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This research study aims to delve into the enduring relationship between housing property prices and economic policy uncertainty across eight major Indian cities.

Using the panel non-linear autoregressive distributed lag model, this study meticulously investigates the asymmetric impact of economic policy uncertainty on apartment and house (unit) prices in India during the period from 2000 to 2022.

The findings of this study indicate that economic policy uncertainty exerts a negative influence on property prices, but noteworthy asymmetry is observed, with positive changes in effect having a more pronounced impact than negative changes. This asymmetrical effect is particularly prominent in the case of unit prices.

This research reveals that long-run price trends are also influenced by factors such as interest rates, building costs and housing loans. Through a comprehensive analysis of these factors and their interplay with property prices, this research paper contributes valuable insights to the understanding of the real estate market dynamics in Indian cities.

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.

This study aims to analyse how consumers' perceptions of justice in a service recovery scenario vary, not only due to the company's actions but also due to the comparisons they make with the experiences of other consumers.

Based on justice theory, social comparison theory and referent cognitions theory, this study describes an eight-scenario experiment with better or worse interactional, procedural and distributive justice (better/worse interactional justice given to other consumers) × 2 (better/worse procedural justice given to other consumers) × 2 (better/worse distributive justice given to other consumers).

First, consumers' perceptions of interactional, procedural and distributive justice vary based on the comparisons they draw with other consumers' experiences. Second, the results confirmed that interactional justice has a moderating effect on procedural justice, whereas procedural justice does not significantly moderate distributive justice.

First, based on justice theory, social comparison theory and referent cognitions theory, we focus on the influence of the treatment received by other consumers on the consumer's perceived justice in the same service recovery situation. Second, it is proposed that the three justice dimensions follow a defined sequence through the service recovery phases. Third, to the best of the authors' knowledge, this study is the first to propose a multistage model in which some justice dimensions influence other justice dimensions.

: 本研究擬探討在服務補救的處境裡, 消費者對公平的看法不但會受公司的行動所影響, 同時也會因他們與其他消費者的經驗作比較而有所改變。

: 本研究根據正義理論、社會比較理論和參照認知理論, 描述一個涵蓋八個處境的實驗, 實驗包含更好的或更差的互動的、程序上的和分配性的公平 (給予其他消費者更好的/更差的互動公平) × 2(給予其他消費者更好的/更差的程序上的公平) × 2 (給予其他消費者更好的/更差的分配性的公平)。

: 研究結果顯示, 消費者對互動的、程序上的和分配性公平的看法, 是會根據他們與其他消費者的體驗所作的比較而有所改變; 研究結果亦確認了互動的公平對程序上的公平會有調節作用, 而程序上的公平對分配性的公平則沒有顯著的調節作用。

: 首先, 我們根據正義理論、社會比較理論和參照認知理論, 把研究焦點放在於相同的服務補救情景中, 其他消費者受到的待遇, 如何影響消費者自身的認知公平; 另外, 我們建議, 這三個公平維度, 在各個服務補救階段裡, 均會跟隨一個清晰的次序。最後, 就研究人員所知, 本研究為首個提出一個公平維度互為影響的多階段模型的研究。

Under the trend of global economic integration and the new context of stagflation, frequent fluctuations in international interest rates are exerting far-reaching impacts on the world economy. In this paper, the transmission mechanism of the impact of fluctuations in international interest rates (specifically, the American interest rate) on the bankruptcy risk in China's pillar industry, the construction industry (which is also sensitive to interest rates), is examined.

Using an improved contingent claims analysis, the bankruptcy risk of enterprises is calculated in this paper. Additionally, an individual fixed-effects model is developed to investigate the mediating effects of international interest rates on the bankruptcy risk in the Chinese construction industry. The heterogeneity of subindustries in the industrial chain and the impact of China's energy consumption structure are also analysed in this paper.

The findings show that fluctuations in international interest rates, which affect the bankruptcy risk of China's construction industry, are mainly transmitted through two major pathways, namely, commodity price effects and exchange rate effects. In addition, the authors examine the important impact of China's energy consumption structure on risk transmission and assess the transmission and sharing of risks within the industrial chain.

First, in the research field, the study of international interest rate risk is extended to domestic-oriented industries. Second, in terms of the research content, this paper is focused on China-specific issues, including the significant influence of China's energy consumption structure characteristics and the risk contagion (and risk sharing) as determined by the current development of the Chinese construction industry. Third, in terms of research methods a modified contingent claim analysis approach to bankruptcy risk indicators is adopted for this study, thus overcoming the problems of data frequency, market sentiment and financial data fraud, which are issues that are ignored by most relevant studies.

The COVID-19 outbreak has disrupted the habits of customers as well as their shopping behavior. This study aims to critically examine the associated benefits and challenges of online shopping from the perspective of customers in the COVID-19 pandemic.

