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Cognition, Technology & Work

, Volume 21, Issue 4, pp 621–630 | Cite as

Self-determined nudging: a system concept for human–machine interaction

  • G. WeßelEmail author
  • E. Altendorf
  • M. Schwalm
  • Y. Canpolat
  • C. Burghardt
  • F. Flemisch
Original Article

Abstract

Humans sometimes struggle when making decisions, because what they want to do in a specific moment can differ from what they feel they should do in general. This phenomenon can also be found in situations of human–machine interaction. In order to support humans in making decisions about their behavior, a new form of support is proposed, which is especially suitable for human–machine interaction: self-determined decision-making with nudging methods (or shortly: self-determined nudging). In this concept, firstly the aspirations of the human are assessed and then supporting mechanisms are offered to guide humans towards their self-set goals. With this procedure, machines can for example support humans in driving safely or economically, help them refraining from scheduling other appointments in their gym-timeslots or push them towards going to bed on time. While originally nudging is based on libertarian paternalism, the concept of self-determined nudging enables the person to decide which goals to get nudged towards. By different examples, it is shown that nudging ideas are already present in numerous technical applications. Then, it is demonstrated how the aspect of self-determination can enrich these approaches. Moreover, already existing as well as potential new implementations of self-determined nudging in the automotive domain are described. As an outlook, the set-up of a study on automated driving is presented.

Keywords

Human–machine interaction Human–machine cooperation Behavior change Behavioral economics Nudging Driver behavior 

Notes

Acknowledgements

The research conducted was partly funded by the Deutsche Forschungsgemeinschaft (DFG) within the projects “Arbitration of cooperative movement for highly-automated human machine systems” respectively “Systemergonomics for cooperative interacting vehicles: transparency of automation behavior and intervention possibilities of the human during normal operation, at system limits and during system failure” and partly by RWTH Aachen University.

