Shop Owners Said Data Tools Helped, and Their Budgets Still Held Them Back

interviews · Amfiteatru Economic · la publicación, 1 may 2026 · gratis

Herramientas de datos en tiendas en línea: lo que reportan 25 dueños rumanos

Entrevistas telefónicas a gerentes y dueños de pymes de comercio electrónico en Rumania: dicen que sus decisiones mejoraron, aunque el estudio no puede probar que las herramientas causaran las ganancias.

Versión breve · la versión detallada sigue, unos 11 min

Pregunte al chat de weeklyAI

Pregúnteme por este estudio: a quiénes se estudió, qué encontró y qué no dice.

Las conversaciones se guardan mientras exista weeklyAI, para mejorar la publicación. Se responde en el idioma en que usted escribe.

El estudio, de un vistazo
Quiénes: Dueños y gerentes de pymes de comercio electrónico
Cuántos: 25 entrevistas
Dónde: Rumania
Cuándo: entre septiembre y octubre de 2025
Tipo de estudio: entrevistas
Quién lo hizo: Universidad de Craiova, Rumania
El límite que importa: Solo recoge lo que los dueños percibieron; no prueba que las herramientas causaran las ganancias.

Qué frena a cada tamaño de empresa

Microempresasfrente aEmpresas más grandes

Las microempresas están limitadas sobre todo por el dinero; las más grandes chocan con problemas de implementación y optimización.

Los beneficios son lo que los dueños percibieron, no una medición.
Lectura de weeklyAI

Ver · 0:53

Usted tiene una tienda en línea, poco personal y ninguna persona dedicada a los datos. Quiere saber si vale la pena pagar por analítica o inteligencia artificial.

Un grupo de investigadores de la Universidad de Craiova, en Rumania, entrevistó por teléfono a 25 dueños y gerentes de pymes de comercio electrónico de ese país. Fueron 13 microempresas, 7 pequeñas y 5 medianas. Las llamadas se hicieron entre septiembre y octubre de 2025. Los resultados se publicaron en 2026 en la revista Amfiteatru Economic.

El patrón que apareció fue claro. Las microempresas se quedaban con lo gratis: Google Analytics, los reportes de su propia plataforma de venta y Excel. Las empresas más grandes pagaban sistemas como Power BI, ERP o CRM, y algunas usaban modelos predictivos para calcular demanda y precios.

Los dueños describieron resultados parecidos. Ventas más estables, mejores márgenes por categoría, menos gasto en promociones que no rendían, y control más fino del inventario. Un gerente de una empresa mediana dijo que sin esos análisis las decisiones serían "mucho más especulativas". Una microempresa reportó que las ventas se volvieron más estables y las decisiones, mejor fundadas.

También contaron lo que costó. El software profesional es caro y armarlo con los sistemas que ya tenían tomó tiempo y pruebas. Más de la mitad de los entrevistados mencionó que sus empleados no tenían las habilidades necesarias para usar estas herramientas. Las microempresas cargaban sobre todo con el problema del presupuesto, y las más grandes, con la dificultad técnica de integrar todo.

Los beneficios son lo que los dueños percibieron, no una medición. El estudio no comparó empresas que usan estas herramientas con empresas que no las usan, no midió cuánto mejoró cada negocio y no puede afirmar que las herramientas causaran las ganancias. Tampoco dice nada sobre otros países, otros sectores ni otras regulaciones. Fueron 25 entrevistas en un solo país.

Antes de contratar cualquier sistema, revise si los reportes que su plataforma ya le entrega y una hoja de cálculo simple le muestran qué productos y qué canales de venta le dejan margen.

Qué significa para usted

Los dueños de estas tiendas describieron mejoras en sus decisiones y en sus márgenes, pero nadie midió cuánto ni comparó con negocios que no usan estas herramientas, así que tome esos relatos como experiencias y no como una promesa de ganancias. Antes de pagar por un sistema, pregunte quién de su equipo lo va a usar y qué pasará cuando esa persona se vaya.

Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650

Quién pagó: El artículo dice que la investigación se realizó con el apoyo del Centro de Investigación Interdisciplinaria en Economía y Ciencias Sociales, INCESA (Infraestructura de Investigación en Ciencias Aplicadas), Universidad de Craiova; no dice si algún financiador tuvo influencia ni si se prestó equipo o software.

Versión detalladaLos pasajes copiados del artículo, las ilustraciones y cada fuente con cuánto leímos de ella · 2105 palabras · unos 11 minLeerla →Cerrar

Las herramientas de datos ya están en las tiendas en línea pequeñas. Un estudio rumano preguntó a 25 dueños qué cambió de verdad.

No midió ventas ni ganancias: recogió lo que los propios gerentes dicen que vieron. Y ahí aparece lo que suele frenar estas inversiones.

Así podría verse · ilustración generada por weeklyAI.watch, no es una fotografía

Un equipo de la Universidad de Craiova, en Rumania, entrevistó a 25 dueños y gerentes de pequeñas y medianas empresas de comercio electrónico de ese país¹. La muestra final quedó repartida así: trece microempresas, siete pequeñas y cinco medianas, y las empresas se eligieron entre las ocho regiones de desarrollo del país. Fueron conversaciones semiestructuradas por teléfono, entre septiembre y octubre de 2025, de 25 a 40 minutos cada una². Antes hubo una prueba piloto con cinco empresas, con entrevistas de unos 40 minutos cada una³. Los autores leyeron y codificaron a mano, con apoyo de un programa de análisis cualitativo, sin dejar que el software asignara las categorías solo⁴⁵.

El retrato que surgió es el de una madurez digital intermedia: lo que domina es el análisis descriptivo —mirar lo que ya pasó—, mientras que las aplicaciones de inteligencia artificial están solo parcialmente implementadas⁶. Los dos frenos que aparecieron con más fuerza fueron la falta de competencias digitales y las restricciones de dinero, con diferencias según el tamaño de la empresa⁷. Y cuando esos obstáculos se van superando, los entrevistados relacionan el uso de estas herramientas con decisiones mejor fundamentadas y con mejoras en el desempeño: ventas más optimizadas, costos más bajos y un manejo más eficiente del inventario⁸.

