, but its full e= ffect depends on overcoming technical, financial, and institutional coordin= ation limitations. The proposed strategies=E2=80=94modular training, adapte= d financing mechanisms, organizational strengthening, and institutional sup= port networks=E2=80=94are consolidated as viable solutions to foster effect= ive and sustainable digitalization. General field of study: Business sciences. Specific field of study: Business mana= gement / transformation in microenterprises. Type of study: Original article.</td></tr= ></table> <h1 style=3D"margin-right:0pt;= margin-left:17.75pt; margin-bottom:7.8pt; text-align:left; line-height:115= %">1. Introducci=C3=B3n </h1>La cuarta revoluci=C3=B3n ind= ustrial o Industria 4.0, se caracteriza por la integraci=C3=B3n de la </spa= n>Inteligencia Artificial (IA</sp= an>), la anal=C3=ADtica de datos masivos, la rob=C3=B3tica avanzada, = la computaci=C3=B3n en la nube, la tecnolog=C3=ADa blockchain y el Internet de las Cosas (IoT), todas ellas c= on un alto potencial disruptivo sobre los procesos tradicionales (<s= pan style=3D"background-color:#ffff00">Flores et al., 2021). <= /span>A nivel eco= n=C3=B3mico redefini=C3=B3 los patrones de competitividad y eficiencia; en = lo social reconfiguro las formas de interacci=C3=B3n y consumo; y en el =C3= =A1mbito empresarial, obligaron a las organizaciones a replantear sus estra= tegias para mantenerse vigentes. Aquellas que lograron adaptarse a la digit= alizaci=C3=B3n reportan incrementos sustanciales en productividad, eficienc= ia operativa y capacidad de innovaci=C3=B3n, mientras que las que se rezaga= n enfrentan mayores riesgos de exclusi=C3=B3n del mercado (Prieto & Loredo, 2025). El= estudio de Brito<sp= an> et al. (2023) se=C3=B1ala que cerca del 70 % de estas iniciativas no al= canzan sus objetivos debido a la falta de liderazgo digital, la resistencia= cultural dentro de las organizaciones y la carencia de una visi=C3=B3n int= egral que abarque no solo la dimensi=C3=B3n tecnol=C3=B3gica, sino tambi=C3= =A9n la humana y organizacional. Desde una perspectiva internacional, organismos como= la Organizaci=C3=B3n para= la Cooperaci=C3=B3n y el Desarrollo Econ=C3=B3micos (OCDE) y = el Banco Mundial reconocen a la digitalizaci=C3=B3n como un motor esencial = para el cumplimiento de los Objetivos de Desarrollo Sostenible (ODS), particularmente en= lo relacionado con el ODS 9: industria, innovaci=C3=B3n e infraestructura = (Espejo et al., 2023= ). En consecuencia, se concibe a la transformaci=C3=B3n digital no so= lo como un mecanismo para incrementar la competitividad empresarial, sino t= ambi=C3=A9n como una estrategia de desarrollo sostenible y la generaci=C3= =B3n de empleo digno (Dur= =C3=A1n, 2025).<h2 style=3D"margin-right:0pt; margi= n-left:18pt; text-indent:-18pt; text-align:left; line-height:115%">1.1.<span= > Justificaci= =C3=B3n e importancia del problema cient=C3=ADfico </h2>En Am=C3=A9rica Latina las </span= >Micro, Peque=C3=B1as y Medianas = Empresas (MIPYMES) representan m=C3=A1s del 99 % de las unidad= es productivas y aportan alrededor del 60 % del empleo formal (<span= style=3D"background-color:#ffff00">Bartels, 2021). Estas orga= nizaciones, por su importancia econ=C3=B3mica y social, constituyen la colu= mna vertebral de las econom=C3=ADas nacionales. Sin embargo, enfrentan m=C3= =BAltiples limitaciones estructurales que reducen su capacidad para adaptar= se a las exigencias de la era digital. Las microempresas se encuentran toda= v=C3=ADa en una etapa incipiente en comparaci=C3=B3n a grandes corporacione= s como la falta de competencias digitales en el personal, la resistencia al= cambio organizacional, las restricciones presupuestarias y la ausencia de = pol=C3=ADticas claras de apoyo a la innovaci=C3=B3n (Banka et al., 2024). En la pandemia del COVID-= 19, diversas microempresas tuvieron que suspender o cerrar sus operaciones = por la imposibilidad de migrar a entornos digitales (Quinallaz, 2022). Esta situaci=C3=B3= n evidenci=C3=B3 que la digitalizaci=C3=B3n no es un lujo opcional, sino un= a necesidad estrat=C3=A9gica para la resiliencia empresarial. <p= style=3D"margin-right:0pt; line-height:115%">En el contexto ecuatori= ano, las microempresas representan m=C3=A1s del 90 % de las unidades produc= tivas registradas y contribuyen de manera significativa a la generaci=C3=B3= n de empleo, en especial en sectores como el comercio, los servicios, la ag= ricultura y la manufactura artesanal (Ortiz-Choez et al., 2024). No obstante, su rezago d= igital limita su competitividad frente a un mercado globalizado donde la in= novaci=C3=B3n tecnol=C3=B3gica es el factor diferenciador. El prop=C3=B3sito de este = estudio es analizar la transformaci=C3=B3n digital en las microempresas ecu= atorianas, mediante un enfoque descriptivo y anal=C3=ADtico de sus procesos= , para la determinaci=C3=B3n de su impacto en la eficiencia operativa y la = competitividad empresarial y as=C3=AD proponer estrategias que promuevan la= transformaci=C3=B3n digital en las microempresas, con base en el an=C3=A1l= isis de barreras y experiencias exitosas. Este argumento da paso a la formu= laci=C3=B3n de la siguiente hip=C3=B3tesis: La implementaci=C3=B3n efectiva de estrat= egias de transformaci=C3=B3n digital en las microempresas ecuatorianas se a= socia significativamente con la mejora de su eficiencia operativa, siempre = que se superen barreras como la insuficiente capacitaci=C3=B3n digital, la = resistencia al cambio organizacional y los costos iniciales de adopci=C3=B3= n tecnol=C3=B3gica. <h2 style=3D"margin-right:0pt; margin-left:1= 8pt; text-indent:-18pt; text-align:left; line-height:115%">1.2. </span= >Antecedentes del pro= blema </h2>En = muchos casos, la falta de adaptaciones tecnol=C3=B3gicas se reduce a una pr= esencia b=C3=A1sica en redes sociales o al uso de aplicaciones de mensajer= =C3=ADa, sin integrar sistemas de Planificaci=C3=B3n de Recursos Empresariales (ERP), pl= ataformas de comercio electr=C3=B3nico o soluciones de ciberseguridad (</sp= an>=C3=81lvarez, 2025= ). Tamayo= (2025) subraya que en Ecuador las microempresas enfrentan debilidade= s estructurales adicionales como la falta de planes de contingencia, la esc= asa capacitaci=C3=B3n del personal, la debilidad financiera y la limitada d= iversificaci=C3=B3n productiva, factores que restringen su capacidad de ada= ptaci=C3=B3n frente a contextos de crisis. A ello se suma la dificultad par= a acceder a financiamiento formal, dado que muchos de estos negocios no cum= plen con requisitos contables o carecen de planes de negocio estructurados = (Ortiz-Choez e= t al., 2024).Otro obst=C3=A1culo cr=C3=ADtico identificado es la= carencia de estrategias de ciberseguridad, lo que expone a las microempres= as a riesgos de p=C3=A9rdida de datos, ataques inform=C3=A1ticos y p=C3=A9r= dida de confianza por parte de los clientes (Largo, 2025). Por su parte Ortiz et al. (2025) destaca que= la insuficiente capacitaci=C3=B3n digital de los microempresarios y de sus= empleados limita no solo la adopci=C3=B3n de herramientas tecnol=C3=B3gica= s, sino tambi=C3=A9n el aprovechamiento de oportunidades en mercados digita= les. A pes= ar de estas limitaciones se document=C3=B3 experiencias exitosas en las que= la digitalizaci=C3=B3n permiti=C3=B3 a las microempresas reducir costos, o= ptimizar la gesti=C3=B3n de clientes y expandir su alcance comercial, espec= ialmente en ecosistemas de innovaci=C3=B3n urbana o en emprendimientos lide= rados por j=C3=B3venes (Ban= ka et al., 2024). Estas evidencias sugieren que, si se dise=C3= =B1an estrategias adecuadas de acompa=C3=B1amiento, capacitaci=C3=B3n y fin= anciamiento, las microempresas ecuatorianas tienen el potencial de aprovech= ar las oportunidades que ofrece la econom=C3=ADa digital. <h1 st= yle=3D"margin-right:0pt; margin-left:17.75pt; margin-bottom:7.8pt; text-ali= gn:left; line-height:115%">2. Metodolog=C3=AD= a </h1>La inve= stigaci=C3=B3n se desarroll=C3=B3 bajo el paradigma pragm=C3=A1tico, que pe= rmiti=C3=B3 abordar la transformaci=C3=B3n digital en microempresas como va= riable independiente y la eficiencia operativa como variable dependiente, p= riorizando la generaci=C3=B3n de soluciones aplicadas a los problemas detec= tados. Se adopt=C3=B3 un enfoque mixto, combinando la aplicaci=C3=B3n de en= cuestas estructuradas y la validaci=C3=B3n mediante juicio de expertos, lo = que permiti=C3=B3 triangular resultados y garantizar mayor rigor cient=C3= =ADfico (Cutanda-L=C3=B3pez= , 2021). El estudio tuvo un alcance descriptivo-explicativo: describi=C3= =B3 el estado actual de la transformaci=C3=B3n digital en microempresas ecu= atorianas del sector comercio y servicios a su vez, explic=C3=B3 las barrer= as que limitan su desarrollo, as=C3=AD como su relaci=C3=B3n con la eficien= cia operativa (Ocampo</span= >, 2023). El dise=C3=B1o fue no experimental, transeccional y de camp= o, ya que no se manipularon variables, sino que estas se observaron en su c= ontexto natural en un =C3=BAnico momento temporal. La poblaci=C3=B3n estuvo conformad= a por microempresas ecuatorianas de los sectores comercio y servicios, cons= iderando su peso en el tejido productivo y en la generaci=C3=B3n de empleo.= Seg=C3=BAn Fern=C3=A1ndez-= Ronquillo & Romero-C=C3=A1rdenas (2015) =E2=80=9Cdefinen a= las microempresas (RIMI) como negocios populares que tienen ingresos de $2= 0.000 a $300.000 (p.14)=E2=80=9D. La muestra se seleccion=C3=B3 mediante un muestreo = no probabil=C3=ADstico por conveniencia, tomando en cuenta la disposici=C3= =B3n de los participantes y la accesibilidad al campo de estudio. Finalment= e, se aplic=C3=B3 una encuesta estructurada a 200 microempresarios, n=C3=BA= mero potencial que permiti=C3=B3 obtener informaci=C3=B3n suficiente para e= l an=C3=A1lisis descriptivo y exploratorio que permiten identificar patrone= s, tendencias y percepciones vinculadas a la transformaci=C3=B3n digital. <= /span><h2 style=3D"margin-right:0pt; text-align:left; line-height:115%"= >2.1.</s= pan>&#= xa0;M=C3=A9tod= os, t=C3=A9cnicas e instrumentos de recolecci=C3=B3n de datos </h2><= p style=3D"margin-right:0pt; line-height:115%">Con base en el enfoque= metodol=C3=B3gico, la ejecuci=C3=B3n pr=C3=A1ctica de la investigaci=C3=B3= n se estructur=C3=B3 en cuatro fases que guiaron el proceso desde la aplica= ci=C3=B3n de los instrumentos hasta la organizaci=C3=B3n e interpretaci=C3= =B3n de los datos. a) &#x= a0; Recolecci=C3=B3n emp=C3=ADrica<= /span>: se aplic=C3=B3 una encuesta estructurada a 200 microempresari= os, que consto de cinco dimensiones relacionadas a: Adopci=C3=B3n digital b= =C3=A1sica/presencia en l=C3=ADnea, barreras t=C3=A9cnicas/econ=C3=B3micas,= disposici=C3=B3n/actitudes hacia la digitalizaci=C3=B3n, capacidades, lide= razgo/toma de decisiones y ecosistema de apoyo e inversi=C3=B3n. Esta etapa= permiti=C3=B3 captar datos tanto cuantitativos como cualitativos en base a= la observaci=C3=B3n sobre el nivel de adopci=C3=B3n tecnol=C3=B3gica, los = procesos internos y las estrategias organizacionales vinculadas a la transf= ormaci=C3=B3n digital, siendo temas claves para el levantamiento de informa= ci=C3=B3n de este estudio en base a los datos cuantitativos. b) &= #xa0; Procesamiento y a= n=C3=A1lisis de datos: los datos cuantitativos fueron analizad= os mediante estad=C3=ADstica descriptiva (frecuencias, porcentajes y medida= s de tendencia central) con el fin de identificar patrones, tendencias y re= laciones entre variables. <= p class=3D"ListParagraph" style=3D"margin-right:0pt; margin-left:36.5pt; ma= rgin-bottom:0pt; text-indent:-18pt; line-height:115%">c)&#= xa0; Formul= aci=C3=B3n de estrategias: esta fase se centr=C3=B3 en la form= ulaci=C3=B3n de estrategias orientadas a fomentar la transformaci=C3=B3n di= gital en las microempresas, tomando como base las barreras