Exploring the Complexity of Algorithmic Normative Control: Internalizing Opacity During the Pursuit of Entrepreneurial Opportunities

Abstract The emerging literature on entrepreneurship‐based algorithmic normative control examines how micro‐and‐small complementors are motivated to join and engage with platforms in pursuit of entrepreneurial opportunities. However, their entrepreneurial experiences on platforms are ambivalent—some achieve entrepreneurial growth, while others increasingly become vulnerable. We suggest the key to understanding this ambivalence lies in how complementors internalize algorithmic opacity while pursuing entrepreneurial opportunities. Yet, we have a limited understanding of how micro‐and‐small complementors internalize such opacity during the pursuit of entrepreneurial opportunities within algorithm‐driven environment. To address the research gap, we employ an explorative, case‐study approach to collect data from various sources, including 79 semi‐structured interviews with the complementors and platform, informal conversations and archival documentation. Our study provides a research model that explains how micro‐and‐small complementors internalize opacity in their pursuit of entrepreneurial opportunities. Specifically, we identify three internalization mechanisms: adaptive identity work, selective (de)coupling and community‐based continuous learning, and unpack their interlinking relationships. We highlight these internalizations foster algorithmically shaped entrepreneurial growth. This study advances the literature on algorithmic normative control, entrepreneurial effectuation and complementor response strategy.