2.1 Integration of Organizational Learning Theory

Organizational Learning Theory highlights the ability to acquire, apply, and manage internal and external knowledge, which helps in adopting technological innovations by enabling continuous adaptation of processes and structures to changing environments (Lin & Lee, 2005). Digital knowledge acquisition (through training, informal learning, or collaborations with external partners) can be seen as a strategy for exploring knowledge, as it broadens the range of digital capabilities available to the firm. In contrast, digitalization often functions as a way to exploit resources, as it turns acquired knowledge into routines, processes, and business models based on digital technologies. However, in specific contexts, digitalization may also involve exploration, particularly when it involves adopting new technologies or experimenting with new digitally enabled approaches or models.

Integrating the lens of organizational learning, therefore, strengthens the theoretical foundation by suggesting a link between digital knowledge acquisition (exploration) and digitalization (exploitation or exploration), which ultimately improves firm performance. This connection suggests that firms able to turn exploratory digital knowledge into exploitative digitalization practices-by sensing, learning, and reconfiguring-are better positioned to absorb new information, adapt their operations, and turn technological initiatives into better performance results. From an organizational learning perspective, this view explains how digital knowledge is absorbed and utilized to produce tangible results. It emphasizes that firms with stronger learning and adaptive capabilities are more capable of transforming digital knowledge into effective digitalization practices, which accounts for the different outcomes seen across organizations of various sizes and levels of digital maturity (Rojas-Segura et al., 2023; Lafuente et al., 2023).

This perspective provides a more nuanced understanding of how digital knowledge leads to tangible results, while also considering the different effects seen across firms of varying sizes and levels of digital maturity.

2.2 Acquisition of Digital Knowledge in Firms

The acquisition of digital knowledge is a crucial phase for building capabilities that support and determine the success of digitalization efforts. Through organizational learning processes, companies develop the cognitive and technical skills needed to understand, integrate, and apply digital technologies in their operations. This stage of knowledge gathering and combining prepares organizations to adopt and adapt technological innovations to their productive and strategic realities (Lin & Lee, 2005). In contrast, digitalization is the implementation phase, where these capabilities are turned into practical applications of digital tools aimed at transforming processes, improving collaboration, and creating new forms of value (Lafuente et al., 2023).

Digital knowledge refers to the ability to understand, adopt, and effectively use digital technologies such as cloud computing, artificial intelligence, big data analytics, and digital platforms to innovate in products, services, processes, and business models (Bharadwaj et al., 2013; Li et al., 2018). To improve digitalization outcomes, involving users and providing training are essential. By engaging and encouraging workers in co-creating and adopting new technology, the organization can foster innovation both across different levels and within teams. The digital skills that boost digital productivity, such as digital information evaluation, critical thinking, creativity, and problem-solving, are regarded as 21st-century digital skills (Wechsler et al., 2018).

Acquiring digital knowledge is a crucial factor in a firm's digital transformation, and it has grown more important for businesses trying to improve their competitive edge and financial results in changing markets (Lafuente et al., 2020). According to Bharadwaj et al. (2013, p. 477), "the digital business context brings new opportunities to create value from information". In this sense, digital knowledge acquisition enables firms to reconfigure their resources, capabilities, and strategies to meet the needs of the digital economy. In other words, digital knowledge is essential in flattening organizational structures and supporting real-time, data-based decision-making.

The process that drives digitalization works through interorganizational collaboration and the development of capabilities that improve knowledge creation and use. Digital technologies and collaborative platforms enable firms to identify new opportunities, learn from one another, and refine their processes by sharing data continuously and coordinating flexibly. By strengthening grounding, bounding, and recasting abilities, technology-enabled collaboration helps organizations improve learning, build trust, and adjust structures flexibly. These sensing, learning, and reconfiguration processes enhance absorptive capacity and show how digital knowledge leads to more effective digitalization and better innovation performance (Lafuente et al., 2023; Vaillant et al., 2025).

In emerging economies, acquiring digital knowledge often happens through informal learning, digital upskilling programs, and partnerships with external entities like universities, public agencies, and international organizations (Cirera et al., 2021). These unconventional sources of learning are essential in places where formal mechanisms for developing digital skills may be weak. Despite infrastructural and institutional limitations, firms that successfully absorb and apply digital knowledge are better equipped to leverage the advantages of digital transformation across products, services, processes, and strategic management-ultimately boosting their financial performance and resilience in uncertain environments.

