Emotional contagion triggered by online consumer reviews: Evidence from a neuroscience study

Herrando, Carolina; Martínez, Julio Jiménez; Hoyos, María José Martín‐De; Constantinides, Efthymios

doi:10.1016/j.jretconser.2022.102973

Cited by 27 publications

(10 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Frontal asymmetry has also been found to be predictive self-reported preference [ 9 , 110 , 118 , 119 , 122 , 148 , 191 , 192 , 260 ], and emerged as the only EEG TF measure that is consistently associated with behavioural measures of willingness to pay (WTP) [ 50 , 138 , 148 , 217 – 219 , 239 ]. This suggests that frontal asymmetry may reflect actional/motivational responses to brands/products while evaluative ratings and recall may be better investigated using other TF measures such as relative-band power changes [ 26 , 87 , 99 , 115 , 117 , 138 , 145 , 226 , 237 ].…”

Section: Systematic Reviewmentioning

confidence: 99%

“…Several studies have shown that measures of arousal and EEG TF responses are modulated by advertisement preference and memorability [ 1 , 44 , 46 , 58 , 83 , 203 , 266 , 266 , 267 , 267 , 269 , 269 , 270 , 270 , 271 , 271 , 272 , 272 , 273 , 273 , 274 , 274 ], as well as differences in product features [ 43 , 50 , 278 ]. However, physiological measures of arousal appear to be unable to differentiate between different emotional and cognitive responses, while EEG TF measures can [ 1 , 22 , 24 , 50 , 52 , 83 , 110 , 189 , 191 , 192 , 195 , 232 , 266 , 273 ]. For example, [ 50 ] Chen [ 49 ] showed that HRV measures of arousal could only differentiate between the intensity of mouthwash flavours, while FAA distinguished between flavours and was predictive of self-reported preference and purchase intention.…”

Section: Systematic Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

et al. 2022

View full text Add to dashboard Cite

Introduction The present paper discusses the findings of a systematic review of EEG measures in neuromarketing, identifying which EEG measures are the most robust predictor of customer preference in neuromarketing. The review investigated which TF effect (e.g., theta-band power), and ERP component (e.g., N400) was most consistently reflective of self-reported preference. Machine-learning prediction also investigated, along with the use of EEG when combined with physiological measures such as eye-tracking. Methods Search terms ‘neuromarketing’ and ‘consumer neuroscience’ identified papers that used EEG measures. Publications were excluded if they were primarily written in a language other than English or were not published as journal articles (e.g., book chapters). 174 papers were included in the present review. Results Frontal alpha asymmetry (FAA) was the most reliable TF signal of preference and was able to differentiate positive from negative consumer responses. Similarly, the late positive potential (LPP) was the most reliable ERP component, reflecting conscious emotional evaluation of products and advertising. However, there was limited consistency across papers, with each measure showing mixed results when related to preference and purchase behaviour. Conclusions and implications FAA and the LPP were the most consistent markers of emotional responses to marketing stimuli, consumer preference and purchase intention. Predictive accuracy of FAA and the LPP was greatly improved through the use of machine-learning prediction, especially when combined with eye-tracking or facial expression analyses.

show abstract

Section: Systematic Reviewmentioning

confidence: 99%

Section: Systematic Reviewmentioning

confidence: 99%

A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The apathy of impartial reviews is also transferred to the observer, causing arousal deactivation and displeasure (Herrando et al, 2022). However, I claim emotional contagion cannot be described as a precursor to the entire understanding of why the participants reacted the way they did.…”

Section: Emotional Contagion Digital Platforms and Mental Disordersmentioning

confidence: 89%

Emotional Contagion on Social Media Platforms: New Directions in Cognitive Science and Practice

Bamijoko-Okungbaye

2022

JSMI

View full text Add to dashboard Cite

Social media platforms have been hypothesised to be a source of mental disorders to develop, and emotional contagion has been described as a precursor of these disorders. This paper systematically explores the key features of emotional contagion as a psychophysiological and behavioural experience required for the conversion of these disorders to propose an innovative characterisation of mental disorders on digital platforms as socially construed and value-laden phenomena.

show abstract

“…In OBCs, the emotions beneath online conversations decide the consumers’ reactions to active firms’ actions ( Homburg et al, 2015 ). The neural analyses (through electroencephalography) showed the valence of the arousal, which indicates that emotional valence of online consumer review activate arousal and emotional status in the observer ( Herrando et al, 2022 ). However, little work has been done in conducting sentimental analysis in OBCs setting (e.g., Situmeang et al, 2019 ), especially the cross-influences from different sources.…”

Section: Literature Reviewmentioning

confidence: 99%

The analysis of dynamic emotional contagion in online brand community

Liu

Zhang²,

Li³

2022

Front. Psychol.

View full text Add to dashboard Cite

Online brand communities (OBCs) could benefit firms in many usages, ranging from collecting consumers’ suggestions or advice to interacting with community members directly and transparently. Creating a positive emotional atmosphere is essential for such communities’ healthy development as its boosts the continuous involvement of each member. However, the dynamic cross-influences and evolution of emotions in OBCs have not been fully explored, which was the research gap this paper tried to fill. Based on emotional contagion theory, this study identifies three sources of textual sentiment through machine learning methods in OBCs: member’s posts, other members’ feedback, and the focal firm’s official feedback. This study further tested the dynamic emotional contagion process among these sources on valence (mean) and volatility (dispersion), namely how they affected each other. Data was collected from the MIUI forum, a large forum launched by Xiaomi corporate on August 1, 2011, which contained 17,622 posts and 99,426 feedback. Results showed that: (1) in the emotional contagion process, there existed differences in the influence of emotional valence and volatility from different sources; (2) all emotional interactions were temporary and mostly lasted no more than three days; (3) the most significant contributor of each sources’ emotion was itself, which could be explained by lagged effect; (4) the valence of focal firm’s emotion (focal firm’s official feedback) was the second contributor of the valence of member’s emotion (member’s posts) and other members’ emotion (other members’ feedback). Three sources of emotion in OBCs and emotional valence/volatility should be considered when firms try to guide the emotional changes in such communities. Furthermore, firms could proactively influence members’ emotions by carefully designing the feedback to members’ posts. Besides, since all interactions are temporary, firms need to engage in online communities frequently, like consistently offering feedback.

show abstract

Emotional contagion triggered by online consumer reviews: Evidence from a neuroscience study

Cited by 27 publications

References 51 publications

A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

Emotional Contagion on Social Media Platforms: New Directions in Cognitive Science and Practice

The analysis of dynamic emotional contagion in online brand community

Contact Info

Product

Resources

About