A questionnaire with 34 questions related to absorptive capacity, financial performance and organizational performance was elaborated. It should be noted that the data collection instrument underwent a pre-test when evaluated by a group of experts (composed of researchers and business professionals) who reviewed the questionnaire and provided feedback on the ease of understanding of the instrument, consistency, and adequacy of the sequence of items, which led to some specific changes. The final questionnaire has thirty-four statements measured on a five-point Likert type scale and the applied version is shown in Appendix A.

It should be noted that the data collection instrument underwent a pre-test when evaluated by a group of experts (composed of researchers and business professionals) who reviewed the questionnaire and provided feedback on the ease of understanding of the instrument, consistency, and adequacy of the sequence of items, which led to some specific changes. The final questionnaire has statements measured on a five-point Likert type scale. Study with scales in SMEs is pointed out by Manthey, de Lorenzi Cancellier and Tezza, R. (2021) as suitable for this type of study

Data collection took place between August and November 2020 through research applied in the Google Forms tool. With the same tool, the verification of the filling out and validation of the questionnaires received was initially carried out.

In this research, the sample size was calculated using the G*Power 3.1.9 software (http://www.gpower.hhu.de/en.html) was used with the following parameters: Effect Size 0.15; alpha error 0.05; Power 0.80; Number of predictors 2. The total sample size was 68.

A total of 295 questionnaires were collected, all the companies studied were micro or small commercial companies in the city of Florianópolis. Florianópolis is a coastal city, and the commerce of micro and small companies is important for a tourist city. Then, the data collected were inserted into Excel spreadsheets and analyzed using descriptive statistics and the Partial Least Square Structural Equation Modeling (PLS-SEM) technique. The PLS-SEM is a method of structural equation modeling which allows estimating complex cause-effect relationship models with latent variables. PLS-SEM is a technique widely used for research in Small Business and Enterprise Development (Ng, Kee and Ramayah, 2020; Pulka, Ramli and Mohamad, 2021)

With the same tool, the verification of the filling out and validation of the questionnaires received was initially carried out. A total of two hundred and ninety-five questionnaires were collected and validated. Despite the non-probabilistic sampling, this can be considered a homogeneous group, with at least one common characteristic: companies of the greater Florianópolis region, as recommended by Flynn, Kakibara, Schroeder, Bates, and Flynn (1990), Hourneaux Jr, Gabriel, Gallardo-Vázquez (2018) and Dabić et al (2019).

The creation of models / frameworks is common in entrepreneurship and small business research (Victorino et al, 2020; Santos and Teixeira, 2021; Lopes et al, 2021). Then, the data collected were inserted into Excel spreadsheets and analyzed using descriptive statistics and the partial least square structural equation modeling technique, with the support of the SmartPLS software, version 3.

Finally, it is clarified that the procedures applied here are quantitative, because in this kind of research, data collection uses standardized methods and generates numerical data, which can be analyzed using graphs and statistical techniques. For the authors, the quantitative approach allows formulating hypotheses that can be tested (contributing to the development of the theory) or examined in future research. The research is conclusive as it seeks to test specific hypotheses and examine relationships. It is also descriptive, as it seeks to describe characteristics of a given phenomenon, seeking to establish relationships between existing variables, such as trust and perceived influence.

After exporting the collected primary data to the SmartPLS software, version 3, and carrying out its configurations, a report of the preliminary data obtained was generated. The evaluation of the model started through its convergent validity, discriminant validity and reliability, as recommended by Hair Jr et al. (2017).

It should be noted, however, that the sequence of the analyses occurred in accordance with the recommendation of Bido and Silva (2019), namely: 1) convergent validity; 2) discriminant validity; 3) reliability. Since one of the assumptions for assessing reliability is that its convergent validity and discriminant validity be adequate. Thus, if any problem in the convergent or discriminant validity is diagnosed, it is suggested not to proceed further to the reliability assessment.

Thus, it was found that the AVE of Latent Variables (LV), according to Ringle, Silva and Bido (2014), values greater than 0.50 are acceptable, and values greater than 0.40 may be acceptable in applied social sciences.

Once the convergent validity is assured, the next step was to assess the Discriminant Validity, which indicates whether the constructs or variables are independent of each other (Hair Jr et al., 2017). According to Ringle, Silva and Bido (2014) for the discriminant validity of the observed variables, cross loads must be observed, that is, indicators with higher factor loads in their respective latent variables than in the others, as recommended by Chin (1998). For the discriminant validity of latent variables, we can use, HTMT criterion - Heterotrait-Monotrait Ratio (Henseler; Ringle and Sarstedt, 2015).

First, the crossed factor loadings were evaluated according to Chin ‘s (1998) criterion, which proved to be adequate, as shown in the Table 1.

Table 1 - Values of the cross loads of the variables observed in the latent variables

Table 1
Values of the cross loads of the variables observed in the latent variables

Source: Prepared by the authors, based on research data (SmartPLS3®, 2021)

Source: Prepared by the authors, based on research data (SmartPLS3®, 2021).

By analyzing Table 1, the factor loads of the Variables Observed in the original Latent Variables are higher when compared to the other constructs. Variables ACAP 09, ACAP 11, had cross loads below acceptable standards and therefore were excluded from the model.

The results from the Heterotrait-Monotrait Ratio (HTMT) criteria are shown in Table 2.

Table 2 – HTMT Criterion

Table 2
HTMT Criterion

Source: Prepared by the authors, based on research data (SmartPLS3®, 2022)

Source: Prepared by the authors, based on research data (SmartPLS3®, 2022).

It is noteworthy that, based on the results of the studies by Henseler, Ringle and Sarstedt (2015) and on previous research, the referred authors suggest a threshold value of 0.90, therefore the discriminant validity of latent variables is also attested by the HTMT criterion.

Finally, the values of internal consistency were evaluated using Cronbach's alpha and composite reliability. The Cronbach's alpha of the constructs is greater than 0.80. Also, the reliability criterion met was considered, through the composite reliability indices, which were higher than the minimum limit of 0.7 (Hair Jr et al., 2017). Table 3 shows the referred values, together with the values related to the AVE:

Table 3 - Values related to the internal consistency of the model

Table 3
Values related to the internal consistency of the model

Source: Prepared by the authors, based on research data (SmartPLS3®, 2022).

Source: Prepared by the authors, based on research data (SmartPLS3®, 2022).

In Table 3 we can see that the Cronbach's alpha of the constructs. Also, the reliability criterion met was considered, through the composite reliability indices, which were higher than the minimum limit of 0.7 (Hair Jr et al., 2017).

The first evaluation carried out consisted of the analysis of collinearity, using the Variance Inflation Factor (VIF). Table 4 shows these values.

Table 4 - Values related to the Variation Inflation Factor (VIF).

Table 4
Values related to the Variation Inflation Factor VIF

Source: Prepared by the authors, based on research data (SmartPLS3®, 2022).

Source: Prepared by the authors, based on research data (SmartPLS3®, 2022).

As all values are less than five, all variables were maintained. Subsequently, Pearson's determination coefficients (R²) were evaluated. According to Ringle, Silva and Bido (2014, p. 67), R² “assesses the variance portion of the endogenous variables, which is explained by the structural modeling”. Figure 2 presents the structure of the measurement model, with the values of R² and path coefficients.

Figure 2 – Proposed model, R² and path coefficients.

Figure 2
Proposed model, R² and path coefficients.

It appears that the endogenous latent variables present R² above the percentage suggested as large/high, according to Hair Jr et al. (2011) classifications, that is, all latent variables have a large effect on the model. The model explained a substantial part of the variation of the endogenous variable: 56.3% of organizational performance.

Financial performance has a moderating effect on organizational performance. The direct relationship between absorptive capacity and organizational performance has a path of 0.346 (Fig.2), while the moderating effect of financial performance generates a PLS path result of 0.324. with f squared, it was evaluated how much each construct is “useful” for the adjustment of the model. Table 5 shows the referred values:

Table 5 - f squared

Table 5
f squared

Source: Prepared by the authors, based on research data (SmartPLS3®, 2022)

Source: Prepared by the authors, based on research data (SmartPLS3®, 2022).

Values of 0.02, 0.15 and 0.35 are considered small, medium, and large, respectively. the values of Q2 and f2 indicate that the model is accurate and that the constructs are important for the overall fit of the model.

To test the significance of the relationships pointed out, the bootstrapping technique was used is a resampling technique used to assess the significance (p-value) of the correlations (measurement models) and the regressions (structural model).

Table 6 – Hypothesis

Table 6
Hypothesis

Source: Authors (2022).

Source: Authors (2022).

The effect of absorptive capacity on organizational performance has a moderating effect of financial performance of SMEs are significant at the p = .000 level (Table 6). The Absorptive Capacity to Financial Performance and Financial Performance to Organizational Performance path coefficients of 0.692 and 0.468, respectively, are relatively stronger than the paths from Absorptive Capacity to Organizational Performance of 0.346.

Another important issue relates to the fit statistics for the models, and while in the past the priority was the predictive relevance of the model in determining a given dependent variable, today PLSSEM offers fit measures such as SRMR, d_ULS, d_G or NFI (Henseler, Ringle and Sarstedt, 2015).

Table 7 – Fit measures

Table 7
– Fit measures

Source: Authors (2022).

Source: Authors (2022).

In this vein, the Standardized Root Mean Squared Residual (SRMR) value of the measurement model should be lower than 0.08 (Hair et al., 2017). In addition, all discrepancies (Squared Euclidean distance -d_ULS- and Geodesic Distance -d_G-) should be below the 99 per cent quantile of the bootstrap discrepancies. This model has a very good measurement model fit.

The results indicate that the absorptive capacity as an independent variable impact on the organizational performance, as it allows to expand and assimilate new knowledge, according to Zahra and George (2002). In addition, the studies by Glabiszewski and Zastempowski (2017) show that absorption can provide economic growth resulting from the innovation absorbed by the organizations.

On the other hand, the results show that the absorptive capacity influences financial performance when observing the relationship of the variables to have a positive impact on the development of the organization. This can lead to an absorption perspective, as reported in the Strom-Andersen studies (2020).

When analyzing the independent variable “absorptive capacity”, the dependent variables are “Financial” and “Firm Performance”. It was observed that Financial is influenced by the Absorptive Capacity in 48% with an R² of 0.69. However, the dependent variable Firm Performance is influenced by Financial and Absorptive Capacity by 56%. But the R2 for ACAP is 0.34 and for Financial 0.46. The research by Silajdzic and Mehic (2015) reached the same conclusion that absorptive capacity has a positive influence on economic growth and Rathakrishnan et al (2021) absorptive capacity influences company performance.

It can thus be inferred that the companies studied in this investigation have managers who encourage support between the organization’s departments to solve problems and spread ideas, and that such attitudes impact both the finance and organizational performance; however, they impact finance in a more relevant way. But, considering that the absorptive capacity influences 48% of finance and that finance influences 46% of the organizational performance, then, the absorptive capacity has an indirect impact on the organizational performance.

The PLS-SEM analysis resulted in retention of 29 indicators and more importantly, the absorptive capacity construct had 9 indicators after elimination of 5 item. The comparative retention of indicators in the PLS-SEM approach enhances the validity and reliability of that model and provides insights for future research and managerial implications that otherwise would be lost.

These results corroborate the research by Strom-Andersen (2020), who in his study concluded that companies that invest in absorptive capacity present evidence that the financial investment made is reflected in the companies' results. A small and medium enterprise (SME) absorbs knowledge to innovation, but also improve organizational performance (Benhayoun, Le-Dain, Dominguez-Pery, 2021).

The variables were attested by the HTMT criterion, where the threshold value is 0.90 and in this study the correlations were between 0.745 and 0.761. The criteria of composite reliability were also met, as values higher than the minimum limit of 0.7 were found.

The results found in this work are in line with the results obtained by several authors, who emphasize that the business absorption capacity is directly linked to results and competitive advantage. In addition, financial performance impacts performance when considering the organizational absorptive capacity. It is evident that there is a relationship between SMEs absorptive capacity, financial performance, and organizational performance.

Moderating effects in the context of PLS path modeling describe a moderated relationship within the structural model. So, absorptive capacity positively influences organizational performance (Ebers, Maurer, 2014, Ali, Kan, Sarsted, 2016, Oliveira et al, 2020; AlTaweel, Al-Hawary, 2021) and the main finding of this research: financial performance has a moderating effect on organizational performance.

Small businesses ought to recognize and take advantage of the practical benefits of absorptive capacity. However, entrepreneurs should find motivation to develop more formalized absorptive capacities to raise the organizational performance. The results can help companies identify ways to better appropriate external information and knowledge to improve absorptive capacity.

Previous studies on SME have already been produced to find mediating effects on organizational performance: Absorptive Capacity and Firm Performance in SMEs: The Mediating Influence of Strategic Alliances (Flatten, Greve, Bretel, 2011), Absorptive capacity, firm performance, and the moderating role of entrepreneurial orientation (Wales, Pankaj and Patel, 2012), Technological constraints to firm performance, the moderating effects of firm linkages and cooperation (Kolade, Obembe, and Salia, 2019), Mediating role of innovativeness in the link between core competencies and SME performance (Ng, Kee and Ramayah, 2020), Growing SMEs and internal financing: the role of business practices (Fadil and St-Pierre, 2021), Entrepreneurial competencies, entrepreneurial orientation, entrepreneurial network, government business support and SMEs performance. The moderating role of the external environment (Pulka, Ramli and Mohamad, 2021). Adapting and repositioning makes a difference in organizational performance and competitiveness (Lafuente, Szerband, Rideg, 2020). Thus, acquisition, assimilation, transformation, and exploration must be continuous actions on organizations (Miroshnychenko et al., 2020). Previous studies were not found relating financial performance as a mediating variable of the absorptive capacity and organizational performance relationship in SME.

The existence of the relationship absorptive capacity and financial performance is not enough to assume an improvement in absorptive capacity. In addition to obtaining information, it is important to analyze other elements such as content, trust and vision. Some resources were responsible for generating organizational performance. According to Pagotto, et al. (2022) these relationships strengthen a set of practices that catalyze the development of innovation, as there is a significant and positive relationship between innovation capacity and innovation, so companies do not compete for innovation, but for the ability to achieve it.

Several managerial implications ensue from our study suggesting that SME managers need to understand how different components of absorptive capacity relate to organizational performance. Thus, firms with better absorptive capacity have advantages in sustaining competitive advantage. Thus, gaining access to knowledge, adapting, and incorporating knowledge in the current business are key to organizational performance. According Miroshnychenko, et al. (2020) providing opportunities to access external knowledge sources (e.g., enabling employees to take part in industry specific meetings) and spreading such knowledge throughout the organization (e.g., through cross-departmental meetings) should foster potential absorptive capacity in SMEs.

The absorptive capacity of SME generally increases their capacity to access, internalize, and exploit new knowledge, as well as their flexibility. Furthermore, our findings support real-world observations. Another interesting aspect of our work is that we shed light on absorptive capacity as a previously overlooked determinant of organizational performance in SME. Specifically, our study demonstrates that absorptive capacity affects organizational performance via financial performance. These findings are in line with those presented by Miroshnychenko, et al. (2020).

Our findings also suggest that business consultants advising SME should consider the importance of possible relationships between financial performance and the different components of absorptive capacity when providing advice and support on the topic of organizational performance.