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This study aimed to identify distinctive characteristics of hotel guests and understand their perception of guestroom technologies (GRTs) in hotels by utilizing the theory of consumer innovativeness and the social influence theory.

The data were collected from 268 hotel guests. The K-means clustering algorithm was employed to identify participants based on their views on the significance of technologies provided in guest rooms. A multivariate analysis of variance was applied to investigate if there were significant differences among clusters regarding social influence on hotel bookings, technology innovativeness and technology expertise and knowledge.

Two clusters were identified: technology compassionates and casual users of technology. Findings revealed that technology compassionates are more influenced by their friends when booking a hotel compared to casual users of technology. The ability to link up multiple wireless mobile devices, accessible outlets and mobile websites were the most critical GRTs for technology compassionates.

Technology should be considered a crucial part of the hotel guest experience. Hoteliers ought to continue investing in smart technologies to improve their guests' experiences. GRTs can reduce overhead staff costs while giving guests more control over their stay by utilizing everyday items like smartphones and offering them more power over their lodging experience.

This study advances the existing literature on GRTs by identifying which GRTs produce the most customer satisfaction. Moreover, this study explores the impact of social influence, innovativeness as a personality trait and having expert knowledge of technologies on preferences for GRTs.

This study aims to explore the use of time series analyses to examine changes in travelers’ preferences in accommodation features by disentangling seasonal, trend and the COVID-19 pandemic’s once-off disruptive effects.

Longitudinal data are retrieved by online traveler reviews (n = 519,200) from the Canary Islands, Spain, over a period of seven years (2015 to 2022). A time series analysis decomposes the seasonal, trend and disruptive effects of six prominent accommodation features (view, terrace, pool, shop, location and room).

Single accommodation features reveal different seasonal patterns. Trend analyses indicate long-term trend effects and short-term disruption effects caused by Covid-19. In contrast, no long-term effect of the pandemic was found.

The findings stress the need to address seasonality at the single accommodation feature level. Beyond targeting specific features at different guest groups, new approaches could allow dynamic price optimization. Real-time insight can be used for the targeted marketing of platform providers and accommodation owners.

A novel application of a time series perspective reveals trends and seasonal changes in travelers’ accommodation feature preferences. The findings help better address travelers’ needs in P2P offerings.