Dataset Information

Use numbers not words! Communicating hotels' cleaning programs for COVID-19 from the brand perspective.

ABSTRACT: After hotels in many countries were forced to close in government-imposed lockdowns during the COVID-19 pandemic, there is an inherent need to communicate how they deal with the coronavirus to motivate guests to visit. However, lack of knowledge about how to persuasively communicate about hotels' cleaning programs for COVID-19 can challenge the industry's survival. We investigated how hotels that position their brand as a particular personality (sincere vs. exciting) could benefit from different communication styles (inclusion of numerical vs. verbal quantifiers) when presenting their COVID-19 cleaning procedures. Study 1 explored tourists' central attitudinal responses toward hotels' cleaning programs. Study 2 demonstrated that sincere hotel brands would benefit from using numerical and verbal quantifiers to communicate their cleaning policies, whereas exciting hotel brands would benefit only from numerical quantifiers. Our results invite hotel managers to use their brand personality positioning to influence tourists' attitudes and intentions in a pandemic context.

SUBMITTER: Jimenez-Barreto J

PROVIDER: S-EPMC8055116 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:The increased availability of patient reported outcome data makes it feasible to provide patients tailored risk information of cancer treatment side effects. However, it is unclear how such information influences patients' risk interpretations compared to generic population-based risks, and which message format should be used to communicate such individualized statistics. METHODS:A web-based experiment was conducted in which participants (n?=?141) read a hypothetical treatment decision-making scenario about four side effect risks of adjuvant chemotherapy for advanced colon cancer. Participants were cancer patients or survivors who were recruited from an online Dutch cancer patient panel. All participants received two tailored risks (of which the reference class was based on their age, gender and tumor stage) and two generic risks conveying the likelihood of experiencing the side effects. The risks were presented either in words-only ('common' and 'very common'), or in a combination of words and corresponding numerical estimates ('common, 10 out of 100' and 'very common, 40 out of 100'). Participants' estimation of the probability, accuracy of their estimation, and perceived likelihood of occurrence were primary outcomes. Perceived personal relevance and perceived uncertainty were secondary outcomes. RESULTS:Tailored risks were estimated as higher and less accurate than generic risks, but only when they were presented in words; Such differences were not found in the verbal and numerical combined condition. Although tailoring risks did not impact participants' perceived likelihood of occurrence, tailored risks were perceived as more personally relevant than generic risks in both message formats. Finally, tailored risks were perceived as less uncertain than generic risks, but only in the verbal-only condition. CONCLUSIONS:Considering current interest in the use of personalized decision aids for improving shared decision-making in oncology, it is important that clinicians consider how tailored risks of treatment side effects should be communicated to patients. We recommend both clinicians who communicate probability information during consultations, and decision aid developers, that verbal descriptors of tailored risks should be supported by numerical estimates of risks levels, to avoid overestimation of risks.

Project description:BackgroundVarious types of framing can influence risk perceptions, which may have an impact on treatment decisions and adherence. One way of framing is the use of verbal terms in communicating the probabilities of treatment effects. We systematically reviewed the comparative effects of words versus numbers in communicating the probability of adverse effects to consumers in written health information.MethodsNine electronic databases were searched up to November 2012. Teams of two reviewers independently assessed studies.Inclusion criteriarandomised controlled trials; verbal versus numerical presentation; context: written consumer health information.ResultsTen trials were included. Participants perceived probabilities presented in verbal terms as higher than in numeric terms: commonly used verbal descriptors systematically led to an overestimation of the absolute risk of adverse effects (Range of means: 3% - 54%). Numbers also led to an overestimation of probabilities, but the overestimation was smaller (2% - 20%). The difference in means ranged from 3.8% to 45.9%, with all but one comparison showing significant results. Use of numbers increased satisfaction with the information (MD: 0.48 [CI: 0.32 to 0.63], p < 0.00001, I2 = 0%) and likelihood of medication use (MD for very common side effects: 1.45 [CI: 0.78 to 2.11], p = 0.0001, I2 = 68%; MD for common side effects: 0.90 [CI: 0.61 to 1.19], p < 0.00001, I2 = 1%; MD for rare side effects: 0.39 [0.02 to 0.76], p = 0.04, I2 = not applicable). Outcomes were measured on a 6-point Likert scale, suggesting small to moderate effects.ConclusionsVerbal descriptors including "common", "uncommon" and "rare" lead to an overestimation of the probability of adverse effects compared to numerical information, if used as previously suggested by the European Commission. Numbers result in more accurate estimates and increase satisfaction and likelihood of medication use. Our review suggests that providers of consumer health information should quantify treatment effects numerically. Future research should focus on the impact of personal and contextual factors, use representative samples or be conducted in real life settings, measure behavioral outcomes and address whether benefit information can be described verbally.

Dataset Information

Use numbers not words! Communicating hotels' cleaning programs for COVID-19 from the brand perspective.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets