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mature updated: 2026-07-04

Bibliography

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.

SME AI adoption (processual STST)

  • Amanollahnejad, A., Fosso-Wamba, S., Shabbir, M. S. & Pakseresht, A. (2026). “Aligning Socio-Technical Systems: Rethinking AI Adoption and Digital Transformation in SMEs.” Information Systems Management 43(2), 103–117. DOI 10.1080/10580530.2025.2612175. Qualitative study (27 UK SMEs): recursive/episodic adoption, technical bricolage, episodic leadership, productive misalignment. Geographic caveat: illustrative mechanisms, not automatic prediction for Latin America.

Organizational culture and digital transformation (emerging economies)

  • Shokrollahi Yancheshmeh, M. (2026). “AI-Driven Digital Transformation and Organizational Culture Alignment in Emerging Economies: A Review of the Literature and Future Research Directions.” Zenodo. DOI 10.5281/zenodo.18094050. SLR 2015–2023: triple constraint (infrastructure, talent, culture) in emerging economies; strategy–culture– technology alignment. Caveat: Zenodo preprint; do not cite aggregated r coefficients.
  • Gupta, S., Bhardwaj, A. & Singh, R. (2020). “Digital transformation and organizational culture.” Journal of Business Research 121, 1–15.
  • Vial, G. (2019). “Understanding digital transformation: A review and a research agenda.” The Journal of Strategic Information Systems 28(2), 118–144.
  • Henderson, J. C. & Venkatraman, N. (1993). “Strategic Alignment: Leveraging Information Technology for Transforming Organizations.” IBM Systems Journal 32(1), 4–16.
  • Schein, E. H. (2010). Organizational Culture and Leadership (4th ed.). Jossey-Bass.
  • Westerman, G., Bonnet, D. & McAfee, A. (2011). “The Digital Advantage.” MIT Sloan Management Review 52(2).

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.

Agentic sociotechnics and human-AI teams

  • Donta, P. K. et al. (2025). “Socio-technical aspects of Agentic AI.” arXiv:2601.06064. DOI 10.48550/arXiv.2601.06064. First framework (MAD-BAD-SAD) to treat agentic systems as sociotechnical systems whose behavior is co-produced by algorithms, data, organizational practices, regulation and norms.
  • Xu, W. & Gao, Z. (2024). “An intelligent sociotechnical systems (iSTS) framework: Enabling a hierarchical human-centered AI (hHCAI) approach.” arXiv:2401.03223. Extends Tavistock’s joint optimization across four levels: individual, organizational, ecosystem and societal.
  • Masters, C. et al. (2025). “Orchestrating Human-AI Teams: The Manager Agent as a Unifying Research Challenge.” arXiv:2510.02557. Manager agents (GPT-5, 20 workflows) still fail to jointly optimize goals, constraints and runtime.
  • Karpathy, A. (2025). “2025 LLM Year in Review.” Software 3.0 and context engineering: the context assembled for an agent matters more than the model.

Psychology of adoption

  • Beane, M. (2024). The Skill Code: How to Save Human Ability in an Age of Intelligent Machines. Harper Business. The three C’s —challenge, complexity, connection— of the expert-novice bond; AI that absorbs entry-level tasks erodes skill formation.
  • Gillespie, N., Lockey, S., Ward, T., Macdade, A. & Hassed, G. (2025). Trust, Attitudes and Use of Artificial Intelligence: A Global Study 2025. University of Melbourne & KPMG. n>48,000 across 47 countries: 66% use AI, 46% are willing to trust it; almost half use it against their organization’s policies.
  • Dell’Acqua, F. et al. (2025). “The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise.” NBER Working Paper 33641. DOI 10.3386/w33641. n=776 at Procter & Gamble: individuals with AI match teams without AI; AI breaks functional silos.
  • Högemann, M., Hein, L., Britsche, J.-O. & Thomas, O. (2025). “Technostress and generative AI in the workplace: a qualitative analysis of young professionals.” Frontiers in Artificial Intelligence 8, 1728881. DOI 10.3389/frai.2025.1728881. Gray-zone technostress: regulatory ambiguity, data protection, perceived dependence.

