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This research aims to examine how consumer minimalism (CM), self-conscious feelings (such as consumer guilt (CG) and consumer pride (CP)) and the inclination to use rental services interact. It also looks at how attitudes toward pro-environmental advertisements affect these relationships as a moderator, recognizing the importance of pro-environmental advertising in influencing consumer behavior.

This study employed a quantitative methodology to investigate the aforementioned associations. Survey questionnaires are used to collect data, which is then analyzed using AMOS 25 and Process Macro to generate meaningful insights.

The findings indicate that the willingness to use rental services is directly associated with CM, while self-conscious emotions (SCEs) play the role of a mediator in this relationship.

It is essential to recognize the limitations of this study. There may be other variables at play, but the research focuses on SCEs (CG and CP) and their role as mediators. The findings must be interpreted based on the selected research methodology and sample size. Future research could investigate additional variables and enlarge the sample size to increase generalizability.

Targeted marketing can leverage CM, SCEs and willingness to use rental services. Recognizing the moderating effect of attitude toward pro-environmental advertisements can help create more effective campaigns promoting environmental behavior.

Underpinned by SCEs, the current study is one of the initial studies to explain how CM encourages responsible environmental behavior through access-based consumption models.

The purpose of this paper is to investigate how the pandemic affects tenants’ response to their lease obligations. This paper commences with examining the adopted tenant selection criteria during the COVID-19 pandemic. Then, this paper statistically tests if there is a relationship between selection criteria and response on whether the pandemic has effects or not. Then, this paper investigates the specific areas of impact on tenants’ ability to adequately keep to lease agreements in the Nigerian rental market. Finally, this paper proceeds to confirm if there is a relationship between selection criteria and the aspects of tenants’ deficiencies in rental obligations because of COVID-19.

Survey data, backed with interviews, is elicited from practicing estate surveyors and valuers and licensed property managers in Lagos, the largest property market in Nigeria and sub-Sahara Africa. Policy solutions and implications were solicited from personnel at the ministry of housing and senior professionals in the property sector. Data were analyzed using descriptive statistics, factor analysis and computer-aided qualitative data analysis, Atlas.ti.

Tenant’s health status is now accorded a priority together with others. Numbers of tenants are challenged with keeping to the prompt-rent-payment rule. Other areas of slight breaches included livestock rearing, subletting, alteration and repair covenants. Except for tenant reputation and tenant family size, there was no significant relationship between tenant’s health status consideration and the COVID-19 effect on tenant non-compliance with lease obligation. Tenants’ non-compliance with tenancy obligations has a connection with the tenants’ affordability, reputation, ability to sign an undertaking and health conditions during the pandemic. This paper recommends rental housing policy review.

It is recommended that the rental policy should be reviewed to give room for rental allowance or palliatives, private rental market regulation, exploration of the national housing fund and, if possible, social housing adoption policy in Nigeria.

This paper draws policymakers’ attention to the need to prepare for the future safety net that caters to citizenry welfare in challenging times.

This article seeks to offer an answer. It explores the criteria on which commercial property market participants can develop strategies in hugely challenging circumstances. For this purpose, a survey-based approach was developed with work conducted with property-market professional in the United Kingdom (UK), France, Germany and Sweden to produce a criteria-based tool supporting adaption to changing market circumstances.

The data have been analyzed using statistical analysis. The data's statistical analysis included Cronbach's alpha's application to evaluate the respondents' replies' reliability. A entral tendency test was used to identify the means of relevance of the criteria. The Mann–Whitney U test was used to determine potential material differences between the UK and other countries with Bonferroni corrections applied to minimize type-I errors.

Thirty characteristics have been identified that impact the dynamics of the commercial property market. Their relevance to the commercial property market was determined using a survey. The literature analysis showed that the researchers paid more attention to quantitative criteria and their comparison. The survey showed that the relevance of criteria to the commercial property market dynamics is unequal. However, the survey results showed that it is most important to pay attention to emotional criteria to adapt to uncertainty changing conditions. The problem of the environment has been on the agenda for the last four decades. Therefore, the fact that the results of the study showed that the environmental criteria are the least significant is unexpected.

