A hotel that is not bad isn’t good: The effects of valence framing and expectation in online reviews on text, reviewer and product appreciation [Dataset]

DOI

Description [in Dataverse: Description] In online hotel reviews, reviewers use both direct and indirect positive and negative evaluations (e.g. ‘good’, ‘not bad’, ‘bad’, ‘not good’). In four studies, we examined the effects of these wording alternatives. In Study 1, participants rated hotel reviews that were manipulated with respect to the wording. In positive reviews, direct evaluations (‘good’) received higher evaluations than indirect wordings (‘not bad’). In negative reviews, however, no such difference was observed between direct and indirect expressions (‘not good’/‘bad’). These results apply to evaluations of the hotel, text and reviewer alike. Study 2 showed that this pattern of results generalizes to restaurant reviews. To investigate an underlying cause for the effects in Study 1 and 2, we manipulated participants’ a priori expectation of the attitude object (hotel) in two subsequent studies. The lack of an interaction effect between the wording and expectation manipulations shows that the pattern of results may be attributed to Verbal Politeness: wordings like ‘not bad’ convey a weakened meaning as compared to ‘good’, whereas the use of ‘not good’ instead of ‘bad’ is interpreted as expressing the same evaluation, albeit in a more polite way.

Identifier
DOI https://doi.org/10.34894/CDWC0T
Metadata Access https://dataverse.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.34894/CDWC0T
Provenance
Creator Kamoen, Naomi; Mos, Maria B. J.; Dekker, W. F. S. Robbin
Publisher DataverseNL
Contributor Kamoen, Naomi; DataverseNL
Publication Year 2016
Rights info:eu-repo/semantics/restrictedAccess
OpenAccess false
Contact Kamoen, Naomi (Tilburg University, The Netherlands: Tilburg School of Humanities: Department of Communication and Information Sciences and Utrecht University, The Netherlands)
Representation
Resource Type experimental data; De analyse van de data hebben we in multi-level programma MLWIN gedaan (.wsz files). Ik upload ook SPSS-versies van die bestanden omdat dataverse geen .wsz als standard opneemt.; Dataset
Format application/msword; application/octet-stream; application/x-spss-sav; application/pdf
Size 40752; 49097; 144017; 42159; 42318; 46538; 243377; 48703; 48335; 31545; 31410; 86825; 1359701; 98016
Version 2.1
Discipline Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Humanities; Life Sciences; Social Sciences; Social and Behavioural Sciences; Soil Sciences