Skip to main content
SpringerLink
Log in

Leveraging Design Thinking Towards the Convergence of AI, IoT and Blockchain: Strategic Drivers and Human-Centered Use Cases

  • Conference paper
  • First Online:
HCI International 2022 - Late Breaking Papers. Design, User Experience and Interaction (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13516))

Included in the following conference series:

  • 1303 Accesses

Abstract

Artificial Intelligence (AI) is increasingly transforming and reshaping human interactions, severely impacting organizational processes and operations. However, it faces substantial challenges, such as collecting, evaluating, and anonymizing data, which brings along privacy risks for sensitive user data and tends to diminish the human perspective as the principal focus of many activities in our world. The relationship between Design Thinking (DT) and AI is meaningful on two interrelated and reciprocal levels: (1) The impact and perceived benefits of AI on the DT process; (2) DT as an important concept to understand the opportunities offered by the combination of AI with Blockchain and the Internet of Things. Hence, we investigate human-centered use-cases building on AI, Blockchain, and IoT by interviewing experts such as entrepreneurs, technology researchers, investors, and academics. We find that AI significantly affects streamlining and enhancing the DT process while the DT process offers great potential to create human-centered use cases leveraging AI, Blockchain, and IoT. Notably, we suggest that the DT process should pay particular attention to industrial and organizational capabilities during the empathize and define stages, the process performance requirements throughout the ideation and prototyping stages, and the output at the testing stage.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Braganza, A., et al.: Productive employment and decent work: the impact of AI adoption on psychological contracts, job engagement and employee trust. J. Bus. Res. 131, 485–494 (2021)

    Article  Google Scholar 

  2. Csaszar, F., Steinberger, T.: Organizations as artificial intelligences: the use of artificial intelligence analogies in organization theory. Acad. Manag. Ann. 16, 1–37 (2021)

    Article  Google Scholar 

  3. George, G., Merrill, R.K., Schillebeeckx, S.J.D.: Digital sustainability and entrepreneurship: how digital innovations are helping tackle climate change and sustainable development. Entrep. Theory Pract. 45(5), 999–1027 (2021)

    Article  Google Scholar 

  4. Alvarez, S.A., et al.: Developing a theory of the firm for the 21st century. Acad. Manag. Rev. 45(4), 711–716 (2020)

    Article  Google Scholar 

  5. Nambisan, S., Wright, M., Feldman, M.: The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes. Res. Policy 48(8), 103773 (2019)

    Article  Google Scholar 

  6. Hilb, M.: Toward artificial governance? The role of artificial intelligence in shaping the future of corporate governance. J. Manag. Gov. 24(4), 851–870 (2020). https://doi.org/10.1007/s10997-020-09519-9

    Article  Google Scholar 

  7. Misselhorn, C.: Artificial systems with moral capacities? A research design and its implementation in a geriatric care system. Artif. Intell. 278, 103179 (2020)

    Article  Google Scholar 

  8. Dell’Era, C., et al.: Four kinds of design thinking: from ideating to making, engaging, and criticizing. Creat. Innov. Manag. 29(2), 324–344 (2020)

    Article  Google Scholar 

  9. Buterin, V.: A next-generation smart contract and decentralized application platform. White Paper 3.37, p. 2-1 (2014)

    Google Scholar 

  10. Liedtka, J.: Perspective: Linking design thinking with innovation outcomes through cognitive bias reduction. J. Prod. Innov. Manag. 32(6), 925–938 (2015)

    Article  Google Scholar 

  11. Verganti, R., Vendraminelli, L., Iansiti, M.: Innovation and design in the age of artificial intelligence. J. Prod. Innov. Manag. 37(3), 212–227 (2020)

    Article  Google Scholar 

  12. Sodhi, M.M.S., et al.: Why emerging supply chain technologies initially disappoint: Blockchain, IoT, and AI. Prod. Oper. Manag. (2022)

    Google Scholar 

  13. Sick, N., Bröring, S.: Exploring the research landscape of convergence from a TIM perspective: a review and research agenda. Technol. Forecast. Soc. Chang. 175, 121321 (2021)

