ADOPTION AND DIFFUSION OF TECHNOLOGIES 4.0 BASED ON THE INNOVATIVE TRAJECTORY AND THE SCALE OF OPERATION: THE CASE OF CHILE

Gatica-Neira, Francisco

doi:10.1590/S0034-759020220303x

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ARTICLES • Rev. adm. empres. 62 (03) • 2022 • https://doi.org/10.1590/S0034-759020220303x copy

ADOPTION AND DIFFUSION OF TECHNOLOGIES 4.0 BASED ON THE INNOVATIVE TRAJECTORY AND THE SCALE OF OPERATION: THE CASE OF CHILE

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

The factors that can explain the diffusion of 4.0 technologies in the chilean economy are analyzed. We work with companies that can be classified as "specialized suppliers" and "science-based" relevant to the analyzed technological field. The data from the Tenth National Innovation Survey carried out by the National Statistics Institute (INE, 2018) are used. A confirmatory factor analysis of the second order is performed to identify the variables that best explain the diffusion. Lisrel 8.8 software was used, making the estimations with the robust diagonal weighted least squares method (DWLS) because we work with ordinal variables. The main results show the relevance of future innovation projects and the number of workers hired by each company to stimulate technological diffusion.

Keywords:
Technological diffusion processes; government policies; confirmatory factor analysis; industry 4.0; technological innovation

Identification of economic activity	Description of the economic activity for the sectors “science-based” and “specialized suppliers”
26	Manufacture of computers, electronic, and optical products.
27	Manufacture of electrical equipment
28	Manufacture of machinery and equipment
61	Telecommunications
72	Scientific research and development.

