Paper Number
ECIS2026-1875
Paper Type
SP
Abstract
Generative AI (gAI) systems are no longer passive tools but increasingly act as semi-autonomous agents that co-create and influence outcomes. Existing IT identity theory (Carter et al., 2020a), however, is grounded in assumptions of controllable tools and enactive mastery, limiting its ability to explain emerging forms of human-AI collaboration. Building on prior work in IT identity, sociomateriality, and research on distributed and perceived algorithmic agency, we introduce Socio-Algorithmic Identity (SAI), defined as an individual’s self-concept reflecting their internalized orientation toward key dynamics of human-AI interaction, including Perceived Algorithmic Agency, Identity Fusion or Separation, and Autonomy Preference. We adopt a two-phase construct development methodology modeled on Carter et al. (2020b). Phase 1 employs qualitative inquiry to explore these dimensions and refine a typology of SAI archetypes, while Phase 2 translates these insights into a validated measurement instrument. Together, this approach positions SAI as a relational identity lens for explaining how individuals make sense of collaboration with agentic and socially responsive AI systems.
Recommended Citation
Taer, Jenna, "Socio-Algorithmic Identity In The Age Of Generative AI" (2026). ECIS 2026 Proceedings. 15.
https://aisel.aisnet.org/ecis2026/cog_hbis/cog_hbis/15
Socio-Algorithmic Identity In The Age Of Generative AI
Generative AI (gAI) systems are no longer passive tools but increasingly act as semi-autonomous agents that co-create and influence outcomes. Existing IT identity theory (Carter et al., 2020a), however, is grounded in assumptions of controllable tools and enactive mastery, limiting its ability to explain emerging forms of human-AI collaboration. Building on prior work in IT identity, sociomateriality, and research on distributed and perceived algorithmic agency, we introduce Socio-Algorithmic Identity (SAI), defined as an individual’s self-concept reflecting their internalized orientation toward key dynamics of human-AI interaction, including Perceived Algorithmic Agency, Identity Fusion or Separation, and Autonomy Preference. We adopt a two-phase construct development methodology modeled on Carter et al. (2020b). Phase 1 employs qualitative inquiry to explore these dimensions and refine a typology of SAI archetypes, while Phase 2 translates these insights into a validated measurement instrument. Together, this approach positions SAI as a relational identity lens for explaining how individuals make sense of collaboration with agentic and socially responsive AI systems.
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