Skills-based on technological knowledge in the digital economy activity
SSkills-based on technological knowledge in the digital economy activity
Dr. Cesar R Salas-Guerra
EDUCTUM Business School, Miami Lakes, United States of America UAB, Universitat Autónoma de Barcelona, Spain [email protected] [email protected] ORCID ID: 0000-0001-7182-3002 bstract
This research seeks to measure the impact of people with technological knowledge on regional digital economic activity and the implications of prosperous cities' contagion effect on neighbouring ones. The focus of this study is quantitative, cross-sectional, and its design is correlational-causal. This study covers seven micro-regions of Minas Gerais in Brazil, organized in 89 municipalities, with 69% urban population and 31% rural. The data used consisted of 4,361 observations obtained in the Brazilian government's public repositories, organized into panel data, and analysed using partial least squares, micro-regional spatial regression, and identification patterns with machine learning. The confirmatory analysis of the regression test establishes a significant impact between the CE's technological knowledge and the digital economic activity AED through a predictive value of R2 = .749, β = .867, p = .000 (value t = 18,298). With high notoriety among the variables, public and private university institutions (IUPP), professors with doctorates and masters (DCNT), and information technology occupations (CBO). A geographic concentration of companies that demand technology-based skills had effects by slowing down the development of small municipalities, suggesting the development of new government technology initiatives that support new business models based on technological knowledge. Keywords: technological knowledge; digital economy; digital transformation; technology innovation. ntroduction
Economic theories say that the level of production increases due to the transfer of knowledge in the human capital and information technologies (Cvetanović et al., 2015); this has led to the development of new production processes based on digitization and data management (Agrawal et al., 2019). With COVID-19, companies face many challenges; however, for years, the new characteristics of human digital adaptation (Engen et al., 2017) have promoted new business models focused on new technologies (Boisier, 2016; Espino-Timón, 2017), favouring diversification of regional economies (Boschma, 2017). These new business models use technologies based on information architectures (Paea & Baird, 2018) with the use of artificial intelligence (AI) algorithms and predictive analysis models (Agrawal et al., 2019), minimizing in this way dependence on technological externalities (Ma & Zhao, 2019). Consequently, the knowledge generated by technological innovation and the transformation of traditional economic activity to digital (Mcadam & Mcadam, 2016; Vega-Gomez et al., 2018) has driven the need for skilled labour in technological innovation (Grillitsch & Trippl, 2018). However, some regions may be affected by the sudden agglomeration of prosperous cities (Nagy et al., 2018a), which could polarize economic growth and regional development (Ma & Zhao, 2019). Therefore, based on the problem previously exposed, we propose the following research question: What impact does technological knowledge have on digital economic activity, and what are the effects of business agglomeration? Next, we will describe the study variables. lassification and definition of the study variables
This study's variables are made up of constructs and indicators, described below according to the order they were considered in the conceptual model. Table 1. Variables definition matrix
Code
Variable
Definition
References CE Technological knowledge
It comprises higher-level institutions with great importance in regional economic development and knowledge individuals related to technological, scientific knowledge to create and capture value for a company. (Giones & Brem, 2018; Bialetti, 2012; Ogundari & Awokuse, 2018; Cortright, 2001)
AED
Digital economic activity
It can generate productivity, economic growth, and well-being through regional economies' construction based on innovation and technological knowledge. (Brynjolfsson et al., 2018; Mendez, 2016; Cortright, 2001; Gomez, 2017; Manuel et al., 2016; dos Santos,
Construct technological knowledge (CE) • Number of public and private university institutions (IUPP) that comprise institutions that offer professional technical education, technological education institutions, educational institutions of undergraduate education, educational institutions of graduate education, and educational institutions of comprehensive education by the municipality between 2009 and 2018. • Number of teachers (DCNT) with doctorates and masters, professional studies instructors, computer science professors, science professors, economics and administrative science professors, biological sciences professors, engineering rofessors, mathematics and statistics professors, human sciences professors of public and private university institutions by the municipality between 2009 and 2018; • Amount of government economic contribution in the municipal education system (AEF) between 2009 and 2018.
