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Article

Absorptive Capacity and Its Dual Effect on Technological Innovation: A Structural Equations Model Approach

by
Héctor Cuevas-Vargas
1,*,
Héctor Abraham Cortés-Palacios
2,
Cid Leana-Morales
3 and
Eduardo Huerta-Mascotte
1
1
Multidisciplinary Technological Development Department, Universidad Tecnológica del Suroeste de Guanajuato, Valle de Santiago, Guanajuato 38400, Mexico
2
Agribusiness Department, Faculty of Business Science, Universidad Autónoma de Aguascalientes, Aguascalientes 20131, Mexico
3
Business Department, Arkansas State University Campus Querétaro, Querétaro 76270, Mexico
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(19), 12740; https://doi.org/10.3390/su141912740
Submission received: 26 August 2022 / Revised: 22 September 2022 / Accepted: 26 September 2022 / Published: 6 October 2022
(This article belongs to the Section Sustainable Management)
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Abstract

:
Knowledge absorptive capacity (ACAP) is a key dynamic capability that boosts business innovation, particularly in developing economies. However, scarce studies focus on ACAP and technological innovation (TI) in the context of small- and medium-sized enterprises (SMEs). Therefore, this study aims to examine the effects of two different types of knowledge absorptive capacities to generate TI in the context of the Mexican manufacturing industry and determine whether realized absorptive capacity (RACAP) mediates the relationship between potential absorptive capacity (PACAP) and TI. This analysis was carried out through an empirical study of predictive type and quantitative approach. A survey-type questionnaire was randomly applied to a sample of 249 SMEs. The survey confers 200 managers’ opinions on the manufacturing industry in Mexico. The outcomes obtained through the variance-based structural equations (PLS-SEM) approach revealed that PACAP significantly influences RACAP, whereas, RACAP does impact TI and has a full mediating effect on the relationship between PACAP and TI. Nevertheless, although the significant direct effect of PACAP on TI is not verified, this research demonstrated the significant indirect effect of PACAP on TI. The findings reveal important implications for managers and decision-makers who must direct their strategies and ensure that the external knowledge acquired is assimilated by their employees so that PACAP will result in the transformation and exploitation of the internal and external knowledge acquired (RACAP), converting it into new products and processes.

1. Introduction

Due to the current competition in increasingly global markets, organizations from all sectors around the world find themselves competing in conditions that change day by day [1,2]. Changes force companies to restructure their business models, as well as their innovations in products and services, because they do not guarantee their competitiveness or even their survival, due to the ease of replication elsewhere or by the same competition [3]. Moreover, the internal generation of new knowledge and the ability of companies to absorb external knowledge is becoming increasingly crucial; some innovative companies seem to possess the organizational characteristics and human resources necessary to successfully absorb the value of external knowledge, while many others fail [4,5,6].
Some authors such as Tortorella et al. [7] and Alvarenga et al. [8] have addressed the topic with an analysis of digital transformation and its relationship with knowledge in manufacturing companies and in the public sector, as has Machado et al. [9] in which they analyzed knowledge management and digital transformation 4.0 with innovation ecosystems and strategic planning; however, information and studies on the influence that absorptive capacity has on innovation, particularly on technological innovation, are still scarce. In addition, there is a general lack of absorptive capacity (ACAP) research in the context of SMEs [1], which is critical, considering how important it is for them to adapt to changing market conditions using scarce resources.
From the knowledge-based perspective, innovation and absorptive capacity are tied and have been studied across different perspectives. Theoretically and empirically, they share part of the analysis of firms to rapidly adapt to a globalized business scenario and its market changes [10]. Introduced by Cohen and Levinthal [11], absorptive capacity (ACAP) is the capacity to process new information and knowledge transformation converted into novel opportunities in business, to use external knowledge and apply it to internal innovation, or to develop internal knowledge and apply it to business opportunities [12].
As one of the main hurdles for businesses in growing and creating knowledge, innovation relates to organizational absorptive capacity in determining how competent firms are in managing external knowledge for their decision-making process [13]. Accordingly, the readiness to recognize and assimilate value to business purposes is critical for a company [11]. Moreover, knowledge obtained through technology has the potential to produce novel outputs via internal processes in a firm, and this is allowed by the absorptive capacity [14].
