1.1 Information and Communication Technologies in Small and Medium-Sized Enterprises

The impacts generated by steam engines, electricity, and fuel during previous industrial revolutions have now been succeeded by Information and Communication Technologies. These technologies—encompassing hardware, software, communications, and associated services—have fundamentally altered processes and activities across all aspects of life (Tricoci, 2015[55] Tricoci, G. A. (2015). Un meta-análisis de los efectos de las TIC sobre el nivel de empleo y los roles laborales: comentarios y direcciones futuras. Buenos Aires: Universidad de Buenos Aires, Argentina.
).

ICTs are regarded as general-purpose technologies that affect the entire economy. This includes communicative devices and applications such as radios, televisions, mobile phones, computer networks, satellite systems, videoconferencing, and e-learning (Van Ark et al., 2011[58] Van Ark, B., Gupta, A., & Azeez, A. (2011). Midiendo la contribución de las TIC al crecimiento económico . En B. Van Ark, Un mundo conectado: Las TIC transforman sociedades culturas y economías (págs. 7-89). Barcelona : Fundación Telefónica.
). These advancements have brought about a transformation in education, employment, and production systems, giving rise to new paradigms.

Unlike past technological advancements that focused on knowledge production, the current revolution centers on the application of knowledge and information for generating new knowledge and improving communication. This creates a cumulative feedback loop between innovation and its uses (Avgerou et al., 2006[5] Avgerou, C., Hanseth, O., & Willcocks, L. (2006). Claudio Ciborra and the information systems field: legacy and development. Journal of Information Technology, 21, 127-128. https://doi.org/10.1057/palgrave.jit.2000061.
). Thus, these new technologies are not mere tools but processes to be developed, leading to significant changes in the traditional work environment.

The integration of ICTs enables firms to enhance both internal and external operations, providing substantial benefits while maintaining competitive advantages (Dibrell et al., 2008[17] Dibrell, C., Davis, P., & Craig, J. (2008). Fueling innovation through information technology in SMEs. Bond University , 86. DOI: https://doi.org/10.1111/j.1540-627X.2008.00240.x
). Consequently, it is essential for SMEs to increasingly adopt ICTs as part of their business activities and invest in technological infrastructure.

Studies such as Naggi (2011)[37] Naggi, R. (2011). Understanding the ICT adoption process in small and medium enterprises (SMEs). In A. D’Atri, M. Ferrara, J. F. George, & P. Spagnoletti (Eds.), Information technology and innovation trends in organizations (pp. 515-522). Springer. https://doi.org/10.1007/978-3-7908-2632-6_58
demonstrate that the adoption of ICTs is significantly related to employee performance in SMEs, particularly when accompanied by appropriate training and managerial support. Similarly, ICT adoption enables SMEs to compete globally, develop closer relationships with customers and suppliers, and embed technology into their commercial strategies (Tarutė & Gatautis, 2013[54] Tarute, A., & Gatautis, R. (2013). ICT impact on SMEs performance. Social and Behavioral Sciences, 1218-1225. https://doi.org/10.1016/j.sbspro.2013.12.968
).

The upward trend in ICT adoption among SMEs over the last decade stems from two main factors: (1) changes in economic market dynamics and (2) the positive impact of ICTs on business goals (Oyelaran & Lal, 2006[44] Oyelaran-Oyeyinka, B., & Lal, K. (2006). Learning new technologies by small and medium enterprises in developing countries. Technovation, 26(2), 220-231. https://doi.org/10.1016/j.technovation.2004.07.005
). However, despite their benefits, SMEs often face financial constraints that limit their ability to adopt emerging technologies such as software, hardware, and infrastructure (Molina-Ycaza et al., 2016[36] Molina-Ycaza, D., & Sánchez-Riofrío, A. (2016). Obstáculos para la micro, pequeña y mediana empresa en América Latina. Pymes, Innovación y Desarrollo, 4, 21-36 - ISSN-e 2344-9195.
).

