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The psychological and ethological antecedents of human consent to techno-empowerment of autonomous office assistants

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Abstract

Human organizations’ adoption of the paradigm of the Fourth Industrial Revolution is associated with the growth of techno-empowerment, which is the process of transferring autonomy in decision-making to intelligent machines. Particular persuasive strategies have been identified that may coax people to use intelligent devices. However, there is a substantial research gap regarding what antecedents influence human intention to assign decision-making autonomy to artificial agents. In this study, ethological and evolutionary concepts are applied to explain the drivers for autonomous assistants’ techno-empowerment. The method used in the study was a 4 × 2 between-subject experiment made with 278 persons. The research tool used to collect the data was an online survey. The results show that more positive attitudes and higher trust, perceived usefulness, and perceived ease of use are correlated with higher intention to allow the autonomous assistant independence in decision-making. Second, the results suggest that the more human-like a non-human agent is, the higher the intention to empower it—but only if this agent simultaneously provides functional and visual anthropomorphic cues explainable by the mimicry effect.

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References

  • Abe R (2019) Introducing autonomous buses and taxis: quantifying the potential benefits in Japanese transportation systems. Transport Res A Policy Practice 126:94–113

    Article  Google Scholar 

  • Ahmad W, Mohamad N, Rizal A (2020) Understanding user emotions through interaction with persuasive technology. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2020.0110926

    Article  Google Scholar 

  • Akerkar R (2019) Artificial intelligence for business. Springer, New York

    Book  Google Scholar 

  • Aleshinloye KD, Woosnam KM, Tasci ADA, Ramkissoon H (2021) Antecedents and outcomes of resident empowerment through tourism. J Travel Res. https://doi.org/10.1177/0047287521990437

    Article  Google Scholar 

  • Aly S, Tyrychtr J, Vrana I (2021) Optimizing design of smart workplace through multi-objective programming. Appl Sci 11(7):3042

    Article  Google Scholar 

  • Appelbaum SH, Karasek R, Lapointe F, Quelch K (2015) Employee empowerment: factors affecting the consequent success or failure (Part II). Ind Commer Train 47(1):23–30

    Article  Google Scholar 

  • Bansal P, Kockelman KM (2018) Are we ready to embrace connected and self-driving vehicles? A case study of Texans. Transportation 45:641–675

    Article  Google Scholar 

  • Barnes C, Mertens DM (2008) An ethical agenda in disability research: rhetoric or reality. In: Mertens DM, Ginsberg PE (eds) The handbook of social research ethics. SAGE, London, pp 485–493

    Google Scholar 

  • Benzidia S, Makaoui N, Bentahara O (2021) The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technol Forecast Soc Change 165:120557

    Article  Google Scholar 

  • Bickmore TW, Caruso L, Clough-Gorr K, Heeren T (2005) ‘It’s just like you talk to a friend’ relational agents for older adults. Interact Comput 17(6):711–735

    Article  Google Scholar 

  • Borau S, Otterbring T, Laporte S, FossoWamba S (2021) The most human bot: female gendering increases humanness perceptions of bots and acceptance of AI. Psychol Mark. https://doi.org/10.1002/mar.21480

    Article  Google Scholar 

  • Bromuri S, Henkel AP, Iren D, Urovi V (2020) Using AI to predict service agent stress from emotion patterns in service interactions. J Serv Manage. https://doi.org/10.1108/JOSM-06-2019-0163

    Article  Google Scholar 

  • Brooks, B. (2021). Get ready for self-driving banks. Financial Times. Retrieved from https://www.ft.com/content/c1caca5b-01f7-41be-85a4-3ecb883f2417

  • Chartrand TL, Bargh JA (1999) The chameleon effect: the perception–behavior link and social interaction. J Pers Soc Psychol 76(6):893–910

    Article  Google Scholar 

  • Chaudhuri T, Yeatts DE, Cready CM (2013) Nurse aide decision making in nursing homes: factors affecting empowerment. J Clin Nurs 22(17–18):2572–2585

    Article  Google Scholar 

  • Cheung MFY, To WM (2017) The influence of the propensity to trust on mobile users’ attitudes toward in-app advertisements: an extension of the theory of planned behavior. Comput Hum Behav 76:102–111

    Article  Google Scholar 

  • Chung TS, Wedel M, Rust RT (2016) Adaptive personalization using social networks. J Acad Mark Sci 44(1):66–87

    Article  Google Scholar 

  • Dalziell AH, Welbergen JA (2016) Mimicry for all modalities. Ecol Lett 19(6):609–619

