The Threshold Effects on Consumer Choice and Pricing Decisions

Problem definition: This paper investigates the threshold effects on the consideration set formation for consumers and the associated pricing decisions for firms. Academic/practical relevance: In the class of random utility maximization choice models, consumers choose the alternative with the largest utility after resolving utility uncertainties for all options. However, in practice, consumers may not inspect all available alternatives due to bounded rationality or search cost. Methodology: This paper incorporates bounded rationality into consumer choice behavior: in the first stage, a representative consumer forms her consideration set by removing alternatives whose nominal utility is lower than the largest by a threshold; in the second stage, she examines all alternatives in her consideration set and chooses the one with the highest realized utility. Results: For the Gumbel-distributed random utilities, we derive the two-stage threshold multinomial logit model with a consideration set. For the pricing problem, we show the quasi-same-markup/same-utility policy is optimal: high-cost products charge the prices such that their profit markups are the same, and low-cost products charge prices such that their nominal utilities are the same. For the price competition, there may exist zero, one, two, or infinite Nash equilibria, depending on the magnitude of the threshold effects. Managerial implications: Our analysis shows that the consideration set and bounded rationality play an important role in consumer choice behavior, so they should be taken into account in firms’ decision making.

Consumer Choice and Market Expansion: Modeling, Optimization, and Estimation

The growth of market size is crucially important to firms, although researchers often assume that market size is constant in assortment and pricing management. I develop a model that incorporates the market expansion effects into discrete consumer choice models and investigate various operations problems. Market size, measured by the number of people who are interested in the products from the same category, is largely influenced by firms’ operations strategy, and it also affects assortment planning and pricing decisions. Failure to account for market expansion effects may lead to substantial losses in demand estimation and operations management. Based on real data, this paper uses an alternating-optimization expectation-maximization method that separates the estimation of consumer choice behavior and market expansion effects to calibrate the new model. The end-to-end solution approach on modeling, operations, and estimation is readily applicable in real business.

Impact of Trade-In Remanufacturing Policy and Consumer Behavior on Remanufacturer Decisions

To develop more competitive strategies for different types of remanufacturing companies under the trade-in remanufacturing policy, this paper investigates the impact of the trade-in remanufacturing policy and consumer choice behavior on decisions of a traditional brand remanufacturer and a third-party brand remanufacturer by using a consumer utility model. The results suggest that the trade-in remanufacturing policy increases demand for the third-party brand, but does not increase demand for the traditional brand. Further, although trade-in remanufacturing policy increases both brand prices, it also increases consumer surplus and corporate profits. Neither the traditional brand remanufacturer nor the third-party brand remanufacturer can completely monopolize the remanufacturing market. In this situation, the traditional brand remanufacturer should strive to increase consumer loyalty, and the third-party brand remanufacturer should strive for consumer recognition of third-party remanufactured products.

The Influencing Factors on Choice Behavior Regarding Green Electronic Products: Based on the Green Perceived Value Model

Electronic/electrical waste (e-waste) has now become a global concern due to its negative impact on the environment and health. This negative effect of e-waste is increasing with the advancement in the electronics industry, especially in Pakistan, which is the leading e-waste disposal destination. Therefore, this study aims to find consumer choice behavior regarding green electronics in Pakistan. For this purpose, a multidimensional modified green perceived model (functional value quality, functional value price, social value identity, social value responsibility, emotional value, and conditional value) is used. Cross-sectional data is collected from 237 consumers through a self-administrative questionnaire. The results of variance-based structural equation modeling (SEM) (partial least squares (PLS)-SEM) suggested that functional value price, quality, social value identity, responsibility, emotional value, and conditional value positively and significantly influence the consumer choice behavior regarding green electronics. the possible reasons for the findings and the implications for managers and policymakers are discussed. Limitations of the study and future research directions are also suggested.

A scarcity mindset alters neural processing underlying consumer decision making

Not having enough of what one needs has long been shown to have detrimental consequences for decision making. Recent work suggests that the experience of insufficient resources can create a “scarcity” mindset; increasing attention toward the scarce resource itself, but at the cost of attention for unrelated aspects. To investigate the effects of a scarcity mindset on consumer choice behavior, as well as its underlying neural mechanisms, we used an experimental manipulation to induce both a scarcity and an abundance mindset within participants and examined the effects of both mindsets on participants’ willingness to pay for familiar food items while being scanned using fMRI. Results demonstrated that a scarcity mindset affects neural mechanisms related to consumer decision making. When in a scarcity mindset compared with an abundance mindset, participants had increased activity in the orbitofrontal cortex, a region often implicated in valuation processes. Moreover, again compared with abundance, a scarcity mindset decreased activity in dorsolateral prefrontal cortex, an area well known for its role in goal-directed choice. This effect was predominant in the group of participants who experienced scarcity following abundance, suggesting that the effects of scarcity are largest when they are compared with previous situations when resources were plentiful. More broadly, these data suggest a potential neural locus for a scarcity mindset and demonstrate how these changes in brain activity might underlie goal-directed decision making.

Product Line Pricing under Marginal Moment Model with Network Effect

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.

A Reference Point Logit Model for Estimating Substitution Probabilities Using Point of Sale Data

This article presents a practical approach to estimate the substitution probabilities between products at a retail store by using the store's point of sale data and prospect theory based structural restrictions on the consumer choice behavior. The prospect theory-based reference dependent preference structure imposed on the consumer choice behavior (a) accounts for how consumers make their original choice as well as how they substitute, (b) eliminates the IIA and IPS assumptions that the standard utility theory based models impose on consumer choice, and (c) alleviates the need for inventory information for estimating the substitution probabilities. Simulations and empirical studies have been used to show that the estimates of the substitution probabilities are efficient and are robust to stock-out rates.