Heart rate variability in marketing research: A systematic review and methodological perspectives

Abstract

Heart rate variability is a promising physiological measurement that accesses psychophysiological variations in response to a marketing stimulus. While its application spans diverse fields, there is a limited understanding of the usability and interpretation of heart rate variability in marketing research. Therefore, this hybrid literature review provides an overview of the emerging use of heart rate variability in marketing research, along with essential methodological considerations. In this context, we blend marketing mix framework with stimulus-organism-response theory, segregating the use of heart rate variability in various marketing research contexts. We follow the preferred reporting items for systematic reviews and metaanalyses (PRISMA) framework to reflect on 33 records obtained from six databases.Our findings suggest that 42% of studies used heart rate variability to investigate promotion-related topics. Overall, heart rate variability is mostly used in combination with Galvanic skin response (48%). Further, 39% of studies used non-portable systems for data collection. Last, using the theory characteristics methodology (TCM) framework, we identified six research avenues: (1) affective, cognitive, and sensorial constructs; (2) personality, thinking style, and demographics; (3) product experience; (4) advertising and branding; (5) correlation with immersive technologies; and (6) triangulation with other neurophysiological tools.

Hallmarks and potential pitfalls of customer‐ and consumer engagement scales: A systematic review

“…To ensure that all relevant articles were included in our review, the reference lists of these articles were also scanned, which did not identify any additional eligible articles. Our sample of 28 articles is comparable to those deployed in prior studies adopting PRISMA, including Kakaria et al (2023) review of heart rate variability in marketing research (33 studies), Rehman et al's (2020) analysis of social media‐based perceived risk antecedents, consequences, and reducers (42 studies), or Serrano‐Arcos et al (2022) review of affinity research (25 studies).…”

“…To ensure that all relevant articles were included in our review, the reference lists of these articles were also scanned, which did not identify any additional eligible articles. Our sample of 28 articles is comparable to those deployed in prior studies adopting PRISMA, including Kakaria et al (2023)…”

Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research

“…To guide our analysis, we adopted the widely‐used PRISMA protocol (e.g., Hollebeek et al, 2023; Moher, 2009), which comprises three main phases, including Identification, Screening, and Inclusion (Page et al, 2021). First, in the Identification phase, we searched the titles, abstracts, and keywords of eligible Scopus‐indexed journals for the following keywords (Shobhit et al, 2023): (engagement AND (“artificial intelligence” OR AI OR “machine learning” OR “deep learning” OR automated OR automation OR self‐driving OR autonomous OR chatbot* OR “voice assistant*” OR robot* OR “digital assistant*” OR “virtual assistant*” OR “recommender system*” OR “recommendation agent*” OR “supervised learning” OR “unsupervised learning” OR smart)). We focused on original research, thus excluding prior (e.g., systematic or bibliometric) reviews (e.g., Lim et al, 2022; Mehta et al, 2022) from our article sample (Clarke, 2011).…”

“…This review paper makes the following contributions to the CE, AI, and the broader consumer psychology/behavior‐ and marketing literatures. First, adopting the Preferred Reporting Items for Systematic reviews and Meta‐Analyses (PRISMA) approach (e.g., Page et al, 2021; Shobhit et al, 2023), we obtain a sample of 89 AI‐based CE studies that are analyzed to deduce their theoretical hallmarks (e.g., deployed theories or methods; Rehman et al, 2020) and to uncover their main themes (Creswell & Creswell, 2018). We also develop a model of AI‐based CE, reflecting MacInnis' (2011) notion of relating and, thus, unlocking acumen of the concept's position vis‐à‐vis its antecedents and consequences, a widely‐adopted approach in prior PRISMA‐based studies (e.g., Ameen et al, 2022; Rehman et al, 2020).…”

Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver’s Physiological Condition

“…Six ultra-short HRV features, including MeanNN, StdNN, MeanHR, StdHR, HF, and SD2, demonstrated consistency across all excerpt lengths, which ranged from 1 to 5 minutes when used in IBK to detect drivers' mental health [17]. Feature selection algorithms like Principal Component Analysis (PCA) and Genetic Algorithm (GA) can simplify a classifier, improve classification accuracy, and shorten classification times [18][19][20][21] by selecting a subset of the most representative features from ECG data [19] for different types of drivers' state monitoring systems.…”