Identifying The Innovation Capabilities Of Information And Communication Technologies Companies in the Context of an Emerging Country

SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 Identificación de las capacidades de innovación de las empresas de Tecnologías de la Información y Comunicación en el contexto de un país emergente* Identifying The Innovation Capabilities of Information and Communication Technologies Companies in the Context of an Emerging Country VANESSA GARCÍA PINEDA Instituto Tecnológico Metropolitano Facultad de Ingenierías [email protected] https://orcid.org/0000-0003-3418-8956 JACKELINE ANDREA MACÍAS URREGO Instituto Tecnológico Metropolitano Facultad de Ciencias Económicas y Administrativas [email protected] https://orcid.org/0000-0001-5899-7462 Recibido/Received: 21/10/2023. Aceptado/Accepted: 20/11/2024 Cómo citar/How to cite: Apellidos,García Pineda, Vanessa & Macías Urrego, Jackeline Andrea (2025). Identifying The Innovation Capabilities of Information and Communication Technologies Companies in the Context of an Emerging Country Sociología y Tecnociencia, 15 (1), 16-38. DOI: https://doi.org/10.24197/st.1.2025.16-38. Artículo de acceso abierto distribuido bajo una Licencia Creative Commons Atribución 4.0 Internacional (CC-BY 4.0). / Open access article under a Creative Commons Attribution 4.0 International License (CC-BY 4.0). Resumen: En años recientes, la sociedad y la economía han experimentado cambios significativos que han requerido que las organizaciones se reinventen y se adapten a nuevas metodologías y procesos, lo cual se logra a través de la innovación. A pesar de los avances en tecnologías de información y comunicación (TIC), las empresas de este sector no siempre reconocen la necesidad de incorporar la innovación en áreas más allá del desarrollo y la infraestructura de las TIC. Este estudio se enfoca en evaluar las capacidades de innovación de * Este trabajo se ha realizado en el marco de la tesis de maestría titulada “Análisis de las capacidades de innovación en las medianas empresas dedicadas a la gestión de servicios de tecnologías de información como mecanismo para el fomento de su competitividad en el sector de las TIC de la ciudad de Medellín”, en la Institución Universitaria – Instituto Tecnológico Metropolitano, Maestría En Gestión De Innovación Tecnológica, Cooperación Y Desarrollo Regional.! Identificación de las capacidades de innovación de las empresas de Tecnologías….. SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 17 empresas medianas que ofrecen Servicio de Gestión de Tecnología de la Información (ITSM) en el sector de las TIC en Medellín, Colombia, mediante un modelo de ecuaciones estructurales. Los resultados señalan que solo siete de las 42 variables del modelo obtuvieron resultados inferiores al umbral normal. Estas siete variables incluyen la evaluación de la marca corporativa, el desarrollo sostenible y la capacidad de improvisación. El sector de TIC se encuentra en constante innovación debido a su enfoque en el desarrollo y provisión de servicios relacionados con la tecnología y la Industria 4.0. El modelo propuesto puede contribuir a la evaluación de dichas capacidades y alcanzar ese objetivo. Palabras clave: Sector TIC; modelado de ecuaciones estructurales; Industria 4.0; capacidades de innovación, país emergente. Abstract: In recent years, the rapidly changing landscape of society and the economy has compelled organizations to adapt and innovate. Information and communication technologies (ICTs) have seen significant advancements in hardware and software. However, many companies within the ICT sector often overlook the need to infuse innovation into areas beyond ICT development and infrastructure. This study evaluates the innovation capabilities of medium-sized enterprises providing Information Technology Service Management (ITSM) in Medellín, Colombia. Utilizing a structural equation model, the study examined causal variables related to competitiveness and innovation capacity. Of the 42 variables considered, seven, including corporate branding evaluation, sustainable development, and improvisation capability, showed results below the normal threshold. The ICT sector, focused on the development and provision of technological and Industry 4.0-related services, continuously evolves. This model contributes to assessing and achieving that objective. Keywords: ICT sector; structural equation modelling; industry 4.0; innovation capabilities, emerging country. 