The role of positive online reviews in risk-based consumer behaviours: an information processing perspective

Lam, Tri; Heales, Jon; Hartley, Nicole

doi:10.1108/ajim-03-2023-0102

Cited by 5 publications

(1 citation statement)

References 118 publications

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“…This method can be complemented with sentiment analysis tools (Punetha & Jain, 2023) to assess opinions and comments on social media, online reviews, and other sources, helping predict the public perception of a product or brand, which, in turn, will affect consumer behaviour. Recommendation models, commonly used in e-commerce platforms and streaming services (Lam et al, 2023), analyse user behaviour to predict products or content that may be of interest, enhancing customer personalisation and satisfaction.…”

Section: Predicting Consumer Behaviourmentioning

confidence: 99%

Application of Artificial Intelligence in Neuromarketing to Predict Consumer Behaviour Towards Brand Stimuli

Juárez-Varón,

Mengual-Recuerda,

Zuluaga

et al. 2024

International Journal of Software Science and Computational Intelligence

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This research aims to analyse the current level of accuracy in predicting consumer behaviour in response to brand stimuli using artificial intelligence, comparing the results with an analysis conducted using neuromarketing biometrics. The study aims to determine the accuracy achieved in predicting consumer levels of attention and visual attraction towards visual stimuli, compared to the results recorded in a neuromarketing investigation with real users, through eye tracking. The implications of these comparative analyses are discussed in the final part of the article, concluding that the emotional intelligence tool provides very accurate predictions of consumer behaviour in response to visual stimuli. The results of this study revealed that the prediction of the percentage of users who would view each area of interest is very good, and regarding visual attraction (time until the first viewing of each area of interest), it is quite similar to the order observed by the consumer group; consequently, the level of approximation to reality of AI is very good.

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Section: Predicting Consumer Behaviourmentioning

confidence: 99%

Application of Artificial Intelligence in Neuromarketing to Predict Consumer Behaviour Towards Brand Stimuli

Juárez-Varón,

Mengual-Recuerda,

Zuluaga

et al. 2024

International Journal of Software Science and Computational Intelligence

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Determinants of multimodal fake review generation in China’s E-commerce platforms

Liu,

He,

2024

Sci Rep

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This paper develops a theoretical model of determinants influencing multimodal fake review generation using the theories of signaling, actor-network, motivation, and human–environment interaction hypothesis. Applying survey data from users of China’s three leading E-commerce platforms (Taobao, Jingdong, and Pinduoduo), we adopt structural equation modeling, machine learning technique, and Bayesian complex networks analysis to perform factor identification, path analysis, feature factor importance ranking, regime division, and network centrality analysis of full sample, male sample, and female sample to reach the following conclusions: (1) platforms’ multimodal recognition and governance capabilities exert significant negative moderating effects on merchants’ information behavior, while it shows no apparent moderating effect on users’ information behavior; users’ emotional venting, perceived value, reward mechanisms, and subjective norms positively influence multimodal fake review generation through perceptual behavior control; (2) feature factors of multimodal fake review generation can be divided into four regimes, i.e., regime 1 includes reward mechanisms and perceived social costs, indicating they are key feature factors of multimodal fake review generation; merchant perception impact is positioned in regime 2, signifying its pivotal role in multimodal fake review generation; regime 3 includes multimodal recognition and governance capabilities, supporting/disparaging merchants, and emotional venting; whereas user perception impact is positioned in regime 4, indicating its weaker influence on multimodal fake review generation; (3) both in full sample, male sample, and female sample, reward mechanisms play a crucial role in multimodal fake review generation; perceived value, hiring review control agency, multimodal recognition and governance capabilities exhibit a high degree of correlation; however, results of network centrality analysis also exhibit heterogeneity between male and female samples, i.e., male sample has different trends in closeness centrality values and betweenness centrality values than female sample. This indicates that determinants influencing multimodal fake review generation are complex and interconnected.

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Investigating the impact of fear of missing out on inaction inertia in live streaming purchase intention

Kao,

Huang

2024

AJIM

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PurposeThe main purpose of the study is to investigate the influence of live streaming participation on purchase intention, specifically focusing on how fear of missing out (FOMO) impacts live streaming purchase intention and its subsequent effect on inaction inertia. Additionally, the study aims to ex-amine the indirect influence of FOMO on the relationship between live streaming participation and purchase intention.Design/methodology/approachThe research employs SEM to assess the properties of measures. The investigation consists of four main constructs: live streaming shopping participation, fear of missing out, live streaming purchase intention, and inaction inertia. Live streaming shopping participation is further divided into two dimensions: live streaming immersion and live streaming social presence, with the latter encompassing social presence and telepresence.FindingsThe study reveals the significant role of FOMO in livestream shopping. Viewers' fear of missing out on information and products presented in livestreams results in heightened attention to these streams and an elevated willingness to purchase. This finding emphasizes the impact of FOMO emotions in driving consumer action and purchasing intent, particularly in situations of product scarcity.Originality/valueThe study uniquely explores FOMO as a factor disrupting consumer inertia, influencing decisive purchasing. It shows that FOMO enhances perceived value of products, altering consumer behavior in live streaming and e-commerce, thus providing a novel perspective on FOMO’s extended impact.

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The role of positive online reviews in risk-based consumer behaviours: an information processing perspective

Cited by 5 publications

References 118 publications

Application of Artificial Intelligence in Neuromarketing to Predict Consumer Behaviour Towards Brand Stimuli

Application of Artificial Intelligence in Neuromarketing to Predict Consumer Behaviour Towards Brand Stimuli

Determinants of multimodal fake review generation in China’s E-commerce platforms

Investigating the impact of fear of missing out on inaction inertia in live streaming purchase intention

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