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These are the sources cited in the book. The rule is strict: only what could be verified appears here, and every concrete figure is attributed to its source. Where a fact is an estimate or a design decision, the corresponding chapter says so.

Sociotechnical theory and participatory design

  • Baxter, G. & Sommerville, I. (2011). “Socio-technical systems: From design methods to systems engineering.” Interacting with Computers. Iterative, user-involving sociotechnical design.
  • Mumford, E. ETHICS method (participatory sociotechnical design). Design must involve those who are going to use the system.
  • Trist, E. & Bamforth, K. (1951). “Some Social and Psychological Consequences of the Longwall Method of Coal-Getting.” Human Relations. Origin of sociotechnical theory; joint optimization of the social and technical subsystems.

Sociology of technology

  • Callon, M. (1986). “Some Elements of a Sociology of Translation: Domestication of the Scallops and the Fishermen of St Brieuc Bay.” In J. Law (ed.), Power, Action and Belief. The four moments of translation: problematization, interessement, enrolment and mobilization.
  • Latour, B. (2005). Reassembling the Social. Oxford University Press. Actor-network theory; the concept of translation (also developed by Callon, M., 1986).

Human-centered and responsible artificial intelligence

  • Dignum, V. (2019). Responsible Artificial Intelligence. Springer. Systematization of responsible AI.
  • O’Neil, C. (2016). Weapons of Math Destruction. The harm of opaque models that hide biases at scale.
  • Shneiderman, B. (2020). “Human-Centered AI” (and the Oxford University Press book, 2022). Combining high automation with high human control.

Data quality

  • Redman, T. (2008). Data Driven. Harvard Business Press. Data quality as a problem of management and processes.
  • Redman, T. (2017). “Seizing Opportunity in Data Quality.” MIT Sloan Management Review. Poor data quality costs most organizations between 15% and 25% of their revenue.
  • Wang, R. & Strong, D. (1996). “Beyond Accuracy: What Data Quality Means to Data Consumers.” Journal of Management Information Systems. Data quality as fitness for use; contextual dimensions.

Maturity, absorption and diffusion

  • Cohen, W. M. & Levinthal, D. A. (1990). “Absorptive Capacity: A New Perspective on Learning and Innovation.” Administrative Science Quarterly 35(1), 128–152. The capacity to absorb external knowledge depends on prior knowledge and is cumulative.
  • Jöhnk, J., Weißert, M. & Wyrtki, K. (2021). “Ready or Not, AI Comes.” Business & Information Systems Engineering 63(1), 5–20. DOI 10.1007/s12599-020-00676-7. 18 factors of AI readiness across 5 categories.
  • Moore, G. (1991). Crossing the Chasm. HarperBusiness. The “chasm” between early adopters and the early majority.
  • Rogers, E. M. (1962; 5th ed. 2003). Diffusion of Innovations. Free Press. The S-shaped adoption curve, five adopter categories and five perceived attributes.

Empirical evidence on the impact of AI

  • Otis, N., Clarke, R., Delecourt, S., Holtz, D. & Koning, R. (2024). The Uneven Impact of Generative AI on Entrepreneurial Performance. Working paper, Harvard Business School / UC Berkeley Haas (SSRN 4671369). Experiment with 640 entrepreneurs in Kenya: high performers around +15%, low performers around −8%.
  • UNCTAD (2025). Technology and Innovation Report 2025: Inclusive Artificial Intelligence for Development. Axes of infrastructure, data and skills; the “compute gap” for small businesses and low-income countries.

AI adoption in Latin America

  • CEPAL (2024). AI penetration in Latin America below 4% (against more than 20% in Europe); in Brazil, 41% of large firms use AI versus 11% of small businesses.
  • CEPAL. “Factores determinantes de la adopción de la IA en empresas: caso Brasil” (publication 81911). Source of the 41% large vs. 11% small businesses figure.
  • CEPAL (2024). Overcoming Development Traps in Latin America and the Caribbean in the Digital Age. Corpus on digital government and State modernization.
  • Jung, J. & Katz, R. (2024/2025). “Impacto económico de la inteligencia artificial en América Latina.” CEPAL (publication 81909).
  • nodo nadIA (CEPE-UTDT + Fundar) (2025). National survey of AI adoption in Argentine small businesses (n=402). 41.6% use at least one AI; mostly basic tools; very low governance and internal capability indicators.

AI governance

  • NIST (2023). AI Risk Management Framework (AI RMF 1.0). Four functions: Govern, Map, Measure, Manage.
  • Regulation (EU) 2024/1689 — EU AI Act. In force since 1 August 2024; staggered application. Binding, with sanctions.

Organizational change

  • Cummings, S., Bridgman, T. & Brown, K. G. (2016). “Unfreezing change as three steps: Rethinking Kurt Lewin’s legacy for change management.” Human Relations 69(1), 33–60. Documents that the unfreeze–change–refreeze model is a later canonization: Lewin never presented it as a closed model.
  • Díaz Barrios, J. (2005). Cambio organizacional: una aproximación por valores. Values of integral change: communication, participation, learning.
  • Kotter, J. P. (1996). Leading Change. Harvard Business School Press. The eight steps of organizational change.
  • Lewin, K. (1947). “Frontiers in Group Dynamics.” Human Relations 1(1). Basis of the three-moment model (unfreeze–change–refreeze), canonized afterwards.

Tacit knowledge and sensemaking

  • Nonaka, I. & Takeuchi, H. (1995). The Knowledge-Creating Company. Oxford University Press. Argues that a good part of tacit knowledge can indeed be made explicit and codified; cited as a deliberate counterpoint to Polanyi.
  • Polanyi, M. (1966). The Tacit Dimension. University of Chicago Press. “We know more than we can tell”: tacit knowledge is not entirely codifiable.
  • Weick, K. E. (1995). Sensemaking in Organizations. Sage. Sensemaking is retrospective, social and prioritizes plausibility over accuracy.

Back to the Preface or to The bridge thesis.