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Under Which Conditions Are Humans Motivated to Delegate Tasks to AI? A Taxonomy on the Human Emotional State Driving the Motivation for AI Delegation

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Marketing and Smart Technologies

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 279))

Abstract

The intensity of human–artificial intelligence (AI) interactions has been growing at a rapid pace. Research has acknowledged a simultaneous significant resistance on the part of users towards AI services on the one hand and a profound acceptance of AI solutions on the other. As a remedy for this ambiguity concerning AI delegation, the author takes the next step to explain the decisive relevant factors. This research introduces the concept of the human emotional state driving the motivation for AI-based task delegation. Precisely, the affective state, as a function of the two independent neurophysiological systems of valence and arousal, determines the motivation for AI delegation in the individual decision situation. The interplay between these two determinants of a human’s affective state results in a four-quadrant taxonomy on AI delegation. For instance, a combination of low arousal and negative valence results in an affective state, which motivates the human to decide in favour of AI delegation; an opposite emotional state of high arousal and positive valence yields a low incentive to apply AI services. The implications of the present research provide novel reasons for the presence and extension of AI services in the fulfilment of human tasks.

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References

  1. Abraham, H., Lee, C., Brady, S., Fitzgerald, C., Mehler, B., Reimer, B., Coughlin, J. F.: Autonomous vehicles, trust, and driving alternatives: a survey of consumer preferences (2016)

    Google Scholar 

  2. Abraham, H., Reimer, B., Seppelt, B., Fitzgerald, C., Mehler, B., Coughlin, J. F.: Consumer interest in automation: Change over one year. In: Proceedings of the Transportation Research Board 97th Annual Meeting, 1–20 (2018)

    Google Scholar 

  3. Achar, C., So, J., Agrawal, N., Duhachek, A.: What we feel and why we buy: the influence of emotions on consumer decision-making. Curr. Opin. Psychol. 10(1), 166–170 (2016)

    Article  Google Scholar 

  4. Aggarwal, P., Mazumdar, T.: Decision delegation: a conceptualization and empirical investigation. Psychol. Mark. 25(1), 71–93 (2008)

    Article  Google Scholar 

  5. Alpert, S.R., Karat, J., Karat, C.M., Brodie, C., Vergo, J.G.: User attitudes regarding a user-adaptive eCommerce web site. User Model. User-Adap. Inter. 13(4), 373–396 (2003)

    Article  Google Scholar 

  6. Alvino, C., Basilico, J.: Learning a personalized homepage. Netflix Technology Blog (2015). https://netflixtechblog.com/learning-a-personalized-homepage-aa8ec670359a. Last accessed 21 Apr 2021

  7. Ameen, N., Tarhini, A., Reppel, A., Anand, A.: Customer experiences in the age of artificial intelligence. Comput. Human Behavior 114(1), 106548 (2021)

    Article  Google Scholar 

  8. Andrade, E.B., Ariely, D.: The enduring impact of transient emotions on decision making. Organ. Behav. Hum. Decis. Process. 109(1), 1–8 (2009)

    Article  Google Scholar 

  9. André, Q., Carmon, Z., Wertenbroch, K., Crum, A., Frank, D., Goldstein, W., Huber, J., Van Boven, L., Weber, B., Yang, H.: Consumer choice and autonomy in the age of artificial intelligence and big data. Cust. Needs Solut. 5(1–2), 28–37 (2018)

    Article  Google Scholar 

  10. Aw, D. J.: When health care is horrifying: treating a patient with a fear of MDs. National Post (2012). https://nationalpost.com/health/when-health-care-is-horrifying-treating-a-patient-with-a-fear-of-doctors. Last accessed 29 Apr 2021

  11. Azer, J., Alexander, M.J.: Conceptualizing negatively valenced influencing behavior: forms and triggers. J. Serv. Manag. 29(3), 468–490 (2018)

    Article  Google Scholar 

  12. Banovsky, M.: How Are You Keeping A Passion For Driving Alive? Petrolicious (2016). https://petrolicious.com/articles/how-are-you-keeping-a-passion-for-driving-alive. Last accessed 07 May 2021

  13. Barrett, L.F.: Solving the emotion paradox: categorization and the experience of emotion. Personal. Soc. Psychol. Rev. 10(1), 20–46 (2006)

    Article  Google Scholar 

  14. Baum, S.D., Goertzel, B., Goertzel, T.G.: How long until human-level AI? results from an expert assessment. Technol. Forecast. Soc. Chang. 78(1), 185–195 (2011)

    Article  Google Scholar 

  15. Beach, L.R.: Broadening the definition of decision making: the role of prechoice screening of options. Psychol. Sci. 4(4), 215–220 (1993)

    Article  Google Scholar 

  16. Bechara, A.: The role of emotion in decision-making: evidence from neurological patients with orbitofrontal damage. Brain Cogn. 55(1), 30–40 (2004)

    Article  Google Scholar 

  17. Bechara, A., Damasio, H., Damasio, A.R.: Emotion, decision making and the orbitofrontal cortex. Cereb. Cortex 10(3), 295–307 (2000)

    Article  Google Scholar 

  18. Beisswingert, B.M., Zhang, K., Goetz, T., Fischbacher, U.: Spillover effects of loss of control on risky decision-making. PLoS ONE 11(3), 0150470 (2016)

    Article  Google Scholar 

  19. Bosch, E., Ihme, K., Drewitz, U., Jipp, M., & Oehl, M.: Why drivers are frustrated: results from a diary study and focus groups. Euro. Transp. Res. Rev. 12(1), (2020)

    Google Scholar 

  20. Bryk, W.: Artificial superintelligence: the coming revolution. Harvard Sci. Rev. (2015). https://harvardsciencereview.org/2015/12/04/artificial-superintelligence-the-coming-revolution-2/. Last accessed 11 Jun 2021

  21. Campbell, D.J.: Task complexity: a review and analysis. Acad. Manag. Rev. 13(1), 40–52 (1988)

    Article  Google Scholar 

  22. Canziani, B., MacSween, S.: Consumer acceptance of voice-activated smart home devices for product information seeking and online ordering. Comput. Human Behavior 119(1), 106714 (2021)

    Article  Google Scholar 

  23. Carter, S., Nielsen, M.: Using artificial intelligence to augment human intelligence. Distill 2(12), 9 (2017)

    Article  Google Scholar 

  24. Castelo, N.: Blurring the line between human and machine: marketing artificial intelligence. ProQuest Dissertations Publishing, New York (2019)

    Google Scholar 

  25. Castelo, N., Bos, M.W., Lehmann, D.R.: Task-dependent algorithm aversion. J. Mark. Res. 56(5), 809–825 (2019)

    Article  Google Scholar 

  26. Chan, E.S.W., Okumus, F., Chan, W.: The applications of environmental technologies in hotels. J. Hosp. Market. Manag. 26(1), 23–47 (2017)

    Google Scholar 

  27. Christie’s: Is artificial intelligence set to become art’s next medium? (2018) https://www.christies.com/features/A-collaboration-between-two-artists-one-human-one-a-machine-9332-1.aspx. Last accessed 15 Mar 2021

  28. Clarke, K., Belk, R.W.: The effects of product involvement and task definition on anticipated consumer effort. Adv. Consum. Res. 6(1), 313–318 (1979)

    Google Scholar 

  29. Clarke, L.: Domino’s Pizza bets on the future of voice ordering with Twilio. Channel Asia (2018). https://www.channelasia.tech/article/648623/domino-pizza-bets-future-voice-ordering-twilio/?fp=2&fpid=1. Last accessed 11 Jun 2021

  30. Constantinou, E., Bogaerts, K., Van Diest, I., Van den Bergh, O.: Inducing symptoms in high symptom reporters via emotional pictures: The interactive effects of valence and arousal. J. Psychosom. Res. 74(3), 191–196 (2013)

    Article  Google Scholar 

  31. Conti, S., Gourvennec, B., Billot, R.: Impact of artificial intelligence in customer service: state of the art. In: Proceedings of the Global Marketing Conference at Seoul, 807–823 (2020)

    Google Scholar 

  32. Costa, H., Macedo, L.: Emotion-based recommender system for overcoming the problem of information overload. In J. M. Corchado, J. Bajo, J. Kozlak, P. Pawlewski, J. M. Molina, V. Julian, R. Azambuja Silveira, R. Unland, S. Giroux (eds.), Highlights on Practical Application of Agents and Multi-agent Systems, 178–189 (2013)