The study involved economically developed countries of Europe. Extending the study's geographical scope would be valuable in revealing whether the same differences exist in other geographical areas (such as Australia or the USA).

The practical implication of the analysis may be to facilitate the decision-making process of either selecting a country for commercial property investment or selecting the most sensitive and relevant criteria for the decision-making.

Criteria for commercial property market performance which promote successful property investment have been developed. Moreover, the criteria affecting the commercial property market have been weighted by their relevance to the market and their sequence of relevance has been established. And finally, the developed criteria have been placed into five groups that could serve as a foundation for a macro-level assessment of commercial property market dynamics.

This study aims to explore the influence of external factors and the characteristics of young Indian consumers on their behavioral intention toward fashion clothing rental (FCR), using the theory of reasoned action (TRA) as a theoretical framework.

This study used quantitative research methods, collecting data from 396 Indian participants, and tested the proposed hypotheses using PLS-structural equation modeling.

The results indicated that young Indian consumers' favorable attitudes toward FCR are mainly driven by perceived usefulness, novelty-seeking, fashion orientation, narcissism and environmental consciousness. These drivers, together with subjective norms, further lead to their intention to rent fashion clothing. This study also found that perceived risk has a negative impact on consumers' attitudes toward FCR, but minimalism does not significantly affect consumer attitudes.

By integrating additional constructs into traditional TRA, this study contributes to existing literature and provides insight for fashion retailers on the role of consumer characteristics in the adoption of FCR in emerging markets.

The New Zealand city of Christchurch provides a leading example of post-disaster rebuilding in a Central Business District (CBD) area. In its rebuilding programme, the city has given emphasis to the greening of hospitality and traditional retail space through a combination of development of shared pedestrian spaces (with traffic exclusion and calming) and the integration of greening within the streetscape design. This paper aims to assess whether the development of greened pedestrian areas leads to higher retail spending and, thus, retail rental rates.

This study uses pedestrian movement data collected from several CBD locations, as well as spending data on retail and hospitality, to assess relationships between pedestrian movements and spending. This study explores retail spending in greened pedestrian shared spaces, and explores how this differs from retail spending in traditional street areas within the Christchurch CBD.

Spending patterns are location-related, depending on the characteristics of pedestrian space in the selected area. Greened shared pedestrian areas have the highest spending per measured pedestrian for retail and hospitality, whereas traditional street areas have lower spending for retail and hospitality per measured pedestrian, demonstrating the benefits in redeveloped central city areas.

The scope of smart data continues to develop as a research area within urban studies to develop an open and connected city. This research demonstrates the use of innovative technologies for data collection, use and sharing. The results support commercial benefits of greening and pedestrianisation of retail and hospitality areas for CBDs and providing an example for other cities to follow.

This study systematically explores the literature on global niche market strategies within the fashion industry to allow the mapping of niche practices and examine the benefits, success factors and characteristics of a niche strategy. Additionally, it identifies data gaps and necessitates a detailed examination to uncover areas with inadequate information.

This study utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA 2020) method for systematic review and included 70 studies to analyze their findings.

This systematic literature review pinpoints niche strategies shaping the future of the fashion industry while developing sectors of the textile and apparel industry, fashion technology, fashion retail business, fashion media and communication, luxury fashion, sustainable fashion, adaptive clothing and transgender fashion within the fashion supply chain. A niche market strategy utilizes both pull and push marketing in the fashion industry. Scholarly literature commonly underscores the understanding of the consumer as a pivotal factor in the success of fashion niche market strategy.

This review offers a comprehensive overview of fashion niche strategy practices, aiming to inspire fashion industry professionals. It also serves as a guide for fashion industry professionals, summarizing best practices across various fashion industry sectors to help develop effective niche strategy competencies for firms.