1. INTRODUCTION Information and communications technologies have been an essential part in the development of society and the economy in recent years (Zambrano, 2020). This has been more evident since the beginning of the COVID-19 pandemic (Kitukutha et al., 2021) because society needed to adapt to a different daily life and carry out different regular activities through information and communications technologies (Zimmerling & Chen, 2021). As a result, the demand for this type of services has increased worldwide. However, society was not ready for this, and change and adaptation had to be immediate (Kwon et al., 2021). In 2016, according to The World Bank (2018)the global digital economy was worth usd 11.5 trillion, i.e., 15.5% of the world's gdp. This figure is expected to reach 25% in less than a decade. In Colombia, there are approximately 4,016 firms performed telecommunications and software development activities in 2014 (MINTIC et al., 2015). Most of these companies are located in the central region of the country (García Pineda & Macías Urrego, 2021), of these regions, Antioquia concentrates a large part of these companies, with most of them (80%) headquartered in the city of Medellín (SENA et al., 2015). Therefore, ICT organizations have become essential service and product providers because they are responsible for the activities, development, and tools that Vanessa García Pineda, Jackeline Andrea Macías Urrego SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 18 facilitate the operation of other organizations in economic and social sectors (Zimmerling & Chen, 2021). Consequently, ICT organizations should constantly keep up to date and pay attention to different demands they should meet and changes they should make in order to adapt, innovate, be competitive in the market (Adeosun & Shittu, 2021; Essmann & Preez, 2010), and, therefore, avoid obsolescence (Hari et al., 2014). This, given the importance of small and medium-sized companies in developing countries, mainly those that offer ICT services, which is why the development of innovation capacities is vital for the sector and the country's sustainability (Adeosun & Shittu, 2021). To innovate, these companies should establish the competencies, abilities, and knowledge they have to develop their innovation capabilities. The foregoing, both with the intention of being competitive in the market, as well as being able to establish agile strategies that allow them to adapt to future changes quickly (Bhāle, 2020). Hence, ICT organizations should clearly identify their innovation capabilities and utilize them to gain dynamic competitive advantage (Barrales et al., 2015; Tang & Chi, 2011), the foregoing through different frameworks, recommendations or establishment of strategies that also allow them to adequately evaluate their innovation capacity in order to establish strategies for an adequate obtaining and use of financing resources for innovation (Giménez, 2020). They can also take advantage of the knowledge that all the actors that collaborate with them possess and acquire (Boscherini et al., 2003; Gutierrez et al., 2018) and then generate different strategies to create and produce value-added services (Ferrer, 2007). In addition, to make use of the different resources and prevalence that have been given to the ICT sector due to its growth in an emerging economy such as Colombia, where different companies are based on the innovations, activities and strategies implemented by large ICT companies from First World countries (Adeosun & Shittu, 2021; Fan et al., 2019). However, these organizations do not always manage to obtain innovative results, although their nature is technology-based and their products are related to ICT, sometimes they do not manage to establish strategies that allow them to innovate in their services or in their processes since they do not know the best methods, strategies and even the elements with which they can acquire capacity for innovation and dynamism. On the other hand, being mediumsized companies, they do not always have the resources to be able to innovate, nor do they have the economic, political or social support (Palma & Guzmán, 2023). This paper aims to evaluate the innovation capabilities of medium-sized enterprises that provide Information Technology Service Management (ITSM) in the ICT sector in Medellín using causal variables of competitiveness and structural evaluation modeling (SEM). This given that, as stated by Adeosun & Shittu, 2021; Bhāle, 2020; Giménez (2021; 2020; 2020), although innovation capacities have been widely studied even in the ICT sector, there is still a gap in the literature for the study of them in this sector in countries with emerging economies. A structural equation model was designed based on the constituent variables of innovation capabilities Identificación de las capacidades de innovación de las empresas de Tecnologías….. SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 19 (ICs) proposed by Yam et al. (2004a) and causal variables of ICs obtained from a literature review. In addition, a survey was administered to a group of companies that provide ITSM in Medellín. The document initially presents a context of theoretical capabilities on technology innovations and industry 4.0, detailing the variables related to the research topic. the methodology is presented, where the method used to obtain the data and the sample is concisely indicated, then the methodology used for the development of the model of structural structures is presented. Then, the results are presented and finally the conclusions. 2. LITERATURE REVIEW 2.1 Technological innovation capabilities Innovation has been defined by Schumpeter as the main promoter of capitalist development and one of the main drivers of the profits obtained by companies (Freeman & Soete, 1997). In addition, innovation is one of the greatest generators of knowledge, as well as competitive advantage (Arredondo et al., 2016). In order to obtain innovative results, organizations should have the competencies, abilities, and capabilities required to innovate. These capabilities have been defined by different authors. According to (Burgelman et al., 2008), technological innovation capabilities (TICs) are a group of characteristics of an organization that facilitate the generation of technological innovation strategies. Yam et al. (2004b, p. 1124) defined these capabilities as “a comprehensive set of characteristics of an organization that facilitates and supports its technological innovation strategies”. Osorio et al. (2014) defined them as abilities developed by organizations based on their daily activities that enable them to acquire the capability to innovate. However, for TICs to properly work and guide an organization’s innovation strategy, its activities and resources should be adequately articulated and work together; more specifically, its special resources, e.g., technology, product, process, knowledge, experience, and organization (Guan & Ma, 2003). In this regard, organizations dedicated to ICT should make greater efforts to conserve resources focused on knowledge (Gutierrez et al., 2018). This refers to the brain drain that widely affects countries in emerging economies (Adeosun & Shittu, 2021). This should allow organizations to coordinate their innovation strategy with their technological strategy and research and development (R&D) activities (Yam et al., 2004a). Different authors have proposed groups of capabilities that compose innovation capability. Yam et al. (2004b) listed seven: learning capability, R&D capability, resources allocation capability, manufacturing capability, marketing capability, organizing capability, and strategic capability. In turn, Wang, Lu, and Chen (2008) referred to five constituent capabilities: R&D capabilities, innovation decision capabilities, marketing capabilities, manufacturing capabilities, and capital Vanessa García Pineda, Jackeline Andrea Macías Urrego SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 20 capabilities. Finally, (Robledo et al., 2010), based on the study by Yam et al. (2004b), proposed seven capabilities: strategic direction, R&D, manufacturing, marketing, organizational learning, resource management, and networking. Said capabilities are defined according to what was proposed by Yam et al. (2004b), as follows: • Learning capability: understanding and applying knowledge in the organization. • R&D capability: the ability to integrate the R&D strategy with the implementation of projects and the innovation management portfolio. • Resources allocation capability: the ability to properly devote resources to activities aimed at innovation. • Manufacturing capability: the ability to transform the results obtained from R&D activities into services and products that respond to the market needs and innovate at the same time. • Marketing capability: an organizations’ ability to offer its products and services according to the needs of the environment and innovation acceptance. • Organizing capability: the ability to maintain harmony in the organization. • Strategic capability: the ability to identify strengths, weaknesses, opportunities, and threats according to the objectives of the organization and adjust them to its strategic implementation plans. Among them, R&D capability is the one that generates innovative results through research and the implementation of different activities. The resources allocation capability facilitates the allocation of strategically oriented capital and resources to projects aimed at innovation (Yam et al., 2004a). 2.2. Industry 4.0 The technological advances witnessed by mankind have marked different eras for the industry and the economy, and an increasingly faster progress has seen the rise and fall of different technologies—from steam-powered machines to the birth of the internet, Wi-Fi technology, and Bluetooth. The introduction of important technologies for the global economy has brought along industrial revolutions—from the first industrial revolution to what is currently known as the Fourth Industrial Revolution or Industry 4.0. “The term “Industry 4.0” was first coined by the German government in 2013 as a strategic plan by Industry Science Research Alliance in partnership with Acatech” (Ellahi et al., 2019). The objective of Industry 4.0 is that new technologies are developed by organizations in the ICT sector because the latter advances hand in hand with the digital economy. Two of the most important components for the development of this industry are electronics and informatics. They facilitate the progress and implementation of activities in different economic sectors because the industries understand that it is increasingly necessary to implement innovative models, Identificación de las capacidades de innovación de las empresas de Tecnologías….. SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 21 embedded systems, manufacture automatization, and artificial intelligence (Gutarra & Valente, 2018, p. 756) in order to improve organizational performance and productivity. In addition, Industry 4.0 is considered a new industrial stage in which the integration of horizontal and