    Google Scholar 

  33. Danise, A.: Women say no thanks to driverless cars, survey finds; Men say tell me more. NerdWallet (2015). https://www.nerdwallet.com/blog/insurance/auto/survey-consumer-fears-self-driving-cars/. Last accessed 22 Apr 2021

  34. Davenport, T., Guha, A., Grewal, D., Bressgott, T.: How artificial intelligence will change the future of marketing. J. Acad. Mark. Sci. 48(1), 24–42 (2020)

    Article  Google Scholar 

  35. De Bellis, E., Johar, G.V.: Autonomous shopping systems: Identifying and overcoming barriers to consumer adoption. J. Retail. 96(1), 74–87 (2020)

    Article  Google Scholar 

  36. Diab, D.L., Pui, S.Y., Yankelevich, M., Highhouse, S.: Lay perceptions of selection decision aids in US and non-US samples. Int. J. Sel. Assess. 19(2), 209–216 (2011)

    Article  Google Scholar 

  37. Dietvorst, B.J., Simmons, J.P., Massey, C.: Algorithm aversion: people erroneously avoid algorithms after seeing them err. J. Exp. Psychol. 144(1), 114–126 (2015)

    Article  Google Scholar 

  38. Dietvorst, B.J., Simmons, J.P., Massey, C.: Overcoming algorithm aversion: people will use imperfect algorithms if they can (even slightly) modify them. Manage. Sci. 64(3), 983–1476 (2018)

    Google Scholar 

  39. DiSalvo, C., Gemperle, F., Forlizzi, J., Montgomery, E.: The hug: an exploration of robotic form for intimate communication. In: Proceedings of the 12th International Workshop on Robot and Human Interactive Communication, 403–408 (2003)

    Google Scholar 

  40. Eastwood, J., Snook, B., Luther, K.: What people want from their professionals: attitudes toward decision-making strategies. J. Behav. Decis. Mak. 25(5), 458–468 (2012)

    Article  Google Scholar 

  41. Gendron, M., Feldman Barrett, L.: Reconstructing the past: a century of ideas about emotion in psychology. Emotion Rev. 1(4), 316–339 (2009)

    Article  Google Scholar 

  42. Grace, K., Salvatier, J., Dafoe, A., Zhang, B., Evans, O.: Viewpoint: when will AI exceed human performance? evidence from AI experts. J. Artific. Intell. Res. 62, 729–754 (2018)

    Article  MathSciNet  Google Scholar 

  43. Guo, X., Shen, Z., Zhang, Y., Wu, T.: Review on the application of artificial intelligence in smart homes. Smart Cities 2(3), 402–420 (2019)

    Article  Google Scholar 

  44. Gursoy, D., Chi, O.H., Lu, L., Nunkoo, R.: Consumers acceptance of artificially intelligent (AI) device use in service delivery. Int. J. Inf. Manage. 49(1), 157–169 (2019)

    Article  Google Scholar 

  45. Haboucha, C.J., Ishaq, R., Shiftan, Y.: User preferences regarding autonomous vehicles. Transp. Res. Part C: Emerg. Technol. 78(1), 37–49 (2017)

    Article  Google Scholar 

  46. Haenlein, M., Kaplan, A.: A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. Calif. Manage. Rev. 61(4), 5–14 (2019)

    Article  Google Scholar 

  47. Häubl, G., Trifts, V.: Consumer decision making in online shopping environments: the effects of interactive decision aids. Mark. Sci. 19(1), 4–21 (2000)

    Article  Google Scholar 

  48. Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transp. Res. Part A: Policy Pract. 94(1), 374–385 (2016)

    Google Scholar 

  49. Hollander, M.A.G., Greene, M.G.: A conceptual framework for understanding iatrophobia. Patient Educ. Couns. 102(11), 2091–2096 (2019)