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.
  • Sadiq, R. B., Safie, N., Abd Rahman, A. H. & Goudarzi, S. (2021). “Artificial intelligence maturity model: a systematic literature review.” PeerJ Computer Science 7, e661. DOI 10.7717/peerj-cs.661. AI maturity literature is biased toward the technical and fewer than half the models report empirical validation.
  • Heger, A. K., Passi, S., Dhanorkar, S., Kahn, Z., Wang, R. & Vorvoreanu, M. (2025). “Towards a Responsible AI Organizational Maturity Model.” Proc. ACM Human-Computer Interaction 9(7), CSCW. DOI 10.1145/3757514. 24 dimensions derived from more than ninety responsible-AI specialists.

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.
  • Challapally, A., Pease, C., Raskar, R. & Chari, P. (2025). The GenAI Divide: State of AI in Business 2025. MIT Project NANDA (v0.1, preliminary). 95% of organizations get zero return from GenAI; the cause is an organizational learning gap, not the models. Preliminary report, not peer-reviewed.
  • Brynjolfsson, E., Li, D. & Raymond, L. (2025). “Generative AI at Work.” The Quarterly Journal of Economics 140(2), 889–942. DOI 10.1093/qje/qjae044. +15% cases resolved per hour in support; the least experienced workers improve most.
  • McKinsey (2025). The State of AI in 2025: Agents, innovation, and transformation (November 2025). Only ≈6% of respondents attribute ≥5% of their EBIT to AI use; 39% report any EBIT impact.
  • IBM (2025). Cost of a Data Breach Report 2025. Organizations with high levels of shadow AI see on average USD 670,000 more in breach cost; 1 in 5 suffered a shadow-AI breach. Industry report.
  • Stanford HAI (2026). Artificial Intelligence Index Report 2026. Annual state of the art; covers agentic AI within technical performance and economy.
  • Anthropic (2026). Anthropic Economic Index (January 2026 report, November 2025 data). Augmentation 52% vs. automation 45%; time series, cite with date.

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.
  • OECD (2025). Governing with Artificial Intelligence: The State of Play and Way Forward in Core Government Functions. Around 200 government AI use cases; a three-pillar framework —enablers, guardrails, engagement— across eleven core functions.
  • CEPAL & CENIA (2025). Latin American Artificial Intelligence Index (ILIA) 2025 (3rd ed.). 19 countries; classification into pioneers, adopters and explorers by ecosystem maturity.

Worker voice and codetermination

  • Doellgast, V. & Geiser, S. (2025). Boosting U.S. Worker Power and Voice in the AI-Enabled Workplace. Washington Center for Equitable Growth. Works councils negotiating AI use in the German ICT industry after the 2021 reform of the works constitution act.
  • NBER (2026). “Worker Voice and Firm Governance.” NBER Reporter, 2026, Number 1. State of the art on worker voice, works councils and codetermination.
  • Mollick, E. (2024). Co-Intelligence: Living and Working with AI. Portfolio / Penguin. Practical pro-adoption writing.
  • Narayanan, A. & Kapoor, S. (2024). AI Snake Oil. Princeton University Press. Rigorous anti-hype: telling what AI can do from what it cannot.
  • Narayanan, A. & Kapoor, S. (2025). “AI as Normal Technology.” Knight First Amendment Institute, Columbia. AI diffuses as normal technology, gradual and mediated by institutions —akin to adoption without teleology.
  • Hoffman, R. & Beato, G. (2025). Superagency: What Could Possibly Go Right with Our AI Future. Authors Equity. Technological optimism about AI as an amplifier of agency.
  • Bender, E. M. & Hanna, A. (2025). The AI Con. Harper. Critique of Big Tech hype.
  • Bornet, P., Wirtz, J., Davenport, T. H., De Cremer, D. et al. (2025). Agentic Artificial Intelligence. World Scientific. A management overview of the agentic wave.

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.

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