estructurales, o= rganizativas y tecnol=C3=B3gicas detectadas durante el an=C3=A1lisis. Estas= propuestas se fundamentan en los hallazgos emp=C3=ADricos obtenidos y busc= an aportar soluciones pr=C3=A1cticas, viables y contextualizadas para mejor= ar la eficiencia operativa mediante una digitalizaci=C3=B3n efectiva, con e= l objetivo de fortalecer la eficiencia operativa de las microempresas media= nte la adopci=C3=B3n digital. d) Validaci=C3=B3n por expertos: la propuesta fue evalu= ada por un panel de cinco especialistas en transformaci=C3=B3n digital y ge= sti=C3=B3n empresarial. Los criterios de selecci=C3=B3n incluyeron formaci= =C3=B3n acad=C3=A9mica de cuarto nivel, experiencia comprobada y participac= i=C3=B3n en proyectos de digitalizaci=C3=B3n. La validaci=C3=B3n se estruct= uro en base a las cinco dimensiones claves del estudio, donde =C3=A9l exper= to valor=C3=B3 la propuesta mediante una escala ordinal, y para medir el gr= ado de consenso se aplic=C3=B3 el Coeficiente de Concordancia de Kendall (W= ) (Ecuaci=C3=B3n 1), t= =C3=A9cnica estad=C3=ADstica no param=C3=A9trica id=C3=B3nea para validar j= uicios colectivos sobre variables (Castillo & Villanueva, 2024).12S<= span style=3D"height:0pt; display:block; position:absolute; z-index:2"><img= src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGYAAAAeCAYAAADermvOA= AAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAhBJREFUaIHtmr2q4kAYht/Z= TrAVCy28AEGCnSAoWNjZ2FhaeA1egZ2FYJPawkqw9QIUrARFEYMIIkYtBBEFIZJ3m1VW3D1kcg6= ccDIPpJifj++Dh0mGmQiShMIzCCEEAPz67kIU/0aJ8ShKjEdRYjyKEuNRlBiPosR4FCXGo0iLMU= 0T1WoVh8MBAFCpVFAoFCCEgBAChmE859ZqNQwGg6+r1k9Qgvl8Tk3T2Ol0aNv2s/9yuRAAAXCxW= Dz7l8sls9ks2+22TBpfIy3mdDoxnU6z0Wi8jf1PDEmORiMC4HA4/HzVPkBaTLPZJABuNpu3sY/E= kGSxWGQ+n+f9fv9c1T7g4cXRN8ayLOi6jkQigUgkIv26TKVS6PV6GI1G0rF+xZGY1WqF2WyGeDy= OP4efUkSjUQDAeDyWjvUrjsQcj0cAQDAYdJUkEAgAAHa7nat4P+JIzGOVuFktAGDbNgCA6urHMY= 7EhEIhAMD1enWV5Ha7AQDC4bCreD/iSEwsFoOmaZhOp29jJGGa5rO93+/fVsZ2uwUAJBKJz9TqL= 5xu43RdJwCu1+uX/nK5/NwqP55KpfIyp1QqMZfL0bKsL9hQ/mykxZzPZ2YyGdbrdalEk8mEANjv= 96WL9CPSYkjSMAwmk0l2u92XI5mP5mezWbZaLdeF+o2HF/F3Q/H9qL9kPI4S41GUGI+ixHgUJUa= hUPwAfgN/h1JHKjQpRgAAAABJRU5ErkJggg=3D=3D" width=3D"102" height=3D"30" alt= =3D"" style=3D"margin-top:6.26pt; margin-left:132.7pt; position:absolute" /= ><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAIsAAAACC= AYAAABopi/vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABZJREFUOI1j= ZGBg+M8wCkbBKBgF1AQAWpoBAV1qK/UAAAAASUVORK5CYII=3D" width=3D"139" height=3D= "2" alt=3D"" />W =3D =F0=9D=91=9A2(=F0=9D=91=9B2 =E2=88=92 =F0=9D=91=9B) =E2=88=92 =F0=9D=91=9A =E2=88=91 =F0=9D=91=87 <h2 style=3D"margin-right:0pt; text-align:left; line-height:= 115%">2.2.<spa= n style=3D"width:18.15pt; text-indent:0pt; display:inline-block"> </sp= an>Aspectos =C3=A9tic= os </h2>La inv= estigaci=C3=B3n cumpli=C3=B3 con los principios =C3=A9ticos de consentimien= to informado, confidencialidad y respeto a la autonom=C3=ADa de los partici= pantes. Antes de la aplicaci=C3=B3n de la aplicaci=C3=B3n de la encuesta di= rigida a los microempresarios, se inform=C3=B3 a los participantes sobre lo= s objetivos del estudio y se garantiz=C3=B3 que su participaci=C3=B3n fuera= voluntaria. Asimismo, se protegi=C3=B3 la identidad de los informantes y s= e asegur=C3=B3 el uso exclusivo de los datos con fines acad=C3=A9micos. El = protocolo fue revisado y aprobado por el comit=C3=A9 cient=C3=ADfico de la = instituci=C3=B3n acad=C3=A9mica donde se desarroll=C3=B3 el programa de mae= str=C3=ADa. <h2 style=3D"margin-right:0pt; ma= rgin-left:17.75pt; margin-bottom:7.9pt; text-align:left; line-height:115%">= 3. Resultados </h2>Los resultados de l= a encuesta aplicada a 200 microempresarios muestran una adopci=C3=B3n digit= al b=C3=A1sica consolidada. El 74 % de los encuestados son mujeres, predomi= nando el sector comercio (57 %) y servicios (20.5 %). El 71.5 % utiliza de = manera constante herramientas digitales, mientras que un 73.5 % promociona = sus productos en redes sociales y el 61 % ya vende mediante plataformas de = comercio electr=C3=B3nico, pero en base a las 5 dimensiones abarcadas en el= levantamiento de informaci=C3=B3n, se identificaron barreras cr=C3=ADticas= en dos dimensiones abordadas que son, barreras t=C3=A9cnicas/ econ=C3=B3mi= cas, identificando como problem=C3=A1tica la falta de conocimiento (54%), d= ificultad de integrar la tecnolog=C3=ADa (45.5%), los costos de herramienta= s digitales (66%) y la falta de presupuestos <spa= n>para capacitaciones (89%), a su vez, la segunda dimensi=C3=B3n se relacio= na con el apoyo institucional e inventarios, identificando falencia bajas e= n la fomentaci=C3=B3n de capacitaciones digitales (39.5%), poco apoyo insti= tucional (32%), falencia de asesor=C3=ADas externas (8%), falta de particip= aci=C3=B3n en programas de digitalizaci=C3=B3n (22%) y la no inversi=C3=B3n= en tecnolog=C3=ADa (33%), estos datos, reflejan que la falta de compromiso= en procesos tecnol=C3=B3gicos es cr=C3=ADtica y se aleja en gran medida de= una estabilidad tecnol=C3=B3gica microempresaria, como se muestra en la </= span>Tabla 1. <h1 = style=3D"margin-right:0pt; margin-bottom:0pt; text-align:center; line-heigh= t:115%">Tabla 1</h1>Resumen de resultados relevan</span= >tes de la ado= pci=C3=B3n digital b=C3=A1sica en microempresas <table style=3D"width:100%; margin-left:0.5pt; mar= gin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr style=3D"height:= 27.95pt"><td style=3D"width:39.18%; border-top:0.75pt solid #000000; border= -bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Dimensiones = </td><td style=3D"width:41.48%; border-top:0.75pt solid #000000;= border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top"= >Ind= icador </td><td style=3D"width:19.34%; border-top:0.75pt solid #= 000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-ali= gn:top">Porcentaje (%) </td></tr><tr style=3D"height:26.15pt"><= td style=3D"width:39.1%; border-top:0.75pt solid #000000; border-bottom:0.7= 5pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Perfil y Adopci=C3=B3n= digital b=C3=A1sica </td><td style=3D"width:41.48%; border-top:= 0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt= ; vertical-align:top">Mujeres empresarias Uso de herramientas digitales <= /p></td><td style=3D"width:18.8%; border-top:0.75pt solid #000000; border-b= ottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">74 <= p style=3D"margin-right:3pt; margin-left:0pt; margin-bottom:0pt; text-inden= t:0pt; text-align:right; line-height:normal; font-size:10pt">71,5 </s= pan></td></tr><tr style=3D"height:13.8pt"><td style=3D"width:39.1%; bor= der-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0= pt 5.4pt; vertical-align:top"> </td><td style=3D"width:41.48%; bord= er-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0p= t 5.4pt; vertical-align:top">Promoci=C3=B3n en redes sociales </td><td s= tyle=3D"width:18.8%; border-top:0.75pt solid #000000; border-bottom:0.75pt = solid #000000; padding:0pt 5.4pt; vertical-align:top">73,5 </td></tr><t= r style=3D"height:13.8pt"><td style=3D"width:39.1%; border-top:0.75pt solid= #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-a= lign:top"><spa= n> </td><td style=3D"width:41.48%; border-top:0.75pt solid = #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-al= ign:top"><span= >Ventas por e-commerce </td><td style=3D"width:18.8%; border-top= :0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4p= t; vertical-align:top">61 </td></tr><tr style=3D"height:13.8pt"><td sty= le=3D"width:39.1%; border-top:0.75pt solid #000000; border-bottom:0.75pt so= lid #000000; padding:0pt 5.4pt; vertical-align:top"> </td><td style= =3D"width:41.48%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:0pt 5.4pt; vertical-align:top">Sector comercio </td>= <td style=3D"width:18.8%; border-top:0.75pt solid #000000; border-bottom:0.= 75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">57 </td></tr= ><tr style=3D"height:27.6pt"><td style=3D"width:39.1%; border-top:0.75pt so= lid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertica= l-align:top">Barreras T=C3=A9cnicas y econ=C3=B3micas </td><td style= =3D"width:41.48%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:0pt 5.4pt; vertical-align:top">Falta de conocimiento </p= >Dificul= tad para integrar tecnolog=C3=ADa </td><td style=3D"width:18.8%;= border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; paddi= ng:0pt 5.4pt; vertical-align:top">54 45,5 </td></tr><tr style=3D"he= ight:13.8pt"><td style=3D"width:39.1%; border-top:0.75pt solid #000000; bor= der-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top"> </spa= n></td><td style=3D"width:41.48%; border-top:0.75pt solid #000000; bord= er-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Costo de her= ramientas digitales </td><td style=3D"width:18.8%; border-top:0.= 75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; = vertical-align:top">66 </td></tr><tr style=3D"height:13.8pt"><td style= =3D"width:39.1%; border-top:0.75pt solid #000000; border-bottom:0.75pt soli= d #000000; padding:0pt 5.4pt; vertical-align:top"> </td><td style= =3D"width:41.48%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:0pt 5.4pt; vertical-align:top">Sin presupuesto para capacitaci= =C3=B3n </td><td style=3D"width:18.8%; border-top:0.75pt solid #= 000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-ali= gn:top"><span= >89 </td></tr><tr style=3D"height:13.8pt"><td style=3D"width:39.= 1%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pa= dding:0pt 5.4pt; vertical-align:top">Disposici=C3=B3n a la digitalizaci=C3=B3n </sp= an></td><td style=3D"width:41.48%; border-top:0.75pt solid #000000; bor= der-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Inter=C3=A9= s en herramientas digitales </td><td style=3D"width:18.8%; borde= r-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt= 5.4pt; vertical-align:top">93 </td></tr><tr style=3D"height:13.8pt"><t= d style=3D"width:39.1%; border-top:0.75pt solid #000000; border-bottom:0.75= pt solid #000000; padding:0pt 5.4pt; vertical-align:top"> </td><td = style=3D"width:41.48%; border-top:0.75pt solid #000000; border-bottom:0.75p= t solid #000000; padding:0pt 5.4pt; vertical-align:top">Adopci=C3=B3n con apoyo t= =C3=A9cnico </td><td style=3D"width:18.8%; border-top:0.75pt sol= id #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical= -align:top"><= span>90 </td></tr><tr style=3D"height:13.75pt"><td style=3D"widt= h:39.1%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #00000= 0; padding:0pt 5.4pt; vertical-align:top"> </td><td style=3D"width:= 41.48%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000= ; padding:0pt 5.4pt; vertical-align:top">Transformaci=C3=B3n es esencial </p= ></td><td style=3D"width:18.8%; border-top:0.75pt solid #000000; border-bot= tom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">91 <= /td></tr><tr style=3D"height:13.75pt"><td style=3D"width:39.1%; border-top:= 0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt= ; vertical-align:top"> </td><td style=3D"width:41.48%; border-top:0= .75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt;= vertical-align:top">Digitalizaci=C3=B3n como oportunidad </td><td style= =3D"width:18.8%; border-top:0.75pt solid #000000; border-bottom:0.75pt soli= d #000000; padding:0pt 5.4pt; vertical-align:top">94,5 </td></tr><tr st= yle=3D"height:13.8pt"><td style=3D"width:39.1%; border-top:0.75pt solid #00= 0000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align= :top">Fo= rmaci=C3=B3n y liderazgo digital </td><td style=3D"width:41.48%;= border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; paddi= ng:0pt 5.4pt; vertical-align:top">Tiene l=C3=ADder digital </td><td styl= e=3D"width:18.8%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:0pt 5.4pt; vertical-align:top">76 </td></tr><tr sty= le=3D"height:13.8pt"><td