vertical manufacturing processes and product connectivity can help companies achieve a better industrial performance (Dalenogare et al., 2018). According to Ghobakhloo (2020), different elements compose Industry 4.0, and they should be acknowledged and utilized by organizations for them to perceive the benefit that this industry provides in order to be sustainable. Said elements are the following: • Business model novelty and innovation • Carbon/harmful gas emission reduction • Corporate profitability improvement • Economic development • Energy and resource sustainability • Environmental responsibility development • Human resource development • Increased production efficiency and productivity • Job creation • Manufacturing cost reduction • Manufacturing agility and flexibility • Production modularity • Product personalization • Risk and safety management • Supply chain digitization and integration • Social welfare enhancement In addition, due to the emergence of Industry 4.0 and the COVID-19 pandemic, the digitization of different sectors has been more noticeable, the use of smart devices has become more frequent, and more digital platforms and environments have been implemented to improve productivity, efficiency, and sustainability (Balogun et al., 2020). In order for organizations in the sector to address the previously described aspects, they should correctly implement innovation capabilities to develop and utilize an innovation strategy that enables them to fulfill the current demands of Industry 4.0. Thus, they can respond to the development and growth of the latter by adequately using tools and technologies such as hyperconnectivity and super-intelligence (Im et al., 2018). It is necessary to understand that, in addition to the previously described aspects, Industry 4.0 is also based on tools and technologies such as the Internet of Things (IoT), Cyber-Physical Systems (CPS), Enterprise Architecture (EA), and Vanessa García Pineda, Jackeline Andrea Macías Urrego SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 22 Enterprise Integration (EI) (Dalenogare et al., 2018); other technologies such as blockchain, data science, and quantic computing; trends such as the orange economy (which are implemented, supported, and developed by the ICT sector); and topics such as digital economy, big data, and artificial intelligence (Bustamante & Guillén, 2017). Organizations in the ICT sector should implement and develop their innovation and R&D strategies based on these technologies (Otles & Sakalli, 2019; Fernández et al., 2024). Based on the above, the literature shows that Industry 4.0 is composed of eight constituent variables that are influenced by multiple causal variables (García & Macías, 2023). 3. RESEARCH METHODS 3.1. Data compilation To compile the data, the ICT sector in Medellín was characterized based on information obtained from the database of the Departamento Administrativo Nacional de Estadística (DANE) in Colombia (García & Macías, 2023). As a result, a new database was created in Excel, where the data were classified using the CIIU code (version 4) and filtered using codes 61, 62, and 63, which correspond to companies that provide ITSM. Three other Colombian databases were consulted: Registo Único Empresarial (RUES), DIAN, and MUISCA. Data such as age, size, status, and registration city were used to confirm that the selected companies were medium-sized, complied with their legal obligations, and were actively operating. Applying the previous criteria, a total of 26 companies registered in Medellín were found. Subsequently, considering a 95% confidence level and an error margin of 10%, the sample final sample included 21 companies. However, only 16 of them participated in the process by taking an online survey composed by 42 questions with a Likert scale. This instrument, which was previously validated by 16 experts in the field of ICTs, was administered to executives and project managers at the companies studied here. 3.2. Design of the structural equation modeling (SEM) SEM can identify the effect and type of relationship between different variables. With this information, causal relationships between latent and measurement variables can be determined. (Escobedo et al., 2015). In other words, structural equation models present causal relationships between observable variables in a set, as well as between observable and non-observable variables (Álvarez & Vernazza, 2017). According to Escobedo et al. (Escobedo et al., 2015), these models resulted from the combination of two different perspectives, i.e., prediction and a psychometric approach, and their objective is to model concepts using latent (non-observable) variables and infer multiple observed measurements (manifest variables). In this study, a path diagram (measurement sub-model) was created, and the model was established and defined in AMOS software. Afterward, a statistical Identificación de las capacidades de innovación de las empresas de Tecnologías….. SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 23 descriptive analysis was conducted using SPSS Statistics software, and the factors were verified. Multiple can be applied strategies to identify the model; one of them is the degrees of freedom (DF) rule, where the following formula is used: 𝐷𝐹 = ( #&𝑜𝑓&𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑&𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠 ) ∗ ( #&𝑜𝑓&𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑&𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠& + &1 ) 2− #&𝑝𝑎𝑟𝑎𝑚𝑒𝑡𝑒𝑟𝑠&𝑡𝑜&𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑒 (1) According to this, if the DF equal zero, the model has zero degrees of freedom and it is an identified model. However, although it presents an optimal fit for this study, it does not present significant relevance because the model cannot be generalized. An overidentified model is one in which the DF are greater than zero because its matrix presents more data than parameters to be estimated; this indicates positive degrees of freedom and that the model can be generalized (Cupani, 2012). In contrast, DF lower than zero mean that the model has not been fully identified and that the user is trying to estimate more parameters than there is information in the matrix. Therefore, the model should be applied constraints and reformulated (Ruiz et al., 2014). 4. RESEARCH RESULTS The results reported in this section were obtained by following the steps described in the methodology: 4.1. Model specification The Structural Equation Model (SEM) designed here is based on the variables found in the literature. Said variables and their relationships were established to design the model in Figure 1, which shows eight latent endogenous variables and 42 observable variables. The model is composed of eight latent variables. Seven of them correspond to the innovation capabilities that were defined in previous sections, and the eight one represents innovation capability as a whole. The variables were named using the Ci set (from C1 to C7), as follows: C1 = Learning capability C5 = Marketing capability C2 = R&D capability C6 = Organizing capability C3 = Resource allocation capability C7 = Strategic planning capability C4 = Manufacturing capability C8 = Innovation capability Vanessa García Pineda, Jackeline Andrea Macías Urrego SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 24 Figure 1. Measurement sub-model of the structural equation model Source: Created by the authors In addition, the 42 observable variables were distributed among the Ci, as follows: The following observable variables, which have a causal relationship with C1, are in the Xj group (from X1 to X7): X1 = Education X4 = Intellectual capital X2 = Innovation capability X5 = Obsolescence X3 = Knowledge transfer X6 = Technology prospecting The following observable variables, which have a causal relationship with C2, are in the Yj group (from Y1 to Y5): Y1 = Technological innovation capability Y2 = Social innovation Y3 = Innovation performance Y4 = Industry 4.0 technologies (blockchain, data science, and quantic computing) Identificación de las capacidades de innovación de las empresas de Tecnologías….. SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 31 Table 3: ANOVA with Friedman’s chi-square test ANOVA with Friedman test Sum of squares DF Quadratic median Friedman’s chi-square test Sig Inter-subject 380.284 15 25.352 Intrasubject Between elements 128.132a 41 3.125 135.838 .000 Remainder 490.653 615 .798 Total 618.786 656 .943 Total 999.070 671 1.489 Global mean = 3.7961 a. Concordance coefficient of W = .128. Source: Authors’ own work using SPSS Statistics software. According to the above, the critical point for a chi-square distribution with 41 degrees of freedom, for a quadratic mean of 3.125, is 135.838. Given the high value obtained in the chi-square and that a significance level of 0.05 is necessary to reject the null hypothesis, the latter can be rejected based on the high chi-square and the results obtained. Thus, H1 can be confidently supported because the set of selected variables are causal of the group of seven capabilities that facilitate innovation capability. The results presented in the previous tables, allow to affirm that the variables included and used for the construction of the model are adequate to evaluate the innovation capacity of ICT organizations, in addition to being replicable in other types of technology-based organizations given the results in the degrees of freedom of the model and of the chi-square. This is significant given that by not having to eliminate any of the variables, it allows future studies and organizations greater scope and freedom for the analysis of innovation capacity, since they have a greater variety in the selection of factors or items. to be studied, allowing more diverse results in the analysis of said capacity. Finally, the model was not respecified because the statistic results of the model and the data obtained were significant. This reason is sufficient to say that the model is valid to evaluate the proposed variables, which were found to be causal based on the literature review about innovation capabilities of mediumsized enterprises that provide information technology service management in the ICT sector in Medellín. 5. DISCUSSION OF THE RESULTS First, these latent and observable variables have been previously acknowledged and discussed by multiple authors, some of which were cited in this paper. As a result, Vanessa García Pineda, Jackeline Andrea Macías Urrego SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 32 the model has enough theoretical support to be proposed here, and its design is saturated. In relation to descriptive statistics, it was found that the asymmetry values were not higher than in any of the cases. However, the kurtosis values were higher than , which indicates that the data do not follow a normal distribution. This indicates that the mean, median, and mode can differ. Despite the above, the data normality tests, which were mainly based on the results of the Shapiro–Wilk test due to the sample size, show that only seven variables obtained results lower than 0.05: technological innovation capability, innovation performance, R&D&I strategies, evaluation of corporate brand, sustainable development, improvisation capability, and R&D&I policies. That is, the nonnormality could have occurred specifically in these variables. In addition, a correlation analysis using Spearman’s Rho coefficient was necessary due to the data abnormality. As a result, it was found that said abnormality was not very strong, which had not been taken into account when the model was designed. Therefore, the model only considers relationships between latent and observable variables and not between variables of the same type. Although the sample was small, the data and variables facilitated the validation of the model using different descriptive statistics and fit and reliability measures of the proposed model. Thus, it was found that, as it had a great number of observable variables, the model was saturated. The result of the degrees of freedom indicates that the model is overidentified and it can be used in a general manner for other types of companies in the sector. The values of the communalities calculated using the unweighted least squares method range between 0.7 and 1.0, which indicates that the items were valid and that it is not necessary to extract any of them to improve the results of the model. Likewise, the data can be considered reliable enough because the Cronbach’s alpha was 0.969 and the evaluation of each item separately resulted in similar values. This confirms the reliability of the data of the model and indicates that it is not necessary to eliminate any of the items considered here, which was confirmed by the Lambda results in the reliability statistics. Regarding the total explained variance, the results indicate that eight factors explain 88.762% of the model, which confirms once again the validity and reliability of the data. In addition, the results of the chi-square statistic in terms of significance level were so low that the null hypothesis can be rejected. This means that at least some of the causal variables found in the literature and can and that have been detailed in the methodology section, in the model specification can explain the development of the seven capabilities that compose innovation capability: learning capability, R&D capability, resource allocation capability, manufacturing capability, marketing capability, organizing capability, and strategic planning capability. Although the model was not re-specified, considering that this is a new proposal based on a thorough theoretical review, the results indicate that the items included here can be valid to explain the generation and development of the seven capabilities proposed by Yam et al. (2004a) as components of innovation capability, specifically Identificación de las capacidades de innovación de las empresas de Tecnologías….. SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 33 at enterprises offering ITSM in the ICT sector in Medellín. The decision not to respecify the model was justified by these results and the fact that this scale and factors were proposed here for the first time. Thus, this is considered an initial investigation of these variables and an exploratory analysis. In addition, as mentioned in the study by Adeosun & Shittu (2021), the development and promotion of innovation capacities in the ICT sector is more than adequate to achieve an improvement in the development of the territory and social inclusion, this given the improvement significant that the sector has had in different developing countries such as Colombia. 6. CONCLUSIONS The results presented in this article can serve as a framework to guide the identification of innovation capabilities. In addition, to allow from the results presented here the design and implementation of strategies aimed mainly at innovation in processes and services in different companies in the ICT sector, focused especially on aspects such as; the capacity for technological innovation, performance in innovation, R + D + i strategies, corporate brand evaluation, sustainable development, improvisation capacity and R + D + i policies. In conclusion, this paper can guide the assessment of the current state of organizations regarding innovation and innovation capabilities, which can be a valuable tool to review and reconsider strategies to improve these aspects and therefore their competitiveness. Future studies can define guidelines based on the proposed model and a confirmatory analysis that includes a larger sample of this sector. Acknowledges: The author thanks ITM Translation Agency ([email protected]o) for editing the manuscript. REFERENCES. Adeosun, O. T. & Shittu, I. A. (2021). Business incubation initiatives and innovation capabilities of micro-sized enterprises: Exploring the software ICT value chain. African Journal of Science, Technology, Innovation and Development. https://doi.org/10.1080/20421338.2021.1985202 Álvarez, R. & Vernazza, E. (2017). Satisfacción estudiantil : Análisis a través de Modelos de Ecuaciones Estructurales. X SEMANA INTERNACIONAL DE ESTADÍSTICA Y PROBABILIDAD, June, 7. https://doi.org/10.13140/RG.2.2.18031.59045 Arredondo, F., Vázquez, J. C. & de la Garza, J. (2016). Factores de innovación para la competitividad en la Alianza del Pacífico. Una aproximación desde el Foro Vanessa García Pineda, Jackeline Andrea Macías Urrego SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 34 Económico Mundial. Estudios Gerenciales, 32(141), 299–308. https://doi.org/10.1016/j.estger.2016.06.003 Balogun, A. L., Marks, D., Sharma, R., Shekhar, H., Balmes, C., Maheng, D., Arshad, A. & Salehi, P. (2020). Assessing the Potentials of Digitalization as a Tool for Climate Change Adaptation and Sustainable Development in Urban Centres. Sustainable Cities and Society, 53(October 2019), 101888. https://doi.org/10.1016/j.scs.2019.101888 Barrales, V., Montes, F. & Gutierrez, L. J. (2015). Dynamic capabilities, human resources and operating routines A new product development approach. Industrial Management and Data Systems, 115(8), 1388–1411. https://doi.org/10.1108/IMDS-02-2015-0058 Berlanga, V. & Rubio, M. (2012). Clasificación de pruebas no paramétricas. Cómo aplicarlas en SPSS. Revista d’Innovació i Recerca en Educació, 5(2), 101– 113. https://doi.org/10.1344/reire2012.5.2528 Bhāle, S. (2020). Enduring Industry Dynamics through Agile Business Strategy. Indian Journal of Ecology, 47, 47–50. Boscherini, D. F., Novick, M. & Yoguel, G. (2003). Nuevas Tecnologías de Información y Comunicación . Los Límites en la Economía del Conocimiento. Burgelman, R. A., Christensen, C. M. & Wheelwright, S. C. (2008). Strategic Management of Technology and Innovation (5a ed.). McGraw-Hill/Irwin ©2008. https://dl.acm.org/citation.cfm?id=1479640 Bustamante, N. B. & Guillén, S. T. (2017). Un acercamiento al Big Data y su utilización en comunicación. Mediaciones Sociales, 16. https://doi.org/10.5209/MESO.58112 Cupani, M. (2012). Análisis de Ecuaciones Estructurales: conceptos, etapas de desarrollo y un ejemplo de aplicación. National Scientific and Technical Research Council, 2(1), 186–199. Dalenogare, L. S., Benitez, G. B., Ayala, N. F. & Frank, A. G. (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics, 204(December 2017), 383– 394. https://doi.org/10.1016/j.ijpe.2018.08.019 Identificación de las capacidades de innovación de las empresas de Tecnologías….. SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 35 Ellahi, R. M., Ali, M. U. & Shah, A. (2019). Redesigning Curriculum in line with Industry 4.0. Procedia Computer Science, 151, 699–708. https://doi.org/10.1016/j.procs.2019.04.093 Escobedo, T. M., Hernández, J. A., Estebané, V. & Martínez, G. (2015). Modelos de Ecuaciones Estructurales: Características, Fases, Construcción, Aplicación y Resultados. Ciencia & Trabajo, 55, 16–22. Essmann, H. & Preez, N. (2010). An Innovation Capability Maturity Model – Development and initial application. Social Sciences, 44–56. Fan, P., Urs, N. & Hamlin, R. E. (2019). Rising innovative city-regions in a transitional economy: A case study of ICT industry in Cluj-Napoca, Romania. Technology in Society, 58, 101139. https://doi.org/10.1016/j.techsoc.2019.05.003 Fernandez-Zubieta, A., García Sánchez, A., & Molero Zayas, J. (2024). The Impact of Public Funding to Private R&D: Evidence from Spain. Sociología Y Tecnociencia, 14(2), 40–70. https://doi.org/10.24197/st.2.2024.40-70 Ferrer, J. (2007). La competitividad desde el mercadeo social: Conjunción estratégica para el fortalecimiento de la pequeña industria zuliana. Revista de Ciencias Sociales, 13(3), 468–483. https://doi.org/10.31876/rcs.v13i3.25376 Freeman, C. & Soete, L. (1997). Chapter 1: The Economics of Industrial Innovation. En The Economics of Industrial Innovation. https://users.dcc.uchile.cl/~cgutierr/cursos/INV/economics.pdf García, M. Á. (2011). Análisis causal con ecuaciones estructurales de la satisfacción ciudadana con los servicios municipales [Universidad de Santiago de Compostela]. http://eio.usc.es/pub/mte/descargas/proyectosfinmaster/proyecto_610.pdf García Pineda, V., & Macías Urrego, J. A. (2021). Analysis of the Variables Leading to the Identification and Incorporation of Innovation Capabilities by Firms in the Colombian ICT Sector. Innovar, 32(84). https://doi.org/10.15446/innovar.v32n84.99867 García, V., & Macías, J. (2023). Identification of the information and communication technology (ICT) sector in the city of Medellín through innovation capacities and elements of Industry 4.0. International Journal of Technology, Policy and Management, 23(2), 216–244. https://doi.org/10.1504/IJTPM.2023.131376 Vanessa García Pineda, Jackeline Andrea Macías Urrego SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 36 Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252, 119869. https://doi.org/10.1016/j.jclepro.2019.119869 Giménez, M. (2020). Towards an agile innovation capability maturity framework to enhance investments on ICT organizations. CEUR Workshop Proceedings, 2613, 21–31. Guan, J. & Ma, N. (2003). Innovative capability and export performance of Chinese firms. Technovation, 23(9), 737–747. https://doi.org/10.1016/S01664972(02)00013-5 Gutarra, R. & Valente, A. (2018). Las mipymes tecnológicas peruanas al 2030. Estrategias para su inserción a la industria 4.0. Nova Scientia, 10(1), 754–778. Gutiérrez, L. J., Barrales, V. & Kaynak, H. (2018). The role of human resourcerelated quality management practices in new product development: A dynamic capability perspective. International Journal of Operations and Production Management, 38(1), 43–66. https://doi.org/10.1108/IJOPM-072016-0387 Hari, A. P. N., Subramaniam, S. R. & Dileep, K. M. (2014). Impact of innovation capacity and anticipatory competence on organizational health: A resource based study of nokia, motorola and blackberry. International Journal of Economic Research, 21(0972–9380), 395–415. https://www.researchgate.net/publication/264162759_Impact_of_Innovation _Capacity_and_Anticipatory_Competence_on_Organizational_Health_A_R esource_based_Study_of_Nokia_Motorola_and_Blackberry Im, I., Shin, D. & Jeong, J. (2018). Components for Smart Autonomous Ship Architecture Based on Intelligent Information Technology. Procedia Computer Science, 134, 91–98. https://doi.org/10.1016/j.procs.2018.07.148 Kitukutha, N. M., Vasa, L. & Oláh, J. (2021). The Impact of COVID-19 on the Economy and Sustainable E-commerce. 9(2). https://doi.org/10.23762/FSO Kwon, W. S., Woo, H., Sadachar, A. & Huang, X. (2021). External pressure or internal culture? An innovation diffusion theory account of small retail businesses’ social media use. Journal of Retailing and Consumer Services, 62(May), 102616. https://doi.org/10.1016/j.jretconser.2021.102616 Identificación de las capacidades de innovación de las empresas de Tecnologías….. SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 37 Levy, J.-P., Varela, J., Gonzalez, N., Abad, J. & Levy, J.-P. (2006). Normalidad y otros supuestos en analisis de covarianzas. En Modelizacion con estructuras de covarianzas en Ciencias Sociales. Temas esenciales, avanzados y aportaciones especiales (pp. 31–58). https://doi.org/10.4272/84-9745-1368.ch3 Osorio, O., Quintero, J. & Arias, J. (2014). Capacidades de innovación, desempeño innovador y desempeño organizacional en empresas del sector servicios. Cuad. admon.ser.organ. Bogotá (Colombia), 27(49), 87–108. http://www.scielo.org.co/pdf/cadm/v27n49/v27n49a05.pdf Otles, S. & Sakalli, A. (2019). Industry 4.0: The Smart Factory of the Future in Beverage Industry. En Production and Management of Beverages. Elsevier Inc. https://doi.org/10.1016/b978-0-12-815260-7.00015-8 Palma-Amestoy, C., & Guzmán-Valenzuela, C. (2023). Modernity, globalization and development: the role of the Knowledge-Based Economy in science, technology and innovation policies in Chile. Sociología Y Tecnociencia, 13(2), 73–93. https://doi.org/10.24197/st.2.2023.73-93 Robledo, J., Lopez, C., Zapata, W. & Pérez, J. D. (2010). Desarrollo de una Metodología de Evaluación de Capacidades de Innovación. Perfil de Coyuntura Económica, 15(1657–4214), 133–148. Ruiz, M. A., Pardo, A. & San Martín, R. (2014). Modelos de Ecuaciones Estructurales [Structural Equation Models]. Papeles del Psicólogo, 31(1), 34– 45. https://doi.org/10.5195/reviberoamer.2003.5609 Tang, L. & Chi, R. (2011). The Evaluation Criteria on ICT Enterprises Innovation Capability-Evidence from Zhejiang Province. Journal of Convergence Information Technology, 6(1), 69–78. https://doi.org/10.4156/jcit.vol6.issue1.9 Wang, C., Lu, I. & Chen, C. (2008). Evaluating firm technological innovation capability under uncertainty. Technovation, 28(6), 349–363. https://doi.org/10.1016/j.technovation.2007.10.007 Yam, R. C. M., Guan, J. C., Pun, K. F. & Tang, E. P. Y. (2004a). An audit of technological innovation capabilities in Chinese firms: Some empirical findings in Beijing, China. En Research Policy (Vol. 33, Número 8, pp. 1123– 1140). https://doi.org/10.1016/j.respol.2004.05.004 Vanessa García Pineda, Jackeline Andrea Macías Urrego SOCIOLOGÍA Y TECNOCIENCIA, 15.1 (2025): 16-38 ISSN: 1989-8487 38 Yam, R. C. M., Guan, J. C., Pun, K. F. & Tang, E. P. Y. (2004b). An audit of technological innovation capabilities in chinese firms: some empirical findings in Beijing, China. Research Policy, 33(8), 1123–1140. https://doi.org/10.1016/j.respol.2004.05.004 Zambrano, L. L. (2020). Uso de la Tecnología de la Información y Comunicación en educación virtual y su correlación con la Inteligencia Emocional de docentes en el Ecuador en contexto COVID-19. Revista Ibérica de Sistemas e Tecnologías de Información, 40(12), 31–44. https://doi.org/10.17013/risti.40.31 Zimmerling, A. & Chen, X. (2021). Innovation and possible long-term impact driven by COVID-19: Manufacturing, personal protective equipment and digital technologies. Technology in Society, 65(August 2020), 101541. https://doi.org/10.1016/j.techsoc.2021.101541