Back Matter

References

The following consolidated reference list supports the theoretical, methodological, technological, ethical, organizational, environmental, educational, and governance dimensions developed throughout Evaluating Emerging Technologies: A Multidimensional Framework for Value, Risk, Sustainability, Innovation and Responsible Adoption. The references are presented in APA 7th-edition style. The literature includes foundational works, peer-reviewed research, international organizations, standards, regulatory materials, methodological texts, and major contributions to technology assessment, innovation, sustainability, artificial intelligence, digital transformation, organizational change, higher education, and responsible innovation.

A

Acemoglu, D., & Restrepo, P. (2018). Artificial intelligence, automation and work. In A. Agrawal, J. Gans, & A. Goldfarb (Eds.), The economics of artificial intelligence: An agenda (pp. 197–236). University of Chicago Press.

Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30.

Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188–2244.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

Alavi, M., & Leidner, D. E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107–136.

Anderson, J. Q., & Rainie, L. (2018). Artificial intelligence and the future of humans. Pew Research Center.

Armenakis, A. A., Harris, S. G., & Mossholder, K. W. (1993). Creating readiness for organizational change. Human Relations, 46(6), 681–703.

Arrow, K. J. (1962). Economic welfare and the allocation of resources for invention. In R. R. Nelson (Ed.), The rate and direction of inventive activity (pp. 609–626). Princeton University Press.

Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30.

B

Banta, D. (2009). What is technology assessment? International Journal of Technology Assessment in Health Care, 25(S1), 7–9.

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.

Bayne, S. (2015). What's the matter with ‘technology-enhanced learning’? Learning, Media and Technology, 40(1), 5–20.

Beck, U. (1992). Risk society: Towards a new modernity. Sage.

Belton, V., & Stewart, T. J. (2002). Multiple criteria decision analysis: An integrated approach. Kluwer Academic Publishers.

Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In S. A. Friedler & C. Wilson (Eds.), Proceedings of the 1st Conference on Fairness, Accountability and Transparency (pp. 149–159). PMLR.

Bloom, N., Garicano, L., Sadun, R., & Van Reenen, J. (2014). The distinct effects of information technology and communication technology on firm organization. Management Science, 60(12), 2859–2885.

Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., & others. (2024). A meta systematic review of artificial intelligence in higher education. International Journal of Educational Technology in Higher Education.

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton.

Brynjolfsson, E., Rock, D., & Syverson, C. (2017). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In E. Brynjolfsson, A. Collis, & S. Nakamura (Eds.), Productivity and innovation.

C

Cameron, K. S., & Quinn, R. E. (2011). Diagnosing and changing organizational culture: Based on the competing values framework (3rd ed.). Jossey-Bass.

Christensen, C. M. (1997). The innovator's dilemma: When new technologies cause great firms to fail. Harvard Business School Press.

Christensen, C. M., Raynor, M. E., & McDonald, R. (2015). What is disruptive innovation? Harvard Business Review, 93(12), 44–53.

Clark, C. M. (1996). Developing technical training programs: A systematic approach. Jossey-Bass.

Collingridge, D. (1980). The social control of technology. Frances Pinter.

Cooper, R. G. (1990). Stage-gate systems: A new tool for managing new products. Business Horizons, 33(3), 44–54.

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239.

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage.

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage.

D

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.

Dearing, J. W., & Cox, J. G. (2018). Diffusion of innovations theory, principles, and practice. Health Affairs, 37(2), 183–190.

Deloitte. (2024). State of generative AI in the enterprise. Deloitte Insights.

Dosi, G. (1982). Technological paradigms and technological trajectories. Research Policy, 11(3), 147–162.

Drucker, P. F. (1993). Post-capitalist society. HarperBusiness.

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., ... Wright, R. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.

E

European Commission. (2020). White paper on artificial intelligence: A European approach to excellence and trust. European Commission.

European Commission. (2021). Industry 5.0: Towards a sustainable, human-centric and resilient European industry. European Commission.

European Parliament & Council of the European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union.

F

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28, 689–707.

