Digital visibility boosts recognition but breeds performative labor: monitoring tools can raise measured activity while encouraging simulated engagement that undermines genuine belonging and raises turnover, so firms should design algorithmic systems to value relational outcomes alongside efficiency.

Main Finding

The paper introduces the "Digital Visibility–Belonging Paradox": digital infrastructures that increase employee visibility simultaneously enable recognition and relational connection while creating performative pressures that can weaken authentic belonging. Visibility thus has dual, divergent effects—supporting felt recognition for some, but encouraging simulated displays of connection, availability, and engagement that erode relational foundations.

Key Points

• Conceptual framing: Digital visibility is treated as a sociomaterial HRM condition—an affordance created by technology and organizational practice that reshapes recognition, evaluation, and connection.
• Paradox: The same visibility infrastructures that make employees observable (and thus recognizable) can foster performative behaviors (e.g., constant availability, signaling engagement) that undermine genuine relational ties.
• Authentic vs simulated belonging:
  • Authentic belonging: felt recognition and genuine relational connection arising from meaningful interactions and trust.
  • Simulated belonging: outward displays or cues (online status, rapid replies, curated presence) intended to signal connection or availability without corresponding relational substance.
• Mechanisms through which visibility drives outcomes:
  • Cognitive: attention allocation, impression management, increased self-monitoring.
  • Emotional: anxiety, stress from visibility, perceived lack of privacy, reduced psychological safety.
  • Structural: changes in norms, work schedules, monitoring policies, platform metrics that reconfigure incentives.
• Boundary conditions: organizational culture, governance of visibility (policies and managerial practices), work modality (remote/hybrid vs co‑located), job type (creative vs routine), platform vs employment relationship, and embedded AI/algorithmic evaluation shape whether visibility supports belonging or intensifies performative availability.
• Contribution to HRM: reframes governance of visibility as a relational and humanistic responsibility—not merely a technical or efficiency issue.

Data & Methods

• Paper type: Conceptual/theoretical.
• Theoretical anchoring: Relational cohesion theory and sociomaterial perspectives.
• Methods: Concept development, literature synthesis, and theorization of mechanisms and boundary conditions. No primary quantitative or qualitative empirical data are presented.
• Outputs: A conceptual model (Digital Visibility–Belonging Paradox) delineating trajectories from visibility infrastructures to authentic or simulated belonging via cognitive, emotional, and structural mechanisms.

Implications for AI Economics

• Measurement and productivity:
  • Visibility-enabled metrics (typing activity, response times, screen time) can improve measurement precision, but may amplify gaming and performative labor that inflate measured productivity without commensurate output quality.
  • Empirical models of productivity should account for signaling/performative behaviors as distortions of measured effort.
• Incentives and compensation:
  • Algorithmic monitoring tied to pay or ratings can create incentives for simulated engagement (e.g., always-on presence) rather than substantive contributions, leading to inefficient allocations and potential negative welfare effects.
  • Compensation schemes should differentiate between observable signals and substantive outputs; otherwise labor supply may shift toward behaviors that maximize visibility metrics rather than value creation.
• Labor supply, turnover, and bargaining:
  • Increased visibility leading to stress and reduced authentic belonging may raise turnover and reduce job attachment—affecting firm-specific human capital and wage bargaining dynamics.
  • Platforms and firms may face hidden costs (burnout, reputational risk) that standard models omitting relational factors will miss.
• Market design and platform governance:
  • Designers of algorithmic management systems should incorporate relational outcomes as objectives (not just efficiency/throughput). This implies multi-objective optimization including employee well-being, trust, and long-term retention.
  • Transparency, contestability of metrics, and governance mechanisms (appeals, human oversight) can mitigate performative escalation.
• Externalities and regulation:
  • There are social welfare externalities from pervasive monitoring (privacy loss, mental health). Regulators might consider limits on continuous monitoring, data minimization, or requirements for human-centered metrics.
  • Antitrust and labor policy debates should incorporate how visibility infrastructures alter bargaining power and information asymmetries between employers/platforms and workers.
• Empirical research agenda for AI economics:
  • Quantify the gap between visible signals and productive output across tasks and sectors.
  • Estimate causal effects of visibility tools (e.g., activity trackers, surveillance algorithms) on turnover, mental health, measured productivity, and true output quality.
  • Study heterogeneous effects by job type, worker demographics, and work modality.
  • Design field experiments that compare metric-driven incentives vs output-focused incentives and measure long-run relational outcomes (trust, collaboration).
• Policy/design recommendations:
  • Incorporate “relational metrics” (e.g., measures of psychological safety, peer-rated authenticity) alongside performance metrics in algorithmic evaluations.
  • Implement decay or smoothing of short-term visibility signals in performance algorithms to reduce incentives for gaming.
  • Provide workers with transparency, control, and reclamation mechanisms over visibility data to preserve autonomy and reduce stress.

Summary: For AI economics, the paper warns that increased digital visibility—especially when coupled with algorithmic evaluation—creates efficiency gains but also distortions via performative labor and weakened relational capital. Models, policy, and system designs should internalize these relational externalities to avoid inefficiencies and welfare losses.