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This study aims to assess housing affordability and estimate demand using a hedonic regression model in the context of Lucknow city, India. This study assesses housing affordability by considering various housing and household-related variables. This study focuses on the impoverished urban population, as they experience the most severe housing scarcity. This study’s primary objective is to understand the demand dynamics within the market comprehensively. An understanding of housing demand can be achieved through an examination of its characteristics and components. Individuals consider the implicit values associated with various components when deciding to purchase or rent a home. The components and characteristics have been obtained from variables relating to housing and households.

A socioeconomic survey was conducted for 450 households from slums in Lucknow city. Two-stage regression models were developed for this research paper. A hedonic price index was prepared for the first model to understand the relationship between housing expenditure and various housing characteristics. The housing characteristics considered for the hedonic model are dwelling unit size, typology, condition, amenities and infrastructure. In the second stage, a regression model is created between household characteristics. The household characteristics considered for the demand estimation model are household size, age, education, social category, income, nonhousing expenditure, migration and overcrowding.

Based on the findings of regression model results, it is evident that the hedonic model is an effective tool for the estimation of housing affordability and housing demand for urban poor. Various housing and household-related variables affect housing expenditure positively or negatively. The two-stage hedonic regression model can define willingness to pay for a particular set of housing with various attributes of a particular household. The results show the significance of dwelling unit size, quality and amenities (R² > 0.9, p < 0.05) for rent/imputed rent. The demand function shows that income has a direct effect, whereas other variables have mixed effects.

This study is case-specific and uses a data set generated from a primary survey. Although household surveys for a large sample size are resource-intensive exercises, they provide an opportunity to exploit microdata for a better understanding of the complex housing situation in slums.

All the stakeholders can use the findings to create an effective housing policy. The variables that are statistically significant and have a positive relationship with housing costs should be deliberated upon to provide the basic standard of living for the urban poor. The formulation of policies should duly include the housing preferences of the economically disadvantaged population residing in slum areas.

This paper uses primary survey data (collected by the authors) to assess housing affordability for the urban poor of Lucknow city. It makes the results of the study credible and useful for further applications.

This study aims to develop an educational tourism demand model, particularly in respect to dynamic effects, university quality (QU) and competitor countries. Educational tourism has been identified as a new tourism sub-sector with high potential, and is thus expected to boost economic growth and sustainability.

This study reviews the literature on the determinants of educational tourism demand. Even though the existing literature is intensively discussed, mostly focusing on the educational tourism demand from an individual consumer's perspective, this study makes an innovation in line with the aggregate demand view. The study uses data that consist of the enrolment of international students from 47 home countries who studied in Malaysia from 2008 to 2017. The study utilised the dynamic panel method of analysis.

This study affirms that income per capita, educational tourism price, price of competitor countries and quality of universities based on accredited programmes and world university ranking are the determinants of educational tourism demand in both the short and the long term. Also, a dynamic effect exists in educational tourism demand.

The results imply that government should take the quality of services for existing students, price decisions and QU into account to promote the country as a tertiary education hub and achieve sustainable development.

Research on the determinants of the demand for educational tourism is rare in terms of macro data, and this study includes the roles of QU, competitor countries and dynamic effects.

We explore the driving forces behind the channel choices of the manufacturer and the platform by considering asymmetric selling cost and demand information.

This paper develops game-theoretical models to study different channel strategies for an E-commerce supply chain, in which a manufacturer distributes products through a platform that may operate in either the marketplace channel or the reseller channel.

Three primary models are built and analyzed. The comparison results show that the platform would share demand information in the reseller channel only if the service cost performance is relatively high. Besides, with an increasing selling cost, the equilibrium channel might shift from the marketplace to the reseller. With increasing information accuracy, the manufacturer tends to select the marketplace channel, while the platform tends to select the reseller channel if the service cost performance is low and tends to select the marketplace channel otherwise.

All these results have been numerically verified in the experiments. At last, we also resort to numerical study and find that as the service cost performance increases, the equilibrium channel may shift from the reseller channel to the marketplace channel. These results provide managerial guidance to online platforms and manufacturers regarding strategic decisions on channel management.

Although prior research has paid extensive attention to the driving forces behind the online channel choice between marketplace and reseller, there is at present few study considering the case where a manufacturer selling through an online platform faces a demand information disadvantage in the reseller channel and sales inefficiency in the marketplace channel. To fill this research gap, our work illustrates the interaction between demand information asymmetry and selling cost asymmetry to identify the equilibrium channel strategy and provides useful managerial guidelines for both online platforms and manufacturers.

To answer the questions: what roles windows of opportunity act in the catchup process of latecomers, what strategies latecomer enterprises should adopt to size windows of opportunity to catch-up with incumbents even going beyond?

This paper studies the catch-up history of the Chinese mobile phone industry and proposes a sectoral innovation system under scenario of technology paradigm shifts. Then a history-friendly simulation model and counterfactual analysis are conducted to learn how different windows of opportunity and catch-up strategies influence the catch-up performance of latecomers.

Results show latecomers can catch up with technology ability by utilizing technology window and path-creating strategy. However, catching up with the market is not guaranteed. Demand window can help latecomers to catch up with market as it increases their survival rates, different sized windows benefit different strategies. However, it also enlarges incumbents' scale effect. Without technology window technology catch up is not guaranteed. Two windows have combination effects. Demand window affects the “degree” of change in survival rates, while the technology window affects the “speed” of change. Demand window provides security; technology window provides the possibility of a breakthrough for technology ability.

The findings of this paper provide theoretical guidance for latecomer enterprises to choose appropriate catch-up strategies to seize different opportunity windows.

This paper emphasizes the abrupt change of industrial innovation system caused by technology paradigm shifts, which makes up for the shortcomings of previous researches on industrial innovation system which either studied the influence of static factors or based on the influence of continuous changes.

