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In this paper, the authors explore the consequences of showrooming and price matching strategy to combat showrooming under the consideration that brick-and-mortar (BM) stores and online retailers hold different costs.

The authors use a duopoly model to analyze the impact of showrooming behavior on competition between a BM store and an online retailer with different types of customers and different costs. Then, they consider the price matching strategy that a BM store could employ to combat showrooming and explore the effect of such a strategy.

Showrooming behavior is detrimental to the profit of the BM store, and the online retailer suffers a loss of their profit unless the relative cost of the BM store is high and only part of the customers exhibit showrooming behavior. As the fraction of customers who seek price matching increases, profits of both the BM store and the online retailer initially decrease and then may increase, even if there is no showrooming.

Unlike existing studies that ignore different costs between online and offline retailers, the authors set different costs between the BM store and the online retailer to consider the effects of showrooming and price matching strategy.

This study aims to explore the impact of decentralized long-term rental apartments on the value of in-community housing from two perspectives of housing price and rent.

This study uses the hedonic model to identify the factors affecting the housing value, and the influence of distributed long-rented apartments on the housing value in the community is analyzed from two aspects of housing price and rent by using the ordinary least square method and propensity score matching method.

The primary finding indicates that decentralized long-term rental apartments increase housing prices while decreasing general rental housing rents in the community, with the average degree of increase ranging from 0.93% to 2.59% and the average degree of decrease ranging from 2.23% to 4.34%. According to additional research, the prices of houses within communities rise by 0.042% for every 1% increase in the share of decentralized long-term rentals, while the rents for other types of rental property fall by 0.162%.

The government can regulate the housing market by regulating the access and layout of distributed long-rent apartments.

The findings of this study indicate that the existence and share of distributed long-rent apartments have a heterogeneous impact on the housing price and rent in the community, respectively.

This paper applies a volume-price probability wave differential equation to propose a conceptual theory and has innovative behavioral interpretations of intraday dynamic market equilibrium price, in which traders' momentum, reversal and interactive behaviors play roles.

The authors select intraday cumulative trading volume distribution over price as revealed preferences. An equilibrium price is a price at which the corresponding cumulative trading volume achieves the maximum value. Based on the existence of the equilibrium in social finance, the authors propose a testable interacting traders' preference hypothesis without imposing the invariance criterion of rational choices. Interactively coherent preferences signify the choices subject to interactive invariance over price.

The authors find that interactive trading choices generate a constant frequency over price and intraday dynamic market equilibrium in a tug-of-war between momentum and reversal traders. The authors explain the market equilibrium through interactive, momentum and reversal traders. The intelligent interactive trading preferences are coherent and account for local dynamic market equilibrium, holistic dynamic market disequilibrium and the nonlinear and non-monotone V-shaped probability of selling over profit (BH curves).

The authors will understand investors' behaviors and dynamic markets through more empirical execution in the future, suggesting a unified theory available in social finance.

The authors can apply the subjects' intelligent behaviors to artificial intelligence (AI), deep learning and financial technology.

Understanding the behavior of interacting individuals or units will help social risk management beyond the frontiers of the financial market, such as governance in an organization, social violence in a country and COVID-19 pandemics worldwide.

It uncovers subjects' intelligent interactively trading behaviors.

Both issuing firms and underwriters shall benefit from the associations in underwriting contracts for seasoned equity offerings (SEOs). Issuing firms that are offered underwriting contracts with clustered gross spreads do not have strong incentives to switch away from the firms' prior SEO underwriters, and thus these existing underwriters are able to maintain or gain greater market share. This study investigates how the clustering of percentage gross spreads affects the likelihood of underwriter switching.

Using the investment bank-underwritten SEOs in Hong Kong, the authors find that the percentage gross spreads of 40% of these SEOs are clustered at 2.5%. The seemingly unrelated bivariate probit model, Weibull survival mixed model and trivariate probit model are applied to analyse this phenomenon.

The authors' study provides first direct evidence that the clustering of percentage gross spreads lowers the likelihood of underwriter switching. Investment banks as underwriters can explicitly price underwriting contracts at a clustered level, more likely in periods of greater market volatility, and intentionally retain the banks' client firms using pricing arrangements. The authors' finding and approach offer more direct and distinct support that the issuer–underwriter association can be relationship-based.

