Abstract
Diagnosticity of a given online review is defined as the extent to which it helps users make informed purchase decisions. Users' perception of review diagnosticity can be associated with five factors, namely, review rating, review depth, review readability, reviewer profile and product type. Review rating refers to the numerical valence of reviews on a scale of one to five. Review depth refers to the quantity of textual arguments provided in reviews. Review readability measures the extent to which the textual arguments are comprehensible. Reviewer profile indicates the past track record of users who contribute reviews. Product type includes experience products and search products. Few studies hitherto have analyzed review diagnosticity taking into account all these factors concurrently. Hence, this paper attempts to augment prior research by developing a theory of diagnosticity for online reviews. The theory posits that review diagnosticity is shaped by the interplay among review rating, review depth, review readability and reviewer profile albeit differently between experience products and search products.
| Original language | English |
|---|---|
| Pages (from-to) | 477-482 |
| Number of pages | 6 |
| Journal | Lecture Notes in Engineering and Computer Science |
| Volume | 2209 |
| Issue number | January |
| Publication status | Published - 2014 |
| Externally published | Yes |
| Event | International MultiConference of Engineers and Computer Scientists, IMECS 2014 - Kowloon, Hong Kong Duration: Mar 12 2014 → Mar 14 2014 |
ASJC Scopus Subject Areas
- Computer Science (miscellaneous)
Keywords
- Moderated multiple regression
- Online reviews
- Review diagnosticity
- Theoretical model