style=3D"width:39.1%; border-top:0.75pt solid #000= 000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:= top">&#x= a0;</td><td style=3D"width:41.48%; border-top:0.75pt solid #0000= 00; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:t= op">Toma= decisiones t=C3=A9cnicas </td><td style=3D"width:18.8%; border-= top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5= .4pt; vertical-align:top">60,5 </td></tr><tr style=3D"height:27.6pt"><t= d style=3D"width:39.1%; border-top:0.75pt solid #000000; border-bottom:0.75= pt solid #000000; padding:0pt 5.4pt; vertical-align:top"> </td><td = style=3D"width:41.48%; border-top:0.75pt solid #000000; border-bottom:0.75p= t solid #000000; padding:0pt 5.4pt; vertical-align:top">Sabe resolver problemas tec= nol=C3=B3gicos </td><td style=3D"width:18.8%; border-top:0.75pt = solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; verti= cal-align:top">71,5 </td></tr><tr style=3D"height:13.8pt"><td style=3D"= width:39.1%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0= 00000; padding:0pt 5.4pt; vertical-align:top"> </td><td style=3D"wi= dth:41.48%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #00= 0000; padding:0pt 5.4pt; vertical-align:top">Fomenta capacitaci=C3=B3n digital </sp= an></td><td style=3D"width:18.8%; border-top:0.75pt solid #000000; bord= er-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">39,5 </span= ></td></tr><tr style=3D"height:12.35pt"><td style=3D"width:39.1%; borde= r-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt= 5.4pt; vertical-align:top">Apoyo institucional= e inversi=C3=B3n </td><td style=3D"width:41.48%; border-top:0.7= 5pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; v= ertical-align:top">Percibe apoyo institucional = = 0; </spa= n></td><td style=3D"width:18.8%; border-top:0.75pt solid #000000; borde= r-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">32 </td></tr><tr style=3D"height:12.35pt= "><td style=3D"width:39.1%; border-top:0.75pt solid #000000; border-bottom:= 0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top"> </td>= <td style=3D"width:41.48%; border-top:0.75pt solid #000000; border-bottom:0= .75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Recibi=C3=B3 asesor=C3= =ADa externa </td><td style=3D"width:18.8%; border-top:0.75pt so= lid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertica= l-align:top">8 participan en programas de 22 digitalizaciones </td></t= r><tr style=3D"height:12.35pt"><td style=3D"width:39.1%; border-top:0.75pt = solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; verti= cal-align:top"> </td><td style=3D"width:41.48%; border-top:0.75pt s= olid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertic= al-align:top">= No invirti=C3=B3 en tecnolog=C3=ADa </td><td style=3D"widt= h:18.8%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #00000= 0; padding:0pt 5.4pt; vertical-align:top">33</td></tr></table>Nota: res= ultados de la encuesta a 200 microempresarios. <p= style=3D"margin-right:0pt; line-height:115%">Los resultados obtenido= s evidencian que la mayor=C3=ADa de las microempresas ecuatorianas avanzaro= n principalmente en la adopci=C3=B3n digital b=C3=A1sica, a trav=C3=A9s del= uso de redes sociales, mensajer=C3=ADa instant=C3=A1nea y plataformas de c= omercio electr=C3=B3nico. Esta tendencia coincide con lo se=C3=B1alado por = Banka et al (2024<sp= an>) y =C3=81lvarez<= span> (2025) quienes sostienen que dichas herramientas constituyen el punto= de partida m=C3=A1s accesible para la integraci=C3=B3n digital de las micr= o y peque=C3=B1as empresas en Am=C3=A9rica Latina. En este sentido, los dat= os del presente estudio confirman que, aunque las iniciativas de digitaliza= ci=C3=B3n a=C3=BAn son incipientes, estas contribuyen significativamente a = la ampliaci=C3=B3n de mercados, la optimizaci=C3=B3n de procesos comerciale= s y la mejora de la visibilidad empresarial. <img src=3D"data:image/png;base64,iVBORw0KGgoA= AAANSUhEUgAAAiAAAAABCAYAAADn5hWmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQ= BlSsOGwAAABtJREFUSIntwTEBAAAIwKDZP7S28AKm2gAAHh2F/QEBXUBBtwAAAABJRU5ErkJggg= =3D=3D" width=3D"544" height=3D"1" alt=3D"" />Sin embargo, persisten = barreras t=C3=A9cnicas y econ=C3=B3micas estructurales que condicionan el = =C3=A9xito de la transformaci=C3=B3n digital. M=C3=A1s del 50 % de los micr= oempresarios encuestados report=C3=B3 deficiencias en competencias digitale= s, mientras que un 66 % identific=C3=B3 los altos costos tecnol=C3=B3gicos = como el principal obst=C3=A1culo para adoptar soluciones digitales avanzada= s. Estos hallazgos son consistentes con los estudios de Ortiz-Choez et al (2024) quienes= enfatizan que la falta de formaci=C3=B3n tecnol=C3=B3gica y la limitada ca= pacidad financiera son los factores m=C3=A1s restrictivos en el proceso de = modernizaci=C3=B3n de las microempresas ecuatorianas. La coincidencia entre= estos resultados refuerza la necesidad de pol=C3=ADticas p=C3=BAblicas ori= entadas a fortalecer las capacidades digitales y el acceso a cr=C3=A9dito p= roductivo. <span= >Otro hallazgo relevante se relaciona con la actitud positiva hacia la digi= talizaci=C3=B3n, observada en m=C3=A1s del 90 % de los encuestados, quienes= manifestaron disposici=C3=B3n para capacitarse y adoptar nuevas herramient= as. Este resultado representa un activo intangible de alto valor, ya que la= disposici=C3=B3n al cambio es un elemento clave para la innovaci=C3=B3n or= ganizacional. En l=C3=ADnea con Tamayo (2025) la actitud proactiva de los emprendedores p= uede convertirse en un catalizador para la resiliencia empresarial, siempre= que se acompa=C3=B1e de formaci=C3=B3n y asesoramiento t=C3=A9cnico contex= tualizado. En el bloque de barreras t=C3=A9cnicas y econ=C3=B3m= icas, el 54 % reporta falta de conocimientos digitales, el 66 % se=C3=B1ala= el costo de la tecnolog=C3=ADa como obst=C3=A1culo y apenas un 11 % cuenta= con presupuesto para capacitaci=C3=B3n. Estos datos confirman que la falta= de capital humano especializado y los altos costos de implementaci=C3=B3n = y mantenimiento limitan la digitalizaci=C3=B3n como se muestra en la </span= >Tabla 2. <h1 styl= e=3D"margin-right:0pt; margin-bottom:0pt; text-align:center; line-height:11= 5%">Tabla 2</h1>Principales barreras t=C3=A9cnicas/ econ=C3=B3micas y apoyo institucio= nal e inversi=C3=B3n reportadas por microempresarios<table style= =3D"width:100%; margin-left:0.5pt; margin-bottom:0pt; padding:0pt; border-c= ollapse:collapse"><tr style=3D"height:14.25pt"><td style=3D"width:78.94%; b= order-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding= :1.45pt 5.75pt 0pt 0pt; vertical-align:top">Problemas identificados </td= ><td style=3D"width:21.06%; border-top:0.75pt solid #000000; border-bottom:= 0.75pt solid #000000; padding:1.45pt 5.75pt 0pt 0pt; vertical-align:top"><p= style=3D"margin-right:0pt; margin-left:0pt; margin-bottom:0pt; text-indent= :0pt; text-align:left; line-height:115%; font-size:10pt">Porcentaje% = </td></tr><tr style=3D"height:15.8pt"><td style=3D"width:78.94%;= border-top:0.75pt solid #000000; padding:1.45pt 5.75pt 0pt 0pt; vertical-a= lign:top"><spa= n>Falta de conocimientos en barreras t=C3=A9cnicas y econ=C3=B3micas </span= ></td><td style=3D"width:21.06%; border-top:0.75pt solid #000000; paddi= ng:1.45pt 5.75pt 0pt 0pt; vertical-align:top">54% </td></tr><tr style=3D"h= eight:14.3pt"><td style=3D"width:78.94%; padding:1.45pt 5.75pt 0pt 0pt; ver= tical-align:top">Dificultad para integrar nuevas tecnolog=C3=ADas </td><= td style=3D"width:21.06%; padding:1.45pt 5.75pt 0pt 0pt; vertical-align:top= ">45.5% </= span></td></tr><tr style=3D"height:14pt"><td style=3D"width:78.94%; pad= ding:1.45pt 5.75pt 0pt 0pt; vertical-align:top">Costos elevados = </td><td style=3D"width:21.06%; padding:1.45pt 5.75pt 0pt 0pt; v= ertical-align:top">66% </td></tr><tr style=3D"height:14pt"><td style=3D"wi= dth:78.94%; padding:1.45pt 5.75pt 0pt 0pt; vertical-align:top">D=C3=A9ficit presupu= estario para capacitaciones </td><td style=3D"width:21.06%; padd= ing:1.45pt 5.75pt 0pt 0pt; vertical-align:top">89% </td></tr><tr style=3D"= height:14pt"><td style=3D"width:78.94%; padding:1.45pt 5.75pt 0pt 0pt; vert= ical-align:top">Falta de capacitaciones digitales </td><td style=3D"widt= h:21.06%; padding:1.45pt 5.75pt 0pt 0pt; vertical-align:top">39,5% </td></= tr><tr style=3D"height:13.8pt"><td style=3D"width:78.94%; padding:1.45pt 5.= 75pt 0pt 0pt; vertical-align:top">D=C3=A9ficit de apoyo institucional&= #xa0; </td><td style=3D"width:21.06%; padding:1.45pt 5.75pt 0pt = 0pt; vertical-align:top">32% </td></tr><tr style=3D"height:13.8pt"><td sty= le=3D"width:78.94%; padding:1.45pt 5.75pt 0pt 0pt; vertical-align:top">Falencia de = asesor=C3=ADas externas </td><td style=3D"wid= th:21.06%; padding:1.45pt 5.75pt 0pt 0pt; vertical-align:top">8% </td></tr= ><tr style=3D"height:14.1pt"><td style=3D"width:78.94%; padding:1.45pt 5.75= pt 0pt 0pt; vertical-align:top">Falta de participaci=C3=B3n en programas de digital= izaci=C3=B3n </td><td style=3D"width:21.06%; padding:1.45pt 5.75= pt 0pt 0pt; vertical-align:top">22% </td></tr><tr style=3D"height:12.95pt"= ><td style=3D"width:78.94%; border-bottom:0.75pt solid #000000; padding:1.4= 5pt 5.75pt 0pt 0pt; vertical-align:top">No hay inversi=C3=B3n en tecnolog=C3=ADa </= span></td><td style=3D"width:21.06%; border-bottom:0.75pt solid #000000= ; padding:1.45pt 5.75pt 0pt 0pt; vertical-align:top">33% </td></tr></table= >Nota:</sp= an> base en los resultados de la encuesta a 200 microempresarios </sp= an> </spa= n>Los datos m= =C3=A1s cr=C3=ADticos muestran porcentajes altos, evidenciando una problem= =C3=A1tica latente, con respecto a la adopci=C3=B3n de la tecnolog=C3=ADa a= microempresarios, revelando que el 54% muestra una falta de conocimientos = en t=C3=A9rminos de barreras t=C3=A9cnicas y econ=C3=B3micas, el 45.5% pres= enta dificultadas para integrar tecnolog=C3=ADas en sus departamentos empre= sariales, 66% muestra que uno de los problemas m=C3=A1s cr=C3=ADticos se as= emeja a los elevados costos de implementaci=C3=B3n y adquisici=C3=B3n de te= cnolog=C3=ADa, 89% la falta de presupuesto para capacitaciones del personal= , 39.50% falta de capacitaci=C3=B3n digital, 32% el d=C3=A9ficit de apoyo i= nstitucional entre entidades, 8% a la falta de asesor=C3=ADas externas, 22%= falta de participaci=C3=B3n en programas de digitalizaci=C3=B3n y por =C3= =BAltimo el 33% no existen inversiones de tecnolog=C3=ADa en las microempre= sas, tambi=C3=A9n se id=C3=A9ntica que m=C3=A1s de la mitad invirti=C3=B3 m= enos de $100 en procesos de digitalizaci=C3=B3n, lo que refleja una descone= xi=C3=B3n significativa con programas p=C3=BAblicos o privados de apoyo. Es= tos puntos, son claves para la optimizaci=C3=B3n de procesos y sobre todo l= a incorporaci=C3=B3n y adopci=C3=B3n de nuevos inversionistas, para mantene= r la calidad de los procesos. 3.1. Transformaci=C3=B3n digital en eficiencia operativa y competitividad= En t=C3=A9rminos de transformaci=C3=B3n digital se puede consi= derar como un motor que redise=C3=B1a la eficiencia operativa y posicionami= ento de manera estrat=C3=A9gica en las diferentes organizaciones modernas, = por ello en la Tabla 3= se desglosan las distintas dimensiones claves de estos procesos que se vin= culan estrictamente en la adopci=C3=B3n de herramientas tecnol=C3=B3gicas q= ue mantienen resultados tangibles en la productividad y gesti=C3=B3n de los= recursos, partiendo de un an=C3=A1lisis exhaustivo de factores como la aut= omatizaci=C3=B3n de la contabilidad y digitalizaci=C3=B3n de los servicios = al cliente, demostr=C3=A1ndose una disminuci=C3=B3n dr=C3=A1stica en los er= rores manuales o p=C3=A9rdidas de documentos, lo cual, hace que se optimice= n los tiempos de respuesta en la operatividad y atenci=C3=B3n. Estas funcio= nes convergen a una alta competitividad empresarial, que se constituye como= una relaci=C3=B3n de confianza y fidelidad en el mercado, a su vez, mantie= ne una innovaci=C3=B3n constante y precautela la seguridad de la informaci= =C3=B3n de la empresa.