    Article  Google Scholar 

  50. Howard, D., Dai, D.: Public perceptions of self-driving cars. Transp. Res. Board 93rd Ann. Meet. (2014)

    Google Scholar 

  51. Hyken, S.: Half of people who encounter artificial intelligence don’t even realize it. Forbes (2017). https://www.forbes.com/sites/shephyken/2017/06/10/half-of-people-who-encounter-artificial-intelligence-dont-even-realize-it/?sh=340a96bc745f. Last accessed 19 Mar 2021

  52. Isen, A.M.: Positive affect, cognitive processes, and social behavior. Adv. Exp. Soc. Psychol. 20, 203–253 (1987)

    Article  Google Scholar 

  53. Jussupow, E., Benbasat, I., Heinzl, A.: Why are we averse towards algorithms? a comprehensive literature review on algorithm aversion. In: Proceedings of the 28th European Conference on Information Systems (2020)

    Google Scholar 

  54. Kahneman, D., Tversky, A.: Choices, values, and frames. Am. Psychol. 39(4), 341–350 (2013)

    Article  Google Scholar 

  55. Kapp, B. S., Whalen, P. J., Supple, W. F., & Pascoe, J. P.: Amygdaloid contributions to conditioned arousal and sensory information processing. The Amygdala: Neurobiological Aspects of Emotion, Memory, and Mental Dysfunction. 229–254 (1992)

    Google Scholar 

  56. König, M., Neumayr, L.: Users’ resistance towards radical innovations: the case of the self-driving car. Transport. Res. F: Traffic Psychol. Behav. 44(1), 42–52 (2017)

    Article  Google Scholar 

  57. Kowalczuk, P.: Consumer acceptance of smart speakers: a mixed methods approach. J. Res. Interact. Mark. 12(4), 418–431 (2018)

    Article  Google Scholar 

  58. Kowalski, J., Biele, C., Krzysztofek, K.: Smart home technology as a creator of a super-empowered user. In Karwowski W, Ahram T. (eds.), Intelligent Human Systems Integration 2019, pp. 175–180. Springer (2019)

    Google Scholar 

  59. Kranz, J., Gallenkamp, J., Picot, A.: Exploring the role of control—smart meter acceptance of residential consumers. In: Proceedings of the 16th Americas Conference on Information Systems (2010)

    Google Scholar 

  60. Kunz, W.H., Heinonen, K., Lemmink, J.G.A.M.: Future service technologies: Is service research on track with business reality? J. Serv. Mark. 33(4), 479–487 (2019)

    Article  Google Scholar 

  61. Kyriakidis, M., Happee, R., De Winter, J.C.F.: Public opinion on automated driving: results of an international questionnaire among 5000 respondents. Transport. Res. F: Traffic Psychol. Behav. 32(1), 127–140 (2015)

    Article  Google Scholar 

  62. Lang, A., Chung, Y., Lee, S., Zhao, X.: It’s the product: do risky products compel attention and elicit arousal in media users? Health Commun. 17(3), 283–300 (2005)

    Article  Google Scholar 

  63. Lee, Y., Kozar, K., Larsen, K.R.: The technology acceptance model: past, present, and future. Commun. Assoc. Inf. Syst. 12(1), 50 (2003)

    Google Scholar 

  64. Leino, J., Räihä, K. J.: Case Amazon: Ratings and reviews as part of a recommendations. In: RecSys’07 Proceedings of the 2007 ACM Conference on Recommender Systems, 137–140 (2007)

    Google Scholar 

  65. Leotti, L.A., Iyengar, S.S., Ochsner, K.N.: Born to choose: the origins and value of the need for control. Trends Cogn. Sci. 14(10), 457–463 (2010)

    Article  Google Scholar 

  66. Leung, E., Paolacci, G., Puntoni, S.: Man versus machine: resisting automation in identity-based consumer behavior. J. Mark. Res. 55(6), 818–831 (2018)

    Article  Google Scholar 

  67. Lewinsohn, S., Mano, H.: Multi-attribute choice and affect: the influence of naturally occurring and manipulated moods on choice processes. J. Behav. Decis. Mak. 6(1), 33–51 (1993)

    Article  Google Scholar 

  68. Linden, G., Smith, B., York, J.: Amazon com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)

    Article  Google Scholar 

  69. Lindquist, K.A., Barrett, L.F.: Constructing emotion: the experience of fear as a conceptual act. Psychol. Sci. 19(9), 898–903 (2008)

