Absorption capacity

The concept that competitive advantage is no longer based on internal knowledge, but on external knowledge, has had a significant impact on the way businesses operate. This notion has given rise to absorptive capacity, which allows organizations to access and assimilate external knowledge for their own competitive advantage. By leveraging the resources of other organizations, companies can identify new opportunities and develop competitive strategies.

^{Cohen and Levinthal (1989)} paved the way for introducing the concept of absorptive capacity, derived from organizational learning theory. This theory suggests that organizations can leverage external knowledge to create value by recognizing and understanding new information, assimilating it into existing organizational structures, and applying it to achieve beneficial outcomes. By utilizing absorptive capacity, organizations can stay ahead of the competition and use their resources more efficiently.

The impact of learning processes on absorptive capacity has been the subject of debate in recent years. Researchers have argued that while both processes have an impact, their effects differ. Therefore, it is suggested that specific strategies are needed to maximize the potential of absorption capacity (^{Cohen & Levinthal, 1990}). In this regard, ^{Zahra and George (2002)} proposed a new framework for understanding absorptive capacity by linking it to exploration and exploitation learning activities.

This framework divides absorption capacity into two distinct categories: proactive absorption capacity (PACAP) and realized absorption capacity (RACAP). This new approach provides insights into how organizations can better manage the acquisition, assimilation, use and transfer of knowledge. Knowledge exploration is an essential component of the learning process and is often divided into four subsets: acquisition, assimilation, transformation, and exploitation.

Each subset of knowledge exploration activities represents two dimensions of understanding; Acquisition and assimilation are analogous to the exploration of new information. While transformation and exploitation are related to the application of what has been learned.

Exploitation is a major problem in today's world, which has important implications for people and organizations. Individuals often modify their beliefs to fit existing organizational codes in the individual context, which can lead to exploitation. This process of knowledge dissemination creates an environment in which exploitation can occur and has negative impacts on both individuals and organizations (^{Tejedo-Romero & Ferraz, 2018}).

^{Nielsen et. al. (2018)} points out that exploitation strategies are often considered the fastest way to acquire knowledge but can be detrimental in the long term. While they may produce quick results in the short term, they may not provide a comprehensive understanding of a topic. As such, it is important to consider both exploitation and exploration strategies to ensure sustained learning and knowledge acquisition over time.

Organizational codes and individual beliefs have the potential to shape reality in unique ways. Exploration occurs when these codes are modified by individuals, leading to a creative process that can result in the formation of new ideas (^{Leiva, Rodríguez, & Monge, 2017}).

^{March (1991)} analyzes that exploring and exploiting knowledge is essential for organizations to create new knowledge. Through the exploitation of existing knowledge, organizations can capitalize on existing resources and capabilities to improve their performance. In this sense, ^{Zollo & Winter (2002)} explain that the exploration of knowledge allows organizations to develop new ideas and strategies that can lead them to success. By combining these two processes, organizations can create a dynamic environment for innovation and growth. Finally, the contributions of ^{Zahra and George (2015)} also invite reflection on whether PACAP and RACAP are two different or complementary processes.

Potential absorption capacity

Identification and assimilation of external knowledge (PACAP) can be a powerful tool for organizations seeking to foster innovation. By leveraging diverse sources of knowledge, everyone in the organization can create an individual combination of knowledge that contributes to innovative solutions. PACAP can enable organizations to access external data and information, allowing them to make smarter decisions quickly and stay ahead of the competition (^{Ávalo, Yagüe, & Cangahuala, 2016}).

In this regard ^{Ruiz-Corrales, et. al. (2022)} investigate that knowledge assimilation is an important process for organizations to remain competitive in the constantly evolving business landscape. Through knowledge assimilation, organizations can continuously update their knowledge stocks, leading to better PACAP levels and more innovation. Thus, establishing that this process can help organizations achieve greater success.

Companies in emerging contexts are one of the main drivers of today's economy. While much research has been done on how certain elements, such as potential absorption capacity (PACAP), can directly affect actual absorption capacity (RACAP) (^{Branstetter & Maskus, 2022}; ^{Guerrero-Sanchez, 2021}; ^{Guajardo & Zapata, 2020}), there is still much to understand about the best ways to take advantage of this knowledge.

