We proposed a method employing deep learning (DL) on eye-tracking data and applied this method to detect intentions to use apparel websites that differed in factors of depth, breadth, and location of navigation. Results showed that users’ intentions could be predicted by combining a deep neural network algorithm and metrics recorded from an eye-tracker. Using all of the eye-tracking metric features attained the best accuracy when predicting usage/not-usage intention to websites. In addition, the results suggest that for apparel websites with the same depth, designers can increase usage intention by using a larger number of navigation items and placing the navigation at the top and left of the homepage. The results show that building intelligent usage intention-detection systems is possible for the range of websites we examined and is also computationally practical. Hence, the study motivates future investigations that focus on design of such systems.
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Almeida VM, Rafael S, Neves M (2020) Natural human-computer interfaces’ paradigm and cognitive ergonomics. In: Rebelo F, Soares M (eds) Advances in Ergonomics in Design AHFE 2019, vol 955. Springer, pp 220–227
Ajzen I (2002) Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior 1. J Appl Soc Psychol 32(4):665–683
Muslim A, Harun A, Ismael D, Othman B (2020) Social media experience, attitude and behavioral intention towards umrah package among generation X and Y. Manage Sci Lett 10(1):1–12
Ajzen I (1985) From intentions to actions: A theory of planned behavior. In: Kuhl J, Beckmann J (eds) Action control. Springer, pp 11–39
Oberauer K (2009) Design for a working memory. In: Ross BH (ed) The psychology of learning and motivation, vol 51. Academic Press, pp 45–100
Xiong A, Proctor RW (2018) The role of task space in action control: Evidence from research on instructions. In: Federmeier KD (ed) The psychology of learning and motivation. Academic Press, Cambridge, MA, pp 325–364
Shojaeizadeh M, Djamasbi S, Paffenroth RC, Trapp AC (2019) Detecting task demand via an eye tracking machine learning system. Decis Support Syst 116:91–101
Ding Y, Cao Y, Duffy VG, Wang Y, Zhang X (2020) Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning. Ergonomics 63(7):896–908
Tzafilkou K, Protogeros N (2017) Diagnosing user perception and acceptance using eye tracking in web-based end-user development. Comput Hum Behav 72:23–37
Deng M, Gu X (2020) Information acquisition, emotion experience and behaviour intention during online shopping: an eye-tracking study. Behav Inf Technol 2:1–11
Guo F, Cao Y, Ding Y, Liu W, Zhang X (2015) A multimodal measurement method of users’ emotional experiences shopping online. Human Factors Ergon Manuf Serv Ind 25(5):585–598
Slanzi G, Balazs JA, Velásquez JD (2017) Combining eye tracking, pupil dilation and EEG analysis for predicting web users click intention. Inf Fusion 35:51–57
Jadue, J., Slanzi, G., Salas, L., & Velásquez, J.D. (2015). Web user click intention prediction by using pupil dilation analysis. In 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (Vol. 1, pp. 433–436): IEEE.
Deng Q, Wang J, Hillebrand K, Benjamin CR, Soffker D (2019) Prediction performance of lane changing behaviors: a study of combining environmental and eye-tracking data in a driving simulator. IEEE Trans Intell Transp Syst 21(8):1–10
Joseph AW, Murugesh R (2020) Potential eye tracking metrics and indicators to measure cognitive load in human-computer interaction research. J Sci Res 64(1):168–175
Zhu Z, Zhou Y, Deng X, Wang X (2019) A graph-oriented model for hierarchical user interest in precision social marketing. Electron Commer Res Appl 35:1–12
Djamasbi S (2014) Eye tracking and web experience. AIS Trans Human-Comput Interact 6(2):37–54
Hwang AH-C, Oh J (2020) Interacting with background music engages E-Customers more: the impact of interactive music on consumer perception and behavioral intention. J Retail Consum Serv 54(5):1–15
Guo F, Wang X-S, Liu W-L, Ding Y (2018) Affective preference measurement of product appearance based on event-related potentials. Cogn Technol Work 20(2):299–308
Gurbuz, A., Aktas, M.S., & Ieee. (2019). Prediction of purchase intention on the E-Commerce clickstream data. In 27th Signal Processing and Communications Applications Conference. New York: IEEE.
