Brain–machine interfaces (BMIs) are emerging as transformative tools with applications in neuroscience, medicine, and virtual reality. Recent breakthroughs, such as Neuralink’s brain implant technology, have showcased the potential to cure neurological diseases and spinal cord injuries. However, as BMIs become more invasive, questions arise about societal acceptance, regulatory challenges, and ethical considerations. This study explores the factors influencing potential users’ attitudes and perceptions toward BMIs. We find that performance and effort expectancy, as well as trust and well-being, positively influence behavioral intention to use BMIs. Conversely, the level of invasiveness of BMI technology negatively impacts behavioral intention due to raised privacy concerns and technology fear. These results offer valuable insights for policymakers, healthcare professionals, and technology developers seeking to navigate the challenges and opportunities associated with the adoption of BMIs.
AndersonLOstromAL (2015) Transformative service research: Advancing our knowledge about service and well-being. Journal of Service Research18(3): 243–249.
2.
Arias-OlivaMPelegrín-BorondoJLara-PalmaAMJuaneda-AyensaE (2020) Emerging cyborg products: An ethical market approach for market segmentation. Journal of Retailing and Consumer Services55: 102140.
3.
AttiéEMeyer-WaardenL (2019) The acceptance process of the Internet of Things: How to improve the acceptance of the IoT technology. In: SimoesDBarbosaBFilipeS (eds) Smart Marketing With the Internet of Things, pp. 21–45. Hershey, PA: IGI Global.
4.
AttieEMeyer-WaardenL (2023) How do you sleep? The impact of sleep apps on generation Z’s well-being. Journal of Interactive Marketing58(2–3): 222–247.
5.
AttourABaudinoMKrafftJLazaricN (2020) Determinants of energy tracking application use at the city level: Evidence from France. Energy Policy147: 111866.
6.
BaruhLSecintiECemalcilarZ (2017) Online privacy concerns and privacy management: A meta-analytical review. Journal of Communication67(1): 26–53.
BernalSLCeldránAHPérezGM (2023) Eight reasons to prioritize brain-computer interface cybersecurity. Communications of the ACM66(4): 68–78.
9.
BerryLLBendapudiN (2007) Health care: A fertile field for service research. Journal of Service Research10(2): 111–122.
10.
BinyaminSSZafarBA (2021) Proposing a mobile apps acceptance model for users in the health area: A systematic literature review and meta-analysis. Health Informatics Journal27(1): 1460458220976737.
11.
BirkmeyerSWirtzBWLangerPF (2021) Determinants of MHealth success: An empirical investigation of the user perspective. International Journal of Information Management59: 102351.
12.
ChitturiRRaghunathanRMahajanV (2008) Delight by design: The role of hedonic versus utilitarian benefits. Journal of Marketing72(3): 48–63.
13.
CloarecJ (2020) The personalization–privacy paradox in the attention economy. Technological Forecasting and Social Change161: 120299.
14.
CloarecJ (2022) Privacy controls as an information source to reduce data poisoning in artificial intelligence-powered personalization. Journal of Business Research152: 144153.
15.
CloarecJCadieuCAlrabieN (2024a) Tracking technologies in eHealth: Revisiting the personalization-privacy paradox through the transparency-control framework. Technological Forecasting and Social Change200: 123101.
CloarecJMeyer WaardenLMunzelA (2022) The personalization–privacy paradox at the nexus of social exchange and construal level theories. Psychology & Marketing49(3): 21587.
18.
CloarecJMeyer WaardenLMunzelA (2024b) Transformative privacy calculus: Conceptualizing the personalization-privacy paradox on social media. Psychology & Marketing41(7): 15741596.
19.
DavisBPechmannC (2013) Introduction to the special issue on transformative consumer research: Developing theory to mobilize efforts that improve consumer and societal well-being. Journal of Business Research66(8): 1168–1170.
20.
DavisFD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly13(3): 319.
DienerEChanMY (2011) Happy people live longer: Subjective well-being contributes to health and longevity. Applied Psychology: Health and Well-being3(1): 1–43.
25.
DienerEEmmonsRALarsenRJGriffinS (1985) The satisfaction with life scale. Journal of Personality Assessment49(1): 7175.
26.
DrewL (2023) Decoding the business of brain–computer interfaces. Nature Electronics6(2): 90–95.
27.
FarwellLADonchinE (1988) Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology70: 510–523.
28.
FishbeinMAjzenI (1975) Belief Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley Series in Social Psychology.
29.
FoxAKRoyneMB (2018) Private information in a social world: Assessing consumers’ fear and understanding of social media privacy. Journal of Marketing Theory and Practice26(1–2): 72–89.
30.
GaoLBaiX (2014) A unified perspective on the factors influencing consumer acceptance of internet of things technology. Asia Pacific Journal of Marketing and Logistics26(2): 211–231.
31.
HaSStoelL (2009) Consumer E-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research62(5): 565–571.
32.
HarmanHH (1967) Modern Factor Analysis. Chicago, IL: University of Chicago Press.
33.
HayesAF (2021) Introduction to Mediation, Moderation, and Conditional Process Analysis, 3rd edition.New York: Guilford Press.
34.
HermannEWilliamsGYPuntoniS (2024) Deploying artificial intelligence in services to AID vulnerable consumers. Journal of the Academy of Marketing Science52: 1431–1451.
35.
HilkenTChylinskiMDe RuyterKHellerJKeelingDI (2022) Exploring the frontiers in reality-enhanced service communication: From augmented and virtual reality to neuro-enhanced reality. Journal of Service Management33(4/5): 657–674.
36.
HilkenTHellerJMahrD (2023) Closing the customer imagination gap with augmented and virtual reality. NIM Marketing Intelligence Review15(2): 30–35.
37.
HofstedeG (1993) Cultural constraints in management theories. Academy of Management Perspectives7(1): 8194.
38.
HongWThongJYL (2013) Internet privacy concerns: An integrated conceptualization and four empirical studies. MIS Quarterly37(1): 275–298.
39.
HseeCKYangYLiNShenL (2009) Wealth, warmth, and well-being : Whether happiness is relative or absolute depends on whether it is about money, acquisition, or consumption. Journal of Marketing Research46(3): 396409.
40.
MasonSGBirchGE (2003) A general framework for brain-computer interface design. IEEE Transactions on Neural Systems Rehabilitation Engineering11(1): 70–85.
41.
JobstLJBaderMMoshagenM (2021) A tutorial on assessing statistical power and determining sample size for structural equation models. Psychological Methods28: 207–221.
42.
JoinsonAReipsU-DBuchananTSchofieldCBP (2010) Privacy, trust, and self-disclosure online. Human-Computer Interaction25(1): 1–24.
43.
KalemGTurhanÇ (2015) Mobile technology applications in the healthcare industry for disease management and wellness. Procedia-social and Behavioral Sciences195: 2014–2018.
KarahocaAKarahocaDAksözM (2018) Examining intention to adopt to internet of things in healthcare technology products. Kybernetes47(4): 742–770.
46.
KingWRHeJ (2006) A meta-analysis of the technology acceptance model. Information & Management43(6)740–755.
47.
LarocheMSadokierskiR (1994) Role of confidence in a multi-brand model of intentions for a high-involvement service. Journal of Business Research29(1): 1–12.
48.
LebedevMANicolelisMAL (2006) Brain–machine interfaces: Past, present and future. Trends in Neurosciences29(9): 536–546.
49.
LeuthardtECSchalkGRolandJRouseAMoranDW (2009) Evolution of brain-computer interfaces: Going beyond classic motor physiology. Neurosurgical Focus27(1): 1–11.
50.
LijarcioIUsecheSALlamazaresJMontoroL (2019) Perceived benefits and constraints in vehicle automation: Data to assess the relationship between driver’s features and their attitudes towards autonomous vehicles. Data in Brief27: 104662.
51.
LimaVMPessôaLABelkRW (2022) The promethean biohacker: On consumer biohacking as a labour of love. Journal of Marketing Management38(5–6): 483–514.
52.
LutzCHoffmannCPBucherEFieselerC (2017) The role of privacy concerns in the sharing economy. Information, Communication & Society21(10): 1472–1492.
53.
McKnightDHChervanyNL (2001) What trust means in E-commerce customer relationships: An interdisciplinary conceptual typology. International Journal of Electronic Commerce6(2): 3559.
54.
McKnightDHCarterMThatcherJBClayPF (2011) Trust in a specific technology: An investigation of its components and measures. ACM Transactions on Management Information Systems (TMIS)2(2): 1–25.
MalhotraNKKimSSAgarwalJ (2004) Internet users’ information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research15(4): 336355.
57.
MartinKDMurphyPE (2017) The role of data privacy in marketing. Journal of the Academy of Marketing Science45(2): 135155.
58.
MedvedevONLandhuisCE (2018) Exploring constructs of well-being, happiness and quality of life. Peer J6: e4903.
59.
MelenhorstAFiskADMynattEDRogersWA (2004) Potential intrusiveness of aware home technology: Perceptions of older adults. Proceedings of the Human Factors and Ergonomics Society48(2): 266–270.
60.
Meyer-WaardenLCloarecJ (2022) Baby, you can drive my car: Psychological antecedents that drive consumers’ adoption of AI-powered autonomous vehicles. Technovation109: 102348.
61.
Meyer-WaardenLCloarecJAdamsCAlimanDNWirthV (2021) Home, sweet home: How well-being shapes the adoption of artificial intelligence-powered apartments in smart cities. Systèmes D’information & Management26(4): 55–88.
62.
MichaelMGMichaelK (eds) (2014) Uberveillance and the Social Implications of Microchip Implants : Emerging Technologies. Hershey, PA: IGI Global.
63.
MorganRMHuntSD (1994) The commitment-trust theory of relationship marketing. Journal of Marketing58(3): 20.
64.
MoshagenMErdfelderE (2016) A new strategy for testing structural equation models. Structural Equation Modeling: A Multidisciplinary Journal23(1): 54–60.
65.
MunzelAMeyer-WaardenLGalanJ-P (2018) The social side of sustainability: Well-being as a driver and an outcome of social relationships and interactions on social networking sites. Technological Forecasting and Social Change130: 14–27.
66.
MuskENeuralink (2019) An integrated brain-machine interface platform with thousands of channels. Journal of Medical Internet Research21(10): 1–14.
67.
OstromALFotheringhamDBitnerMJ (2019) Customer acceptance of AI in service encounters: Understanding antecedents and consequences. In: MaglioPPKieliszewskiCASpohrerJCLyonsKPatrícioLSawataniY (eds) Handbook of Service Science, Volume II. New York: Springer, pp. 77–103.
PartridgeBDoddsS (2023) Conceptualising and regulating all neural data from consumer-directed devices as medical data: More scope for an unnecessary expansion of medical influence?Ethics and Information Technology25(4): 59.
70.
PavlouPA (2003) Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce7(3): 101–134.
71.
PodsakoffPMMacKenzieSBLeeJ-YPodsakoffNP (2003) Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology88(5): 879–903.
72.
RocaJCGarcíaJJde la VegaJJ (2009) The importance of perceived trust, security and privacy in online trading systems. Information Management & Computer Security17(2): 96–113.
73.
RogersE (1995) Diffusion of Innovations. Mumbai: Free Press.
74.
RosseelY (2012) lavaan: An R package for structural equation modeling. Journal of Statistical Software48(2): 1–36.
75.
RozanskiAKubzanskyLD (2005) Psychologic functioning and physical health: A paradigm of flexibility. Psychosomatic Medicine67: S47–S53.
76.
SchwartzABCuiXTWeberDJMoranDW (2006) Brain-controlled interfaces: Movement restoration with neural prosthetics. Neuron52(1): 205–220.
77.
SionG (2019) Employee microchip implants: Technology acceptance, capability enhancement, and continuous monitoring. Psychosociological Issues in Human Resource Management7(1): 48–53.
78.
SirgyMJ (2012) The Psychology of Quality of Life: Hedonic Well-being, Life Satisfaction, and Eudaimonia. New York: Springer.
79.
SlovicP (1987) Perception of risk. Science236(4799): 280–285.
80.
SmithAD (2007) Evolution and acceptability of medical applications of RFID implants among early users of technology. Health Marketing Quarterly24(1–2): 121–155.
81.
SmithHJMilbergSJBurkeSJ (1996) Information privacy: Measuring individuals’ concerns about organizational practices. MIS Quarterly20(2): 167.
82.
Subero-NavarroÁPelegrín-BorondoJReinares-LaraEOlarte-PascualC(2022) Proposal for modeling social robot acceptance by retail customers: CAN model+ technophobia. Journal of Retailing and Consumer Services64: 102813.
ThatcherJBPerrewePL (2002) An empirical examination of individual traits as antecedents to computer anxiety and computer self-efficacy. MIS Quarterly26(4): 381.
85.
Van PinxterenMMWetzelsRWRügerJPluymaekersMWetzelsM (2019) Trust in humanoid robots: Implications for services marketing. Journal of Services Marketing33(4): 507–518.
86.
VenkateshVMorrisMGDavisGBDavisFD (2003) User acceptance of information technology: Toward a unified view. MIS Quarterly27(3): 425.
87.
VenkateshVThongJYLChanFKYHuPJ-HBrownSA (2011) Extending the two-stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information Systems Journal21(6): 527–555.
88.
WerberBBaggiaAŽnidaršičA (2018) Factors affecting the intentions to use RFID subcutaneous microchip implants for healthcare purposes. Organizacija51(2): 121–133.
89.
WillettFRKunzEMFanCAvansinoDTWilsonGHChoiEYKamdarFGlasserMFHochbergLRDruckmannSShenoyKVHendersonJM (2023) A high-performance speech neuroprosthesis. Nature620(7976): 1031–1036.
90.
WolpawJR (2007) Brain–computer interfaces as new brain output pathways. The Journal of Physiology579(3): 613–619.
91.
WolpawJRBirbaumerNHeetderksWJMcFarlandDJPeckhamPHSchalkGDonchinEQuatranoLARobinsonCJVaughanTM (2000a) Brain–computer interface technology: A review of the first international meeting. IEEE Transactions on Neural Systems and Rehabilitation Engineering8(2): 164–173.
92.
WolpawJRBirbaumerNMcFarlandDJPfurtschellerGVaughanTM (2000b) Brain–computer interfaces for communication and control. Clinical Neurophysiology113(6): 767–791.
93.
YangHLeeWLeeH (2018) IoT smart home adoption : The importance of proper level automation. Journal of Sensors2018: 1–11.
ŽnidaršičAWerberBBaggiaAVovkMBevandaVZakonnikL (2021) The intention to use microchip implants: Model extensions after the Pandemics. In: 16th international symposium on operations research SOR’21, Ljubljana: Slovensko društvo informatika, 22–24 September, pp. 247–252.