The paper proposes a two-level closed-loop supply chain (CLSC) dynamic competitive model based on different competitive cooperation situations, and explores the impact of competitive cooperation methods on the pricing strategies, recycling and remanufacturing strategies and competitive model selection strategies of supply chain firms.

This paper establishes a CLSC game consisting of a manufacturer and two retailers. Firstly, five CLSC models are established in both horizontal and vertical dimensions, each of which competes with one another. Secondly, the recycling and remanufacturing pricing strategies are analyzed under different competition or cooperation models. Finally, the results are verified through numerical analysis.

The overall profitability of the CLSC is highest when the manufacturer–retailer partnership alliance is in place. The relationship between retailers and manufacturers is also found to be the best way to achieve overall optimization of the CLSC.

The paper investigates the relationship between the competitive partnership and the total profit of the CLSC, taking into account how to optimize the overall benefit, and focusing on how to optimize the individual interests of each participating enterprise. The results can provide basis and guidance for managers' pricing decision and competition cooperation.

The purpose of this study is to investigate the pricing strategies for knowledge payment products, taking into account the quality level of pirated knowledge products, in the context of platform copyright supervision.

This study abstracts the knowledge payment transaction process and aims to maximize producer's revenue by constructing a pricing model for knowledge payment products. It discusses pricing strategies for knowledge payment products under two scenarios: traditional supervision and blockchain supervision. The analysis explores the impact of pirated knowledge products quality level and blockchain technology on pricing strategies and consumer surplus, while providing threshold conditions for effective strategies.

Deploying blockchain technology in platform operations can significantly reduce costs and increase efficiency. In both scenarios, knowledge producer needs to balance factors such as the quality of pirated knowledge products, the supervision level of platform, and consumer surplus to dynamically adjust pricing strategies in order to maximize his own revenue.

This study enriches the literature on the pricing models of knowledge payment products and has practical significance in guiding knowledge producer to develop effective pricing strategies under copyright supervision.

This paper aims to examine the blockchain introduction and altruistic preference decisions of the supplier in agricultural food supply chains and discuss how the supplier decisions are influenced by blockchain technology and altruistic preference levels.

The author considers a single period two-level supply chain model to describe the supplier’s decisions. The supplier, as the leader of the game, decides whether to introduce blockchain technology and his own level of altruistic preferences. Consumers have environmental awareness and heterogeneity in green trust. Supply chain members determine their own product pricing and green effort level under Stackelberg game.

The results reveal the negative impact of unit verification cost in the technology introduction process on the supply chain. In addition, the supplier can adjust their profits by adjusting their altruistic preferences after introducing blockchain to offset the impact of blockchain through the influence of altruistic preferences as discussed by the author.

This paper investigates how the profits and green efforts of supply chain members are influenced by blockchain technology and altruistic preferences.

As an important form of the e-commerce industry, online group buying is under the spotlight from with two sides: cheaper price but longer waiting time. The purpose of this paper is to adequately investigate the interaction between saving and waiting time of group buying comprehensively.

To fill the research gap, the authors elaborate a dual-channel supply chain (SC) with regular retail (individual buying) and group-buying channel, and formulate the demand based on the consumer utility with the positive effect of saving money and the negative effect of wasting time.

The authors find that power structure only changes the optimal prices, instead of the waiting time. The selling price mainly influences consumer demands, instead of the price discount of group buying. The SC profits are only positive to the channel preference, and it is the decisive parameter of consumers' choice. The price sensitivity lays a more remarkable impact on the SC compared to the time sensitivity. Above all, the price is the main factor of group buying, instead of time.

These results underscore the improvement for the dual-channel SC of group buying, providing managerial insights for the group-buying industry.

Choosing the proper selling format for online retail has long been a critical issue for many platforms to consider, whereas the emergence and popularity of live-streaming have had a massive impact on the platform's business. As a result, selecting the suitable operating strategy for the live channel has become another critical issue for platforms. In such a context, what will be the impact of live-streaming on selling formats?

In order to explore these issues, we identified two selling formats (wholesale reselling or agency selling) as well as two operating strategies (introduce or discard). Thereby, four channel-structures are constructed, namely the reselling-discard model (WN), the reselling-introduce model (WL), the agency-discard model (AN), and the agency-introduce model (AL). We comprehensively compare how different structures affect stakeholders' interests, consumer surplus, and social welfare through equilibrium analyses.

These results help clarify the impact of critical factors (e.g. self-effort attribute, cross-effort attribute, and commission ratio) on the choice of models. We find that regardless of the selling agreement between the manufacturer and the platform, the introduction of a live store is necessary; specifically, when the commission ratio is high, the platform's optimal decision is first to sign an agency agreement and then apply live selling (AL); conversely, when the commission ratio is low, the platform's optimal strategy is first to enable the live channel and then to select the reselling format (WL), together, this also reveals, from a theoretical perspective.

Our study includes the dual analysis of selling formats and channel operations, considering the inherent dual attributes of service efforts and the external competitive environment.

This paper examines how the level of low-carbon awareness (LCA) affects the remanufacturing strategy in a supply chain with an original equipment manufacturer (OEM) and an independent remanufacturer (IR) competing with each other.

Game theory and operations optimization.

The studies analytically characterize the threshold levels of the LCA in response to which the OEM and the IR will change their remanufacturing strategies from no remanufacturing to partial remanufacturing and then to full remanufacturing. In addition, the studies reveal that as compared with the OEM, the IR has more flexibility in terms of the market entry to remanufacturing with the level of LCA increasing. With the extended studies, it is exhibited that the above findings are robust to a good extent.

It can provide decision support for remanufacturing enterprises.