Driving new technologies in hospitals: association of organizational and personal factors with the readiness of neonatal intensive care unit staff toward webcam implementation

Table 2 Multiple linear regression with 122 physicians and 112 nurses

Physicians
Characteristic	Beta	SE^a	95% CI^b	P-value^c	GVIF^d	Adjusted GVIF^e
(Intercept)	2.84	0.872	1.11–4.57	0.009
Technology acceptance	0.38	0.140	0.10–0.65	0.049	1.1	1.0
Innovation climate	−0.23	0.186	−0.60–0.14	> 0.9	1.0	1.0
Age					1.1	1.0
≤44 years	–	–	–
45–54 years	−0.46	0.237	−0.93–0.01	0.3
≥55 years	−0.34	0.249	−0.83–0.16	> 0.9
Gender					1.0	1.0
Male	–	–	–
Female	−0.27	0.208	−0.68–0.15	> 0.9
R² = 0.118; Adjusted R² = 0.080; Sigma = 1.01; Statistic = 3.11; P-value = 0.011; df = 5; Log-likelihood = -172; AIC = 357; BIC = 377; Deviance = 119; Residual df = 116; n = 122, Power (1 – β error probability) = 0.91
Nursing staff
Characteristic	Beta	SE^a	95% CI^a	P-value^b	GVIF^a	Adjusted GVIF^ab
(Intercept)	2.87	0.762	1.35–4.38	0.002
Technology acceptance	0.03	0.140	−0.25–0.31	> 0.9	1.3	1.2
Innovation climate	0.10	0.183	−0.26–0.46	> 0.9	1.4	1.2
Age					1.2	1.0
≤ 44 years	–	–	–
45–54 years	−0.46	0.216	−0.89–-0.03	0.2
≥ 55 years	0.11	0.255	−0.40–0.61	> 0.9
Gender					1.0	1.0
Male	–	–	–
Female	−0.13	0.375	−0.87–0.62	> 0.9
R² = 0.073; Adjusted R² = 0.029; Sigma = 0.939; Statistic = 1.67; P-value = 0.15; df = 5; Log-likelihood = -149; AIC = 312; BIC = 331; Deviance = 93.4; Residual df = 106; n = 112, Power (1 – β error probability) = 0.63

^a SE = standard error, ^bCI = confidence interval, ^cBonferroni correction for multiple testing, ^d GVIF = generalized variance inflation factor, ^eGVIF^[1/(2*df)]

ISSN: 1472-6963