    Article  Google Scholar 

  • Damioli G, Van Roy V, Vertesy D (2021) The impact of artificial intelligence on labor productivity. Eurasian Bus Rev 11:1–25

    Article  Google Scholar 

  • Daugherty PR, Wilson HJ (2018) Human + machine: reimagining work in the age of AI. Harvard Business Review Press, Harvard

    Google Scholar 

  • Davis FD, Bagozzi R, Warshaw PR (1989) User acceptance of computer technology: a comparison of two theoretical models. Manage Sci 35:982–1003

    Article  Google Scholar 

  • Evertsz R, Thangarajah J, Yadav N, Ly T (2015) A framework for modelling tactical decision-making in autonomous systems. J Syst Softw 110:222–238

    Article  Google Scholar 

  • Eyssel F, Hegel F, Horstmann G, Wagner C (2010) Anthropomorphic inferences from emotional nonverbal cues: a case study. In: 19th international symposium in robot and human interactive communication. IEEE, pp 646–651

  • Fischer K, Lohan K, Foth K (2012) Levels of embodiment: Linguistic analyses of factors influencing HRI. In: 7th ACM/IEEE international conference on human‐robot interaction. IEEE, pp 463–470

  • Fogg BJ (2003) Persuasive technology: using computers to change what we think and do. Morgan Kaufmann Publishers, Boston

    Book  Google Scholar 

  • Goddard MA, Davies ZG, Guenat S, Ferguson MJ, Fisher JC, Akanni A, Antoniou C (2021) A global horizon scan of the future impacts of robotics and autonomous systems on urban ecosystems. Nat Ecol Evol 5(2):219–230

    Article  Google Scholar 

  • Haboucha CJ, Ishaq R, Shiftan Y (2017) User preferences regarding autonomous vehicles. Transp Res C 78:37–49

    Article  Google Scholar 

  • Hancock PA (2016) Imposing limits on autonomous systems. Ergonomics 60(2):284–291

    Article  Google Scholar 

  • Horton RP, Buck T, Waterson PE, Clegg CW (2001) Explaining intranet use with the technology acceptance model. J Inf Technol 16(4):237–249

    Article  Google Scholar 

  • Huang MH, Rust RT (2021) A strategic framework for artificial intelligence in marketing. J Acad Mark Sci 49:30–50

    Article  Google Scholar 

  • Hudson J, Orviska M, Hunady J (2019) People’s attitudes to autonomous vehicles. Transport Res A Policy Pract 121:164–176. https://doi.org/10.1016/j.tra.2018.08.018

    Article  Google Scholar 

  • Hulse LM, Xie H, Galea ER (2018) Perceptions of autonomous vehicles: relationships with road users, risk, gender and age. Saf Sci 102:1–13

    Article  Google Scholar 

  • Ivanov S, Webster C (2018) Adoption of robots, artificial intelligence and service automation by travel, tourism and hospitality companies—a cost-benefit analysis. In: Marinov V, Vodenska M, Assenova MDE (eds) Traditions and innovations in contemporary tourism. Cambridge Scholars Publishing, pp 190–203

    Google Scholar 

  • Joo J, Sang Y (2013) Exploring Koreans’ smartphone usage: an integrated model of the technology acceptance model and uses and gratifications theory. Comput Hum Behav 29(6):2512–2518

    Article  Google Scholar 

  • Karimi L, Leggat SG, Bartram T, Afshari L, Sarkeshik S, Verulava T (2021) Emotional intelligence: predictor of employees’ wellbeing, quality of patient care, and psychological empowerment. BMC Psychol. https://doi.org/10.1186/s40359-021-00593-8

    Article  Google Scholar 

  • Kędzierski J, Kaczmarek P, Dziergwa M, Tchoń K (2015) Design for a robotic companion. Int J Humanoid Rob 12(01):1550007

    Article  Google Scholar 

  • Kessel RT (2005) Apparent reliability: conditions for reliance on supervised automation. Defence R&D Canada, Atlantic

    Google Scholar 

  • Khalili H, Sameti A, Sheybani H (2016) A study on the effect of empowerment on customer orientation of employees. Glob Bus Rev 17(1):38–50. https://doi.org/10.1177/0972150915610674

    Article  Google Scholar 

  • Lai PC (2017) Security as an extension to TAM Model: consumers’ intention to use a single platform E-Payment. Asia-Pac J Manage Res Innov 13(3–4):110–119. https://doi.org/10.1177/2319510x18776405