Conviene retener qué es y qué no es este trabajo. No comparó empresas que usan estas herramientas con empresas que no las usan, no midió un efecto cuantificado ni separó el peso de la analítica del de otros cambios del negocio, y por su diseño cualitativo y transversal no permite generalizar⁹. Lo que ofrece son percepciones de gerentes en un sector concreto, no una prueba de causa.

Vale la pena entender de qué se habla. La analítica de datos es el uso sistemático de técnicas estadísticas y analíticas para generar información útil al manejo del negocio; la inteligencia artificial, en cambio, designa tecnologías capaces de simular procesos cognitivos, como el aprendizaje automático y la generación de predicciones¹⁰. El desempeño financiero, en este marco, son los resultados superiores que genera la actividad de una empresa y que se miran con indicadores como ingresos, utilidad y márgenes de rentabilidad¹¹. La diferencia práctica es sencilla: una herramienta descriptiva le cuenta lo que vendió; una predictiva intenta decirle lo que probablemente venda.

Lo que las empresas usan cambia mucho según su tamaño. Las microempresas se apoyan sobre todo en herramientas gratuitas o baratas —analítica web estándar, los reportes que ya trae la plataforma de venta y planillas de cálculo para análisis financieros básicos—¹². Las pequeñas dan un paso intermedio e integran soluciones por suscripción, como sistemas de gestión de clientes, plataformas de correo y, a veces, algo de automatización¹³. Las medianas muestran el nivel más avanzado: sistemas completos de planificación de recursos, plataformas de inteligencia de negocio y, sobre todo, modelos predictivos para pronosticar demanda, ajustar precios y personalizar el marketing¹⁴. El motivo declarado para adoptar todo esto es parecido en todas: la necesidad de decidir rápido y con datos reales¹⁵.

Los obstáculos que describieron también son concretos: el costo alto del software profesional, la complejidad de integrar sistemas entre sí, la necesidad de pruebas y ajustes repetidos y la falta de experiencia interna¹⁶. Hay un matiz que importa para cualquier política de apoyo: las microempresas están limitadas sobre todo por el dinero, mientras que las empresas más grandes chocan con problemas de implementación y optimización¹⁷. Y las dificultades de competencias de los empleados fueron mencionadas por más del 60% de los participantes, sin importar el tamaño de la empresa, lo que sugiere que la brecha de habilidades digitales es un problema sistémico en el país¹⁸.

Sobre el efecto en las decisiones, los entrevistados describen que el uso de datos reduce la incertidumbre y permite evaluar rápido las alternativas estratégicas¹⁹. Todas las categorías de empresa siguen datos de tráfico web y comportamiento del consumidor —tasa de conversión, rebote, tiempo en el sitio, recorrido de compra—, considerados esenciales para mejorar la experiencia de usuario²⁰. Las pequeñas y medianas miran además datos financieros (márgenes por producto o categoría, rotación de inventario, flujo de caja) y operativos (tiempos de proveedores, desempeño logístico, costo por canal)²¹. Un participante de una empresa pequeña contó que notó por el análisis que ciertas categorías de alto volumen tenían márgenes por debajo del promedio, intervino, renegoció con proveedores y ajustó precios, con lo que aumentó la rentabilidad²². Otro, de una firma mediana, dijo que las tecnologías automatizadas le ayudaron a identificar rápido cambios de tendencia en períodos de alta volatilidad, lo que le permitió ajustar campañas y evitar pérdidas²³. Los datos se usan para anticipar riesgos, evaluar escenarios y ajustar la estrategia de forma proactiva²⁴, y todos los participantes reportaron más confianza en sus decisiones al apoyarlas en datos concretos²⁵.

En el terreno del desempeño, lo que más se repite son mejoras en el rendimiento comercial²⁶: ventas más estables, mayor rentabilidad por categoría, menos costos de promoción y mejor control de gastos²⁷. También describen la capacidad de detectar los canales de marketing que rinden y recortar lo que no: "Uso los datos para decidir rápido dónde vale la pena invertir y evitar gastos innecesarios", dijo un gerente de una mediana²⁸. La optimización de inventario y logística, en cambio, fue lo menos mencionado —nueve referencias: tres en micro, cuatro en pequeñas y dos en medianas—, lo que sugiere que esa área sigue siendo un desafío para la mayoría de las empresas pequeñas, probablemente porque exige sistemas de gestión más complejos²⁹. La optimización de costos y márgenes acumuló 24 segmentos codificados, con beneficios como mayor rentabilidad por categoría, identificación de productos de margen débil y menos costos de promoción³⁰. El monitoreo de indicadores propios del comercio electrónico —conversión, valor promedio de pedido, carritos abandonados, tasa de clientes que vuelven— apareció otras 24 veces³¹, y algunos reportaron más clientes recurrentes gracias a campañas dirigidas basadas en los datos de su sistema de clientes³².

El aspecto más citado de todo el estudio —42 referencias— fue el escalamiento y la transformación organizacional, es decir, la idea de que estas tecnologías dan una ventaja competitiva sostenible³³. Los participantes la describen de varias formas: reacción más rápida a los cambios del mercado y una experiencia de cliente más personalizada, según un gerente de una mediana; mayor relevancia de las ofertas, según uno de una micro; asignación más eficiente de recursos, según uno de una pequeña; y más capacidad de innovación y adaptación³⁴. Un participante de una firma mediana lo resumió así: "Las tecnologías inteligentes optimizaron los procesos de análisis y planificación y redujeron el volumen de tareas repetitivas"³⁵. Aunque la madurez digital varía mucho según el tamaño, emerge un modelo común: implementación gradual, orientada a herramientas accesibles para optimizar procesos operativos y comerciales³⁶, y el impacto en el desempeño se evalúa como consistente y multifacético, con mejor rentabilidad, más eficiencia operativa, menos costos y mejor inventario³⁷. Las empresas que logran integrar estas herramientas en sus procesos internos muestran más capacidad de reaccionar al mercado y de personalizar la experiencia del cliente³⁸. Las limitaciones golpean más fuerte a las microempresas, que suelen depender de soluciones gratuitas o de funciones recortadas, lo que acentúa la brecha de desempeño frente a las pequeñas y medianas³⁹.