<h1 style=3D"margin-right:0pt; margin-bott= om:0pt; text-align:center; line-height:115%">Tabla 3</h1>Operatividad y competitividad</s= pan><table style=3D"width:100.08%; margin-right:auto; margin-left:0.5pt= ; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr style=3D"he= ight:25.7pt"><td style=3D"width:24.18%; border-top:0.75pt solid #000000; bo= rder-bottom:0.75pt solid #000000; padding:1.3pt 4.15pt 0pt 0pt; vertical-al= ign:top"><sp= an>Caracter=C3=ADsticas</td><td style=3D"width:20.5%; border-top= :0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:1.3pt 4.= 15pt 0pt 0pt; vertical-align:top">Causa</td><td colspan=3D"2" style=3D"b= order-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding= :1.3pt 4.15pt 0pt 0pt; vertical-align:top">Impacto operativo</td><td col= span=3D"2" style=3D"border-top:0.75pt solid #000000; border-bottom:0.75pt s= olid #000000; padding:1.3pt 4.15pt 0pt 0pt; vertical-align:top">Competitividad empr= esarial</td><td style=3D"padding:0pt; vertical-align:top"></td><= /tr><tr style=3D"height:64.85pt"><td style=3D"width:24.18%; border-top:0.75= pt solid #000000; border-bottom:0.75pt solid #000000; padding:1.3pt 4.15pt = 0pt 0pt; vertical-align:top">Automatizaci=C3=B3n de procesos. </td><td s= tyle=3D"width:20.5%; border-top:0.75pt solid #000000; border-bottom:0.75pt = solid #000000; padding:1.3pt 4.15pt 0pt 0pt; vertical-align:top">Integraci=C3=B3n d= e software contables. </td><td colspan=3D"2" style=3D"border-top= :0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:1.3pt 4.= 15pt 0pt 0pt; vertical-align:top">Optimiza costos y tiempo operativo, agilizando en= un <= span>70% los procesos administrativos y errores manuales. </td><= td colspan=3D"2" style=3D"border-top:0.75pt solid #000000; border-bottom:0.= 75pt solid #000000; padding:1.3pt 4.15pt 0pt 0pt; vertical-align:top">Mejora la image= n ante proveedores y clientes mejorando los servicios financieros y de aten= ci=C3=B3n. </td><td style=3D"padding:0pt; vertical-align:top"></= td></tr><tr style=3D"height:40.5pt"><td style=3D"width:24.18%; border-top:0= .75pt solid #000000; border-bottom:0.75pt solid #000000; padding:1.3pt 4.15= pt 0pt 0pt; vertical-align:top">Contacto de clientes y gesti=C3=B3n. </t= d><td style=3D"width:20.5%; border-top:0.75pt solid #000000; border-bottom:= 0.75pt solid #000000; padding:1.3pt 4.15pt 0pt 0pt; vertical-align:top">Redes socia= les, WhatsApp Business, p=C3=A1ginas web. </td><td colspan=3D"2"= style=3D"border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0000= 00; padding:1.3pt 4.15pt 0pt 0pt; vertical-align:top">Mejora el tiempo de respuesta m= ejorando la trazabilidad de pedidos con chat autom=C3=A1ticos. <= /td><td colspan=3D"2" style=3D"border-top:0.75pt solid #000000; border-bott= om:0.75pt solid #000000; padding:1.3pt 4.15pt 0pt 0pt; vertical-align:top">= Fidelizaci= =C3=B3n y valor de la marca, fomentando la retenci=C3=B3n de clientes. </sp= an></td><td style=3D"padding:0pt; vertical-align:top"></td></tr><tr sty= le=3D"height:64.8pt"><td style=3D"width:24.18%; border-top:0.75pt solid #00= 0000; border-bottom:0.75pt solid #000000; padding:1.3pt 4.15pt 0pt 0pt; ver= tical-align:top">Visibilidad de marca. </td><td style=3D"width:20.5%; bo= rder-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:= 1.3pt 4.15pt 0pt 0pt; vertical-align:top">Marketing </td><td colspan=3D"2"= style=3D"border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0000= 00; padding:1.3pt 4.15pt 0pt 0pt; vertical-align:top">Reduce la dependencia de loca= l f=C3=ADsico y ayuda al posicionamiento y reconocimiento del local a nivel= nacional. </td><td colspan=3D"2" style=3D"border-top:0.75pt sol= id #000000; border-bottom:0.75pt solid #000000; padding:1.3pt 4.15pt 0pt 0p= t; vertical-align:top">Expansi=C3=B3n de mercado. </td><td style=3D"border= -bottom:0.75pt solid #000000; padding:0pt; vertical-align:top"></td></tr><t= r style=3D"height:36.6pt"><td style=3D"width:24.18%; border-top:0.75pt soli= d #000000; border-bottom:0.75pt solid #000000; padding:0pt; vertical-align:= top">Cultu= ra y talento </td><td colspan=3D"2" style=3D"border-top:0.75pt s= olid #000000; border-bottom:0.75pt solid #000000; padding:0pt; vertical-ali= gn:top">Ca= pacitaci=C3=B3n digital y liderazgo al experimentar nuevas herramientas. </= span></td><td colspan=3D"2" style=3D"border-top:0.75pt solid #000000; b= order-bottom:0.75pt solid #000000; padding:0pt; vertical-align:top">Mejora el desempe= =C3=B1o laboral </td><td colspan=3D"2" style=3D"border-top:0.75p= t solid #000000; border-bottom:0.75pt solid #000000; padding:0pt; vertical-= align:top"><s= pan>Intensiva la innovaci=C3=B3n de manera sostenible. </td></tr= ><tr style=3D"height:36.7pt"><td style=3D"width:24.18%; border-top:0.75pt s= olid #000000; border-bottom:0.75pt solid #000000; padding:0pt; vertical-ali= gn:top">Ge= sti=C3=B3n de riesgo </td><td colspan=3D"2" style=3D"border-top:= 0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt; vert= ical-align:top">= Integrar sistemas de Back-up en la nube. </td><td colspan= =3D"2" style=3D"border-top:0.75pt solid #000000; border-bottom:0.75pt solid= #000000; padding:0pt; vertical-align:top">Asegura la continuidad operacional y re= duce el riesgo de p=C3=A9rdida de informaci=C3=B3n. </td><td col= span=3D"2" style=3D"border-top:0.75pt solid #000000; border-bottom:0.75pt s= olid #000000; padding:0pt; vertical-align:top">Mejora la confianza de los cliente= s e intensifica la credibilidad con la microempresa al mantener est=C3=A1nd= ares de seguridad digital.</td></tr><tr style=3D"height:0pt"><td= style=3D"width:106.3pt"></td><td style=3D"width:90.15pt"></td><td style=3D= "width:1.95pt"></td><td style=3D"width:128.75pt"></td><td style=3D"width:2.= 65pt"></td><td style=3D"width:109.55pt"></td><td style=3D"width:0.35pt"></t= d></tr></table>Nota: tabla operativa y competitividad= en base al levantamiento de informaci=C3=B3n, an=C3=A1lisis y observaci=C3= =B3n de procesos en microempresas. 3.2. Pro= puesta E</s= pan>n contraste a la propuesta reflejada en la Tabla 4 se hace =C3=A9nfasis a una ruta estr= at=C3=A9gica con el prop=C3=B3sito de mitigar brechas tecnol=C3=B3gicas en = los sectores microempresariales, en base a soluciones pr=C3=A1cticas frente= a los desaf=C3=ADos identificados y a un mundo altamente tecnol=C3=B3gico = en la actualidad, por lo cual, la propuesta cuenta con una estructura de oc= ho dimensiones cr=C3=ADticas que abarcan desde las herramientas de bajo cos= to para empezar y que precauteles fortalecimiento del ecosistema institucio= nal como tambi=C3=A9n las acciones a abordar con el uso de software Bajo su= scripci=C3=B3n como lo es (SaaS), programas de vinculaci=C3=B3n acad=C3=A9m= ica y por ende las capacitaciones microLearning que comprometen la estabili= dad financiera de las microempresas, que permita el =C3=A9xito en base a un= a tecnolog=C3=ADa digital y expansi=C3=B3n del negocio.<h1 style= =3D"margin-right:0pt; margin-bottom:0pt; text-align:center; line-height:115= %"> </h1><h1 style=3D"margin-right:0pt; margin-bottom:0pt= ; text-align:center; line-height:115%"> </h1><h1 style=3D= "margin-right:0pt; margin-bottom:0pt; text-align:center; line-height:115%">= </h1><h1 style=3D"margin-right:0pt; margin-bottom:0pt; t= ext-align:center; line-height:115%">Tabla 4</h1>Propuesta estrat=C3=A9gica de implemen= taci=C3=B3n para microempresarios sobre aspectos tecnol=C3=B3gicos</= p><table style=3D"width:98.7%; margin-left:0.5pt; margin-bottom:0pt; paddin= g:0pt; border-collapse:collapse"><tr style=3D"height:13.2pt"><td style=3D"w= idth:17.42%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0= 00000; padding:2.8pt 6.7pt 0pt 0pt; vertical-align:top">Dimensi=C3=B3n </t= d><td style=3D"width:26.56%; border-top:0.75pt solid #000000; border-bottom= :0.75pt solid #000000; padding:2.8pt 6.7pt 0pt 0pt; vertical-align:top">Estrategia </= span></td><td colspan=3D"2" style=3D"border-top:0.75pt solid #000000; b= order-bottom:0.75pt solid #000000; padding:2.8pt 6.7pt 0pt 0pt; vertical-al= ign:top">P= ropuesta </td><td colspan=3D"3" style=3D"border-top:0.75pt solid= #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 6.7pt 0pt 0pt; = vertical-align:top">Acciones Claves </td></tr><tr style=3D"height:149.35pt= "><td style=3D"width:17.42%; border-top:0.75pt solid #000000; border-bottom= :0.75pt solid #000000; padding:2.8pt 6.7pt 0pt 0pt; vertical-align:top">1. Adopci= =C3=B3n tecnol=C3=B3gica. </td><td style=3D"width:26.56%;= border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; paddi= ng:2.8pt 6.7pt 0pt 0pt; vertical-align:top">Adopci=C3=B3n de tecnol=C3=B3gica prog= resiva, enfocado en soluciones digitales accesibles (facturaci=C3=B3n elect= r=C3=B3nica, POS, plataformas de ventas y gesti=C3=B3n) de manera gratuita = o a bajo costo. </td><td colspan=3D"2" style=3D"border-top:0.75p= t solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 6.7pt 0p= t 0pt; vertical-align:top">Kit digital de inicio r=C3=A1pido </spa= n></td><td colspan=3D"3" style=3D"border-top:0.75pt solid #000000; bord= er-bottom:0.75pt solid #000000; padding:2.8pt 6.7pt 0pt 0pt; vertical-align= :bottom"><s= pan>=E2=80=A2</spa= n> = Implementar atenci=C3=B3n automatizada a nuevos clientes, que est=C3=A9n pr= e configuradas en herramientas potenciales como WhatsApp businees y chatbot= . =E2=80=A2 &#= xa0; Adoptar la factura electr=C3=B3nica por medio de he= rramientas gratuitas o de bajo costo. =E2=80=A2 = Uso de la nube con el fin de guardar = y gestionar documentaci=C3=B3n colaborativa. </td></tr><t= r style=3D"height:128.05pt"><td style=3D"width:17.42%; border-top:0.75pt so= lid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 6.7pt 0pt 0p= t; vertical-align:top">2. Barreras t=C3=A9cnicas. </td><td style=3D= "width:26.56%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid = #000000; padding:2.8pt 6.7pt 0pt 0pt; vertical-align:top">Consolidar un programa in= tegral de asistencia t=C3=A9cnica y soporte post implementaci=C3=B3n, que g= arantice la funcionalidad y sostenibilidad de las soluciones digitales adop= tadas. </td><td colspan=3D"2" style=3D"border-top:0.75pt = solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 6.7pt 0pt = 0pt; vertical-align:top">Programa =E2=80=9Cdigital de vinculaci=C3=B3n de j=C3=B3= venes=E2=80=9D </td><td colspan=3D"3" style=3D"border-top:0.75pt= solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 6.7pt 0pt= 0pt; vertical-align:bottom">= =E2=80=A2 = ; Recibir capacitaciones para que incentive el desarroll= o del negocio con menci=C3=B3n en tecnolog=C3=ADa y optimizaci=C3=B3n de pr= ocesos. =E2=80=A2</sp= an> </spa= n>Participar en programas universitarios que le de valor al negocio e= integre la intervenci=C3=B3n de universitarios en tecnolog=C3=ADa. = = ;</td></tr><tr style=3D"height:95.45pt"><td style=3D"width:17.42= %; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pad= ding:2.8pt 6.7pt 0pt 0pt; vertical-align:top">3. Barreras econ=C3=B3micas. <= /p> <= /span></td><td style=3D"width:26.56%; border-top:0.75pt solid #000000; = border-bottom:0.75pt solid #000000; padding:2.8pt 6.7pt 0pt 0pt; vertical-a= lign:top"><spa= n>Dise=C3=B1ar mecanismos de financiamiento adaptados al perfil microempres= arial, combinando cr=C3=A9dito digital, cofinanciamiento p=C3=BAblico-priva= do y leasing tecnol=C3=B3gico. </td><td colspan=3D"2" style=3D"b= order-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding= :2.8pt 6.7pt 0pt 0pt; vertical-align:top">Crear un modelo de suscripci=C3=B3n y econo= m=C3=ADa colaborativa. </td><td colspan=3D"3" style=3D"border-to= p:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 6= .7pt 0pt 0pt; vertical-align:bottom">=E2=80=A2 &#x= a0; Uso de software como servicios (SaaS) con pago= s mensuales que var=C3=ADan entre $15 y$20, para no comprar licencias caras= . = =E2=80=A2</s= pan> <spa= n>Gestionar fondos para proyectos de innovaci=C3=B3n tecnol=C3=B3gica. </sp= an>&#x= a0;</td></tr><tr style=3D"height:76.35pt"><td style=3D"width:17.