This review thoroughly analyzes niche strategy implementation in the fashion industry, presenting an important resource for individuals new to this sector. It highlights the significance of niche strategies in improving the comprehension of emerging participants in the fashion business.

The retail investment landscape in the UK has drastically changed. Understanding owners’ responses to this structural change is critical to gain insight into protecting investment performance. This paper identifies and evaluates the tactics and strategies being adopted.

This paper employs a mixed methods research approach, using data collected from semi-structured interviews with professionals involved in the retail investment process. This is supplemented by secondary market data analysis.

The paper assesses the practical responses made by retail property owners/managers, structured around emerging sub-themes. Actions include mitigating short-term risks through greater use of temporary tenants/licensees and independent retailers, preparing generic “white box” retail space to capture remaining tenant demand, exploiting the tenant mix to provide “the retail experience,” and applying new technologies and processes in a sector where systematic risk remains high. A new framework for retail asset management has been developed.

This study contributes to the retail asset management literature and understanding of the way the contemporary retail landscape is shaping investment management behaviour.

The developed framework provides guidance to real estate managers developing a retail real estate management strategy and will help them recognise tactics to better support the evolving retailing market.

The new framework adds new insights to understanding the process for managing retail assets and the actions necessary for asset managers to address economic/functional obsolescence and sustain asset investment values.

This study aims to analyse the effect of competition on retail fuel prices in a small European Union (EU) country with high market concentration.

The researchers use a panel data set to estimate a fuel price equation that includes supply and demand factors as well as time-fixed effects.

The study finds that more competitors in the local market decrease prices, whereas the high market share of oligopoly brands does not condition this effect. Additionally, independent brands set lower prices than wholesalers, and gas stations located near the borders of almost all neighbouring countries are associated with higher prices.

The study suggests that Slovenia’s retail fuel market maintains competitive pricing despite high oligopolistic shares because of historical regulatory influences that shaped firm behaviour and pricing strategies, along with geographical and economic factors such as Slovenia’s role as a transit country. External competitive pressures from neighbouring countries and high levels of traffic, combined with the remnants of regulatory structures, help prevent market abuses and keep fuel prices lower than in other EU countries.

It also indicates that policy should encourage fiercer competition in the local market by increasing the density of gas stations, especially from independent brands.

These findings may be associated with specific country characteristics. This paper introduces unique findings that shed light on the impact of a small market on competition, with a particular focus on highlighting the effect of oligopolistic brands.

Short-term rentals (STRs) (like Airbnb) are reshaping social behaviour, notably in gastronomy, altering how people dine while travelling. This study delves into revenue management, examining the impact of seasonality and dining options near guests’ Airbnb. Machine Learning analysis of Airbnb data suggests owners enhance revenue strategies by adjusting prices seasonally, taking nearby food amenities into account.

This study analysed 220 Airbnb establishments from Madrid, Spain, using consistent monthly price data from Seetransparent and environment variables from MapInfo GIS. The Machine Learning algorithm calculated average prices, determined seasonal prices, applied factor analysis to categorise months and used cluster analysis to identify tourism-dwelling typologies with similar seasonal behaviour, considering nearby supermarkets/restaurants by factors such as proximity and availability of food options.

The findings reveal seasonal variations in three groups, using Machine Learning to improve revenue management: Group 1 has strong autumn-winter patterns and fewer restaurants; Group 2 shows higher spring seasonality, likely catering to tourists, and has more restaurants, while Group 3 has year-round stability, fewer supermarkets and active shops, potentially affecting local restaurant dynamics. Food establishments in these groups may need to adapt their strategies accordingly to capitalise on these seasonal trends.

Current literature lacks information on how seasonality, rental housing and proximity to amenities are interconnected. The originality of this study is to fill this gap by enhancing the STR price predictive model through a Machine Learning study. By examining seasonal trends, rental housing dynamics, and the proximity of supermarkets and restaurants to STR properties, the research enhances our understanding and predictions of STR price fluctuations, particularly in relation to the availability and demand for food options.

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.