Así lo leemos nosotros. Hay un patrón que se repite cuando una tecnología nueva entra a un negocio chico: las tareas no desaparecen, se rearman. Primero se automatizan pasos sueltos; después una o dos personas vuelven a juntar esas tareas y deciden con la información que el sistema produce. La supervisión intermedia se adelgaza y los controles quedan escritos dentro del programa. En tiendas en línea como la suya, eso probablemente signifique que las herramientas de datos no van a reemplazar a nadie: van a concentrar más decisiones en el dueño o en una persona de confianza, que además tendrá que aprender a leer reportes y a mantener el sistema andando. Ese aprendizaje recaerá sobre quien ya está atendiendo ventas y clientes. Sabríamos que nos equivocamos si vemos microempresas que contratan o asignan personal exclusivo para analizar datos, o que delegan esas decisiones en alguien externo sin supervisión del dueño. Antes de sumar una herramienta, pregúntese quién va a leer los reportes todas las semanas y qué tarea va a dejar de hacer esa persona para atenderlos. Si nadie puede hacerlo, la herramienta quedará sin uso.

Y hay un segundo patrón que nos parece más incómodo: las herramientas que prometen simplificar el trabajo pueden terminar empeorándolo si la organización no cambia al mismo ritmo. La tecnología por sí sola no vuelve más autónomos ni más hábiles a los trabajadores; eso depende de cómo se repartan las tareas y de cuánta preparación se les dé. En tiendas en línea que adoptan análisis de datos sin invertir en capacitación, esperaríamos ver empleados usando las herramientas de forma superficial o resistiéndose, y al dueño notando que los reportes no se aprovechan y que las decisiones siguen tomándose por intuición, aunque ahora haya más pantallas y más datos. Nos desmentiría ver que, sin capacitación adicional, los empleados empiezan a proponer mejoras basadas en los datos y a decidir por su cuenta dentro de su área. Al elegir una herramienta, pregunte qué va a cambiar en el trabajo diario de cada persona y qué apoyo concreto recibirá. Si la respuesta es solo "ya aprenderán", conviene empezar por lo más simple y con acompañamiento.

Hay un tercer punto que conviene tener presente y que este estudio no toca: quién provee las herramientas y los canales de venta tiene más poder del que parece, porque decide qué opciones existen, cómo se ordenan y qué información se muestra. Ese poder no actúa solo; se combina con las reglas de la plataforma, los acuerdos comerciales y las prioridades de quienes la operan. Esperaríamos que las tiendas en línea chicas que dependen de una sola plataforma o de un solo proveedor de análisis sientan que su margen y su visibilidad cambian por decisiones ajenas: comisiones, cambios de algoritmo, nuevas condiciones. No siempre podrán anticiparlo ni negociarlo. Lo sabríamos si viéramos que las tiendas chicas cambian de proveedor o de plataforma sin costos ni pérdidas notables, o que negocian condiciones a su favor con la misma facilidad que las grandes. Anote de qué proveedor o plataforma depende cada parte de su negocio y qué pasaría si cambiaran las condiciones de un día para otro. Tener un plan B, aunque sea modesto, reduce el riesgo de quedar atado.

Otros trabajos que leímos apuntan en la misma dirección sobre lo que frena y lo que ayuda. Una revisión sobre la incorporación de inteligencia artificial en pequeñas empresas, de la que pudimos leer solo el resumen porque el texto completo está detrás de una suscripción, encontró que las firmas chicas suelen usar estas herramientas para atención al cliente, creación de contenido de marketing y tareas administrativas, y reportan ganancias de productividad cuando las herramientas van acompañadas de flujos de trabajo claros y supervisión humana. Ese mismo resumen señala que, más allá de eso, los resultados varían según la preparación y los recursos complementarios de cada empresa, y que las habilidades limitadas y la escasa preparación de los datos son los obstáculos que se repiten con más constancia.

Otro trabajo que leímos, también solo en su resumen, examinó el efecto de tecnologías digitales en la eficiencia operativa de pequeñas y medianas empresas en Lagos, Nigeria, con una encuesta a 166 dueños. Sus hallazgos indican que la inteligencia artificial y la computación en la nube cumplen un papel importante en el pronóstico de productos y la mejora de procesos, pero no contribuyen de forma significativa a la gestión de inventario, y que los efectos de estas tecnologías son situacionales: dependen de una integración adecuada de sistemas, de construir capacidad en el personal y de alinear los procesos operativos.

Con todo esto, lo que queda claro es que ninguna herramienta decide por usted, y que el orden importa: primero saber qué pregunta quiere responder, después quién va a mirar los datos, y solo entonces elegir el programa. El estudio rumano sugiere que las empresas que avanzan lo hacen de a poco, con herramientas accesibles y ajustando sus procesos, no comprando el sistema más caro de una vez. La pregunta que le dejamos para la próxima vez que un proveedor le prometa resultados: ¿qué va a cambiar en el trabajo de cada persona de su equipo, y quién lo va a sostener en el tiempo?

