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Until now, it is not clear whether there are differences in patient perception between multi-bedded rooms with two and four beds. The purpose of this study was to investigate the effect of the physical (i.e. room type) and psychosocial (i.e. kindness of roommates and extraversion) aspects on the patients’ experience (i.e. pleasantness of the room, anxiety, sleep quality) in multi-bedded rooms in an oncology ward.

A group of 84 hospitalized oncology patients completed a questionnaire on the day of departure. Room types were categorized into two groups: two-person and four-person rooms.

Multivariate logistic regression analyses with the minimum Akaike Information Criterion (AIC) showed no direct main effects of room type (two vs. four-person room), kindness of roommates and extraversion on pleasantness of the room, anxiety and sleep quality. However, the authors found an interaction effect between room type and extraversion on pleasantness of the room. Patients who score relatively high in extraversion rated the room as more pleasant when they stayed in a four-person rather than a two-person room. For patients relatively low in extraversion, room type was not related to pleasantness of the room.

The findings allow hospitals to better understand individual differences in patient experiences. Hospitals should inform patients about the benefits of the different room types and potential influences of personality (extraversion) so patients are empowered and can benefit from autonomy and the most appropriate place.

This study emphasizes the importance of including four-person rooms in an oncology ward, while new hospital facility layouts mainly include single-bed rooms.

The purpose of this study is to present a methodology for enhancing the quality and usefulness of online reviews for prospective customers to investigate how this contemporary form of instrumental support can be facilitated to strengthen customer-to-customer support.

This study develops an analytics framework with applications of machine learning models using customer review data from Amazon.com. Linear regression is commonly used for review helpfulness and sales prediction. In this study, Random Forest model is applied because of its strong performance and reliability. To advance the methodology, a custom script in Python is created to generate Partial Dependence Plots for intensive exploration of the dependency interpretations of review helpfulness and sales. The authors also apply K-Means to cluster reviewers and use the results to generate reviewer qualification scores and collective reviewer scores, which are incorporated into the review facilitation process.

The authors find the average helpfulness ratio of the reviewer as the most important determinant of reviewer qualification. The collective reviewer qualification for a product created based on reviewers’ characteristics is found important to customers’ purchase intentions and can be used as a metric for product comparison.

The findings of this study suggest that service improvement efforts can be performed by developing software applications to monitor reviewer qualifications dynamically, bestowing a badge to top quality reviewers, redesigning review sorting interfaces and displaying the consumer rating distribution on the product page, resulting in improved information reliability and consumer trust.

This study adds to the research on customer-to-customer support in the service literature. As customer reviews perform as a contemporary form of instrumental support, the authors validate the determinants of review helpfulness and perform an intensive exploration of its dependency interpretation. Reviewer qualification and the collective reviewer qualification scores are generated as new predictors and incorporated into the helpfulness-based review facilitation services.