Innovation has been recognized as related to absorptive capacity in two ways [15]: its empirical evaluation of the single-step impact of the influence of potential absorptive aptitude (PACAP) on acquired absorptive capacity (RACAP) and the approach in which both APAC types impact on technological innovation, a metaphor of ACAP single one-step and bipartisan modes of learning [16].
ACAP has already been recognized as a dependent, independent, or mediator variable [17]. According to the literature, it can be included in research models [18] along with innovation [19]. Repeatedly in the literature, studies have established a favorable absorptive capacity influence on innovation [20]. However, there is no evidence of the separation of absorptive capacity in its two recognized dimensions [21] as part of a step-by-step analysis to contrast this with a new conceptual and measurement acumen [22] and proper operationalization [17].
Studies have shown a relationship and favorable effect of ACAP on innovation [20]. The relationship between potential absorptive aptitude (PACAP) and realized absorptive capacity (RACAP) on technical innovation exists and occurs as a sequence [23]. Theoretically, ACAP and innovation are related [11,24]. The literature on absorptive capacity has considered the various stages of its adoption process, diversity, and richness simultaneously from a multi-staged perspective ranging from PACAP to RACAP [21]. Its impact on non-technical and technical innovation using multivariate analysis techniques [23] has also been approached.
According to Limaj and Bernroider [1], each component of absorptive capacity (PACAP and RACAP) directly affects innovation. In addition, PACAP impacts innovation through sentiments and selection of innovative external knowledge [25] and the firm’s ability to combine old and new knowledge [26]; however, PACAP does not directly impact innovation when the team does not have such ability [27]. Likewise, PACAP was found to be an independent variable that directly had a positive and significant effect on innovation [28]. Some of the above studies show differing results and should be further analyzed.
The strong development of the existing base of research and its consequent consensus compels the absorptive capacity dynamic attributes (at the base of the application of knowledge elements) to be aligned to innovation, particularly technological innovation to all types of organizations [29]. ACAP reduces problems and enhances capabilities to comprehend organizational and external knowledge [20].
The main motivation of this study is refining a framework considered critical on behalf of innovation in organizations when seen as a stage-by-stage conception sequenced in a dynamic continuous in time [30] to mix, readapt, obtain, and ease resources to incentivize a market reengineering [31]. Absorptive capacity can reduce any potential threats and bring innovative output to a firm [32]. It also strives for a higher-level dialogue of studies on absorptive capacity by verifying the impact of time, single–dual, and mediator effects of RACAP since limitations on this still exists.
This study focuses on the mediating influence of realized absorptive capacity (RACAP) on technological innovation as an attempt to try to replicate those dynamic capabilities in a specific industry faster than rivals and to continue the construct renewal [33] and its empirical testing from potential to realized ACAP. The subsequent questions to research are addressed: What is the effect of PACAP on RACAP in small- and medium-sized enterprises (SMEs) in the manufacturing industry? How do PACAP and RACAP influence technological innovation in SMEs in the manufacturing industry of Mexico? What is the mediating effect of RACAP on the relationship between PACAP and technological innovation in SMEs in the manufacturing industry?
Several studies have included the moderating roles of ACAP on innovation [34] but none of them sequences the effect per variable and the final contribution to technological innovation. This study explores the separation of the two components of ACAP sequentially as a single and dual mode. The model using both dimensions to determine both effects is a gap that exists in the literature, and it is worth exploring since it has multiple levels of analysis from two perspectives of corresponding dimensions. On the other hand, technological innovation has been investigated from individual products, services, and an R&D processes viewpoint [35], or from the characteristics of products [36,37]; however, there is still a lack of literature development in emerging markets for small- and medium-sized companies (SMEs) concerning how RACAP impacts technological innovation [38].
On the basis of these research opportunities, this study plots three important contributions. First, to generate proof to confirm the direct relationship between RACAP and technological innovation, as well as the mediating role of RACAP in the relationship between PACAP and technological innovation in a rising economic system like Mexico. Second, to analyze the after-effect of absorptive capacity in manufacturing firms in generating technological innovations. Finally, to use a relatively novel methodology based on the reasonableness test of the second-order theoretically conceived model, from the standpoint of a second-generation statistical technique: variance-based Structural Equation Modeling (PLS-SEM).

