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INTEGRATING DISPERSED KNOWLEDGE MANAGEMENT WITH ENTERPRISE RISK AND CRISIS MANAGEMENT: EVIDENCE FROM INDONESIA’S HEAVY EQUIPMENT SECTOR

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This study examines how technology and leadership enable dispersed knowledge management (DKM) and how DKM drives operational resilient readiness (ORR)—a KM-grounded indicator of enterprise risk and crisis preparedness—thus integrating DKM with enterprise risk and crisis management in a large multi-site heavy-equipment organization in Indonesia. Cross-sectional employee survey (N = 1,625; Leaders = 485 [29.8%]; Non-Leaders = 1,140 [70.2%]). Reflective PLS-SEM with bootstrapping estimated the model; MICOM and multi-group analysis (MGA) assessed measurement invariance and role-based heterogeneity (Leader vs Non-Leader). The results demonstrate that both technology (β = 0.498, p < .001) and leadership (β = 0.336, p < .001) positively predict DKM, which strongly predicts ORR (β = 0.759, p < .001). The model explains 59.6% of DKM and 57.7% of ORR. MGA indicates no significant path differences between Leaders and Non-Leaders, suggesting role-invariant mechanisms. Cross-sectional, self-reported data; future studies should triangulate with incident/BCP records and track resilience longitudinally or across events. Strengthening the technology backbone and leadership micro-practices that mobilize knowledge across sites can raise crisis readiness consistently across roles; embed DKM routines (briefings, after-action reviews, cross-site communities) in ERM/BCP playbooks. Provides large-sample evidence from an asset-intensive, emerging-market setting showing that DKM is the mechanism through which organizational enablers (technology, leadership) drive operational resilience, with effects that generalize across role groups.
Title: INTEGRATING DISPERSED KNOWLEDGE MANAGEMENT WITH ENTERPRISE RISK AND CRISIS MANAGEMENT: EVIDENCE FROM INDONESIA’S HEAVY EQUIPMENT SECTOR
Description:
This study examines how technology and leadership enable dispersed knowledge management (DKM) and how DKM drives operational resilient readiness (ORR)—a KM-grounded indicator of enterprise risk and crisis preparedness—thus integrating DKM with enterprise risk and crisis management in a large multi-site heavy-equipment organization in Indonesia.
Cross-sectional employee survey (N = 1,625; Leaders = 485 [29.
8%]; Non-Leaders = 1,140 [70.
2%]).
Reflective PLS-SEM with bootstrapping estimated the model; MICOM and multi-group analysis (MGA) assessed measurement invariance and role-based heterogeneity (Leader vs Non-Leader).
The results demonstrate that both technology (β = 0.
498, p < .
001) and leadership (β = 0.
336, p < .
001) positively predict DKM, which strongly predicts ORR (β = 0.
759, p < .
001).
The model explains 59.
6% of DKM and 57.
7% of ORR.
MGA indicates no significant path differences between Leaders and Non-Leaders, suggesting role-invariant mechanisms.
Cross-sectional, self-reported data; future studies should triangulate with incident/BCP records and track resilience longitudinally or across events.
Strengthening the technology backbone and leadership micro-practices that mobilize knowledge across sites can raise crisis readiness consistently across roles; embed DKM routines (briefings, after-action reviews, cross-site communities) in ERM/BCP playbooks.
Provides large-sample evidence from an asset-intensive, emerging-market setting showing that DKM is the mechanism through which organizational enablers (technology, leadership) drive operational resilience, with effects that generalize across role groups.

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