Estudo da capacidade de inovação das empresas de pesca na crise da pandemia de covid-19

Dehyouri, S.; Zand, A.; Arfaee, M.

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Brazilian Journal of Biology

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Sumário

Original Article • Braz. J. Biol. 84 • 2024 • https://doi.org/10.1590/1519-6984.263971 copiar

Estudo da capacidade de inovação das empresas de pesca na crise da pandemia de covid-19

Autoria SCIMAGO INSTITUTIONS RANKINGS

Resumo

Muitos países enfrentaram crises na pandemia de coronavírus em todo o mundo. Por meio do surto dessa doença no Irã, planos preventivos foram implementados para quebrar a cadeia de infecção da doença. Essas medidas no setor econômico afetaram a oferta, a produção e a demanda e, em certa medida, levaram ao fechamento parcial ou total de alguns negócios e serviços. Nesse contexto, parece que medidas de infraestrutura inovadoras e com “capacidade de inovação” podem ser uma estratégia para moderar e otimizar a situação nesta crise. Este estudo tenta desenhar conceitos do modelo de capacidade de inovação das empresas de pesca no contexto da crise da pandemia de COVID-19. Após a coleta dos dados, eles foram analisados por meio dos softwares SPSS e PLS SMART e realizados em duas partes: estatística descritiva e estatística analítica ou inferencial. Um novo modelo de pesquisa é apresentado para examinar o impacto de fatores como gestão da inovação, capital social, cultural, humano e organizacional, estratégias de crise, ações governamentais em situações de crise e estratégias de marketing em situações de crise, bem como reconhecimento e aplicação de vantagens de marketing em situações de crise, que oferece uma reanálise da capacidade de inovação. De acordo com os resultados, entre as variáveis que desempenharam um papel significativo no nível de capacidade de inovação em condições epidêmicas, a gestão da inovação (p > 0,000; β = 0,593) e o reconhecimento e aplicação de vantagens de marketing (p > 0,049; β = 0,209) foram as variáveis independentes mais significativas que afetaram a capacidade de inovação, o que explicou suas variações.

Palavras-chave:
pandemia de covid-19; empresas de pesca; capacidades de inovação; modelo PLS inteligente

No.	Items	Mean	Standard deviation	Coefficient of variation	Priority
1	Ideas of employees for data-based novation find the sound people to jointly implement these ideas.	2.38	1.469	0.617227	25
2	People who are responsible for data-based novation can engage firms, researchists, and citizenry to develop new ideas.	2.34	1.062	0.453846	11
3	They have a robust structural network of firms, researchists, and citizenry connected to data-based novation.	2.06	1.096	0.532039	21
4	People who are in charge of data-based novation can stimulate the development of new ideas among their colleagues.	2.4	1.107	0.46125	14
5	They have a robust network of employees with an interest in data-based novation.	2.56	1.236	0.482813	17
6	A company, researches, or citizenry with good ideas for data-based novation finds the right people to further develop these ideas.	2.54	1.147	0.451575	10
7	They are successful in setting up experiments.	2.52	1.147	0.455159	12
8	They have social support (from the citizenry, NGOs, firms, etc.) to test on data-based novation.	2.72	1.325	0.487132	16
9	Political institutions support data-based novation experiments.	1.94	1.114	0.574227	23
10	Office managers support data-driven innovation testing.	2.24	1.222	0.545536	22
11	They provide sufficient funding for testing.	2.06	0.978	0.474757	15
12	If necessary, they engage further governments, firms, and social organizations in trials on data-based novation.	1.94	1.058	0.545361	19
13	They are successful in scaling experiments.	1.88	0.866	0.460638	13
14	They adopt data-based novation that has been proven to be successful on a small scale in the systematic process.	2.6	0.948	0.364615	3
15	They evaluate experiments well with data-driven innovation.	2.34	1.206	0.515385	20
16	They succeed in piloting cooperation with governments, firms, and social organizations into structural forms of cooperation.	2.44	1.163	0.476639	16
17	They succeed in knowing ventures, detriments, and tensions around data-based novation.	2.74	1.209	0.441241	9
18	They initiate a general discussion of the dangers, disadvantages, and stresses of data-based novation and how to deal with them.	2.82	1.155	0.409574	5
19	If there are contradictions, he/she is skilled in mediating conflicts over data-based novation.	3.18	1.119	0.351887	2
20	The ethical aspects of data-based novation are well discussed.	3.52	1.129	0.320739	1
21	They make financial instruments structurally available for data-based novation.	3.02	1.317	0.436093	7
22	There is a good interchange of information about data-based novation among all actors.	2.9	1.096	0.377931	4
23	They have a culture that fosters data-based novation.	2.85	1.229	0.431228	6
24	They create the proper conditions for data-based novation (teaching, information exchange, tools, etc.).	2.63	1.299	0.493916	18
25	They have an explicit sight for data-based novation.	2.94	1.295	0.440476	8

No.	First variable	Second variable	Correlation coefficient	Significant level
1	Innovation capacity	Innovation management	0.402**	0.004
2		Capitals	0.519**	0.000
3		Government measures	-0.286*	0.044
4		Crisis strategies	0.285*	0.045
5		Recognition and use of the advantages of marketing	0.335*	0.023

Steps	Independent variable	R	R²	R²Adj	F	Sig.
First step	Innovation management	0.479	0.230	0.212	13.126	0.001
Second step	Knowledge of marketing advantages	0.593	0.352	0.322	11.669	0.000

Steps	Independent variable	B	SEB	Beta	T	Sig.
First step	Intercept	10.913	14.691	-	0.743	0.462
First step	Innovation Management	0.548	0.151	0.479	3.623	0.000
Second step	Intercept	-7.741	15.127	-	-0.512	0.611
	Innovation Management	0.560	0.140	0.490	3.989	0.000
	Knowledge and awareness of marketing advantages	1.248	0.439	0.350	2.845	0.007

Relationships	Path coefficient	t-value
CLC estimate	PLS estimate	CLC estimate	PLS estimate	P-value
Innovation management Innovation capacity	0.593	0.601	5.239**	0.000
Government measures Innovation capacity	-0.047	-0.049	0.370	0.712
Crisis strategies Innovation Capacity	0.105	0.110	0.903	0.367
Use of marketing advantages Innovation capacity	0.209	0.211	2.125*	0.049
Company innovation Innovation capacity	-0.19	-0.199	0.991	0.322

Items	Sign in the model	Standard factor load	t	T factor load	Result
Inappropriate organizational culture	Q9	73.0	11.14	94.10	Confirmed
Unawareness and non-support of managers	Q10	81.0	49.16	05.10	Confirmed
No relations between the manager and the organization employees	Q11	81.0	71.16	94.9	Confirmed
Hierarchical structure	Q12	84.0	55.17	42.9	Confirmed
Lack of resource allocation to employee ability measurement	Q13	77.0	38.15	55.10	Confirmed
Inconsistence of staff needs with existing technology systems	Q14	71.0	82.13	04.11	Confirmed
No identification of experts on any field in the organization	Q15	67.0	60.12	32.11	Confirmed

Indicator	Acceptable value	Reported value
$χ^{2}$	05.0≤	02.25
p-value	05.0≤	057.0
$d f$	ـ	15
$\frac{χ^{2}}{d f}$	3≥	668.1
RMSEA	05.0≥	012.0

Items	Sign in the model	Standard factor load	t	T factor load	Result
Development of systemic learning	Q16	38.0	29.6	78.11	Confirmed
Development of innovative methods in the organization	Q17	50.0	04.8	0.11	Confirmed
Creation of a creative and dynamic atmosphere	Q18	57.0	08.9	11.10	Confirmed
Development of teamwork and engagement based on creative ideas	Q19	94.0	67.13	64.10	Confirmed

Indicator	Acceptable value	Reported value
$χ^{2}$	05.0≤	37.62
p-value	05.0≤	089.0
$d f$	ـ	22
$\frac{χ^{2}}{d f}$	3≥	835.2
RMSEA	05.0≥	036.0

GoF indicator	RMSEA	GFI	AGFI	NFI	NNFI	IFI
Acceptable values	<0.1	>0.9	>0.9	>0.9	>0.9	0 - 1
Calculated values	0.035	0.94	0.91	0.98	0.93	0.94

Correlation statistic	Correlation coefficient	Sig.	N	Relationship	Type of relationship
Pearson	0.624	^ Significant at 0.01 level. 0.000<	158	Yes	Direct

Variable	Non-standardized beta coefficient	SE	Standardized beta coefficient	t	Normal Sig.
Knowledge management application and innovation factors	0.345	0.106	0.262	3.250	0.001

Correlation statistic	Correlation coefficient	Sig.	N	Relationship	Type of relationship
Pearson	0.627	^ Significant at 0.01 level. 0.000<	158	Yes	Direct

Variable	Non-standardized beta coefficient	SE	Standardized beta coefficient	t	Normal Sig.
Knowledge management application and deterrents	0.337	0.098	0.275	3.456	0.001

Correlation statistic	Correlation coefficient	Sig.	N	Relationship	Type of relationship
Pearson	0.578	**0.000<	158	Yes	Direct

Variable	Non-standardized beta coefficient	SE	Standardized beta coefficient	t	Normal Sig.
Knowledge management application and facilitators	0.342	0.090	0.047	3.780	0.001

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[1] *e-mail: dehyouri.s@gmail.com