Whilst the clustering of fees is interpreted as a type of anticompetitive price sitting, the authors contribute to literature by providing new empirical evidence on why percentage gross spreads as a price dimension are clustered. On top of contract efficiency and collusion, this study's new evidence provides a third view for the clustering of gross spreads.

The purpose of this study is to understand the profit implications of analytics-driven centralized discriminatory pricing at the headquarter level compared with sales force price delegation in the purchase of an aftermarket good through an indirect retail channel with symmetric information.

Using individual-level loan application and approval data from a North American financial institution and segment-level customer risk as the price discrimination criterion for the firm, the authors develop a three-stage model that accounts for the salesperson’s price decision within the limits of the latitude provided by the firm; the firm’s decision to approve or not approve a sales application; and the customer’s decision to accept or reject a sales offer conditional on the firm’s approval. Next, the authors compare the profitability of this sales force price delegation model to that of a segment-level centralized pricing model where agent incentives and consumer prices are simultaneously optimized using a quasi-Newton nonlinear optimization algorithm (i.e. Broyden–Fletcher–Goldfarb–Shanno algorithm).

The results suggest that implementation of analytics-driven centralized discriminatory pricing and optimal sales force incentives leads to double-digit lifts in firm profits. Moreover, the authors find that the high-risk customer segment is less price-sensitive and firms, upon leveraging this segment’s willingness to pay, not only improve their bottom-line but also allow these marginalized customers with traditionally low approval rates access to loans. This points out the important customer welfare implications of the findings.

Substantively, to the best of the authors’ knowledge, this paper is the first to empirically investigate the profitability of analytics-driven segment-level (i.e. discriminatory) centralized pricing compared with sales force price delegation in indirect retail channels (i.e. where agents are external to the firm and have access to competitor products), taking into account the decisions of the three key stakeholders of the process, namely, the consumer, the salesperson and the firm and simultaneously optimizing sales commission and centralized consumer price.

Due to product value uncertainty, consumers do not know the product matching rate before they get the product, which is the probability of product fitness. Taking the consumers’ anticipated regret into account, this paper aims to develop a theoretical model to explore how the anticipated regret affects pricing and advertising decisions and profits of retailers in the online to offline (O2O) supply chain.

This paper considers an O2O supply chain consisting of an e-retailer and a brick-and-mortar retailer; both retailers cooperate to provide buying online and pick up in-store (BOPS) for consumers.

It shows three major findings. Retailers should decide whether to introduce BOPS channel according to the matching rate of the product when the BOPS channel is not very convenient for consumers. When the BOPS channel does not exist in the market, the profits of two retailers increase with the online regret of consumers, while the BOPS channel exists in the market and the matching rate of the product is low, the higher offline regret can enable both retailers to increase the profits; furthermore, when the matching rate is high, the higher degree of online regret can bring more profit to the O2O supply chain. Therefore, both retailers can take measures together to induce consumers’ regrets according to the different matching rates, which makes both retailers obtain more profits. Counterintuitively, consumer surplus will not always increase due to consideration of anticipated regret.

The model has some limitations that are worth further discussing. First, in practice, the O2O supply chain includes many forms except the BOPS channel, for example, order online and pick-up in-store (ROPS) channel; future research can discuss and consider the impact of consumers’ anticipated regret on ROPS. Second, the authors consider that O2O is a supply chain composed of two retailers. In reality, there is also a situation where an oligopoly retailer opens two channels to realize O2O supply chain, in the case the inventory decision-making of the product is worth studying. Finally, to highlight the impact of the anticipated regret on consumers’ decision-making, the return of the product is not considered. Future research can take the return of the product into account to assess the robustness of the results.

The contributions are in two main aspects. First, this paper considers an O2O supply chain with consumer value uncertainty, where there are duopoly retailers in the market and most of the existing literature focus on oligopoly retailer operates both online and offline channels; meanwhile, consumers’ value perceptions of the product is deterministic. Second, this paper explores how the consumer anticipated channel regret affects the pricing and advertising decisions of O2O supply chain, and the authors take behavioral theory into account when studying omnichannel operations, while most studies on anticipated regret consider traditional two-stage price reduction management, product innovation, etc.

