Consumer Choice Models with Endogenous Network Effects

Wang, Ruxian; Wang, Zizhuo

doi:10.1287/mnsc.2016.2520

Cited by 114 publications

(37 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They found that the optimal prices might be different when network effect existed even for homogeneous case. Different from the literature Du et al [9], Wang and Wang [1] investigated the assortment optimization problem under MNL with endogenous network effects. They found that the quasi-revenue-ordered assortment was optimal under certain conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We next analyze the influence of the product's quality on the optimal solutions when the network effect exists. We assume the product's quality lies in [1,10]; let = −2, = 1, = 1, and V 0 = 0.5. Figure 2 shows the variation of optimal solutions with the quality.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…With the rapid development of Internet economy, the traditional consumption structure has been changed greatly, and the research on the influence of complex network effect for development and pricing of new products has become an important academic issue. Many products will exhibit network effect or network externality when the utility a consumer obtains from buying the products depends on the number of sales of the same or similar products [1]. In traditional process of product line pricing, it is assumed that utility is only determined by customer's personal features.…”

Section: Introductionmentioning

confidence: 99%

“…The IIA property shows that the ratio of choice probabilities of two alternatives does not depend on other alternatives, regardless of their similarities. This is usually exemplified by the famous "red bus/blue bus" paradox [1]. Some other probabilistic choice models, such as nested logit (NL) model, multinomial probit (MNP) model, or mixed logit (MIXL) model, are also limited in application due to the complex calculation process or pregiven specific distributions of measuring errors.…”

Section: Introductionmentioning

confidence: 99%

“…However, the issue of product optimization with considering negative network effect cannot be ignored and needs to be further solved. In many product line design optimization problems, there exist a lot of products with negative network effect [1] and the problem of IIA may not be met. In this case, consumer choice behavior simulation and product pricing would be very interesting practical issues.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Product Line Pricing under Marginal Moment Model with Network Effect

Luo

Liu

et al. 2019

Complexity

View full text Add to dashboard Cite

The complex network effect of the product exhibits has a significant impact on the product line optimization design. The multinomial logit (MNL) model which is used to simulate consumer choice behavior is applied in most of product line optimization problems. However, its assumptions, independence of irrelevant alternatives (IIA) and the same Gumbel distribution of random error terms, are usually difficult to be met in practice. The marginal moment model (MMM) can be used when the mean and variance of consumer’s utility error are known. The MMM not only has weak assumption conditions but also overcomes the IIA problem of MNL model. In this paper, we study the product pricing problem based on MMM with endogenous negative network effect. Firstly, we construct a variant of MMM considering network effect in product line optimization design. Secondly, we prove that the revenue function is concave in market share. We propose the solving methods of the model to obtain the optimal price, the corresponding market share, and the revenue under three different scenarios, i.e., developing single product, homogeneous products, and heterogeneous products. Finally, numerical experiments show that the proposed model can better simulate consumer choice behavior and potentially increase revenue.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Numerical Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Product Line Pricing under Marginal Moment Model with Network Effect

Luo

Liu

et al. 2019

Complexity

View full text Add to dashboard Cite

show abstract

Pricing for a product with network effects and mixed logit demand

Nosrat

Cooper

Wang

2020

Naval Research Logistics

Self Cite

View full text Add to dashboard Cite

We consider a pricing problem for a single product that experiences network effects. Demand is described by a consumer choice model in which each individual chooses between purchasing the product and not purchasing the product. We assume that there are multiple segments in the population of potential buyers, and that individuals' intrinsic values for the product and sensitivities to the network effect (ie, the extent to which their values are affected by how many others buy the product) vary across segments. The demand model may be viewed as a version of the mixed multinomial logit model, modified to incorporate network effects. We formulate and analyze an optimization problem that aims to find the seller's revenue‐maximizing price. In settings with an arbitrary number of demand segments, we present a simple, effective heuristic solution approach. In settings with two segments, we obtain a solution method that outputs provably near‐optimal prices. We close with an extensive numerical study.

show abstract

Pricing a product with network effects for sale to a fixed population of customers

Chen,

Cooper

2024

Naval Research Logistics

View full text Add to dashboard Cite

We consider a deterministic dynamic pricing problem for a product that exhibits network effects and that is sold to a fixed heterogeneous population of customers. We begin by introducing a demand model wherein those customers are arrayed over two‐dimensional space according to a bivariate probability distribution. Each customer's location in space provides a description of that customer's intrinsic value for the product as well as the extent to which the customer is influenced by the network effect. In the pricing problem, as sales accumulate over time, the set of customers who have already purchased the product grows, while the set of customers who have not yet purchased the product shrinks. The total customer population remains fixed. Those who have not yet purchased constitute the remaining population of potential buyers of the product. As time moves forward, the mix of customers that remain as potential buyers evolves endogenously. The demand model yields a geometric interpretation of the remaining population of potential buyers, and gives rise to a dynamic program with states that are sets in two‐dimensional space. It is not practical to solve the dynamic pricing problem to optimality, so we present bounds and comparative statics results that help us identify tractable heuristics and obtain rigorous performance guarantees. In numerical experiments, we find that fixed‐price policies may perform poorly, especially when the network effect is strong or the time horizon is long. We also introduce a stochastic version of the problem that uses a spatial Poisson process to describe the customers, and we develop and analyze a heuristic approach for that formulation.

show abstract

Consumer Choice Models with Endogenous Network Effects

Cited by 114 publications

References 39 publications

Product Line Pricing under Marginal Moment Model with Network Effect

Product Line Pricing under Marginal Moment Model with Network Effect

Pricing for a product with network effects and mixed logit demand

Pricing a product with network effects for sale to a fixed population of customers

Contact Info

Product

Resources

About