However, the digital knowledge-digitalization-performance chain is influenced by contextual factors such as firm size, digital maturity, and infrastructural readiness. Larger firms, with more financial and human resources, are better equipped to adopt and integrate new technologies to solve complex operational problems and maintain higher levels of innovation. In contrast, smaller firms often encounter greater obstacles due to limited expertise, financial capacity, and changing regulatory requirements. Additionally, successful digitalization requires not only technical skills but also complementary abilities, such as adaptability, strategic foresight, and change management, which are unevenly distributed among firms of different sizes and maturity levels (Clemente-Almendros et al., 2024). These contextual differences help explain why the benefits of digital knowledge acquisition are not consistently reflected in digitalization and performance outcomes. In this context, absorptive capacity-defined as the organization's ability to recognize the value of new knowledge, assimilate it, and apply it for productive or commercial purposes-becomes a crucial element (Argote et al., 2021).

Currently, collaborating with business partners has become a common priority for organizations across different sectors and contexts (Chesbrough, 2024). It is likely to help firms improve their digital capabilities (Bogers et al., 2018). Digitalization is considered successful when digital technologies are effectively integrated into the organization, such as changing organizational structures and adjusting business models (Lee & Berente, 2012). Several recent studies have explored the impact of digitalization on a firm's performance (Liu et al., 2023; Seclen-Luna et al., 2022a), showing that adopting digital technologies is a key source of a firm's competitive advantage (Chatterjee et al., 2020). Empirical evidence also suggests that firms can benefit financially from digitalization (Hanelt et al., 2021; Sestino et al., 2020). However, other research agrees that performance improvements do not happen until the firm and its human resources develop the necessary "expertise or capabilities" (Kumar et al., 2016).

In this context, many firms need expert help to understand how digital technologies function, allowing them to adjust their activities accordingly. The literature indicates that technological suppliers and KIBS usually compensate for and fill gaps in knowledge and skills in small firms. Conversely, the larger firms strengthen their expertise since they handle it internally (Seclen-Luna & Barrutia-Guenaga, 2018). Additionally, KIBS significantly contributes to digitalization efforts and enhances numerous capabilities, such as developing a connected equipment base, digitizing products and services, and transforming business models (Rapaccini et al., 2023). However, there is limited research on KIBS that focuses on helping other firms develop and adopt emerging digital technologies (Ribeiro-Navarrete et al., 2021), especially for smaller firms in developing countries (Seclen-Luna et al., 2024).

The literature on KIBS identifies two main categories: P-KIBS and T-KIBS. The first focuses on organizational and management fields, aiming to improve the efficiency of its clients' business processes (Miles, 2005). The second concentrates on science and technology, being more closely related to digital processes (Seclen-Luna et al., 2022b; Vaillant et al., 2021), and it allows firms to leverage smart technologies (Bustinza et al., 2021). The distinction between technical assistance related to P-KIBS or T-KIBS is important because they may rely on different knowledge bases (Asheim et al., 2011), especially in the context of KIBS performance, which involves both analytical and synthetic knowledge. Therefore, we affirm that firms with different knowledge bases and modes of organizational learning may produce different outcomes. For example, many KIBS firms are engaged in what can be called a "doing, using, and interacting" mode of organizational learning, which is facilitated through network interactions, learning by doing, and experience, along with flexible organizational structures to promote collective and interdisciplinary learning (Jensen et al., 2007). Firms with this type of organizational learning focus on developing the capabilities of "knowing how" and "knowing who," aiming to mobilize tacit knowledge and build cognitive skills. Therefore, considering organizational learning curves in smaller firms is important (Gardner, 2015).

In any case, acquiring digital knowledge through training programs, informal learning methods, or collaborations with external partners is a vital prerequisite for firms to leverage digital technologies effectively. From an organizational learning perspective, these activities constitute an exploratory process designed to generate and integrate new knowledge, thereby supporting future performance improvements (Argote et al., 2021). Therefore, based on these arguments, we propose the following hypothesis:

H1a. Training in digital tools is positively associated with the firm's sales.

H1b. Training in digital tools is positively associated with the firm's productivity.

H2a. The technical assistance related to P-KIBS is positively associated with the firm's sales.

H2b. The technical assistance related to P-KIBS is positively associated with the firm's productivity.

H3a. The technical assistance related to T-KIBS is positively associated with the firm's sales.

H3b. The technical assistance related to T-KIBS is positively associated with the firm's productivity.

2.3 Digital Knowledge and Digitalization in Firms

Digitalization involves transforming processes, operations, and offerings within companies through digital technologies to generate value, boost efficiency, and enhance effectiveness (Gebauer et al., 2021; Opazo-Basáez et al., 2023). Empirical research has demonstrated that digital knowledge allows firms to create digital products that include smart features, connectivity, or data-driven capabilities. These digital technologies often help firms stand out in competitive markets and gain a larger market share, which in turn positively affects revenues (Porter & Heppelmann, 2014). Besides functionality, digitized products often provide scalability and the ability to connect with digital platforms and ecosystems, boosting customer engagement and creating new sources of value (Vendrell-Herrero et al., 2024).

In parallel, online services allow firms to expand their offerings, customize user experiences, and lower operational costs. In addition to cost savings, earlier studies identified benefits such as increased sales and entry into new markets (Zhu et al., 2004). Xia and Zhang (2010) found that online channels have a more significant long-term effect on sales volumes. To implement online selling, organizations follow a process where they adjust their routines and use both internal and external collaboration (Sanders, 2007). From this perspective, digital knowledge not only supports efficiency but also acts as a catalyst for service innovation. As Nambisan et al. (2019) note, "digital technologies can have a generative effect on innovation" by enabling traditionally goods-producing firms to expand their innovation activities into service domains. This generativity increases firms' capacity to deliver ongoing value to customers, shifting from one-time transactions to continuous digital engagement. The gains in flexibility, responsiveness, and customer satisfaction strengthen the positive link with the firm's revenues. Additionally, firms that invest in digitalization are better prepared to adopt digitized internal processes, such as automation, data integration, and digital workflow management. These digitally enabled practices streamline operations, reduce human error, improve data accuracy, and boost responsiveness to market changes (Vial, 2019). As a result, firms become more agile and cost-effective-attributes essential for growth in volatile environments. These efficiencies ultimately lead to improved financial performance.

Beyond operational improvements, the strategic acquisition of digital knowledge supports comprehensive business digitalization. It requires developing a digitization capability that serves as the foundation upon which firms can build processes, structures, and business models that sustainably leverage digital resources. In this way, it acts as the link between acquiring digital knowledge and exploiting it to generate tangible results, fostering not only internal efficiencies but also new value propositions in products, services, and business relationships (Ritter & Pedersen, 2020). This includes developing a digital strategy, redesigning value creation and delivery mechanisms, and implementing broader organizational changes (Sebastian et al., 2017). Such transformations indicate a shift toward digital maturity, where firms not only adopt technologies but also deeply embed them into their organizational fabric and strategic vision. This comprehensive transformation- covering leadership, culture, infrastructure, and business models-connects holistic business digitalization to revenue growth. Therefore, we assert that acquiring digital knowledge is not a static event but an ongoing, dynamic process that supports firm digitalization across multiple dimensions.

By promoting the development of digital products, enabling online services, enhancing internal processes, and leading strategic change, digital knowledge acts as a key mechanism for firms to create value and boost revenues in the digital economy. To achieve sustainable results in this ongoing process, firms need to build capabilities that allow them to understand and utilize this knowledge effectively. Absorptive capacity is essential for transforming learning into organizational resources. However, this potential must be supported by a digitization capability that converts data into value by integrating technical, analytical, and regulatory skills (Ritter & Pedersen, 2020). Based on these points, we propose the following hypotheses:

H4a. The digitalized product is positively associated with the firm's sales.

H4b. The digitalized product is positively associated with the firm's productivity.

H5a. The provision of service online is positively associated with the firm's sales.

H5b. The provision of service online is positively associated with the firm's productivity.

H6a. The digitalization of processes is positively associated with the firm's sales.

H6b. The digitalization of processes is positively associated with the firm's productivity.

H7a. Business digitalization is positively associated with a firm's sales.

H7b. Business digitalization is positively associated with a firm's productivity.

Figure 1 presents the set of hypotheses formulated within the conceptual model.

3.1 Population and sample

The data were collected from the National Enterprises Survey in Peru (ENE, in its Spanish abbreviation) in 2019. Additionally, the ENE data have been gathered annually by the Institute of Statistics and Informatics of Peru from 2015 to 2019. The survey features a comprehensive questionnaire designed to collect information on the business environment of firms through direct, face-to-face interviews with firm managers and owners involved in decision-making. The survey population includes micro, small, medium, and large Peruvian firms, totaling 9,609 firms from all industries except education and health, excluding those in their first three years of operation. The ENE uses stratified random sampling based on location, size, industry, and other country-specific details. In line with our research goals (to assess whether acquiring digital knowledge and digitalization positively impact firms' sales and productivity), the final sample includes 2,834 firms from the service industries (Table 1). The ENE has been used in previous studies and published in relevant journals; for example, see Castro et al. (2022) and Seclen-Luna et al. (2024).

3.2 Measurement

This study examines firm sales and productivity as dependent variables. The independent variables are divided into two groups: i) acquisition of digital knowledge and ii) digitalization. To measure the first, we used three questions from the ENE, where respondents were asked, for example: 1) "In 2019, did workers receive training in digital tools?" 2) "In 2019, did your firm receive technical assistance for process improvement?" This measurement approach is similar to that used in previous studies, such as Seclen-Luna et al. (2022b). Additionally, to measure digitalization, four questions from the survey were included, where respondents were asked, for example: 1) "Does your firm currently sell digital products?" and 2) "In 2019, did your firm acquire technology to digitize your business?" This measurement approach is similar to that used in previous studies on digitalization, such as those by Sestino et al. (2020), among others.

Finally, as in previous studies, this research includes firm size and firm age as control variables. On the one hand, previous research has emphasized that business size is the most important determinant of whether digital technologies are adopted or not (Spinelli et al., 2013). Additionally, empirical evidence suggests that large firms possess more resources, which accelerate the adoption of technologies and innovation development, thereby impacting performance more significantly than in small firms (Lafuente & Vaillant, 2023).

On the other hand, based on the accumulation of experience and knowledge (S0rensen & Stuart, 2000), it could be argued that older firms are more likely to adopt digital technologies than younger firms; however, it can also be argued that newer firms find it easier to reach higher levels of digitalization. However, these arguments are complex because there are situations where digital technologies are visible but not seen as a strategic issue, which can lead to their active rejection (Fernandes & Burcharth, 2024). In any case, a more established and prominent firm is likely to have higher sales levels (Audretsch & Belitski, 2023). Lastly, dummy variables for each sector and region are also included to control for unobservable variables at both levels of aggregation. The variables used in this work are described in Table 2.

3.3 Methods and test

The OLS regression model was applied using STATA 17 software, which is suitable for our continuous dependent variables. The descriptive statistics in Table 3 reveal no strong correlation among the independent variables, suggesting that multicollinearity is not a significant issue in the data (Myers, 1990). This insight was confirmed by the variance inflation factor (VIF) test conducted on the independent variables, which yielded an average value of 1.14, indicating a satisfactorily low level of multicollinearity (Hair et al., 2010).

4.1 Descriptive analysis

On the one hand, Table 1 shows that the sample is mainly composed of transport (21.1%), accommodation, food, and beverage activities (10.1%), real estate activities (8.0%), architectural and engineering activities, technical testing and analysis (7.1%), financial services, insurance, and pension fund activities (6.4%). All these services account for 52.7% of the total sample. Additionally, the analysis by firm size indicates that SMEs are the most significant segment (48.1%), followed by micro firms (40.7%), and large firms (11.2%).

On the other hand, as shown in Table 3, Peruvian service firms generally have not adopted a clear digitalization strategy; in fact, there is a low proportion of firms that have embraced digital knowledge and digitalization. Specifically, it is clear that the percentage of firms that received technical assistance for process improvement and specialized technical information, and also implemented a digitalization strategy, was very low. However, in relative terms, the main factor influencing digital knowledge acquisition is training in digital tools, which, on average, makes up 21% of the firms' expenses. Likewise, digitalization is the primary digital strategy for 20% of service firms.

