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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 coronavirus disease 2019 (COVID-19) pandemic adversely affected small and medium-sized enterprises (SMEs) in India. Amongst the challenges faced were the adjustments required in leadership skills to address pandemic-induced technological changes and the necessity for employee skill upgrading. This study examined the factors that influenced organisational performance in Indian SMEs, particularly in the context of the digital transformations that were brought about by the pandemic.

The study employed a cross-sectional design to investigate a set of hypotheses that were formulated to understand the relationships amongst digital leadership, digital training, empowerment and organisational performance. The data were collected during the pandemic from 487 employees who were working in various SMEs in India. Questionnaires were distributed through email and social media platforms, and electronic consent was obtained from each participant.

The study's findings indicated positive associations amongst digital leadership, digital training, empowerment and organisational performance. They also highlighted the mediating role of empowerment in these relationships. Furthermore, organisational resilience was found to positively moderate the relationship between empowerment and performance.

The study stands amongst the pioneers in exploring the role of digital leadership and digital training during the pandemic and their impact on employee empowerment amongst SMEs in a developing country.

The purpose of this multi-voiced paper is to propose a prosocial paradigm for the field of performance management and management control systems. This new paradigm suggests cultivating prosocial behaviour and prosocial groups in organizations to simultaneously achieve the objectives of economic performance and sustainability.

The authors share a common concern about the future of humanity and nature. The authors challenge the influential assumption of economic man from neoclassical economic theory and build on evolutionary science and the core design principles of prosocial groups to develop a prosocial paradigm.

Findings are based on the premise of the prosocial paradigm that self-interested behaviour may outperform prosocial behaviour within a group but that prosocial groups outperform groups dominated by self-interest. The authors explore various dimensions of performance management from the prosocial perspective in the private and public sectors.

The authors call for theoretical, conceptual and empirical research that explores the prosocial paradigm. They invite any approach, including positivist, interpretive and critical research, as well as those using qualitative, quantitative and interventionist methods.

This paper offers implications from the prosocial paradigm for practitioners, particularly for executives and managers, policymakers and educators.

Adoption of the prosocial paradigm in research and practice shapes what the authors call the prosocial market economy. This is an aspired cultural evolution that functions with market competition yet systematically strengthens prosociality as a cultural norm in organizations, markets and society at large.