De dónde sale cada dato de contexto, y cuánto leímos de cada documento

Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "The research was conducted using a qualitative approach based on 25 semi-structured interviews with managers and business owners."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "The interviews were conducted by telephone between September and October 2025, lasting 25-40 minutes, depending on respondent availability and the level of details provided."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "The pilot study involved five SMEs with more than 5 years of experience in e-commerce, with each interview lasting approximately 40 minutes."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Qualitative data analysis was performed through manual coding assisted by QDA Miner Lite software, specialised in qualitative data analysis and provided by Provalis Research (provalisresearch.com)."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "QDA Miner Lite was used to structure codes, extract citations, and perform frequency and percentage analyses, while avoiding automatic coding due to the risks of generating redundant codes (Van Haneghan, 2021)."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "The results revealed an intermediate level of digital maturity, dominated by the use of descriptive analytics, while AI applications were only partially implemented."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "The main barriers identified were the deficit of digital competencies and financial constraints, with variations across firm size categories."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "As these barriers are overcome, the adoption of data analytics and artificial intelligence technologies contributed to a more rigorous grounding of decision-making and to improved financial performance through sales optimisation, cost reduction, and inventory management efficiency."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "The limitations include the qualitative nature and cross-sectional design, which do not allow for statistical generalisation; however, this does not diminish the theoretical and exploratory value of the findings."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Data analytics refers to the systematic use of statistical and analytical techniques to generate insights relevant to the managerial process (Mikalef et al., 2019; Chatterjee et al., 2023), while artificial intelligence denotes technologies capable of simulating cognitive processes, such as machine learning and prediction generation, contributing to the optimisation of SMEs’ financial performance (Arroyabe et al., 2024)."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Financial performance reflects the superior results generated by a firm’s activities under digitalisation and the adoption of analytical technologies (Radicic and Petković, 2023; Verdeș, Mironescu and Mazăre, 2024), measured through indicators such as revenue, profit, and profitability ratios."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Microenterprises predominantly rely on free or low-cost tools such as Google Analytics, standard reports offered by the e-commerce platform, and Excel for basic financial analyses."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Small enterprises present an intermediate level of technological endowment, integrating SaaS solutions such as CRMs, email marketing platforms, and occasionally automation solutions."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Medium SMEs demonstrate the most advanced level of adoption, implementing full ERP systems, advanced Business Intelligence platforms (Power BI, Tableau), sophisticated CRMs, and notably, predictive AI models for demand forecasting, price optimisation, and marketing personalisation."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "The main reasons for technology adoption focus on the need for rapid decisions based on real data."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "The high costs of professional software, the complexity of technical integration between systems, the need for repeated tests and the adjustments, and lack of internal expertise represent the major identified obstacles."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "The essential difference is that microenterprises are primarily constrained by financial barriers, while larger firms face challenges related to implementation and optimisation – an important consideration for support policies."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Difficulties related to employee competencies were mentioned by over 60% of the participants, regardless of firm size, suggesting that the digital skills gap is a systemic issue in Romania"
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Response analysis reveals that data use reduces uncertainty and allows rapid evaluation of strategic alternatives."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "All SME categories monitor data regarding website traffic and consumer behaviour (conversion rates, bounce rate, time on site, purchase funnel), considered essential for optimising user experience."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Small and medium enterprises pay increased attention to financial data (product/category margins, inventory turnover, cash flow) and operational data (supplier lead time, logistics performance, and costs per channel)"
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "A participant from a small enterprise describes: “I noticed through data analysis that certain high-volume categories had below-average margins and I intervened”, a decision that led to renegotiating terms with suppliers and adjusting prices, resulting in increased profitability."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Another participant from a medium firm explains: “Automated technologies helped me quickly identify trend changes during periods of high volatility”, allowing rapid adjustment of marketing campaigns and loss avoidance."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Data are used for risk anticipation, evaluation of possible scenarios, and proactive strategy adjustment."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "All participants reported increased confidence in their decisions as a result of grounding them in concrete data."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "The most consistent results are related to improvements in commercial performance."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Benefits reported by respondents include “more stable sales”, “increased profitability per category”, “reduced promotion costs”, and “better control over expenses”."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Participants also describe the ability to identify the high-performing marketing channels and elimination or reduction of investments in inefficient ones: “I use data to quickly decide where it is worth investing and avoid unnecessary expenses” (Medium)."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Inventory and logistics optimisation presents the lowest coding frequency (9 mentions: 3 micro, 4 small, 2 medium), suggesting that this area remains a challenge for most small SMEs, probably due to the need for more complex inventory management systems."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Cost and profitability margin optimisation accumulated 24 coded text segments (8 micro, 9 small, 7 medium), highlighting benefits such as “increased profitability per category”, “identification of products with weak margins”, and “reduced promotion costs”."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "This sub-theme was coded in 24 occurrences (8 micro, 9 small, 7 medium), reflecting the focus on e-commerce-specific KPIs such as: conversion rate, average order value, cart abandonment, and customer return rate."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Respondents report that they “noticed an increase in customer return rate, due to targeted campaigns based on CRM data” (Small)."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "The most frequently cited aspect (42 occurrences) relates to scaling and organisational transformation, through which digital technologies provide sustainable competitive advantage."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Participants describe multiple dimensions of this advantage: rapid reaction to market changes (“The advantage is reflected in faster response to market changes and a more personalised customer experience” - Medium), increased relevance of offers (“Our competitive advantage is absolute relevance” - Micro), superior efficiency in resource allocation (“Resources are allocated much more efficiently now” - Small), and greater capacity for innovation and adaptation."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "A participant from a medium firm summarised: “Intelligent technologies have optimised analysis and planning processes and diminished the volume of repetitive tasks”."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "Although digital maturity level varies significantly according to SME size, a common evolutionary model emerges: gradual implementation, oriented toward accessible tools necessary for optimising operational and commercial processes."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "The impact on performance is assessed as consistent and multifaced, materialising through improved profitability, increased operational efficiency, cost reduction, and inventory optimisation."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "SMEs that successfully integrate these instruments into their internal processes demonstrate superior capacity to react to market changes and personalise customer experience, thereby consolidating their competitive advantage."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - el artículo del que trata esta nota — el artículo completo — el pasaje: "These limitations particularly affect microenterprises, which often rely on free or limited-functionality solutions, thus accentuating the performance gap compared to small and medium enterprises."

interviews · Amfiteatru Economic · the paper, 1 May 2026 · free

Shop Owners Said Data Tools Helped, and Their Budgets Still Held Them Back

In interviews with 25 Romanian online sellers, owners described what worked and what they could not afford. The study reports what they perceived, not what the tools proved.