Construct digital economic activity (AED) • Number of IT companies per municipality (ICT) between 2009 and 2018. • Brazilian classification of occupations (CBO) composed by number of network analysts data communication, information technology services manager, information technology production manager, information technology project manager, information technology technical support manager, database administrator, electrical and telephone line maintenance electrician data communication, data communication and telephone network installer-repairer, data communication technician, information security administrator, IT services manager, information analyst (information network researcher), IT project manager Information Technology Production Manager, Information Technology Technical Support Manager training, information systems programmer, computer equipment maintenance technician, computer user support technician, information technology management technologist, computer programming engineer, computer hardware engineer, computer operating systems engineer , installer of electronic equipment (computers and auxiliary equipment), computer operator (including microcomputer), internet programmer, all by municipality between 2009 and 2018. heoretical framework
Endogenous growth theory
This economic theory incorporates two essential points; First, it describes technological progress as a product of economic activity; second, this theory holds that unlike physical objects, knowledge and technology contribute to economic growth (Cortright, 2010). Although the neoclassical school affirms that the growth of the value of production at the regional level is the result of an increase in the quantity and quality of labour, for the new growth theory, the nature of the change is based on the technological factor (Cvetanović et al., 2015). The technological level of production increases through the transfer of knowledge and innovation activities through the contribution of innovative educational systems (Giones & Brem, 2018). Endogenous economic growth develops at a rate determined by the economic system, mainly those that provide stimuli for the creation of technological knowledge (Howitt, 2010). This theoretical basis is reflected by the following equation: y = AK (1) Where, A should be understood as an expression that represents technological factors, while K includes human capital as knowledge (Pack, 1994). The model most used in the theoretical and empirical analysis of growth and productivity over many years was the Cobb-Douglas function, according to Adams (2005); because the estimation of the parameters as seen in Equation (1) is fundamental in input determinants specific such as knowledge, labour skills, the distinction between capital ICT), non-capital (ICT) and the factor of technological progress (Hanclova et al., 2015). Akcigit (2013) found that regions far from the technological frontier enjoy a "lagged advantage"; This disadvantage implies that, in the long term, a region with a low rate of innovation will lag (Paunov, 2017) due to competitiveness in regional technological development.
The new industrial revolution
The industry is known as the economic process for highly automated material goods (Lasi et al., 2014). However, the fourth industrial revolution has transformed production thanks to technology in production processes; it includes, but is not limited to, artificial intelligence and the internet of things (Vasin et al., 2018). The flow of data managed locally and remotely between the different elements of this new value creation ecosystem called "Industry 4.0" (Stock & Seliger, 2016) allows companies to connect their machinery in the physical-cybernetic spectrum, for this unleash actions through the exchange of information autonomously (Du et al., 2018). Industry 4.0, as the fourth industrial revolution is known, has allowed the developing an environment where all the elements coexist together continuously and ubiquitously (Zambon et al., 2019). Although computers and automation have existed since previous decades, this interaction process seeks to establish coordination capacities to improve product management through the correct use of data (Nagy et al., 2018b). Consequently, recent studies found that the pressure of competition towards organizations motivates companies to trust more in knowledge and data use (Manesh et al., 2019), seeking to maintain a long-term competitive advantage, thus generating a riority for many research university’s (Du et al., 2018) that support professionals in the development of appropriate solutions.
Technological knowledge
The current literature review establishes that technological knowledge brings together people closely related to scientific knowledge and innovation, which contribute to the development of digital economic activity (Giones & Brem, 2018), notably establishing academic institutions' role in regional economic development (Feliu & Rodríguez, 2017). As a basis for regional development, Vega-Gomez et al. (2018) mention that technological knowledge seeks at the regional level to alter economic structures through a) innovation, b) detection of opportunities and c) business creation; thus producing knowledge providers, sophisticated demand, skilled labour, educational research activities, and support services, thus creating a competitive advantage (Grillitsch & Trippl, 2018). Consequently, the impetus that academic institutions exercise in economic development through university technology (Mcadam & Mcadam, 2016) implies a high range of university-community knowledge exchange. This new business approach based on new technologies is also known as academic spin-offs (Vega-Gomez et al., 2018), which positively affect innovation processes and business technology entrepreneurship (Li & Nuccialleri, 2016). Finally, the review of the literature recognizes that there is a direct relationship between business creation and economic growth, as mentioned by Lupiáñez Carrillo et al. (2017) where they affirm that the fundamental factor of economic growth is innovation through digital economic activity, which is driven by technological knowledge. he discussion of the literature presented above allows us to propose the hypothesis of this research: H1: Technological knowledge drives digital economic activity.
Digital Economic Activity
The digital revolution, also known as the digital economy (Li & Nuccialleri, 2016), has affected how conventional markets for products and services compete through the transition to the information economy. According to Gutiérrez et al. (2016), since through innovation, companies optimize a profit function and establish models of search and selection of technologies. The literature review establishes that digital economic activity can generate productivity, economic growth, and well-being through regional economies' construction based on innovation and knowledge (Brynjolfsson et al., 2018; Mendez, 2016). Similarly, it establishes that economies' competitiveness depends on the use of new technologies, the same ones that contribute to the gross domestic product (Neffati & Gouidar, 2019). Information technology companies have been characterized lately by their influence on production (Giones & Brem, 2018) marketing processes, job creation (Neffati & Gouidar, 2019), the transformation of the workforce, and business innovation through the development of new business models based on digital platforms (Agrawal et al., 2019), which maximize business management (Lu et al., 2018). In conclusion, the promotion of technology transfer through technological knowledge by information technology companies solves the need to build regional economies based on innovation and knowledge (Mendez, 2016), since innovation hrough the economy of digital activity (Lupiáñez et al., 2017) is one of the fundamental causes of economic growth (Kumar & Dahiya, 2016).