    Article  Google Scholar 

  70. Linville, P.W., Jones, E.E.: Polarized appraisals of out-group members. J. Pers. Soc. Psychol. 38(5), 689–703 (1980)

    Article  Google Scholar 

  71. Longoni, C., Bonezzi, A., Morewedge, C.: Resistance to medical artificial intelligence. J. Consumer Res. 46(4), 629–650 (2019)

    Article  Google Scholar 

  72. Longoni, C., Cian, L.: Artificial Intelligence in utilitarian vs. hedonic contexts: The “Word-of-Machine” effect. J. Market. (2020)

    Google Scholar 

  73. Lu, L., Cai, R., Gursoy, D.: Developing and validating a service robot integration willingness scale. Int. J. Hosp. Manag. 80(1), 36–51 (2019)

    Article  Google Scholar 

  74. Mano, H.: Emotional states and decision making. Adv. Consum. Res. 17(1), 577–584 (1990)

    Google Scholar 

  75. Mano, H.: Judgments under distress: Assessing the role of unpleasantness and arousal in judgment formation. Organ. Behav. Hum. Decis. Process. 52(2), 216–245 (1992)

    Article  Google Scholar 

  76. Mano, H.: Risk-taking, framing effects, and affect. Organ. Behav. Hum. Decis. Process. 51(1), 38–58 (1994)

    Article  Google Scholar 

  77. Mano, H.: Affect and persuasion: The influence of pleasantness and arousal on attitude formation and message elaboration. Psychol. Mark. 14(4), 315–335 (1997)

    Article  Google Scholar 

  78. Mehrabian, A., Russell, J.A.: An approach to environmental psychology. Electronics Power 18(5), 167–170 (1974)

    Google Scholar 

  79. Meng, Y., Zhang, W., Zhu, H., Shen, X.S.: Securing consumer IoT in the smart home: architecture, challenges, and countermeasures. IEEE Wirel. Commun. 25(6), 53–59 (2018)

    Article  Google Scholar 

  80. Minh, V.T., Khanna, R.: Application of artificial intelligence in smart kitchen. Int. J. Innov. Technol. Interdisciplin. Sci. 1(1), 1–8 (2018)

    Google Scholar 

  81. Mills, R.C.: A new understanding of self: the role of affect, state of mind, self-understanding, and intrinsic motivation. Journal of Experimental Education 60(1), 67–81 (1991)

    Article  Google Scholar 

  82. Mitchell, A.: Involvement: a potentially important mediator of consumer behavior. Adv. Consumer Res. 6(1), 191–196 (1979). http://acrwebsite.org/volumes/5703/volumes/v06/NA-06. Last accessed 14 May 2021

  83. Mitchell, D.: The nexus between decision making and emotion regulation: a review of convergent neurocognitive substrates. Behav. Brain Res. 217(1), 215–231 (2011)

    Article  Google Scholar 

  84. Mizgajski, J., Morzy, M.: Affective recommender systems in online news industry: how emotions influence reading choices. User Model. User-Adap. Inter. 29(2), 345–379 (2019)

    Article  Google Scholar 

  85. Müller, V. C., Bostrom, N.: Future progress in artificial intelligence: a survey of expert opinion. Fundamental Issues Artific. Intell., 555–572 (2016)

    Google Scholar 

  86. Nass, C., Jonsson, I. M., Harris, H., Reaves, B., Endo, J., Brave, S., Takayama, L.: Improving automotive safety by pairing driver emotion and car voice emotion. In: Proceedings of the Conference on Human Factors in Computing Systems, 1973–1976 (2005)

    Google Scholar 

  87. Nawijn, J., Mitas, O., Lin, Y., Kerstetter, D.: How do we feel on vacation? a closer look at how emotions change over the course of a trip. J. Travel Res. 52(2), 265–274 (2012)

    Article  Google Scholar 

  88. Nielsen: (Smart) Speaking my language: despite their vast capabilities, smart speakers are all about the music (2018). https://www.nielsen.com/us/en/insights/article/2018/smart-speaking-my-language-despite-their-vast-capabilities-smart-speakers-all-about-the-music/. Last accessed 05 May 2021