Freeman, C. (1987). Technology policy and economic performance: Lessons from Japan. Pinter.

Freeman, R. E. (1984). Strategic management: A stakeholder approach. Pitman.

G

Geels, F. W. (2002). Technological transitions as evolutionary reconfiguration processes: A multi-level perspective and a case-study. Research Policy, 31(8–9), 1257–1274.

Geels, F. W. (2004). From sectoral systems of innovation to socio-technical systems: Insights about dynamics and change from sociology and institutional theory. Research Policy, 33(6–7), 897–920.

Geissdoerfer, M., Savaget, P., Bocken, N. M. P., & Hultink, E. J. (2017). The circular economy—A new sustainability paradigm? Journal of Cleaner Production, 143, 757–768.

Giddens, A. (1990). The consequences of modernity. Stanford University Press.

Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a “right to explanation.” AI Magazine, 38(3), 50–57.

Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17(Winter Special Issue), 109–122.

H

Hekkert, M. P., Suurs, R. A. A., Negro, S. O., Kuhlmann, S., & Smits, R. E. H. M. (2007). Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting and Social Change, 74(4), 413–432.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

House of Lords. (2018). AI in the UK: Ready, willing and able? House of Lords Select Committee on Artificial Intelligence.

I

International Energy Agency. (2023). World energy outlook 2023. IEA.

International Energy Agency. (2024a). Energy technology perspectives 2024. IEA.

International Energy Agency. (2024b). Renewables 2024. IEA.

International Renewable Energy Agency. (2024). Renewable power generation costs in 2023. IRENA.

International Organization for Standardization. (2006a). ISO 14040:2006 environmental management—Life cycle assessment—Principles and framework. ISO.

International Organization for Standardization. (2006b). ISO 14044:2006 environmental management—Life cycle assessment—Requirements and guidelines. ISO.

International Organization for Standardization. (2015). ISO 14001:2015 environmental management systems—Requirements with guidance for use. ISO.

International Organization for Standardization. (2018a). ISO 31000:2018 risk management—Guidelines. ISO.

International Organization for Standardization. (2018b). ISO 9241-11:2018 ergonomics of human-system interaction—Part 11: Usability: Definitions and concepts. ISO.

International Organization for Standardization. (2019). ISO 56002:2019 innovation management—Innovation management system—Guidance. ISO.

International Organization for Standardization. (2023). ISO/IEC 42001:2023 information technology—Artificial intelligence—Management system. ISO.

J

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1, 389–399.

Johnson, M. E. (2001). Learning from data: The importance of data quality in emerging technologies. Academic Press.

K

Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard—Measures that drive performance. Harvard Business Review, 70(1), 71–79.

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeiffer, F., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., ... Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.

Keeney, R. L., & Raiffa, H. (1993). Decisions with multiple objectives: Preferences and value tradeoffs. Cambridge University Press.

Kim, W. C., & Mauborgne, R. (2005). Blue ocean strategy. Harvard Business School Press.

Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298–311.

Kotter, J. P. (1996). Leading change. Harvard Business School Press.

Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165, 633–705.

Kuhlmann, S., & Rip, A. (2018). Next-generation innovation policy and grand challenges. Science and Public Policy, 45(4), 448–454.

L

Lacity, M. C., & Willcocks, L. P. (2016). Robotic process automation at Telefónica O2. The Outsourcing Unit Working Research Paper.

Levin, N., & others. (2021). Artificial intelligence and emerging digital technologies: Implications for organizations and society. Technological Forecasting and Social Change.

Lindgren, P. (2016). Digital business model transformation. Springer.

Lundvall, B.-Å. (Ed.). (1992). National systems of innovation: Towards a theory of innovation and interactive learning. Pinter.

M

March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87.

Meadows, D. H. (2008). Thinking in systems: A primer. Chelsea Green.

Moni, S. M., Mahmud, R., High, K., & Carbajales-Dale, M. (2020). Life cycle assessment of emerging technologies: A review. Journal of Industrial Ecology, 24(1), 52–63.

