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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 study aims to study the extent of research done in luxury marketing in an emerging economy like India by conducting a bibliometric analysis. A period of 21 years has been considered to present a comprehensive picture for results and analysis. Key findings indicate the gaps and scope of further research for academics in India and abroad. The findings indicate a dearth of research by scholars and academicians in luxury, counterfeit and masstige, especially when there is a surge of the upper middle class in India. More specifically, Indian-grown luxury brands also present a massive scope for future research.

The fast fashion industry is one of the most polluting industries. For this reason, the industry should look into new circular business models in order to reduce its material footprint as well as the amount of waste produced. This article focuses on the question of how the sharing economy, as one possible circular business model, can contribute to achieving Sustainable Development Goal 12 (SDG 12) “Ensuring Sustainable Consumption and Production.” After a brief introduction to SDG 12, a short outline of the current development of the sharing economy in the fast fashion sector is given. To develop consumer buying behavior toward environmental sustainability, it is important to understand their motives. Utilitarian and hedonic motives are examined in order to determine to what extent they can positively influence buying intention and thus the acceptance of fashion sharing platforms. The database gathered through a master thesis is used to investigate the specific influence these motives have on buying intention. To increase the acceptance and thus the use of fashion sharing platforms, recommendations for action are developed in the final step of this chapter throughout the five steps of the buying cycle model. Circular business models will play a key role in the context of sustainable transformation in the future. Therefore, it is particularly important to derive concrete recommendations for action based on research in order to get the ecological footprint of environmentally harmful industries – such as the fast fashion industry – under control.

Subscription offerings are being hailed as the next service growth engine for companies in both business-to-consumer (B2C) and business-to-business (B2B) markets. The study analyzes how a manufacturing firm can develop and implement a scalable service-based subscription business model for B2C and B2B customers alongside its existing product-centric model.

A longitudinal case study is conducted, drawing on 25 in-depth interviews with company executives and dealers in key European markets.

The study outlines an iterative process model for subscription business model innovation. It reveals key events and decisions taken in developing, implementing, and scaling the new business model and how internal and external tensions involving intermediaries arose and were mitigated during the four stages of the process.

The findings highlight the dynamics of business model innovation processes and underscore the importance of organizational learning, collaborative relationships with channel partners, and strategic talent acquisition during business model innovation.

The findings suggest how product-centric firms can implement new service business models alongside existing product models and what this means for partner and customer journey management.

While servitization research predominantly concerns B2B manufacturers, B2C research focuses on digital subscription contexts. The study bridges this divide by investigating the move to subscriptions in both markets.

Product-as-a-Service (PaaS) has the potential to enable closed-loop supply chains (CLSC) and decrease environmental impact, but it is only applied on a small scale. The purpose of this paper is to explore and develop a framework of challenges and corresponding mitigations encountered by Business-to-Consumer (B2C) retailers when transitioning to PaaS.

Data collection drew on a qualitative interview study with two industry experts and four PaaS B2C retailers from different Dutch industries.

A framework was developed linking 26 challenges in eight clusters—financial, product-related, supply chain-related, consumer-related, human resources, research and development/technology, regulatory and industry-related—to 24 mitigations. The mitigations were elaborated, and theoretical insights for matching challenges with mitigations were provided.

This study expands PaaS literature to the generally under-researched retail context. It contributes to CLSC literature by applying it to a less-studied context, thereby revealing many supply chain-related challenges and mitigations encountered by B2C retailers.

The framework offers practical guidance to retail managers for overcoming or preventing challenges in PaaS, in their endeavours toward adopting environmentally sustainable practices.

The study creates awareness about environmental sustainability and the potential to reduce societal impact, in which a PaaS-enabled CLSC is one step.

Studying PaaS and CLSC in a retail context is timely and novel.

Food quality and safety issues have always been imperative topics discussed by people. The anti-tampering of blockchain technology and the transparency of information make it possible to improve food traceability and safety quality. How to effectively apply blockchain traceability technology to food safety has great research significance for improving food safety and consumer quality trust.