= 42%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; p= adding:0pt; vertical-align:top">4. Actitud hacia la digitalizaci=C3=B3n. <= /p> <= /span></td><td colspan=3D"2" style=3D"border-top:0.75pt solid #000000; = border-bottom:0.75pt solid #000000; padding:0pt; vertical-align:top">Consolidar = una cultura digital empresarial, basada en liderazgo adaptativo, gesti=C3= =B3n del cambio y formaci=C3=B3n motivacional en innovaci=C3=B3n. </= p> </= span></td><td style=3D"width:18.58%; border-top:0.75pt solid #000000; b= order-bottom:0.75pt solid #000000; padding:0pt; vertical-align:top">Taller de Digital= izaci=C3=B3n tecnol=C3=B3gica </td><td style=3D"width:0.86%; bor= der-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0= pt; vertical-align:top">=E2=80=A2 =E2=80=A2 </td><td style=3D"width:3= 1.76%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000;= padding:0pt; vertical-align:top">Acudir a campa=C3=B1as de sensibilizaci=C3=B3n so= bre beneficios digitales. Crear comunidades de pr=C3=A1ctica y aprendizaje= entre microempresarios.=E2=80=A2Desarrollar programas de liderazgo digita= l con enfoque motivacional. </td><td style=3D"border-top:= 0.75pt solid #000000; padding:0pt; vertical-align:top"></td></tr><tr style= =3D"height:77.55pt"><td style=3D"width:17.42%; border-top:0.75pt solid #000= 000; border-bottom:0.75pt solid #000000; padding:0pt; vertical-align:top"><= p style=3D"margin-right:0pt; margin-left:0pt; margin-bottom:0pt; text-inden= t:0pt; text-align:left; line-height:115%; font-size:10pt">5. Capacida= des organizacionales/ Talento humano. </td><td colspan=3D= "2" style=3D"border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0= 00000; padding:0pt; vertical-align:top">Implementar un modelo de formaci=C3=B3n m= odular y certificaci=C3=B3n en competencias digitales para propietarios y p= ersonal operativo. </td><td style=3D"width:18.58%; border-top:0.= 75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt; vertic= al-align:top"><s= pan>Incorporar el MicroLearning </td><td style=3D"width:0.86%= ; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padd= ing:0pt; vertical-align:top">=E2=80=A2 =E2=80=A2 </td><td style=3D"wi= dth:31.76%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #00= 0000; padding:0pt; vertical-align:top">Tomar m=C3=B3dulos de capacitaci=C3=B3n en T= IC, marketing digital y ciberseguridad. Contratar personal especializado e= n el uso de herramientas tecnol=C3=B3gicas. </td><td style=3D"pa= dding:0pt; vertical-align:top"></td></tr><tr style=3D"height:63.25pt"><td s= tyle=3D"width:17.42%; border-top:0.75pt solid #000000; border-bottom:0.75pt= solid #000000; padding:0pt; vertical-align:top">6. Mejora de procesos. </= td><td colspan=3D"2" style=3D"border-top:0.75pt solid #000000; border-botto= m:0.75pt solid #000000; padding:0pt; vertical-align:top">Promover la automatizaci= =C3=B3n y digitalizaci=C3=B3n de los procesos internos, fortaleciendo la ef= iciencia administrativa y la gesti=C3=B3n de clientes. </td><td = style=3D"width:18.58%; border-top:0.75pt solid #000000; border-bottom:0.75p= t solid #000000; padding:0pt; vertical-align:top">Digitalizaci=C3=B3n de puntos cr= =C3=ADticos </td><td style=3D"width:0.86%; bo= rder-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:= 0pt; vertical-align:top">=E2=80=A2 </td><td style=3D"width:31.76= %; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pad= ding:0pt; vertical-align:top">Incentivar la adopci=C3=B3n de ERP y CRM adaptados al s= egmento micro. </td><td style=3D"padding:0pt; vertical-align:top= "></td></tr><tr style=3D"height:90.35pt"><td style=3D"width:17.42%; border-= top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt; = vertical-align:top">7. Ventaja competitiva. </td><td colspan=3D"2" style= =3D"border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pa= dding:0pt; vertical-align:top">Desarrollar un modelo de posicionamiento digital y c= omercializaci=C3=B3n online que incremente la competitividad y la proyecci= =C3=B3n de marca. </td><td style=3D"width:18.58%; border-top:0.7= 5pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt; vertica= l-align:top"><= span>Estrategia de geolocalizaci=C3=B3n n comercial entre microempresarios = </td><td style=3D"width:0.86%; border-top:0.75pt solid #000000; = border-bottom:0.75pt solid #000000; padding:0pt; vertical-align:top">=E2=80=A2 </s= pan>= =E2=80=A2 </td><td style=3D"width:31.76%; border-top:0.75pt soli= d #000000; border-bottom:0.75pt solid #000000; padding:0pt; vertical-align:= top">Cre= ar estrategias de marketing digital personalizadas para la microempresa, qu= e ayude a expandirse. Promover la inserci=C3=B3n en plataformas de e-comme= rce y marketplaces. =E2=80=A2 Implementar la geolocalizaci=C3=B3n en redes= o p=C3=A1ginas web del negocio.</td><td style=3D"padding:0pt; v= ertical-align:top"></td></tr><tr style=3D"height:89.6pt"><td style=3D"width= :17.42%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #00000= 0; padding:0pt; vertical-align:top">8. Apoyo institucional / Ecosistema <p= style=3D"margin-right:0pt; margin-left:0pt; margin-bottom:0pt; text-indent= :0pt; text-align:left; line-height:115%; font-size:10pt">digital. </s= pan></td><td colspan=3D"2" style=3D"border-top:0.75pt solid #000000; bo= rder-bottom:0.75pt solid #000000; padding:0pt; vertical-align:top">Fortalecer el ec= osistema interinstitucional de apoyo a la digitalizaci=C3=B3n, articulando = esfuerzos entre Estado, academia y sector privado. </td><= td style=3D"width:18.58%; border-top:0.75pt solid #000000; border-bottom:0.= 75pt solid #000000; padding:0pt; vertical-align:top">Red de alianza local = </td><td style=3D"width:0.86%; border-top:0.75pt solid #000000; border-bott= om:0.75pt solid #000000; padding:0pt; vertical-align:top">=E2=80=A2 </td><= td style=3D"width:31.76%; border-top:0.75pt solid #000000; border-bottom:0.= 75pt solid #000000; padding:0pt; vertical-align:top">Establecer convenios universitar= ios de Transformaci=C3=B3n digital para MIPYMES que ayuden a autocapacitars= e y obtener nociones estrat=C3=A9gicas por parte de la nueva generaci=C3=B3= n profesional. =E2=80=A2Participar en proyectos de innovacion con financia= miento compatido entre microempresarios</td><td style=3D"padding= :0pt; vertical-align:top"></td></tr><tr style=3D"height:0pt"><td style=3D"w= idth:75.5pt"></td><td style=3D"width:115.2pt"></td><td style=3D"width:19.5p= t"></td><td style=3D"width:80.55pt"></td><td style=3D"width:3.75pt"></td><t= d style=3D"width:137.75pt"></td><td style=3D"width:1.4pt"></td></tr></table= >Nota: </= span>en base a los datos obtenidos en la Tabla 2 El an=C3=A1lisis integrado de las encuestas y la validac= i=C3=B3n experta muestra que las microempresas ecuatorianas poseen un poten= cial significativo de transformaci=C3=B3n digital, sustentado en su alta di= sposici=C3=B3n al cambio (5.0) y fuerte adopci=C3=B3n tecnol=C3=B3gica inic= ial (4.6). <h2 style=3D"margin-right:0pt; line-height:115%"><spa= n style=3D"font-weight:normal; font-style:italic">3.3. Validaci=C3=B3n por expertos= </h2>La propuesta metodol=C3=B3gica fue evaluada por un panel de c= inco especialistas con trayectoria en transformaci=C3=B3n digital, gesti=C3= =B3n de microempresas y pol=C3=ADticas p=C3=BAblicas, seleccionados con bas= e en formaci=C3=B3n de cuarto nivel y experiencia profesional comprobada. <= /span>La valor= aci=C3=B3n se efectu=C3=B3 mediante una escala ordinal del 1 al 5, donde: <= /span>1 =3D De= ficiente, 2 =3D Regular, 3 =3D Bueno, 4 =3D Muy Bueno, 5 =3D Excelente. </s= pan>Se evaluaron cuatro criterios: pertinencia, claridad, coherencia y = relevancia como se detalla en la Ta= bla 5. <h1 style=3D"margin-right:0pt; margin-bottom= :0pt; text-align:center; line-height:115%">Tabla 5</h1>Escala de valoraci=C3=B3n y cri= terios de evaluaci=C3=B3n<table style=3D"width:100%; margin-left= :0.5pt; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr style= =3D"height:13.1pt"><td style=3D"width:10.02%; border-top:0.75pt solid #0000= 00; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.3pt; ver= tical-align:top">Valor </td><td style=3D"width:26.88%; border-top:0.75pt= solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0p= t 5.3pt; vertical-align:top">Criterio cualitativo </td><td style=3D"widt= h:63.1%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #00000= 0; padding:2.8pt 2.65pt 0pt 5.3pt; vertical-align:top">Descripci=C3=B3n </= td></tr><tr style=3D"height:13.2pt"><td style=3D"width:10.02%; border-top:0= .75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.3pt; vertical-align:top"><p= style=3D"margin-right:2.65pt; margin-left:0pt; margin-bottom:0pt; text-ind= ent:0pt; text-align:center; line-height:115%; font-size:10pt">1 </spa= n></td><td style=3D"width:26.88%; border-top:0.75pt solid #000000; padd= ing:2.8pt 2.65pt 0pt 5.3pt; vertical-align:top">Deficiente </td><td styl= e=3D"width:63.1%; border-top:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt= 5.3pt; vertical-align:top">El indicador no cumple con el criterio propuesto. = </td></tr><tr style=3D"height:13.2pt"><td style=3D"width:10.02%; paddin= g:2.8pt 2.65pt 0pt 5.3pt; vertical-align:top">2 </td><td style=3D"wid= th:26.88%; padding:2.8pt 2.65pt 0pt 5.3pt; vertical-align:top">Regular <= /td><td style=3D"width:63.1%; padding:2.8pt 2.65pt 0pt 5.3pt; vertical-alig= n:top">Cum= ple parcialmente; requiere ajustes importantes. </td></tr><tr st= yle=3D"height:13.1pt"><td style=3D"width:10.02%; padding:2.8pt 2.65pt 0pt 5= .3pt; vertical-align:top">3 </td><td style=3D"width:26.88%; padding:2= .8pt 2.65pt 0pt 5.3pt; vertical-align:top">Bueno </td><td style=3D"width= :63.1%; padding:2.8pt 2.65pt 0pt 5.3pt; vertical-align:top">Cumple adecuadamente; adm= ite mejoras menores. </td></tr><tr style=3D"height:13.2pt"><td s= tyle=3D"width:10.02%; padding:2.8pt 2.65pt 0pt 5.3pt; vertical-align:top"><= p style=3D"margin-right:2.65pt; margin-left:0pt; margin-bottom:0pt; text-in= dent:0pt; text-align:center; line-height:115%; font-size:10pt">4 </sp= an></td><td style=3D"width:26.88%; padding:2.8pt 2.65pt 0pt 5.3pt; vert= ical-align:top">Muy Bueno </td><td style=3D"width:63.1%; padding:2.8pt 2= .65pt 0pt 5.3pt; vertical-align:top">Cumple adecuadamente </td></tr><tr st= yle=3D"height:13.1pt"><td style=3D"width:10.02%; border-bottom:0.75pt solid= #000000; padding:2.8pt 2.65pt 0pt 5.3pt; vertical-align:top">5 </td>= <td style=3D"width:26.88%; border-bottom:0.75pt solid #000000; padding:2.8p= t 2.65pt 0pt 5.3pt; vertical-align:top">Excelente </td><td style=3D"widt= h:63.1%; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.3pt= ; vertical-align:top">Cumple plenamente con el criterio evaluado. </td></t= r></table>Fu= ente: Ocampo</= span> (2023). La validaci=C3=B3n de la propuesta por parte de cinco expertos ap= ort=C3=B3 solidez y fiabilidad a los resultados emp=C3=ADricos. Las valorac= iones m=C3=A1s altas se concentraron en las dimensiones de adopci=C3=B3n te= cnol=C3=B3gica (4.6/5), actitud hacia la digitalizaci=C3=B3n (5.0/5) y vent= aja competitiva (4.6/5), lo que demuestra un consenso t=C3=A9cnico sobre el= potencial transformador de la digitalizaci=C3=B3n para incrementar la efic= iencia operativa y la competitividad empresarial. Estas coincidencias reafi= rman lo planteado por Priet= o & Loredo (2025) y Brito et al. (2023) quienes argumentan que la incorp= oraci=C3=B3n estrat=C3=A9gica de tecnolog=C3=ADas digitales incrementa la a= daptabilidad organizacional y la sostenibilidad econ=C3=B3mica. = En contraste, las val= oraciones m=C3=A1s bajas correspondieron a las dimensiones barreras