Short version · the longer version follows, about 10 min

Ask the weeklyAI chat

Ask me about this study: who was studied, what it found, and what it does not say.

Conversations are saved for as long as weeklyAI exists, to improve the publication. Answers come in the language you write in.

The study at a glance
Who: owners and managers of Romanian online shops
How many: 25
Where: Romania
When: September and October 2025
Kind of study: interviews
Who did it: University of Craiova
The limit that matters: It reports what owners said they noticed, with no second group for comparison.

How often each idea came up in the interviews

Scaling and organisational transformation42mentions

Cost and profitability margin optimisation24mentions

Monitoring technical indicators and customer experience24mentions

Inventory and logistics optimisation9mentions

These are counts of how often each idea appeared in the 25 interviews, not measures of business results.

What held firms back, by size

microenterprisesagainstlarger firms

microenterprises were held back mainly by money, while larger firms struggled more with implementation and optimisation

What the pattern does suggest is a way in: start with the simple, low-cost tools, and treat your staff's skills, not the software, as the main thing holding you back.
weeklyAI's reading

Watch · 0:50

You run an online shop without a technical staff, and you wonder whether data tools are worth the money.

To see what owners in that position did, researchers at the University of Craiova interviewed 25 owners and managers of Romanian online shops by telephone in September and October 2025. The group covered the country's eight regions: 13 micro businesses, 7 small, and 5 medium.

Size decided what they could afford. Micro firms stuck to free tools: Google Analytics, the standard reports their selling platform already produced, and Excel. Small firms added systems like GA4, Power BI and customer databases. Medium firms ran fuller systems, including predictive models for demand and pricing.

Owners described what came back. Owners said sales became steadier. They spotted products with weak margins and cut spending on ads that did not pay. They managed stock better and watched conversion rates and cart abandonment. One small-firm owner saw that some high-volume product lines had below-average margins and renegotiated with suppliers.

What matched across the group was the obstacle. More than 60 percent of the people interviewed, at every size, said their staff lacked the skills to use the tools. Software costs and hard technical integration came up too. Micro firms said their budgets simply did not stretch to advanced tools.

The study cannot show that the tools caused the gains. It reports what owners said they noticed, with no second group for comparison. The study did count how often each idea came up in the interviews. It covered 25 e-commerce firms in one country, so costs, rules and supplier conditions elsewhere may differ. Treat the benefits as what these owners perceived, not as results you can count on.

What the pattern does suggest is a way in: start with the simple, low-cost tools, and treat your staff's skills, not the software, as the main thing holding you back.

Look at the reports your own platform already gives you, and at a plain spreadsheet, and see whether they already show which products and channels are worth keeping.

What this means for you

For your own shop, the useful signal here is modest: these owners said the tools helped, and their staff's skills held them back more than the software did. So look at what your platform already reports, and ask whether anyone on your team can read it. Treat the gains as what these owners noticed, not what the tools proved.

Who paid: The article says the research was conducted with the support of the Center for Interdisciplinary Research in Economics and Social Sciences, INCESA (Research Infrastructure in Applied Sciences), University of Craiova; it does not say whether any funder had a say or lent equipment or software.

The longer versionThe passages copied from the paper, the pictures, and every source with how much of it we read · 1992 words · about 10 minRead it →Close

Romanian Shop Owners Say Data Tools Steady Sales and Cut Costs — With the Skills Gap Deciding Who Benefits

Twenty-five e-commerce managers describe what changed in their decisions. The study records perceptions, not proof.

How it could look · illustration generated by weeklyAI.watch, not a photograph

Managers of Romanian online shops say that using data analytics and artificial intelligence has made their decisions better grounded and their finances stronger — steadier sales, lower promotion costs, leaner inventory. The finding comes from 25 semi-structured interviews with owners and administrators of Romanian e-commerce small and medium-sized enterprises: 13 microenterprises, 7 small firms and 5 medium firms, reached by telephone between September and October 2025¹². The interviews were coded by hand with the help of QDA Miner Lite, a qualitative analysis program, and the paragraphs were read in full by pairs of authors who sorted them into 15 sub-themes drawn from three main themes³⁴.

What the owners described was not one technology but a ladder of them. Microenterprises mostly rely on free or low-cost tools — Google Analytics, the standard reports their e-commerce platform already produces, and Excel for basic financial analysis⁵. Small firms sit in the middle, adding subscription tools such as customer relationship management systems and email marketing platforms⁶. Medium firms go furthest: full ERP systems, business intelligence platforms such as Power BI and Tableau, and predictive models used for demand forecasting, price setting and personalised marketing⁷.

The stated reasons for adopting any of it were practical: the wish for faster decisions based on real data⁸. Owners described wanting more control over sales, inventory and margins, wanting to understand customers better, and wanting to streamline costs. Across the whole sample, the study describes an intermediate level of digital maturity, dominated by descriptive analytics — looking at what already happened — with AI applications only partially implemented⁹.

The obstacles were equally consistent. High costs of professional software, the complexity of connecting systems to each other, repeated testing and adjustment, and a lack of expertise inside the firm were the major barriers named¹⁰. There was a difference by size: microenterprises were held back mainly by money, while larger firms struggled more with implementation and optimisation¹¹. Skill gaps were mentioned by more than 60 percent of participants regardless of firm size, which the authors read as a systemic problem rather than a few unlucky firms¹².

