DYNAMIC MANAGERIAL CAPABILITIES’ IMPACT ON INTERNATIONAL OVERALL PERFORMANCE

Gomes, Joana Marçal; Carvalho, Fernando; Alves, André; Cabral, Ângelo

doi:10.1590/S0034-759020250605

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ARTICLES • Rev. adm. empres. 65 (6) • 2025 • https://doi.org/10.1590/S0034-759020250605 linkcopy

DYNAMIC MANAGERIAL CAPABILITIES’ IMPACT ON INTERNATIONAL OVERALL PERFORMANCE

Impacto de las capacidades dinámicas de los gestores en el desempeño internacional

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

This article investigates the relationship between top managers’ dynamic managerial capabilities and the international overall performance of small and medium enterprises (SMEs). It addresses the limited understanding of the mediating effects of digital maturity and the moderating effect of market turbulence on international overall performance. Data was collected using a questionnaire survey applied to international Portuguese SMEs. The responses were analyzed using covariance-based structural equation modeling and path analysis using IBM® SPSS® Amos™. The main results show evidence of a positive impact of dynamic managerial capabilities on international overall performance and digital maturity. Additionally, we found a mediating effect of digital maturity between dynamic managerial capabilities and international overall performance, and a negative moderation of market turbulence between digital maturity and such performance, contributing to the enlightenment of top managers’ dynamic managerial capabilities as an antecedent of international overall performance, international overall performance, and digital maturity as a mediator on this relationship.

Keywords:
dynamic managerial capabilities; international overall performance; digital maturity; market turbulence; SMEs.

Company Category	Staff headcount	Turnover	Balance Sheet total
Medium-sized	< 250	≤ EUR 50 m	≤ EUR 43 m
Small	< 50	≤ EUR 10 m	≤ EUR 10 m
Micro	< 10	≤ EUR 2 m	≤ EUR 2 m

Sector
I	17	5%
II	131	37%
III	207	58%
Size
Micro	5	1%
Small	8	2%
Medium	342	96%
Position
Accountant	8	2%
Board of Directors	39	11%
CEO	180	51%
Manager	128	36%
Gender
Feminine	127	36%
Masculine	209	59%
N/A	19	5%
Education
Graduate degree (PhD)	3	1%
Basic school (grades 5-9)	7	2%
Primary school (grades 1-4)	2	1%
High school (grades 10 -12)	81	23%
Undergraduate degree	261	74%
N/A	1	0.3%

	DM	IOP	DMCs
N	355	355	355
Skewness	-.084	-.449	-1.049
Standard Error of Skewness	.129	.129	.129
Kurtosis	-.757	-.553	1.423
Standard Error of Kurtosis	.258	.258	.258

Variable	VIF
DM	1.571
DMCs	1.672

Extraction Sums of Squared Loadings
Total	% of Variance	Cumulative %
20,310	36.927	36.927

Latent Variable	Standardized Loadings
COG
SENS1	0.421
SENS2	0.500
SENS3	0.765
SENS4	0.863
SENS5	0.840
SOC
SOC1	0.891
SOC2	0.854
SOC3	0.924
SOC4	0.932
HUM
HUM1	0.677
HUM2	0.718
HUM3	0.863
HUM4	0.804
HUM5	0.900
DIN
DIN1	0.762
DIN2	0.730
DIN3	0.676
DIN4	0.796
DIN5	0.845
DIN6	0.676
DIN7	0.695
DIN8	0.704
DIN9	0.859
DIN10	0.797
TMI
TMI1	0.852
TMI2	0.868
TMI3	0.736
TMI4	0.858
TMI5	0.892
TMI6	0.791
TMI7	0.866
TMI8	0.800
TMI9	0.807
TMI10	0.825
IOP
IOP1	0.858
IOP2	0.868
IOP3	0.909

	IOP	DMCs	DM	CR	AVE	MAXR(H)
IOP	0.879			0.910	0.772	0.914
DMCs	0.329^***	0.814		0.853	0.674	0.926
DM	0.217^***	0.317^***	0.982	0.980	0.995	1.792

Indicator	Threshold	Model
χ2		990.071
df		541
χ2/df	<5	1.830
RMSEA	< 0.07	0.048
CFI	> 0.95	0.959
TLI	> 0.91	0.955
PClose	> 0.05	0.702

Hypotheses	Path Analysis			SRW	value	ρ	Results
H1	DMCs	→	IOP	0.194	2.448	0.014	Supported
H2	DMCs	→	DM	0.658	10.566	***	Supported
H3	DM	→	IOP	0.257	3.472	***	Supported
	Sector I	→	IOP	0.036	0.695	0.487
	Sector II	→	IOP	0.255	4.710	***
	Firm Age	→	IOP	-0.091	-1.740	0.082

Hypothesis	Indirect Patha	Lower	Upper	ρ	β	Result
H3a	DMCs→DM→IOP	0.200	0.451	0.001	0.174***	0.200

Hypothesis	Relationship	β	t value	ρ	Result
	MKTurb →IOP	-0.143	0.768	0.443
	DMCs→ IOP	0.042	0.242	0.809
H4	MKTurb ^*DMCs→IOP	0.179	0.672	0.502	Not supported

Hypothesis	Relationship	β	value	ρ	Result
	MKTurb →IOP	0.144	2.368	0.018
	DM→IOP	0.512	9.659	***
H5	MKTurb ^*DM→IOP	-0.336	-4.587	***	Not supported (the reverse is supported)

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Fundação Getulio Vargas, Escola de Administração de Empresas de S.Paulo Avenida Nove de Julho, 2.029, Bela Vista, CEP: 01313-902, Telefone: +55 (11) 3799-7718 - São Paulo - SP - Brazil
E-mail: rae@fgv.br

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[1] Note:

[TFN1] ²
= Chi-square; dF = Degrees of freedom; /df = Chi-squared divided by degrees of freedom; RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index; TLI = Tucker-Lewis’ index; PClose = closeness of fit. Thresholds from /df, RMSEA, CFI, and TLI (Hair et al., 2019; Hu & Bentler, 1999; Xia & Yang, 2019) and PClose from (Browne & Cudeck, 1992).