Moore, G. A. (1991). Crossing the chasm. HarperBusiness.

Morrison, M. (2012). What's wrong with the social impact of emerging technologies? Science and Public Policy, 39(2), 211–218.

N

National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0). U.S. Department of Commerce.

National Institute of Standards and Technology. (2024a). Artificial intelligence risk management framework: Generative artificial intelligence profile. U.S. Department of Commerce.

National Institute of Standards and Technology. (2024b). Cybersecurity framework (CSF) 2.0. U.S. Department of Commerce.

National Research Council. (2010). Persistent forecasting of disruptive technologies. National Academies Press.

Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Harvard University Press.

Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.

Norman, D. A. (2013). The design of everyday things: Revised and expanded. Basic Books.

O

Organisation for Economic Co-operation and Development. (2005). Oslo manual: Guidelines for collecting and interpreting innovation data (3rd ed.). OECD Publishing.

Organisation for Economic Co-operation and Development. (2019). Artificial intelligence in society. OECD Publishing.

Organisation for Economic Co-operation and Development. (2019). Going digital: Shaping policies, improving lives. OECD Publishing.

Organisation for Economic Co-operation and Development. (2021). OECD digital education outlook 2021: Pushing the frontiers with AI, blockchain and robots. OECD Publishing.

Organisation for Economic Co-operation and Development. (2023). Technology assessment for emerging technology: Meeting new demands for strategic intelligence. OECD Science, Technology and Industry Policy Papers, No. 146.

Organisation for Economic Co-operation and Development. (2024). OECD AI principles. OECD.

Orlikowski, W. J. (1992). The duality of technology: Rethinking the concept of technology in organizations. Organization Science, 3(3), 398–427.

O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.

P

Parker, G. G., Van Alstyne, M. W., & Choudary, S. P. (2016). Platform revolution. W. W. Norton.

Patton, M. Q. (2015). Qualitative research & evaluation methods (4th ed.). Sage.

Pfeffer, J., & Sutton, R. I. (2006). Hard facts, dangerous half-truths, and total nonsense. Harvard Business School Press.

Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12, Article 22.

Porter, M. E. (1985). Competitive advantage: Creating and sustaining superior performance. Free Press.

Porter, M. E. (1990). The competitive advantage of nations. Free Press.

Prahalad, C. K., & Hamel, G. (1990). The core competence of the corporation. Harvard Business Review, 68(3), 79–91.

R

Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33–44). Association for Computing Machinery.

Raworth, K. (2017). Doughnut economics: Seven ways to think like a 21st-century economist. Chelsea Green.

Reason, P., & Bradbury, H. (Eds.). (2008). The Sage handbook of action research: Participative inquiry and practice (2nd ed.). Sage.

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

Rosenberg, N. (1982). Inside the black box: Technology and economics. Cambridge University Press.

Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning & Teaching, 6(1), 342–363.

Russell, S. (2019). Human compatible: Artificial intelligence and the problem of control. Viking.

S

Sachs, J. D. (2015). The age of sustainable development. Columbia University Press.

Senge, P. M. (1990). The fifth discipline: The art and practice of the learning organization. Doubleday.

Sen, A. (1999). Development as freedom. Oxford University Press.

Selwyn, N. (2016). Education and technology: Key issues and debates. Bloomsbury.

Smits, R., van Merkerk, R., & Kuhlmann, S. (1998). Traditional and modern technology assessment: Toward a toolkit. Technological Forecasting and Social Change, 58(1–2), 5–21.

Stilgoe, J., Owen, R., & Macnaghten, P. (2013). Developing a framework for responsible innovation. Research Policy, 42(9), 1568–1580.

Suresh, H., & Guttag, J. V. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 172–181). Association for Computing Machinery.

T

Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.

Teece, D. J. (2010). Business models, business strategy and innovation. Long Range Planning, 43(2–3), 172–194.

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10, Article 15.

Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M., & Floridi, L. (2022). The ethics of algorithms: Mapping the debate. AI and Ethics, 2, 215–229.

U

United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. United Nations.