Realized absorption capacity

RACAP is a proven method for companies to identify and develop new sources of knowledge. Developed by ^{Zahra and George in (2002)}, RACAP can provide companies with insights into their current market position and help them create innovative strategies that will increase their overall performance. By using RACAP, companies can gain an edge over their competitors and create long-term success. Organizations are increasingly focusing on the ability to quickly create and develop new strategies to remain competitive in their respective industry sectors.

RACAP is a concept that reflects the ability of an organization to integrate the knowledge acquired and assimilated from abroad, to put it into practice in its processes, routines, and operations. RACAP can help organizations gain a competitive advantage by enabling them to rapidly reconfigure their existing knowledge base into new and efficient competencies (Cruz, Characterization of Technology-Based Companies in Higher Education Institutions in Mexico, 2017; ^{García, León, & Nuno, 2017}).

The relationship between PACAP and RACAP has sufficient evidence to support it is necessary for organizational efficiency (^{Ponce-Espinosa, Segarra-Oña, & Peiró-S, 2020}; ^{Máynez, 2016}; ^{Cruz, 2014}). While some studies have reported that the correlation between these two variables is not significant, others suggest that they are complementary and both are necessary to achieve innovation (^{Meraz-Ruiz, Olague, & Perez-Cruz, 2023}a; ^{Kantis, Federico, & Girandola, 2020}; ^{Perez-Cruz, 2020}).

Previous research on the relationship between these two dimensions of absorption capacity has revealed that they are interrelated, demonstrating that the correlation will only have positive results if PACAP positively complements the RACAP, thus generating dynamic capacities (^{Saavedra-García, Demuner-Flores, & Choy-Zevallos, 2020}; ^{Restrepo & Vanegas, 2015}).

H1. The potential absorption capacity positively influences the realized absorption capacity.

Responsiveness

Business research provides valuable insights into how a company's organizational form relates to its strategic choices and the outside environment. By understanding the structure of a company and its relationship to different strategies and market conditions, companies can make better decisions that lead to better performance and greater efficiency (^{Irwin, Gilstrap, Drnevich, & Sunny, 2022}; ^{Meraz-Ruiz, Olague, Flores-Villanueva, & Perez-Cruz, 2023b}).

These decisions involve shifts between investment approaches and expanding production or technology, or maintaining installed capacity (^{Pérez, Lara, & Gómez, 2017}). By carefully balancing their decision-making between both approaches (investing or not), companies can adjust their development stages and marketing cycles to better meet market needs (^{Leyva, Cavazos, & Espejel, 2018}). This allows them to maximize their profits while ensuring they remain competitive in their respective industries (^{Monge, Rodriguez, & Leiva, 2015}).

^{Teece (2017)}, points out that the ability to respond implies skills to characterize and exploit business opportunities that arise in the economic context, allowing companies to reorient their strategies and stay ahead of their competition. Thus, allowing companies to save time and resources to generate reliable knowledge that accurately reflects changing market conditions (^{Flores, García, & Zevallos, 2022}; ^{Sánchez, Saavedra, & Choy, 2020}).

^{Teece et. al. (1997)} explore the concept of response capacity (CR) as a vital tool for companies to manage their vulnerability in a dynamic environment. The authors argue that CR can be used to proactively assess and respond to external threats, such as market changes, new technologies, and customer needs. By responding quickly and in a timely manner, companies can gain an edge over their competitors in the marketplace. In this order of ideas, the following hypothesis is proposed:

H₂. The potential absorption capacity measures the realized absorption capacity and determines the responsiveness.

In this sense, Figure 1 shows the theoretical model specified for this research:

Source: Authors.

Figure 1 Theoretical model 

However, CR varies significantly depending on the economic sector in which a company operates, whether it is in commerce, construction, manufacturing, or services (^{Pérez-Cruz, 2018}; ^{Leal-Rodríguez, Roldán, Ariza-Montes, & Leal-Millán, 2014}; ^{Ynzunza & Izar, 2013}). Thus, in some sectors, CR is high while in others it is relatively low. This variation influences how companies operate and can influence their profitability. Understanding the differences between sectors and their respective CRs is critical to the successful management of any business. In this way, four hypotheses related to the economic sectors to be analyzed are proposed:

H₃. In companies in the commercial sector, potential absorption capacity mediates the realized absorption capacity and determines the response capacity.

H₄. In companies in the construction sector, the potential absorption capacity mediates the realized absorption capacity and determines the response capacity.

H₅. In companies in the manufacturing sector, the potential absorption capacity measures the realized absorption capacity and determines the response capacity.

