Multicriteria Analysis of Innovation Ecosystems and the Impact of Human Capital and Investments on Brazilian Industries

Abstract

Innovation is one of the main factors associated with industrial growth, as it contributes to increased productivity, sustainability, and international competitiveness. There is a certain degree of complexity in measuring innovation, since there are various metrics for this purpose, and each sector of the economy handles it differently. In Brazil, the Industrial Survey of Technological Innovation (PINTEC) was developed to construct sectorial indicators on innovation activities. Based on these data, this paper investigates how ten sectors of the economy performed during two historical series (2011 to 2014 and 2014 to 2017) considering five criteria linked to the innovation process and the impact of qualified human capital. The problem is analyzed in light of multicriteria decision analysis (MCDA), using preference ranking organization methods for enrichment evaluation (PROMETHEE II) to provide a ranking of Brazilian industrial sectors. The results show how the sectors have changed over the years, highlighting that innovation expenditure proved crucial in measuring companies’ commitment to innovation, but also show that a highly qualified workforce emerged as a leading factor. Furthermore, the research presents which criteria have contributed positively and negatively to each sector, which can serve as drivers for policy formulation to strengthen the Brazilian industry.

1. Introduction

Innovation has been the subject of study for understanding the economic capacity of organizations and how the factors linked to it increase business competitiveness (Schumpeter 1934; Porter 1991; Damanpour 1991; Drucker 1998; OECD and Eurostat 2005; Silva et al. 2023). Innovation is one of the alternatives for improving organizations’ competitive positioning and profitability (Moreira and de Vargas 2015; Guimarães et al. 2016). In the 21st century, the ability to generate innovations regarding technologies and their use, business strategies, and processes is one of the prerequisites for the success of companies (Boehm et al. 2014). To this end, innovation can be a competitive advantage for companies, whether through introducing new products and services or improving existing ones (Taques et al. 2021). In contrast, National and Regional Innovation System actors should observe the environment where they are inserted, searching for laws and strategic development policies that favor the implantation of new innovative ventures that support them in the market and the maintenance of existing ones that support their growth (Moreira et al. 2024).

Despite the relevance discussed in the literature, common problems hinder the innovation process, such as the lack of qualified human capital (personnel) and high costs (Jacoski et al. 2014). Research into innovation for the business sector is essential due to its significance and rapid dynamics (Becheikh et al. 2006; Zanello et al. 2016). So, new studies are necessary to understand the impacts of these advancements. In addition, the activities linked to the innovation process are a critical factor in innovation performance (Santos et al. 2012). Despite the extensive literature on innovation and its impact on industrial growth considering the knowledge-generating organization paradigm, there is still a gap in understanding the best practices of innovation that are effective in different economic and sectoral contexts (Satarova et al. 2023).

Studies present various innovation indicators and even use the PINTEC database to study the Brazilian innovation ecosystem, although they are limited to specific topics. In this context, we can cite the positive statistical relationships found between innovation performance and knowledge-intensive business services (Santos 2019); between obtaining cooperation with foreign partners and the adoption of eco-innovation (da Silva Rabêlo and Melo 2019); and between the incidence of organizational innovation and the degree of technological intensity of industry sectors (de Oliveira 2023). Although these studies provide interesting insights into the subject, there is a lack of a systematic and comparative approach to how specific innovation practices can be adapted and implemented for performance in diverse sectors.

Brazilian companies were described considering the analysis model of previous versions of PINTEC (Kannebley et al. 2005). It was possible to characterize the companies as those that innovated by developing new processes and by introducing new products to the market. In this context, they found that at that time, the main determinants for characterizing innovative companies were inter-industry differences, foreign capital origin, firm size, and exporting orientation.

It is interesting to note that, even after almost two decades, innovation in the Brazilian industrial sector is still concentrated on the acquisition of equipment and innovations that already exist on the global market. Moreover, there is a dependence on government support and there is no continuity in the efforts made by Brazilian firms to innovate (Caliari et al. 2021; Cirani et al. 2021). In other words, it is necessary to understand the characteristics of each sector, taking into account the specificities of the organizations, in order to diagnose the barriers and draw up specific strategies that boost innovation and consequently the competitiveness of the Brazilian industry at a world level, which is corroborated by (de Moraes Silva et al. 2023).

Teixeira (2007) states that the fuel for the innovation process in companies is qualified human capital. When a company has qualified employees, it increases the potential for developing innovation so that improving the quality of goods and services becomes possible (Drucker 1998; Teixeira 2007). Researchers are key players in innovation systems through the transfer of knowledge (Etzkowitz 1998) and skilled labor (Brown 2016). In practical terms, introducing new products and services and other innovation processes are also driven by this professional qualification and knowledge exchange (Silva et al. 2023). This dynamic creates innovative opportunities to meet market needs (Moreira et al. 2024).

On the other hand, expenditures on innovation are often directed toward research and development (R&D) activities, which form the basis of innovation. Therefore, issues relating to investment in innovation are another significant factor to be investigated. In the studies conducted by Lazzarotti et al. (2015) and Santos et al. (2012), investments in resources that can generate innovation have been shown to impact companies’ performance.

Innovation is one of the alternatives for improving organizations’ competitive positioning and profitability (Moreira and de Vargas 2015; Guimarães et al. 2016). In Brazil, one of the potential determinants of industrial growth is investment in innovation (Arruda et al. 2006; Fischer et al. 2009; Confederação Nacional da Indústria 2010; Spinosa et al. 2021). However, despite the importance of innovation for growth and business positioning, the Brazilian industrial sector reported a 2.55% decrease in the 2009–2011 innovation rate compared to that of the previous triennium (Instituto de Pésquisa Economica Aplicada 2013). This indicator assesses the ratio between the number of companies that innovated at least once and the total number of companies surveyed during the period in question.

Thus, this work seeks to measure which sector of Brazilian industry and services has the best results, considering decision criteria such as the value of expenditure on innovation by companies and the number of employees with higher education (undergraduate) and specialization at the master’s level. These analyses focused on two historical series covering the three-year periods from 2011 to 2014 and from 2014 to 2017 based on comparisons between them. To this end, the work uses data from the historical series extracted from the Industrial Survey of Technological Innovation (Pesquisa Industrial de Inovação Tecnológica, PINTEC) published by the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística, IBGE). For this analysis, the following indicators were used, also called criteria: (i) expenditure by companies on innovation; (ii) percentage of employees with postgraduate degrees (masters); (iii) percentage of employees with undergraduate degrees; (iv) net revenue; and (v) percentage of new and substantially improved products in total domestic sales.

The contribution of this study lies in its adoption of an analytical framework based on a multicriteria analysis that provides a detailed assessment of the dynamic changes in sectoral performance in the context of innovation. By presenting the importance of digital competence and leadership in digital transformation as fundamental catalysts of innovation, this study emphasizes the need for new funding policies for science, technology, and innovation that enhance the educational qualifications of the workforce to foster the competitiveness of economic sectors. This study offers valuable insights for policymakers and industrial stakeholders, aiming to promote sustainable industrial growth through strategic investments in innovation and organizational results. In addition, this article aims to investigate which sectors of the Brazilian industry performed best based on the established criteria to aid government bodies in forming public policies to encourage these sectors. This research aims to provide researchers, practitioners, and entrepreneurs with a summary analysis based on rankings and innovation prospects.

This work is divided into four sections. In addition to this introduction, Section 2 presents the method for analyzing the data used to obtain and explore the results. Section 3 examines the results of the multicriteria models, while Section 4 discusses the findings and limitations of the research. Finally, the last section presents the final considerations.

2. Materials and Methods

2.1. Data

PINTEC is an innovation survey that provides information on sectoral, regional, and national indicators of Brazilian companies’ innovation activities. This study used PINTEC data published in 2014 and 2017, the last years of the Brazilian Institute of Geography and Statistics (IBGE 2024) surveys. Although the data used were published in these two years, they refer to two historical series, the first covering 2011 to 2014 and the second from 2014 to 2017.

Using a multicriteria classification methodology, this study seeks to assess which of Brazil’s industrial and service sectors perform best based on criteria linked to innovation. In this sense, it is crucial to measure the innovation potential of industry and service sectors, given that better use of labor and financial expenditure on innovation can lead to better revenues and more innovative or substantially improved products. To this end, 10 of these sectors of the national economy were selected to measure their performance: extractive industries, manufacture of food products, manufacture of clothing and accessories, manufacture of chemical products, manufacture of rubber and plastic products, manufacture of machinery, equipment, and electrical materials, manufacture of machinery and equipment, electricity and gas, custom software development, and development of customizable software.

The sectors were chosen according to their presence and importance in the Brazilian economy. For this purpose, the sectors with the highest share of aggregate gross domestic product (GDP) from 2011 to 2017 were adopted. When outlining the methodological framework for assessing innovative performance in the various sectors of the Brazilian economy, five key indicators were selected that cover different dimensions of the innovation process.

2.2. Evaluation Structure of the PROMETHEE II Method

Behzadian et al. (2010), suggest that multicriteria decision-making methods are often used when analyzing many alternatives based on conflicting criteria. Roy (1996) articulates that multicriteria methods address specific problems such as choice (P.α), sorting (P.β), and ordering (or ranking, P.γ).

Making a simplistic summary of the multicriteria analytical process, it looks for advantages and disadvantages and then compares them until it becomes clear which is the best alternative, which is the one that makes the best use of the criteria considered, or, if not the most viable, which is the scenario that comes closest to the optimum (De Carvalho et al. 2022; Liu and Liu 2024).

Multicriteria decision analysis (MCDA) is widely used in the literature and can be conducted using various preference ranking organization methods for enrichment evaluation (PROMETHEE). Karasakal et al. (2022) evaluated two classification approaches with PROMETHEE to determine weights and threshold values inspired by data envelopment analysis (DEA). Guney et al. (2020) applied the PROMETHEE method to assess corporate governance quality in U.S. companies. Husin et al. (2024) conducted a study on the physicochemical properties of drugs for renal cancer treatment. Using a multicriteria decision-making approach, the authors employ PROMETHEE II to classify the target drugs of the study. Mir et al. (2024) assessed the risk of disaster for educational infrastructure in mountainous regions using the PROMETHEE-II method. Another contribution is made by Wang et al. (2024), who analyze barriers to adopting resilience in the food supply chain industry.

De Carvalho et al. (2018) employed the PROMETHEE II method to evaluate the relative importance of factors related to information technology outsourcing considering a group of companies in a Brazilian metropolitan region. Silva et al. (2023) also applied PROMETHEE II to evaluate the importance of factors related to information technology outsourcing, which is considered a technological innovation hub in a Brazilian city. De Carvalho et al. (2020) used PROMETHEE I and II to assess the creative–innovative potential of Brazilian cities, creating a ranking of the nine capitals of the Northeast Region.

In the context of the research reported in this article, an outranking model from the PROMETHEE family was developed using Visual PROMETHEE software (version 1.4.0), which is freely accessible. This model’s selection relies on the non-compensatory characteristics between the adopted criteria and the possibility of analyzing each criterion in its own units, eliminating scale effects.

The PROMETHEE II method, selected for application in this study, works with ordering problems (P.γ) and has two essential phases: one dedicated to constructing outranking relations and the other dedicated to exploring these relations (Brans and Vincke 1985a, 1985b). The related procedure can be explained in the following steps (Behzadian et al. 2010; Brans and Vincke 1985b; De Carvalho et al. 2018):

Step 1: Determination of deviation based on pairwise comparisons:

$$d_j\left(a,b\right)=g_j\left(a\right)-g_j\left(b\right)$$

(1)

where d_j(a, b) is the difference between alternatives a and b for each criterion.

Step 2: Application of preference function:

$$P_j\left(a,b\right)=F_j\left[d_j\left(a,b\right)\right] j=1,\cdots ,k$$

(2)

Pj(a, b) is the preference of alternative a concerning alternative b for each criterion as a function of d_j(a, b).

Step 3: Calculation of a global preference index:

$$\forall a,b\in A \pi \left(a,b\right)={{\displaystyle \sum }}_{j=1}^k{P}_j\left(a,b\right)w_j$$

(3)

where π(a, b) of a over b (from 0 to 1) is the weighted sum of P_j(a, b) for each criterion, and w_j is the weight associated with the jth criterion.

Step 4: Calculation of the input and output flows:

$${\mathsf{\Phi }}^{+}\left(a\right)={\displaystyle \frac{1}{n-1}}{{\displaystyle \sum }}_{b \in A}\pi \left(a,b\right)$$

(4)

$${\mathsf{\Phi }}^{-}\left(a\right)={\displaystyle \frac{1}{n-1}}{{\displaystyle \sum }}_{b \in A}\pi \left(b,a\right)$$

(5)

where Φ⁺(a) is the positive flow and Φ⁻(a) is the negative flow for each alternative.

Step 5: Calculation of the net flow:

$$\mathsf{\Phi }\left(a\right)={\mathsf{\Phi }}^{+}\left(a\right)-{\mathsf{\Phi }}^{-}\left(a\right)$$

(6)

where Φ(a) is the net flow for each alternative.

This research applied the usual criterion defined through (7) in Step 2:

$$P\left(x\right) \left\{\begin{array}{c}0 \forall d_j\left(a,b\right)\le 0\\ 1 \forall d_j\left(a,b\right)>0\end{array}\right.$$

(7)

The following sentences define the binary relations Preferences (P) and Indifferences (I) in PROMETHEE II:

$$a\mathrm{P}b if \mathsf{\Phi }\left(a\right)>\mathsf{\Phi }\left(b\right)$$

(8)

$$a\mathrm{I}b if \mathsf{\Phi }\left(a\right)=\mathsf{\Phi }\left(b\right)$$

(9)

At the end of this process, a complete preorder is created using the net flow, delivering the ranking of the alternatives.

2.3. Application of the PROMETHEE II Method in Innovation Ecosystems

In terms of input data, the first input selected was expenditure on innovation, as this is fundamental data for capturing companies’ financial commitment to innovation. The second and third inputs are the proportion of employees with advanced degrees, in this case, those with master’s and undergraduate degrees. Studies indicate that companies with highly qualified professionals have a greater propensity to innovate, as advanced education is correlated with the ability to absorb knowledge and creativity.

In this sense, net revenue was selected because it is an essential financial indicator offering a global perspective on companies’ economic performance. Finally, to round off the model, the percentage share of new products in domestic sales introduces a more detailed analysis of the percentage share ranges, enriching the evaluation and providing a more refined understanding of innovative performance.

Table 1. Description of model indicators.

Indicator	Description	Objectives	Process
Spending by companies on innovation (BRL)	Spending on innovative activities includes internal research and development (R&D) activities and external R&D procurement, as well as other activities.	Maximize	Input
Postgraduate (%)	Percentage of workers with postgraduate degrees in the workforce by sector as a proportion of the overall number of workers employed.	Maximize	Input
Graduation (%)	Percentage of workers in the workforce with a bachelor’s degree by sector as a proportion of the overall number of workers employed.	Maximize	Input
Net revenue (BRL)	Net revenue from sales of products in each sector.	Maximize	Output
Percentage share of new or substantially improved products in total domestic sales (%)	Refers to the proportion of a company’s domestic sales attributed to new or substantially improved products.	Maximize	Output

summarizes these indicators evaluated with the same degree of importance, where three correspond to the inputs to feed the model and stimulate the process studied, and two indicators make up the outputs.

These indicators help outline an overview of the impact of innovation on operations and the market, providing interesting insights by always considering a standard set of factors associated with innovation, and enabling an understanding framed within the same comparison rule. Dziallas and Blind (2019) and Taques et al. (2021) present a survey of indicators for use in innovation assessments. Both consider innovation from a sectoral perspective and consider comparability with equivalent metrics.

Furthermore, the PINTEC survey covers companies across all industries and sectors without distinction. This reinforces the notion that indicators, both from the innovation perspective and the financial perspective, can be collected for any organization and, consequently, any industry. Finally, we chose accounting metrics that can broadly apply to both manufacturing and service companies. This contrasts with metrics like total assets, patents, trademarks, machinery, and equipment, among others, whose empirical evidence shows significant differences between industries.

To seek a better evaluation based on a comparison, two models were created: one comprising the three years of the first time-lapse and the second containing the three years immediately afterward, characterizing the following historical series (see

Table 2. Establishing the time-lapse images of the models.

	Inputs (Years)		Outputs (Years)
Model 1	2011		2014
Model 2	2014		2017

).

As shown in Table 2, the input data were collected in previous years compared to the output data because it takes a certain amount of time for the investments in the most varied scenarios to be processed, and only then can the results be achieved. As this problem makes it impossible to obtain results instantly, the current study decided to use the data described in Table 2.

To build the multicriteria model, it is necessary to establish the objective (maximize or minimize) of each criterion, and in this sense, the maximize direction was adopted for all the criteria. In other words, increasing the values of all the criteria adopted in this study means moving towards achieving better results in terms of innovation.

3. Results and Discussion

This section discusses the data and results of the assessment of Brazilian innovation systems. Section 3.1 reports a comprehensive exploration of descriptive statistics, which serve as a fundamental aspect in understanding the breadth of this study. Moving forward to Section 3.2, the multicriteria analysis is developed using PROMETHEE II. Based on a net flow framework, this methodology facilitates the ranking of sectors by subtracting outflows from inflows, as described in Equation (6). This process elucidates and highlights sectoral performances from 2011 to 2014, reporting notable findings such as the exemplary performance of chemical product manufacturing. In Section 3.3, the research implications are discussed, scrutinizing sectoral dynamics over time while considering unique sector characteristics and the nuanced impacts of innovation investment.

3.1. Descriptive Statistics

This pursuit aimed to foster a comprehensive understanding of the studied scenario, thereby facilitating the identification of patterns, conducting intergroup comparisons, and enabling assessments of data dispersion. A comprehensive presentation of these descriptive statistics can be found in

Table 3. Descriptive statistics of Model 1 variables.

	Innovation Spending in 2011 (BRL)	Postgraduate % in 2011	Graduation % in 2011	Net Revenue in 2014	% of Substantially Improved Products in Total Sales in 2014
Max	7,814,360.57	23.81	58	525,606,581.00	57
Q3	2,188,277.02	11.02	52	186,762,263.82	34
Average	2,266,535.66	9.09	43	150,243,708.98	27
Median	1,793,904.56	6.25	41	112,821,141.50	25
Q1	667,002.92	5.37	35	53,669,513.00	16
Min	310,073.74	0.32	28	12,719,474.00	7

.

In this first model, which covers the time series from 2011 to 2014, the clothing and accessories sector had the lowest employment of employees with undergraduate and postgraduate degrees in 2011. This may indicate that this sector has low salaries and low production complexity.

The food products manufacturing sector has the highest values for the indicators relating to expenditure by companies on innovation in 2011 and net revenue in 2014. These figures may be directly related, but more detailed analysis is needed to confirm this relationship.

The custom software development sector is projected to have the lowest values in the indicators of expenditures made by companies in innovation” and in net revenue. In other words, even though this line of business has invested less financially than other lines of business in innovation issues, it has still achieved more than 50% of new or improved items in total internal sales. This phenomenon may be linked to possible past investments, which made the branch invest less because it already has a solid base to achieve these levels of innovation.

Box plot graphs in Figure 1 were generated to aid in visualizing and analyzing the descriptive data, delineating the values corresponding to the utilized criteria. These graphs are systematically arranged to align with the sequence of indicators outlined in Table 3.

Notably, the initial plot pertains to the expenditures on innovation in 2011, as depicted in Figure 1a. Most of the data are concentrated below the average, with a few outliers above it. Two outliers in the food and chemical products manufacturing sectors may have increased the average value. The data set seems to have a positive asymmetric distribution, where the higher values are more dispersed than the lower values.

The box plot (Figure 1b) shows that the percentage of employees with postgraduate degrees is concentrated in a moderate range, with one outlier, which in this case corresponds to the electricity and gas sector. In this scenario, it can be inferred that there is a greater concentration of data above the average but with a strong tendency toward heterogeneity.

The box plot (Figure 1c), which shows the percentage of workers with a degree, shows that most of the data are grouped in a moderate range, with a reasonable dispersion around the average. The presence of outliers indicates some variability in the data, but the distribution generally appears relatively concentrated and uniform.

The box plot of net revenue (Figure 1d) shows only one outlier, which in this case refers to the food manufacturing sector. However, the data distribution suggests positive asymmetry since the median is below the mean, and the notable difference between Q3 and Q1 indicates significant variability. The lowest value identified in the data is BRL 12,719,474.00, and the highest is BRL 525,606,581.00, indicating the presence of outliers at both extremes.

The box plot (Figure 1e) interpretation of the enhanced product share range data suggests that most values are concentrated in a moderate range. However, the presence of outliers, especially on the upper side, highlights significant variability in the data. Higher values influence the average, indicating a positive asymmetry in the distribution.

Table 4. Descriptive statistics of Model 2 variables.

	Innovation Spending in 2014	Postgraduate % in 2014	Graduation % in 2014	Net Revenue in 2017	% of Substantially Improved Products in Total Sales in 2017
Max	7,106,515.74	19	62	667,024,159.16	49
Q3	2,671,620.21	11	46	234,590,278.51	31
Average	2,424,906.16	8	45	172,447,090.56	24
Median	1,916,461.74	6	44	108,317,788.62	25
Q1	1,151,547.33	5	41	51,853,713.97	14
Min	430,415.76	1	31	14,972,712.33	4

shows the descriptive statistics for Model 2, which covers the range of aggregated data from 2014 to 2017.

The descriptive statistics are the same as those for the 2011–2014 period, as is the case with the food products manufacturing sector, which maintains its position with the highest levels in the indicators’ expenditure by companies on innovation and net revenue, while the clothing and apparel and accessories manufacturing sector continues to have the lowest values in the indicators relating to the educational level of the employees being assessed—undergraduates and postgraduates. Box plot graphs in Figure 2 follow in parallel to those previously presented.

The customizable software development sector had the highest value for the indicator percentage share of new or substantially improved products in total internal sales in 2017 and had the highest number of employees with a degree in 2014, but in 2017, it had the lowest net revenue. Meanwhile, the extractive industries sector showed the highest value for the number of employees with postgraduate degrees. Still, it was the sector with the lowest share of products that received innovation incentives in total domestic sales. Box plots in Figure 2 were constructed with the values of the criteria used to enhance the descriptive data analysis.

The box plot (Figure 2b) for the percentage of employees with postgraduate degrees indicates no outliers. Furthermore, the minimum value is 1%, and the maximum value is 19%, indicating the presence of outliers at both extremes. This suggests that most of the data are concentrated within a moderate range, and the average is affected by higher values, indicating a tendency toward positive asymmetry in the distribution.

Box plot (Figure 2c) shows three outliers, two of which are positive and concern the custom software development and customizable software development sectors. The negative outlier refers to the clothing and accessories sector. Overall, the box plot values imply that most data are grouped within a relatively narrow range, suggesting consistency in the distribution.

Regarding net revenue in 2017, box plot (Figure 2d) shows that there is one outlier, which refers to the food product sector. However, the other values are above average, as shown in the graphical representation. In addition, the distribution suggests positive asymmetry since the median is below the mean, indicating the influence of higher values.

Regarding the percentage of substantially improved products in total sales in 2017 (Figure 2e), the data suggest a concentrated distribution in the interquartile range of 14% to 31%. A median close to the average of 24% implies a slight positive asymmetry. Most of the data are centered in a moderate range, with some variability at the extremes, indicating an overall symmetrical or slightly asymmetrical positive distribution.

3.2. Multicriteria Analysis with PROMETHEE II

As demonstrated previously, the PROMETHEE II method is designed based on a net flow, which refers to subtracting the outflow from the inflow, to create the rank of the alternatives. Brans and Vincke (1985a, 1985b) define the two concepts of outflow as the “intensity of preference” of the specific alternative over all the other alternatives in the set, where the higher the value of the flow, the better the alternative. The input flow, on the other hand, is the opposite, being the “intensity of preference” of all the alternatives over the specific alternative, and the lower the input flow is, the better the alternative.

It should be noted that the flow values are used to rank the alternatives and do not have a direct algebraic relationship of better or worse performance between them. The rankings for Model 1 in this study are presented in

Table 5. Model 1 net flows.

Ranking	Sector	Net Flow	Output Flow	Input Flow
1st	Manufacture of chemical products	0.5556	0.7778	0.2222
2nd	Manufacture of food products	0.3778	0.6889	0.3111
3rd	Electricity and gas	0.2444	0.6222	0.3778
4th	Custom software development	0.0667	0.5333	0.4667
5th	Manufacture of machinery and equipment	0.0000	0.4889	0.4889
5th	Manufacture of electrical materials	0.0000	0.4889	0.4889
7th	Extractive industries	−0.1111	0.4444	0.5556
8th	Manufacture of rubber and plastic products	−0.1556	0.4222	0.5778
8th	Customizable software development	−0.1556	0.4222	0.5778
10th	Manufacture of clothing and accessories	−0.8222	0.0889	0.9111

.

According to PROMETHEE II’s net flow analysis, the best-performing sector in the 2011–2014 period was chemical product manufacturing, followed by food product manufacturing and electricity and gas.

The worst sectors in this ranking were manufacturing rubber and plastic products, developing customizable software, and the clothing and accessories sector. It should be noted that Visual PROMETHEE provides a visualization of the criteria that acted positively and negatively in constructing this ranking.

The behavior of the criteria according to the method is described in

Table 6. The behavior of the criteria in Model 1.

Positive Criteria	Sectors	Negative Criteria
- Spending by companies on innovation (BRL) - Graduation - Postgraduate - Ranges of percentage share of new or substantially improved products in total domestic sales - Net income (BRL)	1st—Manufacture of chemical products
- Innovation spending by companies - Postgraduate - Net Revenue (BRL)	2nd—Manufacture of food products	- Graduation - Percentage share of new products
- Postgraduate - Graduation - Net Revenue (BRL)	3rd—Electricity and gas	- Expenditure by companies on innovation (BRL) - Percentage share of new products
- Postgraduate - Graduation - Percentage share of new products	4th—Custom software development	- Spending by companies on innovation (BRL) - Net revenue (BRL)
- Spending by companies on innovation (BRL) - Graduation - Percentage share of new products	5th—Manufacture of machinery and equipment	- Postgraduate - Graduation
- Expenditure by companies on innovation (BRL) - Net revenue (BRL) - Percentage share of new products	6th—Manufacture of electrical materials	- Postgraduate - Graduation
- Postgraduate - Net revenue (BRL)	7th—Extractive industries	- Expenditure by companies on innovation (BRL) - Graduation - Percentage share of new products
- Expenditure by companies on innovation (BRL)	8th—Manufacture of rubber and plastic products	- Postgraduate - Graduation - Net revenue (BRL) - Percentage share of new products
- Graduation - Percentage share of new products	9th—Customizable software development	- Spending by companies on innovation (BRL) - Postgraduate - Net revenue (BRL)
	10th—Manufacture of clothing and accessories	- Spending by companies on innovation (BRL) - Postgraduate - Graduation - Net revenue (BRL) - Percentage share of new products

.

The table shows which criteria made it possible to raise the sector’s position in the ranking or which did the opposite. In this case, the first-placed sector on the list (manufacturing chemical products) had all its indicators show positive results. In other words, it can be understood from this that this sector invested in innovation, increased the number of undergraduate and postgraduate employees, and had new or improved products and higher net revenues.

In addition, it is important to note that the sectors in second place in this ranking, which are the manufacture of food products and electricity and gas, respectively, had a positive postgraduate degree and net revenue criteria. In contrast, the percentage share of new products’ criterion was common to both and proved to be a negative indicator for this analysis. Therefore, even though these sectors invested in employees with fourth-degree qualifications, they had higher revenues but did not achieve the same triumph when analyzing the number of new products.

Looking at the last four places in the ranking, it can be seen that all of them had the investment in innovation indicator as a negative criterion—except for the rubber and plastics manufacturing sector. In addition, the indicators for the number of employees with undergraduate and postgraduate degrees also contributed to the lower rankings in these sectors. It is particularly interesting to note that although the customizable software development sector has invested in employees with postgraduate degrees, it has still been unable to obtain better positions since the other criteria caused it to decline.

The rankings for Model 2 in this study are presented in

Table 7. Model 2 net flows.

Ranking	Sector	Net Flow	Output Flow	Input Flow
1st	Manufacture of chemical products	0.6444	0.8000	0.1556
2nd	Manufacture of food products	0.5556	0.7556	0.2000
3rd	Customizable software development	0.0889	0.5333	0.4444
4th	Manufacture of electrical materials	0.0667	0.5333	0.4667
5th	Electricity and Gas	0.0222	0.5111	0.4889
6th	Manufacture of machinery and equipment	0.0000	0.4889	0.4889
7th	Extractive industries	−0.1111	0.4444	0.5556
8th	Custom software development	−0.1778	0.4000	0.5778
9th	Manufacture of rubber and plastic products	−0.3111	0.3333	0.6444
10th	Manufacture of clothing and accessories	−0.7778	0.1111	0.8889

.

From the net flow of Model 2, it is possible to infer that there were some changes in the positioning of the sectors after this period, such as the rise of the customizable software development sector and the decline of the custom software development sector. There have been few changes in this new ranking, especially in the last- and first-placed sectors.

Table 8. The behavior of the criteria in Model 1.

Positive Criteria	Sectors	Negative Criteria
- Expenditure by companies on innovation (BRL) - Postgraduate - Graduation - Net revenue (BRL) - Percentage share of new products	1st—Manufacture of chemical products
- Expenditure by companies on innovation (BRL) - Postgraduate - Graduation - Net revenue (BRL)	2nd—Manufacture of food products	- Percentage share of new products
- Graduation - Percentage share of new products	3rd—Customizable software development	- Expenditure by companies on innovation (BRL) - Postgraduate - Net revenue (BRL)
- Spending by companies on innovation (BRL) - Postgraduate - Graduation - Percentage share of new products	4th—Manufacture of electrical materials	- Net revenue (BRL)
- Postgraduate - Net revenue (BRL)	5th—Electricity and gas	- Expenditure by companies on innovation (BRL) - Graduation - Percentage share of new products
- Expenditure by companies on innovation (BRL) - Net revenue (BRL) - Percentage share of new products	6th—Manufacture of machinery and equipment	- Postgraduate - Graduation
- Postgraduate - Net revenue (BRL)	7th—Extractive industries	- Expenditure by companies on innovation (BRL) - Graduation - Percentage share of new products
- Graduation - Percentage share of new products	8th—Custom software development	- Spending by companies on innovation (BRL) - Postgraduate - Net revenue (BRL)
- Expenditure by companies on innovation (BRL)	9th—Manufacture of rubber and plastic products	- Postgraduate - Graduation - Net revenue (BRL) - Percentage share of new products
	10th—Manufacture of clothing and accessories	- Expenditure by companies on innovation (BRL) - Postgraduate - Graduation - Net revenue (BRL) - Percentage share of new products

shows the direction taken by each criterion.

Table 8 shows that Model 2 behaves very similarly to Model 1 in this scenario, with a few minor exceptions. However, it is still possible to infer in a broader sense that most of the negative criteria of the sectors in the worst positions are related to the indicator expenditure in BRL made by companies on innovation, as well as the number of employees with higher levels of training. The opposite is also true: looking at the top two, you can see that this indicator boosts their position.

Finally,

Table 9. Ranking of economic sectors.

2011–2014
Sectors	Spending by Companies on Innovation (BRL)	Postgraduate (%)	Graduation (%)	Net Revenue (BRL)	Percentage Share of New or Substantially Improved Products in Total Domestic Sales (%)
1st—Manufacture of chemical products	+	+	+	+	+
2nd—Manufacture of food products	+	+	−	+	−
3rd—Electricity and gas	−	+	+	+	−
4th—Custom software development	−	+	+	−	+
5th—Manufacture of machinery and equipment	+	−	+	−	+
6th—Manufacture of electrical materials	+	−	−	+	+
7th—Extractive industries	−	+	−	+	−
8th—Manufacture of rubber and plastic products	+	−	−	−	−
9th—Customizable software development	+	+	−	+	−
10th—Manufacture of clothing and accessories	−	−	−	−	−
2014–2017
Sectors	Spending by Companies on Innovation (BRL)	Postgraduate (%)	Graduation (%)	Net Revenue (BRL)	Percentage Share of New or Substantially Improved Products in Total Domestic Sales (%)
1st—Manufacture of chemical products	+	+	+	+	+
2nd—Manufacture of food products	+	+	+	+	−
3rd—Customizable software development	−	−	+	−	+
4th—Manufacture of electrical materials	+	+	+	−	+
5th—Electricity and gas	−	+	−	+	−
6th—Manufacture of machinery and equipment	+	−	−	+	+
7th—Extractive industries	−	+	−	+	−
8th—Custom software development	−	−	+	−	+
9th—Manufacture of rubber and plastic products	+	−	−	−	−
10th—Manufacture of clothing and accessories	−	−	−	−	−

shows a comparison between the positions of the sectors between the periods in which the changes in the last positions are small, which may reflect the lack of search for innovation and internal development by the companies that make up these sectors.

Understanding an economic sector’s strengths and weaknesses is crucial for various stakeholders, including companies, investors, and governments. This in-depth understanding allows for more informed strategic decision-making. Companies can capitalize on their strengths and develop competitive advantages while mitigating weaknesses to improve efficiency.

Understanding your strengths inspires innovation and continuous development while understanding your weaknesses allows you to prepare for challenges and adapt to changes in the market. In addition, this analysis influences recruitment and workforce training decisions, contributing to job creation and the development of specific skills. In a broader context, understanding strengths and weaknesses contributes to resilience to economic, technological, and social changes, promoting long-term sustainability. This includes the efficient management of natural resources, the minimization of environmental impacts, and the promotion of ethical practices.

3.3. Research Implications

By analyzing the results shown above using the multicriteria analysis and the descriptive statistics, it can be inferred how the scenarios between the sectors have changed over the years. Each sector has its specific characteristics, and the results of investments in innovation reflect these characteristics since high-value standards in a given criterion do not have the same impact on different sectors.

It is worth noting that this study was divided into two different time scenarios, thus encompassing various events that certainly influenced the results obtained, given the economic variations, global paradigm shifts, and investment programs in strategic sectors by the public authorities involved in this period.

Furthermore, from an internal perspective of the organization, the temporal perspective reveals that the dynamics of innovation can change rapidly over time in the same sector. This can occur due to the uncertainty of investment in innovation, competitive or regulatory aspects, or the stock of innovation. This stock can represent new innovations stemming from previous ones, thereby mitigating costs and generating learning capacity.

This scenario is observed in some sectors across the three-year periods. In the manufacture of food products, the negative signal for the percentage of graduates in the first three-year period turned positive in the second, demonstrating greater relevance of the indicator.

For electricity and gas, the percentage of graduates switched from positive to negative between the periods. In custom software development, the proportion of postgraduates reversed from positive to negative between the three-year periods.

In the manufacture of machinery and equipment, the proportion of graduates changed from positive to negative, while the direction for net revenue was the opposite between the intervals. In the manufacture of electrical materials, the proportion of graduates and postgraduates changed from negative to positive, whereas the direction for net revenue was the opposite.

In customizable software development, all five indicators reversed their signals, with the percentage of graduates and the percentage share of new or substantially improved products in total domestic sales changing from negative to positive.

Therefore, the results of the method allow for an evaluation of how indicators related to the relevance of human capital and innovation can affect the financial perspective. When prioritizing the context for a sector, it becomes clear that it is not static, as it is influenced by changes in both the internal and external environments. Monitoring over time allows for not only understanding possible short- and medium-term strategy adaptations but also assessing each sector’s priorities over time by identifying the most appropriate metrics.

It is also important to mention that Brazil has a concentration of sectors throughout its territory. This concentration is not uniform, so it is only natural that certain sectors studied in this study are located in specific states, which means that the benefits or development effects they bring are naturally not spread across the entire national territory, as is the demand that the sectors lack, such as the availability of skilled labor, especially those with undergraduate and postgraduate degrees, which are different in certain Brazilian regions or states.

By analyzing the situation and dynamics of the budgets of the Ministry of Science, Technology, and Innovations and the allocation of resources from the National Fund for Scientific and Technological Development, it is evident that Brazil is struggling to face the challenges of the 21st century and international competitiveness among the leading developed nations. The country’s funding bases and science, technology, and innovation models are in crisis (Tavares 2024). There is a reconcentration of investments in science, technology, and innovation in already developed regions of the country based on the execution of no reimbursable resources from the National Fund for Scientific and Technological Development. The previous strategy of allocating 30% of the resources to regions such as the North and Northeast Regions is no longer sufficient to mitigate regional asymmetries (McManus et al. 2022).

This concentration is not uniform, resulting in the localization of certain sectors analyzed in this study in specific states. This implies that their benefits and developmental effects are not evenly distributed across the national territory. This disparity manifests itself, for example, in the demand for sectors lacking in areas such as the availability of qualified labor, particularly those with higher education and postgraduate degrees, which vary between different regions or states in Brazil. The funding bases for science, technology, and innovation concerning the needs of the national knowledge production system are currently limited (McManus et al. 2023).

Based on the results of this study, to achieve a high level of innovation, a robust ecosystem is necessary, including a well-established research and development infrastructure, policies to incentivize innovation, a quality education system that trains highly qualified professionals, and a regulatory environment that facilitates the creation and growth of new businesses (Berndt et al. 2024). However, one of the main internal barriers companies face is the lack of digital competencies among leaders (Carvalho et al. 2023). Managers do not understand emerging technologies or how they can be strategically applied to improve processes, products, and services (Kowalski et al. 2024). Additionally, companies face difficulties attracting and retaining talent due to high demand and a shortage of qualified professionals.

Although this research presents a novel contribution through the PROMETHEE II method for innovation in Brazil, a factor that limits comparability with other studies using the same method, there is convergence with findings that demonstrate the importance of innovation indicators in explaining the financial performance of organizations.

There is a wealth of empirical evidence in the literature that explores this relationship. Specifically, with data on the number of graduates, master’s degree holders, or PhDs within a company, or their respective proportions, the contributions of Guo et al. (2012), Han and Bae (2014), de Oliveira et al. (2018), Santos et al. (2018), and Dankwah et al. (2024) can be highlighted. This same body of research also utilizes R&D investment indicators or similar metrics that demonstrate the innovative efforts of companies.

As with the evidence from this research, both spending on innovation and employee education are shown to be appropriate indicators for testing this relationship. Despite differences in methodology, studying this relationship remains relevant. Similarly, the sectoral or industry perspective can reveal differences in the relationship between innovation and financial performance, as suggested by the studies of Kostopoulos et al. (2011), Gök and Peker (2017), Chouaibi (2021), and Lu and Chesbrough (2022).

Finally, it is worth noting that this study was carried out using a low number of evaluation criteria, which can certainly be expanded in future studies and applied to statistical tests. The exploratory nature of this study has not been replicated in any recent research in the literature, and the model proposed here is also a framework for future analysis.

4. Conclusions

Throughout this study, an effort was made to evaluate and understand innovative performance in the various sectors of the Brazilian economy using a multicriteria approach based on the PROMETHEE II method.

From these results, it is possible to see which sectors performed best in each historical series and which criteria made them rise or fall in the ranking. These findings are for government bodies to formulate incentive policies, for example, for sectors that cannot exit the lowest positions, such as (i) clothing and accessories manufacturing and (ii) rubber and plastics manufacturing. These sectors were ranked lowest in the ranking lists of both models, and the negative criterion for these sectors was the number of employees with undergraduate and postgraduate degrees.

In other words, with these findings, government bodies can encourage formal education for these sectors to bring qualified professionals into the market. In addition, financial incentives, such as credit access policies for innovative processes, can be formulated.

Another important finding of this study is the sector organizations. At the top of both models, there were positive criteria, such as spending on innovation and the number of qualified employees with academic qualifications. From this, it is possible to infer that these specific criteria can influence organizations’ performance.

This study’s managerial contribution is that it allows managers to develop a roadmap for industry sectors’ digital transition aligned with business and management strategies. This framework provides a structured approach to identifying and implementing emerging technologies, promoting digital integration at all levels of the organization.

It is plausible for entrepreneurs to see these findings as a way of improving their businesses and to focus on these specific criteria when seeking innovation in their companies. At this point, we would like to point out that further studies could investigate how the behavior of these markets is shaped by the maximization of this indicator in a multicriteria model. According to this study, businesses can infer that improved or innovative products can affect revenue over time.

From a methodological perspective, one limitation is the impossibility of identifying, the practical reasons, why the criteria were positive or negative for the economic sectors selected for analysis. In other words, although the results show which criteria boosted or lowered the sector’s ranking, it was not possible to determine the real reasons for it reaching that level. But it is hoped that this result will encourage studies to develop this understanding.

This work also limits itself to not assigning weights to the criteria, even though it is known that this can directly impact flows and, consequently, the ranking of sectors. It is suggested that further investigations can assign these weights to the criteria, such as the Roc Weight classification.

Regarding data characterization, this study only addresses two specific periods (2011 to 2014 and 2014 to 2017) grouped into two historical series, which suggests that it is possible that innovative processes have not yet taken place, as the analysis does not even consider a decade. Similarly, the expansion of both innovation indicators and the financial perspective can provide a broader understanding of this phenomenon under study.

Finally, using general data from all regions of the country is also a limitation of this study, leaving aside the demographic, economic, and social considerations of many Brazilian locations.

In future work, it is suggested to study PINTEC microdata to determine whether the behaviors outlined here are the same across Brazil’s regions and states. With this sample set, it is possible to access over 200 answered questions regarding the perspective of organizational innovation. Combined with the microdata from the Annual Industrial Survey (PIA) and the Annual Services Survey (PAS), both conducted by the IBGE, various other financial performance metrics can be obtained, thus providing a deeper perspective on this relationship. In addition, more comprehensive studies are suggested, incorporating external factors, the size of each company, and the business sector.