References

  1. Abadie A, Gay S (2006) The impact of presumed consent legislation on cadaveric organ donation: a cross-country study. J Health Econ 25(4):599–620CrossRefGoogle Scholar
  2. Abbink DA (2006) Neuromuscular analysis of haptic gas pedal feedback during car following. Delft University of Technology, DelftGoogle Scholar
  3. Ajzen I, Fishbein M (2010) Predicting and changing behavior: the reasoned action approached. Taylor and Francis Group, New YorkGoogle Scholar
  4. Altendorf E, Weßel G, Baltzer M, Canpolat Y, Flemisch FO (2016) Joint decision making and cooperative driver-vehicle interaction during critical driving situations. I-Com 15(3):265–281CrossRefGoogle Scholar
  5. Ashraf N, Karlan D, Yin W (2006) Tying odysseus to the mast: evidence from a commitment savings product in the Philippines. Q J Econ 121:635–672CrossRefGoogle Scholar
  6. Auberlet J-M, Rosey F, Anceaux F, Aubin S, Briand P, Pacaux M-P, Plainchault P (2012) The impact of perceptual treatments on driver’s behavior: from driving simulator studies to field tests—first results. Accid Anal Prev 45:91–98CrossRefGoogle Scholar
  7. Baldwin TT, Ford JK (1988) Transfer of training: a review and directions for future research. Pers Psychol 41(1):63–105CrossRefGoogle Scholar
  8. Bazerman MH, Tenbrunsel AE, Wade-Benzoni K (1998) Negotiating with yourself and losing: making decisions with competing internal preferences. Acad Manag Rev 23(2):225–241CrossRefGoogle Scholar
  9. Bubb H, Schmidtke H (1993) Systemergonomie. Ergonomie 3:305–458Google Scholar
  10. Carsten OMJ, Tate FN (2005) Intelligent speed adaptation: accident savings and cost-benefit analysis. Accid Anal Prev 37(3):407–416CrossRefGoogle Scholar
  11. Chaiken S, Trope Y (1999) Dual-process theories in social psychology. Guilford Press, New YorkGoogle Scholar
  12. Comte SL, Jamson AH (2000) Traditional and innovative speed-reducing measures for curves: an investigation of driver behaviour using a driving simulator. Saf Sci 36(3):137–150CrossRefGoogle Scholar
  13. Czapla M, Simon JJ, Richter B, Kluge M, Friederich H-C, Herpertz S, Loeber S (2016) The impact of cognitive impairment and impulsivity on relapse of alcohol-dependent patients: implications for psychotherapeutic treatment. Addict Biol 21(4):873–884CrossRefGoogle Scholar
  14. Dinner I, Johnson EJ, Goldstein DG, Liu K (2011) Partitioning default effects: why people choose not to choose. J Exp Psychol Appl 17(4):332CrossRefGoogle Scholar
  15. Fishbein M, Ajzen I (1977) Belief, attitude, intention, and behavior: an introduction to theory and research. Philos Rhetor 10(2):130–132Google Scholar
  16. Flemisch FO (2016) Professor Flemisch during a discussion about the advantages and drawbacks of nudging (personal communication)Google Scholar
  17. Flemisch F, Schieben A, Kelsch J, Löper C (2008) Automation spectrum, inner/outer compatibility and other potentially useful human factors concepts for assistance and automation. In: de Waard D, Flemisch FO, Lorenz B, Oberheid H, Brookhuis KA (eds) Human factors for assistance and automation. Shaker Publishing, Maastricht, pp 1–6Google Scholar
  18. Flemisch FO, Bengler K, Bubb H, Winner H, Bruder R (2014) Towards cooperative guidance and control of highly automated vehicles: H-mode and conduct-by-wire. Ergonomics 57(3):343–360CrossRefGoogle Scholar
  19. Flemisch F, Abbink D, Itoh M, Pacaux-Lemoine MP, Weßel G (2019) Shared control is the sharp end of cooperation: towards a common framework of joint action, shared control and human machine cooperation. Cognit Technol Work (19)Google Scholar
  20. Fogg BJ (2002) Persuasive technology: using computers to change what we think and do. Ubiquity 2002(December):5CrossRefGoogle Scholar
  21. Fogg BJ (2009) A behavior model for persuasive design. In Proceedings of the 4th international Conference on persuasive technology (p 40)Google Scholar
  22. Fudenberg D, Levine DK (2006) A dual-self model of impulse control. Am Econ Rev 96(5):1449–1476CrossRefGoogle Scholar
  23. Hanks AS, Just DR, Smith LE, Wansink B (2012) Healthy convenience: nudging students toward healthier choices in the lunchroom. J Public Health 34(3):370–376CrossRefGoogle Scholar
  24. Hausman DM, Welch B (2010) Debate: to nudge or not to nudge. J Political Philos 18(1):123–136CrossRefGoogle Scholar
  25. Hoc J-M (2000) From human–machine interaction to human–machine cooperation. Ergonomics 43(7):833–843CrossRefGoogle Scholar
  26. Hollnagel E, Woods DD (1983) Cognitive systems engineering: new wine in new bottles. Int J Man Mach Stud 18(6):583–600CrossRefGoogle Scholar
  27. Hon AHY, Bloom M, Crant JM (2014) Overcoming resistance to change and enhancing creative performance. J Manag 40(3):919–941Google Scholar
  28. Hunt WA, Bespalec DA (1974) Relapse rates after treatment for heroin addiction. J Commun Psychol 2(1):85–87CrossRefGoogle Scholar
  29. Kahneman D, Frederick S (2002) Representativeness revisited: attribute substitution in intuitive judgment. Heuristics Biases 49:81Google Scholar
  30. Khan U, Dhar R (2006) Licensing effect in consumer choice. J Mark Res 43(2):259–266CrossRefGoogle Scholar
  31. Khan U, Dhar R (2007) Where there is a way, is there a will? The effect of future choices on self-control. J Exp Psychol Gen 136(2):277CrossRefGoogle Scholar
  32. Lee JD, See KA (2004) Trust in automation: designing for appropriate reliance. Hum Factors 46(1):50–80CrossRefGoogle Scholar
  33. Lockton D, Harrison D, Stanton NA (2010) The design with intent method: a design tool for influencing user behaviour. Appl Ergon 41(3):382–392CrossRefGoogle Scholar
  34. Meschtscherjakov A, Wilfinger D, Scherndl T, Tscheligi M (2009) Acceptance of future persuasive in-car interfaces towards a more economic driving behaviour. In Proceedings of the 1st International Conference on automotive user interfaces and interactive vehicular applications, pp 81–88Google Scholar
  35. Michon JA (1985) A critical view of driver behavior models: what do we know, what should we do? Human behavior and traffic safety. Springer, Berlib, pp 485–524CrossRefGoogle Scholar
  36. Milkman KL, Rogers T, Bazerman MH (2008) Harnessing our inner angels and demons: what we have learned about want/should conflicts and how that knowledge can help us reduce short-sighted decision making. Perspect Psychol Sci 3(4):324–338CrossRefGoogle Scholar
  37. Montano DE, Kasprzyk D (2015) Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. Health behavior: theory, research and practice. Jossey-Bass, San FranciscoGoogle Scholar
  38. Nudging traffic (2016) How to save lives in a hurry—iNudgeyou. Retrieved from http://inudgeyou.com/archives/260. Accessed 2 Nov 2016.
  39. Oinas-Kukkonen H (2010) Behavior change support systems: a research model and agenda. In International Conference on persuasive technology, pp 4–14CrossRefGoogle Scholar
  40. Oinas-Kukkonen H, Harjumaa M (2009) Persuasive systems design: key issues, process model, and system features. Commun Assoc Inf Syst 24(1):28Google Scholar
  41. Pacaux-Lemoine M-P, Debernard S (2002) Common work space for human–machine cooperation in air traffic control. Control Eng Pract 10(5):571–576CrossRefGoogle Scholar
  42. Pacaux-Lemoine M-P, Flemisch F (2016) Layers of shared and cooperative control, assistance and automation. IFAC-PapersOnLine 49(19):159–164CrossRefGoogle Scholar
  43. Parasuraman R, Manzey DH (2010) Complacency and bias in human use of automation: an attentional integration. Hum Factors 52(3):381–410CrossRefGoogle Scholar
  44. Plotnikoff RC, Brez S, Hotz SB (2000) Exercise behavior in a community sample with diabetes: understanding the determinants of exercise behavioral change. Diabetes Educ 26(3):450–459CrossRefGoogle Scholar
  45. Prochaska JO (2013) Transtheoretical model of behavior change. Encyclopedia of behavioral medicine. Springer, Berlin, pp 1997–2000Google Scholar
  46. Prochaska JO, DiClemente CC (1982) Transtheoretical therapy: toward a more integrative model of change. Psychother Theory Res Pract 19(3):276CrossRefGoogle Scholar
  47. Prochaska JO, DiClemente CC (1986) Toward a comprehensive model of change. Treating addictive behaviors. Springer, Berlin, pp 3–27CrossRefGoogle Scholar
  48. Rasmussen J (1983) Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models. IEEE Trans Syst Man Cybern 3:257–266CrossRefGoogle Scholar
  49. SAE International (2016) ®Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems (Revision Sep 2014). Surface vehicle information report. SAE International, WarrendaleGoogle Scholar
  50. Sheridan TB (2002) Humans and automation: system design and research issues. Wiley, HobokenGoogle Scholar
  51. Shiv B, Fedorikhin A (1999) Heart and mind in conflict: the interplay of affect and cognition in consumer decision making. J Consum Res 26(3):278–292.  https://doi.org/10.1086/209563 CrossRefGoogle Scholar
  52. Sunstein CR (2014) Nudging: a very short guide. J Consum Policy 37(4):583–588CrossRefGoogle Scholar
  53. Sunstein C, Thaler R (2008) Nudge: improving decisions about health, wealth, and happiness. Yale University Press, New HavenGoogle Scholar
  54. Thaler RH, Sunstein CR, Balz JP (2014) Choice architecture. The behavioral foundations of public policy. Princeton University Press, Princeton, pp 428–439Google Scholar
  55. Zhu B, Kaber DB, Zahabi M, Ma J (2017). Effect of feedback type and modality on human motivation. In IEEE International Conference on systems, man, and cybernetics (SMC), pp 2838–2843Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Industrial Engineering and ErgonomicsRWTH Aachen UniversityAachenGermany
  2. 2.Institute for Automotive EngineeringRWTH Aachen UniversityAachenGermany

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