Successful firms can manufacture their own strategic blindness: internal feedback loops, sunk commitments and algorithmic personalization can make vigorous digital transformation accelerate movement toward obsolete objectives rather than away from them.

Main Finding

Capability rigidity is a distinct, observable form of incumbent adaptive failure: high-performing firms can actively sense, seize, and transform yet still converge repeatedly on obsolete strategic objectives when environmental change redefines what matters. Three self-reinforcing mechanisms produce this outcome—attention narrowing from sustained success, feedback endogeneity (the firm's configuration generates the feedback used to validate its representation), and accumulated commitments that raise the cost of revising the representation. Digital instrumentation amplifies these mechanisms by increasing endogenous, high‑precision evidence and by letting algorithmic systems determine what the environment is permitted to show (closing the loop). Under capability rigidity, strengthening reconfiguration (seizing/transforming) without changing the representational standard (sensing) can accelerate movement toward the wrong objective.

Key Points

• Definition: Capability rigidity is defined behaviorally — the stability of a firm's strategic objective across successive change episodes in a changing environment — not merely as lack of change or path dependence.
• Three mechanisms:
• Attention narrowing: sustained success channels managerial attention toward cues and explanations that past performance validated.
• Feedback endogeneity: performance feedback is produced within the firm's current configuration, making tests partly circular and self‑confirming.
• Commitments: resources, coalitions, identity claims form around the validated representation and make revision costly.
• Digital intensification:
  • Digital systems produce large volumes of precise but often endogenous signals (e.g., recommender outputs, conversion funnels, churn models).
  • Personalisation and experimentation platforms narrow what customers see and thus what is observable; exploration/exploitation settings are typically technical choices, not strategic ones.
  • This creates a confidence-to-correspondence gap: higher measurement precision of the current operating envelope can increase confidence without improving correspondence to the wider environment.
• Theoretical innovations:
  • Separates capability rigidity from structural inertia, core rigidity, path dependence, competency traps; proposes discriminating empirical observations between these accounts.
  • Extends endogenous sampling literature to the evidentiary standard itself (firms undersample regions the current representation does not touch, including regions where no option was ever posed).
  • Shows that strengthening seizing/transforming alone can worsen adaptation when sensing is biased by the representation.
• Empirical structure: the paper develops 14 numbered propositions comprising 18 testable statements, specifies release conditions (what external feedback can break rigidity) and ex ante criteria for identifying exogenous feedback channels.

Data & Methods

• Nature of the paper: conceptual/theoretical development grounded in literature review across organization theory, dynamic capabilities, managerial cognition, and digital transformation. No original empirical dataset.
• Methods used:
  • Integrative literature synthesis to identify gaps and align theoretical pieces.
  • Formalization of a conceptual model linking performance → attention → endogenous feedback → commitments → persistent representation (capability rigidity).
  • Derivation of numbered propositions and explicit boundary conditions.
  • Identification of observable implications and criteria to distinguish capability rigidity from other accounts.
• Empirical agenda and suggested methods for testing:
  • Longitudinal case studies and process tracing (e.g., firms like Nokia, Kodak, Polaroid used illustratively).
  • Field experiments inside firms that manipulate personalization intensity or exploration rate in algorithmic systems.
  • Natural experiments exploiting exogenous shocks or access to external feedback channels (e.g., regulatory data disclosures, entry of third‑party measurement platforms).
  • Lab and online experiments on managerial updating under endogenous vs. exogenous feedback.
  • Quantitative analyses using firm digital traces: A/B testing logs, recommender outputs, conversion funnels, customer cohorts, R&D and M&A activity, strategic statements (to measure objective stability).
  • Simulation/agent‑based models to explore dynamic interaction of learning, endogenous signals, and commitment formation.

Implications for AI Economics

• Platform & market dynamics:
  • Algorithmic personalization and experimentation tighten feedback loops that can entrench incumbents’ representational blind spots; endogenous data can reduce informational exposure to outside opportunities, affecting entry, competition, and market reallocation.
  • Measured improvements (conversion, engagement) can mask declining correspondence to broader market preferences; valuation models that treat digital performance metrics as direct proxies for market fitness will be biased.
• Policy & regulation:
  • Policies that require increased exogenous visibility (e.g., data portability, third‑party access to recommendation outputs, mandated exploration quotas) could serve as release mechanisms to correct capability rigidity.
  • Antitrust and competition assessments should consider not just scale and market share but the degree to which firm instrumentation produces endogenous evidence and constrains observable alternatives.
• Firm strategy & AI investment:
  • Investing in ML-driven sensing (more instrumentation, higher measurement precision) without simultaneous attention to the representational categories managers use risks accelerating migration toward obsolete objectives. Thus, AI investments should be paired with interventions that alter representational standards (diverse external benchmarks, contrarian experiments, independent feedback channels).
  • Technical knobs that look purely implementational (personalisation intensity, exploration rate in bandit/A/B systems) are strategic levers that should be intentionally managed to mitigate capability rigidity.
• Measurement & empirical practice for AI economists:
  • Useful empirical proxies: personalization intensity (% of decisions influenced by algorithms), exploration rate in experimentation platforms, fraction of user exposures shaped by firm-side ranking/recommendation, concentration of training data originating from incumbent-controlled segments.
  • Proposed tests to discriminate accounts:
  • Sampling-account prediction: supplying outcome data on alternatives should correct the firm’s error.
  • Representation-account (capability rigidity) prediction: supplied outcomes will fail to correct belief when the evidentiary standard (categories/representation) itself is at issue.
  • Designable interventions: randomized increases in exploration or externalized signals (e.g., externally generated recommendation lists) to measure whether and how representations update.
• Broader economic consequences:
  • Endogenous feedback loops can produce systematic mismatches between measured firm performance and true social value creation, with possible welfare losses if incumbents misallocate resources toward declining value propositions.
  • For macro models of technological diffusion and productivity, capability rigidity implies incumbent activity (investment, reconfiguration) can be uninformative or misleading about future industry direction—policy and forecasting models should incorporate representational inertia and endogenous data generation.

Summary recommendation for AI economists: treat algorithmic feedback channels and their technical settings as strategic variables in models of firm adaptation. Empirical work should measure the endogeneity of signals and test whether external, exogenous information sources (or enforced exploration) produce representational correction, not merely incremental performance changes.