Modeling Toothpaste Brand Choice: An Empirical Comparison of Artificial Neural Networks and Multinomial Probit Model

Kaya, Tolga; Aktaş, Emel; Topçu, Y. İlker; Ülengin, Burç

doi:10.1080/18756891.2010.9727732

Cited by 5 publications

(5 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The process of computing appropriate weights is called a learning law or learning algorithm. The learning process of ANN can be thought of as a reward and punishment mechanism [23], whereby when the system reacts appropriately to an input, the related weights are strengthened. In this case, it is possible to generate outputs, which are similar to those corresponding to the previously encountered inputs.…”

Section: Artificial Neural Network Artificial Neural Networkmentioning

confidence: 99%

A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

Ding

Wang

et al. 2013

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The neural network may learn and incorporate the uncertainties to predict the driver’s lane-changing behavior more accurately. In this paper, we will discuss in detail the effectiveness of Back-Propagation (BP) neural network for prediction of lane-changing trajectory based on the past vehicle data and compare the results between BP neural network model and Elman Network model in terms of the training time and accuracy. Driving simulator data and NGSIM data were processed by a smooth method and then used to validate the availability of the model. The test results indicate that BP neural network might be an accurate prediction of driver’s lane-changing behavior in urban traffic flow. The objective of this paper is to show the usefulness of BP neural network in prediction of lane-changing process and confirm that the vehicle trajectory is influenced previously by the collected data.

show abstract

Section: Artificial Neural Network Artificial Neural Networkmentioning

confidence: 99%

A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

Ding

Wang

et al. 2013

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…2 For toothpaste, previous studies include Kaya et al (2010), Gutierrez (2005), Yang, Zhou and Chen (2005), and Shin (2008); and for chewing gum, Chung and Szymanski (1997). All of these studies examine factors that affect brand selection.…”

Section: Introductionmentioning

confidence: 99%

Quality Differentiation with Flavors: Demand Estimation of Unobserved Attributes

Toro-González¹,

Gallardo²,

Jia³

et al. 2013

Journal of Agricultural & Food Industrial Organization

View full text Add to dashboard Cite

This article estimates the demand for mint-flavored gum products using grocery store sales data and accounting for consumers' valuation of quality. Unobserved product attributes, such as flavor quality, are important elements to consider when estimating the demand for gum. The estimation results suggest that gum is an inelastic product. A positive relationship between willingness to pay and unobserved quality was identified, implying that gum industry should be able to command a premium for higher quality mint-flavored products.

show abstract

“…This prediction helps the retail industry to maximize profit. It is a nonlinear statistical model created for the human brain and can be programmed and trained to identify data patterns (Kaya et al, 2010).…”

Section: Artificial Neural Network and Brand Choicementioning

confidence: 99%

The Impact of Artificial Intelligence on Branding

Varsha¹,

Akter

Kumar

et al. 2021

Journal of Global Information Management

View full text Add to dashboard Cite

Understanding the growth paths of artificial intelligence (AI) and its impact on branding is extremely pertinent of technology-driven marketing. This explorative research covers a complete bibliometric analysis of the impact of AI on branding. The sample for this research included all 117 articles from the period of 1982-2019 in the Scopus database. A bibliometric study was conducted using co-occurrence, citation analysis and co-citation analysis. The empirical analysis investigates the value propositions of AI on branding. The study revealed the nine clusters of co-occurrence: Social Media Analytics and Brand Equity; Neural Networks and Brand Choice; Chat Bots-Brand Intimacy; Twitter, Facebook, Instagram-Luxury Brands; Interactive Agent-Brand Love and User Choice; Algorithm Recommendations and E-Brand Experience; User-Generated Content-Brand Sustainability; Brand Intelligence Analytics; and Digital Innovations and Brand Excellence. The findings also identify four clusters of citation analysis—Social Media Analysis and Brand Photos, Network Analysis and E-Commerce, Hybrid Simulating Modelling, and Real-time Knowledge-Based Systems—and four clusters of co-citation analysis: B2B Technology Brands, AI Fostered E-Brands, Information Cascades and Online Brand Ratings, and Voice Assistants-Brand Eureka Moments. Overall, the study presents the patterns of convergence and divergence of themes, narrowing to the specific topic, and multidisciplinary engagement in research, thus offering the recent insights in the field of AI on branding.

show abstract

Modeling Toothpaste Brand Choice: An Empirical Comparison of Artificial Neural Networks and Multinomial Probit Model

Cited by 5 publications

References 42 publications

A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

Quality Differentiation with Flavors: Demand Estimation of Unobserved Attributes

The Impact of Artificial Intelligence on Branding

Contact Info

Product

Resources

About