A neural network based price sensitive recommender model to predict customer choices based on price effect

Chen, Shiuann Shuoh; Choubey, Bhaskar; Singh, Vinay

doi:10.1016/j.jretconser.2021.102573

Cited by 29 publications

(11 citation statements)

References 84 publications

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“…AR, VR) can increase consumers' feelings of psychological ownership while decreasing price sensitivity. Previous studies also indicate that economic background, advertising, special occasions, risk attitude and individual differences can influence consumers' price sensitivity levels (Goldsmith et al, 2010;Baucells et al, 2017;Chen et al, 2021). In this research, price sensitivity refers to consumers' willingness to pay for branded color cosmetics.…”

Section: Price Sensitivitymentioning

confidence: 87%

The e-WOM intention of artificial intelligence (AI) color cosmetics among Chinese social media influencers

Simay

Wei

Gyulavári

et al. 2022

APJML

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PurposeThe recent advancements in smartphone technology and social media platforms have increased the popularity of artificial intelligence (AI) color cosmetics. Meanwhile, China is a lucrative market for various foreign beauty products and technological innovations. This research aims to investigate the adoption of AI color cosmetics applications and their electronic word-of-mouth (e-WOM) intention among Chinese social media influencers. Several key concepts have been proposed in this research, namely body esteem, price sensitivity, social media addiction and actual purchase.Design/methodology/approachAn online questionnaire design was used in this research. A combination of purposive sampling and snowball sampling of AI color cosmetics users who are also social media influencers in China yields 221 respondents. To analyze the data, this research employs Structural Equation Modelling (SEM) method via SPSS and AMOS software. A 2-step approach, Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), is implemented to prove the hypotheses and generate the results.Findings1) Social media addiction is a positive predictor of AI color cosmetics usage, (2) AI color cosmetics usage is a positive predictor of actual purchase, (3) actual purchase is a positive predictor of e-WOM intention and lastly, (4) there is a full mediation effect of actual purchase.Originality/valueThis research draws on the uses and gratification (U&G) theory to investigate how specific user characteristics affect Chinese social media influencers' adoption of AI color cosmetics, as well as how this may affect their decision to purchase branded color cosmetics and their e-WOM.

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Section: Price Sensitivitymentioning

confidence: 87%

The e-WOM intention of artificial intelligence (AI) color cosmetics among Chinese social media influencers

Simay

Wei

Gyulavári

et al. 2022

APJML

View full text Add to dashboard Cite

show abstract

“…To predict the behaviour of consumers using modern tools and approaches, the neural network can be used for the more advanced models. Chen et al (2021) and Yang (2021) mention the importance of the price in models using a neural network to make a prediction. Those models can be used to make a new way of prediction in revenue management that is not based on the time series.…”

Section: Discussionmentioning

confidence: 99%

Understanding customer's online booking intentions using hotel big data analysis

Chalupa

Petříček

2022

Journal of Vacation Marketing

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The presented article focuses on the issue of customer segmentation in the hospitality industry and its use for price optimisation. To identify the market segments article uses the Two-Step cluster analysis. The analysis was based on the seven variables (length of stay, average room rate, distribution channel, reservation day, day of arrival, lead time and payment conditions). Six clusters were identified as following segments: Corporates, Early Bird Bookers, Last Minute Bookers, Product Seekers, Values Seekers and Last Minute Bookers. To optimise the price for these segments, article works with the coefficient of price elasticity of demand for the presented market segments. The price elasticity of demand is measured by the log-log regression analysis. Data were colected from the four-star hotel in Prague, Czech Republic and analysis is based on more than 9000 transactions. Last minute bookers segment was the only one with the positive coefficient of price elasticity and has the lowest value of lead time (9,27 in average). Product seekers have the highest coefficient of price elasticity (−3,413) and highest average room rate (3573 CZK in average). Early bird bookers, Long time stayers, Corporates and Value seekers was identified as pricely inelastic.

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“…In the same way, the articles also examined children's classification (rating) behavior toward certain trademarks based on emotions and loyalty [118], the credibility in the classification of most popular users on consumer reviews platforms such as Yelp [119], the prediction of customer responses to campaigns [120], and the future behaviors of a panel of customers [55]. Furthermore, the price sensitivities and the importance of consumer behavior in supermarkets [121] or purchases of ecological buildings [122] have been studied. Studies also investigated the adoption of payment according to the attributes of products coincidences in shopping baskets [123] and the use of peer-to-peer mobile payment systems and key backgrounds of clients' intention [124].…”

Section: A Consumer Behaviormentioning

confidence: 99%

Machine Learning and Marketing: A Systematic Literature Review

2022

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Even though machine learning (ML) applications are not novel, they have gained popularity partly due to the advance in computing processing. This study explores the adoption of ML methods in marketing applications through a bibliographic review of the period 2008-2022. In this period, the adoption of ML in marketing has grown significantly. This growth has been quite heterogeneous, varying from the use of classical methods such as artificial neural networks to hybrid methods that combine different techniques to improve results. Generally, maturity in the use of ML in marketing and increasing specialization in the type of problems that are solved were observed. Strikingly, the types of ML methods used to solve marketing problems vary wildly, including deep learning, supervised learning, reinforcement learning, unsupervised learning, and hybrid methods. Finally, we found that the main marketing problems solved with machine learning were related to consumer behavior, recommender systems, forecasting, marketing segmentation, and text analysis-content analysis.

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A neural network based price sensitive recommender model to predict customer choices based on price effect

Cited by 29 publications

References 84 publications

The e-WOM intention of artificial intelligence (AI) color cosmetics among Chinese social media influencers

The e-WOM intention of artificial intelligence (AI) color cosmetics among Chinese social media influencers

Understanding customer's online booking intentions using hotel big data analysis

Machine Learning and Marketing: A Systematic Literature Review

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