Wu I-C, Yu H-K (2020) Sequential analysis and clustering to investigate users’ online shopping behaviors based on need-states. Inf Process Manage 57(6):1–18
Yan H, Wang Z, Lin T-H, Li Y, Jin D (2018) Profiling users by online shopping behaviors. Multimed Tools Appl 77(17):21935–21945
Ahmad IS, Bakar AA, Yaakub MR (2020) Movie revenue prediction based on purchase intention mining using YouTube trailer reviews. Inf Process Manage 57(5):1–15
Hibbeln MT, Jenkins JL, Schneider C, Valacich J, Weinmann M (2017) How is your user feeling? inferring emotion through human-computer interaction devices. MIS Q 41(1):1–21
Leiva LA, Huang J (2015) Building a better mousetrap: compressing mouse cursor activity for web analytics. Inf Process Manage 51(2):114–129
Liu W, Liang X, Wang X, Guo F (2019) The evaluation of emotional experience on webpages: an event-related potential study. Cogn Technol Work 21(2):317–326
Sung B, Wilson NJ, Yun JH, LEE EJ (2020) What can neuroscience offer marketing research? Asia Pac J Mark Logist 32(5):1089–1111
Xiong J, Zuo M (2020) What does existing NeuroIS research focus on? Inf Syst 89:1–12
Campbell CS, & Maglio PP (2001) A robust algorithm for reading detection. In Proceedings of the 2001 workshop on Perceptive user interfaces (pp. 1–7). ACM.
Guo F, Ding Y, Liu W, Liu C, Zhang X (2016) Can eye-tracking data be measured to assess product design?: visual attention mechanism should be considered. Int J Ind Ergon 53(5):229–235
Guo F, Li M, Qu Q, Duffy VG (2019) The effect of a humanoid robot’s emotional behaviors on users’ emotional responses: evidence from pupillometry and electroencephalography measures. Int J Human-Comput Interact 35(20):1947–1959
Espigares-Jurado F, Munoz-Leiva F, Correia MB, Sousa CMR, Ramos CMQ, Faisca 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(1):1–11
Liu Y, Yttri EA, Snyder LH (2010) Intention and attention: different functional roles for LIPd and LIPv. Nat Neurosci 13(4):495–502
Rayner K (1998) Eye movements in reading and information processing: 20 years of research. Psychol Bull 124(3):372–422
Di Stasi LL, Catena A, Canas JJ, Macknik SL, Martinez-Conde S (2013) Saccadic velocity as an arousal index in naturalistic tasks. Neurosci Biobehav Rev 37(5):968–975
Fuchs A (1967) Saccadic and smooth pursuit eye movements in the monkey. J Physiol 191(3):609–631
Jonikaitis D, Szinte M, Rolfs M, Cavanagh P (2013) Allocation of attention across saccades. J Neurophysiol 109(5):1425–1434
Marchak F (2013) Detecting false intent using eye blink measures. Front Psychol 4:1–9
Stern JA, Boyer D, Schroeder D (1994) Blink rate: a possible measure of fatigue. Hum Factors 36(2):285–297
Noton D, Stark L (1971) Scanpaths in eye movements during pattern perception. Science 171(3968):308–311
Coutrot A, Hsiao JH, Chan AB (2018) Scanpath modeling and classification with hidden Markov models. Behav Res Methods 50(1):362–379
Lim Y, Gardi A, Pongsakornsathien N, Sabatini R, Ezer N, Kistan T (2019) Experimental characterisation of eye-tracking sensors for adaptive human-machine systems. Measurement 140:151–160
Park H, Lee S, Lee M, Chang M-S, Kwak H-W (2016) Using eye movement data to infer human behavioral intentions. Comput Hum Behav 63:796–804
Jang Y-M, Mallipeddi R, Lee M (2014) Identification of human implicit visual search intention based on eye movement and pupillary analysis. User Model User-Adapt Interact 24(4):315–344
Koochaki F, Najafizadeh L (2018) Predicting intention through eye gaze patterns. 2018 IEEE Biomedical Circuits and Systems Conference. IEEE, New York, pp 25–28
Yang, M., Lin, L., Chen, Z., Wu, L., & Guo, Z. (2020). Research on the construction method of kansei image prediction model based on cognition of EEG and ET. International Journal on Interactive Design and Manufacturing, 1–21.