The degree of ICT integration and usage has a significant impact on SME performance. However, ICT adoption alone is insufficient—its proper integration and alignment with business processes are critical for achieving improved performance (Azam, 2015[7] Azam, S. (2015). Diffusion of ICT and SME performance. E-services adoption: Processes by firms in developing nations. Emerald Group Publishing Limited, 7-290. https://doi.org/10.1108/S1069-096420150000023005.
). ICT platforms such as mobile devices, the Internet, and computers offer four main benefits to SMEs: (1) increased visibility, (2) better access to information, (3) the ability to overcome traditional trade barriers, and (4) improved financial transactions (Tarute & Gatautis, 2013[54] Tarute, A., & Gatautis, R. (2013). ICT impact on SMEs performance. Social and Behavioral Sciences, 1218-1225. https://doi.org/10.1016/j.sbspro.2013.12.968
). Aligning ICT investments with internal capabilities and organizational processes further enhances these outcomes (Consoli, 2012[15] Consoli, D. (2012). Literature analysis on determinant factors and the impact of ICT in SME´s. Social and Behaboural Sciences 62, 93-97. https://doi.org/10.1016/j.sbspro.2012.09.016
).

1.2 Effects of Information and Communication Technologies by Economic Activity

As SMEs seek to improve their efficiency, competitiveness, and reach in an increasingly globalized market, analyzing the effects of ICTs across different economic sectors becomes essential. This analysis provides a holistic understanding of how ICTs are redefining SME operations in diverse economic contexts:

A detailed analysis of ICT shows impacts across various economic sectors highlighting their transformative potential. While ICTs can address regional disparities in agriculture, they optimize safety and operations in mining and enhance production in manufacturing. In sectors like utilities, construction, and financial services, ICTs provide critical tools for decision-making and efficiency. Ultimately, aligning ICT adoption with organizational capabilities remains essential for SMEs to fully realize these benefits.

3.1 Data

We utilized the 2017 Longitudinal Survey of Enterprises (ELE 5), conducted by Chile's National Institute of Statistics (INE) and the Ministry of Economy. This survey covers key dimensions for business development, such as: technology, training, productive linkages, research, and human and financial resources.

The ELE 5 sampling design is probabilistic and stratified, where the strata are defined by economic activity section and firm size. Economic activity sections follow the national adaptation of the International Standard Industrial Classification (ISIC4.CL 2012), while firm size is determined based on total annual sales measured in “Unidades de Fomento (UF)”, which is an inflation-indexed monetary unit widely used in Chile to adjust prices, contracts, and economic indicators, ensuring values remain stable relative to inflation (INE, 2020[25] INE. (2 de febrero de 2020). Ministerio de economía. Obtenido de Encuesta Longitudinal de Empresa. Retrieved from https://www.ine.cl/estadisticas/economia/ciencia-y-tecnologia/encuesta-longitudinal-de-empresas
).

The survey was administered to 339,002 companies, excluding those with annual sales of USD $31,595 or less. The sample is representative in terms of firm size (micro, small, medium, and large, based on sales) and economic sector (INE, 2020[25] INE. (2 de febrero de 2020). Ministerio de economía. Obtenido de Encuesta Longitudinal de Empresa. Retrieved from https://www.ine.cl/estadisticas/economia/ciencia-y-tecnologia/encuesta-longitudinal-de-empresas
). However, only 6,480 surveys were successfully completed after undergoing rigorous review and validation processes. These companies represent businesses that began operations in 2016.

Focusing exclusively on SMEs, we filtered companies with annual sales in the range of 2,401 UF to 100,000 UF, which are classified as SMEs in Chile (SII, 2020[52] Servicio de Impuestos Internos (SII), (2020). Nómina de personas jurídicas. Servicio de Impuestos Internos. Retrieved from https://www.sii.cl/sobre_el_sii/nominapersonasjuridicas.html
). This filtering reduced our sample to 2,643 firms, enabling us to work with microdata.

The pre-processing of the data involved eliminating missing values. Specifically, the rule applied was as follows: if a value was missing, we attempted to infer the missing response based on other available data to preserve the case. If this was not possible, the entire case was removed (Table 1).