Methodology
Methodological design of the research
The focus of this research is quantitative, cross-sectional since the variables of interest of a specific population were studied over time using multivariate spatial analysis statistics (Gomez, 2017) and structural equations with partial least squares (Ajamieh, 2016) through the implementation of a matrix of spatial weights to determine the impact relationships of the digital economic activity of a municipality in comparison with its neighbor. The methodology was framed in the cross-sectional correlational-causal design because only the level of correlation between the variables was measured to identify possible causalities in the phenomenon that will later be studied (Orengo, 2008); The data used consisted of 4,361 observations obtained in the Brazilian government's public repositories described below: • Brazilian Classification of Occupations (CBO) • National Classification of Economic Activities (CNAE) • Brazilian Institute of Geography and Statistics (IBGE), and • Central Bank of Brazil (BCB) The panel data allowed identifying systematic and unobserved differences between the units correlated with factors whose effects should be measured (Wooldridge, 2009). They made it possible to generalize the results since this study eeks to obtain from this population the data previously organized in tables methodologically designed for such purposes (IBGE, 2017).
Geographical projection
Brazil's geographical projection is composed of a hierarchical distribution (Bosco, 2009), where the whole country represents the first level of the hierarchy, followed by the second level composed of the north region, northeast region, middle west southeast, and southern region. The third level has twenty-seven states or administrative units. One hundred and thirty-seven mesoregions represent the fourth level, and the fifth level corresponds to the microregions, which are subdivisions of the mesoregions; the country has 558 microregions (IBGE, 2019). Finally, the sixth level corresponds to the municipalities, which are made up of 5,565. However, in reality, two have a special status: the Federal District, which is considered a unit at all hierarchical levels, that is, it is a State, a mesoregion, a microregion, and a municipality at the same time, and the archipelago of Fernando de Noroña, which is a state district, belonging to Pernambuco, but considered as a municipality for analysis purposes. This study covers the total population composed of 7 microregions organized by 89 municipalities that comprise 128,602 km², equivalent to 22% of the state of Minas Gerais, with a population of 1,610,413 inhabitants. 69% is urban, and 31% rural (IBGE, 2017). Table 2. Municipalities of the northern mesoregion of Minas Gerais
Microregion
Municipalities
Municipalities
Municipalities
1. Bocaiuva
Bocaiúva * Engenheiro Navarro
Francisco Dumont
Guaraciama
Olhos de Água
2. Grão-Mogol
Botumirim Cristália
Grão-Mogol *
Itacambira
Josenópolis Padre Carvalho
3. Janaúba
Catuti Espinosa Gameleiras Jaíba
Janaúba *
Mamonas Mato Verde Monte Azul Nova Porteirinha Pai Pedro
Porteirinha Riacho dos Machados Serranópolis de Minas
4. Januária
Bonito de Minas Chapada Gaúcha
Cônego Marinho
Icaraí de Minas Itacarambi
Januária *
São João das Missões Urucuia
Juvenília
Manga Matias Cardoso Miravânia
Montalvânia Pedras de Maria da Cruz
Pintópolis
São Francisco
5. Montes Claros
Brasília de Minas Campo Azul Capitão Enéias Claro dos Poções Coração de Jesus Francisco Sá Glaucilândia
Ibiracatu Japonvar Juramento Lontra Luislândia Mirabela
Montes Claros *
Patis Ponto Chique São João da Lagoa São João da Ponte São João do Pacuí Ubaí Varzelândia
Verdelândia
6. Salinas
Águas Vermelhas Berizal Curral de Dentro Divisa Alegre Fruta de Leite Indaiabira
Ninheira Novorizonte Rio Pardo de Minas Rubelita
Salinas *
Santa Cruz de Salinas
Santo Antônio do Retiro São João do Paraíso Taiobeiras Vargem Grande do Rio Pardo Montezuma
7. Pirapora
Buritizeiro Ibiaí Jequitaí
Lagoa dos Patos
Lassance
Pirapora *
Riachinho
Santa Fé de Minas São Romão Várzea da Palma
Note: * Region capital
Data Analysis
In this study, 4,361 observations were analysed, organized into panel data; the process and tools are detailed below: • The first analysis phase: reflective PLS model (Smart PLS 3.0)
The second analysis phase: microregional spatial regression (Geoda 1.6 and ArcGIS Pro) • The third analysis phase: municipal growth analysis patterns by variable (BigML's platform for Machine Learning)
First analysis phase: reflective PLS model
For this first phase, a non-parametric reflective model of partial least squares PLS and Bootstrapping is used since it is reliable and less sensitive to outliers. The model consists of two constructs, and fifty indicators previously explained. Figure 1. Research empirical model
Model evaluation
Before starting with the respective multivariate analysis; Hair et al. (2012); Martínez & Fierro (2018a) establish the importance of their evaluation, which implies examining the reliability, internal consistency, convergent, and discriminant validation. These evaluations yielded the following results explained below:
CE AED IUPP DCNT AEF TIC CBO able 3. Precision and accuracy tests
Code
Cronbach’s α
AVE PFC Q HTMT VIF
CE .967 .521 .969 AED .988 .814 .989 .494 CE-AED .844 1.00
The results obtained show construct reliability in the model since the tests obtained values higher than p-value = .7. Regarding the convergent validation through the test (AVE), we conclude that the set of indicators represents a single underlying construct since values higher than p-value = .50 were obtained (Martínez & Fierro, 2018). Therefore, each construct explains at least 50% of the variance of the indicators. When evaluating the collinearity level, the test (VIF) did not find problems related to collinearity since its values fluctuated at a p-value = 1.00. In the discriminant validity test or the Heterotrait-Monotrait HTMT methodology, results in less than 0.7 confirm the existence of validity. The model's predictive quality was performed using the Stone-Geisser redundancy test of cross-validation of the construct or Q2, which assesses the structural and theoretical model; with the results obtained with a value greater than zero 0, the conclusion is drawn existence of predictive validity and relevance of the model (Thaisaiyi, 2020).
Magnitude and significance of the model
Path coefficient results (β) and values (p)
The analysis of the PLS algorithm's magnitude and significance allows us to measure nd test the research model's respective hypothesis relationships. The magnitude is observed in the standardized regression coefficient (β) and its significance (p). With the Bootstrapping algorithm, the magnitude is observed in the standardized regression coefficient (β), and the significance in the two-tailed t (4900) values; where the critical value is (0.01; 4999) = 2,576 (Martínez & Fierro, 2018a). The resampling analysis evaluated (5000 subsamples) with a confidence level of 0.01. Figure 2. Total effects EC - AED ratio
Results
The confirmatory analysis of the PLS least squares regression test establishes a high and robust significant impact between CE technological knowledge and AED digital economic activity through a predictive value of R2 = .749, β = .867, p = .000 (t-value = 18.298). The research showed that for each unit of increase in technological knowledge, digital economic activity increases by 74.9%. Table 4. Hypothesis test results
Hypothetical Relationship
Coefficient β t Student Boostrapping R p Hypothesis Accept
CE-----AED .867 .749 .000
YES econd analysis phase: microregional spatial regression
To model the relationship between technological knowledge and digital economic activity, a geographically weighted regression test (GWR) was carried out, which will allow identifying patterns and detecting clusters (Leong & Yue, 2017). It will also allow us to observe its constancy in time and significance (Sabogal, 2013). This model detects outliers that violate the assumptions that give rise to false correlations (Wilcox, 2016), which prevents the identification of significant values. Consequently, the Montes Claros Microregion was eliminated as it had outlier values. Similarly, the Janauba Microregion was eliminated since no significant relationship was found between the study variables. Table 5. Regional Comparative Matrix 2009
Region IUPP - TIC IUPP - CBO
DCNT - CBO EFU - TIC R | P R | P R | P R | P SALINAS .522 | .002 PIRAPORA .580 | .017 .989 | .000 .619 | .014 BOCAIUVA .000 | .000 .000 | .000 .829 | .023 G. MOGOL .000 | .000 .000 | .000 .000 | .000 .921 | .025 JANAURIA .901 | .000 .402 | .005
Table 6. Regional Comparative Matrix 2018
Region IUPP - TIC IUPP - CBO
DCNT - CBO EFU - TIC R | P R | P R | P R | P SALINAS .712 | .000 .916 | .000 .772 | .000 PIRAPORA .993 | .000 .995 | .000 .973 | .044 BOCAIUVA .978 | .001 .940 | .006 .940 | .014 .972 | .042 G. MOGOL .000 | .000 .000 | .000 .000 | .000 .645 | .002 JANUARIA .981 | .000 esults
The results confirm the predictive relationship and hypothesis validation between CE technological knowledge and AED digital economic activity in the following microregions: •
