Table 2. Regression models displaying...

Table 2.

Regression models displaying the best statistical fit

Models	Sum of squares	df	Mean square	F	Sig.	Predictors	Unstandardized coefficients		Standardized coefficients			R	R²	Corrected R²	SE of estimation	Durbin – Watson
Models	Sum of squares	df	Mean square	F	Sig.	Predictors	B	SE B	Beta	T	Sig. T	R	R²	Corrected R²	SE of estimation	Durbin – Watson
M1: Value creation triggered by organizational learning (OL)
Regression	0.134	1	0.134	0.516	0.000	(Constant)	3.777	0.189		19.944	0.000
Rest	52.873	203	0.260			OL	0.034	0.048	0.050	0.718	0.000
In total	53.007	204										0.050	0.003	−0.002	0.51035	1.776
M2: Direct-value creating functions triggered by organizational learning (OL)
Regression	0.234	1	0.244	0.581	0.447	(Constant)	3.666	0.236		15.561	0.000
Rest	81.794	203	0.403			OL	0.045	0.060	0.053	0.762	0.447
In total	82.028	204										0.530	0.003	−0.002	0.63476	1.839
M3: Indirect-value creating functions triggered by organizational learning (OL)
Regression	0.069	1	0.069	0.159	0.691	(Constant)	3.877	0.245		15.854	0.000
Rest	88.133	203	0.434			OL	0.025	0.062	0.028	0.398	0.691
In total	88.201	204										0.028	0.001	−0.004	0.65890	1.868
M4: Crowdsourcing triggered by organizational learning (OL)
Regression	18.661	1	18.661	32.960	0.000	(Constant)	1.833	0.281		6.521	0.000
Rest	114.934	203	0.566			OL	0.407	0.071	0.374	5.741	0.000
In total	133.595	204										0.374	0.140	0.135	0.752448	1.613
M5: Value creation triggered by crowdsourcing (CS)
Regression	0.001	1	0.001	0.003	0.954	(Constant)	3.912	0.147		26.680	0.000
Rest	43.960	164	0.268			CS	−0.002	0.041	−0.005	−0.058	0.954
In total	43.960	165										0.005	0.000	−0.006	0.51773	1.814
M6: Direct value creation triggered by crowdsourcing (CS)
Regression	0.076	1	0.076	0.236	0.628	(Constant)	3.920	0.173		22.686	0.000
Rest	65.642	203	0.323			CS	0.024	0.049	0.034	0.486	0.628
In total	65.718	204										0.034	0.001	−0.004	0.568648	1.770
M7: Indirect value creation triggered by crowdsourcing (CS)
Regression	0.049	1	0.049	0.072	0.789	(Constant)	4.050			16.075	0.000
Rest	139.581	203	0.688			CS	−0.019	0.252		−0.267	0.789
In total	139.630	204						0.072	−0.019			0.019	0.000	−0.005	0.8292	1.944

Models	Sum of squares	df	Mean square	F	Sig.	Predictors	Unstandardized coefficients		Standardized coefficients			R	R²	Corrected R²	SE of estimation	Durbin – Watson
Models	Sum of squares	df	Mean square	F	Sig.	Predictors	B	SE B	Beta	T	Sig. T	R	R²	Corrected R²	SE of estimation	Durbin – Watson
M1: Value creation triggered by organizational learning (OL)
Regression	0.134	1	0.134	0.516	0.000	(Constant)	3.777	0.189		19.944	0.000
Rest	52.873	203	0.260			OL	0.034	0.048	0.050	0.718	0.000
In total	53.007	204										0.050	0.003	−0.002	0.51035	1.776
M2: Direct-value creating functions triggered by organizational learning (OL)
Regression	0.234	1	0.244	0.581	0.447	(Constant)	3.666	0.236		15.561	0.000
Rest	81.794	203	0.403			OL	0.045	0.060	0.053	0.762	0.447
In total	82.028	204										0.530	0.003	−0.002	0.63476	1.839
M3: Indirect-value creating functions triggered by organizational learning (OL)
Regression	0.069	1	0.069	0.159	0.691	(Constant)	3.877	0.245		15.854	0.000
Rest	88.133	203	0.434			OL	0.025	0.062	0.028	0.398	0.691
In total	88.201	204										0.028	0.001	−0.004	0.65890	1.868
M4: Crowdsourcing triggered by organizational learning (OL)
Regression	18.661	1	18.661	32.960	0.000	(Constant)	1.833	0.281		6.521	0.000
Rest	114.934	203	0.566			OL	0.407	0.071	0.374	5.741	0.000
In total	133.595	204										0.374	0.140	0.135	0.752448	1.613
M5: Value creation triggered by crowdsourcing (CS)
Regression	0.001	1	0.001	0.003	0.954	(Constant)	3.912	0.147		26.680	0.000
Rest	43.960	164	0.268			CS	−0.002	0.041	−0.005	−0.058	0.954
In total	43.960	165										0.005	0.000	−0.006	0.51773	1.814
M6: Direct value creation triggered by crowdsourcing (CS)
Regression	0.076	1	0.076	0.236	0.628	(Constant)	3.920	0.173		22.686	0.000
Rest	65.642	203	0.323			CS	0.024	0.049	0.034	0.486	0.628
In total	65.718	204										0.034	0.001	−0.004	0.568648	1.770
M7: Indirect value creation triggered by crowdsourcing (CS)
Regression	0.049	1	0.049	0.072	0.789	(Constant)	4.050			16.075	0.000
Rest	139.581	203	0.688			CS	−0.019	0.252		−0.267	0.789
In total	139.630	204						0.072	−0.019			0.019	0.000	−0.005	0.8292	1.944

Note:

N = 205

[ViewLarge]

Sharing Unavailable