2. Literature Review

As a new perspective of innovation [39], absorptive capacity raised a different discussion in management from the beginning [40]. First, it was perceived as the external value recognition of knowledge to be applied in business [11], then it changed over time through several alternative views that were more oriented to innovation [41]. Among those visions that expanded the concept is the division into potential versus routines and processes in an organization [21].
As a seminal concept, absorptive capacity allows companies to better adapt and reconfigure resources [42]. It represents a foundation for developing dynamic capabilities [43] and helps recognize a particular value and adoption of innovation in organizations [44]. It is depicted by Zahra and George [21] as a “set of organizational routines and processes by which firms acquire, assimilate, transform, and exploit knowledge to produce a dynamic organizational capability” (p. 186). It is conceived as “the capabilities of a firm to innovate and be dynamic” [41] and oriented towards creating a potential gain of a competitive advantage over time [21]. Since then, it is a relevant research subject in the literature under theoretical and empirical orientations [13].
The internal characteristics of a firm comply with the multidimensional nature of ACAP making it possible to separate [45]. Still, for most existing literature, potential and realized capacities are not the same but exist simultaneously. Their relationship in the process of knowledge transformed into innovation has a continually changing set of routines and capabilities determined by absorptive capacity [46]; those routines are seen as dimensions.
Traditionally, four dimensions are recognized in the ACAP literature: first, acquiring capabilities to identify external knowledge or information [47]; second, assimilating procedures and processes about information relating to examining, processing, translating, and understanding it [5]; third, transforming, modifying, adapting, and combining that information with that which was internally created; and last, transforming and exploiting it as a competitive advantage [46]. Each of these relate to different management theories of the firm, ranging from a resource-based view to competitive advantage as its main orientation.
The theory recognizes several studies that address ACAP’s two components together to create RACAP, whereas others, namely, Flatten et al. [5], even referred to a scale development for its validation. Since this validation exists, theory accepts that absorptive capacity dimensions or components can be sequential but also complementary or can overlap through processes and routines.
Despite those variations, PACAP influences RACAP and produces dynamic organizational capabilities [21] and serves as an indicator of knowledge creation [48]. PACAP also reduces the relative distance that may exist in both types of capacities [46] in the multidimensional framework that identifies different aspects and measures related to innovation.
More recently, the literature has recognized that this concept has branched from several dimensions that separate its two factors “potential absorptive capacity” (PACAP) and “realized absorptive capacity” (RACAP) [23]. This latter type confirms that there is an impact on innovation as a competitive advantage that reduces the distance between both absorptive capacity types [46]. RACAP is an indicator of internal movement to use absorbed knowledge by a firm. Considering these arguments, the following hypothesis is presented:
Hypothesis 1.
Potential absorptive capacity of knowledge has a positive effect on realized absorptive capacity of knowledge.
Absorptive capacity (ACAP) is relevant for innovation processes [11]. In a firm, it can increase the speed and frequency of innovation [49]. It has been previously tested and determined that ACAP positively moderates innovation in high-tech manufacturing firms with high absorptive capacity, compared with those with a low one, with a U-shaped curve effect as a result [29]. Other studies have shown a favorable result when testing technologically oriented skills and competencies determining a positive influence on realized and potential capacity when considering technological assets and flexibility [43].
Transformation means the firm’s ability to unify previous information with newly created information and the firm’s ability to improve, use, and create skills to apply acquired knowledge [3,4,11]. RACAP is the firm’s ability to use its knowledge in organizational objectives consisting of transformation, acquisition, and exploitation of external knowledge, a process that, if done correctly, results in innovations [25]. RACAP particularly influences technological innovation because it starts from identifying knowledge externally [1]; thus, these organizations have a constant interest in identifying new technologies to incorporate them into their products and processes [50]. RACAP is a stage that drives innovation through the application of new knowledge and employee collaboration [51]. By significantly influencing innovation, RACAP also requires control and stability on the part of organizations [52]. On the basis of these arguments, the second hypothesis is stated:
Hypothesis 2.
Realized absorptive capacity of knowledge has a positive effect on technological innovation.
Absorptive capacity is defined as the ability and motivation of workers to acquire external knowledge and convert it into innovations [53]. PACAP is the first stage of ACAP where firms obtain external knowledge, assimilate it, and translate it to their benefit; thus, PACAP is divided into two dimensions: acquisition and assimilation [21]. Acquisition is the ability of a firm to identify and obtain knowledge, while assimilation is the ability of a firm to analyze, process, interpret, and understand information obtained [28] from external sources [3,4,11,27]. Each component of PACAP influences innovation, ref. [1] assuming that such knowledge is relevant to the firm’s core business [54], with the dimensions of acquisition and assimilation. Additionally, PACAP influences innovation by providing a flexible strategy that allows firms to change and reconfigure organizational operations [50]. This is most prevalent in managing firms with technology and the ability to conform and adapt to select external knowledge relevant to the cultural organization [1]. Firms with a strong PACAP can absorb the results of gathering new knowledge and combine it with previous knowledge for the innovation process [25], then, the skill factor of the organizational team will influence the PACAP to determine the ability to innovate and achieve success in innovation. This gives rise to our third research hypothesis:
Hypothesis 3.
Potential absorptive capacity of knowledge has a positive effect on technological innovation.
According to theory, this complementarity between PACAP and RACAP does not necessarily translate into innovation [55], yet ACAP is a mediating potential version of this [21]. At some point, organizations that integrate structures, diversity, and structural linkages will likely produce innovation [34]. Some studies have linked absorptive capacity to innovation, such as the study of Cuevas-Vargas et al. [19] who, in the context of small Colombian firms, concluded that ACAP has positive and significant effects on open innovation, and also demonstrated the mediating role of ACAP on the relationship between ICT adoption and open innovation. Absorptive capacity correlates with, and has been tested as, a mediator affecting technological innovation. That analysis has considered lower and lower-medium tech manufacturing companies and proved this relationship is positive through a PLS-SEM [56].
From absorptive capacity theory, its moderating effect has been tested in China’s manufacturing industry, proving that it has certain effects under different search levels on innovation [29]. Once an organization recognizes a structural single mode pattern to innovate and learn and induces a contingency approach of management to deliver value, it also obtains and sustains a competitive advantage [57].
In terms of innovation, one of the most studied branches of this concept recognized its adoption by firms where this occurred [44,58]. Conceptual models from theory can help support the arguments that connect theory with empirical testing. Absorptive capacity is undoubtedly related to the technological capabilities of innovation [59]. If companies can increase their technological capabilities, they can be more competitive through innovation. Analyzing these arguments, the fourth research hypothesis is presented:
Hypothesis 4.
Realized absorptive capacity of knowledge positively mediates the relationship between the potential absorptive capacity of knowledge and technological innovation.
In order to have a complete vision of the research model with the four hypotheses, see Figure 1:

3. Materials and Methods

An empirical study with a predictive type and quantitative approach was conducted, applying a cross-sectional and non-experimental design in which three correlational–causal hypotheses were developed and measured via the statistical technique of variance-based structural equation modeling (PLS-SEM), employing the statistical software SmartPLS 3 [60]. This statistical method was implemented for the following reasons. First, it is the second-generation statistical technique that has had the greatest boon in recent scientific literature; second, it is very useful for working with small samples [61]; third, it works with non-parametric tests, preventing possible problems of non-normality of the data through the use of bootstrapping and blindfolding, since it does not have a unique goodness-of-fit criterion to assess the PLS-SEM results [62]; and fourth, it allows working with reflective and formative models at the same time [63]. The implementation of this technique was carried out by estimating a model that quantifies the association among observations as a reflective–reflective, Type I hierarchical component model [63,64], using the indicator repetition approach [65,66], an action that is necessary to configure when running higher-order models in PLS-SEM [19,61]. This method consists of two phases: first, the measurement model was assessed in order to demonstrate that the lower- and higher-order constructs have reliability and validity; subsequently, the structural model was tested using bootstrapping with 10,000 subsamples [67], allowing the research hypotheses to be contrasted and, thus, being able to assess the indirect effects of PACAP on technological innovation and then determine the mediating role of RACAP in the relationship between PACAP and technological innovation.