econ=C3= =B3micas (3.6/5) y apoyo institucional / ecosistema digital (3.2/5), eviden= ciando la debilidad del entorno estructural en el que operan las microempre= sas. Esta situaci=C3=B3n se alinea con Espejo et al. (2023) quienes destacan que la limit= ada articulaci=C3=B3n de las pol=C3=ADticas de innovaci=C3=B3n con las nece= sidades reales del sector productivo constituye uno de los principales desa= f=C3=ADos de la digitalizaci=C3=B3n en Am=C3=A9rica Latina. La falta de aco= mpa=C3=B1amiento institucional y la escasez de incentivos econ=C3=B3micos c= ontin=C3=BAan restringiendo el alcance de los procesos de transformaci=C3= =B3n digital en el pa=C3=ADs como se detalla en la Tabla 6. Tabla 6Matriz consolidada de validac= i=C3=B3n por dimensiones<table style=3D"width:93.86%; margin-lef= t:0.5pt; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr styl= e=3D"height:38.9pt"><td style=3D"width:22.78%; border-top:0.75pt solid #000= 000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; ve= rtical-align:top">Dimensi=C3=B3n/Criterio </td><td style=3D"width:14.04%; b= order-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding= :2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Pertinencia </td><td style=3D"= width:11.5%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0= 00000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Claridad </td><t= d style=3D"width:14.08%; border-top:0.75pt solid #000000; border-bottom:0.7= 5pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Coherencia </sp= an></td><td style=3D"width:13.52%; border-top:0.75pt solid #000000; bor= der-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-a= lign:top">R= elevancia </td><td style=3D"width:11.9%; border-top:0.75pt solid= #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4p= t; vertical-align:top">Promedio total (1=E2=80=935) </td><td style=3D"wid= th:12.2%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0000= 00; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Nivel </td></t= r><tr style=3D"height:25.8pt"><td style=3D"width:22.78%; border-top:0.75pt = solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt= 5.4pt; vertical-align:top">Adopci=C3=B3n tecnol=C3=B3gica </td><td style= =3D"width:14.04%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">4.0 </= td><td style=3D"width:11.5%; border-top:0.75pt solid #000000; border-bottom= :0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">= 4.8 </= span></td><td style=3D"width:14.08%; border-top:0.75pt solid #000000; b= order-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical= -align:top"><sp= an>4.4 </td><td style=3D"width:13.52%; border-top:0.75pt solid #= 000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt;= vertical-align:top">4.8 </td><td style=3D"width:11.9%; border-top:0.75= pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt = 0pt 5.4pt; vertical-align:top">4.6 </td><td style=3D"width:12.2%; borde= r-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8= pt 2.65pt 0pt 5.4pt; vertical-align:top">Muy alto </td></tr><tr style=3D"= height:13.1pt"><td style=3D"width:22.78%; border-top:0.75pt solid #000000; = border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertica= l-align:top"><spa= n>Barreras t=C3=A9cnicas </td><td style=3D"width:14.04%; border-= top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt= 2.65pt 0pt 5.4pt; vertical-align:top">3.6 </td><td style=3D"width:11.= 5%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pa= dding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">3.6 </td><td style= =3D"width:14.08%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">3.6 </td>= <td style=3D"width:13.52%; border-top:0.75pt solid #000000; border-bottom:0= .75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top"><p= style=3D"margin-right:2.9pt; margin-left:0pt; margin-bottom:0pt; text-inde= nt:0pt; text-align:center; line-height:115%; font-size:9pt">3.8 </spa= n></td><td style=3D"width:11.9%; border-top:0.75pt solid #000000; borde= r-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-ali= gn:top"><span= >4.2 </td><td style=3D"width:12.2%; border-top:0.75pt solid #000= 000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; ve= rtical-align:top">Alto </td></tr><tr style=3D"height:25.7pt"><td style= =3D"width:22.78%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Barreras econ=C3=B3mic= as </td><td style=3D"width:14.04%; border-top:0.75pt solid #0000= 00; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; ver= tical-align:top">3.4 </td><td style=3D"width:11.5%; border-top:0.75pt = solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt= 5.4pt; vertical-align:top">3.6 </td><td style=3D"width:14.08%; border= -top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8p= t 2.65pt 0pt 5.4pt; vertical-align:top">3.6 </td><td style=3D"width:13.52= %; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pad= ding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">3.6 </td><td style=3D"= width:11.9%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0= 00000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">3.6 </td><td= style=3D"width:12.2%; border-top:0.75pt solid #000000; border-bottom:0.75p= t solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Alto = </td></tr><tr style=3D"height:25.8pt"><td style=3D"width:22.78%; border= -top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8p= t 2.65pt 0pt 5.4pt; vertical-align:top">Actitud hacia la digitalizaci=C3=B3n </= p></td><td style=3D"width:14.04%; border-top:0.75pt solid #000000; border-b= ottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:= top">4= .8 </td><td style=3D"width:11.5%; border-top:0.75pt solid #00000= 0; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vert= ical-align:top">5.0 </td><td style=3D"width:14.08%; border-top:0.75pt = solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt= 5.4pt; vertical-align:top">5.0 </td><td style=3D"width:13.52%; border-to= p:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2= .65pt 0pt 5.4pt; vertical-align:top">5.0 </td><td style=3D"width:11.9%;= border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; paddi= ng:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">5.0 </td><td style=3D"wi= dth:12.2%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000= 000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Muy alto </td></= tr><tr style=3D"height:38.9pt"><td style=3D"width:22.78%; border-top:0.75pt= solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0p= t 5.4pt; vertical-align:top">Capacidades organizacionales / Talento humano = </td><td style=3D"width:14.04%; border-top:0.75pt solid #000000; border-bot= tom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:to= p">4.2= </td><td style=3D"width:11.5%; border-top:0.75pt solid #000000;= border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertic= al-align:top">4.2 </td><td style=3D"width:14.08%; border-top:0.75pt so= lid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5= .4pt; vertical-align:top">4.2 </td><td style=3D"width:13.52%; border-top:= 0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.6= 5pt 0pt 5.4pt; vertical-align:top">4.4 </td><td style=3D"width:11.9%; b= order-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding= :2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">4.2 </td><td style=3D"widt= h:12.2%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #00000= 0; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Alto </td></tr>= <tr style=3D"height:13.25pt"><td style=3D"width:22.78%; border-top:0.75pt s= olid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt = 5.4pt; vertical-align:top">Mejora de procesos </td><td style=3D"width:14.04= %; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pad= ding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">4.0 </td><td style=3D= "width:11.5%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #= 000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">4.2 </td><= td style=3D"width:14.08%; border-top:0.75pt solid #000000; border-bottom:0.= 75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">4.2 <= /p></td><td style=3D"width:13.52%; border-top:0.75pt solid #000000; border-= bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align= :top">4= .2 </td><td style=3D"width:11.9%; border-top:0.75pt solid #00000= 0; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vert= ical-align:top">4.4 </td><td style=3D"width:12.2%; border-top:0.75pt so= lid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5= .4pt; vertical-align:top">Alto </td></tr><tr style=3D"height:13.45pt">= <td style=3D"width:22.78%; border-top:0.75pt solid #000000; border-bottom:0= .75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top"><p= style=3D"margin-right:0pt; margin-left:0pt; margin-bottom:0pt; text-indent= :0pt; text-align:left; line-height:115%; font-size:9pt">Ventaja compe= titiva </td><td style=3D"width:14.04%; border-top:0.75pt solid #= 000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt;= vertical-align:top">4.4 </td><td style=3D"width:11.5%; border-top:0.7= 5pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt= 0pt 5.4pt; vertical-align:top">4.6 </td><td style=3D"width:14.08%; bo= rder-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:= 2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">4.6 </td><td style=3D"width:1= 3.52%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000;= padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">4.6 </td><td style= =3D"width:11.9%; border-top:0.75pt solid #000000; border-bottom:0.75pt soli= d #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">4.6 </td= ><td style=3D"width:12.2%; border-top:0.75pt solid #000000; border-bottom:0= .75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top"><p= style=3D"margin-right:0pt; margin-left:2.5pt; margin-bottom:0pt; text-inde= nt:0pt; text-align:left; line-height:115%; font-size:9pt">Muy alto </= span></td></tr><tr style=3D"height:25.9pt"><td style=3D"width:22.78%; b= order-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding= :2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Apoyo institucional / Ecosistema digital </td><= td style=3D"width:14.04%; border-top:0.75pt solid #000000; border-bottom:0.= 75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">3.2 </spa= n></td><td style=3D"width:11.5%; border-top:0.75pt solid #000000; borde= r-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; vertical-ali= gn:top"><spa= n>3.2 </td><td style=3D"width:14.08%; border-top:0.75pt solid #0= 00000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0pt 5.4pt; = vertical-align:top">3.2 </td><td style=3D"width:13.52%; border-top:0.75pt= solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt 2.65pt 0p= t 5.4pt; vertical-align:top">3.2 </td><td style=3D"width:11.9%; border-= top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:2.8pt= 2.65pt 0pt 5.4pt; vertical-align:top">3.2 </td><td style=3D"width:12.2= %; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pad= ding:2.8pt 2.65pt 0pt 5.4pt; vertical-align:top">Moderado </td></tr></table= >Nota:<= span> los promedios se calcularon a partir de las valoraciones individuales= de los cinco expertos, conforme a los criterios definidos. <img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGIAAAAgCAYAAAD= pNa1pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAArBJREFUaIHtmj1L62= AYhu8cuogOLtLBDv4ARULEISCkoOAPcHd0detQByl0qtWii0IXHToJHXVVtApipaIIIqUoVQsGR= OoHVHKf4ZwGQ46Y1HJ8Sd8LMuR5b8gTrrz5VkgSkh9DURQFAH79dCOSP0gRgiBFCIIUIQhShCBI= EYIgRQiCFCEIvkXc3t4iFouhVqsBAF5eXpBOpzE6OoqJiQnk83k7m0wmsb+/375ugwx9cHFxQVV= Vubm5ScuySJLxeJwAHMvOzg5J8urqitFolLlczs9mOgrfIh4fHzk2NsZMJmPXKpUKc7kcX19feX= Z2xqGhIQLg3NycnSkWiwTAw8PD9u5BQPAtYmVlhQB4c3Nj1+r1uiOzvLxMAFxYWHDUp6amODk5y= ff392+2HTyaHjxdIxqNBlZXVzE8PIz+/n673t3d7chZlgUAMAzDUdd1Hdvb2ygWi14215F4ElEu= l3F+fo7BwUH8fVn4TwqFAmZnZ6FpmqMeiUQAAKVS6RutBpuQl5BpmgCAnp6eTzPHx8cwTRNra2u= usa6uLgDA3d1dKz12BJ5mRHMWfDYb6vU6UqkUstksent7XePNUxblp49P8SSir68PAPD8/OwaI4= mlpSXMz89jYGAAlmVha2sLDw8Pdubt7Q0AEA6H29FzMPFyZW80GlRVlaqqusbW19ddzxG6rjsyi= 4uLBMCDg4N23GgEiqYHTzMiFAphZmYGJycnuL6+tuuFQgHT09OuvK7rjvWjoyOMj49jZGSklWOl= M/Bq7unpiYZhuJ4RvuL09JQAuLe35/to6QR8iyDJy8tLaprGfD5vv+L4Kh+NRrmxsdFyo0GnJRE= kWa1WGYvFeH9//2U2kUhwd3e3pQY7haYH5eOK5P8jf6cRDClCEKQIQZAiBEGKEAQpQhCkCInkI7= 8BoaaGNkcjxHEAAAAASUVORK5CYII=3D" width=3D"98" height=3D"32" alt=3D"" style= =3D"margin-top:26.76pt; margin-left:382.04pt; position:absolute" /><= span>Dado a la suma de los rangos obtenemos un total de =E2=88=91 Ri =3D 75= , al desfragmentar esta expresi=C3=B3n con la Ecuaci=C3=B3n 2.