    Article  Google Scholar 

  • Lee S, Kim Y, Kahng H, Lee S, Chung S, Cheong T, Shin K, Park J, Kim SB (2019) Intelligent traffic control for autonomous vehicle systems based on machine learning. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2019.113074

    Article  Google Scholar 

  • MacDonald S, MacIntyre P (1997) The generic job satisfaction scale: scale development and its correlates. Employee Assist Q 13(2):1–16

    Article  Google Scholar 

  • Makridakis S (2017) The forthcoming artificial intelligence (AI) revolution: its impact on society and firms. Futures 90:46–60

    Article  Google Scholar 

  • McKnight DH, Carter M, Thatcher JB, Clay PF (2011) Trust in a specific technology. ACM Trans Manage Inf Syst 2(2):1–25. https://doi.org/10.1145/1985347.1985353

    Article  Google Scholar 

  • McLain D, Hackman K (1999) Trust, risk, and decision-making in organizational change. Public Adm Q 23(2):152–176

    Google Scholar 

  • McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Ann Rev Sociol 27:415–444

    Article  Google Scholar 

  • Mendling J, Recker J, Reijers HA, Leopold H (2019) An empirical review of the connection between model viewer characteristics and the comprehension of conceptual process models. Inf Syst Front 21(5):1111–1135

    Article  Google Scholar 

  • Modliński A, Gladden M (2021a) An organizational metaphor for the 4th industrial revolution: the organization as Cyborg. World Futures. https://doi.org/10.1080/02604027.2021.1996187

    Article  Google Scholar 

  • Modliński A, Gladden M (2021b) Applying Ethology to design human-oriented technology. An experimental study on the signalling role of the labelling effect. Human Technology 17(2)

  • Modliński A, Skowroński D (2021). Robopowers? The phenomenon of techno-empowerment in the socio-organizational context (submitted paper)

  • Modlinski A, Fortuna P, Rożnowski B (2022) Human–machine trans roles conflict in the organization: how sensitive are customers to intelligent robots replacing the human workforce? Int J Consum Stud. https://doi.org/10.1111/ijcs.12811

    Article  Google Scholar 

  • Modliński A, Gwiaździński E, Karpińska-Krakowiak M (2022) The effects of religiosity and gender on attitudes and trust toward autonomous vehicles. J High Technol Manage Res 33(1)

  • Molina-Mula J, Gallo-Estrada J (2020) Impact of nurse-patient relationship on quality of care and patient autonomy in decision-making. Int J Environ Res Public Health 17(3):835

    Article  Google Scholar 

  • Mori M, MacDorman K, Kageki N (2012) The uncanny valley [from the field]. IEEE Robot Autom Mag 19(2):98–100

    Article  Google Scholar 

  • Nass C, Moon Y (2000) Machines and mindlessness: social responses to computers. J Soc Issues 56(1):81–103. https://doi.org/10.1111/0022-4537.00153

    Article  Google Scholar 

  • Natarajan M, Gombolay M (2020). Effects of anthropomorphism and accountability on trust in human robot interaction. In: Proceedings of the 2020 ACM/IEEE international conference on HRI. ACM/IEEE, pp 33–42

  • Nysveen H, Pederson PE, Thorbjørnsen H (2005) Intentions to use mobile services: antecedents and cross-service comparisons. JAMS 33(3):330–346

    Article  Google Scholar 

  • Oinas-Kukkonen H, Harjumaa M (2008) A systematic framework for designing and evaluating persuasive systems. In: Oinas-Kukkonen H, Hasle P, Harjumaa M, Segerståhl K, Øhrstrøm P (eds) Persuasive technology. PERSUASIVE 2008. Lecture notes in computer science, vol 5033. Springer, Berlin, Heidelberg

    Google Scholar 

  • Pak R, Fink N, Price M, Bass B, Sturre L (2012) Decision support aids with anthropomorphic characteristics influence trust and performance in younger and older adults. Ergonomics 55(9):1059–1072

    Article  Google Scholar 

  • Phillips-Wren G, Jain L (2006) Artificial Intelligence for Decision Making. In: Gabrys B, Howlett RJ, Jain LC (eds) Knowledge-based intelligent information and engineering systems. KES 2006. Lecture notes in computer science, vol 4252. Springer, Berlin, Heidelberg

    Google Scholar 

  • Pillai R, Sivathanu B (2020) Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations. Benchmarking Int J. https://doi.org/10.1108/bij-04-2020-0186

    Article  Google Scholar 

  • Qiu L, Benbasat I (2009) Evaluating anthropomorphic product recommendation agents: a social relationship perspective to designing information systems”. J Manage Inf Syst 25(4):145–182

    Article  Google Scholar 

  • Ritvo H (2007) On the animal turn. Daedalus 136(4):118–122

    Article  Google Scholar 

  • Ruijten PA, Haans A, Ham J, Midden CJ (2019) Perceived humanlikeness of social robots: testing the Rasch model as a method for measuring anthropomorphism. Int J Soc Robot 11(3):477–494

    Article  Google Scholar 

  • Salazar J, Pfaffenberg C, Salazar L (2006) Locus of control vs. employee empowerment and the relationship with hotel managers’ job satisfaction. J Hum Resour Hospital Tour 5(1):1–15

    Article  Google Scholar 

  • Salloum SA, Al-Emran M (2019) Factors affecting the adoption of e-payment systems by university students: extending the TAM with trust. Int J Electron Bus 14(4):371

    Article  Google Scholar 

  • Sarter NB, Woods DD (1997) Team play with a powerful and independent agent: operational experiences and automation surprises on the Airbus A-320. Hum Factors 39(4):553–569

    Article  Google Scholar 

  • Shaffer VA, Probst CA, Merkle EC, Arkes HR, Medow MA (2013) Why do patients derogate physicians who use a computer-based diagnostic support system? Med Decis Making 33(1):108–118

    Article  Google Scholar 

  • Siegall M, Gardner S (2000) Contextual factors of psychological empowerment. Pers Rev 29(6):703–722

    Article  Google Scholar 

  • Sohrabpour V, Oghazi P, Toorajipour R, Nazarpour A (2020) Export sales forecasting using artificial intelligence. Technol Forecast Soc Chang. https://doi.org/10.1016/j.techfore.2020.120480

    Article  Google Scholar 

  • Spreitzer GM (1995) Psychological empowerment in the workplace: dimensions, measurement, and validation. Acad Manage J 38:1442–1465

    Article  Google Scholar 

  • Venkatesh V, Morris M, Davis GB, Davis F (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425. https://doi.org/10.2307/30036540

    Article  Google Scholar 

  • Virmani A (2002) A new development paradigm: employment, entitlement and empowerment. Glob Bus Rev 3(2):225–245

    Article  Google Scholar 

  • Watson DP, Scheidt DH (2005) Autonomous systems. Johns Hopkins APL Techn Dig 26(4)

  • Welz A (2020) Decoy tactics: can fake concrete penguins help save the real thing? Retrieved from https://www.theguardian.com/environment/2020/apr/15/decoy-tactics-can-fake-concrete-penguins-help-save-the-real-thing-aoe

  • Wood W (2000) Attitude change: persuasion and social influence. Annu Rev Psychol 51:539–570

    Article  Google Scholar 

  • Wu L-H, Wu L-C, Chang S-C (2016) Exploring consumers’ intention to accept smartwatch. Comput Hum Behav 64:383–392

    Article  Google Scholar 

  • Wynhoff I, van Langevelde F (2017) Phengaris (Maculinea) teleius butterflies select host plants close to Myrmica ants for oviposition, but P. nausithous do not. Entomol Exp Appl 165(1):9–18

    Article  Google Scholar 

  • Xu K, Lombard M (2017) Persuasive computing: feeling peer pressure from multiple computer agents. Comp Hum Behav 74:152–162

    Article  Google Scholar 

  • Yam KC, Bigman YE, Tang PM, Ilies R, De Cremer D, Soh H, Gray K (2020) Robots at work: people prefer—and forgive—service robots with perceived feelings. J Appl Psychol. https://doi.org/10.1037/apl0000834

    Article  Google Scholar 

  • Zhang F (2021) Construction of internal management system of business strategic planning based on artificial intelligence. IseB. https://doi.org/10.1007/s10257-021-00510-x

    Article  Google Scholar 

  • Zhao S (2003) Toward a taxonomy of copresence. Presence Teleoper Virtual Environ 12(5):445–455

    Article  Google Scholar 

  • Ziamou P, Ratneshwar S (2003) Innovations in product functionality: when and why are explicit comparisons effective. J Market 67:49–61

    Article  Google Scholar 

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Appendix 1 Experimental stimuli

Appendix 1 Experimental stimuli

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Modliński, A. The psychological and ethological antecedents of human consent to techno-empowerment of autonomous office assistants. AI & Soc 38, 647–663 (2023). https://doi.org/10.1007/s00146-022-01534-8

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