H₆. In companies in the service sector, potential absorption capacity measures the absorption capacity realized and determines the response capacity.

Hence, Figure 2, shows the theoretical model and its relationship with the working hypotheses previously exposed:

Source: Authors.

Figure 2 Theoretical model 

To evaluate the working hypotheses, structural equation modeling (SEM) with partial least squares (PLS) adjustment was applied using SmartPLS software (^{Ringle, Wende, & Becker, 2015}). The stages proposed by ^{Hair, et. al. (2019)} to evaluate the theoretical model: goodness of fit of the model, measurement model, structural model, hypothesis testing and bootstrapping of the control variables: trade, construction, manufacturing, and services.

Initially, we assessed the adequacy of the theoretical model fit to assess its suitability for analysis. Subsequently, the validity and reliability of the measurement model were examined. In the third section, a structural model is used to explore the correlation between the study variables. Finally, in the fourth part, the research hypotheses are evaluated. Finally, the theoretical models for commercial and service enterprises are examined separately.

Data

To assess potential and actual absorption capacity and its influence on business response, data from the Monthly Business Opinion Survey (EMOE) were used from a sample of more than 12,000 companies belonging to the trade, construction, manufacturing, and services sectors (^{INEGI, 2023}). The survey collected and analyzed data from companies in Mexico between the years 2011 to 2020.

Data analysis

The analysis presented here is a conceptual model SEM-PLS using the SmartPLS software, through which the effects of the absorption capacity (AC) of knowledge and its four elements were sought, as predictors of the moment to invest, as an indicator of the response capacity (CR). This analysis will be mediated by control variables (VC) that were: trade, construction, manufacturing, and services companies in Mexico. Table 1 shows the latent variables, indicators, codes, definitions, and scales used.

Measurement model

To determine the reliability of the model, composite reliability (CC) and Cronbach’s Alpha (AC) were verified. Table 4 shows the results, where it is observed that the WC varied from 0.79 to 0.87 in the PACAP, and from 0.83 to 0.91 in the RACAP, complying with the recommended reliability (^{Nande, Reyes, & Perez-Cruz, 2021}).

The AVE ranged from 0.62 to 0.65, thus meeting the minimum accepted value of 0.5 according to (^{Ringle, Wende, & Becker, 2015}).

Discriminant validity

Regarding discriminant validity, the Heterotrait-Monotrait correlation ratio (HTMT) was used (^{Hair, Risher, Sarstedt, & Ringle, 2019}). Table 5 shows that HTMT values were lower than the cut-off value of 1, thus complying with discriminant validity (^{Hair, Risher, Sarstedt, & Ringle, 2019}).

In the table above, it shows the correlations between constructs and, on the diagonal (in italics and bold), the square root of the AVE. In view of these results, it can be asserted that there is a discriminant validity between the constructs, according to ^{Fornell and Larcker (1981)}.

Based on the analyses presented in Tables 4 and 5, it is argued that the dimensions have the discriminant validity values of the specified theoretical model. The next phase was to evaluate the structural model, which evaluates the relationships between variables raised in the conceptual model.

Structural model

For the evaluation of the robustness and predictive accuracy of the structural model, ^{Hair et. al. (2019)} explain that the Q2 communality index should be examined. These values must be greater than zero to indicate the predictive accuracy of a specific construct (^{Hair, Risher, Sarstedt, & Ringle, 2019}). The results indicate that for PACAP (Q2 = 0.36) and for RACAP (Q2 = 0.41). The values for the CR (Q2 = 1.00), satisfying the condition of values greater than zero.

Results of hypothesis tests

The values obtained between PACAP and RACAP shown in Tables 4 and 5 show concurrent validity. Next, a two-tailed bootstrapping was performed with 5,000 repetitions, including the t values of the six hypothetical relationships specified, which obtained statistically significant relationships, at levels of 0.01 and 0 .05, which are shown in the following Table 6.

In general, the results validate the proposed model and confirm the two hypotheses raised, evidencing the relationship between the PACAP and RACAP dimensions.

Control variables

For a more detailed analysis of these results, two bootstrapping of two tails were run with 5,000 repetitions, with the control variables: companies in the trade, construction, manufacturing, and services sectors. The bootstrapping was run separately, for each of the sectors. Table 7 presents the results.

The table above shows that, in the four business sectors, PACAP and RACAP mediate knowledge for CR, which confirms hypotheses H3, H4, H5 and H6.