Paper Number
ECIS2026-2335
Paper Type
CRP
Abstract
Data analytics projects are central to contemporary organizational value creation, yet outcomes remain uneven. Existing research often treats collaboration in such projects superficially in two ways: technical elements are under-theorized, and human actors are flattened into broad categories such as “developer” and “domain expert.” This paper advances a conceptual framework that addresses both shortcomings. First, collaboration is reframed from a dyad to a triad that includes the machine, defined by data, models, and hyperparameters whose evolving state shapes and is shaped by human work. Second, role differentiation provides micro foundations for who knows what and who does what, replacing blanket labels with coordinated expertise. Guided by Actor–Network Theory and Transactive Memory Systems Theory, the framework specifies interaction mechanisms, predicts breakdowns, and surfaces levers for intervention. The paper derives research directions that enable granular attribution of failure modes and design actionable pathways to more reliable success in data analytics projects.
Recommended Citation
Pedersen, Nikolaj Krebs and Aslan, Aycan, "A Novel Conceptual Framework For Collaboration In Data Analytics Projects" (2026). ECIS 2026 Proceedings. 11.
https://aisel.aisnet.org/ecis2026/bus_analytics/bus_analytics/11
A Novel Conceptual Framework For Collaboration In Data Analytics Projects
Data analytics projects are central to contemporary organizational value creation, yet outcomes remain uneven. Existing research often treats collaboration in such projects superficially in two ways: technical elements are under-theorized, and human actors are flattened into broad categories such as “developer” and “domain expert.” This paper advances a conceptual framework that addresses both shortcomings. First, collaboration is reframed from a dyad to a triad that includes the machine, defined by data, models, and hyperparameters whose evolving state shapes and is shaped by human work. Second, role differentiation provides micro foundations for who knows what and who does what, replacing blanket labels with coordinated expertise. Guided by Actor–Network Theory and Transactive Memory Systems Theory, the framework specifies interaction mechanisms, predicts breakdowns, and surfaces levers for intervention. The paper derives research directions that enable granular attribution of failure modes and design actionable pathways to more reliable success in data analytics projects.
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