This theoretical study aims to clarify the (a priori) ambiguous effect of homeownership on unemployment. In general, in fact, homeownership discourages job mobility, but homeowners are less likely to be unemployed than tenants, since homeownership would seem to be positively related to human capital.

This study develops a modified version of the benchmark theoretic model of the labour market – the well-known “equilibrium unemployment theory” – where homeownership affects both the “Beveridge Curve” (BC, by means of job immobility) and the “Job Creation Condition” (JCC, by means of human capital).

The general result is that an increase in homeownership increases unemployment. Therefore, policymakers could encourage job mobility, before facilitating homeownership. This policy implication, however, may not apply in the case of high inflation and/or low nominal interest rate, and when the job destruction rate depends on the homeownership rate.

The model studies the steady-state equilibrium of the labour market, so the policy implications only relate to the long-run. The model, therefore, does not consider the short-run effects of homeownership on unemployment (which may differ from the long-term results).

The model suggests a public policy characterised by large investment in rail lines and subsidised commuter fares. By promoting a more efficient allocation of workers across regions (and, thus, job mobility), indeed, this policy can be a good way to increase employment, without harming homeownership.

The practical implication of this model is also a social implication, since it relates to homeownership and housing tenure.

To the best of author’s knowledge, this is the first model that introduces the key role of homeownership in the so-called “Equilibrium unemployment theory”. Precisely, the model uses a modified version of both the BC (which includes the negative effect of homeownership on the overall job search intensity of unemployed workers) and the JCC (which includes the positive effect of homeownership on both the business start-up and the human capital of workers). By comparing these two opposite effects, this theoretical work makes clearer the net effect of homeownership on unemployment.

This paper mainly focuses on how to induce all members to represent members' true preferences for supply and demand matching of E-commerce platform in order to generate stable matching schemes with more social welfare of Multi-agent Matching Platform (MMP) and individually stable advantages than traditional methods.

An MMP is designed. Meanwhile, a true preference inducing method, Lower-Bid Ranking (LBR), is proposed to reduce the number of false preferences, which is helpful to solve the problem that too much false preferences leads to low efficiency of platform operation and supply and demand matching. Then, a systematic model of LBR-based Stable Matching (SM-LBR) is proposed.

To obtain an ideal transaction partner, the adequate preference ordering and modifying according to market environment is needed for everyone, and the platform should give full play to the platforms' information advantages and process historical transaction and cooperation data. Meanwhile, the appropriate supply and demand matching is beneficial to improve the efficiency and quality of platform operation, and the design of matching guidance mechanism is essential.

The numerical experiments show that, the proposed model (SM-LBR) can induce members to represent the model's true preferences for stable matching and generate effective matchings with more social welfare of MMP and individually stable advantages than traditional methods, which may provide necessary method and model reference for the research of stable matching and E-commerce platform operation.

This article aims to present a direct solution to handle linear constraints in finite element (FE) analysis without penalties or the Lagrange multipliers introduced.

First, the system of linear equations corresponding to the linear constraints is solved for the leading variables in terms of the free variables and the constants. Then, the reduced system of equilibrium equations with respect to the free variables is derived from the finite-dimensional virtual work equation. Finally, the algorithm is designed.

The proposed procedure is promising in three typical cases: (1) to enforce displacement constraints in any direction; (2) to implement local refinements by allowing hanging nodes from element subdivision and (3) to treat non-matching grids of distinct parts of the problem domain. The procedure is general and suitable for 3D non-linear analyses.

The algorithm is fitted only to the Galerkin-based numerical methods.

The proposed procedure does not need Lagrange multipliers or penalties. The tangential stiffness matrix of the reduced system of equilibrium equations reserves positive definiteness and symmetry. Besides, many contemporary Galerkin-based numerical methods need to tackle the enforcement of the essential conditions, whose weak forms reduce to linear constraints. As a result, the proposed procedure is quite promising.