<img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEMA= AAASCAYAAAAAGGMhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAxJJREF= UWIXV2EuoVWUUB/DfPnd789ULpQhBTAyxDEIRjAiKRiGIBJLUxEkGNVAJwWrUQCc1cqADUUSoSI= mCTBs1F5GKGmgPJF83NHxF3bz37LMdrL05h9M5Z3vvvlduf9jsw/ft71vrW4//Wt9J1MMw1mIlc= iS4jWO4hXV4pJh7AGfwFVo15U4LGjXX51iPxzEPD2MjMqR4E4sxF01cLNbMSKQ117dwAqdERLyI= +3ADc8TBP8HZ4nfTDDbGUM31Oc4JQzyE97Efl0UKbcJsPC8Mf94MTRHqG6NEA89hAY6INGkKjri= IP/Fu8e2PZrBB6qIhUmIvVgsSJQxdPsP4EEcx/x7olBRPNycmhT4pZnU8D1KfM0oBLwgC/UWbEx= 7FK4IzyjT6HeNTIHMQGsLgq/ADrnfMDWEDFnaMzRYFYOtUCJ+Hz0QZLT2RYDm+E6X0U3yBRdqRM= x1oCCd8jJ+xomt+Dk5iRKTvBcFj+5mayLgt+OCSdlTk+BUvaXthBH+bvmqSirL+jCjxDb0N/y9e= F1Hc7BiTurteI9f/EE1RUbrnMxGiZZhWGaHM88nqkQvC/hJv4IkB313DH9rGQBjjaRE+g3BL9Ap= Zj7mk690Pif4HSfAk7q/YYww/Fe9uZPje3fUziThzs9irhTzFTizV/zC5IKLtIsw7Nxw28fI8ps= sjxR7bhGP6RWqOK9gsPNvrsFXRlxfy3xbXhFn4FodwNcVbqg80htGusWG8h2cr1nYiwx5806V4h= ncK5QY5pSWidLK8M44dxT6jWIbdIqW2pPin+HCQElkPBXJBmmcnoEwLN/vMjQoyHqRHaZDJIhNN= X5myF3AcrxGcsR2PVWxyDvsKZUuM44CJX/Za/mvYRITuEoO55wY+wl8TlNkpp1OHcfym4MxUlLw= qj1zqM57pTaqTUXLE4IqSCyPUiYylWCM64Zagh+XiuiAVHWJVJchqKlGFFj5XXV7LStEPQ6LTXS= n+P1khUqHsbxZjV/HtSTyFl3FQheD/GxLRdL0qqtJ8XMVpfCCMskjckZYJIk7wNQ7jyh1ZCM8NJ= BPWQwAAAABJRU5ErkJggg=3D=3D" width=3D"67" height=3D"18" alt=3D"" style=3D"m= argin-right:9pt; margin-left:9pt; float:left; position:relative" />= =E2=88=91Ri siguiente formula R =3D <img src=3D"data:image/png;base6= 4,iVBORw0KGgoAAAANSUhEUgAAABAAAAACCAYAAABR7VzxAAAABHNCSVQICAgIfAhkiAAAAAlwS= FlzAAAOxAAADsQBlSsOGwAAABVJREFUCJljZGBg+M9APmCkQC8EAAB+yAECR0E4BQAAAABJRU5E= rkJggg=3D=3D" width=3D"16" height=3D"2" alt=3D"" /> =F0=9D=91=82=F0=9D=91=8F=F0=9D=91=A1=F0=9D= =91=92=F0=9D=91=9B=F0=9D=91=92=F0=9D=91=9A=F0=9D=91=9C=F0=9D=91=A0<s= pan style=3D"width:29.02pt; text-indent:0pt; display:inline-block"> </= span> <= /span>y al calcular la suma de cuadrados de desviaci= =C3=B3n n =3D5S con la formula S=3D =E2=88=91 = (Ri =E2=80=93 R)2, se obtiene un resultado de S= =3D212 el cual ayuda a ser plasmado para poder realizar el C=C3=A1lculo de W-Kendall (</= span>Ecuaci=C3=B3n 3). </span= >= <img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGY= AAAAeCAYAAADermvOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAqdJRE= FUaIHt2s8rPHEcx/HnfvuWxFXaUpz9aNvkyBokysHBxb/g5qSUoxxIioOLhNq9KLnKwcEWDliR2= qR2hSQlsfnVvr6H79dk2u3bDFum3c+j5rCfz3v7vLdXM/OZaQOShOEbgUAgAPDrpxsx8jPB+JQJ= xqdMMD5lgvEpE4xPmWB8ygTjU56Dubq6YmRkhJubGwAkEYvF6OjooK2tjVgsZteOj48Tj8cL120= pkQenp6cKh8NaXV1VNpuVJC0uLgpwHLu7u5Kks7MzWZalaDTqZZmS5jmY+/t7tba2amZmxh67vb= 3VysqKMpmM0um0+vr6BGhhYcGu2d/fF6CdnZ3C/oIi5TmY2dlZAbq4uLDH3t/fHTUTExMCtL297= RgfGBhQT09PTr2Ry1Mwr6+vamhoUCgUsi9hn2UyGW1sbKi2tlaTk5M589PT0wK0t7f3/c6L3Ecu= rm7+5+fnnJyc0NjYyL+Xnw5zc3N0d3eTSqWIRqMcHh465mtqagBIJBJuljNwGczd3R0AlZWVeee= HhobY3NwkEolwcHDA8PCwY768vByA6+vr7/RaUn67Kfo4S/KdLQAVFRV0dnZSX19PJBJha2uLl5= cXysrKAMhms8DfrbXhjqtgqqqqAHh6evpvXTAYpLe3l6amJjsUgOfnZwCqq6u/2mfJcXUpq6urI= xwOc3x87BhfWlrCsizW19d5fHwknU6TSCQYGxtz1F1eXgIQCoUK1HYJcLtbmJ+fF6BUKmWPxeNx= 9ff3KxgMqqWlRaOjo0omkznfHRwcVFdXl97e3gqycylmnoN5eHhQe3u7pqamPC10dHSU99nGyM9= zMJKUTCbV3NystbW1vM8z+eoty9Ly8vKXGy01H7kEPn8wfp75l4zPmWB8ygTjUyYYnzLBGIZRBP= 4AXxXm5pGMwQ8AAAAASUVORK5CYII=3D" width=3D"102" height=3D"30" alt=3D"" styl= e=3D"margin-top:1.61pt; margin-left:376.33pt; position:absolute" /><= span>W =3D <img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgA= AADMAAAAcCAYAAADMW4fJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAABM= pJREFUWIXt2FmM3WMUAPDfvbMYpTra0o2iNEqslcausUQVoQlPUhIillQoYok1NITUFhKJIFFUx= BI8SCTKS60NorUv0fBQiqRqa6edzng458v85+bOIiOxxEn+uffbznf25asZGmoYh7mYhtvQhRac= gCOwA+q4Gx+iN8+24HgchfFoxT1Ylevb4DAcixuxMfEckucmYDQew6vYPBih9WEwsicuwynYo0J= oDftjCa7Bb7ggGahC2XMtfsKFydTWWIjZOLCBpsOxAtfjLdyCsUPQqnWoDViDu3C6kGKBLbi9Mv= 5BSLoKPVjcsGecEMgmPIxO7F3Z0533FfgRv+f8oDCUZnrxcyLcok8rZa3MTRPmtiTnmu3ZDcfgk= Zzrxrf5W8Ur13uE9ubiBqwfipnhaGYwqGFnXIpF+KwJYTVMruz5uMmeZjAOV+IVvO4v0Ewj1BrG= E4RJPII3c64l99Xzd0fch8eTqGb3NuLtwEWJ8wnB/JC0NjprM2jDDOEz07FSRJ1uEYF2wAYRgc4= Qkjwbu+ML4fgTRYA4OPe8rM/0jsMcEeE2Je5zME/46364SgSCQU1tOJrpFFFnO2Hjl+DQPNsuwv= RsEYHW5Jmtcq0upLwh9xyBtbmnhjNxND7B+TgphTc57zpM+Nn6xPE//A9/N9QwFdv+3YSMEHrwa= Q0Pidroz4bpfwr04lfsU8NMEV4bY/2/CbqxrCS3/wL0DKWNwmxd2GWP5qVIbYD56npJ0AXPSKDQ= 3e/OlspitQQpm0eJQm++yNZfiiRZoC6Cx0RRkDbDURfVwHycLBLg2kZChiC8Eef2Ipn3S6SFmfG= iVJkosu8sfC16mXl4Duclcysql8zN9U+TyOnYKfHsLZqpjaLA/BLf4Aq8JMqb4TAyA1MS5/SkeW= Pee7gog/o1beditai7VuJp0eG1iTK8E0txYuXMZNybDNYxBstFabIqv4Pz8m1EeTMLD+aZ4UAr3= hbd6yq8K8qfetK2UDSN/dzlPFE0TspvrL7qd0weOk3UWUVii0RBWAJIJ57HAYljoqjRir8cgptE= tzqcArcw82SemSSq9A59JrcfnpLCKYSUhmpMLvwubHoULhf+sFyou5bzc/C5PmcujVibsOmaCJk= 17IqzRPPWJjRVS2K3avK16Qsq3TnuzPHm/O3F96KfmlI4h3cSySzhB6txK/YSFfLoXPsI9wvT2x= 6/VKS4ES/mmXHiQeJy4XsLhF8uEBq7TiS6k8RjR2NU/V6YcBeWiTeCaaJdWChcobTeG0Se/KIws= wofCE2txjNCvSuFeZXIs0n/KFQloks0aUVIu4h24WLxMNFSkWjR/DoRFBqZWSe0vAWP5lwH9sWp= SWuxpnqhqTUvOUhEm/X6q3Gz5s87v4l3gfJiUnxrUuIpZ9vz0q4mOOA1vNFkvtzfmUL5KMdd+r8= xdAiLWisZaReSOyjHc/E+XhiAEcKOxyYD7+VFU3FnXraHaKwWixeZgXJKr74kWv3K/t1xhzDJmS= I6PoDvcn2GSB9L0V2iwo7CSUfnxpIcB0ts43FzCmKd0PKuwhm78ZXoFkeS7duFYCYkPV8Jf+oVG= rlavCssQ2/VVqv/h5Oda6IVPhLPivzSa4BSYwTQjK7x4k2iSzx4DGTG/174Ay9kMICbxBFiAAAA= AElFTkSuQmCC" width=3D"51" height=3D"28" alt=3D"" /> = ; W =3D <img src=3D"data:image/png;base64,iVBORw0= KGgoAAAANSUhEUgAAADoAAAAdCAYAAAD7En+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxA= AADsQBlSsOGwAABO1JREFUWIXt2FmMnXMUAPDfvXfa6cy0hqmiVdXYUktQe0ss9WBNLAmxlIoHg= ijCgwQREUo0jSVBPFFbNRFJrUEjxBIPfUAtVaWo1r7V0jKLh3M+93NNzeiMdoKTfLnf/f+/c/5n= /59zKgYOTdgW26EZy7AEPdgc++c3MBr3oDP/VxJvE7yOrhLdZuyHN/FN0ltvqA4EOWFn3IVfMQI= PYpvcm4RzcCCmYji6c6+CNszDJQ28VHEqHhCKGDA09f1Jn/AJLsJbaMG52AMfCYYfxf1CwE51y4= zAVXhZWLpSojkhaSwzOMYYFCJfY7EQoCas9nzudWNrzMDxQhHyu72xAm/kd5XkZzhuEcppK62XF= fG3YVC0lUyMw724Ft8Lwd8UFlsi3PghDMOuuEC4bU/i74tWzMT8xKmIWD1qEHldb6hgJO4UAtQa= 9qq51oJVgvGr8HY+q/AFnsMoPCPCYCm+xXLMbqC7UWATzMVp+b4vjhZCzhDJaoRIKm8I12zKtVY= R0/OEmzYJRTRjU7yGg4QXDMh1ByMZ7YTdMRmX59rs/N0Ss7AIe+V7Vz6dQujxQpBW/Kh+9WyPn0= RIlJPYesGAtJTQii1K/7tFgvpBuNtoYb3V4j4sQ00ICl8lTgEdwpU7sdIQELQ3Oo1MVXpZa8Ttb= f+v9v6H3qCiHuiDZd2hBj1EMjoL0w2Be+ofgm680iRKtRcMgXvqH4JuLC2XV/9q1/3PwGBYsUxj= KGivV36KuKyWnsZatSaSVk1k6DasLe034WBx2f+kXt9WSweV12oNzBRrxTMSv/RTqEbcjuTtWHy= WdH7PukTLdEQKMgFP4JFkaA52SIQW0V/OSbzNRbexSL3qGYcTRaUzTySDdpwsqqB2/CzKxK+wC6= 7Lsws4TjTyfcERuFiUlMW0Yk+8gyuxAC+qN/uuTOKtojh/X4xHhotuYrIo2EeJgltq8DacpO4FU= 3G36EdnltZ3FIV/Wwr6pKiLKzggBd0s6Y/U/5CajusTb1TSLzynHc9iLPW7cxYeE2ZfU3qol28t= QsuFpseKkclT6rOe94SGX2pgdhnOFkX7L0l7VWm/O5VSLZ1bnL2up4zbXMLtybXVWIh9UCtctys= /bBGTgEuFW1XwIW5Ija0WrdfnGJPMFvEq14f545CrYKYQZjcRzw/l3o/CqvthovCSO3JvD3++33= tEkV/0seeLzmhrXI3HhTF68KpoGZ8pt2kjcZNw1adLzJ5TYvIaEXfTsBW+VPL/PqCKQ3AeLlO33= GIcmXTGC3dbIJLJdNH5lKFL5JBPk8+FiTsN9+FwMdkglDhayaLNuFB0+fNFPP6cBMYIDXbl74TE= 6RTJrLd4anSvqhilzMCZSWuMUFRHMrRGeMTaPH9FKqQ3+oV7jhVTiDXJWy1xy+d2U3eLM3B6Ik1= JoRelIHOFGzcJa9wqrNCMY0QWLqzfLCYKF4jEsFi46Rg8LNxtEk4Qk4iFee5p+EBYdiJuV3e/dT= 1wc9JZjlNE2Nwm8kBVJMdOvFwIelgKOUzE6QpR/36bQk4W8TJfuE0xJThdZNjvkk67cPWVKeAkM= f9pEzG+Rrhil0hY7yZuh7iLf8WNeW5/io+VYooxBR/jCuGJhBFnpoI/6QetdUJFXC23imtoqNTK= RZEzTX1kOuCOZYlw10Pzfe1ffr1hoCYKid3EHbt647KzgeE3x9gwUfNz4BcAAAAASUVORK5CYII= =3D" width=3D"58" height=3D"29" alt=3D"" /> W =3D <img src=3D"= data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABsAAAAaCAYAAABGiCfwAAAABHNCS= VQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAz1JREFUSIm91ktonVUQB/Dfd3tzk6Zp= 4gP7UEt81Ed8I4IBXxvFhQvRagRRVGhRizsXgsVFBQVR6UJcKCoooiIUig9ExI0ovooRYquibVE= pSqzVaJsY097rYuZwv0bpqunAx/2+O2fOzPzPnPlPJaSFMzCMDr7Gj6kbwWpUue4kbNKVJi7GFL= 5Du6YbxCX4FPsb+ecabMDPGMVmnIwGbs/nsjTcW9usymBexVh+F2ngPryGY0pU0vNn+AHTuBsr0= nkDr+NtHMinHvlafJPrirNFuAYXZsaN4h12YkdCOIJfEpJOwnJ2BnBFzaaJB/AJZmvZNDCEm/FI= 7lGhUQwLJKflojX4AwfxfGb9Je7EMxn55fgLb9ayWYtjsREvYi6drcL5dUdn4nNRKPODKBFfj1+= xDC9jO77Fb4nGBK7EV6LIdugWzrVlw0E8J6pqIOE6J983ivPry8gn8r2FXizFWwlZH3pStxgXiC= M6FT2lQMZEFV4qzqiNezKrYTyZGYyIyvy7lvnShKo3YZ+rIbI8kViFXaV6js8syncbk7lpC8fl7= 17sc6gsTlg72J0OiyxL/TT21O9F/V0az9d1/L8cTl8dxm7hpBIHWvlvZkdaOk1xd25zaLkfcUfY= 2hQN9wNxKRdK2thZLuxCw3j0C+SoSoGuTg+VwLij1rF170vRzbdT0xWbIh20S7taLbp9CycK2ng= FM6Id3StoA54WnaISPfS6tJvAS6Jd9WEdVqJfMMAXxfMY1ovWMoqfBCX04HE8iiV4EO+mo5X4CK= dkQOOy4Qp22JSO1otGPVBS3YxnBQsvwX7BZ0O4Ch+KPjkumGFFOvldUMuM4Lxb09m6/J7FFpyOo= eLsYG58E+7HLbnJQP7/Z+I+mesHE/pZ3fPbLeaWJi4Szbcj+GwGrfoh9mf03+Nh0bEL3RRZlBsc= 0B0F6rp/8n2+roFOcbY8I3kDDwnKP0vAOaU7yp2QAU1iVy0TYsT7WBTItvyuBH3NYbo4uwOPiXO= 4AXtysylRlWNiXLhRMPc+QfnTuEuMecN4J7N7Im3OFUPRe+J8wXl4QRTAFjGCFTboFRU5LmbL/h= o0o6I638fVaSPXbBBF8pQgX/8ChA/Lm9SdJdsAAAAASUVORK5CYII=3D" width=3D"27" heig= ht=3D"26" alt=3D"" />=3D 0.848 R//Al realizar esta fragmentaci=C3=B3n de la f= =C3=B3rmula de Kendall, obtenemos un resultado de W=3D0.84, que equivale a = un nivel alto de concordancia entre los cinco expertos que evaluaron las pr= opuestas, cabe mencionar que al obtener valores superiores a 0,7 se los con= sidera aceptables para una validaci=C3=B3n de criterios, evidenciando la co= nsistencia de las valoraciones y la validez metodol=C3=B3gica del instrumen= to. Seg=C3=BAn la escala de Juna (2025) este valor corresponde a una concordancia sustanc= ial, lo que respalda la confiabilidad de los resultados y la aplicabilidad = pr=C3=A1ctica de las estrategias evaluadas. El an=C3=A1lisis evidencia un alto consen= so (coeficiente de Kendall W > 0.84), con valoraciones promedio de 4.6 e= n adopci=C3=B3n tecnol=C3=B3gica, 5.0 en actitud hacia la digitalizaci=C3= =B3n, 4.4 en mejora de procesos y 4.6 en ventaja competitiva. En contraste,= las dimensiones de apoyo institucional/ecosistema digital (3.2) y barreras= econ=C3=B3micas (3.6) recibieron las valoraciones m=C3=A1s bajas.