A longitudinal analysis of digitalisation in the Brazilian industry, 2017-2020
1.
Introduction
New digital technologies are transforming every industry and the digitization process has
triggered a broader “digital transformation” phenomenon across most industries (Loebbecke and
Picot, 2015; Richter et al., 2018; Ghosh et al., 2017; Butschan et al., 2018). Industrial digitalization
is emerging as and will become a matter of utmost economic and competitive importance. From an
entrepreneurial perspective, the impact of digitalization can be identified from three viewpoints: i)
the internal efficiency, allowing organizations to streamline their own internal processes through
changes in roles and tasks; ii) the external opportunities, leading to new business opportunities in
existing business domains through new products, services, customers and insight; iii) the promotion
of a more disruptive change, when it can completely change the entire business domains. The
purpose of digital technologies is to support business processes, both within a company or between
a company and its environment.
The temporal path of adoption of digital technologies reflects changes in the perception of the
digital generation already adopted, which may be related to changes in the techno-economic
environment. Among those changes, we highlight the following: (i) the epidemiological effect
associated to technological diffusion, which includes both the observation by each company of the
speed of adoption by its competitors and the access to more information on the possibilities that
digital technologies offer; (ii) the objective determinants of the adoption of digital technologies at
the firm level, which are discussed with basis on the conceptual and operational tools provided by
"probit models" used to explain the characteristics of technological diffusion; (iii) the effects of
the environmental context on investment projects, including a lasting crisis faced by the Brazilian
industry and the impacts of a global economic crisis, aggravated by the Covid pandemics.
Considering the premises of the "probit models", the purpose of this paper is to identify the
main factors affecting the evolution of the perception of adoption of digital technologies in the
Brazilian industry using two direct surveys carried out in 2017 and 2019/2020. It is assumed that
not all industrial firms do perceive in the same way the challenges and windows of opportunities
associated with the adoption of digital solutions in their productive endeavours. Moreover, changes
in the perceived stage of adoption are related to different "generations" of digital technologies taken
as reference identifying situations of advance, stability or regression along the two considered
periods. To do that, we use a model that measure the probability of advance in digitalization
adoption considering variables related to the current adoption, the structural characteristics of the
firm (size and industry), its behavior in terms of P&D and training involvement and its involvement
in competitive markets,
Beyond this introduction, the paper has 4 sections ….
The Brazilian Manufacturing Context
According to Wang et all (2017) a longitudinal research requires a proper perception about
the “time horizon” of this research. At the sphere of research design, it is important to consider the
context and characteristics of the temporal interval between two adjacent time points, in order to
identify the relevance of events and trends that may interfere in the analysis as time passes. In this
sense, the elaboration of a longitudinal analysis about the of the expectations for the adoption of
digital technologies in the Brazilian industry requires a contextualization of the economic
framework conditions more general business environment of the Brazilian economy in which these
expectations are formed. Specifically, it is important to highlight the persistence of an unstable and
relatively hostile environment, with important consequences on the willingness of economic agents
agents' expectations and their predisposition to carry out various types of productive investments,
including , which tends to have important impacts on the adoption of digital technologies.
As external framework conditions must be taken into account in a longitudinal exercise, this section
will illustrate the evolution of aggregate economic indicators. It will be shown that in the sedond half of
the 20s the Brazilian economy was on the downward side of the cycle which inclined negatively even
further by 2020 as a result of the COVID pandemic which is reflected . As a result,
Indeed, the recent period is marked by important changes in the internal and external scenario,
resulting in a relatively turbulent period, marked by mini-cycles of industry retraction and growth,
in which an important aspect to be considered refers to the in the dynamics of economic activities,
investment, idel capacity and confidence levels of economic agents.
As shown in table XXX In recent decades, there has been a trend towards a reduction in the
relative share of investment in GDP, with the lowest investment rate being recorded in 2017
(14.6%). According to a study of thee BCB (2019), the investment rate, measured by the ratio
between Gross Fixed Capital Formation (GFCF) and Gross Domestic Product (GDP), in 2019, was
around 5 percentage points below the level observed in the 2nd quarter of 2013 . Analyzing the
period 2013-2019, it appears that the absorption of capital goods corresponded, in the 2nd quarter
of 2019, to less than 70% of the level observed six years earlier.
Concerning the manufacturing industry, it is possible to consider the broader evolution of the
recent trajectory of investment informed by the PIA for the recent period. In this way, a measure
can be defined by the relationship between the Acquisition of Assets (understood as an investment
"proxis") and the Value of Industrial Transformation (VTI), defining an Investment Rate, in relation
to which a significant growth can be observed between 2005 and 2014, reaching a maximum level
of 20.6%, with a pronounced decrease thereafter, until reaching 14.6% in 2017, with a relative
recovery in 2018, but still well below the level observed before the recession period. Information
collected by the CNI also Indicates that in the last months of 2019 the Installed Capacity Utilization
index evolved from an average close to 83% in 2013 to something around 77% in 2019, with a more
pronounced drop being observed in some industrial sectors with strong linkage effects, such as those
in the Chemicals, Metallurgy, Machinery and Equipment and Automotive vehicles.
Some aspects help to clarify the reasons for the general recent trend of investments. One
critical aspect concerns the role of public investment. In recent decades there has been a clear trend
towards a reduction in the share of government investment in GDP (reflected in the public
investment rate), interrupted only in the 2007/2010 period, in which it prevailed public investments
under the so-called Growth Acceleration Program (PAC). Another important aspect refers to the
evolution of the Industrial Installed Capacity Utilization Level (NUCI), estimated by the FGV. As
the primary objective of making the investment is precisely the expansion of production capacity,
it is expected that a dynamic of investment recovery will be accompanied by increases in the level
of utilization of production capacity. However, despite the investments effectively returned to a
positive trend from the end of 2017, at least until the coronavirus shock in 2020, this movement
doesn't reproduce the most consistent trajectories of investment recovery in the Brazilian economy
(1985/86 and 1993/94), driven by a robust NUCI growth, which is not observed in the 2018/19
biennium.
In this context, an important key issue concerns the "quality" of these investments. In
particular, the acceleration of investments that took place until 2013 occurred in the face of
productivity growth, which remained relatively stagnant during that period, probably indicating that
the activities that supported these investments were not able to promote a generalized increase in
productivity in the industry. In this sense, it can be suggested that a portion of this impact "leaked"
out of the industry, either in the form of the acquisition of services and/or imports. However, there
are signs that the relative recovery of investments at the end of the period - reflected more in the
value than in the investment rate - has been accompanied by a reasonable growth in productivity. It
can be argued that this growth was in part driven by the fall in employment. However, it is important
to consider to what extent this increase in productivity may have been affected by a greater "quality"
of investments due to the incorporation of digital technologies associated with the Industry 4.0
paradigm.
The modernization process anchored in the realization of investments can also be associated
with the innovative efforts of the industrial sector. Concerning these innovative efforts, the analysis
of data from the Brazilian Innovation Survey (PINTEC) for the 2015-2017 triennium, which largely
reflect the serious economic crisis of 2015/2016 and the poor recovery observed in the subsequent
period, points to a general picture of retraction of innovative investments. Despite evidence that the
world is accelerating its efforts to innovate, starting a new technological paradigm, there is evidence
that an environment that is not conducive to innovation persists in Brazil. In fact, according to
information from PINTEC, between 2012-2014 and 2015-2017, the innovation rate of PINTEC's
set of activities, which measures the proportion of companies that introduced new products and
processes in the period, dropped from 36% to 33.6 %. The total volume of resources invested in
innovation in the manufacturing sector dropped - 22%, while the indicator of the innovation effort
in the manufacturing industry declined from 2.16% of sales revenue in 2012-2014 to 1.69% in 2015
-2017. In the case of expenditure on R&D, which is the most technological component of the
innovative efforts, there was a drop of -2.9%, from 0.68% of revenues to 0.62% in the case of the
manufacturing industry between 2012-2014 and 2015 -2017. In particular, there is a sharp drop in
investments in R&D in the core of the equipment producing sectors, be they mechanical, electrical
and electronic.
According to PINTEC, the main difficulties reported by the 3 out of 5 companies in the survey
that have not implemented innovations fall under the broad category “market conditions”. Although
this category is very broad, there is evidence that companies face increasing difficulties in accessing
resources to support innovation, which only increased in the period covered by the last edition of
PINTEC (2015-2017), reflecting a weakness in the national system of innovation. In this sense,
there is a substantial reduction in support for innovation registered in the period. On the one hand,
large companies still have the incentives for innovation and R&D, but they cover a very small
number of the Brazilian business demography. On the other hand, the set of public support
programs reached a much smaller number of companies, with a reduction from 39.9% to 26.2% of
companies compared to the previous survey. In particular, there is a significant reduction in the
number of companies that relied on public resources to finance the acquisition of machinery and
equipment for innovative products and processes, all of which suffered the greatest reduction - from
29.9% to 12.9% - which is consistent with the drop in investments (gross formation of fixed capital)
observed in the period. The low dynamism of GDP in 2018 and 2019 and the shock of the covid19 pandemic in 2020 do not point to the reversal of the situation presented by the latest edition of
PINTEC.
Considering these trends, a controversial point concerns the critical factors to be considered
to drive a new investment cycle in the Brazilian industrial sector. The traditional view (BCB, 2019)
argues that a dynamization of private investment could be obtained from a reduction in risk
premiums, from the favorable perspectives for investments in the area of infrastructure and from
the gradual advancement of business confidence, as well as from initiatives that aim to stimulate
the increase of productivity and efficiency gains, mainly through greater "flexibility" of markets
and through the general improvement of the "business environment". In contrast to the emphasis on
factors that encourage the reactivation of industrial investments strictly associated with the
microeconomic plan, Bueno and Sarti (2019) argue that a new cycle of productive and technological
investment will not be possible without an updating of the domestic productive structure. They
argue, in this direction, that it is unlikely that a new investment cycle will start in a generalized way
in the industrial sector, given the high idle capacity and the low operational profitability in view of
a repressed demand for industrial goods and services. In this sense, the need to generate higher
quality investments is a prerequisite for overcoming the hardships of the called "Brazilian industrial
disease"(Kupfer, 2018), characterized by the persistence of incentives for industrial firms to adopt
strategies that minimize productive investments, with periods of growth being based,
fundamentally, on the use of existing capacity, due to the high aversion to the risks inherent to the
decision to immobilize fixed capital. It is possible to argue that a more accurate analysis of the
digitization process must consider this more general context, both in the sense of being constrained
by it, as well as in the sense of eventually contributing to a reversal of this structurally adverse
situation.
In general, the evidences indicate that there are obstacles for the Brazilian industry to deepen
its insertion in the digital universe, while they also show that companies are not standing still, with
an ongoing process of technological updating driven by digitization being in progress, which,
however, should be accelerated. It is in a context of a relatively unfavorable momentum - marked
by an internal environment still hostile to the acceleration of investments, by the persistence of high
levels of idle capacity, by the relative contraction of innovative efforts and by the difficulty to
reverse a general adverse macroeconomic scenario - that the expectations of industrial firms in
terms of the adoption of digital technologies were mapped, and it is against this background that
the trends presented below should be considered. The external ambiance acted as a deterrent to
adoption by all firms? Alternatively, did all or some firms used digitalisation as a defensive
mechanism of survival? Having an increasingly hostile external framework conditions as the
background, comparing 2020 to 2017, what is the likely profile of firms progressing, stagnating or
regressing in the adoption of digital technologies? These are the issued to be explored in this paper.
1.
Determinants of rhythms and paths of digital adoption technologies
The process of digital adoption technologies can be referred to the general pattern and
conditions for the adoption considered in the theory of technological diffusion. This theory focuses
on how innovations and technology diffuse among the targeted users and how the use of them can
be increased, rather than focusing on barriers and enablers in a wider perspective (Rogers, 1962;
2003). Based on reviews of different kinds of innovations adopted in different contexts, Rogers
concluded that the diffusion process displayed patterns and regularities across a range of conditions,
innovations, and cultures, describing diffusion as a dynamic process by which an innovation is
communicated through certain channels over time among the members of a social system.
For Rogers, adoption is a decision of full use of an innovation as the best course of action
available and rejection is the decision of not-adoption. The newness characteristic of an adoption is
related to the three first steps (knowledge, persuasion, and decision) of the innovation-decision
process. In this context, the perceived characteristics of an innovation is supposed to critically affect
its adoption, with five innovation attributes typically affecting rhythm of diffusion: (1) relative
advantage, referring to the extent or degree to which the innovation is perceived to have significant
advantages over current alternatives; (2) compatibility, referring to the degree to which the
innovation is seen or perceived as being consistent with past practices or experiences, existing
values and needs of potential adopters; (3) complexity, referring to the extent to which the
innovation can readily be understood or perceived and easily used; (4) trialability, referring to new
ideas that can be tried out at low cost before adoption; (5) observability, referring to the degree to
which the use and benefits or results of the innovation are visible to others, and therefore act as a
further stimulus to be adopted by others. Other attributes have also been identified as being
potentially important, such as: adaptability, centrality to the day-to-day work of the organization,
and little requirement for additional visible resources (Nutley et alii, 2002). To reduce the
uncertainty of adopting the innovation, the potential adopters should be informed about its
advantages and disadvantages to make them aware of all its consequences. Moreover, Rogers
(2003) claimed that consequences can be classified as desirable (functional) versus undesirable
(dysfunctional), direct (immediate result) versus indirect (result of the immediate result) and
anticipated (recognized) versus unanticipated (intended or not).
When assessing factors affecting technology adoption in specific contexts, there are many
analytical models with similar focus, such as the Technology Acceptance Model (TAM) (Davis,
1989) and the Technology-Organization-Environment (TOE) (Taherdoost, 2017). TAM focuses on
aspects that affect adoption of technology by the user of the technology, defining two main
categories: perceived usefulness and perceived ease of use. ‘Perceived usefulness’ refers to the
assumption that using a particular system would enhance his or her job performance. ‘Perceived
ease of use’ refers to the supposition that using a particular system would be free of effort. The
perceived usefulness and ease of use are two determinants that affect if the user intends to use the
technology, and consequently if the user uses the technology (Davis, 1989). The TOE framework
assumes a more holistic perspective on how technology adoption must be handled (Tornatzky and
Fleisher,1990). According to this framework, the process by which an organization adopts and
implements technological innovations is influenced by the technological, organizational and
environmental context.
The consequences of the technology adoption are their progressive diffusion to the sector as
a whole and to the economic system, amplifying the impacts generated. Two main general models
have been used to represent this process: the traditional epidemic model and the models that
conceived diffusion as the result of firm’s decision‐ making process (Geroski, 1999). The
‘epidemic’ model assume that what limits the speed of usage is the lack of information available
about the new technology, how to use it and what it does. Reflecting this process, empirical studies
of diffusion observed a time path of adoption which resembles an S-curve: a slow period of early
take-up is followed by a phase of rapid adoption and then a gradual approach to satiation (i.e., the
rate of diffusion first rises and then falls over time) (Karshenas and Stoneman, 1995; Baptista, 1999;
Geroski, 2000).
One of the main problems with the epidemic model is that information typically diffuses much
faster than the use of new technology does; another is that the analogy with epidemics is misleading
– potential users need to be persuaded and not just informed about the new technology. An
alternative approach is the models that empathize on the firm’s decision‐ making process of the
adoption, the intensity of the usage and the benefits a firm can get from the new technology. Such
considerations have led some scholars to develop the so called "probit models" which assume that
different firms, with different goals and abilities, are likely to want to adopt the new technology at
different times. Probit models consider that the benefits derived from technology adoption and use
depend on firm major characteristics, such as size, workforce, skills, involvement in innovation
activities, among other features (Karshenas and Stoneman, 1995; Geroski, 2000). The more
ambitious is the firm, the more likely is adoption given any specific rate of return (Bourke and
Rope, 2018; 2019). There is also strong evidence that firm-level characteristics impact digital
adoption, with micro-businesses with stronger internal resources (business plans, training, external
finance) being, ceteris paribus, more likely to be digital innovators. For example, Fabiani et all
(2005) indicate that the most important determinants of Italians firm’s digital adoption were size,
the human capital of the workforce and the presence of large firms in the local environment. ICT
adoption also tends to be associated with changes in a firm’s organizational structure. Using a probit
model about e‐commerce adoption for Luxembourgish firms, Peltier et all (2012) choose well‐based
explanatory variables in the literature on ICT diffusion, confirming some preliminary hypothesis.
Their work confirmed an expected positive effect of firm size; human capital, measured by workers
with a college/university degree and IT specialists; the absorptive capacity indicated by the firms;
and a variable related to whether the firm belongs to a group of enterprises. With expected positive
effects but with unclear results, they included the age of the firm, which could either have a positive
or negative influence depending on whether age indicates experience or less flexibility and the
geographical market served by the firm. An important point to note is that prior adoption of digital
technologies is negatively linked to subsequent adoption, while prior levels of sectoral adoption are
positively linked to adoption, reflecting informational, competitive, or supply-side effects.
Hypothesis
Assuming the probit approach, the path of evolution about the perception of adoption of
digital technologies between two periods depends, primarily, on firm’s structural behavioral and
performance characteristics. Structural characteristics are firm size, sector, and qualification level.
Behavioral determinants refers to firm’s investments in capability-building activities that involve
learning and allow to create absorption capacity. The performance indicators allows to relate
adoption to leadership in terms of competitiveness, this is, to associate the most aggressive
strategies of adoption (and the pioneers) to the economic leaders.
Beyond these determinants, and given the temporal dimension of the adoption, an additional
determinant is the level of digitalization from which the firm departs. Following the logic of the
convergence and catching-up models, the firms that are in earlier digital generations have more
probability to move forward than those that already had adopted digital solutions in more advanced
generations at the observed starting point. Parallelly, firms that already adopted a more advanced
digital generation in the initial period face a more restrict potential advance in relation to the frontier
or the threshold that represent the most advanced level of adoption. In this sense, the possibility of
evolution among different generations of digitalization depends on the potential of advancement in
relation to the current perception of adoption at the observed starting point.
Under all the above considerations, the hypotheses are the following:
Proposition 1. The less advanced generation adopted at the initial period, the higher the
probability to advance in adoption.
Movements of advance will be concentrated in firms whose perception in current adoption at
the initial period is early generations of digital technologies, in comparison to firms in more
advanced stages. This expected location of the advance represent a natural trend of "convergence"
towards the most advanced digital generation when the movements between generations are strictly
considered. That assumption implies implicitly that the perception of companies in relation to their
relative position in terms of the frontier incorporates, to some extent, the perceived advance of the
frontier between the two moments in time, that is, the frontier is a "moving target".
Proposition 2. (Structural). The higher the size of the firm at the initial period, the higher the
probability to advance in adoption.
Following Fabiani et al (2005), given the specificity of the firms, adoption involves changes
in both, internal organization and external relations (among firms and in the markets). Empirical
evidence shows that adopting firms in information and communication technologies that introduce
organizational changes obtain higher productivity gains and the scope of organizational
improvements is higher in the larger organizations. Also, digital technologies allow to reduce
transaction costs between firms and with customers, which represent an incentive to adoption.
Finally, reorganization require codification and standardization of the organizational routines,
which is easier to happen in large firms, while small firms depend on informal relationships and
tacit knowledge. For these reasons, we expect that the probability of a most rapid adoption augments
with the size firm.
Proposition 3. (Structural). The higher the digital intensity of the industry in which the firm
compete at the initial period, the higher the probability to advance in adoption.
The rate of adoption among firms is also affected by the extend the digital technologies are
more central in terms of competitive advantage in the set of entrepreneurial functions they have
potential applications. According to OEDC …. Industries differ in the intensity of use of digital
technologies having a more systemic character in ones (several functions) than in others. For
example ….
As the degree of application of digital technologies varies among industries, the adoption rate
will be also different among firms that belong to industries with different digital intensity. In this
sense, advances in adoption should be faster in those industries that are more sensitive to the
applicability of digital solutions to the specific functions of the firm.
Proposition 4. (Behavioral). The probability to advance in digital adoption is higher in firms
that perform R&D and training at the initial period.
Adoption generally involves large investment in capability-building activities, such as R&D,
training and in new equipment and machinery. In this sense, financial and human resources play an
important role in digitalization at the firm level (Delera et al , 2020). Even digital technologies are
mainly process innovation (users of innovation are others than the producers of innovation), R&D
is an important source of learning in the Cohen and Levinthal’s sense, that is, to create absorption
capacity to adopt: to identify uses, potentialities, and gains. However, training plays a more decisive
role than R&D in adopting process innovation of incorporated technologies. Training brings a
dynamic idea about labour qualification and firm’s capabilities. Literature on technology diffusion
suggest that adoption usually involves new investment in qualification of the labor force. Workers
must learn new procedures, to identify and solve new problems and to use properly the possibilities
that digital technologies offer. This kind of knowledge and competences is mainly acquired by
training on specific technologies and applications than by formal education (for example STEM
competences). Even more, qualification in static sense as formal education can even be a barrier to
adoption when represents a rigidity to adapt to new knowledge and to leave behind routines based
on old knowledge. Therefore, we assume that adoption rates depend on the firm's behavior to build
new capabilities, competences and routines though learning provided by training and R&D. The
improvement in adopting-capabilites involve absorption capacity, and it should be positively
associated with adoption paths of advance.
Proposition 5. (Performance). The probability to advance in digital adoption is higher in
firms that exported at the initial period.
The adoption pace is also associated to external environment which firms interact in.
Competition (not perfect) in the markets stimulate innovation and adoption at least in two senses:
(i) by the imitation effect, that is, firms get ‘contaminated’ by digitalization when these new
technologies are seen as a source of the rival’s competitive advantage; (ii) by the competition effect,
that is, the stimulus given by the competitive pressure consisting in the search of leadership
positioning (in technological and economic terms). In this sense, firms capable of competing in
foreign markets (exporting firms) have a more competitive pressure and a wider access to
monitoring the technology strategies of their rivals in international markets and have a more rapid
perception of the digital adoption and its importance as a competitive strategy. For example, Delera
et al (2020) suggest that once controlling for innovative behavior and structural firm characteristics,
firms participating into value chains are significantly more likely to adopt advanced digital
technologies. Therefore, we assume that exporting firms have a higher probability of advance in the
adoption path.
2.
Digitalisation in 2017 and in 2020: descriptive and longitudinal analysis
Data sources: I2027 and I2030 surveys
Purpose: to present the basic concepts associated with a focus on generations and what would be perception of
current adoption.
The research programme from behind this paper recognises some characteristics of the rapid
rate of progress of digital technologies and the related adoption process by firms which constitute
the backbones of the surveys carried out in Brazil in 2017 and 2019-2020. First, digital technologies
are transversal to all firms, but at the same time they take strong specificities to each firm. Therefore,
questions about digitalization must be designed to be answered by any industrial firm, regardless
the nature of its activities. Second, digital technologies are adopted across all business functions,
from design to production to relations with clients. What matters then is to raise which digital
solutions are adopted to carry out different business functions; not the specific technology in itself,
such as painting robots or generic ones such as artificial intelligence. Third, technologies have been
around for quite some time; so, it is important to consider that firms may adopt not only state of the
art technologies but also older technologies, eventually well-adapted to their requirements and
expectations; so, in the discussion of the process of adoption, it is necessary to specify “generations”
of digital solutions, naturally having as the ultimate reference the most advanced ones.
Para realizar o estudo longitudinal da adopção de tecnologias digitais, foram utilizadas duas
pesquisas. A primeira, relativa ao projeto I-2027, tinha como objetivo verificar o uso atual e
esperado de tecnologias digitais na indústria brasileira até 2030. Em 2020, realizou-se uma nova
pesquisa no âmbito do Projeto I-2030 estendendo o período de uso esperado de tecnologias digitais
até 2030 e incluindo, entre outras, um conjunto de variáveis de caráter estrutural (tamanho e setor),
comportamental (R&D, treinamento) e de desempenho (exportação). A metodologia de ambas
pesquisas pressupõe que o estado de adopção percebido pelas empresas pode estar referido a 4
gerações digitais para realizar 3 funciones empresariais: (1) relação com provedores; (2) gestão da
produção; (3) relação com clientes.
Tomando como referencia las soluciones digitales más avanzadas, se elaboró una
clasificación de cuatro generaciones (G1, G2, G3 y G4) estilizadas en tres funciones empresariales:
relación con proveedores, gestión de producción y relación con el cliente. La primera generación
(G1) incluye soluciones digitales ya relativamente maduras y que normalmente se utilizan para fines
específicos, esto es, en alguna función particular. En la G1, las tecnologías digitales desempeñan
tareas muy específicas en funciones empresariales localizadas. La relación con proveedores y
clientes se realiza a través de transmisiones manuales o telefónicas. En la segunda generación (G2),
las tecnologías digitales tienen un campo de aplicación más amplio permitiendo hacer la producción
más ágil y flexible. Así, puede haber algún grado de integración entre funciones empresariales como
CAD-CAM, pero sin abarcar todo el ámbito de una función. Su adopción implica incrementos en
la eficiencia de las empresas y en la calidad de sus productos y procesos. La transición de G1 a G2
no requiere grandes esfuerzos en términos de cambio organizacional e inversiones. En la tercera
generación (G3), las tecnologías están más integradas e interconectadas para ejecutar el control de
diferentes funciones empresariales. El grado de compromiso con proveedores y clientes es alto y la
empresa puede activar o responder a las demandas casi en tiempo real. La transición de G2 a G3
requiere esfuerzos significativos de inversión en adopción para integrar plenamente sus funciones
de negocio y estandarizar de forma integral y eficaz sus procesos y sistemas de información. En la
cuarta generación (G4), las empresas logran la producción integrada, conectada e inteligente, es
decir, las tecnologías digitales apoyan en tiempo real los procesos de decisión, con uso intensivo de
inteligencia artificial. Para pasar de G3 a G4, son necesarios cambios sustanciales, ya que la
generación G4 se caracteriza por el uso de dispositivos avanzados de comunicación, robotización,
sensorización, big data e inteligencia artificial, entre otros.
Tabela 1 - Caracterização do painel – dados extraídos da pesquisa de 2020, variáveis de
estrutura e de conduta
Número
Porte
Proporção (%)
Qualificação
Número
Proporção (%)
Pequenas
47
15,7%
Baixa
85
28,4%
Pequenas-Médias
107
35,8%
Média-Baixa
57
19,1%
Médias-Grandes
60
20,1%
Média-Alta
78
26,1%
Grandes
85
28,4%
Alta
59
19,7%
Intensidade
digital**
Low
Número
Proporção (%)
NP*
20
6,7%
53
17,7%
Fez P&D?
Número
Proporção (%)
Medium - Low
120
40,1%
Sim
174
58,2%
Medium - High
101
33,8%
Não
125
41,8%
High
25
8,4%
Fez Treinamento?
Número
Proporção (%)
Número
Proporção (%)
Sim
182
60,9%
Sim
127
42,5%
Não
117
39,1%
Não
172
57,5%
Exportou?
2
Total Geral
9
9
100,0%
NP*; empresas sem dado por problemas com CNPJ; **: According with classification Appendix
Entre as 1003 empresas entrevistadas na pesquisa I-2030, apenas 299 tinham participado da
pesquisa I-1727. This dataset forms the panel for the longitudinal analysis. Even the panel is not
representative of the whole Brazilian industry, it allows to indicate trends about rhythms and paths
of adoption to be considered for analytical, strategic, and policy-making purposes. A distribuição
de empresas se concentra maiormente nos portes médio-pequeno (35,8%) e médio-grande (20,1%),
embora quase um terço se encontra representado nas grandes (28,4) (Tabela1). A maior parte das
empresas se concentra em indústrias de intensidade digital media-baja (40,1%) e média alta
(33,8%), o qual acompanha o padrão de especialização da indústria brasileira. Em termos de
capacitação, a proporção de empresas localizadas no primeiro quartil (Baixo) em seu setor é similar
à proporção do terceiro quartil (Medio-Alto) (entre 26-28%); assim como a proporção de empresas
localizadas no segundo quartil (Medio-Baixo) é similar ao do quarto quartil (Alto) (19,1-19,7). Não
há então um padrão claro sobre a distribuição da qualificação em atividades STEM para o conjunto
das empresas da amostra. Entre as características comportamentais, mais da metade das empresas
da amostra realizam P&D (58,2%), e treinamento (60,9%) e, em termos de desempenho, quase a
metade da amostra é exportadora (42,5%) (Tabela 1).
2017 x 2020: a longitudinal perspective of change
Em termos de função empresarial, a propensão das empresas sobre sus percepção de adoção
em tecnologias digitais de relacionamento com fornecedores registrou pequenos retrocessos nas
gerações G2 e G3 e avanços nas gerações G1 e G4, o que estaria mostrando uma certa evolução
para a polarização (gráfico 1). Na função empresarial relativa à gestão da produção, a tendencia é
diferente. Houve um forte retrocesso na percepção de adopção digital em G2 (de 44% a 28%) e um
retrocesso moderado em G1 (de 46% a 38%). Este forte retrocesso em G1 e G2 teve como
contrapartida um elevado avanço da propensão a adotar tecnologias digitais de geração G3 (de 9%
a 32%) e G4 (de 1% a 2%). Na função de relacionamento com clientes, a tendencia foi similar. O
retrocesso moderado da percepção de adopção nas gerações G1 e G2 se traduziu num aumento da
percepção de adopção nas tecnologias G3 e G4, embora de forma também mais moderada.
Gráfico 1 – Percepção de adoção por função empresarial
A percepção de adoção digital está positivamente relacionada com o porte nos dois momentos
do tempo. As empresas pequenas estão fortemente concentradas na G1 (57% em 2017 e 62% em
2020), o que explica que entre este grupo a propensão a retroceder seja a menor (tabela 2). No
entanto, houve uma leve redução da percepção de adopção em G2 para aumentar em G3 e G4, o
que revela um caminho de polarização entre as empresas deste grupo. Entre as empresas grandes, a
percepção de adopção em G1 se manteve constante, em quanto que se reduziu em G2 e aumentou
em G3 e G4, o que também indica um caminho de polarização entre as empresas deste grupo
(gráfico 2). As empresas de porte médio seguem uma tendencia similar à das empresas de porte
grande, embora com mudanças mais moderadas; uma certa estabilidade com tendencia a retrocesso
em G1; um retrocesso em G2 nas de porte médio-grande e um pequeno avanço nas de porte médiopequeno; avanços relevantes em G3; e estabilidade em G4. Entre as empresas de porte medio, o
caminho para a polarização parece ser menos acusado (gráfico 2).
Gráfico 2 – Percepção de adoção por porte
Uma forma mais especifica de observar as mudanças na percepção de adopção é para cada
uma das três funções empresariais em cada uma das 299 empresas, o que dá um total de 897
observações. As mudanças observadas por função empresarial permitem identificar as diferentes
possibilidades de movimento incluso dentro de uma mesma firma. Desde esta perspectiva mais
focalizada, a tabela 2 apresenta em linhas a geração de partida em 2017 e em colunas a geração de
chegada em 2020. Os valores da diagonal representam a estagnação em cada uma das gerações; os
valores por cima da diagonal (sombreados) representam os avanços; e os valores por debaixo da
diagonal representas os retrocessos. Em termos agregados, a percepção sobre adopção digital entre
2017 e 2020 seguiu a seguinte trajetória: 36% das firmas mantiveram sua posição; outro 36%
avançaram e 28% retrocederam (Tabela 2). Os totais indicam as variações por geração. Assim,
45,5% das empresas se encontram em alguma função em G1 em 2017 e esse percentual passou a
ser 43,1% em 2020. O movimento de retrocesso nos percentuais de adopção também se observa em
G2 (de 34,1% em 2017 a 26,2% em 2020). Alternativamente, os movimentos em G3 e G4 foram
de avanço (de 19,3% em 2017 a 28,2 em 2020 e de 1,1% em 2017 a 2,5% em 2020 respectivamente).
A tabela 2 revela também que 80% dos movimentos entre gerações de avanço, retrocesso e
manutenção se concentram nas posições de partida em gerações G1 e G2 em 2017 e nas posições
de chegada nas gerações G1, G2 e G3 em 2020. Assim, 21,1% das empresas que se encontravam
em G1 em 2017, mantiveram sua posição, 12,4% avançaram para G2 e 11,5% avançaram para G3.
Entre as empresas que se encontravam em G2 em 2017, 14,5% regressou a G1; 8,4% se manteve
em G2 e 10,1% avançou a G3. Nesta seção de movimentos, o avanço (34%) foi menor que o não
avanço (manutenção mais o retrocesso, 43,9%).
Tabela 2 - Mudanças na percepção de adoção*. (%).
Nível de adoção em 2020
Nível de adoção
em 2017
G1
G2
G3
G4
Total
G1
21,0
12,4
11,5
0,7
45,5
G2
14,5
8,4
10,1
1,1
34,1
G3
7,1
5,2
6,2
0,7
19,3
G4
0,6
0,2
0,3
0,0
1,1
Total
43,1
26,2
28,2
2,5
100,0
*; considerando 299 empresas x 3 funções empresariais
A análise por caraterísticas estruturais revela que as empresas grandes e medias apresentaram
uma maior propensão a retroceder, enquanto as empresas pequenas tenderam mais a manter os
mesmos níveis de digitalização nos dos períodos. Em termos gerais não há muita diferenciação dos
movimentos entre firmas grandes, médias-grandes e médias-pequenas. Já as firmas pequenas
apresentam uma percepção de manutenção superior e de avanço inferior. Esse resultado pode se
dever a dois motivos. O primeiro se refere ao caráter auto declaratório da pesquisa, isto é, dado que
não sempre responderam os mesmos funcionários nos dois momentos, é possível que funcionários
de firmas menores não foram capazes de reconhecer melhor as gerações de digitalização de divisões
nas que não atuem. Alternativamente, o entrevistado na primeira pesquisa poderia conhecer uma
divisão da firma com grau de digitalização distinto do entrevistado da segunda pesquisa. O segundo
motivo se deve a que firmas pequenas tem menor propensão ao retrocesso por já estarem em uma
situação digital inferior ou por não precisarem de controles avançados da produção e dos
relacionamentos e, portanto, não haveria para onde retroceder na sua percepção de adoção.
A propensão a avançar é maior nos setores de intensidade alta e média-baixa. Nos setores de
intensidade digital média-alta e baixa, a maior propensão foi a de manter o nível de adopção de
2017 (tabela 3). Em geral, há poucas diferenças no perfil de mudança nos setores de baixa, médiabaixa e média-alta intensidade digital. Apesar disso, o fato de uma firma se encontrar num setor de
alta intensidade digital não exclui a possibilidade de retrocessos na percepção da adoção, porém é
inferior aos retrocessos observados em outras indústrias de intensidade digital menor.
Em termos de comportamento, os resultados revelam que são precisamente os grupos de
empresas mais ativas em criar capacitação através do aprendizado os que se revelam mais dinâmicos
para a adopção. Assim, entre as empresas que não fazem treinamento, 45% mantiveram sua posição
e 41% retrocederam, enquanto entre as que fazem treinamento, 51% avançaram na adopção entre
períodos. Similarmente, entre as empresas que não fazem P&D, 39% retrocederam e 45% se
mantiveram, enquanto entre as que fazem P&D, mais da metade avançaram da adopção digital.
Finalmente, entre as empresas que não exportam, não há uma propensão clara ao avanço ou ao
recuo; mas entre as firmas exportadoras, praticamente a metade registrou avanço.
Tabela 3 – Movimentos de retrocesso, manutenção e avanço da adopção de tecnologias
digitais (2017-2020)
Total
Porte
Grande
Media-grande
Media-pequena
Pequena
Intensidade digital
Alta
Média-alta
Média-baixa
Baixa
Treinamento
Não
Sim
P&D
Não
Sim
Exporta
Não
Sim
Retrocesso
Manutenção
28,0
36,0
36,0
40,0
38,0
38,0
25,0
32,0
35,0
32,0
50,0
28,0
27,0
30,0
25,0
19,0
29,0
28,0
31,0
32,0
37,0
34,0
37,0
49,0
33,0
38,0
32,0
41,0
20,0
45,0
30,0
15,0
51,0
39,0
20,0
45,0
29,0
17,0
51,0
34,0
20,0
38,0
32,0
28,0
48,0
Fonte: Elaboração própria; I-2027; I-2030.
Nova tabela:
Total
Stay put or Falling
behind
100.0%
Moving forward
100.0%
Size
Large/Medium-large
46.3%
52.3%
Medium-small/Small
53.7%
47.7%
Avanço
Digital intensity
High
6.7%
11.3%
Medium-high
35.4%
30.9%
Medium-low
38.9%
42.2%
Low
18.9%
15.6%
High
21.9%
15.9%
Medium-high
24.2%
29.4%
Medium-low
21.4%
15.0%
Low
26.3%
32.1%
N/A
6.1%
7.6%
No
52.6%
15.6%
Yes
47.4%
84.4%
No
54.9%
19.0%
Yes
45.1%
81.0%
No
65.4%
43.7%
Yes
34.6%
56.3%
STEM-related skill level
Training
R&D
Export
Finalmente a tabela 5 caracteriza os caminhos da adoção nas funções empresariais de acordo
com as caraterísticas estruturais, comportamentais e de desempenho das firmas. O movimento de
avanço se concentrou nas empresas de porte médio que se encontravam principalmente em G1 e
grandes que partiam de G2, e atuavam em setores de intensidade digital médio-baixo e médio -alto.
Mais da metade das empresas que avançaram desde G1 realizaram atividades de P&D e
treinamento, assim como aproximadamente um quarto das empresas que avançaram desde G2. A
atividade de exportação, entendida como um fator que revela a capacidade competitiva da firma,
não se presenta caraterística do avanço, embora os dados mostram que quanto maior era a geração
digital adotada em 2017, maior era a propensão a exportar respeito da de não exportar.
A manutenção nas mesmas gerações digitais é uma caraterística da G1 para qualquer que seja
o porte da empresa, embora um pouco superior nas de porte médio-baixo. Este grupo de empresas
que partiam de G1 e não se movimentaram se distribuem na mesma ordem de importância em
indústrias de intensidade digital média (44,0%), o que é esperado dada a estrutura produtiva do país
mais concentrada neste tipo de indústrias. No grupo das estagnadas, aproximadamente a metade
não realiza atividade de P&D nem de treinamento. Empresas que se mantiveram em gerações mais
avançadas (G3 e G4) apresentam uma maior propensão a fazer P&D e treinamento do que a não
fazê-lo. Um padrão similar segue a propensão a exportar: 42% das empresas que se mantiveram em
G1 não são exportadoras e das que permaneceram em G2, a propensão a não exportar é ainda menor.
No entanto, entre as que se mantiveram em G3, a propensão a exportar é claramente superior à de
não exportar.
Finalmente, o retrocesso, que de forma esperada se concentra nas gerações G2 e G3 dada o
pequeno grau de adopção em G4 em 2017, se distribui de forma relativamente uniforme entre todos
os tamanhos de firma, embora de forma mais destacada nas de tamanho médio-baixo (39%). Dado
que é neste tamanho de firma onde se concentram também os principais avanços, cabe prever que
será nesta faixa de tamanho onde a polarização da adoção digital é maior. O retrocesso também
atravessa as indústrias independentemente de sua intensidade digital, tendo um peso relativamente
mais alto nas de intensidade digital média. Novamente, como é nestas indústrias onde a
digitalização avança mais, a polarização entre ritmos de adoção se prevê maior. Entre as
caraterísticas comportamentais destaca o fato da menor propensão relativa a fazer P&D e
treinamento entre as que retrocederam em G2. No entanto, a propensão a fazer ou não P&D ou
treinamento é muito similar entre as que retrocederam em G3. Em termos de desempenho, 70% das
empresas que recuam não são exportadoras e este padrão se segue para qualquer que fosse a geração
digital inicial.
Tabela 4 - Mudanças de percepção de adopção 2017-2020. Distribuição percentual por grupo de movimento
Porte
Avanço
Manutenção
Intensidade digital
P&D
Treinamento
Exporta
P
P-M
M-G
G
B
M-B
M-A
A
SIM
NÃO
SIM
NÃO
SIM
NÃO
ST
G1
7,0
30,0
14,0
16,0
9,0
29,0
22,0
7,0
52,9
14,4
56,0
11,3
36,7
30,6
67,0
G2
4,0
7,0
6,0
14,0
7,0
13,0
7,0
4,0
26,6
4,3
26,9
4,0
18,0
12,8
31,0
G3
0,0
0,0
1,0
1,0
0,0
0,0
1,0
0,0
1,5
0,3
1,5
0,3
1,5
0,3
2,0
0,11
0,37
0,21
0,31
16,0
42,0
31,0
11,0
81,0
19,0
84,4
15,6
56,3
43,7
100,0
G1
18,0
20,0
11,0
10,0
13,0
22,0
22,0
3,0
14,7
44,2
16,9
42,0
14,4
44,5
59,0
G2
3,0
8,0
6,0
6,0
3,0
10,0
10,0
1,0
16,6
6,9
16,9
6,6
10,7
12,9
24,0
G3
1,0
3,0
3,0
10,0
3,0
7,0
3,0
4,0
16,3
1,3
16,9
0,6
12,9
4,7
18,0
G4
0,0
0,0
0,0
0,0
0,0
0,0
0,0
0,0
0,0
0,0
0,0
0,0
0,0
0,0
0,0
22,0
32,0
20,0
26,0
18,0
39,0
35,0
8,0
47,6
52,4
50,8
49,2
37,9
62,1
100,0
G2
8,0
20,0
10,0
14,0
12,0
18,0
19,0
3,0
17,5
34,3
16,7
35,1
14,3
37,5
52,0
Retrocesso G3
6,0
18,0
6,0
13,0
7,0
18,0
16,0
3,0
22,7
21,5
24,3
19,9
14,7
29,5
44,0
G4
0,0
1,0
2,0
1,0
0,0
4,0
0,0
0,0
1,6
2,4
2,0
2,0
1,2
2,8
4,0
14,0
39,0
19,0
28,0
20,0
39,0
35,0
6,0
41,8
58,2
43,0
57,0
30,3
69,7
100,0
N
141
321
180
255
159
360
303
75
15,7
35,8
20,1
28,4
17,7
40,1
33,8
8,4
375
41,8
546
60,9
351
39,1
381
42,5
516
57,5
897
%
522
58,2
N
%
327 36,3
319 35,6
251 28,0
100,0
(N), número de empresas; (ST) subtotais. Porte: (P) pequeno; (P-M) pequeno-médio; (M-G) médio-grande. (G) grande. Intensidade Digital/Capacitação: (B) baixa; (MB) média-baixa; (M-A) media-alta; (A) alta.
*; considerando 299 empresas x 3 funções
Fonte: elaboração própria; I2027; I2030.
3.
Structural, conduct and performance factors affecting the adoption of
digital technologies
a.
The empirical model
Descrição das variáveis e conexão com as hipóteses teóricas
Especificar as variáveis da pesquisa de campo que estarão conectadas com as
hipóteses do theoretical background
The logistic regression is a methodological strategy consensually accepted for
survey-based data and categorical variables. More specifically, the ordered version of
logistic regressions is useful when there is a relative ordering of response values that are
known, but the exact distance between them is not. This feature is essential for the present
analysis as the purpose of the research is to identify the main factors behind the advance
in terms of the adoption of digital technologies between the two surveys: 2017 and 2020.
Then, advancing between one survey and the other means belonging to a higher category
than that verified by firms that have either maintained or retreated in the analysed period.
By means of a logistic function, these models estimate the probability that an
outcome variable is associated to independent variables (both categorical or continuous):
the regression produces the likelihood occurrence of a specific event from the logistic
function to predict the corresponding target class of the categorical response variable
(Long and Freese, 2006; Long and Freese, 2014). Other studies had already implemented
the same model to verify different modes of digital adoption as was implemented in
Ferraz et. al (2018). This study moves one step forward in this context as combines two
surveys with the same question’s design and pool of firms order to capture which
determinants affect the movement between 2017 and 2020.
The general empirical equation to tests the hypotheses already specified in the
theoretical background takes the specification of usual β-convergence models, where the
growth of a variable (𝑦) depends on the initial level from it departs (𝑦0 ) and on a set of
variables that theoretically have influence over 𝑦. The β parameter associated with 𝑦0 is
usually negative, given that it is expected that those that departs from the most delayed
positions present a higher capacity to grow. Here, the dependent variable (∆𝑦) can be
understood as the advance in terms of digital technologies adoption between both surveys.
For the purpose of this paper, the general equation includes also three vectors of variables,
one related to the structural variables (𝑆), a second one related to conduct variables (𝐶)
and a third one related to performance (𝑃) as follows;
(1) ∆𝑦 = 𝛼 + 𝛽0 𝑦0 + 𝛽1 𝑆 + 𝛽2 𝐶 + 𝛽3 𝑃 + 𝜖
However, the general specification of β-convergence models of growth has to be
adapted to serve to the specific purpose of this work. As the outcome and the independent
variables are categorical and the usual OLS model can’t be applied, this reference model
will be rewritten in terms of an ordered logistic regression1 (AGRESTI, XXXX).
1
There are a lot of applied references for ordered logistic regressions. Some of them that were useful
for the current experiment was Abreu (2007), Williams (2016) and Calais (2019).
(2) ∆𝑦 =
As it is possible to observe in equations (3) and (4) ahead, the endogenous variable
is the variation of the current adoption between 2017 and 2020 (∆𝐺0 ). As the model is
categorical, initially this variable took values [1,2,3] when in each function the firm
recedes, remains or advances, respectively, as it was described in the previous section.
However, due to an analytical simplification, ∆𝐺0 was transformed into a dichotomic
variable that assumes value equals to 1 when the firm stood or retreated during the two
surveys or 2 when the firm advances.
The variable related to the initial position is the value of the generation adopted in
2017 (𝐺0−17 ) and takes values [1,2] when the firm reports being, for each function, in
generation 1 or 2 and 3 or 4, respectively. In order to better analyze the isolated influence
of structural, conduct and performance elements, it was decided to implement two
equations, one just for the structural dimension and other to analyze the conduct and
performance factors altogether.
(3) ∆𝐺0 = 𝛼 + 𝛽0 𝐺0−17 + 𝛽1 𝑠 + 𝛽2 𝑖 + 𝛽3 𝑙20 + 𝛽4 𝑟20 + 𝛽5 𝑡20 + 𝜖
(4) ∆𝐺0 = 𝛼 + 𝛽0 𝐺0−17 + 𝛽1 𝑠 + 𝛽2 𝑖 + 𝛽3 𝑙20 + 𝛽4 𝑟20 + 𝛽5 𝑡20 + 𝜖
In relation to the structural variables as defined in (3), three independent variables
are introduced in the model specification: firm size (𝑠), qualification of labour (𝑙) and the
digital intensity 2of the industrial activity (𝑖) the firm is engaged. All of them do not
change between 2017 and 2020, so they are the same independently of the survey. Size is
a dummy variable and was divided between small and medium-small firms (code 0) and
medium-large and large firms (code 1). The variable for the labour’s force qualification
(𝑙) takes values [1,2,3,4] when the relative position of the firm in the industry it belongs
to is low, medium-low, medium-high and high, respectively. Finally, the digital intensity
also assumes 4 categories: (1) low, (2) medium-low, (3) medium-high and (4) high.
The variables related to conduct and performance are specified in equation (4) and
are only available in the I2030 questionnaire. The variables that incorporate these
characteristics in the model are: (1) if the firm does R&D (𝑟) or not [1,0], respectively;
(2) if the firm does training (𝑡) or not [1,0], and (4) if it exports (𝑥) or not [1,0]. Both
equations previously defined have 𝐺0−17 in common, so it is possible to control the
starting point of each firm. The final panel has 897 observations considering the changes
in generations related to the three business functions for each 299 firms.
2
b.
E
Results
4.
Conclusão
All the sectors were classified according to its digital intensity following the suggestion applied in OCDE
(XXXX)
Bibliografia
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Technologies in Developing Countries? M. Delera, C. Michele Delera, Carlo
Pietrobelli, Elisa Calza, and Alejandro Lavopa; October 8, 2020. ICID. The
Italian centre for internacional development
(Paises UNIDO)