The reported benefits ran from the commercial to the organisational. The most consistent results were in commercial performance: steadier sales, higher profitability per product category, lower promotion costs and better control over expenses¹³¹⁴. Owners described spotting which marketing channels performed and cutting spending on the rest¹⁵. One small-firm owner described noticing that some high-volume categories carried below-average margins, then renegotiating supplier terms and adjusting prices, which raised profitability¹⁶. Cost and margin optimisation appeared in 24 coded segments, and monitoring e-commerce measures such as conversion rate, average order value and cart abandonment also appeared in 24¹⁷¹⁸. Inventory and logistics optimisation had the lowest frequency — 9 mentions — which the authors suggest reflects how hard that area remains for small firms¹⁹. All participants reported more confidence in their decisions once those decisions rested on concrete data²⁰.

Two limits matter before anything is taken from this. The design is qualitative and cross-sectional: it captures what 25 managers said at one point in time, and the authors state plainly that it does not allow statistical generalisation²¹. The study reports what managers perceived, not measured effects. The firms were identified as those that reported balance sheets under NACE code 4791 for 2023 and 2024, and they had to have contact details available.

The technology itself is less exotic than the label suggests. Data analytics means using statistical and analytical techniques systematically to produce insights for management; artificial intelligence means software that simulates cognitive processes, such as machine learning and generating predictions²². Financial performance, in this literature, means the superior results a firm generates — read through revenue, profit and profitability ratios²³. The practical difference between the rungs of the ladder is what the tool does with the numbers: a platform report tells you what sold, while a predictive model tells you what is likely to sell next.

Analysts of small-business technology adoption describe a similar split elsewhere. A review of artificial intelligence in small businesses — we could read only the summary; the full paper sits behind a subscription — found that small firms most often use AI for customer engagement, marketing content and administrative automation, and report productivity gains when the tools are paired with clear workflows and human oversight²⁴. The same summary reports that outcomes vary with readiness and complementary assets, and that limited skills and weak data readiness are the barriers cited most consistently²⁵²⁶. That is the same shape this Romanian study describes, in a different country and a different method.

What the tools are used for also shapes what they can deliver. A separate survey study of 166 small-business owners in Lagos State, Nigeria — again, we could read only the summary — found that artificial intelligence and cloud computing played significant roles in product forecasting and process improvement, but did not significantly contribute to inventory management²⁷²⁸. Its authors concluded that the effects of digital technologies are situational, requiring proper system integration, staff capacity building and aligned operational processes²⁹. Note the overlap with the Romanian finding: inventory and logistics was the thinnest category of reported benefit there too¹⁹.

There is a caution about how much any one tool adds. In a controlled experiment with 199 participants, researchers tested whether assistance from a large language model improved the way entrepreneurs developed business ideas; on average it did not, but it helped those whose ideas were less developed and displaced the thinking of those whose ideas were already distinctive³⁰³¹. The summary notes that these models can also push reasoning toward conventional patterns and away from the counterintuitive ideas that produce breakthroughs³². For a small firm weighing an AI subscription, that is a reminder that assistance is not the same as improvement, and that the value depends on what the person brings to it.

The constraints around this kind of adoption are not only technical. Restricted access to financing is described as a major structural barrier to AI adoption in e-commerce SMEs, because the initial costs often exceed what a small firm can invest³³. The literature also names a lack of collateral, high transaction costs and information gaps between lenders and small firms³⁴. High initial costs for infrastructure and software licences can block adoption, although access to markets and logistics services can later reduce the cost of running an e-commerce operation³⁵. This is why the study's own authors stress that money and institutional support are necessary but not sufficient, and that effective policy combines financing, risk management and advisory services³⁶.

In Romania specifically, the backdrop is a digitalisation level below the European Union average — 69.1 percent against 72.91 percent³⁷. Among Romanian SMEs the level remains low, especially for small firms, because of limited financial resources and insufficient IT infrastructure, with services and commerce among the sectors most affected³⁸. Online sellers there face pressure to adopt analytics and AI under conditions of limited organisational resources³⁹. Research on Romanian e-commerce SMEs describes wide variation between firms: managers willing to take risks and invest in digital capability tend to post the best financial results, while financial limits, thin local know-how and uneven adoption remain recurring problems⁴⁰.

The studies behind this kind of finding mostly ask firms to fill in surveys, which gives a broad picture of who has adopted what but says little about how decisions actually get made inside the firm⁴¹. The few qualitative or mixed-method studies that exist have not focused on e-commerce alone⁴². That gap is the reason this research was done the way it was, and it is also the reason its results should be read as a description of experience rather than a measurement of effect.

Here is how we read it. Put the three tiers side by side and a pattern appears that has less to do with software than with who can read it. The microenterprise on free tools and a spreadsheet, the small firm on a subscription dashboard, and the medium firm on predictive models are not simply at different points on a spending curve — they are at different points on a learning curve, and the study's own numbers show that the skill gap was named by a majority of everyone interviewed, not just the smallest firms. That leads us to expect that the firms which gain most from these tools will be the ones where somebody inside the business actually sits with the output and acts on it, and that buying a tool without buying that person's time will produce a dashboard nobody opens. We would be wrong if microenterprises with no analytical staff turned out to adopt advanced tools as effectively as medium firms, or if the gap between the smallest and largest firms narrowed rather than persisted. What you can do with this: before you pay for anything, name the person who will read the reports and decide what changes, and count their learning hours as part of the price. Then watch, over the first quarter, whether the benefit lands with you or with the outside consultant you now depend on.

A second thing we notice is where the decisions sit. In firms this size the owner is usually the only person who interprets the numbers, which means the speed the owners describe depends on the owner being present. The study's participants reported more confidence and faster decisions, and we would expect that to hold in ordinary weeks and to fray during peak trading or when the owner is away. We would be wrong if front-line staff in these firms routinely acted on the data without the owner's sign-off, or if decision speed turned out not to depend on the owner's personal involvement at all. What you can do with this: pick one routine call — reordering stock, pausing an underperforming ad — that someone other than you could make from a simple screen, let them make it for a month, and see whether your own desk, rather than the tool, was the bottleneck.