</= p><sp= an>Finalmente, las estrategias propuestas que incluyen programas de capacit= aci=C3=B3n modular, mecanismos de financiamiento adaptado a la escala micro= empresarial, talleres de gesti=C3=B3n del cambio organizacional, manuales d= e procesos digitales estandarizados y redes de articulaci=C3=B3n interinsti= tucional se consolidan como una respuesta pr=C3=A1ctica, viable y contextua= lizada frente a las brechas identificadas. Su validaci=C3=B3n t=C3=A9cnica = y su anclaje emp=C3=ADrico las posicionan como una ruta integral para promo= ver la digitalizaci=C3=B3n sostenible de las microempresas ecuatorianas, fo= rtaleciendo su capacidad de innovaci=C3=B3n, resiliencia y eficiencia opera= tiva. <h2 style=3D"margin-right:0pt; margin-left:17.75pt; margin= -bottom:0.85pt; text-align:left; line-height:115%">4. Conclusiones </h2>La investigaci=C3=B3n permiti=C3=B3 evid= enciar que la transformaci=C3=B3n digital constituye un factor decisivo par= a la sostenibilidad y competitividad de las microempresas ecuatorianas. El = an=C3=A1lisis emp=C3=ADrico realizado demostr=C3=B3 que, si bien existe un = nivel significativo de adopci=C3=B3n digital b=C3=A1sica y una amplia dispo= sici=C3=B3n hacia la innovaci=C3=B3n tecnol=C3=B3gica, las limitaciones est= ructurales, econ=C3=B3micas y organizativas siguen representando un obst=C3= =A1culo relevante para avanzar hacia procesos de mayor complejidad y valor = agregado. En correspondencia con el objetivo general, se logr= =C3=B3 analizar la relaci=C3=B3n entre la transformaci=C3=B3n digital y la = eficiencia operativa, mostrando que la primera incide positivamente en la s= egunda cuando se articulan estrategias de capacitaci=C3=B3n, financiamiento= y acompa=C3=B1amiento t=C3=A9cnico. Asimismo, se cumplieron los objetivos = espec=C3=ADficos: se identificaron las principales barreras que frenan la d= igitalizaci=C3=B3n, se evalu=C3=B3 el grado de implementaci=C3=B3n de tecno= log=C3=ADas en procesos clave y se formularon estrategias contextualizadas = que aportan soluciones viables y aplicables al entorno de las microempresas= . El principal aporte cient=C3=ADfico del estudio radica en la = integraci=C3=B3n de un enfoque mixto que combina datos cuantitativos y cual= itativos, junto con la validaci=C3=B3n experta, lo cual permiti=C3=B3 const= ruir una propuesta metodol=C3=B3gicamente robusta y adaptada al contexto lo= cal. A nivel pr=C3=A1ctico, se ofrecen lineamientos estrat=C3=A9gicos que p= ueden servir de base para dise=C3=B1ar pol=C3=ADticas p=C3=BAblicas, progra= mas de apoyo institucional y modelos de gesti=C3=B3n empresarial orientados= a la digitalizaci=C3=B3n inclusiva de las microempresas. La di= gitalizaci=C3=B3n representa una oportunidad hist=C3=B3rica para redefinir = el modelo productivo de las micro y peque=C3=B1as empresas ecuatorianas. Si= se consolida mediante una pol=C3=ADtica integral que combine formaci=C3=B3= n, financiamiento y acompa=C3=B1amiento institucional, la transformaci=C3= =B3n digital puede convertirse en el principal motor de innovaci=C3=B3n, co= mpetitividad y equidad empresarial en el siglo XXI. <h2 style=3D= "margin-right:0pt; margin-left:17.75pt; margin-bottom:0.85pt; text-align:le= ft; line-height:115%">5. Conflicto de interes= es </h2>Los autores decla= ran que no existe conflicto de intereses en relaci=C3=B3n con el art=C3=ADc= ulo presentado. <ol start=3D"6" style=3D"margin:0pt; padding-lef= t:0pt"><li style=3D"margin-left:31.25pt; margin-bottom:0.85pt; text-indent:= 0pt; text-align:left; line-height:115%; padding-left:4pt; font-weight:bold;= color:#767171">Declaraci=C3=B3n de contribuci=C3=B3n de los autores = </li></ol>Todos autores contribuyeron significativamente en la elab= oraci=C3=B3n del art=C3=ADculo. <ol start=3D"7" style=3D"margin:= 0pt; padding-left:0pt"><li style=3D"margin-left:31.25pt; margin-bottom:0.85= pt; text-indent:0pt; text-align:left; line-height:115%; padding-left:4pt; f= ont-weight:bold; color:#767171">Costos de financiamiento </li></ol>La presente investigaci=C3= =B3n fue financiada en su totalidad con fondos propios de los autores. </sp= an><h2 style=3D"margin-right:0pt; margin-left:17.75pt; margin-bottom:0.= 85pt; text-align:left; line-height:115%">8. R= eferencias Bibliogr=C3=A1ficas </h2> <div s= tyle=3D"margin-left:24pt">Prieto Albornoz, I. V., & Loredo Mendoza, A. (2025). Fact= ores que inciden en la productividad empresarial en entornos digitales. </s= pan>Perspectiva Revista Cient=C3=ADfica d= e Econom=C3=ADa, 1</s= pan>(1), 16=E2=80=9352. <a href=3D"https://doi.org/10.56469/PR= CE.V1I1.1925" style=3D"text-decoration:none">http= s://doi.org/10.56469/PRCE.V1I1.1925</a> </div><div = style=3D"margin-left:24pt">Ocampo Alvarado, A. M. (2023). Efectos de la transformaci=C3= =B3n digital en el sector contable y financiero en Ecuador. Ciencia y Educaci=C3=B3n, <sp= an style=3D"font-style:italic">4(11), 42=E2=80=9352. <a= href=3D"https://doi.org/10.5281/ZENODO.10212374" style=3D"text-decoration:= none">https://doi.org/10.5281/ZENODO.10212374</sp= an></a> </div><div style=3D"margin-left:24pt"><sp= an style=3D"background-color:#ffff00">=C3=81lvarez Alp=C3=ADzar<span= >, O. C. (2025). Estrategia para e= l mejoramiento de la gesti=C3=B3n y la competitividad empresarial mediante = el uso de TIC: PYMEs de servicios odontol=C3=B3gicos en Tres R=C3=ADos, Car= tago [Tesis de posgrado, Ciudad Universitaria Rodrigo Facio, S= an Jos=C3=A9, Costa Rica]. <a href=3D"https://hdl.handle.net/10669/1= 02752" style=3D"text-decoration:none">https://hdl= .handle.net/10669/102752</a> </div><div style=3D"ma= rgin-left:24pt">Banka, M.= S., Andrade, R. D. de, Tien, N. H., Trai, D. V., & Tinh, = N. H. (2024). La cooperaci=C3=B3n de corporaciones p=C3=BAblicas con empres= as privadas de nueva creaci=C3=B3n en todas las etapas de su ciclo de vida.= Brazilian Journal of Development<= /span>, 10(6), = e70679. <a href=3D"https://doi.org/10.34117/bjdv10n6-051" style=3D"t= ext-decoration:none">https://doi.org/10.34117/bjd= v10n6-051</a></div><div style=3D"margin-left:24pt"><spa= n style=3D"background-color:#ffff00">Bartels Ortega, T. (2021)= . El rol de la Agencia Universitar= ia para la Gesti=C3=B3n del Emprendimiento (AUGE) en la innovaci=C3=B3n e i= nternacionalizaci=C3=B3n de las micro, peque=C3=B1as y medianas empresas (M= ipymes). Pa=C3=ADs: Costa Rica [Tesis de posgrado, Universidad= de Chile, Santiago de Chile, Chile]. <a href=3D"https://repositorio= .uchile.cl/handle/2250/184282" style=3D"text-decoration:none">https://repositorio.uchile.cl/handle/2250/184282</a><= span> Brito Bautista , S. M., Naranjo Navas, N. N., Remache Alarc=C3=B3n, = B. S., & Quishpe Alarc=C3=B3n, S. A. (2023). El Liderazgo y la Comunica= ci=C3=B3n Digital. Dos cauces de un mismo fin. Ciencia Latina Revista Cient= =C3=ADfica Multidisciplinar, 7(2), 649= 1-6507. <a href=3D"https://doi.org/10.37811/cl_rcm.v7i2.5801" style= =3D"text-decoration:none">https://doi.org/10.37811/cl_rcm.v7i2.5801</a></p= ></div><div style=3D"margin-left:24pt">Castillo Inche, C. H., & Villanueva Quispe, = E. V. (2024). Transformaci=C3=B3n digital de los noticieros televisivos y s= u impacto social en Cerro de Pasco, 2023 [Tesis de posgrado, Universidad Na= cional Daniel Alcides Carri=C3=B3n =E2=80=93 UNDAC, Capital Minera de Cerro= de Pasco, Per=C3=BA]. <a href=3D"https://scholar.google.com/scholar= ?hl=3Des&as_sdt=3D0%2C5&q=3DTransformaci%C3%B3n+digital+de+los+noti= cieros+televisivos+y+su+impacto+social+en+Cerro+de+Pasco&btnG=3D" style= =3D"text-decoration:none">https://scholar.google.= com/scholar?hl=3Des&as_sdt=3D0%2C5&q=3DTransformaci%C3%B3n+digital+= de+los+noticieros+televisivos+y+su+impacto+social+en+Cerro+de+Pasco&btn= G=3D</a> </div><div style=3D"marg= in-left:24pt">Cutanda-L= =C3=B3pez, M. T. (2021). M=C3=A9todo mixto de investigaci=C3= =B3n: pertinencia y dificultades en el estudio programas de reenganche. </s= pan>Caribe=C3=B1a de Ciencias Sociales</s= pan>, 31=E2=80=9348. <a href=3D"https://doi.org/10.51896/CARIB= E/MGUI5478" style=3D"text-decoration:none">https:= //doi.org/10.51896/CARIBE/MGUI5478</a> </div><div s= tyle=3D"margin-left:24pt">Dur=C3=A1n Mero, R. A. (2025). Transformaci=C3=B3n digital en= las pymes ecuatorianas: desaf=C3=ADos y oportunidades. Revista Cient=C3=ADfica Multidisciplinaria en Cienci= as Sociales y Humanidades Eucken, 1(1), 40-58. <a href=3D"https://revistaeuc= ken.com/indes/index.php/home/article/view/transformacion-digital-pymes" sty= le=3D"text-decoration:none">https://revistaeucken= .com/indes/index.php/home/article/view/transformacion-digital-pymes<= /a></div><div style=3D"margin-left:24pt">Espejo Villar, L. B., L=C3=A1zaro Herrero,= L., & =C3=81lvarez L=C3=B3pez, G. (2023). Digitalizaci=C3=B3n educativ= a y aprendizaje m=C3=B3vil: tendencias en las narrativas pol=C3=ADticas de = los organismos internacionales. Fo= ro de Educaci=C3=B3n, 21(2), 45=E2=80=9366. <a href=3D"= https://dialnet.unirioja.es/servlet/articulo?codigo=3D9262557" style=3D"tex= t-decoration:none">https://dialnet.unirioja.es/se= rvlet/articulo?codigo=3D9262557</a> </div><div styl= e=3D"margin-left:24pt">Fe= rn=C3=A1ndez-Ronquillo, M., & Romero-C=C3=A1rdenas, E. (20= 15). Estructuraci=C3=B3n de la microempresa establecida en el sector comerc= ial de la ciudad de Milagro, Ecuador. </span= >Revista Ciencia Unemi, </sp= an>8(14), 21-29. <a h= ref=3D"https://www.redalyc.org/pdf/5826/582663828004.pdf" style=3D"text-dec= oration:none">https://www.redalyc.org/pdf/5826/58= 2663828004.pdf</a> </div><div style=3D"margin-left:= 24pt">Flores= Arocutipa, J. P., Manrique Nugent, M. A. L., Serna Silva, G. J., & Ayb= ar Bellido, I. E. (2021). Liderazgo en tiempos de 4ta revoluci=C3=B3n indus= trial. Revista Venezolana de Geren= cia. 26(96), 1096-1107. <a href=3D"https://doi.org/10.5= 2080/rvgluz.26.96.7" style=3D"text-decoration:none">https://doi.org/10.52080/rvgluz.26.96.7</a> </di= v><div style=3D"margin-left:24pt">Juna Pozo, L. P. (2025). Importancia de la investigac= i=C3=B3n cient=C3=ADfica en los profesionales quite=C3=B1os y el gusto en s= u profesi=C3=B3n analizado por la prueba de concordancia Kappa de Cohen. </= span>Ciencia Latina Revista Cient=C3=ADfi= ca Multidisciplinar, = 9(2), 8017=E2=80=938029. <a href=3D"https://doi.org/10.= 37811/CL_RCM.V9I2.17522" style=3D"text-decoration:none">https://doi.org/10.37811/CL_RCM.V9I2.17522</a> <= /p></div><div style=3D"margin-left:24pt">Largo Piedrahita, J. A. (2025). Estrategias de ciberseguridad para la protecci= =C3=B3n de la infraestructura cibern=C3=A9tica de las (PYMES), mediante el = estudio de vectores de ataque para fortalecer la seguridad inform=C3=A1tica= [Tesis de posgrado, Universidad Nacional Abierta y a Distanci= a =E2=80=93 UNAD, Bogot=C3=A1, Colombia]. <a href=3D"http://reposito= ry.unad.edu.co/handle/10596/75474" style=3D"text-decoration:none">http://repository.unad.edu.co/handle/10596/75474</a= ></div><div style=3D"margin-left:24pt">Ortiz-Choez, G., Constantine-Castro, J., Mar= tillo-Mieles, O., & Silva-Idrovo, R. (2024). Las PYMES en el Ecuador y = su participaci=C3=B3n en el PIB. D= igital Publisher CEIT, 9(2), 736=E2=80=93743. <a href=3D"https://doi.org/10.= 33386/593DP.2024.2.2273" style=3D"text-decoration:none">https://doi.org/10.33386/593DP.2024.2.2273</a> <= /p></div><div style=3D"margin-left:24pt">Ortiz Valverde, K., Quir=C3=B3s Fonseca, F., G= onz=C3=A1lez Villalobos, P., & Esquivel Montero, N. (2025). <spa= n style=3D"font-style:italic">Desarrollo de las capacidades empresariales d= e las pymes del sector industrial en el cant=C3=B3n de P=C3=A9rez Zeled=C3= =B3n, per=C3=ADodo 2024-2025 [Tesis de pregrado, Universidad N= acional, Heredia, Costa Rica]. <a href=3D"https://hdl.handle.net/110= 56/33459" target=3D"_blank" style=3D"text-decoration:none">https://hdl.handle.net/11056/33459</a></div><div style= =3D"margin-left:24pt">Qui= nallaz Arango, M. Z. (2022). Caracterizaci=C3=B3n de los efectos producidos por la problem=C3=A1= tica socio econ=C3=B3mica surgida durante la pandemia del COVID 19 en la co= muna 8 del municipio de Armenia en los a=C3=B1os 2020 y 2021 [= Tesis de pregrado, Escuela Superior de Administraci=C3=B3n P=C3=BAblica CET= AP, Armenia, Colombia]. <a href=3D"https://repositoriocdim.esap.edu.= co/items/82c04ef0-da4b-4aa2-ad8d-2034da1db755" style=3D"text-decoration:non= e">https://repositoriocdim.esap.edu.co/items/82c0= 4ef0-da4b-4aa2-ad8d-2034da1db755</a></div><div style=3D"margin-l= eft:24pt">Tamayo<s= pan> Freire, J. A. (2025). Estudio= de los factores determinantes para la creaci=C3=B3n de emprendimientos y/o= microempresas en el sector tur=C3=ADstico de conocimiento de la naturaleza= en el cant=C3=B3n Tulc=C3=A1n de la provincia del Carchi [Tes= is de maestr=C3=ADa, Universidad Andina Sim=C3=B3n Bol=C3=ADvar =E2=80=93 S= ede Ecuador, Quito, Ecuador]. <a href=3D"http://repositorio.uasb.edu= .ec/handle/10644/10498" style=3D"text-decoration:none">http://repositorio.uasb.edu.ec/handle/10644/10498</a> </= span></div> <s= pan> <p= style=3D"text-align:left"> <div style=3D"clear:bo= th"><img src=3D"data:image/png;b= ase64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAACCAYAAACZgbYnAAAABHNCSVQICAgIfAhkiAAAA= AlwSFlzAAAOxAAADsQBlSsOGwAAABJJREFUCJljYGBg+M/AwMDAAAAGBAEA+2Da0AAAAABJRU5E= rkJggg=3D=3D" width=3D"1" height=3D"2" alt=3D"" style=3D"margin-right:9pt; = margin-left:9pt; float:left; position:relative" /> &= #xa0; Nombre 1 = </div></div></body></html>