Learning, Integration, and Re-Configuration: Dynamic Capabilities as Drivers of Data-Driven Insights and Decision Quality
Purpose The study investigates the role of the dynamic capabilities of learning, integration, and re-configuration on the quality of decision-making mediated by data-driven insights. It particularly examines how these dynamic capabilities individually contribute to offering efficient and effective decision-making in the hospitality sector. Design/methodology/approach This study employs a quantitative research approach harnessed through surveys and the application of partial least square structural equation modelling using SMART PLS 4. The hospitality sector being a highly competitive industry was selected for data collection. Findings The findings reveal that learning and reconfiguration capabilities have a significant positive individual impact on generating data-driven insights hence improving the effectiveness and efficiency of decision-making. Contrarily, the integration capabilities standalone did not influence in pointing out the statistical significance of the relationship with data-driven insight or decision-making quality, an indication that integration has to be combined with other dynamic capabilities for it to be effective. Originality/Value This study adds novelty to the literature by examining the individual influences of dynamic capabilities—learning, integration, and reconfiguration on data-driven insights and decision-making quality. Unlike previous studies that have generally explored bundles of capabilities, this study isolates each to answer the more nuanced question of how organizations can, in a strategic manner, invest in certain dynamic capabilities that can improve decision-making.
Authors
- Qasim Ali Nisar (ORCID: https://orcid.org/0000-0003-2191-5165)
- Arab ul Mateen (ORCID: https://orcid.org/0000-0002-5398-490X)
- Shafiqur Rehman (ORCID: https://orcid.org/0000-0002-2209-9617)
Institutions
- Superior University (PK)
Publication Details
- Journal
- The Asian Bulletin of Big Data Management
- Published
- 2024-10-28
- DOI
- https://doi.org/10.62019/abbdm.v4i4.233
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00