Alpaydin E (2020) Introduction to machine learning. MIT press
Kim I-H, Bong J-H, Park J, Park S (2017) Prediction of driver’s intention of lane change by augmenting sensor information using machine learning techniques. Sensors 17(6):1–18
Fu X, Ouyang T, Chen J, Luo X (2020) Listening to the investors: a novel framework for online lending default prediction using deep learning neural networks. Inf Process Manage 57(4):1–13
Marblestone AH, Wayne G, Kording KP (2016) Toward an integration of deep learning and neuroscience. Front Comput Neurosci 10:1–41
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117
Su M-C, Hsieh Y-Z, Yeh Z-F, Lee S-F, Lin S-S (2020) An eye-tracking system based on inner corner-pupil center vector and deep neural network. Sensors 20(1):1–15
Sheela KG, Deepa SN (2013) Review on methods to fix number of hidden neurons in neural networks. Math Probl Eng 2013:1–11
Abbaspour-Gilandeh Y, Fazeli M, Roshanianfard A, Hernández-Hernández M, Gallardo-Bernal I, Hernández-Hernández JL (2020) Prediction of draft force of a chisel cultivator using artificial neural networks and its comparison with regression model. Agronomy 10(4):1–14
Lever J, Krzywinski M, Altman N (2016) Points of significance: classification evaluation. Nat Methods 13(8):603–604
Karanam S, Van Oostendorp H, Tat Fu, W (2016) Performance of computational cognitive models of web-navigation on real websites. J Inf Sci 42(1):94–113
Kumar V, Jenamani M (2017) Context preserving navigation redesign under Markovian assumption for responsive websites. Electron Commer Res Appl 21(1):65–78
Katz MA, Byrne MD (2003) Effects of scent and breadth on use of site-specific search on e-commerce Web sites. ACM Trans Comput-Human Interact (TOCHI) 10(3):198–220
Donovan RD, Rossiter JR (1982) Store atmosphere: An environmental psychology approach. J Retail 58(1):34–57
Tuch AN, Roth SP, Hornbæk K, Opwis K, Bargas-Avila JA (2012) Is beautiful really usable? toward understanding the relation between usability, aesthetics, and affect in HCI. Comput Hum Behav 28(5):1596–1607
Hu Lt, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model 6(1):1–55
Williams BA, Mandrekar JN, Mandrekar SJ, Cha SS, & Furth AF (2006) Finding optimal cutpoints for continuous covariates with binary and time-to-event outcomes. Technical Report Series #79, Department of Health Sciences Research, Mayo Clinic, Rochester, MN.
Haggard P (2005) Conscious intention and motor cognition. Trends in Cognit Sci 9(6):290–295
This work was supported by the National Natural Science Foundation of China (Grant Numbers 71701003, 71801002, 71802002), Ministry of Education Industry-University Cooperation Collaborative Education Project (Grant No. 201901024006), the University Natural Science Research Key Project of Anhui Province (Grant No. KJ2017A108). We thank all the participants for carrying out the experiments.
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Cao, Y., Ding, Y., Proctor, R.W. et al. Detecting users’ usage intentions for websites employing deep learning on eye-tracking data. Inf Technol Manag 22, 281–292 (2021). https://doi.org/10.1007/s10799-021-00336-6
- Behavioral intention
- Deep learning