A systematic review of the relevant literature published between 2020 and 2022 was conducted via performing comprehensive search query in leading scholarly databases “Scopus and Web of Science” with the restriction of their predefined subject category of “Business.” Overall, 30 research studies were selected for the review and a significant number of studies were published in 2021 (n = 15).

The research findings revealed that customers are motivated to shop online because of perceived benefits such as time-saving, convenience, 24/7 accessibility, interactive services without physical boundaries, trust, website attractiveness and cost-saving. However, challenging factors such as financial scams, privacy concerns, poor quality of products and services, fake promotions and reduced social interaction have hindered the growth of online shopping. The recommendations regarding designing marketing strategies, secured transaction, multiple payment options, trust building, protection of privacy, promotion via social media, effective mechanism to secure and timely delivery of product are helpful to improve the service quality of online shopping.

The outcomes of this research are valuable to online retailers and policymakers, as it highlights how the benefits can enhance customers’ shopping intentions and minimize the impact of associated challenges. This study also recommends the redesigning of user-friendly interfaces of online shopping websites and ensures their privacy, security and performance on a regular basis.

This study aims to examine the determinants that influence housing prices in Dammam metropolitan area (DMA), Saudi Arabia, by using the analytic hierarchy process (AHP) model. The study considers determinants such as building age (BLD AG), building size (BLD SZ), building condition (BLD CN), access to parking (ACC PK), proximity to transport infrastructure (PRX TRS), proximity to green areas (PRX GA) and proximity to amenities (PRX AM).

The AHP decision model was used to assess the determinants of housing prices in DMA, using a pair-wise comparison matrix to determine the influence of the investigated factors on housing prices.

The study’s results revealed that building size (BLD SZ) was the most critical determinant affecting housing prices in DMA, with a weight of 0.32, trailed by proximity to transport infrastructure (PRX TRS), with a weight of 0.24 as the second most influential housing price determinant in DMA. The third most important determinant was proximity to amenities (PRX AM), with a weight of 0.18.

This study addresses a research gap by using the AHP model to assess the spatial determinants of housing prices in DMA, Saudi Arabia. Few studies have used this model in examining housing price factors, particularly in the context of Saudi Arabia. Consequently, the findings of this study provide unique insights for policymakers, housing developers and other stakeholders in understanding the importance of building size, proximity to transport infrastructure and proximity to amenities in influencing housing prices in DMA. By considering these determinants, stakeholders can make informed decisions to improve housing quality and prices in the region.

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 Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

The purpose of this study is to construct a new index to assess the impact of an energy price shock on macroeconomic indicators of India. This paper also shows a comparative analysis of the constructed index along with pre-existing World Bank and International Monetary Fund indices on energy.

This paper uses three vector autoregressions and compute the long-term impact of the indices on the considered macroeconomic variables through impulse response functions.

This paper finds that an energy price shock has a detrimental impact on the macroeconomic indicators of India in the long run. This study also finds that the constructed index acts as a relatively more sensitive index in comparison to the International Monetary Fund and World Bank indices, which is bespoke to a developing economy case. This sensitivity is ascribed to dynamic weighting for a different basket of energy components, which are more pertinent to an Indian context.

The novelty of this research lies in the construction of a new index and its comparison to the existing ones. This study justifies why a developing economy would require a different measure of energy as opposed to the existing indices.

Integrating artificial intelligence and quantitative investment has given birth to various agricultural futures price prediction models suitable for nonlinear and non-stationary data. However, traditional models have limitations in testing the spatial transmission relationship in time series, and the actual prediction effect is restricted by the inability to obtain the prices of other variable factors in the future.

To explore the impact of spatiotemporal factors on agricultural prices and achieve the best prediction effect, the authors innovatively propose a price prediction method for China's soybean and palm oil futures prices. First, an improved Granger Causality Test was adopted to explore the spatial transmission relationship in the data; second, the Seasonal and Trend decomposition using Loess model (STL) was employed to decompose the price; then, the Apriori algorithm was applied to test the time spillover effect between data, and CRITIC was used to extract essential features; finally, the N-Beats model was selected as the prediction model for futures prices.

Using the Apriori and STL algorithms, the authors found a spillover effect in agricultural prices, and past trends and seasonal data will impact future prices. Using the improved Granger causality test method to analyze the unidirectional causality relationship between the prices, the authors obtained a spatial effect among the agricultural product prices. By comparison, the N-Beats model based on the spatiotemporal factors shows excellent prediction effects on different prices.

This paper addressed the problem that traditional models can only predict the current prices of different agricultural products on the same date, and traditional spatial models cannot test the characteristics of time series. This result is beneficial to the sustainable development of agriculture and provides necessary numerical and technical support to ensure national agricultural security.