Technological level in the relationship between absorptive capacity and response capacity in manufacturing companies

Demuner Flores, María del Rosario

doi:10.1590/1678-6971/eRAMR230174.en

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Sumário

Resources and Entrepreneurial Development • RAM, Rev. Adm. Mackenzie 24 (03) • 2023 • https://doi.org/10.1590/1678-6971/eRAMR230174.en copy

Technological level in the relationship between absorptive capacity and response capacity in manufacturing companies

Nível tecnológico na relação entre capacidade de absorção e capacidade de resposta em empresas de manufatura

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

Purpose:

This research analyzed the influence of the acquisition, assimilation, and exploitation of knowledge on the organizational response capacity, given the moderation of the technological level in Mexican manufacturing companies.

Originality/value:

A conceptual contribution of response capacity as an understanding of organizational agility is fundamentally necessary for organizations facing changing conditions to use new knowledge to achieve the objectives of the organization, employees, and shareholders. An empirical contribution from the absorptive capacity to make companies respond with innovations to environmental changes and help them develop their absorptive capacity so the opportunities and restrictions in their technological level within a company can be seen.

Design/methodology/approach:

The quantitative study was conducted through a survey with a sample of 102 manufacturing companies that promoted some innovation activity in the State of Mexico, Mexico. The data were analyzed using a hierarchical regression model.

Findings:

The results showed that the technological level only moderated the relationship between the assimilation of knowledge and organizational response capacity. Also, this study contributed empirically by showing that companies have the same opportunity to improve their absorptive capacity and respond to changes in the environment regardless of their technological level.

Keywords:
organizational learning; absorptive capacity; response capacity; technological level; innovation

Manufacturing sector	Nieto and Quevedo (2005)Nieto, M., & Quevedo, P. (2005). Absorptive capacity, technological opportunity, knowledge spillovers, and innovative effort. Technovation, 25(10), 1141–1157. https://doi.org/10.1016/j.technovation.2004.05.001 https://doi.org/10.1016/j.technovation.2...	García et al. (2007)García, M. A. S., Aroche, R. F., & Ramos, C. C. (2007). Determinación de coeficientes importantes por niveles tecnológicos: Una aproximación desde el modelo de Miyazawa. Investigaciones Económicas, XXXI(1), 161–190. https://www.redalyc.org/articulo.oa?id=17331105 https://www.redalyc.org/articulo.oa?id=1...	Eurostat, as cited in García et al. (2007)García, M. A. S., Aroche, R. F., & Ramos, C. C. (2007). Determinación de coeficientes importantes por niveles tecnológicos: Una aproximación desde el modelo de Miyazawa. Investigaciones Económicas, XXXI(1), 161–190. https://www.redalyc.org/articulo.oa?id=17331105 https://www.redalyc.org/articulo.oa?id=1...	CNAE (INE, 2016Instituto Nacional de Estadística (INE). (2016). Indicadores del sector de alta tecnología: Año 2014: Resultados definitivos. http://www.ine.es/prensa/np959.pdf http://www.ine.es/prensa/np959.pdf... )	OCDE (INE, 2012Instituto Nacional de Estadística (INE). (2012). Informe anual 2012. https://www.ine.es/ine/planine/informe_anual_2012.pdf https://www.ine.es/ine/planine/informe_a... )	Gumbau-Albert and Maudos (2013)Gumbau-Albert, M., & Maudos, J. (2013). The evolution of technological inequalities: Country effect vs industry composition. European Journal of Innovation Management, 16(2), 190–210. https://doi.org/10.1108/14601061311324539 https://doi.org/10.1108/1460106131132453...	Galindo-Rueda and Verger (2016)Galindo-Rueda, F., & Verger, F. (2016). OECD taxonomy of economic activities based on R&D intensity (Working Paper No. 2016/04). OECD Science, Technology and Industry Working Papers. https://doi.org/10.1787/5jlv73sqqp8r-en https://doi.org/10.1787/5jlv73sqqp8r-en...	Final
Transport equipment	High	High and medium	Medium	Medium-high	Medium-high	High	Medium	High
Food	Low	Low	Low		Low	Low	Medium-low	Low
Chemicals	High	High and medium	High	Medium-high	Medium-high	High	Medium-high	High
Metal products	Low	High and medium	Medium	Medium-high	Medium-low	Low	Medium-low	Low
Textiles	High	Low	Low		Low	Low	Medium-low	Low
Plastic		High and medium	Medium	Medium-high	Medium-low	High	Medium	Low
Clothes and skins	High	Low	Low		Low		Medium-low	Low
Paper	Low	Low	Low		Low	Low	Medium-low	Low
Pinting and relatec ndustries		Low	Low		Low		Medium-low	Low
Communication equipment	High	High	High	High	High	High	Medium-high	High
Wood	Low		Low		Low		Medium-low	Low
Pharmaceuticals			High	High	High			High

Classification in Table 1	Manufacturing sector	Frequency		Percentage	Accumulated percentage
Classification in Table 1	Manufacturing sector	High	Low	Percentage	Accumulated percentage
High	Transport equipment	20		19.6	19.6
Low	Food		17	16.7	36.3
High	Chemicals	24		23.5	59.8
Low	Metal products		13	12.7	72.5
Low	Textiles		5	4.9	77.5
Low	Plastic		7	6.9	84.3
Low	Printing and related industries		4	3.9	88.2
High	Communication equipment	8		7.8	96.1
Low	Wood		4	3.9	100
	Total	52	50	100.0

Independent variable	Model 1		Model 2		Model 3		Model 4		Model 5		Model 6
Independent variable	β	t	β	t	β	t	β	t	β	t	β	t
Constant	4215	95.055	4.215	88.145	4.215	82.069	4.218	95.489	4.222	89.868	4.216	82.257
Acquisition	0.456	5.525***					0.434	5.211***
Assimilation			0.369	4.9***					0.378	5.076***
Exploitation					0.18	2.787***					0.175	2.717***
Technological leve							0.133	1.503	0.136	1.442	0.158	1.54
Acquisition technological leve							-0.142	-1.022
Assimilation technological leve									-0.298	-1.996**
Exploitation technological leve											-0.071	-0.548
Square R	0.309		0.197		0.073		0.332		0.245		0.099
Adjusted R squarec	0.302		0.189		0.064		0.311		0.221		0.07
F	43.889		24.009		7.767		15.909		10.387		3.499
Change in R square	0.309		0.197		0.073		0.332		0.245		0.099

Variable	Technological level	N	Mean	Standard deviation	Standard error mean
Acquisition	High	51	40	0.58276	0.08160
Acquisition	Low	49	39	0.71272	0.10182
Assimilation	High	51	41	0.67565	0.09461
Assimilation	Low	49	40	0.59994	0.08571
Exploitation	High	51	37	0.80908	0.11329
Exploitation	Low	49	36	0.79550	0.11364
Response capacity	High	51	43	0.48491	0.06790
Response capacity	Low	49	41	0.56615	0.08088

		Levene test of homogeneity of variance		t test for equality of means
		F	Significance	t	gl	Two-tailed significance	Mean difference	Standard error difference
Acquisition	Equal variances assumed	1.347	0249	0.713	98	0.478	0.093	0.130
Acquisition	Equal variances not assumed			0.71C	92.737	0.48C	0.095	0.13C
Assimilatior	Equal variances assumed	1.647	0202	0.769	98	0.444	0.098	0.128
Assimilatior	Equal variances not assumed			0.77C	97.406	0.445	0.098	0.128
Exploitation	Equal variances assumed	0.138	0.711	0.545	98	0.587	0.088	0.161
Exploitation	Equal variances not assumed			0.545	97.946	0.587	0.088	0.16C
Response capacity	Equal variances assumed	1.781	0.185	1.646	98	0.103	0.173	0.105
Response capacity	Equal variances not assumed			1.641	94.460	0.104	0.175	0.106

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[1] Correspondence concerning this article should be addressed to María del Rosario Demuner Flores, Cerro de Coatepec, s/n, Ciudad Universitaria, Toluca, Estado de México, México, ZIP code 50000. Email: demuner7@yahoo.com.