    Article  Google Scholar 

  14. Nishant, R., Kennedy, M., Corbett, J.: Artificial intelligence for sustainability: challenges, opportunities, and a research agenda. Int. J. Inf. Manag. 53, 102104 (2020)

    Article  Google Scholar 

  15. Cautela, C., et al.: The impact of artificial intelligence on design thinking practice: insights from the ecosystem of startups. Strat. Des. Res. J. 12(1), 114–134 (2019)

    Google Scholar 

  16. Weller, A.J.: Design Thinking for a user-centered approach to artificial intelligence. She Ji J. Des. Econ. Innov. 5(4), 394–396 (2019)

    Article  Google Scholar 

  17. Buchanan, R.: Wicked problems in design thinking. Des. Issues 8(2), 5–21 (1992)

    Article  MathSciNet  Google Scholar 

  18. Dorst, K.: The core of ‘design thinking’ and its application. Des. Stud. 32(6), 521–532 (2011)

    Article  Google Scholar 

  19. Kimbell, L.: Rethinking design thinking: Part I. Des. Cult. 3(3), 285–306 (2011)

    Google Scholar 

  20. Razzouk, R., Shute, V.: What is design thinking and why is it important? Rev. Educ. Res. 82(3), 330–348 (2012)

    Article  Google Scholar 

  21. Brown, T.: Design thinking. Harv. Bus. Rev. 86(6), 84 (2008)

    Google Scholar 

  22. Novak, A., Bennett, D., Kliestik, T.: Product decision-making information systems, real-time sensor networks, and artificial intelligence-driven big data analytics in sustainable Industry 4.0. Econ. Manag. Financ. Mark. 16(2), 62–72 (2021)

    Article  Google Scholar 

  23. Erhard, L., McBride, B., Safir, A.: A framework for the evaluation and use of alternative data in the consumer expenditure surveys. Mon. Lab. Rev. 144, 1 (2021)

    Google Scholar 

  24. Hansen, K.B., Borch, C.: Alternative data and sentiment analysis: prospecting non-standard data in machine learning-driven finance. Big Data Soc. 9(1), 20539517211070700 (2022)

    Article  Google Scholar 

  25. Duan, Y., Edwards, J.S., Dwivedi, Y.K.: Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. Int. J. Inf. Manag. 48, 63–71 (2019)

    Article  Google Scholar 

  26. Brenner, W., Uebernickel, F., Abrell, T.: Design thinking as mindset, process, and toolbox. In: Brenner, W., Uebernickel, F. (eds.) Design Thinking for Innovation, pp. 3–21. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-26100-3_1

    Chapter  Google Scholar 

  27. Nemati, H.R., et al.: Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decis. Support Syst. 33(2), 143–161 (2002)

    Article  Google Scholar 

  28. Kannengiesser, U., Gero, J.S.: Design thinking, fast and slow: a framework for Kahneman’s dual-system theory in design. Des. Sci. 5 (2019)

    Google Scholar 

  29. Rolan, G., et al.: More human than human? Artificial intelligence in the archive. Arch. Manuscr. 47(2), 179–203 (2019)

    Article  Google Scholar 

  30. Trunk, A., Birkel, H., Hartmann, E.: On the current state of combining human and artificial intelligence for strategic organizational decision making. Bus. Res. 13(3), 875–919 (2020). https://doi.org/10.1007/s40685-020-00133-x

    Article  Google Scholar 

  31. Nagorny, K., et al.: Big data analysis in smart manufacturing: a review. Int. J. Commun. Netw. Syst. Sci. 10(3), 31–58 (2017)

    Google Scholar 

  32. Ahmed, B., Dannhauser, T., Philip, N.; A lean design thinking methodology (LDTM) for machine learning and modern data projects. In: 2018 10th Computer Science and Electronic Engineering (CEEC). IEEE (2018)

    Google Scholar 

  33. Garrido, A.L., Sangiao, S., Cardiel, O.: Improving the generation of infoboxes from data silos through machine learning and the use of semantic repositories. Int. J. Artif. Intell. Tools 26(05), 1760022 (2017)

    Article  Google Scholar 

  34. Pham, D.T., Pham, P.T.N.: Artificial intelligence in engineering. Int. J. Mach. Tools Manuf 39(6), 937–949 (1999)

    Article  Google Scholar 

  35. Javaid, M., et al.: Artificial intelligence applications for Industry 4.0: a literature-based study. J. Ind. Integr. Manag. 7(01), 83–111 (2022)

    Article  Google Scholar 

  36. Micheli, P., et al.: Doing design thinking: conceptual review, synthesis, and research agenda. J. Prod. Innov. Manag. 36(2), 124–148 (2019)

    Article  Google Scholar 

  37. Cousins, B.: Design thinking: organizational learning in VUCA environments. Acad. Strateg. Manag. J. 17(2), 1–18 (2018)

    Google Scholar 

  38. Rosenberg, N.: Technological change in the machine tool industry, 1840–1910. J. Econ. Hist. 23(4), 414–443 (1963)

    Article  Google Scholar 

  39. Adner, R., Levinthal, D.A.: The emergence of emerging technologies. Calif. Mana. Rev. 45(1), 50–66 (2002)

    Article  Google Scholar 

  40. Hacklin, F., Marxt, C., Fahrni, F.: An evolutionary perspective on convergence: inducing a stage model of inter-industry innovation. Int. J. Technol. Manag. 49(1–3), 220–249 (2010)

    Article  Google Scholar 

  41. Schuelke-Leech, B.-A.: A model for understanding the orders of magnitude of disruptive technologies. Technol. Forecast. Soc. Chang. 129, 261–274 (2018)

    Article  Google Scholar 

  42. Centobelli, P., et al.: Surfing Blockchain wave, or drowning? Shaping the future of distributed ledgers and decentralized technologies. Technol. Forecast. Soc. Chang. 165, 120463 (2021)

    Article  Google Scholar 

  43. Parker, B., Bach, C.: The synthesis of Blockchain, artificial intelligence and internet of things. Eur. J. Eng. Technol. Res. 5(5), 588–593 (2020)

    Google Scholar 

  44. Montes, G.A., Goertzel, B.: Distributed, decentralized, and democratized artificial intelligence. Technol. Forecast. Soc. Chang. 141, 354–358 (2019)

    Article  Google Scholar 

  45. Singh, S.K., Rathore, S., Park, J.H.: Blockiotintelligence: a Blockchain-enabled intelligent IoT architecture with artificial intelligence. Future Gener. Comput. Syst. 110, 721–743 (2020)

    Article  Google Scholar 

  46. Dedehayir, O., Steinert, M.: The hype cycle model: a review and future directions. Technol. Forecast. Soc. Chang. 108, 28–41 (2016)

    Article  Google Scholar 

  47. Leonardi, P.M.: When flexible routines meet flexible technologies: affordance, constraint, and the imbrication of human and material agencies. MIS Q. 35, 147–167 (2011)

    Article  Google Scholar 

  48. Davidson, S., De Filippi, P., Potts, J.: Blockchains and the economic institutions of capitalism. J. Inst. Econ. 14(4), 639–658 (2018)

    Google Scholar 

  49. Lumineau, F., Wang, W., Schilke, O.: Blockchain governance—a new way of organizing collaborations? Organ. Sci. 32(2), 500–521 (2021)

    Article  Google Scholar 

  50. Lacity, M.C.: Addressing key challenges to making enterprise Blockchain applications a reality. MIS Q. Exec. 17(3), 201–222 (2018)

    Google Scholar 

  51. Swedberg, R.: Exploratory research. In: The Production of Knowledge: Enhancing Progress in Social Science, pp. 17–41 (2020)

    Google Scholar 

  52. Gioia, D.A., Corley, K.G., Hamilton, A.L.: Seeking qualitative rigor in inductive research: notes on the Gioia methodology. Organ. Res. Methods 16(1), 15–31 (2013)

    Article  Google Scholar 

  53. Sanka, A.I., Cheung, R.C.C.: A systematic review of Blockchain scalability: issues, solutions, analysis and future research. J. Netw. Comput. Appl. 195, 103232 (2021)

    Article  Google Scholar 

  54. Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. Decent. Bus. Rev., 21260 (2008)

    Google Scholar 

  55. Hajli, N., et al.: Towards an understanding of privacy management architecture in big data: an experimental research. Br. J. Manag. 32(2), 548–565 (2021)

    Article  Google Scholar 

  56. Mohanta, B.K., et al.: Survey on IoT security: challenges and solution using machine learning, artificial intelligence and Blockchain technology. Internet Things 11, 100227 (2020)

    Article  Google Scholar 

  57. Hussain, A.A., Al-Turjman, F.: Artificial intelligence and Blockchain: a review. Trans. Emerg. Telecommun. Technol. 32(9), e4268 (2021)

    Google Scholar 

  58. Sanz, J.L.C., Zhu, Y.: Toward scalable artificial intelligence in finance. In: 2021 IEEE International Conference on Services Computing (SCC). IEEE (2021)

    Google Scholar 

  59. Pfeffers, K., et al.: The design science research process: a model for producing and presenting information systems research. In: Proceedings of the First International Conference on Design Science Research in Information Systems and Technology (DESRIST 2006), Claremont, CA, USA (2006)

    Google Scholar 

  60. Dresch, A., Lacerda, D.P., Antunes, J.A.V.: Design science research. In: Dresch, A., Lacerda, D.P., Antunes, J.A.V. (eds.) Design science research, pp. 67–102. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-07374-3_4

    Chapter  Google Scholar 

  61. Avison, D.E., et al.: Action research. Commun. ACM 42(1), 94–97 (1999)

    Article  Google Scholar 

  62. Okoli, C., Pawlowski, S.D.: The Delphi method as a research tool: an example, design considerations and applications. Inf. Manag. 42(1), 15–29 (2004)

    Article  Google Scholar 

  63. Lerner, J., Tirole, J.: Some simple economics of open source. J. Ind. Econ. 50(2), 197–234 (2002)

    Article  Google Scholar 

  64. Lüdeke-Freund, F., Gold, S., Bocken, N.M.P.: A review and typology of circular economy business model patterns. J. Ind. Ecol. 23(1), 36–61 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Jean Baptiste Say Institute for Entrepreneurship, ESCP Wirtschaftshochschule, Heubnerweg 8-10, Berlin, Germany

    Maximilian Tigges, Chloé Ipert & René Mauer

Authors
  1. Maximilian Tigges
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chloé Ipert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. René Mauer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maximilian Tigges .

Editor information

Editors and Affiliations

  1. The Open University of Japan, Chiba, Japan

    Masaaki Kurosu

  2. Tokyo University of Science, Tokyo, Saitama, Japan

    Sakae Yamamoto

  3. Tokyo City University, Tokyo, Japan

    Hirohiko Mori

  4. Southern University of Science and Technology, Shenzhen, China

    Marcelo M. Soares

  5. World Usability Day and Bubble Mountain Consulting, Newton Center, MA, USA

    Elizabeth Rosenzweig

  6. Aaron Marcus and Associates, Berkeley, CA, USA

    Aaron Marcus

  7. Tsinghua University, Beijing, China

    Pei-Luen Patrick Rau

  8. Coventry University, Coventry, UK

    Don Harris

  9. Cranfield University, Cranfield, UK

    Wen-Chin Li

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tigges, M., Ipert, C., Mauer, R. (2022). Leveraging Design Thinking Towards the Convergence of AI, IoT and Blockchain: Strategic Drivers and Human-Centered Use Cases. In: Kurosu, M., et al. HCI International 2022 - Late Breaking Papers. Design, User Experience and Interaction. HCII 2022. Lecture Notes in Computer Science, vol 13516. Springer, Cham. https://doi.org/10.1007/978-3-031-17615-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-17615-9_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17614-2

  • Online ISBN: 978-3-031-17615-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation