Skip to main content
SpringerLink
Log in

A review of studies on internet of everything as an enabler of neuromarketing methods and techniques

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Preserving customers’ expectations and understanding factors affecting their purchasing decisions are crucial in designing effective marketing and advertising strategies. However, constantly and swiftly changing the customers’ interests and consumption behaviors make it inevitable to utilize sophisticated tools and approaches based on advanced technologies. Among them, by measuring the customers’ physiological and neural signals, studying the customers’ cognitive and affective responses to marketing stimuli, neuromarketing provides deep insight into the customers’ motivations, preferences, and decisions. Recently, the internet of everything (IoE) has brought many new opportunities to the industry and has attracted the attention of many researchers in recent years. The main objective of this paper is to address how the IoE would empower neuromarketing techniques. Hence, an in-depth understanding of current research issues as well as emerging trends would help meet this goal. In this paper, a comprehensive review has been done by reviewing numerous journal and conference papers from various academic databases (sciencedirect.com, IEEE Xplore, Springer, Elsevier, Wiley, ACM digital library) based on applying various filtrations on specific keywords to identify and categorize the IoE devices in the neuromarketing field. In particular, we discussed the importance and the applications of IoE gadgets and devices, especially wearable medical technologies in eight neuromarketing techniques. Finally, we described critical existing challenges and limitations in neuromarketing and IoE gadgets that researchers have dealt with in this field of research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Abed SS, Ezzi SW (2020) Exploring the demographic differences on customers’ adoption of social commerce in Saudi Arabia. In: Rana NP, Slade EL, Sahu GP, Kizgin H, Singh N, Dey B, Gutierrez A, Dwivedi YK (eds) Digital and social media marketing: emerging applications and theoretical development. Springer International Publishing, Cham, pp 57–66

    Google Scholar 

  2. Algharabat RS, Rana NP, Alalwan AA, Baabdullah AM (2020) Investigating the impact of social media commerce constructs on social trust and customer value co-creation: a theoretical analysis. In: Rana NP, Slade EL, Sahu GP, Kizgin H, Singh N, Dey B, Gutierrez A, Dwivedi YK (eds) Digital and social media marketing: emerging applications and theoretical development. Springer International Publishing, Cham, pp 41–55

    Google Scholar 

  3. Alsuradi H, Park W, Eid M (2020) EEG-based neurohaptics research: a literature review. IEEE Access 8:49313–49328. https://doi.org/10.1109/ACCESS.2020.2979855

    Article  Google Scholar 

  4. AlSuwaidan L (2019) Data management model for internet of everything. International Conference on Mobile Web and Intelligent Information Systems

  5. Amin SU, Alsulaiman M, Ghulam Muhammad M, Hossain S, Guizani M (2020) Deep learning for EEG motor imagery-based cognitive healthcare. In: Abdulmotaleb El Saddik M, Hossain S, Kantarci B (eds) Connected health in smart cities. Springer International Publishing, Cham, pp 233–254. https://doi.org/10.1007/978-3-030-27844-1_12

    Chapter  Google Scholar 

  6. Ariely D, Berns GS (2010) Neuromarketing: the hope and hype of neuroimaging in business. Nat Rev Neurosci 11(4):284–292. https://doi.org/10.1038/nrn2795

    Article  Google Scholar 

  7. Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54:2787–2805

    MATH  Google Scholar 

  8. Bamakan SMH, Malekinejad P, Ziaeian M (2022) Towards blockchain-based hospital waste management systems; applications and future trends. J Clean Prod 349:131440. https://doi.org/10.1016/j.jclepro.2022.131440

    Article  Google Scholar 

  9. Benedek M, Kaernbach C (2010) A continuous measure of phasic electrodermal activity. J Neurosci Methods 190(1):80–91. https://doi.org/10.1016/j.jneumeth.2010.04.028

    Article  Google Scholar 

  10. Bercea MD (2013) Quantitative versus qualitative in neuromarketing research.

  11. Braeutigam S (2013) Magnetoencephalography: fundamentals and established and emerging clinical applications in radiology. ISRN Radiol 2013:1–18. https://doi.org/10.5402/2013/529463

    Article  Google Scholar 

  12. Buraga SC, Dospinescu O (2021) A knowledge-based pilot study on assessing the music influence. Comput Mater Contin 66:2857–2873

    Google Scholar 

  13. Burhan M, Rehman RA, Khan B, Kim B-S (2018) IoT elements, layered architectures and security issues: a comprehensive survey. Sensors 18(9):2796

    Google Scholar 

  14. Butler MJ (2008) Neuromarketing and the perception of knowledge. J Consum Behav Int Res Rev 7(4–5):415–419

    Google Scholar 

  15. Caldas OI, Aviles OF, Rodriguez-Guerrero C (2020) A simplified method for online extraction of skin conductance features: a pilot study on an immersive virtual-reality-based motor task. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

  16. Cheng C-Y, Shu W, Tsen H-P (2020) Exploring cognitive distraction of galvanic skin response while driving: an artificial intelligence modeling. J Adv Inform Technol. https://doi.org/10.12720/jait.11.1.35-39

    Article  Google Scholar 

  17. Constantinescu M, Orindaru A, Pachitanu A, Rosca L, Caescu S-C, Orzan MC (2019) Attitude evaluation on using the neuromarketing approach in social media: matching company’s purposes and consumer’s benefits for sustainable business growth. Sustainability 11(24):7094

    Google Scholar 

  18. Curiel E (2020) Considerations on global social media marketing. In: Rana NP, Slade EL, Sahu GP, Kizgin H, Singh N, Dey B, Gutierrez A, Dwivedi YK (eds) Digital and social media marketing: emerging applications and theoretical development. Springer International Publishing, Cham, pp 229–237. https://doi.org/10.1007/978-3-030-24374-6_15

    Chapter  Google Scholar 

  19. Daugherty T, Hoffman E (2017) Neuromarketing: understanding the application of neuroscientific methods within marketing research. In: Thomas AR, Pop NA, Iorga AM, Ducu C (eds) Ethics and neuromarketing. Springer International Publishing, Cham, pp 5–30. https://doi.org/10.1007/978-3-319-45609-6_2

    Chapter  Google Scholar 

  20. Dayanandam G, Rao TV, Bujji Babu D, Nalini Durga S (2019) DDoS attacks—analysis and prevention. In: Saini HS, Rishi Sayal A, Govardhan RB (eds) Innovations in computer science and engineering. Springer, Singapore, pp 1–10. https://doi.org/10.1007/978-981-10-8201-6_1

    Chapter  Google Scholar 

  21. Marques JP, dos Santos H, Ferreira JR, Prata D, Simões SP, Borges ID (2020) The use of consumer neuroscience knowledge in improving real promotional media: the case of worten. In: Rocha Á, Reis JL, Peter MK, Bogdanović Z (eds) Marketing and smart technologies: proceedings of ICMarkTech 2019. Springer, Singapore, pp 202–218. https://doi.org/10.1007/978-981-15-1564-4_20

    Chapter  Google Scholar 

  22. Dospinescu O, Percă-Robu AE (2017) The analysis of E-commerce sites with eye-tracking technologies. BRAIN Broad Res Artif Intell Neurosci 8(3):85–100

    Google Scholar 

  23. Duan Y, Li Z, Tao X, Li Q, Hu S, Lu J (2020) EEG-based maritime object detection for IoT-driven surveillance systems in smart ocean. IEEE Internet Things J 7(10):9678–9687

    Google Scholar 

  24. Elazhary H (2019) Internet of things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: disambiguation and research directions. J Netw Comput Appl 128:105–140

    Google Scholar 

  25. Eser Z, Isin FB, Tolon M (2011) Perceptions of marketing academics, neurologists, and marketing professionals about neuromarketing. J Mark Manag 27(7–8):854–868

    Google Scholar 

  26. Espigares-Jurado F, Muñoz-Leiva F, Correia MB, Sousa CM, Ramos CM, Faísca L (2020) Visual attention to the main image of a hotel website based on its position, type of navigation and belonging to Millennial generation: an eye tracking study. J Retail Consum Serv 52:101906

    Google Scholar 

  27. Fan X, Liu X, Hu W, Zhong C, Lu J (2019) Advances in the development of power supplies for the internet of everything. InfoMat 1(2):130–139

    Google Scholar 

  28. Farnsworth B (2019) What is eye tracking and how does it work? reterived 22 Sept 2020 from https://imotions.com/blog/eye-tracking-work/

  29. Fiaidhi J, Mohammed S (2019) Internet of everything as a platform for extreme automation. IT Prof 21(1):21–25

    Google Scholar 

  30. Filipović F, Baljak L, Naumović T, Labus A, Bogdanović Z (2020) Developing a web application for recognizing emotions in neuromarketing. In: Rocha Á, Reis JL, Peter MK, Bogdanović Z (eds) Marketing and smart technologies: proceedings of ICMarkTech 2019. Springer, Singapore, pp 297–308. https://doi.org/10.1007/978-981-15-1564-4_28

    Chapter  Google Scholar 

  31. Fortunato VCR, de GiraldiOliveira JDMEJHC (2014) A review of studies on neuromarketing: practical results, techniques, contributions and limitations. J Manag Res 6(2):201

    Google Scholar 

  32. Galitsky B, Parnis A (2019) Accessing validity of argumentation of agents of the internet of everything. Artificial intelligence for the internet of everything. Elsevier, pp 187–216. https://doi.org/10.1016/B978-0-12-817636-8.00011-9

    Chapter  Google Scholar 

  33. Ghorpade R (2020) A study on the role of neuromarketing as an innovative approach to consumer behavior research. Stud Indian Pl N 40(4):244–251

    Google Scholar 

  34. Gountas J, Gountas S, Ciorciari J, Sharma P (2019) Looking beyond traditional measures of advertising impact: Using neuroscientific methods to evaluate social marketing messages. J Bus Res 105:121–135

    Google Scholar 

  35. Gross J (2019) Magnetoencephalography in cognitive neuroscience: a primer. Neuron 104(2):189–204

    MathSciNet  Google Scholar 

  36. Gupta N, Dhurandher SK (2020) Efficient caching method in fog computing for internet of everything. Peer-to-Peer Networking and Applications 1–14

  37. Gurgu E, Gurgu I-A, Tonis RBM (2020) Neuromarketing for a better understanding of consumer needs and emotions. Ind Manag Prod 11(1):208–235

    Google Scholar 

  38. Hafez M (2019) Neuromarketing: a new avatar in branding and advertisement. Pac Bus Rev Int 12(4):58–64

    Google Scholar 

  39. Harris JM, Ciorciari J, Gountas J (2018) Consumer neuroscience for marketing researchers. J Consum Behav 17(3):239–252

    Google Scholar 

  40. Hasan MK, Islam S, Sulaiman R, Khan S, Hashim AHA, Habib S, Islam M, Alyahya S, Ahmed MM, Kamil S, Hassan MA (2021) Lightweight encryption technique to enhance medical image security on internet of medical things applications. IEEE Access 9:47731–47742. https://doi.org/10.1109/ACCESS.2021.3061710

    Article  Google Scholar 

  41. Bamakan SMH, Bondarti AB, Bondarti PB, Qiang Q (2021) Blockchain technology forecasting by patent analytics and text mining. Blockchain Res Appl 2(2):100019. https://doi.org/10.1016/j.bcra.2021.100019

    Article  Google Scholar 

  42. Bamakan SMH, Moghaddam SG, Manshadi SD (2021) Blockchain-enabled pharmaceutical cold chain: applications, key challenges, and future trends. J Clean Prod 302:127021. https://doi.org/10.1016/j.jclepro.2021.127021