3.1. Sample Design and Data Gathering

In order to conduct this research, the database of the National Statistical Directory of Economic Units [68] was used as a reference, considering the economic units of the manufacturing sector in the state of Guanajuato, Mexico, as the target population, where a total of 3791 companies in the manufacturing industry with 10 to 250 workers appeared registered. By applying the formula for finite populations with a trust worth confidence level of 95%, a marginal percent of error of 6%, and P = Q = 0.5, we obtained a sample of 249 firms, whose personnel were interviewed using a simple random sampling technique; however, only 200 valid surveys were received during the period from October 2019 to January 2020, representing 80.3 percent of the defined study population.
The technique for collecting the information was through a personal interview (questionnaire) addressed to the managers or owners of the industrial SME in Guanajuato, since they are the ones who know the aspects related to ACAP and technological innovation within their organizations.
Regarding the sample’s size, Cohen [69] power tables were taken as a reference, following the suggestions of Roldan and Sánchez-Franco [70]; considering a medium effect size with the purpose of obtaining a power of 0.8 and alpha level of 0.01, a minimum sample of 97 was required. Thus, this research used a sample of 200 respondents which was greater than the minimum number of participants necessary to evaluate the proposed research model [71].

3.2. Measurement of Variables

To measure the absorptive capacity (ACAP) variable, the higher-order construct (HOC) of reflective type was used, refined from Flatten et al. [5] and used by Cuevas-Vargas et al. [19]. This consisted of four reflective-type dimensions: (1) acquisition, measured by three items; (2) assimilation, measured by four items; (3) transformation, measured by four items; and (4) exploitation, measured by three items. The first two dimensions make up the higher-order scale called potential absorptive capacity (PACAP); the last two dimensions make up the higher-order scale called realized absorptive capacity (RACAP). All scales were measured with a five-point Likert-type scale where responses ranged from strongly disagree to strongly agree.
To measure the technological innovation, the higher-order construct (HOC) of reflective type was used, which consists of two dimensions: (1) product innovation adapted from Cuevas-Vargas [72] and OECD/Eurostat [73], which was measured using six items; and (2) process innovation, composed of seven items adapted from OECD/Eurostat [73]. All of these were measured with a five-point Likert-type scale where responses ranged from strongly disagree to strongly agree.

3.3. Common Method Variance

Regarding the common method variance (CMV), two post-hoc techniques were evaluated to demonstrate that the probable presence of CMV does not significantly disturb the interpretation of the data outcomes [74]. The Harman’s single factor test was performed, incorporating all the manifest variables of the model in exploratory factor analysis (EFA), using the first un-rotated factor that had a value of 35.01%, which was less than 50% in all observed indicators; therefore, CMV was not an issue for the structural equation model [75]. Moreover, the full collinearity assessment was evaluated through the variance inflation factor (VIF) in order to present more robust evidence of CMV, following the suggestions of Kock [76]. The results indicated that none of the second-order latent variables reached the VIF threshold value greater than 3.3—PACAP (VIF = 2.316), RACAP (VIF = 2.530), and Technological innovation (VIF = 1.163). Therefore, CMV was not an issue in this study [76].

3.4. Reliability, Convergent, and Discriminant Validity

First, to evaluate the internal consistency and validity of the measurement scales, the PLS-SEM algorithm was carried out, although the manifest variables (AC1, IP2, IP5, IP6, and IPR7) presented problems of normal distribution according to the asymmetry and kurtosis tests. As mentioned before, PLS-SEM works with non-parametric tests; therefore, the data non-normality was solved with this statistical technique [62].
The results obtained in the measurement model revealed that the model needed to be adjusted since the IPR7 variable did not achieve a factor loading greater than the critical value of 0.7, suggested by Hair et al. [61]. Thus, this manifest variable was eliminated from the process innovation construct. Once the measurement model was adjusted, the high internal consistency of the six first-order constructs was achieved, since the composite reliability (CR) exceeded without problem the critical value of 0.7 suggested by Hair et al. [61]. Similarly, Cronbach’s Alpha coefficient was above 0.7 for each construct, as suggested by Nunnally and Bernstein [77], and finally, all constructs exceeded the average variance extracted (AVE) value of 0.5 [71,78]. Moreover, it was found that all factor loadings of the items were higher than 0.7 [61], as shown in Table 1, and all were significant (p < 0.001); therefore, the communality of each manifest variable was assured. Furthermore, the model showed convergent validity because all the constructs obtained AVE values greater than 0.5 [61]. In the same vein, these psychometric tests were evaluated in the second-order constructs, obtaining satisfactory values of Cronbach’s Alpha greater than 0.7 [77], values of CR greater than 0.7 [61], and values of AVE greater than 0.5 [71,78].
On the other hand, discriminant validity was evaluated with two tests which are shown in Table 2 and Table 3. Firstly, the Heterotrait–Monotrait correlations ratio test is presented above the diagonal, which is considered the criterion with better performance to evaluate discriminant validity [79]. According to this test, it was found that for both the lower- and higher-order constructs, none of the correlation matrix values were above 0.85; therefore, there was evidence of discriminant validity [79,80]. Secondly, the Fornell–Larcker criterion test was performed, which was estimated using the square root of the AVE of each construct, whose values in bold are represented on the diagonal of Table 2 and Table 3, and according to Fornell and Larcker [78], these values are greater than their corresponding correlations, as observed below the diagonals. Therefore, based on these previously presented criteria, it can be concluded that the different measurements performed on both lower- and higher-order constructs in this study indicated adequate evidence of internal consistency and both convergent and discriminant validity of the fitted theoretical research model.

4. Results

For the purpose of testing the research hypotheses, PLS-SEM bootstrapping with 10,000 subsamples was applied [67] using SmartPLS 3 [60] statistical software. This was carried out in order to assess the structural model as a hierarchical component model type I (reflective–reflective) [63,64]. The results indicate that there was empirical evidence to obtain confidence intervals and evaluate the precision of the parameters [19]; as can be seen in Table 4, the structural model had explanatory capacity and predictive relevance.
First, the realized absorptive capacity of knowledge (RACAP) of manufacturing SMEs in the state of Guanajuato, Mexico, is 56.9 percent explained by PACAP (R2 = 0.569). Similarly, the technological innovation of this type of companies is explained in 15.1 percent by the management of PACAP and RACAP by the SMEs that are part of the sample of this study (R2 = 0.151). Therefore, the results allow us to infer that both RACAP and technological innovation, in their role as endogenous constructs, had explanatory capacity since the values of the coefficient of determination were much higher than 0.20 [81] for the case of the RACAP and greater than 0.1 (as suggested by Falk and Miller [82]) for the case of technological innovation. Furthermore, the predictive relevance was evaluated taking as reference the Q2 test known as cross-validated redundancy [83,84] using the blindfolding technique—RACAP (Q2 = 0.368) and technological innovation (Q2 = 0.071). Both Q2 values were above zero; therefore, the model had predictive power [83,84]. Moreover, a post-hoc (computing achieved power) was performed in order to evaluate the statistical power using G*Power 3.1.9.7 [85], taking as reference the R2 value of the endogenous construct, technological innovation, whose calculated effect size f2 = 0.178, α = 0.01, total sample size = 200, number of predictors = 2, and obtaining a power (1 − β err prob) = 0.9985; this means that this research model had statistical power [85]. Therefore, its results are useful for business decision making.
Regarding the first hypothesis H1, the obtained and presented results shown in Table 4, and more specifically in Figure 2 (β = 0.754, p < 0.001), empirically demonstrate that PACAP had positive and highly significant effects on RACAP; therefore, H1 is supported because there was empirical evidence that PACAP had a significant impact of 75.4 percent on the RACAP of manufacturing SMEs in Guanajuato, Mexico.
As for H2, the outcomes indicate that RACAP had positive and significant effects on technological innovation of manufacturing SMEs in the state of Guanajuato, Mexico (β = 0.452, p < 0.001). Therefore, H2 is supported since RACAP showed a positive and significant impact of 45.2 percent on the technological innovation of the manufacturing SMEs under study.
Concerning the third hypothesis H3, the results indicated that PACAP had no significant effects on technological innovation of manufacturing SMEs in the state of Guanajuato, Mexico (β = −0.089, N.S.). Therefore, H3 is not supported.
Moreover, with the purpose of identifying the indirect effect of PACAP on technological innovation, and in this way determining the mediating role of RACAP in the relationship between PACAP and technological innovation, an indirect effect was found (p1 * p2), following the recommendations of Zhao et al. [86]. When contrasting the mediating effect (see Table 5), it was found that PACAP had a positive and significant indirect effect on technological innovation (β = 0.341, t = 4.360, p < 0.001), which means that RACAP played a full mediating effect considered as an indirect-only effect because there was no significant direct effect of PACAP on technological innovation [86]. Therefore, based on these results, H4 is supported, verifying that PACAP had a total significant effect on technological innovation (β = 0.251, t = 3.524, p < 0.001).

5. Discussion

Previous studies have shown that the ability to be proactive and take risks is the best driver of business innovation [87,88]; therefore, companies are encouraged to develop absorptive capacity and its realization.
To obtain tangible benefits in technological innovation, knowledge must be exchanged throughout the organization and other participants in its value chain, focusing on customers and suppliers who know the products the company develops. Also, organizations must have the ability to gain access and transfer knowledge to other firms involved in its value chain and innovation ecosystem [89]. For most SMEs, collaboration with other organizations is not central to their management, rather they focus on efficient making more efficient what they have done from the beginning; thus, it is suggested that SMEs focus on generating new business models based on technology, with an innovative and involvement culture of the owners.
In this sense, government strategy should be initiated to support the flow of knowledge within the technological innovation process, along with the implementation of monetary support systems to industrial associations, research institutions and universities for the follow-up of ACAP. This would allow manufacturing SMEs to gather up, share and exchange knowledge with other companies, thus transforming supply chains into innovation ecosystem networks.
On the other hand, in terms of methodological implications, although the ACAP nexus with technological innovation had been little studied, this study in addition to having evaluated the two phases of ACAP as a hierarchical component model, focused on determining a mediating mechanism of RACAP in the relationship between PACAP and TI, which was of interest to this study. In addition, in this study unlike others, partial least squares structural equation modeling (PLS-SEM) was used, generating more robust information than in previous studies in which the relationships between the variables of interest in this study had been investigated.

5.1. Managerial Implications

This research suggests to managers the decentralization of information and the participation of workers in the generation and transfer of technological information, encouraging their absorption capacity in order to innovate in processes, products, and services, thus developing competitive advantages [39]. Consequently, managers should try to obtain a decentralized information structure, capable of stimulating economic and professional incentives for employees’ participation in the innovation process and to invest in training and courses that manage to improve the absorptive capacity and information transmission of workers, improving innovation and business performance.
Furthermore, managers should keep in mind that the interaction in both the internal and external organizational environments is essential; the activities of workers as far as possible should be planned so that they are properly immersed both at the internal and external environment of the company, as an attempt to favor the exchange of ideas and information [90]. In order to generate this positive feedback loop, all employees, not only the R&D team, must be encouraged to express ideas about new or improved products [91]. Likewise, managers should pay attention to the organizational culture. This culture should strive on driving confidence to employees to share their opinions or ideas, and thus stimulate a more efficient group interaction in different environments, which would boost creativity and business innovation, avoiding formality and bureaucratization at work [92].
Moreover, managers could increase the level of PACAP in their companies by adjusting the structure of the organization. To improve the process of idea generation and communication of departments to solve problems, they need to reduce oversight and delegate responsibilities in the PACAP process. To do this, they must first establish a cross-functional team with relevant tacit knowledge and ensure that its members are communicating and working together.
The team must be headed by a leader with the necessary knowledge and experience in technological innovation and knowledge of the individual capabilities of the team members. The leader is responsible for managing the information flows and the results of the innovative processes and for defining measures to improve the PACAP process in case the results are not as expected.
Finally, in collaboration with partners, managers of SMEs are encouraged to hire IT-related specialists who execute solutions that allow SMEs to intensify escalated growth or create specialized consultancies that strengthen their adaptation of the knowledge generated and learned. Furthermore, it would be important that decision makers encourage collaboration and panels creation between supply chain members in which SME’s information could be shared [89].

5.2. Theoretical Implications

Although there is considerable evidence on the influence of different internal factors on innovation performance, most of them investigate innovation in a general way. However, there is a great interest in today’s fast-changing innovation, such as technological innovation, which requires organizational and structured knowledge and skills. Perhaps the main contribution of our study relates to the open innovation literature because it explains how to manage knowledge and ideas developed internally and externally.
This paper contributes to empirical research on the concept of absorptive capacity and its interaction with technological innovation [88]. Our theoretical model and constructs have been little explored and analyzed [39], and our results show that SMEs make use of both potential and realized absorptive capacity to develop technological innovation strategies. The use of external knowledge is very relevant when the technological environment changes rapidly; this research extends the findings of Möeller et al. [90] and Müller et al. [89] who showed that the greater an organization’s ability to acquire and use new information, the greater its ability to innovate [76].
In addition, Taran et al. [2] found that, due to prior knowledge, organizations generally prefer the same technological innovations, to perform them in a better way, but do not innovate new products or services and generate value to the organization. The findings of this study, however, show that absorptive capacity leads not only to exploitative innovation but also to exploring new innovation strategies. Our results reveal, however, that successful employment of technological innovation depends significantly also on a firm’s absorptive capacity.

6. Conclusions

Based on the results found through the variance-based analysis of the structural equation model (PLS-SEM) it is concluded that PACAP significantly influences RACAP; moreover, RACAP influences technological innovation and has an effect that mediates the relationship between PACAP and technological innovation. Hence, for SMEs to obtain better results in their technological innovation levels, it is necessary that the external knowledge acquired by their personnel could be efficiently assimilated so that PACAP can generate better results in SMEs when going through the transformation and exploitation of knowledge (RACAP). As PACAP has a direct impact on RACAP and an indirect impact on technological innovation, it should be prioritized as an essential dynamic capability for SMEs to develop a more structured competitive advantage.
Furthermore, our findings also provide empirical support to demonstrate the mediating role of RACAP in this research model; more specifically, the study shows that RACAP represents a mechanism that underlies the relationship between PACAP and technological innovation. This means that PACAP leads to RACAP, and this, in turn, leads to greater technological innovation on the part of Mexican manufacturing SMEs.
This study confirms that the RACAP effect improves the ability of a firm to assimilate and apply knowledge for decision making by taking risks and exploiting market opportunities with new technological products or services [93,94], which means those firms that are capable to ignite acquisition, assimilation, transformation, and external knowledge usage are better prepared to develop new technological innovation strategies and new business models [89]. As emerging markets continue to grow, capabilities for innovation will become key elements of growth for global firms [53].
As this study shows, it can be concluded that SMEs, in particular, need support to implement technological innovations considering their specific characteristics. For SMEs to use the absorption and application of knowledge to realize innovations, the support of the State is fundamental, providing specific information, such as best practices in different application areas as well as access to funds and helping to transfer experience and knowledge between industries and between regions through networks. This is especially important for SMEs not only to exploit their current products, but also to explore new products and services; however, considering their limited resources, they require security and actions to develop them. This could be achieved through the formation of ecosystems among supply chains, research institutions and universities, industrial, and political associations, as suggested above.

Limitations and Future Research Avenues

The limitations of this study could be attributed to the unexplored novelty of the research field and it has some restrictions that could lead the way for future research. Studies in emerging markets relating ACAP and technological innovation are still scarce, which prevents a more extensive comparison in different contexts. A larger population covering other regions and different industry types could help extend the findings of this research in a promising set of future studies.
The second limitation of this study is that it was cross-sectional, so the data were collected at a single point in time, suggesting that future research could conduct a longitudinal study to identify in time how technological innovation is affected by ACAP. A third limitation was the use of opinions to obtain the results; therefore, more objective measures may give more accurate results (objective measures related to a firm’s performance). However, previous studies showed that there are strong correlations between objective and subjective measures [39].
Another limitation is that the sample of our study is composed only of companies from one nation. Therefore, it is suggested for future lines of research, replication of the model in other countries could provide more information and support for generalization of the results. The fifth and last limitation is that the information was collected from SMEs, without differentiating among services, commerce, or manufacturing; therefore the results between the different types of companies may not coincide, being more noticeable in manufacturing, due to its relationship and dependence on technology
To conclude, there are some opportunity areas for future research. The implementation of technological innovations is the result of group decisions, only they can decide what to put into practice [26]; future researchers could analyze how individual absorptive capacity affects absorptive capacity as a group and how the latter affects the ability to develop technological innovation. In this way, it would be interesting to investigate how absorptive capacity affects radical and incremental innovation in organizations. Finally, it is relevant to analyze how these variables contribute to the implementation of Industry 4.0.
Another line of research could be how the implementation of technological innovations becomes the result of group decisions [39] between technological supervisors and administrative management; thus, future researchers could analyze how individual absorptive capacity affects absorptive capacity at the team level and how the latter affects performance in the organization. Our theoretical model has a limited set of constructs, so mediating and moderating effects could be added by examining the relationship between absorptive capacity in a decentralized way and the performance of other types of innovations. For example, an interesting avenue for future research would be the incorporation of the literature regarding open or frugal innovation, which emphasize an open mind of all participants in the innovation process and with limited resources. Thus, future research should analyze internal and external mechanisms which could influence organizational innovation capacity in all its facets.

Author Contributions

Conceptualization, H.C.-V., C.L.-M. and H.A.C.-P.; methodology, H.C.-V.; software, H.C.-V.; validation, H.C.-V., H.A.C.-P., C.L.-M. and E.H.-M.; formal analysis, H.C.-V.; investigation, H.C.-V.; resources, H.C.-V.; data curation, H.C.-V.; writing—original draft preparation, H.C.-V. and C.L.-M.; writing—review and editing, H.C.-V. and H.A.C.-P.; visualization, H.C.-V. and E.H.-M.; supervision, H.C.-V. and H.A.C.-P.; project administration, H.C.-V.; funding acquisition, H.C.-V. and E.H.-M. All authors have read and agreed to the published version of the manuscript.

Funding

This research project received funding for publication by the Program for the Development of Research, Science, and Technology (PRODICYT) 2022 of the Universidad Tecnológica del Suroeste de Guanajuato.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

MDPI Research Data Policies.

Acknowledgments

The authors would like to thank to Universidad Tecnológica del Suroeste de Guanajuato for the financial support in the publication of this article. Moreover, our thanks to the reviewers for their valuable comments and suggestions to improve this article. In addition, our recognition to the managers or owners of the companies participating in this research for their availability and valuable support.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Theoretical Research Model of Type I (reflective–reflective) Hierarchical Component Model.
Figure 1. Theoretical Research Model of Type I (reflective–reflective) Hierarchical Component Model.
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Figure 2. Structural equation model results with direct, indirect, and total effects, and R2 and Q2. Source: Own contribution based on PLS-SEM bootstrapping results [60].
Figure 2. Structural equation model results with direct, indirect, and total effects, and R2 and Q2. Source: Own contribution based on PLS-SEM bootstrapping results [60].
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Table
Table 1. Reflective measurement model assessment.
Table 1. Reflective measurement model assessment.
ConstructsIndicators
(Manifest Variables)		Convergent ValidityReliability
Factor Loading
>0.7		t-Value
>2.57		AVECRCronbach’s Alpha
>0.5>0.7>0.7
AcquisitionAC1-Information about our industry0.903 ***54.2840.8600.9490.918
AC2-Motivation to use information from our industry0.956 ***116.291
AC3-Our management encourages workers to use information outside of the industry0.922 ***71.813
AssimilationAS1-Communication between departments0.880 ***47.1720.7500.9230.889
AS2-Support between departments to solve problems0.875 ***30.908
AS3-There is information flow0.882 ***48.319
AS4-There are departmental meetings and take advantage of the flow of information0.827 ***24.707
TransformationTR1-The flow of information is structured0.881 ***49.5600.7820.9350.907
TR2-Our employees are willing to absorb knowledge0.892 ***30.612
TR3-Employees standardize the knowledge acquired0.879 ***40.062
TR4-Employees apply new knowledge0.885 ***40.660
ExploitationEX1-New prototypes are developed0.918 ***61.2020.8540.9460.915
EX2-New technologies are freadapted0.941 ***71.975
EX3-The company adapts to the adoption of technologies0.914 ***55.806
Product innovationIP1-New products/services are accepted by the market0.864 ***45.4000.7210.9390.923
IP2-Our new products are environmentally friendly0.830 ***26.292
IP3-Our new or improved products/services are imitated by our competitors0.810 ***22.541
IP4-Our new or improved products/services are brought to market faster than the competition0.864 ***37.524
IP5-We have better R&D capability than our competitors0.865 ***47.481
IP6-We make new products with the existing ones0.861 ***38.731
Process innovationIPR1-Process technology is systematized in real-time0.815 ***34.2590.6750.9250.903
IPR2-New equipment available to improve product quality0.897 ***61.106
IPR3-Our new processes increase production capacity0.847 ***31.806
IPR4-Efficient manufacturing processes are developed0.804 ***26.527
IPR5-We have equipment that makes materials and energy efficient0.793 ***32.007
IPR6-Equipment can be flexible to meet customer requirements0.767 ***21.261
HOCsLatent variablesPath coefficientAVECRCronbach’s Alpha
PACAPAcquisition of knowledge0.8480.6110.9160.893
Assimilation of knowledge0.898
RACAPTransformation of knowledge0.9190.6590.9310.914
Exploitation of knowledge0.877
TIProduct innovation0.8560.5160.9270.914
Process innovation0.867
CRI = Composite Reliability Index; AVE = Average Variance Extracted Index. Significance level = *** = p < 0.001. Source: Own contribution from results of PLS-SEM Algorithm [60].
Table
Table 2. Discriminant validity for the lower-order constructs (LOCs).
Table 2. Discriminant validity for the lower-order constructs (LOCs).
ConstructsAcquisitionAssimilationTransformationExploitationProduct inn.Process inn.
AVE = 0.860AVE = 0.750AVE = 0.782AVE = 0.854AVE = 0.721AVE = 0.675
Acquisition0.927[0.583][0.561][0.596][0.204][0.433]
Assimilation0.5290.866[0.759][0.670][0.138][0.357]
Transformation0.5130.6830.884[0.676][0.098][0.478]
Exploitation0.5470.6050.6160.924[0.188][0.545]
Product inn.0.186−0.1140.0910.1760.849[0.521]
Process inn.0.3920.3160.4300.4940.4850.821
NOTE: The numbers on the diagonal (in bold) represent the square root of the AVE. The Fornell–Larcker criterion is presented below the diagonal. Above the diagonal, the HTMT85 whose values in brackets are less than 0.85. Source: Own contribution from results of PLS-SEM Algorithm [60].
Table
Table 3. Discriminant validity for the higher-order constructs (HOCs).
Table 3. Discriminant validity for the higher-order constructs (HOCs).
HOCsPACAPRACAPTechnological Innovation
AVE = 0.611AVE = 0.659AVE = 0.516
PACAP0.781[0.834][0.370]
RACAP0.7540.812[0.411]
Technological Innovation0.2510.3840.719
NOTE: The numbers on the diagonal (in bold) represent the square root of the AVE The Fornell–Larcker criterion is presented below the diagonal. Above the diagonal, the HTMT85 whose values in brackets are less than 0.85. Source: Own contribution from results of PLS-SEM Algorithm [60].
Table
Table 4. PLS-SEM results of the structural model using bootstrapping with 10,000 subsamples.
Table 4. PLS-SEM results of the structural model using bootstrapping with 10,000 subsamples.
HypothesesPathStandardized Coefficient
β		t-Value DecisionR²
H1PACAP → RACAP0.754 ***24.575Supported0.569
H2RACAP → Technological innovation0.452 ***4.534Supported0.151
H3PACAP → Technological innovation−0.089 N.S.0.832Not supported
H4PACAP → RACAP → Technological innovation0.341 ***4.360Supported
Significance: *** = p < 0.001; N.S. = Non-significant. R2 values: >0.20 = weak; >0.33 = moderate; >0.67 = substantial [81]. Source: Own contribution from results obtained with SmartPLS 3 [60].
Table
Table 5. Specific indirect effects for mediation significance analysis.
Table 5. Specific indirect effects for mediation significance analysis.
Path Mediation RelationshipsDirect Effect
p3		95% CI Bias-CorrectedIndirect Effect 1
p1 * p2 		95% CI Bias-Corrected
PACAP → RACAP→ Technological innovation −0.089[−0.299, 0.121]0.341[0.188, 0.493]
NOTE: The 95% bias-corrected bootstrap confidence intervals are performed where zero is not presented, thereby demonstrating the strength and magnitude of the mediation. Source: Own calculation from results obtained with SmartPLS 3 [60]. 1 This result was obtained from the effects of PACAP on RACAP (0.754) multiplied by the effects of RACAP on technological innovation (0.452). Source: Own contribution from results obtained with SmartPLS 3 [60].
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Cuevas-Vargas, H.; Cortés-Palacios, H.A.; Leana-Morales, C.; Huerta-Mascotte, E. Absorptive Capacity and Its Dual Effect on Technological Innovation: A Structural Equations Model Approach. Sustainability 2022, 14, 12740. https://doi.org/10.3390/su141912740

AMA Style

Cuevas-Vargas H, Cortés-Palacios HA, Leana-Morales C, Huerta-Mascotte E. Absorptive Capacity and Its Dual Effect on Technological Innovation: A Structural Equations Model Approach. Sustainability. 2022; 14(19):12740. https://doi.org/10.3390/su141912740

Chicago/Turabian Style

Cuevas-Vargas, Héctor, Héctor Abraham Cortés-Palacios, Cid Leana-Morales, and Eduardo Huerta-Mascotte. 2022. "Absorptive Capacity and Its Dual Effect on Technological Innovation: A Structural Equations Model Approach" Sustainability 14, no. 19: 12740. https://doi.org/10.3390/su141912740

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