Explanatory variable	Metric	Explanation	The hypothesis regarding the latent variable and references in the literature
Past innovation rate (Pinn)	= [Ʃ score by company i (New goods + New services + Improved production method + Improved logistics method + Improved support + New practices of organizational process + New methods of organizational responsibility + New methods of external organizational relations + Changes in packaging design + New means of promotion + New methods for distribution channels + New pricing methods + Social innovation) / Ʃ maximum score] * 100	The company that presents a higher past innovation rate is more likely to incorporate technologies 4.0 and to contribute to their diffusion	H1: Positive relationship (+) between the past innovation rate and innovative capacity Horváth et al. (2019)Horváth, D., & Szabo, R. Z. (2019). Driving forces and barriers of Industry 4.0: Do multinational and small and medium-sized companies have equal opportunities? Technological Forecasting and Social Change, 146, 119-132. doi: 10.1016/j.techfore.2019.05.021 https://doi.org/10.1016/j.techfore.2019.... , Chauhan et al. (2021)Chauhan, C., Singh, A., & Luthra, S. (2021). Barriers to industry 4.0 adoption and its performance implications: An empirical investigation of emerging economy. Journal of Cleaner Production, 285. doi: 10.1016/j.jclepro.2020.124809 https://doi.org/10.1016/j.jclepro.2020.1... , Müller et al. (2018)Müller, J. M., Kiel, D., & Voigt, K.-I. (2018). What drives the implementation of Industry 4.0? The Role of Opportunities and Challenges in the Context of Sustainability. Sustainability, 10(1), 247. doi: 10.3390/su10010247 https://doi.org/10.3390/su10010247... , Agostini et al. (2019)Agostini, L., & Filippini, R. (2019). Organizational and managerial challenges in the path toward Industry 4.0. European Journal of Innovation Management, 22, 406-421. doi: 10.1108/EJIM-02-2018-0030 https://doi.org/10.1108/EJIM-02-2018-003... , Rojas-Córdova et al. (2020)Rojas-Córdova, C., Heredia-Rojas, B., & Ramírez-Correa, P. (2020). Predicting business innovation intention based on perceived barriers: A machine learning approach. Symmetry, 12(9), 1381. doi: 10.3390/sym12091381 https://doi.org/10.3390/sym12091381...
Future innovation perspective (Finn)	= Ʃ of future innovation present in (product + process + MKT + organizational management + social) * 100	The company with more expectations of generating innovation on different fronts of organizational development has a greater capacity to disseminate technologies 4.0	H2: Positive relationship (+) between plans for future innovation projects and innovative capacity Horváth et al. (2019)Horváth, D., & Szabo, R. Z. (2019). Driving forces and barriers of Industry 4.0: Do multinational and small and medium-sized companies have equal opportunities? Technological Forecasting and Social Change, 146, 119-132. doi: 10.1016/j.techfore.2019.05.021 https://doi.org/10.1016/j.techfore.2019.... , Chauhan et al. (2021)Chauhan, C., Singh, A., & Luthra, S. (2021). Barriers to industry 4.0 adoption and its performance implications: An empirical investigation of emerging economy. Journal of Cleaner Production, 285. doi: 10.1016/j.jclepro.2020.124809 https://doi.org/10.1016/j.jclepro.2020.1... , Müller et al. (2018)Müller, J. M., Kiel, D., & Voigt, K.-I. (2018). What drives the implementation of Industry 4.0? The Role of Opportunities and Challenges in the Context of Sustainability. Sustainability, 10(1), 247. doi: 10.3390/su10010247 https://doi.org/10.3390/su10010247... , Agostini et al. (2019)Agostini, L., & Filippini, R. (2019). Organizational and managerial challenges in the path toward Industry 4.0. European Journal of Innovation Management, 22, 406-421. doi: 10.1108/EJIM-02-2018-0030 https://doi.org/10.1108/EJIM-02-2018-003... , Botha (2019)Botha, A. P. (2019). Innovating for market adoption in the fourth industrial revolution. South African Journal of Industrial Engineering, 30(3), 187-198. doi: 10.7166/30-3-2238 https://doi.org/10.7166/30-3-2238...
Capacity to innovate based on perceived challenges (InnChall)	= 1- [[Ʃ score by company i (Lack of own funds + Lack of external funding + Very high cost of innovation + Lack of qualified personnel + Lack of technology information + Lack of information on markets + Difficulty finding a partner + Market dominated by established companies + Uncertainty regarding the demand for goods + Not necessary due to previous innovation + Not necessary due to lack of demand for innovation + Regulatory challenges) / Ʃ maximum score] * 100]	The greater the perception of innovation facilities, the greater the capacity to innovate	H3: Positive relationship (+) between the capacity to innovate based on perceived challenges and innovative capacity. Reyes et al. (2016)Reyes, P., Li, S., & Visich, J. (2016). Determinants of RFID adoption stage and perceived benefits. European Journal of Operational Research, 254(3), 1. 801-812. doi: 10.1016/j.ejor.2016.03.051 https://doi.org/10.1016/j.ejor.2016.03.0... , Arnold et al. (2018)Arnold, C., Veile, J., & Voigt, K. (2018). What drives Industry 4.0 adoption? An examination of technological, organizational, and environmental determinants. International Association for Management of Technology. IAMOT 2018 Conference Proceedings. Recuperado de https://www2.aston.ac.uk/aston-business-school/documents/IAMOT2018_paper_75.pdf https://www2.aston.ac.uk/aston-business-... , Cabrera-Sánchez (2019), Chege et al. (2020)Chege, S., Wang, D., & Suntu, S., (2020). Impact of information technology innovation on firm performance in Kenya. Information Technology for Development, 26(2), 316-345. doi: 10.1080/02681102.2019.1573717 https://doi.org/10.1080/02681102.2019.15... , Horváth et al. (2019)Horváth, D., & Szabo, R. Z. (2019). Driving forces and barriers of Industry 4.0: Do multinational and small and medium-sized companies have equal opportunities? Technological Forecasting and Social Change, 146, 119-132. doi: 10.1016/j.techfore.2019.05.021 https://doi.org/10.1016/j.techfore.2019.... , Kiraz et al. (2020)Kiraz, A., Canpolat, O., Özkurt, C., & Taşkın, H. (2020). Analysis of the factors affecting the Industry 4.0 tendency with the structural equation model and an application. Computers & Industrial Engineering, 150, 1-10. doi: 10.1016/j.cie.2020.106911 https://doi.org/10.1016/j.cie.2020.10691... , Agostini et al. (2019)Agostini, L., & Filippini, R. (2019). Organizational and managerial challenges in the path toward Industry 4.0. European Journal of Innovation Management, 22, 406-421. doi: 10.1108/EJIM-02-2018-0030 https://doi.org/10.1108/EJIM-02-2018-003...
Percentage of employees who hold a graduate degree (PerEmpGrad)	= [(Number of employees who hold masters or PhD degrees / Total number of employees)] * 100	Having employees who hold a graduate degree increases the probability of innovation, facilitating technological diffusion	H4: Positive relationship (+) between the percentage of employees who hold a graduate degree and innovative capacity Ingaldi et al. (2020)Ingaldi, M., & Ulewicz, R. (2020) Problems with the implementation of Industry 4.0 in enterprises from the SME sector. Sustainability, 12, 217. doi:10.3390/su12010217 https://doi.org/10.3390/su12010217... , Dalenogarea et al. (2018)Dalenogarea, L., Benitez, G., & Ayala, F. (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics, 204, 383-394. doi: 10.1016/j.ijpe.2018.08.019 https://doi.org/10.1016/j.ijpe.2018.08.0... , Reyes et al. (2016)Reyes, P., Li, S., & Visich, J. (2016). Determinants of RFID adoption stage and perceived benefits. European Journal of Operational Research, 254(3), 1. 801-812. doi: 10.1016/j.ejor.2016.03.051 https://doi.org/10.1016/j.ejor.2016.03.0... , Vowles et al. (2011)Vowles, N., Thirkell, P., & Sinha, A. (2011). Different determinants at different times: B2B adoption of a radical innovation. Journal of Business Research, 64(11), 1162-1168. doi: 10.1016/j.jbusres.2011.06.016 https://doi.org/10.1016/j.jbusres.2011.0... , Chege et al (2020)Chege, S., Wang, D., & Suntu, S., (2020). Impact of information technology innovation on firm performance in Kenya. Information Technology for Development, 26(2), 316-345. doi: 10.1080/02681102.2019.1573717 https://doi.org/10.1080/02681102.2019.15... , Agostini et al. (2019)Agostini, L., & Filippini, R. (2019). Organizational and managerial challenges in the path toward Industry 4.0. European Journal of Innovation Management, 22, 406-421. doi: 10.1108/EJIM-02-2018-0030 https://doi.org/10.1108/EJIM-02-2018-003...
Percentage of qualified employees (PerQualEmp)	= ((Number of specialized employees / Total number of employees)) * 100	Having qualified employees increases the probability of innovating	H5: Positive relationship (+) between the percentage of qualified employees and innovative capacity Ingaldi et al. (2020)Ingaldi, M., & Ulewicz, R. (2020) Problems with the implementation of Industry 4.0 in enterprises from the SME sector. Sustainability, 12, 217. doi:10.3390/su12010217 https://doi.org/10.3390/su12010217... , Dalenogarea et al. (2018)Dalenogarea, L., Benitez, G., & Ayala, F. (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics, 204, 383-394. doi: 10.1016/j.ijpe.2018.08.019 https://doi.org/10.1016/j.ijpe.2018.08.0... , Reyes et al. (2016)Reyes, P., Li, S., & Visich, J. (2016). Determinants of RFID adoption stage and perceived benefits. European Journal of Operational Research, 254(3), 1. 801-812. doi: 10.1016/j.ejor.2016.03.051 https://doi.org/10.1016/j.ejor.2016.03.0... , Vowles et al. (2011)Vowles, N., Thirkell, P., & Sinha, A. (2011). Different determinants at different times: B2B adoption of a radical innovation. Journal of Business Research, 64(11), 1162-1168. doi: 10.1016/j.jbusres.2011.06.016 https://doi.org/10.1016/j.jbusres.2011.0... , Chege et al. (2020)Chege, S., Wang, D., & Suntu, S., (2020). Impact of information technology innovation on firm performance in Kenya. Information Technology for Development, 26(2), 316-345. doi: 10.1080/02681102.2019.1573717 https://doi.org/10.1080/02681102.2019.15...
