Recognition of the importance of the quality of the indoor environment (IEQ) to health, comfort and productivity of building end users has produced increasing numbers of voluntary schemes whose assessment embraces a wide spectrum of environmental attributes. Studies which aim to derive appropriate weighting factors for these attributes through soliciting the perceived importance from experts are abundant. This article reports the findings of a study which, based on face-to-face interviews with 548 end users and 66 building professionals, processed their perceived importance of IEQ using the analytical hierarchy process (AHP). Attributes included were thermal comfort, air cleanliness, odor and noise associated with the air conditioning system of typical commercial buildings. Correlation analysis of the ranking results of the AHP weights revealed the difference in perceived importance of the attributes according to gender of the respondents. Other factors also found to have influence on the perceived importance of the IEQ were whether the respondents were professionals or other end users and the reason for them working or visiting the buildings and the duration of their stay. These all varied with psychophysical factors such as personal experiences, needs and expectations. Further work is needed to study whether the weighting factors should be derived from the perceptions of experts, end users, or a balance between the two.

Whereas the use of specific smart technologies in various sectors of the tourism industry has been under greater scrutiny in recent years, research that investigates tourists’ acceptance of smart technologies applied as a whole to hotels is largely underexplored. To address this shortfall, a study with a focus on tourists’ acceptance of experience-enhancement smart technologies has been conducted. A technology acceptance model based conceptual framework was developed, followed by a series of interviews with the managerial staff of ten hotels in Hong Kong that adopt experience-enhancement smart technologies. Based on the interview findings, a questionnaire was designed for use in a survey in which data were collected face-to-face from 312 tourists. Structural equation modelling was utilised to reveal the interrelationships between nine technology acceptance parameters. This study contributes knowledge to both real-world practice and research in tourism management.