4.2 Hypothesis testing

Table 4 shows the results of the OLS regression analysis, broken down by firm size. To examine revenue by sales (columns 1, 2, 3, and 4) and productivity (columns 5, 6, 7, and 8), we estimated the effects of acquiring digital knowledge and digitalization across the total sample, micro, SMEs, and large firms. Along with the significance of the regression for each model, it is also evident that all significant coefficients of the subsamples, based on firm size, are consistently higher than those of the total sample, whether the latter are significant, except for the variable 'training in digital tools' in the sales revenue model. Finally, it is possible to observe a good fit for revenue by sales models, but only for large firms in terms of productivity. Although the values of this indicator are low compared to other productivity models, the results are consistent with those of previous studies, such as Vendrell-Herrero et al. (2023), among others.

First, the focus on revenue by sales in the total sample (column 1) indicates that the variables analyzed concerning the acquisition of digital knowledge do not have a statistically significant effect on sales. Therefore, no hypothesis was supported. One reason for this result is the low proportion of firms that invest in acquiring digital knowledge; therefore, it can be seen as a lack of strategic vision among firms regarding these issues and their limited absorptive capacity to benefit from KIBS. Consequently, this finding contradicts previous studies that highlight the importance of digital capabilities among workers (Kumar et al., 2016; Wechsler et al., 2018). The technical assistance related to KIBS (Ribeiro-Navarrete et al., 2021; Seclen-Luna et al., 2024) positively influences their performance.

On the other hand, the digitalization of the firm, including digitalized products (p < 0.01), digitalization of processes (p < 0.05), and business digitalization (p < 0.01), has positive effects on firms' sales, although with different magnitudes. Specifically, the coefficient for the first variable is twice that of the second, and the second is more than 50% greater than the third. These results, therefore, support hypotheses H4a, H6a, and H7a. These results are consistent with previous studies, such as Porter and Heppelmann (2014), which noted that digitalized products positively impact their revenues. Additionally, it is similar to the study by Vial (2019) because digitalized internal processes allow practices to streamline operations, reduce human error, improve data accuracy, and positively influence the firms' sales. Furthermore, these results confirm that business digitalization deeply integrates digital technologies into their organizational structure, strategic vision, and positively impacts the firm's sales (Sebastian et al., 2017). Finally, since firm size and productivity only positively impact sales, it was appropriate to examine their diverse effects. However, only more experienced companies appear to prioritize productivity over sales improvements, as the latter has a non-significant coefficient.

Second, when the analysis examines the subsamples based on firm size, clear differences emerge between these groups. On the one hand, focusing on micro firms (column 2), the variables related to acquiring digital knowledge generally have a positive, statistically significant impact on their sales, except for technological assistance associated with T-KIBS. Therefore, these findings support hypotheses H1a and H2a. This result aligns with previous studies, which show that workers' digital capabilities (Kumar et al., 2016) positively affect their sales. On the other hand, the variables examined regarding the firm's digitalization, such as digitalized products (p<0.01), online service provision (p<0.05), process digitalization (p<0.01), and overall business digitalization (p<0.01), have positive effects on the firm's sales. Consequently, we can conclude that microenterprises aim to generate sales in the short term. Therefore, these results support hypotheses H4a, H5a, H6a, and H7a. These results align with previous studies indicating that digitalized products (Porter & Heppelmann, 2014), online services (Xia & Zhang, 2010), process digitalization (Vial, 2019), and business digitalization (Sebastian et al., 2017) influence firms' sales.

The analysis of SMEs (column 3) indicates that neither acquiring digital knowledge nor digitalization has positive effects on the firm's sales, except for technological assistance related to T-KIBS. That is, SMEs outsource technical assistance to boost revenue in the short term. Therefore, this finding supports the hypothesis H3a. One explanation for this can be found in the resource-based view theory (Barney, 2001), which suggests that a firm can benefit from collaborations with external partners. This result aligns with previous studies that found T-KIBS positively influence a firm's sales in the short term, especially in the context of Latin American countries (Seclen-Luna et al., 2024). However, these results also indicate that for SMEs, digital technologies are considered important but are not seen as a strategic issue, as Fernandes and Burcharth (2024) mention. Therefore, it can be understood that SMEs are at a basic level of digital maturity, which is common in Latin American countries (Balestrini et al., 2024).

Regarding large firms (column 4), only the variables related to acquiring digital knowledge, such as training in digital tools (p < 0.05), have a positive effect on firms' sales, supporting Hypothesis H1a. Therefore, we can assert that for large firms, training workers is a strategic issue in increasing sales. This finding aligns with previous studies, which demonstrate that workers' digital capabilities positively influence their performance (Kumar et al., 2016). However, digitalization does not have a statistically significant impact on a firm's sales. Therefore, no hypothesis was supported. One possible explanation is that firms faced challenges in meeting their demand for digital talent. In fact, Novella & Rosas-Shady (2022) report that eight out of ten Peruvian firms struggled to recruit digital talent.

Third, when examining productivity across the entire sample (column 5), it is notable that neither the acquisition of digital knowledge nor digitalization shows any positive impact on the productivity of service firms. Only technical assistance related to P-KIBS (p<0.10) has a statistically significant adverse effect on their productivity. This suggests that digital skills and digitalization in Peruvian service firms are underdeveloped and not effectively linked to productivity, highlighting an area that requires improvement. Therefore, no hypothesis was supported. Comparing these results with those previously observed for sales revenue (column 1), we can conclude that the resource-based view logic is more closely related to digitalization than to the firm's productivity. However, when analyzing the subsamples based on firm size, it is possible to observe significant differences between these firms.

Regarding micro firms (column 6), the previous results still stand. Therefore, hypothesis H2b was not supported. One reason is that micro firms are vulnerable to market fluctuations (in terms of sales in the short run), and focusing on efficiency and productivity is a subsequent step. This aligns with their organizational inertia and the firm's age; additionally, because P-KIBS are based on professional services and support activities that rely on personal expertise and are less likely to transfer their knowledge to other local firms (Doloreux, et al., 2010).

The analysis of SMEs (column 7) reveals that, on the one hand, the acquisition of digital knowledge has no statistically significant positive effects on the productivity of service firms. Perhaps one explanation is that, for SMEs, digital technologies are seen as important but are not perceived as a strategic issue, as mentioned by Fernandes and Burcharth (2024). This result can be understood since SMEs are at the initial level of digital maturity, which is common in Latin American countries (Balestrini et al., 2024). On the other hand, results show that SMEs' digitalization has a statistically significant effect on a firm's productivity. Specifically, digitalized products (p < 0.10) have a positive impact on productivity. Therefore, this result supports hypothesis H4b and aligns with previous studies that have found digitalized products (Porter & Heppelmann, 2014) to have a positive effect on firm performance. It is important to note that, although SMEs do not invest in acquiring digital knowledge, they still implement digitalized products; that is, digitalization enhances productivity in SMEs.

Lastly, regarding large firms (column 8), only the variables related to acquiring digital knowledge, such as training in digital tools (p<0.05), have a positive effect on the productivity of service firms. Therefore, these results support only hypothesis H1b. This finding aligns with previous studies that show the necessity of digital skills among workers (Kumar et al., 2016) to positively influence a firm's performance. On the other hand, variables related to the digitalization of the firm, such as digitalized processes (p < 0.05), have positive effects on the productivity of service firms. Therefore, this supports hypothesis H6b. These findings align with previous research indicating that the digitalization of processes (Vial, 2019) influences firms' productivity. In summary, it appears that under the organizational learning theory, large firms combine the acquisition and development of digitalization.

5.1 Theoretical implications

The first contribution of this study relates to the organizational learning theory (March, 1991), as it shows that the complementarity between acquiring digital knowledge and digitalization improves firm performance in sales and productivity. This mainly applies to investments in digital tools, training, and digitalizing products and processes. This relationship is especially strong for larger firms' productivity and micro firms' sales. Therefore, this study emphasizes that combining digitalization with the acquisition of digital knowledge is more effective than doing them separately.

Second, this study helps expand the understanding of how KIBS's technical assistance influences the acquisition of digital knowledge and sales by their client firms (Rapaccini et al., 2023; Ribeiro-Navarrete et al., 2021; Seclen-Luna et al., 2024). Specifically, SMEs are linked to T-KIBS, while micro firms are linked to P-KIBS.

5.2 Managerial and policy implications

First, since several strategies for gaining digital knowledge and digitalization positively relate to sales and productivity, managers should oversee a broad range of digital capabilities and the technological portfolio. They should also develop tailored strategies based on firm size and maturity level. This is particularly important because the use of multiple digital technologies can have a stronger impact on firm performance (Seclen-Luna et al., 2025). For example, micro and small firms could focus on cost-effective training programs centered on basic digital tools and online services. Meanwhile, large firms might benefit from advanced programs that incorporate artificial intelligence, data analytics, and comprehensive digital business models. Therefore, establishing knowledge management mechanisms and leveraging knowledge assets is crucial for technological and organizational innovation (Hall & Andriani, 2003; Lafuente et al., 2010).

Additionally, empirical studies show that team processes related to knowledge management-such as team building, information seeking, and problem-solving-positively impact the internalization of technology, thereby strengthening the adoption of innovations (Bong et al., 2004).

Second, adopting digitalization may also require ad hoc policies when they are closely linked to innovation processes within the firm. In this context, creating advanced services should be encouraged to connect digital service innovation (Lafuente et al., 2018; Opazo-Basaéz et al., 2024), especially to promote digital servitization (Gebauer et al., 2021). Similarly, policy tools could incentivize partnerships among firms, universities, and public agencies to offer targeted digital training and consulting services.

The positive effects of digital training on small and large firms suggest that human-capital-based initiatives might need different approaches depending on the firm's absorptive capacity and the development of the surrounding digital ecosystem. Micro firms could benefit from simplified, subsidized training programs and shared digital service centers that reduce entry barriers. Meanwhile, large firms may require more advanced programs that incorporate data analytics, cybersecurity, and artificial intelligence into their management practices.

This aligns with the framework proposeds by Acs et al. (2022) for Latin America, Szerb et al. (2019) for Europe, and Lafuente et al. (2024) at the global scale, which highlights that the success of entrepreneurship and innovation policies depends on their ability to target local ecosystem bottlenecks rather than using one-size-fits-all solutions. Based on this perspective, national and regional governments should create tailored policies that strengthen specific aspects of the entrepreneurial and digital ecosystem, such as human capital, technological infrastructure, and institutional coordination, depending on firm type and sector (Bayon et al., 2016; Lafuente et al., 2017; Lafuente et al., 2022). Consequently, the policy approach should shift from generic digital inclusion programs to context-aware, capability-focused interventions that enhance firms' digital knowledge and help them turn digitalization into measurable performance results.

5.3 Limitations and future research

This study has limitations. First, although it provides valuable insights into the business's productivity and sales, the database used is from 2019, which is the most recent survey available on these topics for Peruvian firms. Since 2020, due to the COVID-19 pandemic, business digital transformation has sped up worldwide; therefore, the study's findings may not reflect current conditions.

Second, the measurement of digital knowledge acquisition uses three binary indicators, which may not fully reflect variations in intensity, frequency, or depth of training and technical assistance. Likewise, the binary format of the digitalization variables reduces their explanatory power. Therefore, the Peruvian business survey (ENE) or future studies should consider this limitation when designing their questionnaires. Additionally, some key moderating variables for the explored determinants were not included in the ENE 2019, such as the education level of the firm's workers, which serves as a primary proxy for the firm's technological absorption capabilities, as well as their academic specialization in STEM careers, among others. Including these in the ENE would help improve and provide better evidence related to the digital transformation of Peruvian firms.

Finally, our findings are based on the experiences of Peruvian service firms, which may raise concerns about their relevance in other sectoral, geographical, and cultural contexts. Future research could adopt a cross-country approach to consider the influence of different institutional environments and cultural differences. Additionally, the cross-sectional design of our dataset limits the ability to analyze how these relationships change over time. Therefore, it is recommended that future studies employ a longitudinal approach to explore these relationships further.