We also read the confidence finding with some care. All the participants said they trusted their decisions more once those decisions rested on data²⁰, which is a good sign and also a familiar one: it takes some knowledge to recognise the limits of your own reading of a chart. We would expect the owners working from free, fragmented tools to feel the same lift in confidence as those working from integrated systems, without the same accuracy behind it. We would be wrong if confidence in these firms tracked objective performance improvements, or if the owners with the thinnest toolkits were the ones most careful about what their numbers could and could not show. What you can do with this: when a report makes you feel certain, write down the decision and the figure behind it, then check the outcome a month later — and ask your accountant or a peer in the same trade what they would have done differently with that same number.

The last thing worth holding onto is the direction the authors themselves point. They recommend investing in analytics infrastructure such as integrated systems, building employees' digital skills including formal training in data management and AI models, and tightening logistics, inventory and financial operations⁴³. They also argue that public support programmes, professional training and competency networks are what would spread advanced tools across the sector⁴⁴. For a reader running an online shop, the practical translation is unglamorous: the tool is the cheap part, and the person who understands it is the expensive part. Ask any vendor which of your staff will be able to run the thing without them, and get the answer in writing before you sign.

What one number would you check a month from now to see whether your data tools are actually earning their keep?

Where each piece of context comes from, and how much of it we read

Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "The research was conducted using a qualitative approach based on 25 semi-structured interviews with managers and business owners."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "The interviews were conducted by telephone between September and October 2025, lasting 25-40 minutes, depending on respondent availability and the level of details provided."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Qualitative data analysis was performed through manual coding assisted by QDA Miner Lite software, specialised in qualitative data analysis and provided by Provalis Research (provalisresearch.com)."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Thematic coding followed an inductive-deductive process, in which paragraphs were read entirely by cross-teams of two authors and associated with the 15 sub-themes derived from the three main study themes."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Microenterprises predominantly rely on free or low-cost tools such as Google Analytics, standard reports offered by the e-commerce platform, and Excel for basic financial analyses."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Small enterprises present an intermediate level of technological endowment, integrating SaaS solutions such as CRMs, email marketing platforms, and occasionally automation solutions."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Medium SMEs demonstrate the most advanced level of adoption, implementing full ERP systems, advanced Business Intelligence platforms (Power BI, Tableau), sophisticated CRMs, and notably, predictive AI models for demand forecasting, price optimisation, and marketing personalisation."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "The main reasons for technology adoption focus on the need for rapid decisions based on real data."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "The results revealed an intermediate level of digital maturity, dominated by the use of descriptive analytics, while AI applications were only partially implemented."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "The high costs of professional software, the complexity of technical integration between systems, the need for repeated tests and the adjustments, and lack of internal expertise represent the major identified obstacles."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "The essential difference is that microenterprises are primarily constrained by financial barriers, while larger firms face challenges related to implementation and optimisation – an important consideration for support policies."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Difficulties related to employee competencies were mentioned by over 60% of the participants, regardless of firm size, suggesting that the digital skills gap is a systemic issue in Romania"
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "The most consistent results are related to improvements in commercial performance."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Benefits reported by respondents include “more stable sales”, “increased profitability per category”, “reduced promotion costs”, and “better control over expenses”."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Participants also describe the ability to identify the high-performing marketing channels and elimination or reduction of investments in inefficient ones: “I use data to quickly decide where it is worth investing and avoid unnecessary expenses” (Medium)."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "A participant from a small enterprise describes: “I noticed through data analysis that certain high-volume categories had below-average margins and I intervened”, a decision that led to renegotiating terms with suppliers and adjusting prices, resulting in increased profitability."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Cost and profitability margin optimisation accumulated 24 coded text segments (8 micro, 9 small, 7 medium), highlighting benefits such as “increased profitability per category”, “identification of products with weak margins”, and “reduced promotion costs”."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "This sub-theme was coded in 24 occurrences (8 micro, 9 small, 7 medium), reflecting the focus on e-commerce-specific KPIs such as: conversion rate, average order value, cart abandonment, and customer return rate."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Inventory and logistics optimisation presents the lowest coding frequency (9 mentions: 3 micro, 4 small, 2 medium), suggesting that this area remains a challenge for most small SMEs, probably due to the need for more complex inventory management systems."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "All participants reported increased confidence in their decisions as a result of grounding them in concrete data."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "The limitations include the qualitative nature and cross-sectional design, which do not allow for statistical generalisation; however, this does not diminish the theoretical and exploratory value of the findings."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Data analytics refers to the systematic use of statistical and analytical techniques to generate insights relevant to the managerial process (Mikalef et al., 2019; Chatterjee et al., 2023), while artificial intelligence denotes technologies capable of simulating cognitive processes, such as machine learning and prediction generation, contributing to the optimisation of SMEs’ financial performance (Arroyabe et al., 2024)."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Financial performance reflects the superior results generated by a firm’s activities under digitalisation and the adoption of analytical technologies (Radicic and Petković, 2023; Verdeș, Mironescu and Mazăre, 2024), measured through indicators such as revenue, profit, and profitability ratios."
Minifie J, Arango Mesa S, Ayer S. (2026). Incorporating Artificial Intelligence in Small Businesses: A Practitioner-Oriented Literature Review (2020–2025). Small Business Institute Journal. https://doi.org/10.53703/001c.168695 — only the abstract - the full text could not be fetched — the passage: "Small firms most often use AI for customer engagement, marketing content creation, and administrative automation, and they report productivity gains when tools are paired with clear workflows and human oversight."