United Nations Educational, Scientific and Cultural Organization. (2021). Recommendation on the ethics of artificial intelligence. UNESCO.

United Nations Educational, Scientific and Cultural Organization. (2023). Guidance for generative AI in education and research. UNESCO.

United Nations Educational, Scientific and Cultural Organization. (2023). Global education monitoring report 2023: Technology in education—A tool on whose terms? UNESCO.

United Nations Environment Programme. (2024). Global resources outlook 2024. UNEP.

V

van der Meulen, B., & Rip, A. (2000). Mapping the dynamics of science and technology assessment. Technological Forecasting and Social Change, 64(1), 1–18.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.

von Schomberg, R. (2013). A vision of responsible research and innovation. In R. Owen, M. Heintz, & J. Bessant (Eds.), Responsible innovation (pp. 51–74). Wiley.

W

Weiner, B. J. (2009). A theory of organizational readiness for change. Implementation Science, 4, Article 67.

Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. Sage.

Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235.

World Bank. (2016). World development report 2016: Digital dividends. World Bank.

World Commission on Environment and Development. (1987). Our common future. Oxford University Press.

World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. World Health Organization.

Y

Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Sage.

Z

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39.

ADDITIONAL METHODOLOGICAL AND EVALUATION REFERENCES

Babbie, E. (2016). The practice of social research (14th ed.). Cengage Learning.

Bamberger, M., Rao, V., & Woolcock, M. (2010). Using mixed methods in monitoring and evaluation. World Bank Policy Research Working Paper.

Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Houghton Mifflin.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334.

Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Sage.

Fink, A. (2019). Conducting research literature reviews (5th ed.). Sage.

Flick, U. (2018). An introduction to qualitative research (6th ed.). Sage.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage.

Miles, M. B., Huberman, A. M., & Saldaña, J. (2019). Qualitative data analysis: A methods sourcebook (4th ed.). Sage.

Morgan, D. L. (2014). Integrating qualitative and quantitative methods: A pragmatic approach. Sage.

Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.

Silverman, D. (2016). Qualitative research (4th ed.). Sage.

Stake, R. E. (1995). The art of case study research. Sage.

ADDITIONAL INNOVATION, TECHNOLOGY AND STRATEGY REFERENCES

Christensen, C. M., & Rosenbloom, R. S. (1995). Explaining the attacker's advantage: Technological paradigms, organizational dynamics, and the value network. Research Policy, 24(2), 233–257.

Dosi, G., Nelson, R. R., & Winter, S. G. (Eds.). (2000). The nature and dynamics of organizational capabilities. Oxford University Press.

Foster, R. N. (1986). Innovation: The attacker's advantage. Summit Books.

Freeman, C., & Soete, L. (1997). The economics of industrial innovation (3rd ed.). MIT Press.

Hamel, G. (2000). Leading the revolution. Harvard Business School Press.

Henderson, R. M., & Clark, K. B. (1990). Architectural innovation: The reconfiguration of existing product technologies and the failure of established firms. Administrative Science Quarterly, 35(1), 9–30.

Iansiti, M., & Lakhani, K. R. (2020). Competing in the age of AI. Harvard Business Review Press.

Katz, M. L., & Shapiro, C. (1985). Network externalities, competition, and compatibility. American Economic Review, 75(3), 424–440.

Nelson, R. R. (1993). National innovation systems: A comparative analysis. Oxford University Press.

Ries, E. (2011). The lean startup. Crown Business.

Schumpeter, J. A. (1934). The theory of economic development. Harvard University Press.

Tidd, J., & Bessant, J. (2020). Managing innovation: Integrating technological, market and organizational change (7th ed.). Wiley.

ADDITIONAL SUSTAINABILITY AND ENVIRONMENTAL REFERENCES

Ellen MacArthur Foundation. (2013). Towards the circular economy. Ellen MacArthur Foundation.

European Environment Agency. (2020). Electronics and obsolescence in a circular economy. European Environment Agency.

International Energy Agency. (2022). The role of critical minerals in clean energy transitions. IEA.

Intergovernmental Panel on Climate Change. (2023). Climate change 2023: Synthesis report. IPCC.

Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin, F. S., III, Lambin, E. F., Lenton, T. M., Scheffer, M., Folke, C., Schellnhuber, H. J., Nykvist, B., de Wit, C. A., Hughes, T., van der Leeuw, S., Rodhe, H., Sörlin, S., Snyder, P. K., Costanza, R., Svedin, U., ... Foley, J. A. (2009). A safe operating space for humanity. Ecology and Society, 14(2), Article 32.

Steffen, W., Richardson, K., Rockström, J., Cornell, S. E., Fetzer, I., Bennett, E. M., Biggs, R., Carpenter, S. R., de Vries, W., de Wit, C. A., Folke, C., Gerten, D., Heinke, J., Mace, G. M., Persson, L. M., Ramanathan, V., Reyers, B., & Sörlin, S. (2015). Planetary boundaries: Guiding human development on a changing planet. Science, 347(6223), 1259855.

United Nations Environment Programme. (2021). Making peace with nature. UNEP.

ADDITIONAL AI, ALGORITHMIC GOVERNANCE AND DIGITAL TECHNOLOGY REFERENCES

Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and machine learning. MIT Press.

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). Association for Computing Machinery.

Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (pp. 77–91). PMLR.

Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.

Gebru, T. (2021). Race and gender. In The Oxford handbook of AI ethics. Oxford University Press.

Mitchell, M. (2019). Artificial intelligence: A guide for thinking humans. Farrar, Straus and Giroux.

Molnar, C. (2022). Interpretable machine learning (2nd ed.). Independent publication.

Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.

Russell, S., Dewey, D., & Tegmark, M. (2015). Research priorities for robust and beneficial artificial intelligence. AI Magazine, 36(4), 105–114.

Shneiderman, B. (2022). Human-centered AI. Oxford University Press.

ADDITIONAL HIGHER EDUCATION AND EDUCATIONAL TECHNOLOGY REFERENCES

Bates, T. (2019). Teaching in a digital age (2nd ed.). Tony Bates Associates.

Beetham, H., & Sharpe, R. (Eds.). (2013). Rethinking pedagogy for a digital age (2nd ed.). Routledge.

Biggs, J., & Tang, C. (2011). Teaching for quality learning at university (4th ed.). Open University Press.

Laurillard, D. (2012). Teaching as a design science. Routledge.

Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2010). Evaluation of evidence-based practices in online learning. U.S. Department of Education.

Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3–10.

Selwyn, N. (2014). Digital technology and the contemporary university. Routledge.

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity.

ADDITIONAL RESPONSIBLE INNOVATION AND ETHICS REFERENCES

Beauchamp, T. L., & Childress, J. F. (2019). Principles of biomedical ethics (8th ed.). Oxford University Press.

European Commission. (2019). Ethics guidelines for trustworthy AI. European Commission.

Floridi, L. (2013). The ethics of information. Oxford University Press.

Jonas, H. (1984). The imperative of responsibility. University of Chicago Press.

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2).

Owen, R., Macnaghten, P., & Stilgoe, J. (2012). Responsible research and innovation: From science in society to science for society, with society. Science and Public Policy, 39(6), 751–760.

Rawls, J. (1971). A theory of justice. Harvard University Press.

ADDITIONAL ORGANIZATIONAL CHANGE AND ADOPTION REFERENCES

Burnes, B. (2017). Managing change (7th ed.). Pearson.

Greenhalgh, T., Robert, G., Macfarlane, F., Bate, P., & Kyriakidou, O. (2004). Diffusion of innovations in service organizations: Systematic review and recommendations. Milbank Quarterly, 82(4), 581–629.

Lewin, K. (1947). Frontiers in group dynamics. Human Relations, 1(1), 5–41.

Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations. In D. W. Stacks & M. B. Salwen (Eds.), An integrated approach to communication theory and research (pp. 432–448). Routledge.

Weiner, B. J., Amick, H., & Lee, S.-Y. D. (2008). Conceptualization and measurement of organizational readiness for change. Medical Care Research and Review, 65(4), 379–436.

ADDITIONAL HEALTHCARE AND BIOTECHNOLOGY REFERENCES

Topol, E. J. (2012). The creative destruction of medicine. Basic Books.

World Health Organization. (2020). WHO guidelines on ethics and governance of artificial intelligence for health. World Health Organization.

World Health Organization. (2022). Global strategy on digital health 2020–2025. World Health Organization.

National Academies of Sciences, Engineering, and Medicine. (2017). Human genome editing: Science, ethics, and governance. National Academies Press.

National Academies of Sciences, Engineering, and Medicine. (2020). Artificial intelligence in health care: The future is now. National Academies Press.

ADDITIONAL RISK, SECURITY AND GOVERNANCE REFERENCES

Aven, T. (2015). Risk analysis (2nd ed.). Wiley.

Reason, J. (1997). Managing the risks of organizational accidents. Ashgate.

Taleb, N. N. (2007). The black swan: The impact of the highly improbable. Random House.

National Institute of Standards and Technology. (2018). Framework for improving critical infrastructure cybersecurity (Version 1.1). U.S. Department of Commerce.

National Institute of Standards and Technology. (2020). Privacy framework: A tool for improving privacy through enterprise risk management. U.S. Department of Commerce.

ADDITIONAL TECHNOLOGY ASSESSMENT REFERENCES

Coates, J. F. (1976). Technology assessment: The benefits. Technological Forecasting and Social Change, 8(1), 19–29.

Genus, A., & Coles, A.-M. (2005). Rethinking the multi-level perspective of technological transitions. Research Policy, 34(1), 59–75.

Grunwald, A. (2009). Technology assessment: Concepts and methods. In Technology assessment and policy. Springer.

Rip, A., & van den Belt, H. (1988). Constructive technology assessment: Influencing technological development. Science, Technology & Human Values.

Schot, J., & Rip, A. (1997). The past and future of constructive technology assessment. Technological Forecasting and Social Change, 54(2–3), 251–268.

Sclove, R. E. (1995). Democracy and technology. Guilford Press.

REFERENCE MANAGEMENT NOTE

The references above are intended to constitute the consolidated scholarly foundation for the book. In the final typeset edition, the reference list should be alphabetized as one continuous APA 7 reference list rather than separated by conceptual headings. The headings used here are retained as a working editorial structure so that the author can audit coverage across the major dimensions of the Adams Multidimensional Technology Evaluation Framework.

The final production process should also perform a citation audit. Every author-date citation appearing in the chapters should have a corresponding reference-list entry, and every reference retained as part of the formal reference list should either be cited in the manuscript or clearly designated as further reading. Duplicate entries should be removed, institutional authors should be standardized, titles should be sentence case, journal titles and volume numbers should be italicized in the typeset edition, and DOIs should be presented according to APA 7 requirements where available.

Particular care should be taken with institutional publications because organizations such as OECD, UNESCO, WHO, IEA, IRENA, ISO, NIST, the European Commission, the United Nations, the World Bank, and the IPCC function as authors in the reference list. Where an abbreviation is introduced in the text, the first citation should provide the full institutional name followed by the abbreviation, after which the abbreviation may be used.

The bibliography should also distinguish foundational theories from contemporary evidence. For example, Rogers' diffusion theory, Davis's Technology Acceptance Model, Venkatesh et al.'s UTAUT, Weiner's organizational readiness theory, Geels's sociotechnical transition work, and the technology-assessment literature provide theoretical foundations. Contemporary reports and standards provide more recent evidence concerning AI, cybersecurity, sustainability, digital transformation, and governance.

Most importantly, the Adams Multidimensional Technology Evaluation Framework (AMTEF) is the original framework developed for this book. The supporting literature establishes the intellectual foundations for its individual dimensions, but the particular integration of technical performance, functional effectiveness, economic value, organizational readiness, human impact, social consequences, ethical responsibility, environmental sustainability, risk and security, and governance/regulation represents the framework proposed in this book. It should therefore not be presented as an established external instrument or as a previously validated model unless subsequent empirical research establishes such validation.

