Customer orientation, open innovation and enterprise performance, evidence from Ethiopian SMEs

Figures & data

Figure 1. Conceptual model. Sources: Modified from (Tuominen et al., 2022).

Table 1. displays each construct, and its associated reliability coefficients.

No	Variables	No. of items	Correlation (r)	Cronbach Alpha
I. Customer Orientation
1	Customer Info.	3	0.859	0.909
2	Customer Strategy	3	0.827	0.896
3	Responsiveness	3	0.806	0.890
IV. Open Innovation
1	Outside-in	3	0.819	0.893
2	Inside-out	2	0.948	0.971
V. Enterprise Performance
1	Marketing Perf.	3	0.849	0.918
2	Finance Perf.	2	0.885	0.931
3	Innovation Perf.	3	0.743	0.842

Source: Author’s data analysis, 2024 from SPSS output.

Table 2. Composite reliability (using AMOS).

No	Variables	Standard Loadings(r)	(e)=1-(r)	∑(ri). ∑(ri) i = 1_n	∑(ri). ∑(ri) +∑(ei) i = 1_n	Composite reliability
I. Customer Orientation
1	Customer information	0.701	0.299
2	Customer Strategy	0.72	0.28	4.84	5.64	0.858
3	Responsiveness	0.778	0.222
I. Open Innovation
1	Outside-in	0.85	0.15
2	Inside-out	0.766	0.234	9.14	10.12	0.904
I. Innovative Performance
1	Market Performance	0.828	0.172
2	Finance Performance	0.795	0.205	6.04	6.58	0.918
3	Operational Performance	0.835	0.165

Source: Authors’ calculation, 2024 from SPSS and Amos output.

Table 3. Convergent Validity based on loading factors (using SPSS) and AVE on constructs from Standardized estimate (Using AMOS).

V	Factor Loadings	AVE
Customer Information	0.847	0.5
Customer Strategy	0.794	0.518
Responsiveness	0.793	0.605
Outside-in open Innovation	0.792	0.640
Inside-out open Innovation	0.814	0.955
Market Performance	0.763	0.686
Finance Performance	0.85	0.632
Innovation Performance	0.871	0.697

Source: Authors’ calculation, 2024. Extraction Method: Principal Component Analysis Varimax rotation with Kaiser normalization.

Table 4. Discriminant validity (using AMOS).

Fornell–Larcker Criterion	VIF	Cust. Info	Cust. Strategy	Response	Outside-in IC	Inside-out IC	Cust. Sat	Fin. Perf	Oper. Perf
Cust Info	2.00	0.701
Cust Stra	2.07	0.561	0.720
Response	2.53	0.554	0.492	0.778
Outside-in	2.78	0.148	0.189	0.26	0.800
Inside-Out	2.22	0.208	0.257	0.276	0.78	0.977
Cust Sat.	3.18	0.301	0.321	0.314	0.51	0.59	0.828
Fin Perf	2.72	0.226	0.244	0.257	0.39	0.5	0.64	0.795
Oper Perf	3.30	0.29	0.308	0.182	0.37	0.52	0.68	0.7	0.835

Source: Author’s data analysis, 2024 from SPSS and Amos output. The off-diagonal values in the Fornell–Larcker Criterion matrix are the correlations between the latent constructs, and diagonal are square root values of AVEs.

Figure 2. Structural regression model by AMOS (standardized estimates(r)). Source: Self structured, 2024.

Table 5. Below Presents, the results of mediation conditions and Regression Weight estimates. Standardized Effects (rr).

Standardized Effects (rr)	St. total Effects	St. direct Effects	St. indirect Effects	indirect/Total
Customer Orientation→ Enterprise performance	0.45***	0.25***	0.20***	0.20/0.45 = 0.45
Customer Orientation→ Open Innovation	0.34**	0.34***
Open innovation→ enterprise performance	0.58***	0.58***

Source: Author’s data analysis, 2024 from Amos output.

*** is significant at the p < .001 (2-tailed), n = 321.

Table 6. Maximum likelihood estimates, relationship between variables in the proposed model.

Variables	Loadings	Variables	Unstandardized Estimate	Standardized Estimate	S. E	p
OI	<—	CuO	0.312	0.339	0.062	***
EP	<—	CuO	0.303	0.255	0.072	***
EP	<—	OI	0.75	0.58	0.08	***
Responsiveness	<—	CuO	1	0.701
Cust Strategy	<—	CuO	1.124	0.72	0.11	***
Cust Info	<—	CuO	1.276	0.778	0.123	***
Outside-in	<—	OI	1	0.8
Inside-out	<—	OI	1.609	0.977	0.083	***
Finance Perform	<—	EP	1	0.795
Market Perform	<—	EP	0.882	0.828	0.058	***
Innovation Perform	<—	IP	1.085	0.835	0.07	***

N = 321, *p < .05; **p < .01; ***p < .001, Source: Self- elaborated from Amos output, 2024.

Table 7. Correlation coefficients sample table.

Variables	Customer orientation	Open innovation	Enterprise performance
Customer orientation	1
Open Innovation	0.300	1
Enterprise performance	0.348	0.598	1

Source: Authors’ calculation, 2024 from SPSS.

Table 8. Below presents, the results of mediation conditions and regression weight estimates. Unstandardized Effects (ββ).

Unstandardized Effects (ββ)	Total Effects	Direct Effects	Indirect Effects	indirect/Total
Customer Orientation→ Enterprise performance	0.54***	0.30***	0.24***	0.24/0.54 = 0.45
Customer Orientation→ Open Innovation	0.31***	0.31***
Open Innovation → Enterprise performance	0.75***	0.75***

Source: Author’s data analysis, 2024 from Amos output.

***Significant at the p < .001 (2-tailed), n = 321.

Table 9. Output of standardized total effects.

Relations	Model 1	Model 2	Model 3
CustO→ EP (H1)	0.44***		0.26***
CustO→ OI (H2)		0.35***	0.34***
OP→ EP (H3)		0.67***	0.58***
	χ2/df = 3 CFI = 0.979 GFI = 0.976	χ2/df = 2.7, CFI = 0.963 GFI = 0.947	χ2/d.f = 2.2 CFI = 0.957 GFI = 0.975

Source: Authors’ calculation, 2024 from Amos.

Table 10. Summary of findings and decision of the hypothesis.

Hypothesis: H	Proposed hypothesis	Findings	Decisions
H1	Customer orientation has positive effect on performance of Hawassa SMEs in Ethiopia	Significant	Supported
H2	Customer orientation significantly influences open innovation of Hawassa SMEs in Ethiopia	Significant	Supported
H3	Open Innovation has significant influence on the performance of Hawassa SMEs in Ethiopia	Significant	Supported
H4	Open Innovation mediate the relationship between customer orientation and performance of Hawassa SMEs in Ethiopia	Significant	Supported

Source: Verified by Authors, 2024 from Amos.

Tuominen, S., Reijonen, H., Nagy, G., Buratti, A., & Laukkanen, T. (2022). Customer-centric strategy driving innovativeness and business growth in international markets. International Marketing Review, 40(3), 479–496. https://doi.org/10.1108/IMR-09-2020-0215

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Data availability statement

The raw data supporting the results of this article will be made available by the authors, upon reasonable request.