Sales variation (SalesVari)	= [((Sales $ 2016-sales $ 2015) / Sales $ 2015)] * 100	An increase in sales can generate an incentive to adopt technology	H6: Positive relationship (+) between the sales variation and the innovative capacity Prause et al. (2019)Prause, M., & Günther, C. (2019). Technology diffusion of Industry 4.0: an agent-based approach. Int. J. Computational Economics and Econometrics, 9(1/2), 29-48. doi: 10.1504/IJCEE.2019.097793 https://doi.org/10.1504/IJCEE.2019.09779... , Reyes et al. (2016)Reyes, P., Li, S., & Visich, J. (2016). Determinants of RFID adoption stage and perceived benefits. European Journal of Operational Research, 254(3), 1. 801-812. doi: 10.1016/j.ejor.2016.03.051 https://doi.org/10.1016/j.ejor.2016.03.0...
Sales volume (lnSales)	Natural logarithm of sales volume ($)	The larger the sales volume, the greater the diffusion effect, since the company’s size facilitates technology adoption)	H7: Positive relationship (+) between the volume of sales and the scale of operation of companies that disseminate technologies 4.0 Ingaldi et al. (2020)Ingaldi, M., & Ulewicz, R. (2020) Problems with the implementation of Industry 4.0 in enterprises from the SME sector. Sustainability, 12, 217. doi:10.3390/su12010217 https://doi.org/10.3390/su12010217... , Reyes et al. (2016)Reyes, P., Li, S., & Visich, J. (2016). Determinants of RFID adoption stage and perceived benefits. European Journal of Operational Research, 254(3), 1. 801-812. doi: 10.1016/j.ejor.2016.03.051 https://doi.org/10.1016/j.ejor.2016.03.0... , Kiraz et al. (2020Kiraz, A., Canpolat, O., Özkurt, C., & Taşkın, H. (2020). Analysis of the factors affecting the Industry 4.0 tendency with the structural equation model and an application. Computers & Industrial Engineering, 150, 1-10. doi: 10.1016/j.cie.2020.106911 https://doi.org/10.1016/j.cie.2020.10691...
Total number of employees (lnTotEmp)	Natural logarithm of the number of employees	The larger the company, the greater its diffusion power.	H8: Positive relationship (+) between the total number of employees and the scale of operation of companies that disseminate technologies 4.0 Ingaldi et al. (2020)Ingaldi, M., & Ulewicz, R. (2020) Problems with the implementation of Industry 4.0 in enterprises from the SME sector. Sustainability, 12, 217. doi:10.3390/su12010217 https://doi.org/10.3390/su12010217... , Reyes et al. (2016)Reyes, P., Li, S., & Visich, J. (2016). Determinants of RFID adoption stage and perceived benefits. European Journal of Operational Research, 254(3), 1. 801-812. doi: 10.1016/j.ejor.2016.03.051 https://doi.org/10.1016/j.ejor.2016.03.0... , Chege et al. (2020)Chege, S., Wang, D., & Suntu, S., (2020). Impact of information technology innovation on firm performance in Kenya. Information Technology for Development, 26(2), 316-345. doi: 10.1080/02681102.2019.1573717 https://doi.org/10.1080/02681102.2019.15... , Brambilla (2018)Brambilla, I. (2018). Digital technology adoption and jobs: A model of firm heterogeneity. World Bank. Policy Research (Working Paper N. 8326). Recuperado de https://ssrn.com/abstract=3115833 https://ssrn.com/abstract=3115833...
Years of operation (CompYear)	Years of operation of the company	The number of years since the company started its operations. The lower diffusion capacity is due to difficulties in adapting to new production models	H9: Negative relationship (-) between the years of operation of companies that disseminate technologies 4.0 and these companies’ scale of operation Ingaldi et al. (2020)Ingaldi, M., & Ulewicz, R. (2020) Problems with the implementation of Industry 4.0 in enterprises from the SME sector. Sustainability, 12, 217. doi:10.3390/su12010217 https://doi.org/10.3390/su12010217... , Gatica (2018)Gatica, F. (2018). Elementos explicativos de la innovación en la industria TIC en Chile. Revista Interciencia, 43(6), 434-440. Recuperado de https://www.interciencia.net/wp-content/uploads/2018/06/434-GATICA-43_5.pdf https://www.interciencia.net/wp-content/...

	InnChall	Finn	CompYear	lnSales	SalesVari	lnTotEmp	PerEmpGrad	PerQualEmp	Pinn
InnChall	1.00
Finn	0.01 (PS)	1.00
CompYear	0.04 (PE)	-0.07 (PS)	1.00
lnSales	0.19 (PS)	0.11 (PC)	0.26 (PS)	1.00
SalesVari	0.08 (PE)	0.05 (PS)	-0.09 (PE)	0.16 (PS)	1.00
lnTotEmp	0.14 (PS)	0.13 (PC)	0.25 (PS)	0.80 (PC)	0.09 (PS)	1.00
PerEmpGrad	-0.02 (PE)	0.02 (PS)	-0.07 (PE)	-0.02 (PS)	0.09 (PE)	-0.06 (PS)	1.00
PerQualEmp	0.09 (PE)	0.20 (PS)	-0.15 (PE)	-0.05 (PS)	-0.03 (PE)	-0.13 (PS)	-0.01 (PE)	1.00
Pinn	0.20 (PS)	0.58 (PC)	-0.12 (PS)	0.23 (PC)	-0.03 (PS)	0.26 (PC)	0.06 (PS)	0.20 (PS)	1.00

		Number of companies	Percentage (313 companies)
Future innovation (a company may intend to innovate in more than one area)	Future product innovation	162	51.6%
	Future process innovation	132	42.2%
	Does not contemplate innovation in the future	115	36.6%
	Future innovation in organizational management	100	32.0%
	Future innovations in MKT	94	30.1%
	Future innovations of a social nature	40	12.6%
		Number of companies	Percentage (313 companies)
Past innovation (a company may have innovated in more than one area)	It does not feature past innovation	213	68.0%
	New services	49	15.6%
	New practices of process organization.	47	15.1%
	New methods of organizing responsibilities	44	14.0%
	Improve support	40	12.7%
	Improved production method	34	10.8%
	New goods	31	10.0%
	New means of promotion.	27	8.6%
	New methods of external relations	25	8.1%
	New pricing methods	23	7.3%
	New methods for distribution channels	19	6.2%
	Changes in packaging design	18	5.7%
	Improved logistics method	13	4.0%
	Social innovation	6	1.9%
		Number of companies	Percentage on a 313 basis
Main barriers to innovate (a company may appoint more than one item)	High costs of innovation	128	41.0%
	Lack of own funds	127	40.7%
	Lack of external funding	101	32.3%
	A market dominated by established companies	94	30.2%
	Uncertainty regarding the demand for goods	94	30.2%
	Difficulty finding a partner	83	26.4%
	Lack of qualified personnel	68	21.8%
	Lack of technology information	53	17.0%
	Lack of market information	52	16.7%
	Regulatory barriers	40	12.9%
	Lack of demand for innovation	30	9.7%
	Innovation is not required due to previous innovations	30	9.4%

Types of Settings	Statistics	Observed value First Model	Reference value	Fit quality
Goodness-of-fit Indexes	Root Mean Square Error of Approximation (RMSEA)	0.00	<0.08	Acceptable
	Standardized RMR	0.07	<0.7 and CFI > 0.92	Acceptable
	Root Mean Square Residual (RMR)	0.07	<0.7 and CFI > 0.92	Acceptable
	Chi square / degrees of freedom	= (74.09/26) = 2.84	Between 2-5	Acceptable
Incremental fit Indexes	Normed Fit Index (NFI)	1.00	> 0.92	Acceptable
	Comparative Fit Index (CFI)	1.00	> 0.95	Acceptable
	Adjusted Goodness of Fit Index (AGFI)	0.96	>0.90	Acceptable
Parsimony fit -- Indexes	Parsimony Normed Fit Index (PNFI)	0.72	Between 0.5 and 0.7, considered acceptable	Out of range

Latent variables	Explanatory variables	Estimate	Test T		Average variance extracted (AVE) (1)		Ordinal reliability coefficient (Ꙍ) (2)
Innovative Capacity	← Future innovation perspective (Finn)	0.60	7.65^ 95% confidence and ^* * 90% confidence.		0.2		0.5
Innovative Capacity	← Capacity to innovate based on perceived challenges (InnChall)	0.20	4.02^ 95% confidence and ^* * 90% confidence.
Innovative Capacity	← Percentage of qualified employees (PerQualEmp)	0.047	0.82
Innovative capacity	← Past innovation rate (Pinn)	0.21	4.11^ 95% confidence and ^* * 90% confidence.
Innovative Capacity	← ln Total number of employees (lnTotEmp)	0.0067	0.14
Innovative Capacity	← ln Sales (InSales)	1.00
Scale of operation	← Years of operation (CompYear)	1.00			0.5		0.7
Scale of operation	← Innovative capacity	1.13	3.15^ 95% confidence and ^* * 90% confidence.
Scale of operation	← Scale of operation scale (Scale)	0.15	0.98
Diffusion power	← Future innovation perspective (Finn)	1.00			0.38		0.4
Diffusion power	← Capacity to innovate based on perceived challenges (InnChall)	0.33	2.07^ 95% confidence and		0.38		0.4
	Discriminant validity check
		√AVE	Innovative Capacity	Scale of operation		Diffusion power
	Innovative capacity	0.47		<0.18 (3)		<0.54 (3)
	Scale of operation	0.74				>0.18
	Diffusion power	0.56

Types of Settings	Statistics	Improved observed value	Reference value	Quality of fit.
Goodness-of-fit Indexes	Root Mean Square Error of Approximation (RMSEA)	0.00	<0.08	Acceptable
	Standardized RMR	0.06	<0.7 con CFI > 0.92	Acceptable
	Root Mean Square Residual (RMR)	0.06	<0.7 con CFI > 0.92	Acceptable
	Chi square / degrees of freedom	=(41.9/12) = 3.4	Between 2-5	Acceptable
Incremental fit indices Indexes	Normed Fit Index (NFI)	1.00	About 0.92	Acceptable
	Comparative Fit Index (CFI)	1.00	About 0.95	Acceptable
	Adjusted Goodness of Fit Index (AGFI)	0.97	>0.90	Acceptable
Parsimony fit indices Indexes	Parsimony Normed Fit Index (PNFI)	0.57	Between 0.5 and 0.7 is considered acceptable	Acceptable

Latent variables	Explanatory variables	Estimate	Test T		Average variance extracted (AVE )(1)		Ordinal reliability coefficient (Ꙍ) (2)
Innovative Capacity	← Future innovation perspective (Finn)	0.59	7.56^ 95% confidence and ^* * 90% confidence.		0,4		0.6
Innovative Capacity	← Capacity to innovate based on perceived challenges (InnChall)	0.20	3.99^ 95% confidence and ^* * 90% confidence.
Innovative capacity	← Percentage of qualified employees (PerQualEmp)	0.21	4.03^ 95% confidence and ^* * 90% confidence.
Innovative Capacity	← Past innovation rate (Pinn)	1.00
Operation scale	← ln Total number of employees (lnTotEmp)	1.04	5.72^ 95% confidence and ^* * 90% confidence.		0.6		0.8
Operation scale	← ln Sales (InSales)	1.00
Operation scale	← Years of operation (CompYear)	0.29	2.80^ 95% confidence and ^* * 90% confidence.
Diffusion power	← Innovative capacity	1.00			0.3		0.5
Diffusion power	← Scale of operation scale (Scale)	0.42	3.74^ 95% confidence and ^* * 90% confidence.		0.3		0.5
	Discriminant validity check
		√AVE	Innovative capacity	Scale of operation		Diffusion power
	Capacidad innovativa	0.63		> 0.21		> 0.51
	Escala de operación	0.77				> 0.21
	Potencia difusora	0.56

Fundação Getulio Vargas, Escola de Administração de Empresas de S.Paulo Av 9 de Julho, 2029, 01313-902 S. Paulo - SP Brasil, Tel.: (55 11) 3799-7999, Fax: (55 11) 3799-7871 - São Paulo - SP - Brazil
E-mail: rae@fgv.br

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[1] Author's contribution

Francisco Gatica-Neira affirms that he has participated in all stages of the development of the manuscript, which includes: conceptualization and theoretical-methodological construction, theoretical review, data collection, data analysis, writing and final review.