18 Municipalities of the Microregion of Salinas among the IUPP - CBO; IUPP - TIC and EFU - TIC indicators. • • • •
16 Municipalities of the Microregion of Juanuaria between the DCNT - CBO and EFU - TIC indicators.
Third phase of analysis: municipal growth analysis patterns by variable
The previously geographically weighted regression analysis results suggest the need to distinguish the geographical concentration caused by the phenomena under study (Prat & Cànoves, 2014). Therefore, with the Geoda program, municipalities' growth and concentration patterns were mapped using a statistical grouping technique. The findings identified concentration levels of> 75%. able 7. Spatial matrix of similarities at the regional level variable IUPP
IUPP 2009 (>75%)
IUPP 2018 (>75%)
Municipalities:
San Francisco; Pirapora; Montes Claros; Janauba; Nova Porterinha; Porterinha; Salinas
Municipalities:
Januária; San Francisco; Pirapora; Montes Claros; Corazón de Jesús; Bocaiúva; Janauba; Mato Verde; Porterinha; Salinas; Rio Pardo de Minas; Taiobeiras; Sao Joao do Paraíso; Itacarambi
Table 8. Spatial matrix of similarities at the regional level variable DNCT
DNCT 2009 (>75%)
DNCT 2018 (>75%)
Municipalities:
Januária; Pirapora; Mirabela; Montes Claros; Bocaiúva; Jaiba; Francisco Sa; Janauba; Nova Porterinha; Mato Verde; Salinas; Sao Joao do Paraíso; Itacarambi; Lassance; Varzea da Palma
Municipalities:
Januária; Pirapora; Montes Claros; Bocaiúva; Francisco Sa; Janauba; Nova Porterinha; Riacho dos Machados; Mato Verde; Salinas; Buritizeiro; Varzea da Palma able 9. Spatial matrix of similarities at the regional level variable AEF
AEF 2009 (>75%)
AEF 2018 (>75%)
Municipalities:
Januária; San Francisco; Pirapora; Sao Joao da Ponte; Montes Claros; Bocaiuva; Janauba
Municipalities:
Januária; San Francisco; Pirapora; Montes Claros; Janauba
Table 10. Spatial matrix of similarities at the regional level variable TIC
TIC 2009 (>75%)
TIC 2018 (>75%)
Municipalities:
Janauria; Pirapora; Montes Claros; Bocaiuva; Jaiba; Janauba; Salinas; Varzea da Palma
Municipalities:
Janauba; Pirapora; Montes Claros; Bocaiuva; Jaiba; Salinas able 11. Spatial matrix of similarities at the regional level variable CBO
CBO 2009 (>75%)
CBO 2018 (>75%)
Municipalities:
Janauria; Pirapora; Montes Claros; Bocaiuva; Janauba; Grao Mogol; Fruta de Leite; Salinas; Taiobeiras; Varzea da Palma
Municipalities:
Janauria; Pirapora; Corazon de Jesus; Montes Claros; Bocaiuva; Jaiba; Olhos de Agua; Janauba; Espinosa; Porteirinha; Mato Verde; Salinas; Taiobeiras
The geographic concentration caused by the study phenomena identified several that were statistically analyzed by creating binary dummy variables to be used through the supervised learning model with the use of logistic regression. The Municipalities with the identifier (1) reached values higher than 75% of their neighbors' growth. The municipalities that did not reach 75% growth were categorized with an identifier (0). The use of machine learning requires the validation of the parameters to identify the appropriate model; therefore, the data was divided into two parts, 85% of data for training, and 15% for testing. To test the model, we used Deep Neural Network, Logistic Regression, and Tree; after carrying out the respective cross-validation, we identified that the logistic regression scores were the highest 0.83 AUC; Therefore it was the model to be used to test the model the remaining 15% of data was used. Finally, the evaluation of the logistic regression model is presented below: able 12. Machine Learning evaluation model
Accuracy F value Precision Recall Coefficient Phi Lift Value K-S Kendall Tau Spearman Rho FPR Values
50% .66 66.7% 66.7% -.33 88.9% 66.7% .235 .258 100%
Note: The regression model's evaluation allowed us to measure and compare its performance to evaluate predictions of new instances that the model has never seen before.
Table 13. Machine Learning model prediction results.
Predictors Prediction >75% Prediction <75%
San Francisco 0.94998 0.02226 Mato Verde 0.80181 0.17121 Salinas 0.7727 0.20008 Riacho dos Machados 0.73077 0.23663 Nova Porterinha 0.72089 0.24927 Jaiba 0.70734 0.2565 Buritizeiro 0.6555 0.31602 Porterinha 0.64916 0.32188 Bocaiuva 0.64916 0.32188 Rio Pardo do Minas 0.64916 0.32188 Sao Joao do Paraiso 0.64916 0.32188 Varzea da Palma 0.64916 0.32188 Corazon de Jesus 0.64874 0.32167 Itacarambi 0.64792 0.32126 Taiobeiras 0.63943 0.33081 Sao Joao da Ponte 0.63383 0.31427 Januária 0.63187 0.34119 Grao Mogol 0.45364 0.52389 Fruta de Leite 0.4409 0.51521 igure 3. Evaluation curves for classification models
Results
The results through machine learning found high predictive values concerning the study variables in 17 municipalities where Sao Francisco (Januária) stood out with 0.94, Mato Verde (Janauba) with 0.80, Salinas (Salinas) with 0.77, Buritizeiro (Pirapora) with 0.65; Bocaiuva (Bocaiuva) with 0.64. However, in Grao Mogol, a predictive value of 0.45 was found, and Fruta de Leite one of 0.44. A side-by-side bar graph is presented to explain better these findings, which compares each municipality with its predictive value.
