Forecasting Buying Intention Through Artificial Neural Network: An Algorithmic Solution on Direct-to-Consumer Brands

Prasad, Bikram; Ghosal, Indrajit

doi:10.1177/23197145211046126

Cited by 17 publications

(6 citation statements)

References 66 publications

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“…The data can be assessed and predicted using an ANN (Artificial Neural Network) by employing a suitable neural network program to anticipate the consumer's buying characteristics [17]. The ANN can also be used as a connectionist system, which is made up of a collection of nodes that process non-linear relationships.…”

Section: Discussionmentioning

confidence: 99%

Transforming Consumer Behavior to New Paradigms Through Deep Learning Applications

Ghosal,

Prasad

2023

IJABMR

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Direct-to-consumer (DTC) businesses are gaining popularity as a way to reach a larger number of customers and better suit their needs. Vertical brands are distinguished by their metamorphosis, in which they offer their products straight from the manufacturer to consumers without the use of distribution intermediaries, like in traditional business models. They're obliterating themselves on virtual platforms and undermining their old linear sales processes in the process. In the current scenario, the ability of connectionist models to explain consumer behavior, with a focus on the feed-forward neural network model, should be emphasized, and the possibility of expanding the implications of ANN (Artificial Neural Network) for predicting buying behavior for DTC (direct-to-consumer) brands should be explored. To forecast consumer loyalty as a critical feature of consumer behavior, a variety of neural network models of various complexity are constructed. When compared to the more standard logistic regression approach, neural networks outperform logistic regression in predicting customer loyalty. Utilitarian and Informational Reinforcement factors, both independently determined, are shown to contribute significantly to the explanation of consumer choice. The potential of connectionist models for predicting and explaining consumer behavior is discussed, and future research directions are proposed for investigating the predictive and explanatory capacity of connectionist models, such as neural network models, and their integration into consumer behavior analysis within the theoretical framework.

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Section: Discussionmentioning

confidence: 99%

Transforming Consumer Behavior to New Paradigms Through Deep Learning Applications

Ghosal,

Prasad

2023

IJABMR

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“…Online product sales are becoming increasingly popular as a way to reach more consumers and better meet their expectations (Prasad and Ghosal, 2021). Consequently, the number of clients purchasing online is rapidly increasing (Sarkar and Ghosal, 2018; Ghosal, 2015), owing to the increased number of shopping websites.…”

Section: Literature Reviewmentioning

confidence: 99%

The impact of website quality on online compulsive buying behavior: evidence from online shopping organizations

Rahman

Hossain

2022

SAJM

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PurposeThe influence of website quality on online compulsive buying behavior (OCBB) in the context of online shopping based on the usage of a credit card (UCC) and online impulsive buying behavior (OIBB) was investigated in this study.Design/methodology/approachThe authors used a research model to examine the relationships between the study components as per the prescription. For this investigation, the authors used an online survey form to obtain primary data from 350 respondents on social media. A covariance-based structural equation modeling approach was used to evaluate the structural research model and data.FindingsThe findings reveal that the quality of online shopping websites positively affects consumers' UCC and OIBB, and these in turn positively influence their OCBB.Practical implicationsThe study emphasized impacting elements on consumer behavior and gave advice for future research based on the results. Using several dimensions of website quality, this study bridges the knowledge gap between UCC, OIBB and OCBB.Originality/valueBased on UCC and OIBB, the authors developed a new model to investigate the link between website quality and OCBB. To the best of the authors' knowledge, it is the first experimental result that assesses the impact of website quality on OCBB.

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“…A key cause of inventory shortage is the imbalance between consumer demand and retailer's supply. Information factors that influence e-commerce customer demand, such as availability of products, product customization and variety, quality of the products and price comparator, can be considered by the online retailer to make scientific decisions to balance supply and demand (Prasad and Ghosal, 2021;Dospinescu et al, 2022;Ghosal et al, 2015). We demonstrate that inventory information disclosure can affect customer's purchasing behavior and thus demand.…”

Section: Literature Reviewmentioning

confidence: 99%

Disclosing opaque inventory information in a two-period sales setting: substitute products' selling strategy

Kong

2022

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PurposeThe purpose of this paper is to investigate the opaque inventory information disclosure strategy for an online retailer who sells two substitutable products to customers in two selling periods.Design/methodology/approachThe authors develop a two-period model where an online retailer sells two substitute products with two inventory composition structures to maximize profits. The authors investigate the optimal inventory disclosure decision from both ex post and ex ante perspectives. Sensitivity analysis is performed to investigate the effects that discount rate, transaction cost and the probability of agreeable inventory situation have on the equilibrium disclosure outcome. The authors also consider risk-averse customers and horizontally differentiated products to highlight the robustness of our results.FindingsThe authors find that the online retailer will choose the opaque information disclosure when attempting to increase revenue and reduce the mismatch of supply and demand in both ex post and ex ante inventory information conditions. Comparing with ex post disclosure strategies, ex ante opaque disclosure is optimal in a larger price region, and the total revenues gap between opaque disclosure and complete disclosure gradually increase as discount rate, transaction cost or the probability of agreeable inventory situation decreases. Furthermore, strategic customers may tend to be risk neutral when faced with opaque inventory information in a two-period sales setting.Originality/valueThis current paper is the first paper to study the online retailer's inventory information disclosure strategy in two selling periods. Moreover, this paper presents the conditions under which the online retailer should share complete or opaque inventory information with customers to maximize the online retailer's total revenues.

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Forecasting Buying Intention Through Artificial Neural Network: An Algorithmic Solution on Direct-to-Consumer Brands

Cited by 17 publications

References 66 publications

Transforming Consumer Behavior to New Paradigms Through Deep Learning Applications

Transforming Consumer Behavior to New Paradigms Through Deep Learning Applications

The impact of website quality on online compulsive buying behavior: evidence from online shopping organizations

Disclosing opaque inventory information in a two-period sales setting: substitute products' selling strategy

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