The ICT variables considered are ordinal, as they represent categories with an implicit ranking (e.g., the company uses or does not use a specific technology). Annex 1 presents the descriptive statistics for each of these variables.

Below, we list the selected variables related to the incidence of ICT usage, supported and framed by previous literature:

3.2 Methodology

This research employs two quantitative techniques. The first is an Analysis of Variance (ANOVA), which aims to determine the significance of the use of Information and Communication Technology (ICT) variables across different economic activities of Chilean SMEs. The second technique is Factor Analysis, designed to reduce data dimensionality. Its objective is to identify the minimum number of dimensions capable of explaining the maximum amount of information contained in the dataset (Gonzalez, 2009[21] Gonzalez, J. A. (2009). Manual Básico SPSS- Manual de introducción SPSS. Universidad de Talca, Centro de inserción laboral.
). As part of this analysis, we will generate ICT factors relevant to small and medium-sized enterprises.

To simplify the subsequent formation of ICT usage clusters, we have chosen to organize the variables based on the categorization proposed by Aral and Weill (2007). This classification comprises of four ICT groupings, enabling a more fluid and effective structuring of the variables according to their nature and application in each economic sector.

Below is a summary of the four main classifications, along with their expected operational benefits:

4.1 General ANOVA Analysis of the Model

This section analyzes whether the use of Information and Communication Technologies (ICT) significantly influences the operational performance of Chilean SMEs across different economic sectors. To do so, we applied a one-way Analysis of Variance (ANOVA), a statistical method used to determine whether there are significant differences in the means of a dependent variable across multiple groups (Gonzalez, 2009[21] Gonzalez, J. A. (2009). Manual Básico SPSS- Manual de introducción SPSS. Universidad de Talca, Centro de inserción laboral.
).

In our case, we used the variable “CIIU_RECO” (economic sectors) as the factor and considered ICT indicators as the dependent variables. The null hypothesis assumes no significant differences in ICT usage across sectors, implying a uniform impact. Conversely, the alternative hypothesis—tested in this study—proposes that at least one ICT variable differs significantly between sectors, indicating a differentiated effect on operational capabilities.

Although the data used in this analysis are based on a 2017 pre-pandemic survey (ELE5), the findings provide a valid structural benchmark for understanding ICT adoption in SMEs. Scientific literature confirms that the COVID-19 pandemic accelerated digital transformation globally: over 70% of SMEs increased their use of digital tools between 2020 and 2021 (OECD, 2021[39] Organisation for Economic Co-operation and Development (OECD). (2021). The digital transformation of SMEs. OECD Publishing. https://doi.org/10.1787/bdb92b6e-en
). Moreover, technologies such as artificial intelligence, automation, and digital platforms became essential for maintaining business continuity and boosting resilience (McKinsey, 2023[35] McKinsey. (2023). How SMEs are using AI and automation to boost productivity. Obtenido de https://www.mckinsey.com/business-functions/mckinsey-digital
).

However, most of the post-pandemic research has focused on large enterprises or digitally mature/advanced firms, leaving a gap in the literature regarding SMEs, especially in emerging economies. Therefore, this pre-pandemic diagnostic remains highly relevant for identifying foundational ICT adoption patterns in SMEs—particularly those underrepresented in recent data. These patterns offer a critical baseline to assess the evolving impact of emerging technologies on SME competitiveness, productivity, and digital strategy (Zafar, 2022[60] Zafar, A. K. (2022). Digital transformation of small and medium enterprises (SMEs) in the post-COVID-19 era: Challenges, enablers, and policy implications. Frontiers in Public Health, 10, 885067, https://doi.org/10.3389/fpubh.2022.885067.
).

Table 4 summarizes the ANOVA results and identifies the sectors in which ICT variables show statistically significant differences, supporting the hypothesis that ICT adoption impacts operational development heterogeneously across economic sectors.

Based on Table 4, a one-way ANOVA test was conducted by performing nineteen pairwise comparisons between variables. The independent variable considered was “CIIU_RUBROS”, representing the economic activities of SMEs, while the dependent variables were: Computer, Smartphone, Administrative Software, Sales and Marketing Software, Industry-Specific Software, Cloud Computing Software, IT Security Software, Dial-Up Connection, Broadband, Fixed Broadband (ADSL or Cable), Dedicated Internet, Use of Smartphone or Tablet, Email, Company and Product Information, Online Product or Service Sales, Online Payments, Online Reservations, Internet Purchases, Internet Sales, and Social Media.

A significant difference was observed in the majority of the ICT variables based on the p-value criterion (Sig. ≤ 0.05). This indicates that statistical evidence was found to reject the null hypothesis for most ICT variables, suggesting that these differences are statistically significant at a 5% significance level. Consequently, at least one ICT variable plays a relevant role in the operational development of various economic activities.

In other words, there is a statistically significant relationship between the use of ICT variables and the economic activities of small and medium-sized enterprises, which supports their operational development. However, the variable “Email” did not show a significant difference. This could be attributed to its status as one of the oldest operational technologies in the market (Albarrán, 2001[2] Albarrán, L. (2001). El impacto del uso del correo electrónico en el profesorado de las universidades públicas madrileñas. Reis. Revista Española de Investigaciones Sociológicas, 201-223. https://doi.org/10.2307/40184389
).

4.2 Factor Analysis

Factor analysis was employed to identify the most relevant ICT categories among small and medium-sized enterprises (SMEs) across various economic activities. This analysis aims to generate one or more policy proposals that support the operational development of these enterprises.

To achieve this goal, the variable "size" was recoded, excluding the "Large" enterprises category. Only the categories “Medium,” “Small 1,” and “Small 2” were retained, creating a new single category variable, which we named “SME.”

According to Table 5, the KMO value of 0.721 confirms that the correlation structure among the variables is suitable for factor analysis. This value exceeds the typically acceptable threshold of 0.6 and approaches what is considered a good value of 0.7. Similarly, Bartlett's test, with a chi-square statistic of 6,899 and a p-value of 0.000, provides strong evidence to reject the null hypothesis, indicating significant correlations among the variables in the population.

This analysis was further refined using anti-image correlation matrices, which enabled us to exclude certain variables with limited relevance for SMEs. For instance, the variable “Dial-Up Connection” was removed due to a p-value exceeding 0.05, suggesting its usage is predominantly associated with large enterprises rather than SMEs.

Table 6 presents the decomposition of variance across the various factors. As shown, the first four components explain 49% of the total variance, leading to the generation of four dimensions. This suggests that nearly half of the variance can be attributed to these four factors.

It is important to note that the remaining 51% of the variance could be explained by other ICT variables or by additional factors not included in this study. However, derived from the literature reviewed (as summarized in Table 2), we focused on the most significant ICT variables that directly impact small and medium-sized enterprises.

Table 7 presents the rotated component matrix, showcasing the factorial solution for the selected ICT variables. Four significant dimensions were identified, marked with an asterisk (*), which provide a clear structure for categorizing ICT tools. These dimensions align with the classification proposed by Aral & Weill (2007):

4.3 Public Policy Proposals by Axis

Before presenting the policy recommendations derived from this study, it is necessary to reflect briefly on the analytical scope and methodological boundaries that frame the interpretation of our findings. Acknowledging these limitations not only enhances the transparency of the research but also provides direction for future academic and policy study in this field.

First, the analysis is based on cross-sectional data from the 2017 Fifth Longitudinal Survey of Enterprises, which restricts the ability to assess causal relationships or long-term effects of ICT adoption. Second, the database does not capture post-pandemic technological dynamics, particularly the emergence of advanced tools such as artificial intelligence, automation, or digital collaborative platforms, which limits the contemporaneity of some conclusions. Third, most ICT variables are binary, lacking granularity on usage intensity or technological maturity. Finally, some economic sectors with low representation in the sample have limited statistical reliability, which restricts the validity of sector-specific comparisons. These limitations, while not undermining the validity of the results, do signal important opportunities for future research with updated and diversified methodological approaches.