Discussion
The results of this study show that the regions that took advantage of skilled labor based on technological innovation (Manesh, Pellegrini & Marzi, 2019) maintained a more significant competitive advantage over their neighbors and minimized dependence on technological externalities (Agrawal et al., 2019). hese regions have reevaluated their existing strategies and operations to emphasize their objectives of building regional economies based on innovation and technological knowledge (Mendez, 2016), as is the case of the Municipalities of Montes Claros and Pirapora with high results in academic investment and not traditional. Productive processes, quite the opposite with the Municipality of Grao Mogol. The causes that contributed to the findings in this case study, the gross value added (GVA) of Grao Mogol broke down, and limited investment was discovered in the education and service industry sectors, which generated unfavorable economic results about others. Municipalities such as Montes Claros and Pirapora. Table 14. Gross value-added matrix (GVA)
Municipality
Grao Mogol
Pirapora
Montes Claros
Note: Analysis of three cities in the north of Minas Gerais (Unit: R $ x1000)
Figure 4. Gross added value at current prices of economic activity
Note: Non-traditional (left); Traditional (right), unit: R $ x1000
In the traditional production model used in the Municipality of Grao Mogol, it was possible to identify two determining factors that respond to this study's need: the low level of schooling of employers and workers and the minimal investment in knowledge technological education. These factors negatively impacted the development of digital economic activity in this region, which is consistent with what was stated by Brynjolfsson et al. (2018); dos Santos (2017); Gomez (2017); Mendez (2016); Meijer & Bolívar (2016); Century et al. (2007) & Cortright (2001). The evidence also showed that in regions where there is a higher concentration of labor and qualified suppliers in technological innovation, digital economic activity receives a significant boost promoting regional poles of economic development (PRDE) as argued by Sabogal (2013); Siabato & Guzmán-Manrique (2019). igure 5. Gross added value at current prices of economic activity
Note: education activity with other services, unit: R $ x1000
Figure 6. Gross added value at current prices of economic activity
Note: education activity with other services, unit: R $ x1000
The second-order contagion effect was another critical finding in this study, noting that the Municipality of Bocaiúva was favored by its proximity to the Municipalities of Montes Claros and Pirapora. Proving in this way what was argued by Sabogal (2013); Siabato & Guzmán-Manrique (2019) affirm that there may be results in heir neighbors in the contagion effect because companies tend to focus on geographic locations in order to use highly qualified labor and suppliers. Figure 7. Gross added value at current prices of economic activity
Note: education activity with other services, unit: R $ x1000
Conclusions
The theory of economic growth proposed in this study made it possible to identify and test the causal relationship of the variables that establish that the production level increases due to the transfer of knowledge in human capital and information technologies (Agrawal et al., 2019). This article also provides new evidence that contrasts with arguments that propose urban decentralization as a factor that would alleviate urban pressures on large cities by promoting small cities (Lu et al., 2018) due to the contagion effect of cities with economies based on innovation and technological knowledge affects services, production, and marketing, generating new opportunities in the labor market. Consequently, municipal governments must initiate a transformation framed in a new governmental, technological management (a subject proposed in this study for uture research). This management model would help develop new business models linked to technological innovation through specialized technological training, preventing the effects that slow down the development of small municipalities due to the geographic concentration of companies that demand technology-based skills. eferences
Adams, F. G. (2005). “A Theory of Production” The Estimation of the Cobb-Douglas Function: A Retrospective View.
Eastern Economic Journal , The Economics of Artificial Intelligence , 237–290. https://doi.org/10.7208/chicago/9780226613475.003.0009 Ajamieh, A. A. (2016). Essays on information technology and operational capabilities. In
Universidad de Granada . Universidad de Granada. https://dialnet.unirioja.es/servlet/tesis?codigo=56570 Akcigit, U. (2013). What Do We Learn From Schumpeterian Growth Theory ? Terms of Use.
Swedish Entrepreneurship Forum
Interacoes. Revista Internacional de Desenvolvimiento Local. , , 9–28. https://doi.org/10.20435/interações.v2i3.583 Boschma, R. (2017). Relatedness as driver of regional diversification: a research agenda. Regional Studies , (3), 351–364. https://doi.org/10.1080/00343404.2016.1254767 Bosco, D. (2009). Meso e microrregiões do ibge
Conference on Economic Measurement , Oct , 1–39. http://research.economics.unsw.edu.au/kfox/assets/bcdef_digitaleconomy_8oct2018.pdf entury, X. X. I., Capello, R., & Nijkamp, P. (2007).
Regional Growth and Development Theories. Regional Studies, 44:1, 132-135 . https://doi.org/10.1080/00343400903569174 Cortright, J. (2001). New Growth Theory , Technology and Learning : A Practitioner ’ s Guide (Reviews of Economic Development Literature and Practice: No. 4).
U.S. Economic Development Administration , , 35. https://e-tcs.org/wp-content/uploads/2012/10/Cortright-nueva_teoria_del_crecimiento.pdf Cvetanović, S., Filipović, M., Nikolić, M., & Belović, D. (2015). Endogenous growth theory and regional development policy. Spatium , (34), 10–17. https://doi.org/10.2298/SPAT1534010C dos Santos, U. P. (2017). Distribución espacial de los entes del sistema nacional de innovación Brasileño: Análisis de la década de 2000. Cepal Review , (122), 235–253. https://repositorio.cepal.org/bitstream/handle/11362/42039/1/RVE122_DosSantos.pdf Du, P., Luo, L., Huang, X., & Yu, J. S. (2018). Ultrafast synthesis of bifunctional Er 3+ /Yb 3+ -codoped NaBiF 4 upconverting nanoparticles for nanothermometer and optical heater. Journal of Colloid and Interface Science , (01), 172–181. https://doi.org/10.1016/j.jcis.2017.12.027 Engen, V., Walland, P., Uders, M. L., & Bravos, G. (2017). Understanding Human-Machine Networks: A Cross-Disciplinary Survey . . https://doi.org/10.1145/3039868 Espino Timón, C. (2017). Análisis predictivo: técnicas y modelos utilizados y aplicaciones del mismo - herramientas Open Source que permiten su uso . http://hdl.handle.net/10609/59565 abian Gomez, L. S. (2017). Convergencia interregional en Colombia 1990-2013: un enfoque sobre la dinámica espacial.
Ensayos Sobre Politica Publica , , 161–187. https://doi.org/10.1016/j.espe.2016.03.004 Feliu, V. R., & Rodríguez, A. D. (2017). Knowledge transfer and university-business relations: Current trends in research. Intangible Capital , (4), 697–719. https://doi.org/10.3926/ic.990 Feng Li, Alberto Nuccialleri, G. G. (2016). How Smart Cities Transform Operations Models: A New Research Agenda for Operations Management in the Digital Economy. Production Planing and Control , (6), 1–45. https://doi.org/10.1080/09537287.2016.1147096 Giones, F., & Brem, A. (2018). Digital Technology Entrepreneurship: A Definition and Research Agenda. Technology Innovation Management Review , (5), 44–51. https://doi.org/10.22215/timreview/1076 Grillitsch, M., & Trippl, M. (2018). Innovation Policies and New Regional Growth Paths: A Place-Based System Failure Framework. Innovation Systems, Policy and Management , October , 329–358. https://EconPapers.repec.org/RePEc:hhs:lucirc:2016_026 Gutiérrez, C., Heijs, J., Buesa, M., & Baumert, T. (2016). Configuración de los sistemas nacionales de innovación y su impacto sobre el crecimiento económico.
Economía y Política , (2), 37–83. https://doi.org/10.15691/07194714.2016.006 Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science , (3), 414–433. https://doi.org/10.1007/s11747-011-0261-6 Hanclova, J., Doucek, P., Fischer, J., & Vltavska, K. (2015). Does ICT capital affect conomic growth in the EU-15 and EU-12 countries? Journal of Business Economics and Management , (2), 387–406. https://doi.org/10.3846/16111699.2012.754375 Howitt, P. (2010). endogenous growth theory BT - Economic Growth (S. N. Durlauf & L. E. Blume (eds.); pp. 68–73). Palgrave Macmillan UK. https://doi.org/10.1057/9780230280823_10 IBGE. (2017). Síntese de indicadores sociais Uma análise das condições de vida da população brasileira. In Ibge/Pnad
Sistema IBGE de Recuperação Automática - SIDRA . Instituto Brasilero de Geografía y Estadística. https://sidra.ibge.gov.br/pesquisa/pib-munic/tabelas Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0.
Business and Information Systems Engineering , (4), 239–242. https://doi.org/10.1007/s12599-014-0334-4 Leong, Y. Y., & Yue, J. C. (2017). A modification to geographically weighted regression. International Journal of Health Geographics , (1), 1–18. https://doi.org/10.1186/s12942-017-0085-9 Lu, H., Zhang, M., Sun, W., & Li, W. (2018). Expansion Analysis of Yangtze River Delta Urban Agglomeration Using DMSP/OLS Nighttime Light Imagery for 1993 to 2012. ISPRS International Journal of Geo-Information , (2), 52. https://doi.org/10.3390/ijgi7020052 Lupiáñez Carrillo, L., López-Cózar Navarro, C., & Priede Bergamini, T. (2017). El capital intelectual y el capital emprendedor como impulsores del crecimiento económico regional. Cuadernos de Economia , (114), 223–235. https://doi.org/10.1016/j.cesjef.2016.09.005 a, C., & Zhao, T. (2019). The Correlation between Industrial Organization and Sustainable Growth of City Cluster Spatial: A Case Study of Harbin-Changchun City Cluster. Energy Procedia , (2018), 773–781. https://doi.org/10.1016/j.egypro.2018.11.243 Martínez Ávila, M., & Fierro Moreno, E. (2018a). Aplicación de la técnica PLS-SEM en la gestión del conocimiento: un enfoque técnico práctico / Application of the PLS-SEM technique in Knowledge Management: a practical technical approach. In RIDE Revista Iberoamericana para la Investigación y el Desarrollo Educativo (Vol. 8, Issue 16). https://doi.org/10.23913/ride.v8i16.336 Martínez Ávila, M., & Fierro Moreno, E. (2018b). Aplicación de la técnica PLS-SEM en la gestión del conocimiento: un enfoque técnico práctico / Application of the PLS-SEM technique in Knowledge Management: a practical technical approach. In
RIDE Revista Iberoamericana para la Investigación y el Desarrollo Educativo (Vol. 8, Issue 16). https://doi.org/10.23913/ride.v8i16.336 Mcadam, K., & Mcadam, R. (2016). A Systematic Literature Review of University Technology Transfer from a Quadruple Helix Perspective: Towards a Research Agenda.
R & D Management , (1), 7–24. https://doi.org/10.1111/radm.12228 Meijer, A., & Bolívar, M. P. R. (2016). Governing the smart city: a review of the literature on smart urban governance. International Review of Administrative Sciences , (2), 392–408. https://doi.org/10.1177/0020852314564308 Mendez, R. (2016). Renovar economías urbanas en crisis: un debate actual sobre la innovación. Desenvolvimiento Regional Em Debate , (3), 4–31. https://doi.org/10.24302/drd.v6i3.1293 Mohammad Fakhar Manesh, Massimiliano Matteo Pellegrini, Giacomo Marzi, M. D. (2019). Knowledge Management in the Fourth Industrial Revolution : Mapping the iterature and Scoping Future Avenues Published in IEEE Transactions on Engineering Management. IEEE Transactions on Engineering Management , 1–7. https://doi.org/10.1109/TEM.2019.2963489 Nagy, J., Oláh, J., Erdei, E., Máté, D., & Popp, J. (2018a). The role and impact of industry 4.0 and the internet of things on the business strategy of the value chain-the case of hungary.
Sustainability (Switzerland) , (10). https://doi.org/10.3390/su10103491 Nagy, J., Oláh, J., Erdei, E., Máté, D., & Popp, J. (2018b). The role and impact of industry 4.0 and the internet of things on the business strategy of the value chain-the case of hungary. Sustainability (Switzerland) , (10). https://doi.org/10.3390/su10103491 Neffati, M., & Gouidar, A. (2019). Socioeconomic Impacts of Digitisation in Saudi Arabia. International Journal of Economics and Financial Issues , (3), 65–72. https://doi.org/10.32479/ijefi.7718 Orengo, J. (2008). Qué Es Un Protocolo De Investigación (pp. 1–25). SUAGM. Pack, H. (1994). Endogenous Growth Theory: Intellectual Appeal and Empirical Shortcomings.
Journal of Economic Perspectives , (1), 55–72. https://doi.org/10.1257/jep.8.1.55 Paea, S., & Baird, R. (2018). Information Architecture ( IA ): Using M ultidimensional S caling ( MDS ) and K - M eans C lustering A lgorithm for Analysis of C ard S orting D ata . (3), 138–157. https://uxpajournal.org/information-architecture-card-sort-analysis/ Paunov, اd. G. C. (2017). DIGITAL INNOVATION AND THE DISTRIBUTION OF INCOME. Conference on the Economics of Digital Change , 1–47. https://doi.org/10.3386/w23987 rat Forga, J. M., & Cànoves Valiente, G. (2014). Análisis de la evolución de la concentración geográfica de los establecimientos de turismo rural en Cataluña.
Anales de Geografía de La Universidad Complutense , (1), 155–177. https://doi.org/10.5209/rev_AGUC.2014.v34.n1.45196 Sabogal, C. R. (2013). Análisis espacial de la correlación entre cultivo de palma de aceite y desplazamiento forzado en Colombia. Cuadernos de Economia (Colombia) , (61 ESPECIAL), 683–719. https://revistas.unal.edu.co/index.php/ceconomia/article/view/42494 Siabato, W., & Guzmán-Manrique, J. (2019). La autocorrelación espacial y el desarrollo de la geografía cuantitativa. Cuadernos de Geografia: Revista Colombiana de Geografia , (1), 1–22. https://doi.org/10.15446/rcdg.v28n1.76919 Stock, T., & Seliger, G. (2016). Opportunities of Sustainable Manufacturing in Industry 4.0. Procedia CIRP , (Icc), 536–541. https://doi.org/10.1016/j.procir.2016.01.129 Thaisaiyi Opati, M. G. (2020). Impact of Mobile Payment Applications and Transfers on Business (T. Z. Opati & M. K. Gachukia (eds.)). IGI Global. https://doi.org/10.4018/978-1-7998-2398-8 Vasin, S., Gamidullaeva, L., Shkarupeta, E., Palatkin, I., & Vasina, T. (2018). Emerging trends and opportunities for industry 4.0 development in Russia.
European Research Studies Journal , (3), 63–76. https://doi.org/10.35808/ersj/1044 Vega-Gomez, F. I., Miranda, F. J., Mera, A. C., & Mayo, J. P. (2018). The spin-off as an instrument of sustainable development: Incentives for creating an academic USO. Sustainability (Switzerland) , (11). https://doi.org/10.3390/su10114266 Vinod Kumar, T. M., & Dahiya, B. (2016). Smart Economy in Smart Cities (Issue November). https://doi.org/10.1007/978-981-10-1610-3_1 Wilcox, R. R. (2016). Comparing dependent robust correlations.
British Journal of athematical and Statistical Psychology , (3), 215–224. https://doi.org/10.1111/bmsp.12069 Wooldridge, J. M. (2009). Econometrics: Panel Data Methods. Complex Systems in Finance and Econometrics , 215–237. https://doi.org/10.1007/978-1-4419-7701-4_12 Zambon, I., Cecchini, M., Egidi, G., Saporito, M. G., & Colantoni, A. (2019). Revolution 4.0: Industry vs. agriculture in a future development for SMEs.
Processes ,7