AI in hospitality can hit profits at first before boosting them later: firms face short-run adjustment costs but gain from differentiation and productivity over time, producing a U-shaped adoption–profit curve. Widespread robot acceptance needs favorable initial conditions and incentives, and household tourism demand is nonlinearly affected by women’s economic empowerment, with gender-equal households allocating spending most efficiently.

Main Finding

The dissertation comprises three essays showing that AI and women’s economic empowerment affect hospitality and tourism through nonlinear, context-dependent channels. Key overall findings: - AI adoption has a U-shaped effect on hospitality firm profit: short-term costs and adjustment can reduce profits, while longer-term gains from differentiation and productivity raise profits. - Stakeholder attitudes toward AI service robots evolve strategically; widespread positive adoption requires favorable initial conditions and incentives (lower training costs, higher labor efficiency, effective marketing). - Women’s economic empowerment influences household tourism expenditure nonlinearly; intra-household gender equality produces the most efficient/optimal tourism spending outcomes.

Key Points

• Essay 1 (AI and firm profit)

  • Uses a differentiated Bertrand competition framework where AI increases service differentiation and alters pricing strategies.
  • Integrates demand-side and productivity mechanisms to show a U-shaped relationship between AI adoption intensity and firm profits.
  • Implies dynamic, long-run profit gains from AI after overcoming early adoption costs and strategic adjustments.

• Essay 2 (Service robots and stakeholder dynamics)

  • Models hotel owners, employees, and customers as a three-player evolutionary game.
  • Simulations (MATLAB) trace how behavior evolves toward an “ideal equilibrium” where all parties accept service robots.
  • Adoption likelihood depends on initial conditions and parameters: sensitivity to robots, training costs, perceived risks, marketing influence, and labor efficiency.
  • Identifies levers (e.g., reducing training costs, improving perceived safety, targeted marketing) that shift the system toward positive equilibrium.

• Essay 3 (Women’s empowerment and household tourism spending)

  • Builds a household decision-making model anchored in feminist theory and rational choice to capture intra-household bargaining and preferences.
  • Shows a nonlinear relationship between women’s economic empowerment and tourism expenditure, with gender equality within the household yielding optimal spending allocations.
  • Emphasizes how power dynamics and preference aggregation shape demand for tourism services.

Data & Methods

• Essay 1

  • Theoretical model: differentiated Bertrand competition capturing pricing and service differentiation effects of AI.
  • Empirical component: firm-level analysis linking AI adoption measures to profit, demand and productivity indicators (modeling and robustness checks align with theoretical predictions).
  • Identification strategy: combines structural predictions from theory with observed firm behavior to detect nonlinear (U-shaped) effects.

• Essay 2

  • Analytical framework: three-player evolutionary game theory to represent strategic interactions among owners, employees, customers.
  • Computational methods: MATLAB simulations exploring dynamic trajectories under varying parameter values and initial states.
  • Sensitivity analysis: systematic variation of training costs, perceived risks, marketing strength, and labor efficiency to map basins of attraction for equilibria.

• Essay 3

  • Theoretical model: household decision-making and bargaining model drawing on feminist and rational choice theories.
  • Analytical exploration: comparative statics and theoretical characterization of nonlinear impacts of women’s empowerment on tourism spending.
  • No primary empirical estimation reported; framework intended to guide future empirical work.

Implications for AI Economics

• Investment timing and policy design

  • The U-shaped profit profile implies early adopters may face transitional losses; subsidies, tax incentives, or targeted support (training, integration assistance) can accelerate welfare-improving adoption.
  • Policies should account for dynamic complementarities (e.g., service redesign, staff retraining) that realize long-run productivity and demand gains.

• Stakeholder-centered adoption strategies

  • Evolutionary game results highlight that adoption is not purely technological or cost-driven—customer perceptions, worker acceptance, and managerial actions matter.
  • Managers should reduce perceived risks (safety, service quality), lower training costs, and use marketing to shape customer and employee expectations.
  • Labor-market and retraining policies can align worker incentives and ease transitions to robot-augmented service models.

• Demand-side and equity considerations

  • Household bargaining and women’s empowerment shape tourism demand; AI-driven changes in jobs and incomes will interact with intra-household allocation decisions.
  • Designing AI deployment and tourism products with gender-aware demand effects can improve inclusivity and market uptake.

• Research directions

  • Empirical validation: more granular firm- and household-level panel data to estimate the nonlinear effects and causal channels suggested by the models.
  • Behavioral extensions: incorporate heterogeneous customer preferences and worker risk attitudes in dynamic adoption models.
  • Policy evaluation: experimentally test interventions (training subsidies, marketing campaigns, safety certifications) that the evolutionary model predicts will move systems toward positive equilibria.

If you want, I can (a) draft potential empirical strategies and data sources to test each essay’s theoretical predictions, or (b) produce a one-page slide-ready summary highlighting managerial and policy recommendations. Which would be most useful?