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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 study aims to show the difference between the two types of oil price volatility resulting from either increases or decreases in oil prices and find evidence of the differential effect of oil price volatility on firms' environmental initiatives.

This paper examines how volatility in crude oil prices affect corporate environmental responsibility among US firms (excluding oil and gas producers) between 2002 and 2020, with a particular focus on the differential impact of oil price volatility.

The authors find that a one standard deviation increase in oil volatility resulting from positive changes in oil prices corresponds to a 12.7% decrease in environmental score, while the same increase in volatility from negative changes in oil prices leads to a 5.5% decrease in environmental score. Financial constraints are identified as a potential channel through which oil price volatility influences environmental activities. Specifically, a one standard deviation increase in oil volatility from positive price changes leads to an 18% decrease in environmental score for firms with high financial constraints, compared to an 8% decrease for firms with low financial constraints.

This study builds on the research of Phan et al. (2021) and Maghyereh and Abdoh (2020). Pan et al. reveal a negative association between oil price uncertainty and corporate social responsibility in the oil and gas sector, yet they overlook 1) the asymmetric impacts of oil price changes and sectoral disparities. Moreover, 2) their inclusion of a year-fixed effect undermines their findings’ reliability, as the oil price volatility variable remains constant across all firm-year observations, and including a year-fixed effect diminishes its explanatory power.

This study aims to explore the relationship between house prices and time-on-market (TOM) in Silicon Valley. Previous findings have been inconclusive due to variations in property characteristics. This paper highlights the discrepancy between listing and selling prices and identifies differences among housing types such as condominiums, detached houses and townhouses based on housing orientations and customer groups. Additionally, this study considers the impact of the COVID-19 pandemic and the Fed’s interest rate policies on the housing market.

The authors analyze 63,853 transactions from the Bay East Board of Realtors’ Multiple Listing Service during 2018 to 2022. The study uses a multiple-stage methodology, including a nonlinear hedonic pricing model, search theory and two-stage least squares method to address concerns relating to endogeneity.

The Silicon Valley housing market shows resilience, with low-end properties giving buyers more bargaining power without significant price drops. High-end properties, on the other hand, attract more attention over time, leading to aggressive bidding and higher final sale prices. The pandemic, despite reducing housing supply, did not dampen demand, leading to price surges. Post-COVID, price correlations with TOM changed, indicating a more cautious buyer approach toward high premiums. The Fed’s stringent monetary policies post-2022 intensified these effects, with longer listing times leading to greater price disparities due to financial pressures on buyers and shifting dynamics in buyer interest.

Results reveal a nonlinear positive correlation between TOM and the price formation process, indicating that the longer a listed property is on the market, the greater the price changes. For low-end properties, TOM becomes significantly negative, while for high-end properties, the coefficient becomes significantly positive, with effects and magnitudes varying by type of dwelling. Moreover, external environmental factors, especially those leading to financial strain, can significantly impact the housing market.

The experience of Silicon Valley is valuable for cities using it as a development model. The demand for talent in the tech industry will stimulate the housing market, especially as the housing supply will not improve in the short term. It is important for government entities to plan for this proactively.

The purpose of this study is to examine the effects of the Odd-Even Road Rationing Policy (RRP) on housing prices in Jakarta, Indonesia. It aims to evaluate the net effect of the RRP on housing prices.

The study uses the monocentric model and employs the difference-in-differences (DD) method. Annual neighborhood-level housing price data is analyzed to assess the impact of the RRP on housing prices. Additionally, propensity score matching is used to address potential biases resulting from non-random policy assignments.

The results demonstrate that houses located within the RRP-restricted area experience a decrease in price that is relative to those in the control group. The findings indicate a decrease in housing prices ranging from 7.59% to 14.7% within the RRP-restricted area. This suggests that the positive impacts resulting from the RRP have not fully compensated for the restricted accessibility experienced by individuals who have limited behavioral changes. The study also confirms the significance of commuting costs in individuals' location decisions, aligning with predictions from urban economics models.

This study contributes to the literature by providing insights into the effects of a RRP on housing prices. It expands understanding beyond the immediate effects on traffic conditions and air pollution, which previous studies have primarily focused on. Furthermore, to the best of the authors’ knowledge, this research will be the first conducted to identify the impacts of RRP on housing prices in Indonesia.