The paper aims to analyze the differences in product quality levels and market participants’ profits before and after the use of blockchain-driven traceability technology in the food agricultural product supply chain (SC) in the dynamic game frameworks of supplier-led and retailer-led modes, respectively, and explores the willingness, social welfare and consumer surplus of each member of the agricultural product SC to participate in the blockchain. Besides, We investigate the SC performance improvement with the mechanism of central centralized decision-making and revenue-sharing contract, compared to the SC performance in dynamic games.

The results are obtained as follow: The adoption of blockchain traceability technology can help improve the quality of food agricultural products, consumer surplus and social welfare, but the application and popularization of technology is hindered by traceability technology installment costs. Compared with the supplier leadership model, retailer-led food quality level, customer surplus and social welfare are higher.

How to effectively apply blockchain traceability technology to food safety has great research significance for improving food safety and consumer quality trust.

Food quality and safety issues have always been hot topics discussed by people. The anti-tampering of blockchain technology and the transparency of information make it possible to improve food traceability and safety quality.

The research results enrich the theories related to food safety and quality, and provide a valuable reference for food enterprises involved in the decision-making exploration of blockchain technology.

Based on the characteristics of blockchain technology, the demand function is adjusted and the product loss risk of channel members is transferred through a Stackelberg game SC composed of agricultural products suppliers and retailers.

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  We introduce two features of blockchain: quality trust and product information tracking.

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  The willingness of each member of the supply chain to use blockchain for product traceability was explored.

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  The overall traceability effect of the retailer-led blockchain is better than that of the manufacturer-led blockchain.

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  The cost of blockchain technology is a barrier to its adoption.

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  Blockchain brings higher consumer surplus and social welfare.

We introduce two features of blockchain: quality trust and product information tracking.

The willingness of each member of the supply chain to use blockchain for product traceability was explored.

The overall traceability effect of the retailer-led blockchain is better than that of the manufacturer-led blockchain.

The cost of blockchain technology is a barrier to its adoption.

Blockchain brings higher consumer surplus and social welfare.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

The real estate industry has rapidly changed due to technological advances across residential and commercial real estate from the perspective of occupiers, investors, and service providers. Owners and buyers of properties have access to increasing information in the marketplace, including access to residential real estate platforms such as Zillow. Automated appraisals and artificial intelligence (AI) in the mortgage application process speed up home buying. Commercial real estate uses fintech to source deals, perform due diligence, and execute property management requests. This chapter includes a practitioner's view of the current and future information data needs, processes, and point solutions in the evolving technology landscape, including how tools such as ChatGPT apply. It concludes that the real estate fintech revolution has only begun, as data gaps in the real estate market require resolution before yielding better process automation and as the business model of real estate service providers shifts to strategic advisory roles.

This paper aims to contribute to understanding Brazilian fashion consumer behavior. The subsequent research question is formulated as follows: How are the consumers purchasing new clothes and disposing of used ones, and how is their awareness of sustainable fashion consumption and disposal of used clothes?

An online questionnaire was sent to nearly one thousand e-mails. A database was formed with 182 complete answers to 13 questions concerning consumer behavior toward sustainability, especially clothing acquisition, use and disposal. A multimethod approach was used to analyze the initial attributes, applying descriptive statistics, cluster analysis and data mining.

This survey obtained valuable answers from Brazilian fashion consumers grouped into four clusters. Age and yearly income were more critical in determining the clusters. Only four attributes were chosen by the algorithm to build the trees (age, annual income, yearly spending on clothes and how long the clothes are worn). The consumer's profile may help the fashion industry redirect investments in sustainability. The most critical factor leading to the sustainability of clothing fashion was the duration of the clothes. The study dealt with a limited sample size that was not representative of Brazil's broader population. Despite numerous attempts to seek responses through e-mail, the participant pool was predominantly composed of highly educated individuals.

This assessment of Brazilian consumer behavior toward sustainability and fashion presents essential knowledge to understand the relationships among variables affecting the purchase and discharge of clothes.