This study aims to provide a critical lens and a body of empirical observations to reexamine the assumptions and explanatory boundaries of internalization theory in the age of surveillance capitalism.
Adopting Jaakkola’s (2020) theoretical synthesis approach alongside Bouncken et al. (2021) flexible pattern matching, this study systematically integrates multidisciplinary concepts from surveillance capitalism into the core architecture of internalization theory to explore how empirical breakdowns problematize and extend the theory.
The study presents five thematic constructs extracted from the literature on surveillance capitalism to advance internalization theory: commodification, to examine the shift toward a new kind of firm-specific advantage; panopticon and synoptic surveillance, to interrogate the logic of asset-light costs of digital assets from tech-based companies; Oligopolization, to map power asymmetries in Tech-Based multinational enterprises (MNEs) that actively shape institutional gaps; and the social uncontract and the technological sublime, to question a new market hierarchy that internalizes the power asymmetry created by behavioral modification.
A primary limitation of this conceptual study stems from multidisciplinary heterogeneity across regulation, behavioral engineering and digitalization literature. While synthesizing surveillance capitalism into internalization theory advances international business research beyond platform firm behavior to broader tech MNEs, integrating disparate paradigms risks epistemological inconsistencies and loss of contextual nuance. Furthermore, adopting the Gioia methodology carries risks of hermeneutic oversimplification, as the five synthesized thematic constructs may not fully capture the complexity of surveillance practices by tech-based MNEs (Mees-Buss et al., 2022). Nevertheless, this framework provides the essential foundation for future qualitative content analysis and empirical validation.
For tech-based MNE executives and emerging competitors, reliance on behavioral surplus creates severe regulatory, political and reputational risks. Decision-makers must navigate tensions between short-term data monetization and long-term institutional legitimacy. To build durable non-location-bound firm-specific advantages, practitioners must prioritize ethical differentiation, privacy-by-design, transparent governance and user autonomy over coercive adhesion contracts. Strategically, moving away from extractive data practices mitigates consumer distrust, reduces regulatory exposure and prevents corporate lock-in that stifles internal innovation. Ultimately, embedding ethical platform governance into core business models serves as a crucial nonmarket strategy to sustain long-term competitive advantage in data-driven global markets.
The algorithmic governance of tech MNEs poses systemic challenges at the intersection of corporate value maximization, public welfare and national sovereignty. To counter digital oligopolization, behavioral commodification and platform dependency, policymakers must transition from retrospective penalties to proactive regulatory mechanisms, such as taxing behavioral prediction services. Furthermore, emerging economies and international institutions require robust digital sovereignty frameworks to mitigate digital colonization, safeguard national security and restrict acquisitions that stifle domestic innovation. Enforcing explicit user consent over data monetization protects individual autonomy, preserves the social contract and rebalances the power asymmetries generated by tech oligopolies operating across global markets.
This work establishes a critical framework that redefines the MNE role in the digital economy and under a new form of capitalism. The article offers strategic insights into the practices of tech-based MNEs while expanding the core assumptions of internalization theory.