This paper aims to study the joint decision making of advance selling and service cancelation for service provides with limited capacity when consumers are overconfident.

For the case in which consumers encounter uncertainties about product valuation and consumption states in the advance period and are overconfident about the probability of a good state, we study how the service provider chooses the optimal sales strategy among the non-advance selling strategy, the advance selling and disallowing cancelation strategy, and the advance selling and allowing cancelation strategy. We also discuss how overconfidence influences the service provider’s decision making.

The results show that when service capacity is sufficient, the service provider should adopt advance selling and disallow cancelation; when service capacity is insufficient, the service provider should still implement advance selling but allow cancelation; and when service capacity is extremely insufficient, the service provider should offer spot sales. Moreover, overconfidence weakens the necessity to allow cancelation under sufficient service capacity and enhances it under insufficient service capacity but is always advantageous to advance selling.

The obtained results provide managerial insights for service providers to make advance selling decisions.

This paper is among the first to explore the effect of consumers’ overconfidence on the joint decision of advance selling and service cancelation under capacity constraints.

This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable).

The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map.

Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities.

Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.

This study seeks to investigate role of the coronavirus disease 2019 (COVID-19) pandemic on clean energy stocks for the United States for the period 21 January 2020–16 August 2021.

At the empirical stage, the Fourier-augmented vector autoregression approach has been used.

According to the empirical results, the response of the clean energy stocks to the feverish sentiment, lockdown stringency, oil volatility, dirty assets, and monetary policy dies out within a short period of time. In addition, the authors find that there is a unidirectional causality from the feverish sentiment index and the lockdown stringency index to the clean energy stock returns; and from the monetary policy to the clean energy stocks. At the same time, there is a bidirectional causality between the lockdown stringency index and the feverish sentiment index. The empirical findings can be helpful to both practitioners and policy-makers.

Among the COVID-19 variables used in this study is a new feverish sentiment index, which has been constructed using principal component analysis. The importance of the feverish sentiment index is that it allows us to examine the impact of the aggregate level of fear in the economy on clean energy stocks.

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 study aims to examine how fairness concerns and power structure in dyadic green supply chains impact retail price, supply chain profits and greening level decisions.

This study develops game-theoretic models considering fairness concerns and asymmetric power structures under an iso-elastic demand setting. The research paper employs the Stackelberg game approach, taking into consideration the fairness concern of the channel leader.

The findings indicate that under fairness, there is an increase in both wholesale and retail prices, as well as greening expenditures. Notably, when comparing the two models (manufacturer Stackelberg and retailer Stackelberg), double marginalization is more pronounced in the retailer Stackelberg setup than in the manufacturer Stackelberg setup. In a traditional supply chain with iso-elastic demand, the follower typically extracts higher profit compared to the leader; however, our results show that, under fairness conditions, the leader achieves higher profit than the follower. Additionally, our study suggests that supply chain coordination is unattainable in a fairness setup. This paper provides insights for managers on the optimal supply chain structure and the level of fairness to maximize profit.

This paper investigates the impact of a leader's fairness on the optimal decisions within a green supply chain, an area that has received limited attention previously. Additionally, the study investigates how fairness concerns manifest in distinct power dynamics, specifically, in the contexts of manufacturer Stackelberg and retailer Stackelberg.

With revolutions in the telecommunication sector having led to wide unprecedented consequences in all facets of human life, this paper aims to examine the relationship between cell phone tower base stations (CPTBSs) and residential property prices within the City of Johannesburg (CoJ), South Africa.

The authors align their work with global literature and assess how the impact of CPTBSs influences residential property values in South Africa. The authors use a semi-log hedonic pricing model to test the hypothesis that proximity of CPTBSs to residential properties does not account for any variation in residential property prices.

The results show a significant impact that proximity of CPTBS has on residential property sale prices. However, the impact of CTPBSs’ proximity on residential property prices depends on their distance from the residential properties. The closer a residential property is to the CTPBS, the greater the impact that the CTPBS will have on the selling price of the residential property.

With international studies offering mixed findings on the impact of CPTBSs on residential property values, there is limited research on their impact in South Africa. The findings of this study offer crucial insights for the real estate practitioners, property owners, telecommunications companies and the public, providing a nuanced understanding of the relationship between CPTBSs and property values. This research helps property owners understand the effects of CPTBSs on their properties, and it assists property valuers in gauging the impact of CPTBSs on property values.