  89. Niven, K.: Affect. In Gellman, M. D., Turner, J. R. (eds.), Encyclopedia of Behavioral Medicine, pp. 49–50. Springer (2013)

    Google Scholar 

  90. Nowak, K. L., Biocca, F.: The effect of the agency and anthropomorphism on users’ sense of telepresence, copresence, and social presence in virtual environments. Presence: Teleoperators Virtual Environ. 12(5), 481–494 (2003)

    Google Scholar 

  91. Ostrom, A. L., Fotheringham, D., Bitner, M. J.: Customer acceptance of AI in service encounters: understanding antecedents and consequences. In Maglio, P. P., Kieliszewski, C. A., Spohrer, J. C., Lyons, K., Patrício, L., Sawatani, Y. (eds.), Handbook of Service Science, pp. 77–103. Springer (2019)

    Google Scholar 

  92. Ovide, S.: Driverless cars go humble to get real. The New York Times (2020). https://www.nytimes.com/2020/10/26/technology/driverless-cars.html. Last accessed 21 Apr 2021

  93. Pajkovic, N.: Algorithms and taste-making: exposing the Netflix recommender system’s operational logics. Convergence (2021)

    Google Scholar 

  94. Palmeira, M., Spassova, G.: Consumer reactions to professionals who use decision aids. Eur. J. Mark. 49(3/4), 302–326 (2015)

    Article  Google Scholar 

  95. Park, E., Kim, S., Kim, Y.S., Kwon, S.J.: Smart home services as the next mainstream of the ICT industry: determinants of the adoption of smart home services. Univ. Access Inf. Soc. 17(1), 175–190 (2018)

    Article  Google Scholar 

  96. Park, K., Kwak, C., Lee, J., Ahn, J.H.: The effect of platform characteristics on the adoption of smart speakers: empirical evidence in South Korea. Telematics Inform. 35(8), 2118–2132 (2018)

    Article  Google Scholar 

  97. Paulus, M.P., Yu, A.J.: Emotion and decision-making: affect-driven belief systems in anxiety and depression. Trends Cogn. Sci. 16(9), 476–483 (2012)

    Article  Google Scholar 

  98. Payre, W., Cestac, J., Delhomme, P.: Intention to use a fully automated car: attitudes and a priori acceptability. Transp. Res. Part F: Traffic Psychol. Behav., 27(B), 252–263 (2014)

    Google Scholar 

  99. Peters, E., Västfjäll, D., Gärling, T., Slovic, P.: Affect and decision making: a “hot” topic. J. Behav. Decis. Mak. 19(2), 79–85 (2006)

    Article  Google Scholar 

  100. Petty, R. E., Cacioppo, J. T.: The elaboration likelihood model of persuasion. In Petty, R. E., Cacioppo, J. T. (Eds.), Communication and Persuasion, pp. 1–24. Springer (1986).

    Google Scholar 

  101. Pfister, H.R., Böhm, G.: The multiplicity of emotions: a framework of emotional functions in decision making. Judgm. Decis. Mak. 3(1), 5–17 (2008)

    Google Scholar 

  102. Phelps, E.A., LeDoux, J.E.: Contributions of the amygdala to emotion processing: from animal models to human behavior. Neuron 48(2), 175–187 (2005)

    Article  Google Scholar 

  103. Porter, M. E., Heppelmann, J. E.: How smart, connected products are transforming competition. Harvard Bus. Rev. (2014). https://hbr.org/2014/11/how-smart-connected-products-are-transforming-competition. Last accessed 11 Jun 2021

  104. Posner, J., Russell, J.A., Peterson, B.S.: The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17(3), 715–734 (2005)

    Article  Google Scholar 

  105. Promberger, M., Baron, J.: Do patients trust computers? J. Behav. Decis. Mak. 19(5), 455–468 (2006)

    Article  Google Scholar 

  106. Raue, M., D’Ambrosio, L.A., Ward, C., Lee, C., Jacquillat, C., Coughlin, J.F.: The influence of feelings while driving regular cars on the perception and acceptance of self-driving cars. Risk Anal. 39(2), 358–374 (2019)

