Variabilitas Detak Jantung dalam Penelitian Preferensi Produk: Tinjauan Bibliometrik Sistematis
DOI:
https://doi.org/10.30872/jatri424873Keywords:
Heart Rate Variability, Neurosains Konsumen, Pengambilan Keputusan, Preferensi Produk, Analisis Bibliometrik.Abstract
Pengukuran preferensi produk dan perilaku pengambilan keputusan konsumen secara tradisional sangat bergantung pada metode survei subjektif (self-report) yang rentan terhadap bias kognitif dan sosial. Sebagai solusinya, neurosains konsumen mulai mengadopsi indikator fisiologis otonom yang objektif, di mana Heart Rate Variability (HRV) muncul sebagai instrumen non-invasif yang andal untuk menangkap emosi implisit secara real-time. Penelitian ini bertujuan untuk memetakan lanskap bibliometrik, struktur konseptual, serta tren evolusi metodologi HRV dalam riset preferensi produk dan pemasaran selama dua dekade terakhir. Menggunakan korpus data Scopus yang mencakup 770 artikel ilmiah, analisis jaringan kolaborasi (co-authorship) dan kemunculan bersama kata kunci (co-occurrence) dieksekusi secara komprehensif melalui perangkat lunak VOSviewer dan Biblioshiny.Hasil analisis co-authorship menunjukkan karakteristik jaringan akademik global yang masih terfragmentasi secara ekstrem ke dalam kelompok-kelompok kecil mandiri (disconnected clusters) tanpa adanya aktor penghubung (boundary spanner) utama. Sementara itu, analisis co-occurrence kata kunci berhasil mengonfirmasi pergeseran metodologis yang kuat dari pendekatan subjektif menuju pemodelan fisiologis prediktif. Struktur konseptual tersebut terbagi menjadi tiga klaster utama: pemodelan parameter HRV otonom, aplikasi psikofisiologis dalam pengambilan keputusan kognitif konsumen, serta standarisasi prosedur eksperimen terkontrol berbasis profil demografi. Konvergensi antarklaster mengindikasikan integrasi mutakhir yang kuat antara sinyal HRV dengan algoritma machine learning. Studi ini merumuskan enam jalur penelitian strategis masa depan yang berfokus pada pengujian preferensi implisit, atribut sensorik kemasan, ekosistem digital (e-commerce), serta triangulasi multi-modal (HRV dan eye tracking) untuk memprediksi dinamika perilaku pembelian konsumen secara presisi.
References
Achar, C., So, J., Agrawal, N., & Duhachek, A. (2016). What we feel and why we buy: the influence of emotions on consumer decision-making. Current Opinion in Psychology, 10, 166–170. https://doi.org/10.1016/j.copsyc.2016.01.009
Alvino, L., Pavone, L., Abhishta, A., & Robben, H. (2020). Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research. Frontiers in Neuroscience, 14. https://doi.org/10.3389/fnins.2020.577666
Araújo, T., & Fontainha, E. (2017). Big Missing Data: are scientific memes inherited differently from gendered authorship? https://doi.org/10.48550/arXiv.1706.05156
Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377–386. https://doi.org/10.1162/qss_a_00019
Bell, L., Vogt, J., Willemse, C., Routledge, T., Butler, L. T., & Sakaki, M. (2018). Beyond Self-Report: A Review of Physiological and Neuroscientific Methods to Investigate Consumer Behavior. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.01655