    Article  Google Scholar 

  43. Hou J, Qu L, Shi W (2019) A survey on internet of things security from data perspectives. Comput Netw 148:295–306

    Google Scholar 

  44. Hsu MY-T, Cheng JM-S (2018) fMRI neuromarketing and consumer learning theory: word-of-mouth effectiveness after product harm crisis. Eur J Mark 52(1/2):199–223. https://doi.org/10.1108/EJM-12-2016-0866

    Article  Google Scholar 

  45. Hubert M, Hubert M (2020) Consumer neuroscience: recent theoretical and methodological developments for research and practice using a cube model. In: Martineau JT, Racine E (eds) Organizational neuroethics: reflections on the contributions of neuroscience to management theories and business practices. Springer International Publishing, Cham, pp 67–86

    Google Scholar 

  46. imotions (2020) Galvanic skin response (GSR): the complete pocket guide. reterived 22 Sept 2020 from https://imotions.com/blog/galvanic-skin-response/

  47. Ionescu A-M, Romanelli M (2019) Rediscovering Neuromarketing For Sustainable Companies. Strategica, 251

  48. Isa SM, Mansor AA, Razali K (2019) Ethics in neuromarketing and its implications on business to stay vigilant. KnE Social Sciences, 687–711–687–711

  49. Isabella G, Mazzon JA, Dimoka A (2015) Culture differences, difficulties, and challenges of the neurophysiological methods in marketing research. J Int Consum Mark 27(5):346–363

    Google Scholar 

  50. Jara AJ, Ladid L, Gómez-Skarmeta AF (2013) The internet of everything through IPv6: an analysis of challenges, solutions and opportunities. J Wirel Mob Networks Ubiquitous Comput Dependable Appl 4(3):97–118

    Google Scholar 

  51. Jayashree K, Rao VV (2020) A conceptual study on neuromarketing and its implications on customers. Stud Indian Pl N 40(67):1–8

    Google Scholar 

  52. Jentsch VL, Wolf OT, Merz CJ (2020) Temporal dynamics of conditioned skin conductance and pupillary responses during fear acquisition and extinction. Int J Psychophysiol 147:93–99

    Google Scholar 

  53. Joshi A, Kiran R (2020) Gauging the effectiveness of music and yoga for reducing stress among engineering students: an investigation based on galvanic skin response. Work 65(3):671–678. https://doi.org/10.3233/WOR-203121

    Article  Google Scholar 

  54. Kang B, Kim D, Choo H (2017) Internet of everything: a large-scale autonomic IoT gateway. IEEE Trans Multi-Scale Comput Syst 3(3):206–214

    Google Scholar 

  55. Karthiban MK, Raj JS (2019) Big data analytics for developing secure internet of everything. J ISMAC 1(02):129–136

    Google Scholar 

  56. Kaskela J (2021) The added value of neuromarketing in Finland https://lutpub.lut.fi/handle/10024/162359

  57. Kenning P, Plassmann H (2005) Neuroeconomics: an overview from an economic perspective. Brain Res Bull 67(5):343–354

    Google Scholar 

  58. Khan MA, Salah K (2018) IoT security: review, blockchain solutions, and open challenges. Futur Gener Comput Syst 82:395–411

    Google Scholar 

  59. Kiełtyka L (2019) Application of the internet of things and internet of everything in the activities that implement the concept of ecosystem. Przegląd Organizacji. https://doi.org/10.33141/po.2019.06.05

    Article  Google Scholar 

  60. Koshizuka N, Sakamura K (2010) Ubiquitous ID: standards for ubiquitous computing and the internet of things. IEEE Pervasive Comput 9(4):98–101

    Google Scholar 

  61. Kubba ZMJ, Hoomod HK (2019) The internet of everything based smart systems: applications and challenges. In: 2019 1st AL-Noor International Conference for Science and Technology (NICST)

  62. Kusano H, Horiguchi Y, Baba Y, Kashima H (2020) Stress Prediction from Head Motion.In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 6–9 Oct 2020

  63. Lal B, Ismagilova E, Dwivedi YK, Kwayu S (2020) Return on investment in social media marketing: literature review and suggestions for future research. In: Rana NP, Slade EL, Sahu GP, Kizgin H, Singh N, Dey B, Gutierrez A, Dwivedi YK (eds) Digital and social media marketing: emerging applications and theoretical development. Springer International Publishing, Cham, pp 3–17. https://doi.org/10.1007/978-3-030-24374-6_1

    Chapter  Google Scholar 

  64. Langley DJ, van Doorn J, Ng IC, Stieglitz S, Lazovik A, Boonstra A (2021) The internet of everything: smart things and their impact on business models. J Bus Res 122:853–863

    Google Scholar 

  65. Lankinen K, Saari J, Hlushchuk Y, Tikka P, Parkkonen L, Hari R, Koskinen M (2018) Consistency and similarity of MEG-and fMRI-signal time courses during movie viewing. Neuroimage 173:361–369

    Google Scholar 

  66. Lee E-J, Choi H, Han J, Kim DH, Ko E, Kim KH (2020) How to “Nudge” your consumers toward sustainable fashion consumption: An fMRI investigation. J Bus Res 117:642–651

    Google Scholar 

  67. Lee N, Broderick AJ, Chamberlain L (2007) What is ‘neuromarketing’? A discussion and agenda for future research. Int J Psychophysiol 63(2):199–204

    Google Scholar 

  68. Li X, Dai H-N, Shukla MK, Li D, Xu H, Imran M (2021) Friendly-jamming schemes to secure ultra-reliable and low-latency communications in 5G and beyond communications. Comput Stand Interfaces 78:103540. https://doi.org/10.1016/j.csi.2021.103540

    Article  Google Scholar 

  69. Lim WM (2018) Demystifying neuromarketing. J Bus Res 91:205–220

    Google Scholar 

  70. Liu Y, Dai H-N, Wang Q, Shukla MK, Imran M (2020) Unmanned aerial vehicle for internet of everything: opportunities and challenges. Comput Commun 155:66–83

    Google Scholar 

  71. Liu Y, Zhou G (2012) Key Technologies and Applications of Internet of Things In: 2012 Fifth International Conference on Intelligent Computation Technology and Automation

  72. Loued-Khenissi L, Döll O, Preuschoff K (2019) An overview of functional magnetic resonance imaging techniques for organizational research. Organ Res Methods 22(1):17–45

    Google Scholar 

  73. Marjani M, Nasaruddin F, Gani A, Karim A, Hashem IAT, Siddiqa A, Yaqoob I (2017) Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access 5:5247–5261

    Google Scholar 

  74. Marsili IA, Biasiolli L, Masè M, Adami A, Andrighetti AO, Ravelli F, Nollo G (2020) Implementation and validation of real-time algorithms for atrial fibrillation detection on a wearable ECG device. Comput Biol Med 116:103540

    Google Scholar 

  75. Martineau JT, Racine E (2020) Organizational neuroethics

  76. Maslowska E, Segijn CM, Vakeel KA, Viswanathan V (2020) How consumers attend to online reviews: an eye-tracking and network analysis approach. Int J Advert 39(2):282–306

    Google Scholar 

  77. Miraz MH, Ali M, Excell PS, Picking R (2015) A review on internet of things (IoT), internet of everything (IoE) and internet of nano things (IoNT). In: 2015 internet technologies and applications (ITA)

  78. Mohanta BK, Jena D, Satapathy U, Patnaik S (2020) Survey on IoT security: challenges and solution using machine learning, artificial intelligence and blockchain technology. Internet Things 11:1–50

    Google Scholar 

  79. Mouammine Y, Azdimousa H (2019) » Using neuromarketing and AI to collect and analyse consumer’s emotion: literature review and perspectives «. Int J Bus Econ Strategy 12(2):34–38

    Google Scholar 

  80. Murdoch B (2021) Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics 22(1):122. https://doi.org/10.1186/s12910-021-00687-3

    Article  Google Scholar 

  81. Nurelmadina N, Hasan MK, Memon I, Saeed RA, Ariffin KAZ, Ali ES, Mokhtar RA, Islam S, Hossain E, Md Hassan A (2021) A systematic review on cognitive radio in low power wide area network for industrial IoT applications. Sustainability 13(1):338. https://doi.org/10.3390/su13010338

    Article  Google Scholar 

  82. Narayana S, Prasad RV, Warmerdam K (2019) Mind your thoughts: BCI using single EEG electrode. IET Cyber-Phys Syst Theory Appl 4(2):164–172

    Google Scholar 

  83. Neeli J, Patil S (2021) Insight to security paradigm, research trend & statistics in internet of things (IoT). Glob Transit Proceed 2(1):84–90

    Google Scholar 

  84. Ohira H, Hirao N (2015) Analysis of skin conductance response during evaluation of preferences for cosmetic products. Front Psychol. https://doi.org/10.3389/fpsyg.2015.00103

    Article  Google Scholar 

  85. Panda D, Chakladar DD, Dasgupta T (2020) Multimodal system for emotion recognition using eeg and customer review. In: Mandal JK, Mukhopadhyay S (eds) Proceedings of the global ai congress 2019. Springer, Singapore, pp 399–410. https://doi.org/10.1007/978-981-15-2188-1_32

    Chapter  Google Scholar 

  86. Perrachione TK, Perrachione JR (2008) Brains and brands: Developing mutually informative research in neuroscience and marketing. J Consum Behav Int Res Rev 7(4–5):303–318

    Google Scholar 

  87. Rachit Bhatt S, Ragiri PR (2021) Security trends in internet of things: a survey. SN Appl Sci 3(1):121. https://doi.org/10.1007/s42452-021-04156-9

    Article  Google Scholar 

  88. Rahiminasab A, Tirandazi P, Ebadi M, Ahmadian A, Salimi M (2020) An energy-aware method for selecting cluster heads in wireless sensor networks. Appl Sci 10(21):7886. https://doi.org/10.3390/app10217886

    Article  Google Scholar 

  89. Ramsøy TZ, Skov M, Christensen MK, Stahlhut C (2018) Frontal brain asymmetry and willingness to pay. Front Neurosci. https://doi.org/10.3389/fnins.2018.00138

    Article  Google Scholar 

  90. Rathod A, Ayare P, Bobhate R, Sachdeo R, Sarode S, Malhotra J (2020) IoE-enabled smart embedded system: an innovative way of learning. In Information and Communication Technology for Sustainable Development, Springer, pp 659-668

  91. Reutov V, Mitina E, Kalkova N (2020) Formation and promotion of the territory brand of the neuromarketing tools. International Scientific Conference Far East Con (ISCFEC 2020)

  92. Salam A (2020) Internet of things for sustainability: perspectives in privacy, cybersecurity, and future trends. In: Salam A (ed) Internet of things for sustainable community development: wireless communications, sensing, and systems. Springer International Publishing, Cham, pp 299–327. https://doi.org/10.1007/978-3-030-35291-2_10

    Chapter  Google Scholar 

  93. Sato W, Minemoto K, Ikegami A, Nakauma M, Funami T, Fushiki T (2020) Facial EMG correlates of subjective hedonic responses during food consumption. Nutrients 12(4):1174

    Google Scholar 

  94. Sebastian V (2014) Neuromarketing and evaluation of cognitive and emotional responses of consumers to marketing stimuli. Procedia Soc Behav Sci 127:753–757

    Google Scholar 

  95. Shilpa A, Muneeswaran V, Sherin J (2019) Exploring the benefits of sensors in internet of everything (IoE). In: 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)

  96. Silberstein RB, Nield GE (2008) Brain activity correlates of consumer brand choice shift associated with television advertising. Int J Advert 27(3):359–380

    Google Scholar 

  97. Singh J, Goyal G, Gill R (2020) Use of neurometrics to choose optimal advertisement method for omnichannel business. Enterp Inform Syst 14(2):243–265

    Google Scholar 

  98. Singh P, Nayyar A, Kaur A, Ghosh U (2020) Blockchain and fog based architecture for internet of everything in smart cities. Future Inter 12(4):61

    Google Scholar 

  99. Sobin C (2020) A survey on architecture, protocols and challenges in iot. Wireless Pers Commun 112(3):1383–1429

    Google Scholar 

  100. Solnais C, Andreu-Perez J, Sánchez-Fernández J, Andréu-Abela J (2013) The contribution of neuroscience to consumer research: a conceptual framework and empirical review. J Econ Psychol 36:68–81

    Google Scholar 

  101. Soni A, Rawal K (2020) A review on physiological signals: heart rate variability and skin conductance. Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019)

  102. Sun Li, Qinghe Du (2018) A review of physical layer security techniques for internet of things: challenges and solutions. Entropy 20(10):730. https://doi.org/10.3390/e20100730

    Article  Google Scholar 

  103. TawalbehMuheidat LAF, Tawalbeh M, Quwaider M (2020) IoT Privacy and security: challenges and solutions. Appl Sci 10(12):4102

    Google Scholar 

  104. Tirandazi P, Rahiminasab A, Ebadi MJ (2022) An efficient coverage and connectivity algorithm based on mobile robots for wireless sensor networks. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03597-9

    Article  Google Scholar 

  105. Utriainen T (2020) Neuromarketing and consumer neuroscience–the evolution and current state of the Art, an integrative review

  106. Vasiljević T, Bogdanović Z, Rodić B, Naumović T, Labus A (2019) Designing iot infrastructure for neuromarketing research. World Conference on Information Systems and Technologies

  107. Vaya D, Hadpawat T (2020) Internet of everything (IoE): a new era of IoT. In: ICCCE 2019, Springer, pp 1-6

  108. Vrána J, Mokrý S (2020) Haptics and its effect on consumers intentions using neuroscientific methods: literature review. Acta universitatis agriculturae et silviculturae mendelianae brunensis 68(2):451–457

    Google Scholar 

  109. Wohlgenannt I, Simons A, Stieglitz S (2020) Virtual reality. Bus Inf Syst Eng 62:455–461. https://doi.org/10.1007/s12599-020-00658-9

    Article  Google Scholar 

  110. Xu Q, Gregor S, Shen Q, Ma Q, Zhang W, Riaz A (2020) The power of emotions in online decision making: a study of seller reputation using fMRI. Decis Support Syst 131:113247

    Google Scholar 

  111. Yugha R, Chithra S (2020) A survey on technologies and security protocols: reference for future generation IoT. J Netw Comput Appl 120:1–22

    Google Scholar 

  112. Zhang J, Rajendran S, Sun Z, Woods R, Hanzo L (2019) Physical layer security for the internet of things: authentication and key generation. IEEE Wirel Commun 26(5):92–98. https://doi.org/10.1109/MWC.2019.1800455

    Article  Google Scholar 

  113. Zhuang W, Shen Y, Li L, Gao C, Dai D (2020) A brain-computer interface system for smart home control based on single trial motor imagery EEG. Int J Sens Netw 34(4):214–225. https://doi.org/10.1504/IJSNET.2020.111780

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Data Science, Yazd University, Yazd, 89195-741, Iran

    Peyman Tirandazi & Seyed Mojtaba Hosseini Bamakan

  2. Department of Management Science, Yazd University, Yazd, 89195-741, Iran

    Seyed Mojtaba Hosseini Bamakan & Aref Toghroljerdi

Authors
  1. Peyman Tirandazi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seyed Mojtaba Hosseini Bamakan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aref Toghroljerdi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

This article has three authors, and all contributed equally.

Corresponding author

Correspondence to Seyed Mojtaba Hosseini Bamakan.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest, financial or otherwise.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tirandazi, P., Bamakan, S.M.H. & Toghroljerdi, A. A review of studies on internet of everything as an enabler of neuromarketing methods and techniques. J Supercomput 79, 7835–7876 (2023). https://doi.org/10.1007/s11227-022-04988-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04988-1

Keywords

Search

Navigation