    Article  Google Scholar 

  107. Reed, A., Forehand, M.R., Puntoni, S., Warlop, L.: Identity-based consumer behavior. Int. J. Res. Mark. 29(4), 310–321 (2012)

    Article  Google Scholar 

  108. Reeve, J.: The interest-enjoyment distinction in intrinsic motivation. Motiv. Emot. 13(2), 83–103 (1989)

    Article  Google Scholar 

  109. Reisenzein, R.: Pleasure-arousal theory and the intensity of emotions. J. Pers. Soc. Psychol. 67(3), 525–539 (1994)

    Article  Google Scholar 

  110. Rijsdijk, S.A., Hultink, E.J.: “Honey, have you seen our hamster?” consumer evaluations of autonomous domestic products. J. Prod. Innov. Manag. 20(3), 204–216 (2003)

    Article  Google Scholar 

  111. Rijsdijk, S.A., Hultink, E.J.: How today’s consumers perceive tomorrow’s smart products. J. Prod. Innov. Manag. 26(1), 24–42 (2009)

    Article  Google Scholar 

  112. Risteska Stojkoska, B.L., Trivodaliev, K.V.: A review of internet of things for smart home: challenges and solutions. J. Clean. Prod. 140, 1454–1464 (2017)

    Article  Google Scholar 

  113. Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)

    Article  Google Scholar 

  114. Russell, J.A.: Core affect and the psychological construction of emotion. Psychol. Rev. 110(1), 145–172 (2003)

    Article  Google Scholar 

  115. Schafer, J. B., Konstan, J., & Riedl, J.: Recommender systems in e-commerce. In: Proceedings of the 1st ACM Conference on Electronic Commerce, 158–166 (1999).

    Google Scholar 

  116. Schmelzer, R.: Should we be afraid of AI? Forbes (2019).https://www.forbes.com/sites/cognitiveworld/2019/10/31/should-we-be-afraid-of-ai/?sh=569bc8b4331d. Last accessed 16 Mar 2021

  117. Schoettle, B., Sivak, M.: A survey of public opinion about connected vehicles in the U.S., the U.K., and Australia. In: Proceedings of the 2014 International Conference on Connected Vehicles and Expo, 687–692 (2014)

    Google Scholar 

  118. Schwartz, E. H.: Voice assistants see uptick in daily use during the pandemic: Report. Voicebot (2020). https://voicebot.ai/2020/05/01/voice-assistants-see-uptick-in-daily-use-during-the-pandemic-report/. Last accessed 21 Apr 2021

  119. Schwarz, N., Clore, G.L.: Mood, misattribution, and judgments of well-being: informative and directive functions of affective states. J. Pers. Soc. Psychol. 45(3), 513–523 (1983)

    Article  Google Scholar 

  120. Schweitzer, F., Van den Hende, E.A.: To be or not to be in thrall to the march of smart products. Psychol. Mark. 33(10), 830–842 (2016)

    Article  Google Scholar 

  121. Seymour, B., Dolan, R.: Emotion, decision making, and the amygdala. Neuron 58(5), 662–671 (2008)

    Article  Google Scholar 

  122. Shaffer, V.A., Probst, C.A., Merkle, E.C., Arkes, H.R., Medow, M.A.: Why do patients derogate physicians who use a computer-based diagnostic support system? Med. Decis. Making 33(1), 108–118 (2013)

    Article  Google Scholar 

  123. Shankar, V.: How Artificial Intelligence (AI) is reshaping retailing. J. Retail. 94(4), vi–xi (2018)

    Article  Google Scholar 

  124. Sharar, S.R., Alamdari, A., Hoffer, C., Hoffman, H.G., Jensen, M.P., Patterson, D.R.: Circumplex model of affect: a measure of pleasure and arousal during virtual reality distraction analgesia. Games Health J. 5(3), 197–202 (2016)

    Article  Google Scholar 

  125. Shetty, D.: Women Across 17 Countries Report Increased Stress, Household Work Due To Covid-19. Forbes (2021). https://www.forbes.com/sites/dishashetty/2021/01/29/women-across-17-countries-report-increased-stress-household-work-due-to-covid-19/?sh=4ece1b852f0a. Last accessed 03 Jun 2021

  126. So, J., Achar, C., Han, D.H., Agrawal, N., Duhachek, A., Maheswaran, D.: The psychology of appraisal: specific emotions and decision-making. J. Consum. Psychol. 25(3), 359–371 (2015)

    Article  Google Scholar 

  127. Sohn, K., Kwon, O.: Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telematics Inform. 47(December), 1–14 (2020)

    Google Scholar 

  128. Stein, M.B., Simmons, A.N., Feinstein, J.S., Paulus, M.P.: Increased amygdala and insula activation during emotion processing in anxiety-prone subjects. Am. J. Psychiatry 164(2), 318–327 (2007)

    Article  Google Scholar 

  129. Svebak, S., Stoyva, J.: High arousal can be pleasant and exciting: the theory of psychological reversals. Biofeedback Self Regul. 6(1), 443 (1981)

    Article  Google Scholar 

  130. Tkalčič, M., Burnik, U., Košir, A.: Using affective parameters in a content-based recommender system for images. User Model. User-Adap. Inter. 20(4), 279–311 (2010)

    Article  Google Scholar 

  131. Tseng, A., Bansal, R., Liu, J., Gerber, A.J., Goh, S., Posner, J., Colibazzi, T., Algermissen, M., Chiang, I.-C., Russel, J.A., Peterson, B.S.: Using the circumplex model of affect to study valence and arousal ratings of emotional faces by children and adults with autism spectrum disorders. J. Autism Dev. Disord. 44(6), 1332–1346 (2014)

    Article  Google Scholar 

  132. Van’t Wout, M., Kahn, R. S., Sanfey, A. G., Aleman, A.: Affective state and decision-making in the Ultimatum Game. Experiment. Brain Res., 169(4), 564–568 (2006)

    Google Scholar 

  133. 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 

  134. Wang, X., McGill, T.J., Klobas, J.E.: I want it anyway: consumer perceptions of smart home devices. J. Comput. Inf. Syst. 60(5), 437–447 (2020)

    Google Scholar 

  135. Watson, D., Tellegen, A.: Toward a consensual structure of mood. Psychol. Bull. 98(2), 219–235 (1985)

    Article  Google Scholar 

  136. Wei, K., Huang, J., Fu, S.: A survey of E-commerce recommender systems. In: Proceedings of the 2007 International Conference on Service Systems and Service Management (2007)

    Google Scholar 

  137. Williams, R.: Domino’s lets AI assistant “Dom” handle incoming phone orders. Marketing Dive (2018). https://www.marketingdive.com/news/dominos-lets-ai-assistant-dom-handle-incoming-phone-orders/522163/. Last accessed 17 Mar 2021

  138. Yang, H., Lee, H., Zo, H.: User acceptance of smart home services: an extension of the theory of planned behavior. Ind. Manag. Data Syst. 117(1), 68–89 (2017)

    Article  Google Scholar 

  139. Zajonc, R.B.: The interaction of affect and cognition. Approach. Emotion 239(1), 239–246 (1984)

    Google Scholar 

  140. Zeelenberg, M., Nelissen, R.M.A., Breugelmans, S.M., Pieters, R.: On emotion specificity in decision making: Why feeling is for doing. Judgm. Decis. Mak. 3(1), 18–27 (2008)

    Google Scholar 

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  1. University of St. Gallen, Dufourstrasse 50, 9000, St. Gallen, Switzerland

    Anna Bouwer

  2. Institute of Behavioral Science and Technology, Torstrasse 25, 9000, St. Gallen, Switzerland

    Anna Bouwer

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  3. Suffolk Business School, University of Suffolk, Ipswich, UK

    Luiz Moutinho

  4. University of Maia - ISMAI, Maia, Portugal

    José Paulo Marques dos Santos

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Bouwer, A. (2022). Under Which Conditions Are Humans Motivated to Delegate Tasks to AI? A Taxonomy on the Human Emotional State Driving the Motivation for AI Delegation. In: Reis, J.L., López, E.P., Moutinho, L., Santos, J.P.M.d. (eds) Marketing and Smart Technologies. Smart Innovation, Systems and Technologies, vol 279. Springer, Singapore. https://doi.org/10.1007/978-981-16-9268-0_4

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