Minifie J, Arango Mesa S, Ayer S. (2026). Incorporating Artificial Intelligence in Small Businesses: A Practitioner-Oriented Literature Review (2020–2025). Small Business Institute Journal. https://doi.org/10.53703/001c.168695 — only the abstract - the full text could not be fetched — the passage: "Beyond that, outcomes vary, reflecting differences in readiness and complementary assets."
Minifie J, Arango Mesa S, Ayer S. (2026). Incorporating Artificial Intelligence in Small Businesses: A Practitioner-Oriented Literature Review (2020–2025). Small Business Institute Journal. https://doi.org/10.53703/001c.168695 — only the abstract - the full text could not be fetched — the passage: "Limited skills and weak data readiness are the barriers reported most consistently."
Alu C, Shiyanbola A, Olurin O, Olalere M, Ahannaya C, Atejioye O, et al. (2026). Digital technologies and operational efficiency: evidence from small and medium enterprises in Lagos State, Nigeria. Cogent Business & Management. https://doi.org/10.1080/23311975.2026.2744827 — only the abstract - the full text could not be fetched — the passage: "The findings indicate that artificial intelligence and cloud computing play significant roles in product forecasting and improving business processes, but do not significantly contribute to inventory management."
Alu C, Shiyanbola A, Olurin O, Olalere M, Ahannaya C, Atejioye O, et al. (2026). Digital technologies and operational efficiency: evidence from small and medium enterprises in Lagos State, Nigeria. Cogent Business & Management. https://doi.org/10.1080/23311975.2026.2744827 — only the abstract - the full text could not be fetched — the passage: "A structured questionnaire was used to collect data from 166 SME owners, and both stratified and purposive sampling techniques were employed."
Alu C, Shiyanbola A, Olurin O, Olalere M, Ahannaya C, Atejioye O, et al. (2026). Digital technologies and operational efficiency: evidence from small and medium enterprises in Lagos State, Nigeria. Cogent Business & Management. https://doi.org/10.1080/23311975.2026.2744827 — only the abstract - the full text could not be fetched — the passage: "The paper further reveals that the effects of digital technologies are situational, requiring appropriate system integration, personnel capacity building, and alignment of operational processes."
Park SH, Murray A. (2026). Entrepreneurial theorizing with generative artificial intelligence. Strategic Entrepreneurship Journal. https://doi.org/10.1002/sej.70060 — only the abstract - the full text could not be fetched — the passage: "In a within‐subject experiment with 199 participants theorizing both with and without LLM assistance, we find no significant average treatment effect on either structural or content properties."
Park SH, Murray A. (2026). Entrepreneurial theorizing with generative artificial intelligence. Strategic Entrepreneurship Journal. https://doi.org/10.1002/sej.70060 — only the abstract - the full text could not be fetched — the passage: "However, this null average effect masks a redistribution effect, where LLM assistance supplemented theorizing when unaided theories were less developed or distinctive but displaced participants' own theorizing when their theories were already more developed or distinctive."
Park SH, Murray A. (2026). Entrepreneurial theorizing with generative artificial intelligence. Strategic Entrepreneurship Journal. https://doi.org/10.1002/sej.70060 — only the abstract - the full text could not be fetched — the passage: "At the same time, LLMs may also push reasoning toward conventional patterns and away from the counterintuitive ideas that are a source of breakthrough opportunities."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Restricted access to financing constitutes a major structural barrier to AI solution adoption in e-commerce SMEs, as initial costs frequently exceed the investment capacity of small firms (EIB, 2023)."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Other barriers identified in the literature include lack of collateral, high transaction costs, and information asymmetries between financial institutions and SMEs (Kergroach, 2021)."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "High initial costs for infrastructure and software licenses can represent a barrier; however, access to markets and logistics services can subsequently reduce the costs of e-commerce integration (Kahveci, 2025)."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Financial resources and institutional support represent necessary but not sufficient conditions; effective policies combine financing, risk management, and advisory services to transform AI investments into efficient decision-making and improved financial performance (Arroyabe et al., 2024)."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "In Romania, e-commerce is experiencing rapid growth, yet the overall level of digitalisation remains below the European Union average (69.1% compared to 72.91%) (EC, 2025)."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "In Romania, the level of digitalisation among SMEs remains low, particularly for small firms, due to limited financial resources and insufficient IT infrastructure, with the services and commerce sectors being the most affected (Mihu and Herciu, 2024; Verdeș, Mironescu and Mazăre, 2024)."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "In this context, SMEs in e-commerce face pressure to adopt analytical technologies and AI solutions (online platforms, digital marketing) under conditions of limited organisational resources (Iordache et al., 2023)."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Research on Romanian SMEs in the e-commerce domain highlights significant heterogeneity among firms: managers oriented toward risk-taking and investment in digital capabilities tend to achieve the best financial results, whereas recurring challenges include financial limitations, lack of local know-how, and uneven levels of technology adoption (Iordache et al., 2023; Mihu and Herciu, 2024)."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Most studies on Romanian e-commerce SMEs employ a quantitative approach based on surveys (Cazabat et al., 2019; Türkeș et al., 2019; Măiţă, Pădurean and Cazacu, 2021; Iordache et al., 2023; Mihu and Herciu, 2024), providing a general overview of the level of digitalisation and its determinants, but limiting the understanding of organisational mechanisms and managerial experiences."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Some studies use qualitative approaches (González-Varona et al., 2021; Lupșa-Tătaru, Marinescu and Rădulescu, 2025) or mixed methods (Borcoși et al., 2023); however, these are limited and do not focus exclusively on the e-commerce sector."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "Based on the research findings, several recommendations can be formulated for Romanian e-commerce SMEs: investing in data analytics infrastructure (integrated solutions such as ERP and automation platforms); developing employees’ digital skills, including certifications in data management and AI models; optimising logistics workflows, inventory management, and financial operations."
Vasilescu, L., Sichigea, M., Brabete, V. et al. (2026). Decision-Making and Financial Performance in E-Commerce SMEs: Perceptions on Data Analytics and Artificial Intelligence. Amfiteatru Economic. https://doi.org/10.24818/EA/2026/72/650 - the article this story is about — the whole article — the passage: "In this context, the development of public support programs, professional training, and competency ecosystems becomes crucial, as such initiatives can accelerate the adoption of advanced technologies across the SMEs."

The findings of other studies mentioned here are known to us through this document, which is the one we read; we did not open each of those studies.