Less Artificial, More Intelligent: Understanding Affinity, Trustworthiness, and Preference for Digital Humans

References

• Badcock NA, Mousikou P, Mahajan Y, de Lissa P, Thie J, McArthur G (2013) Validation of the Emotiv Epoc® EEG gaming system for measuring research quality auditory ERPs. PeerJ 1:e38.Crossref, Google Scholar
• Baird A, Maruping LM (2021) The next generation of research on IS use: A theoretical framework of delegation to and from agentic IS artifacts. MIS Quart. 45(1):315–341.Crossref, Google Scholar
• Başar E, Başar-Eroğlu C, Karakaş S, Schürmann M (1999) Are cognitive processes manifested in event-related gamma, alpha, theta and delta oscillations in the EEG? Neuroscience Lett. 259(3):165–168.Crossref, Google Scholar
• Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Ser. B Statist. Methodology 57(1):289–300.Crossref, Google Scholar
• Berger H (1929) Über das elektrenkephalogramm des menschen. Archiv Psychiatrie Nervenkrankheiten 87:527–570.Crossref, Google Scholar
• Blascovich J, Loomis J, Beal AC, Swinth KR, Hoyt CL, Bailenson JN (2002) Immersive virtual environment technology as a methodological tool for social psychology. Psych. Inquiry 13(2):103–124.Crossref, Google Scholar
• Blut M, Wang C, Wünderlich NV, Brock C (2021) Understanding anthropomorphism in service provision: A meta-analysis of physical robots, chatbots, and other AI. J. Acad. Marketing Sci. 49(4):632–658.Crossref, Google Scholar
• Burton JW, Stein MK, Jensen TB (2020) A systematic review of algorithm aversion in augmented decision making. J. Behav. Decision Making 33(2):220–239.Crossref, Google Scholar
• Cafaro A, Vilhjálmsson HH, Bickmore T (2016) First impressions in human-agent virtual encounters. ACM Trans. Comput. Human Interaction 23(4):1–40.Crossref, Google Scholar
• Cohen L (1995) Time-Frequency Analysis (Prentice Hall, Hoboken, NJ).Google Scholar
• Compeau D, Marcolin B, Kelley H, Higgins C (2012) Generalizability of information systems research using student subjects—A reflection on our practices and recommendations for future research. Inform. Systems Res. 23(4):1093–1109.Link, Google Scholar
• Critchley M (1953) The Parietal Lobes (Hafner, New York).Google Scholar
• Cummings J, Dennis AR (2018) Virtual first impressions matter: The effect of enterprise social networking sites on impression formation in virtual teams. MIS Quart. 42(3):697–718.Crossref, Google Scholar
• Dahlbäck N, Jönsson A, Ahrenberg L (1993) Wizard of Oz studies—Why and how. Knowledge Based Systems 6(4):258–266.Crossref, Google Scholar
• Debener S, Minow F, Emkes R, Gandras K, Vos M (2012) How about taking a low‐cost, small, and wireless EEG for a walk? Psychophysiology 49(11):1617–1621.Crossref, Google Scholar
• Delorme A, Makeig S (2004) EEGlab: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neuroscience Methods 134(1):9–21.Crossref, Google Scholar
• Dietvorst BJ, Simmons JP, Massey C (2015) Algorithm aversion: People erroneously avoid algorithms after seeing them err. J. Experiment. Psych. 144(1):114–126.Crossref, Google Scholar
• Dimoka A, Davis FD, Gupta A, Pavlou PA, Banker RD, Dennis AR, Ischebeck A, Müller-Putz G, Benbasat I, Gefen D (2012) On the use of neurophysiological tools in is research: Developing a research agenda for neuroIS. MIS Quart. 36(3):679–702.Crossref, Google Scholar
• Dwivedi YK, Hughes L, Baabdullah AM, Ribeiro-Navarrete S, Giannakis M, Al-Debei MM, Dennehy D, et al. (2022) Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Internat. J. Inform. Management 66:102542.Crossref, Google Scholar
• Etemad-Sajadi R (2016) The impact of online real-time interactivity on patronage intention: The use of avatars. Comput. Human Behav. 61:227–232.Crossref, Google Scholar
• Faul F, Erdfelder E, Lang AG, Buchner A (2007) G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39(2):175–191.Crossref, Google Scholar
• Friedman D, Shapira S, Jacobson L, Gruberger M (2015) A data-driven validation of frontal EEG asymmetry using a consumer device. 2015 Internat. Conf. Affective Comput. Intelligent Interaction (ACII) (Xi’an, China), 930–937.Google Scholar
• Gambino A, Fox J, Ratan A (2020) Building a stronger CASA: Extending the computers are social actors paradigm. Human Machine Comm. 1:71–85.Crossref, Google Scholar
• Gefen D, Straub DW (2004) Consumer trust in B2c e-commerce and the importance of social presence: Experiments in e-products and e-services. Omega 32(6):407–424.Crossref, Google Scholar
• Gioia DA, Corley KG, Hamilton AL (2012) Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organ. Res. Methods 16(1):15–31.Crossref, Google Scholar
• Glikson E, Woolley AW (2020) Human trust in artificial intelligence: Review of empirical research. Acad. Management Ann. 14(2):627–660.Crossref, Google Scholar
• Hayes AF (2017) Introduction to Mediation, Moderation, & Conditional Process Analysis (Guilford Press, New York).Google Scholar
• Herwig U, Satrapi P, Schönfeldt-Lecuona C (2003) Using the international 10-20 EEG system for positioning of transcranial magnetic stimulation. Brain Topography 16(2):95–99.Crossref, Google Scholar
• Hsu J (2012) Robotics’ uncanny valley gets new translation. Live Sci. (June 12), https://www.livescience.com/20909-robotics-uncanny-valley-translation.html.Google Scholar
• Kahneman D (2011) Thinking, Fast and Slow (Macmillan, London).Google Scholar
• Kelly SP, Lalor EC, Reilly RB, Foxe JJ (2006) Increases in alpha oscillatory power reflect an active retinotopic mechanism for distracter suppression during sustained visuospatial attention. J. Neurophysiology 95(6):3844–3851.Crossref, Google Scholar
• Kilner JM, Mattout J, Henson R, Friston KJ (2005) Hemodynamic correlates of EEG: A heuristic. Neuroimage 28(1):280–286.Crossref, Google Scholar
• Klimesch W (2012) Alpha-band oscillations, attention, and controlled access to stored information. Trends Cognitive Sci. 16(12):606–617.Crossref, Google Scholar
• Klimesch W, Doppelmayr M, Pachinger T, Ripper B (1997) Brain oscillations and human memory: EEG correlates in the upper alpha and theta band. Neuroscience Lett. 238(1):9–12.Crossref, Google Scholar
• Kolb B, Whishaw IQ (2009) Fundamentals of Human Neuropsychology (Macmillan, New York).Google Scholar
• Kramer RM (1999) Trust and distrust in organizations: Emerging perspectives, enduring questions. Annual Rev. Psych. 50:569–598.Crossref, Google Scholar
• Krause CM, Sillanmäki L, Koivisto M, Saarela C, Häggqvist A, Laine M, Hämäläinen H (2000) The effects of memory load on event-related EEG desynchronization and synchronization. Clinical Neurophysiology 111(11):2071–2078.Crossref, Google Scholar
• Lewicki RJ, Bunker BB (1995) Trust in relationships: A model of development and decline. Bunker BB, Rubin JZ, eds. Conflict, Cooperation and Justice: Essays Inspired by the Work of Morton Deutsch (Jossey-Bass, San Francisco), 133–173.Google Scholar
• Logg JM, Minson JA, Moore DA (2019) Algorithm appreciation: People prefer algorithmic to human judgment. Organ. Behav. Human Decision Processes 151:90–103.Crossref, Google Scholar
• Loiacono ET, Watson RT, Goodhue DL (2007) Webqual: An instrument for consumer evaluation of web sites. Internat. J. Electronic Commerce 11(3):51–87.Crossref, Google Scholar
• Mahmud H, Islam AKMN, Ahmed SI, Smolander K (2022) What influences algorithmic decision-making? A systematic literature review on algorithm aversion. Tech. Forecasting Social Change 175:121390.Crossref, Google Scholar
• Makeig S (1993) Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalography Clinical Neurophysiology 86(4):283–293.Crossref, Google Scholar
• Makeig S, Westerfield M, Jung TP, Enghoff S, Townsend J, Courchesne E, Sejnowski T (2002) Dynamic brain sources of visual evoked responses. Science 295(5555):690–694.Crossref, Google Scholar
• Maxwell GM, Cook MW, Burr R (1985) The encoding and decoding of liking from behavioral cues in both auditory and visual channels. J. Nonverbal Behav. 9(4):239–263.Crossref, Google Scholar
• Mayer RC, Davis JH, Schoorman FD (1995) An integrative model of organizational trust. Acad. Management Rev. 20(3):709–734.Crossref, Google Scholar
• McCrea SW, Wieber F, Myers AL (2012) Construal level mind-sets moderate self and social stereotyping. J. Personality Soc. Psych. 102(1):51–68.Crossref, Google Scholar
• McKnight DH, Choudhury V, Kacmar C (2002) Developing and validating trust measures for e-commerce: An integrative typology. Inform. Systems Res. 13(3):334–359.Link, Google Scholar
• Meng M, Cherian T, Singal G, Sinha P (2012) Lateralization of face processing in the human brain. Proc. Biol. Sci. 279(1735):2052–2061.Google Scholar
• Mills AM, Liu LF (2020) Trust in digital humans. ACIS 2020 Proc. (Wellington, New Zealand), vol. 96.Google Scholar
• Minas RK, Potter RF, Dennis AR, Bartelt VL, Bae S (2014) Putting on the thinking cap: Using neuroIS to understand information processing biases in virtual teams. J. Management Inform. Systems 30(4):49–82.Crossref, Google Scholar
• Moravec PL, Minas RK, Dennis AR (2019) Fake news on social media: People believe what they want to believe when it makes no sense at all. MIS Quart. 43(4):1343–1360.Crossref, Google Scholar
• Moretti DV, Paternicò D, Binetti G, Zanetti O, Frisoni GB (2013) EEG upper/low alpha frequency power ratio relates to temporo-parietal brain atrophy and memory performances in mild cognitive impairment. Frontiers Aging Neuroscience 5:63.Crossref, Google Scholar
• Mori M, MacDorman KF, Kageki N (2012) The uncanny valley [from the field]. IEEE Robotics Automation Magazine 19(2):98–100.Crossref, Google Scholar
• Müller-Putz GR, Riedl R, Wriessnegger SC (2015) Electroencephalography (EEG) as a research tool in the information systems discipline: Foundations, measurement, and applications. Comm. Assoc. Inform. Systems 37(1):46.Crossref, Google Scholar
• Nass C, Brave S (2005) Wired for Speech (MIT Press, Cambridge, MA).Google Scholar
• Nass C, Moon Y (2000) Machines and mindlessness: Social responses to computers. J. Soc. Issues 56(1):81–103.Crossref, Google Scholar
• Nass C, Steuer J, Tauber ER (1994) Computers are social actors. Adelson B, Duamis S, Olson J, eds. SIGCHI ‘94 Human Factors Comput. Systems (Association for Computing Machinery, New York), 72–78.Crossref, Google Scholar
• Onton J, Makeig S (2006) Information-based modeling of event-related brain dynamics. Neuper C, Klimesch W, eds. Progress in Brain Research (Elsevier, Amsterdam), 99–120.Google Scholar
• Onton J, Delorme A, Makeig S (2005) Frontal midline EEG dynamics during working memory Neuroimage 27(2):341–356.Crossref, Google Scholar
• Peter S, Riemer K (2024) Creative assistants with style: Making sense of generative AI as “style engines.” 2024 57th Annual Hawaii Internat. Conf. System Sci. (Honululu, Hawaii), 3980–3990.Google Scholar
• Pizzagalli DA (2007) Electroencephalography and high-density electrophysiological source localization. Cacioppo J, Tassinary GB, eds. Handbook of Psychophysiology (Cambridge University Press, New York), 56–84.Crossref, Google Scholar
• Rai A, Constantinides P, Sarker S (2019) Next-generation digital platforms: Toward human–AI hybrids. MIS Quart. 43(1):iii–ix.Google Scholar
• Ramírez-Cortes JM, Alarcon-Aquino V, Rosas-Cholula G, Gomez-Gil P, Escamilla-Ambrosio J (2010) P-300 rhythm detection using Anfis algorithm and wavelet feature extraction in EEG signals. Proc. World Congress Engrg. Comput. Sci., vol. 1 (International Association of Engineers, San Francisco), 963–968.Google Scholar
• Richards L (1999) Using Nvivo in Qualitative Research (Sage, London).Google Scholar
• Riedl R, Minas RK, Dennis AR, Müller-Putz GR (2020) Consumer-grade EEG instruments: Insights on the measurement quality based on a literature review & implications for NeuroIS research. Davis FD, Riedl R, vom Brocke J, Léger PM, Randolph AB, Fischer T, eds. Inform. Systems Neuroscience NeuroIS 2020, Lecture Notes in Information Systems and Organisation, vol. 43 (Springer, Cham, Switzerland), 350–361.Crossref, Google Scholar
• Rietz F, Sutherland A, Bensch S, Wermter S, Hellström T (2021) WoZ4U: An open-source Wizard-of-Oz interface for easy, efficient and robust HRI experiments. Frontiers Robotics AI 8:668057.Crossref, Google Scholar
• Ryan M (2020) In AI we trust: Ethics, artificial intelligence, and reliability. Sci. Engrg. Ethics 26(5):2749–2767.Crossref, Google Scholar
• Salber D, Coutaz J (1993) Applying the Wizard of Oz technique to the study of multimodal systems. Gesamtzentrum für Verkehr Braunschweig, ed. Human-Comput. Interaction: Third Internat. Conf., EWHCI’93 (Springer, Berlin), 219–230.Google Scholar
• Schroeder R, ed. (2001) The Social Life of Avatars: Presence and Interaction in Shared Virtual Environments (Springer Science & Business Media, New York), 130–131.Google Scholar
• Schuetz S, Venkatesh V (2020) Research perspectives: The rise of human machines: How cognitive computing systems challenge assumptions of user-system interaction. J. Assoc. Inform. Systems 21(2):460–482.Google Scholar
• Schutze U (2011) The avatar as sociomaterial entanglement: A performative perspective on identity, agency and world-making in virtual worlds. Thirty Second Internat. Conf. Inform. Systems (Shanghai, China).Google Scholar
• Seymour M, Riemer K, Kay J (2018) Actors, avatars and agents: Potentials and implications of natural face technology for the creation of realistic visual presence. J. Assoc. Inform. Systems 19(10):953–981.Google Scholar
• Seymour M, Yuan LI, Dennis AR, Riemer K (2021) Have we crossed the uncanny valley? Understanding affinity, trustworthiness, and preference for realistic digital humans in immersive environments. J. Assoc. Inform. Systems 22(3):591–617.Google Scholar
• Seymour M, Lovallo D, Riemer K, Dennis AR, Yuan L (2023) AI with a human face. Harvard Bus. Rev. March–April:49–54.Google Scholar
• Silva ES, Bonetti F (2021) Digital humans in fashion: Will consumers interact? J. Retailing Consumer Services 60:102430.Crossref, Google Scholar
• Simonte T (2021) Deepfakes are now making business pitches. Wired (August 16), https://www.wired.com/story/deepfakes-making-business-pitches/.Google Scholar
• Steelman ZR, Hammer BI, Limayem M (2014) Data collection in the digital age: Innovative alternatives to student samples. MIS Quart. 38(2):355–378.Crossref, Google Scholar
• Steina J-P, Appela M, Jostb A, Ohlerb P (2020) Matter over mind? How the acceptance of digital entities depends on their appearance, mental prowess, and the interaction between both. Internat. J. Human Comput. Stud. 142:102463.Crossref, Google Scholar
• Sundar SS, Kim J (2019) Machine heuristic: When we trust computers more than humans with our personal information. Association for Computing Machinery, ed. Proc. 2019 CHI Conf. Human Factors Comput. Systems (CHI ‘19) (Glasgow, Scotland, UK), 1–9.Google Scholar
• Taylor GS, Schmidt C (2012) Empirical evaluation of the Emotiv Epoc Bci headset for the detection of mental actions. Proc. Human Factors Ergonomics Soc. Annual Meeting 56(1):193–197.Crossref, Google Scholar
• Tinwell A, Grimshaw M, Nabi DA, Williams A (2011) Facial expression of emotion and perception of the uncanny valley in virtual characters. Comput. Human Behav. 27(2):741–749.Crossref, Google Scholar
• Tomlinson EC, Schnackenberg AK, Dawley D, Ash SR (2020) Revisiting the trustworthiness–trust relationship: Exploring the differential predictors of cognition- and affect-based trust. J. Organ. Behav. 41:535–550.Crossref, Google Scholar
• Trope YL (2012) Construal level theory. Van Lange PK, ed. Handbook of Theories of Social Psychology (Sage, Washington, DC), 118–134.Crossref, Google Scholar
• Trope Y, Liberman N (2010) Construal-level theory of psychological distance. Psych. Rev. 117(2):440–463.Crossref, Google Scholar
• Venkatesh V, Brown SA, Bala H (2013) Guidelines for conducting mixed methods research in information systems. MIS Quart. 37(1):21–54.Crossref, Google Scholar
• Venkatesh V, Brown SA, Sullivan YW (2016) Guidelines for conducting mixed-methods research: An extension and illustration. J. Assoc. Inform. Systems 17(7):435–494.Google Scholar
• von der Pütten AM, Kamer NC, Gratch J, Kang SW (2010) “It doesn’t matter what you are!” Explaining social effects of agents and avatars. Comput. Human Behav. 26:1641–1650.Crossref, Google Scholar
• Wang W, Benbasat I (2005) Trust in and adoption of online recommendation agents. J. Assoc. Inform. Systems 6(3):4.Google Scholar
• Wang S, Gwizdka J, Chaovalitwongse WA (2015a) Using wireless EEG signals to assess memory workload in the n-back task. IEEE Trans. Human Machine Systems 46(3):424–435.Google Scholar
• Wang S, Lilienfeld SO, Rochat P (2015b) The uncanny valley: Existence and explanations. Rev. General Psych. 19(4):393–407.Crossref, Google Scholar
• Williams M (2001) In whom we trust: Group membership as an affective context for trust development. Acad. Management Rev. 26(3):377–396.Crossref, Google Scholar
• Xu K, Chen X, Huang L (2022) Deep mind in social responses to technologies: A new approach to explaining the computers are social actors phenomena. Comput. Human Behav. 134:107321.Crossref, Google Scholar
• Yamada Y, Kawabe T, Ihaya K (2013) Categorization difficulty is associated with negative evaluation in the “uncanny valley” phenomenon. Japanese Psych. Res. 55:20–32.Google Scholar
• Yanev V (2024) Video game demographics—Who plays games in 2024? Techjury (January 2), https://techjury.net/blog/video-game-demographics/#gref.Google Scholar