Paper Type
Complete
Abstract
This study defines and measures data orchestration as a multidimensional construct within data ecosystems and examines its validity and reliability for empirical research. Grounded in Resource-Orchestration Theory and a management-oriented perspective, data orchestration is structured across four dimensions—Data Strategy, Data Governance, Synergy, and Technological Infrastructure—which represent how organizations coordinate data-related activities across data ecosystems. Construct development combined a literature review with expert evaluations to refine item clarity and relevance. Empirical validation used data from 306 specialists engaged in their organizations' data ecosystems, applying exploratory and confirmatory factor analyses. The results confirm a robust construct with strong reliability (Cronbach’s alpha ≥ 0.90, composite reliability ≥ 0.90) and validity (average variance extracted ≥ 0.71, Heterotrait-Monotrait ratio ≤ 0.81). These findings provide a validated framework for measuring data orchestration, supporting future research on its influence in areas such as data value generation, data-driven innovation, and decision-making within data ecosystems.
Paper Number
1398
Recommended Citation
Salerno, Felipe Fonseca and Maçada, Antônio Carlos Gastaud, "Developing a Data Orchestration Scale: a Validity and Reliability Study" (2025). AMCIS 2025 Proceedings. 3.
https://aisel.aisnet.org/amcis2025/data_eco/data_eco/3
Developing a Data Orchestration Scale: a Validity and Reliability Study
This study defines and measures data orchestration as a multidimensional construct within data ecosystems and examines its validity and reliability for empirical research. Grounded in Resource-Orchestration Theory and a management-oriented perspective, data orchestration is structured across four dimensions—Data Strategy, Data Governance, Synergy, and Technological Infrastructure—which represent how organizations coordinate data-related activities across data ecosystems. Construct development combined a literature review with expert evaluations to refine item clarity and relevance. Empirical validation used data from 306 specialists engaged in their organizations' data ecosystems, applying exploratory and confirmatory factor analyses. The results confirm a robust construct with strong reliability (Cronbach’s alpha ≥ 0.90, composite reliability ≥ 0.90) and validity (average variance extracted ≥ 0.71, Heterotrait-Monotrait ratio ≤ 0.81). These findings provide a validated framework for measuring data orchestration, supporting future research on its influence in areas such as data value generation, data-driven innovation, and decision-making within data ecosystems.
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