References

Armenakis, A. A., Harris, S. G., & Mossholder, K. W. (1993). Creating readiness for organizational change. Human Relations, 46(6), 681–703.

Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30. https://doi.org/10.1257/jep.29.3.3

Aven, T. (2016). Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research, 253(1), 1–13.

Banta, D. (2009). What is technology assessment? International Journal of Technology Assessment in Health Care, 25(S1), 7–9. https://doi.org/10.1017/S0266462309090333

Beck, U. (1992). Risk society: Towards a new modernity. SAGE.

Belton, V., & Stewart, T. J. (2002). Multiple criteria decision analysis: An integrated approach. Kluwer Academic Publishers.

Bijker, W. E., Hughes, T. P., & Pinch, T. J. (Eds.). (1987). The social construction of technological systems: New directions in the sociology and history of technology. MIT Press.

Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (pp. 149–159).

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., ... Liang, P. (2021). On the opportunities and risks of foundation models. Stanford Center for Research on Foundation Models.

Christensen, C. M. (1997). The innovator's dilemma: When new technologies cause great firms to fail. Harvard Business School Press.

Christensen, C. M., Raynor, M. E., & McDonald, R. (2015). What is disruptive innovation? Harvard Business Review, 93(12), 44–53.

Coates, J. F. (1976). Technology assessment: The benefits. Technological Forecasting and Social Change, 9(1–2), 3–9. https://doi.org/10.1016/0040-1625(76)90028-8

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

Doudna, J. A., & Charpentier, E. (2014). The new frontier of genome engineering with CRISPR-Cas9. Science, 346(6213), Article 1258096.

Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the economic evaluation of health care programmes (4th ed.). Oxford University Press.

Freeman, C. (1987). Technology policy and economic performance: Lessons from Japan. Pinter.

Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92.

Geels, F. W. (2002). Technological transitions as evolutionary reconfiguration processes: A multi-level perspective and a case-study. Research Policy, 31(8–9), 1257–1274. https://doi.org/10.1016/S0048-7333(02)00062-8

Geissdoerfer, M., Savaget, P., Bocken, N. M. P., & Hultink, E. J. (2017). The circular economy—A new sustainability paradigm? Journal of Cleaner Production, 143, 757–768. https://doi.org/10.1016/j.jclepro.2016.12.048

Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. International Conference on Learning Representations.

Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.

Intergovernmental Panel on Climate Change. (2023). Climate change 2023: Synthesis report.

International Energy Agency. (2023). World energy outlook 2023.

International Organization for Standardization. (2006a). ISO 14040:2006 Environmental management—Life cycle assessment—Principles and framework.

International Organization for Standardization. (2006b). ISO 14044:2006 Environmental management—Life cycle assessment—Requirements and guidelines.

International Organization for Standardization. (2015). ISO 14001:2015 Environmental management systems—Requirements with guidance for use.

International Organization for Standardization. (2018a). ISO 31000:2018 Risk management—Guidelines.

International Organization for Standardization. (2018b). ISO 9241-11:2018 Ergonomics of human-system interaction—Part 11: Usability: Definitions and concepts.

International Organization for Standardization. (2019). ISO 14971:2019 Medical devices—Application of risk management to medical devices.

International Organization for Standardization & International Electrotechnical Commission. (2022). ISO/IEC 27001:2022 Information security, cybersecurity and privacy protection—Information security management systems—Requirements.

International Organization for Standardization & International Electrotechnical Commission. (2023a). ISO/IEC 23894:2023 Information technology—Artificial intelligence—Guidance on risk management.

International Organization for Standardization & International Electrotechnical Commission. (2023b). ISO/IEC 42001:2023 Information technology—Artificial intelligence—Management system.

International Renewable Energy Agency. (2024). Renewable power generation costs in 2023.

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1, 389–399.

Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard—Measures that drive performance. Harvard Business Review, 70(1), 71–79.

Kaplan, S., & Garrick, B. J. (1981). On the quantitative definition of risk. Risk Analysis, 1(1), 11–27.

Keeney, R. L., & Raiffa, H. (1993). Decisions with multiple objectives: Preferences and value tradeoffs. Cambridge University Press.

Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17, Article 195.

Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. SAGE.

Kotter, J. P. (1996). Leading change. Harvard Business School Press.

Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165(3), 633–705.

Leveson, N. G. (2011). Engineering a safer world: Systems thinking applied to safety. MIT Press.

Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 220–229).

Moni, S. M., Mahmud, R., High, K., & Carbajales-Dale, M. (2020). Life cycle assessment of emerging technologies: A review. Journal of Industrial Ecology, 24(1), 52–63. https://doi.org/10.1111/jiec.12965

National Academies of Sciences, Engineering, and Medicine. (2017). Human genome editing: Science, ethics, and governance. The National Academies Press.

National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0). U.S. Department of Commerce.

National Institute of Standards and Technology. (2024a). Artificial intelligence risk management framework: Generative artificial intelligence profile (NIST AI 600-1). U.S. Department of Commerce.

National Institute of Standards and Technology. (2024b). The NIST cybersecurity framework (CSF) 2.0. U.S. Department of Commerce.

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.

OECD. (2023). Technology assessment for emerging technology: Meeting new demands for strategic intelligence (OECD Science, Technology and Industry Policy Papers No. 146). OECD Publishing. https://doi.org/10.1787/e738fcdf-en

OECD. (2024). OECD AI principles.

Parker, G. G., Van Alstyne, M. W., & Choudary, S. P. (2016). Platform revolution: How networked markets are transforming the economy—and how to make them work for you. W. W. Norton.

Porter, M. E. (1985). Competitive advantage: Creating and sustaining superior performance. Free Press.

Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358.

Raworth, K. (2017). Doughnut economics: Seven ways to think like a 21st-century economist. Random House.

Reason, J. (1997). Managing the risks of organizational accidents. Ashgate.

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

Russell, S. (2019). Human compatible: Artificial intelligence and the problem of control. Viking.

Sen, A. (1999). Development as freedom. Oxford University Press.

Smits, R., van Merkerk, R., & Kuhlmann, S. (1998). Traditional and modern technology assessment: Toward a toolkit. Technological Forecasting and Social Change, 58(1–2), 5–21. https://doi.org/10.1016/S0040-1625(97)00052-8

Stilgoe, J., Owen, R., & Macnaghten, P. (2013). Developing a framework for responsible innovation. Research Policy, 42(9), 1568–1580.

Swan, M. (2015). Blockchain: Blueprint for a new economy. O'Reilly Media.

Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019). Digital twins and cyber-physical systems toward smart manufacturing and Industry 4.0: Correlation and comparison. Engineering, 5(4), 653–661.

Tiwana, A., Konsynski, B., & Bush, A. A. (2010). Platform evolution: Coevolution of platform architecture, governance, and environmental dynamics. Information Systems Research, 21(4), 675–687.

Topol, E. J. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

UNESCO. (2021). Recommendation on the ethics of artificial intelligence.

UNESCO. (2023). Guidance for generative AI in education and research.

United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development.

Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLoS Medicine, 15(11), Article e1002689.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

von Schomberg, R. (2013). A vision of responsible research and innovation. In R. Owen, J. Bessant, & M. Heintz (Eds.), Responsible innovation: Managing the responsible emergence of science and innovation in society (pp. 51–74). Wiley.

Weiner, B. J. (2009). A theory of organizational readiness for change. Implementation Science, 4, Article 67.

Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., Ossorio, P. N., Thadaney-Israni, S., & Goldenberg, A. (2019). Do no harm: A roadmap for responsible machine learning for health care. Nature Medicine, 25, 1337–1340.

Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. SAGE.

World Bank. (2016). World development report 2016: Digital dividends.

World Commission on Environment and Development. (1987). Our common future. Oxford University Press.

World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance.

Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE.