Table 2 Principal component analysis...

Table 2

Principal component analysis results

Factor	Measurement items	Loading	α
Motivation	Mo1- Need to digitize processes	0.74	0.78
	Mo2- Establishing effective communication in company	0.71
	Mo3- Creating new innovative processes	0.73
Selected LSS methods	Me1- Normal probability plot	0.85	0.82
	Me2- Analysis of variance	0.76
	Me3- Regression/statistical hypothesis testing	0.69
Challenges	C1- Wrong selection of projects at start of LSS initiative	0.68	0.76
	C2- Unfavorable identification of projects	0.88
	C3- Identification of suitable projects	0.61
LSS program performance	L1- Willingness to cooperate in further LSS projects	0.77	0.82
	L2- Growth of scope of LSS initiative	0.71
	L3- Acquiring/training new LSS experts	0.69
Organizational performance	O1- Internal customers’ satisfaction	0.67	0.80
	O2- Delivering value to internal customers	0.73
	O3- Delivering what internal customers want	0.63

Factor	Measurement items	Loading	α
Motivation	Mo1- Need to digitize processes	0.74	0.78
	Mo2- Establishing effective communication in company	0.71
	Mo3- Creating new innovative processes	0.73
Selected LSS methods	Me1- Normal probability plot	0.85	0.82
	Me2- Analysis of variance	0.76
	Me3- Regression/statistical hypothesis testing	0.69
Challenges	C1- Wrong selection of projects at start of LSS initiative	0.68	0.76
	C2- Unfavorable identification of projects	0.88
	C3- Identification of suitable projects	0.61
LSS program performance	L1- Willingness to cooperate in further LSS projects	0.77	0.82
	L2- Growth of scope of LSS initiative	0.71
	L3- Acquiring/training new LSS experts	0.69
Organizational performance	O1- Internal customers’ satisfaction	0.67	0.80
	O2- Delivering value to internal customers	0.73
	O3- Delivering what internal customers want	0.63

Note(s): Kaiser-Meyer-Olkin Measure of sampling adequacy = 0.79 (“Middling”; Kaiser and Rice, 1974)

Bartlett's test of Sphericity shows that the result is significant at 0.00 which shows that the matrix is not an identity matrix and the variables relate enough to run a meaningful PCA

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