Table 3 Factor loading and Cronbach's...

Table 3

Factor loading and Cronbach's alpha for KM challenges

KM process	KG challenge (normalized)	Factor loading	Cronbach's alpha
KG	1. Data overload and poor data–knowledge conversion	0.898	0.84 *0.92
	2. Weak organizational learning mechanisms and unskilled manpower	0.886
	3. Rapid technology change disrupting learning cycles	0.754
	4. Lack of strategic vision guiding digital KG	0.911
	5. Low data quality and contextual incompleteness	0.330
	6. Fragmented KG across digital systems	0.390
	7. Insufficient human-AI collaboration in KG	0.789
	8. Over-automation/underutilization of human tacit knowledge	0.380
	9. Limited absorptive capacity for externally generated digital knowledge	0.260
	10. Cultural resistance to digitally driven KG	0.330
	KC Challenge (normalized)		0.86 *0.93
KC	11. Difficulty in codifying tacit and experiential knowledge	0.950
	12. Lack of standardized semantic models and ontologies	0.830
	13. Weak or absent codification frameworks and KM governance	0.415
	14. Lack of time and resources for codification activities	0.810
	15. Heterogeneous data formats hindering formalization	0.240
	16. Low data quality and contextual ambiguity during codification	0.350
	17. Insufficient skills to codify digital and analytical knowledge	0.890
	18. Limited incentives or motivation to codify knowledge	0.380
	19. Rapid obsolescence of codified knowledge in I4.0 settings	0.370
	20. Poor integration of legacy and digital knowledge repositories	0.924
	KS Challenge (normalized)		0.88 *0.94
KS	21. Organizational and cultural resistance to KS	0.930
	22. Trust deficit in AI analytics-mediated knowledge	0.915
	23. Siloed structures and weak cross-departmental collaboration	0.885
	24. Human–machine KS barriers	0.400
	25. Interoperability constraints limiting knowledge exchange	0.830
	26. Lack of standardized communication protocols	0.360
	27. Data security and privacy concern	0.870
	28. Loss of contextual meaning during digital transfer	0.370
	29. Temporal misalignment between KG and use	0.240
	30. Over-reliance on digital platforms reducing social interaction	0.310
	31. Weak boundary-spanning roles and coordination mechanisms	0.320
	32. Limited cross-organizational trust in supply-chain KS	0.280
	KU Challenge (normalized)		0.85 *0.93
KU	33. Lack of tools or infrastructure (hardware and software) to effectively utilize the knowledge	0.920
	34. Low interpretability of machine-generated knowledge	0.900
	35. KM–business process misalignment	0.875
	36. Leadership support and commitment gaps	0.290
	37. Weak feedback and learning mechanism	0.866
	38. Cognitive overload in data-driven decision making	0.450
	39. Poor embedding of knowledge into operational workflows	0.374
	40. Inadequate decision-support tools for real-time utilization	0.260

KM process	KG challenge (normalized)	Factor loading	Cronbach's alpha
KG	1. Data overload and poor data–knowledge conversion	0.898	0.84*0.92
	2. Weak organizational learning mechanisms and unskilled manpower	0.886
	3. Rapid technology change disrupting learning cycles	0.754
	4. Lack of strategic vision guiding digital KG	0.911
	5. Low data quality and contextual incompleteness	0.330
	6. Fragmented KG across digital systems	0.390
	7. Insufficient human-AI collaboration in KG	0.789
	8. Over-automation/underutilization of human tacit knowledge	0.380
	9. Limited absorptive capacity for externally generated digital knowledge	0.260
	10. Cultural resistance to digitally driven KG	0.330
	KC Challenge (normalized)		0.86*0.93
KC	11. Difficulty in codifying tacit and experiential knowledge	0.950
	12. Lack of standardized semantic models and ontologies	0.830
	13. Weak or absent codification frameworks and KM governance	0.415
	14. Lack of time and resources for codification activities	0.810
	15. Heterogeneous data formats hindering formalization	0.240
	16. Low data quality and contextual ambiguity during codification	0.350
	17. Insufficient skills to codify digital and analytical knowledge	0.890
	18. Limited incentives or motivation to codify knowledge	0.380
	19. Rapid obsolescence of codified knowledge in I4.0 settings	0.370
	20. Poor integration of legacy and digital knowledge repositories	0.924
	KS Challenge (normalized)		0.88*0.94
KS	21. Organizational and cultural resistance to KS	0.930
	22. Trust deficit in AI analytics-mediated knowledge	0.915
	23. Siloed structures and weak cross-departmental collaboration	0.885
	24. Human–machine KS barriers	0.400
	25. Interoperability constraints limiting knowledge exchange	0.830
	26. Lack of standardized communication protocols	0.360
	27. Data security and privacy concern	0.870
	28. Loss of contextual meaning during digital transfer	0.370
	29. Temporal misalignment between KG and use	0.240
	30. Over-reliance on digital platforms reducing social interaction	0.310
	31. Weak boundary-spanning roles and coordination mechanisms	0.320
	32. Limited cross-organizational trust in supply-chain KS	0.280
	KU Challenge (normalized)		0.85*0.93
KU	33. Lack of tools or infrastructure (hardware and software) to effectively utilize the knowledge	0.920
	34. Low interpretability of machine-generated knowledge	0.900
	35. KM–business process misalignment	0.875
	36. Leadership support and commitment gaps	0.290
	37. Weak feedback and learning mechanism	0.866
	38. Cognitive overload in data-driven decision making	0.450
	39. Poor embedding of knowledge into operational workflows	0.374
	40. Inadequate decision-support tools for real-time utilization	0.260

Note(s): Cronbach's alpha values *With all dimensions **By removing weak dimensions

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