Bulagang, A. F., Weng, N. G., Mountstephens, J., & Teo, J. (2020). A review of recent approaches for emotion classification using electrocardiography and electrodermography signals. Informatics in Medicine Unlocked, 20, 100363. https://doi.org/10.1016/j.imu.2020.100363
Buyo, K., Kishida, S., & Horita, Y. (2022). Consideration of Majority Voting Method using CNN in Heart Rate Variability Analysis and Preference. IEEJ Transactions on Electronics, Information and Systems, 142(5), 601–602. https://doi.org/10.1541/ieejeiss.142.601
Candia-Rivera, D., Catrambone, V., Thayer, J. F., Gentili, C., & Valenza, G. (2022). Cardiac sympathetic-vagal activity initiates a functional brain–body response to emotional arousal. Proceedings of the National Academy of Sciences, 119(21). https://doi.org/10.1073/pnas.2119599119
Casado-Aranda, L.-A., & Sanchez-Fernandez, J. (2022). Advances in neuroscience and marketing: analyzing tool possibilities and research opportunities. Spanish Journal of Marketing - ESIC. https://doi.org/10.1108/SJME-10-2021-0196
Christoforou, C., Christou-Champi, S., Constantinidou, F., & Theodorou, M. (2015). From the eyes and the heart: a novel eye-gaze metric that predicts video preferences of a large audience. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.00579
Dobbs, W. C., Fedewa, M. V., MacDonald, H. V., Holmes, C. J., Cicone, Z. S., Plews, D. J., & Esco, M. R. (2019). The Accuracy of Acquiring Heart Rate Variability from Portable Devices: A Systematic Review and Meta-Analysis. Sports Medicine, 49(3), 417–435. https://doi.org/10.1007/s40279-019-01061-5
Dulleck, U., Ristl, A., Schaffner, M., & Torgler, B. (2011). Heart rate variability, the autonomic nervous system, and neuroeconomic experiments. Journal of Neuroscience, Psychology, and Economics, 4(2), 117–124. https://doi.org/10.1037/a0022245
Feng, C., Jadram, N., Nakagawa, Y., & Sugaya, M. (2024). Emotion Estimation Using Single-Channel EEG and Heart Rate Variability for Industrial Applications. 2024 12th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), 195–197. https://doi.org/10.1109/ACIIW63320.2024.00040
Ferreira, M. L. V., Sardeli, A. V., Souza, G. V. De, Bonganha, V., Santos, L. D. C., Castro, A., Cavaglieri, C. R., & Chacon-Mikahil, M. P. T. (2017). Cardiac autonomic and haemodynamic recovery after a single session of aerobic exercise with and without blood flow restriction in older adults. Journal of Sports Sciences, 35(24), 2412–2420. https://doi.org/10.1080/02640414.2016.1271139
Gamble, K. R., Vettel, J. M., Patton, D. J., Eddy, M. D., Caroline Davis, F., Garcia, J. O., Spangler, D. P., Thayer, J. F., & Brooks, J. R. (2018). Different profiles of decision making and physiology under varying levels of stress in trained military personnel. International Journal of Psychophysiology, 131, 73–80. https://doi.org/10.1016/j.ijpsycho.2018.03.017
Gaur, S. S., Herjanto, H., & Makkar, M. (2014). Review of emotions research in marketing, 2002–2013. Journal of Retailing and Consumer Services, 21(6), 917–923. https://doi.org/10.1016/j.jretconser.2014.08.009
Geib, R. W., Swink, P. J., Vorel, A. J., Shepard, C. S., Gurovich, A. N., & Waite, G. N. (2015). The bioengineering of changing lifestyle and wearable technology: a mini review. Biomedical Sciences Instrumentation, 51, 69–76.
Halbig, A., & Latoschik, M. E. (2021). A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality. Frontiers in Virtual Reality, 2. https://doi.org/10.3389/frvir.2021.694567
Herman, A. M., Critchley, H. D., & Duka, T. (2018). The role of emotions and physiological arousal in modulating impulsive behaviour. Biological Psychology, 133, 30–43. https://doi.org/10.1016/j.biopsycho.2018.01.014
Hermes, A., & Riedl, R. (2021). Exploring the Influence of Personality Traits on Affective Customer Experiences in Retailing: Combination of Heart Rate Variability (HRV) and Self-report Measures (pp. 20–29). https://doi.org/10.1007/978-3-030-88900-5_3
Ibrahim, A. H., Beaumont, C. T., & Strohacker, K. (2026). “The More You Give the Wearable, the More It Gives You”: How Regular Exercisers Navigate Exercise Using Wearable Devices. Applied Psychophysiology and Biofeedback. https://doi.org/10.1007/s10484-025-09762-6
Kaheh, S., Ramirez, M., & George, K. (2021a). Using Concurrent fNIRS and EEG Measurements to Study Consumer’s Preference. 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 0305–0310. https://doi.org/10.1109/IEMCON53756.2021.9623092
Kaheh, S., Ramirez, M., & George, K. (2021b, August 5). Study on the Effect of Product Brand and Pricing Using Biosignals. Proceedings of the 2021 IEEE 28th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2021. https://doi.org/10.1109/INTERCON52678.2021.9532800
Karmarkar, U. R., & Plassmann, H. (2019). Consumer Neuroscience: Past, Present, and Future. Organizational Research Methods, 22(1), 174–195. https://doi.org/10.1177/1094428117730598
Kleinginna, P. R., & Kleinginna, A. M. (1981). A categorized list of emotion definitions, with suggestions for a consensual definition. Motivation and Emotion, 5(4), 345–379. https://doi.org/10.1007/BF00992553
Kristoffersson, A., & Lindén, M. (2020). Wearable sensors for monitoring and preventing noncommunicable diseases: A systematic review. In Information (Switzerland) (Vol. 11, Number 11, pp. 1–31). MDPI AG. https://doi.org/10.3390/info11110521
Lang, A. (2000). The Limited Capacity Model of Mediated Message Processing. Journal of Communication, 50(1), 46–70. https://doi.org/10.1111/j.1460-2466.2000.tb02833.x
Laros, F. J. M., & Steenkamp, J.-B. E. M. (2005). Emotions in consumer behavior: a hierarchical approach. Journal of Business Research, 58(10), 1437–1445. https://doi.org/10.1016/j.jbusres.2003.09.013
Laureanti, R., Barbieri, R., Cerina, L., & Mainardi, L. (2022). Analysis of physiological and non-contact signals to evaluate the emotional component in consumer preferences. PLoS ONE, 17(5 May). https://doi.org/10.1371/journal.pone.0267429
Lee, N., Chamberlain, L., & Brandes, L. (2018). Welcome to the jungle! The neuromarketing literature through the eyes of a newcomer. European Journal of Marketing, 52(1/2), 4–38. https://doi.org/10.1108/EJM-02-2017-0122
Liu, J., & Mo, Z. (2021). The effects of review’s mobile phone price on consumers’ purchase intention: An event-related potential study. Journal of Neuroscience, Psychology, and Economics, 14(4), 197–206. https://doi.org/10.1037/npe0000152
Massaro, S., & Pecchia, L. (2019). Heart Rate Variability (HRV) Analysis: A Methodology for Organizational Neuroscience. Organizational Research Methods, 22(1), 354–393. https://doi.org/10.1177/1094428116681072
Morganroth, J. (2004). A definitive or thorough phase 1 QT ECG trial as a requirement for drug safety assessment. Journal of Electrocardiology, 37(1), 25–29. https://doi.org/10.1016/j.jelectrocard.2003.11.004
Nelson, B. W., Low, C. A., Jacobson, N., Areán, P., Torous, J., & Allen, N. B. (2020). Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research. Npj Digital Medicine, 3(1), 90. https://doi.org/10.1038/s41746-020-0297-4
Ogunleye, B., Zakariyyah, K. I., Ajao, O., Olayinka, O., & Sharma, H. (2024). A Systematic Review of Generative AI for Teaching and Learning Practice. Education Sciences, 14(6), 636. https://doi.org/10.3390/educsci14060636
Orazi, D. C., & Nyilasy, G. (2019). Straight to the Heart Of Your Target Audience. Journal of Advertising Research, 59(2), 137–141. https://doi.org/10.2501/JAR-2019-020
Perez, A., Singh, A., & Pontes, V. (2025). Purchase Intention in Augmented Reality vs. Web Shopping: A Multimodal Exploration. Proceedings of the 2025 20th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry, 1–4. https://doi.org/10.1145/3779232.3779279
Poels, K., & Dewitte, S. (2006). How to Capture the Heart? Reviewing 20 Years of Emotion Measurement in Advertising. Journal of Advertising Research, 46(1), 18–37. https://doi.org/10.2501/S0021849906060041
Ramirez, M., Kaheh, S., & George, K. (2021). Neuromarketing Study Using Machine Learning for Predicting Purchase Decision. 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 0560–0564. https://doi.org/10.1109/UEMCON53757.2021.9666539
Royo-Vela, M., & Varga, Á. (2022). Unveiling Neuromarketing and Its Research Methodology. Encyclopedia, 2(2), 729–751. https://doi.org/10.3390/encyclopedia2020051
Solhjoo, S., Haigney, M. C., McBee, E., van Merrienboer, J. J. G., Schuwirth, L., Artino, A. R., Battista, A., Ratcliffe, T. A., Lee, H. D., & Durning, S. J. (2019). Heart Rate and Heart Rate Variability Correlate with Clinical Reasoning Performance and Self-Reported Measures of Cognitive Load. Scientific Reports, 9(1), 14668. https://doi.org/10.1038/s41598-019-50280-3
Stokols, D. (1972). On the distinction between density and crowding: Some implications for future research. Psychological Review, 79(3), 275–277. https://doi.org/10.1037/h0032706
Suparwadi, S., Al Musadieq, M., Riza, M. F., & Hutahayan, B. (2025). What Are the Key Determinants of Sustainable Organizational Performance? A PRISMA-Based Systematic Review. F1000Research, 14, 1189. https://doi.org/10.12688/f1000research.168678.1
Švihrová, R., Marzorati, D., Bechný, M., Grossenbacher, M., Ilchenko, Y., Grossenbacher, J., Tzovara, A., & Faraci, F. D. (2025). Toward burnout prevention with Bayesian mixed-effects regression analysis of longitudinal data from wearables: a preliminary study. Frontiers in Digital Health, 7. https://doi.org/10.3389/fdgth.2025.1640900
Tozman, T., Magdas, E. S., MacDougall, H. G., & Vollmeyer, R. (2015). Understanding the psychophysiology of flow: A driving simulator experiment to investigate the relationship between flow and heart rate variability. Computers in Human Behavior, 52, 408–418. https://doi.org/10.1016/j.chb.2015.06.023
Valente, J., Godinho, L., Pintado, C., Baptista, C., Kozlova, V., Marques, L., Fred, A., & Plácido da Silva, H. (2021). Neuroorganoleptics: Organoleptic Testing Based on Psychophysiological Sensing. Foods, 10(9), 1974. https://doi.org/10.3390/foods10091974
Valenza, G., Citi, L., Lanatá, A., Scilingo, E. P., & Barbieri, R. (2014). Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics. Scientific Reports, 4(1), 4998. https://doi.org/10.1038/srep04998
Verhulst, N., De Keyser, A., Gustafsson, A., Shams, P., & Van Vaerenbergh, Y. (2019). Neuroscience in service research: an overview and discussion of its possibilities. Journal of Service Management, 30(5), 621–649. https://doi.org/10.1108/JOSM-05-2019-0135
Viglia, G., Zaefarian, G., & Ulqinaku, A. (2021). How to design good experiments in marketing: Types, examples, and methods. Industrial Marketing Management, 98, 193–206. https://doi.org/10.1016/j.indmarman.2021.08.007
Wen, X., Wang, Y., & Liu, C. (2024). The relationship between emotions and HRV under the stimulation of bra images. International Journal of Industrial Ergonomics, 103, 103633. https://doi.org/10.1016/j.ergon.2024.103633
Zhao, M., Ma, S., Liu, Y., & Song, W. (2023). Multimedia-Based Informal Learning in Museum Using Augmented Reality (pp. 53–64). https://doi.org/10.1007/978-981-99-7549-5_5
Zimmermann-Viehoff, F., Thayer, J., Koenig, J., Herrmann, C., Weber, C. S., & Deter, H.-C. (2016). Short-term effects of espresso coffee on heart rate variability and blood pressure in habitual and non-habitual coffee consumers – A randomized crossover study. Nutritional Neuroscience, 19(4), 169–175. https://doi.org/10.1179/1476830515Y.0000000018
Zohar, A. H., Cloninger, C. R., & McCraty, R. (2013). Personality and Heart Rate Variability: Exploring Pathways from Personality to Cardiac Coherence and Health. Open Journal of Social Sciences, 01(06), 32–39. https://doi.org/10.4236/jss.2013.16007
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