Search results

The purpose of this article is based on the unit patent license fee model in the closed-loop supply chain.

This paper analyzes the impact of the bundling strategy of the retailer selling new products and remanufactured products on the closed-loop supply chain under the condition that the original manufacturer produces new products and the remanufacturer produces remanufacturing products.

The results show that alternative products can be bundled, and in many cases, the bundling of remanufactured products and new products is better than selling alone.

If the retailer chooses bundling, for the remanufacturer, when certain conditions are met, the benefits of bundling are greater than the separate sales at that time; for the original manufacturer, when the recycling price sensitivity coefficient is high, the bundling is better than separate sales.

The purpose of this study is to provide an optimal joint strategy of multi-period pricing and sales effort for a retailer with a logit choice demand in an integrated channel.

Customer demand is characterized by a logit choice model, it varies over time and is influenced by price and sales effort. The multi-period decision model for the retailer is constructed using a discrete-time dynamic programming method to determine the optimal price and sales effort in each period.

When the inventory level does not exceed a certain threshold, decreasing price and increasing sales effort over time or as inventory level increases are the optimal strategies. However, once the inventory level exceeds the threshold, the optimal strategy is to maintain both price and sales effort constant as the inventory level changes or to increase price and decrease sales effort over time. Additionally, the greater the influence of sales effort on demand or the higher the arrival rate of customers, the higher the optimal price and the greater the optimal sales effort level.

This study contributes to the existing research on dynamic pricing and sales effort in integrated channels by incorporating a logit choice model. Furthermore, it provides valuable management insights for retailers operating in an integrated channel to make pricing and sales effort decisions based on inventory level and time period.

This paper aims to study how price and sales level information influence consumer product perceptions and choices in online settings. It, in particular, tests whether displaying sales level information increases consumer price sensitivity, which is a potential strategic risk to retailers.

Study 1 uses eBay data to investigate whether the interaction effects between price and sales level can be observed in an existing market. Study 2 involves online experiments across three product categories. Participants choose from product pairs that are shown with either the same or different prices and with no, the same or different sales levels.

Study 1 shows strong effects of a product’s displayed sales and price level on its daily sales but finds no interaction effect. Study 2 shows strong effects of price and sales levels on product choice but similarly finds no evidence that sales level information influences consumer price sensitivity, although it reveals an effect on quality perceptions. The results show how perceptions of quality, sacrifice and popularity mediate the effects of price and sales level information on product choice.

Study 1 has limited control over prices and sales levels. Study 2 involves only hypothetical choices.

These findings indicate that businesses can use sales level information to manage consumer product quality perceptions and choices without having to be concerned that this will make consumers more price-sensitive.

To the best of the authors’ knowledge, this paper is the first to investigate how sales level information affects consumer responses to price differences in online contexts.

The paper attempts to discuss the optimal pricing decisions under the decentralized and centralized decision and analyze the influence of online reviews and in-sale service on dual-channel supply chain. Finally, the authors design a two-part tariff coordination mechanism.

To deal with this pricing conflict problems of dual-channel supply chain consisting of dominant manufacturer and a retailer, considering the fact that online reviews and in-sale service are important factors on consumers’ purchase decisions, the authors establish some basic models and exploit them to discuss the optimal pricing decisions under the decentralized and centralized decision and analyze the influence of online reviews and in-sale service on dual-channel supply chain. Finally, the authors design a profit-sharing coordination mechanism.

The results show that the optimal online direct selling price is positively correlated with product perceived quality obtained from online reviews and negatively correlated with the in-sale service. The traditional retail price is positively correlated with the in-sale service and weakly correlated with online reviews. For the manufacturer and retailer, whether decentralized decision or coordination contract, their profits increase with the increase of the in-sale service in a certain range and quality perceived from spontaneous online reviews. Online reviews and in-sale service are important factors on consumers’ purchase decisions. Positive in-sale services and online reviews can provide consumers with a better shopping experience, thereby promoting their enthusiasm for shopping and improving their quality of life. The two-part tariff coordination mechanism improves the profits of the manufacturer and the traditional retailer, respectively, through the transfer fee.

The proposed approach can well analyze the channel conflicts and pricing problems between retailers and manufacturers with respect to product offline price and online price. The analysis and results can inform decision-making for manufacturers and retailers.

This study delves into the nuanced implications of short-sale constraints on stock prices within the context of stock market efficiency. While existing research has explored this relationship, inconsistencies persist in their findings. The purpose of this study is to conduct a comprehensive review of literature to elucidate the reasons behind these disparities.

A systematic review of existing theoretical and empirical studies was conducted following the PRISMA method. The analysis centered on discerning the factors contributing to the divergence in projected stock prices due to these constraints. Key areas explored included assumptions related to expectations homogeneity, revisions, information uncertainty, trading motivations and fluctuations in supply and demand of risky assets.

The review uncovered multifaceted reasons for the disparities in findings regarding the influence of short-sale constraints on stock prices. Variations in assumptions related to market expectations, coupled with fluctuations in perceived information uncertainty and trading motivations, were identified as pivotal factors contributing to differing projections. Empirical evidence disparities stemmed from the use of proxies for short-sale constraints, varied sample periods, market structure nuances, regulatory changes and the presence of option trading.

This study emphasizes the significance of not oversimplifying the impact of short-sale constraints on stock prices. It highlights the need to understand these effects within the broader context of market structure and methodological considerations. By delineating the intricate interplay of factors affecting stock prices under short-sale constraints, this review provides a nuanced perspective, contributing to a more comprehensive understanding in the field.

The purpose of this study is to conduct a thorough empirical investigation of the intricate relationship between urban housing sales prices and land supply prices in China, with the aim of elucidating the underlying economic principles governing this dynamic interplay.

Using monthly data of China, the authors use the asymmetry nonlinear autoregressive distributed lag (NARDL) model to test for nonlinearity in the relationship between land supply price and urban housing prices.

The empirical results confirm the existence of an asymmetric relationship between land supply price and urban housing prices. The authors find that land supply price has a positive and statistically significant impact on urban housing prices when land supply is increasing. Policymakers should strive to strike a balance between safeguarding residents’ housing rights and maintaining market stability.

Although the asymmetric effect of land supply price has been identified as a significant contributor in this study, it is important to note that the research primarily relies on time series data and focuses on analysis at the national level. Although time series data offer a macroscopic perspective of overall trends within a country, they fail to adequately showcase the structural variations among different cities.

To ensure a stable housing market and meet residents’ housing needs, policymakers must reexamine current land policies. Solely relying on restricting land supply to control housing prices may yield counterproductive results. Instead, increasing land supply could be a more viable option. By rationally adjusting land supply prices, the government can not only mitigate excessive growth in housing prices but also foster the healthy development of the housing market.

First, the authors have comprehensively evaluated the impact of land supply prices in China on urban housing sales prices, examining whether they play a facilitating or mitigating role in the fluctuation of these prices. Second, departing from traditional linear analytical frameworks, the authors have explored the possibility of a nonlinear relationship existing between land supply prices and urban housing sales prices in China. Finally, using an advanced NARDL model, the authors have delved deeper into the asymmetric effects of land supply prices on urban housing sales prices in China.

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.

The current literature on energy certificates shows that Energy Performance Certificate labels have an important effect on real estate prices. However, interestingly, the limited studies that address the rental market find significantly lower price premiums than the sales market. The purpose of this paper is to add to this literature, by doing a comparative analysis of price premiums in the sales and rental market in Flanders (Belgium).

This study uses a hedonic regression model to analyze 177,670 real estate listings between 2016 and 2021. The data is provided by Immoweb – the largest online real estate platform in Belgium. The data set was divided in sold and rented properties: the authors evaluated 126,217 sales listings and 51,453 rent listings.

The results confirm that energy efficient properties generate a price premium, but that this premium is significantly larger in the sales market than in the rental market. In addition, the findings indicate that both investors and landlords could benefit strongly from renovating dwellings – especially when renovating from an F label to an A label.

Previous research focuses strongly on the sales market, although in many countries the rental market is similar in size and responsible from much energy consumption. Interestingly, the few studies that are addressing the rental market, find singificantly smaller price premiums than in the sales market. The findings add to this literature tradition and offer a comparative analysis of price premiums in the sales and rental market in Flanders. This allows us to not only show the similarities between both markets but also highlight the differences – creating valuable insights for academia, governments and real estate professionals.

The purpose of this paper is to examine the effect of visual technology on the price discovery process in listings of residential properties in Singapore from 2015 to 2018.

The authors empirically model the effects of 360 virtual tours and drone video on four dimensions in price discovery – buyers’ arrival rate, sale probability, transaction prices and time-on-market – using a comprehensive data set for the residential properties in Singapore.

The analysis shows that the availability of virtual tours or drone video in a listing increases the arrival rate from potential buyers, the probability of a successful sale and the selling price. These findings are consistent with the hypothesis that technologically enhanced tools improve the quality of information and the marketability of property. However, listings with virtual tours tend to be associated with longer marketing time, which is consistent with the prediction of the information overload hypothesis.

This paper extends the housing and price discovery literature by examining how technologically enabled new information affects property transactions.

To the best of the authors’ knowledge, this is the first paper to consider the impact of drone video on property market outcome.

Many studies have shown that the intensity and the number of hurricanes are likely to increase. This paper aims to look at the immediate effects of hurricanes on the time on the market, share of houses sold and percentage of houses with price cuts in the housing market using the metropolitan statistical area-level data in Florida.

Using a difference-in-difference method, the authors estimate the impact that a hurricane has on the housing markets.

The authors find that a hurricane has a positive and significant effect on the time on the market. A hurricane leads to a delay of the sale of a typical house in Florida by five days. The authors test for within-year seasonality and show that these effects change with seasonality of the housing market. Markets with seasonal housing prices tend to be affected more by hurricanes than those where housing prices are not seasonal. The authors also show that effects of a hurricane are transient and fade away in a few months. The results remain significant as the hurricane intensity changes.

This is the first study to look at the short-term effects of the hurricanes and how their effects vary based on seasonality of the markets.