Motivation for writing long online reviews
A big data analysis of an anime community
Bibliographic Data
| ID | 12420938 |
|---|---|
| Authors | Kevin Leung (0000-0003-2136-0091, Hong Kong Polytechnic University, corresponding author), Vincent Cho (0000-0001-8822-9077, Hong Kong Polytechnic University) |
| Year | 2024 |
| Volume | 34 |
| Issue | 5 |
| Pages | 1845-1871 |
| Publication date | 2024-01-04 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Internet Research (JOURNAL) |
| Journal identifiers | ISSN: 1066-2243 • E-ISSN: 2054-5657 |
| Publisher | Emerald Publishing Limited (PUBLISHER • GB) |
| DOI | 10.1108/intr-07-2022-0548 |
| OpenAlex | W4390583072 |
| Language | EN |
| References cited | 62 |
Purpose Based on self-determination theory (SDT), this study aims to determine the motivation factors of reviewers writing long reviews in the anime industry. Design/methodology/approach This study analyzes 171,188 online review data collected from an online anime community (MyAnimeList.net). Findings The findings show that intensity of emotions, experience in writing reviews and helpful votes in past reviews are the most important factors and positively influence review length. The overall rating of the anime moderates the effects of some motivation factors. Moreover, reviewers commenting on their favorite or nonfavorite anime also have varied motivation factors. Furthermore, this study has addressed the p -value problem due to the large sample size. Research limitations/implications This study provides a comprehensive and theoretical understanding of reviewers' motivation for writing long reviews. Practical implications Online communities can incorporate the insights from this study into website design and motivate reviewers to write long reviews. Originality/value Many past studies have investigated what reviews are more helpful. Review length is the most important factor of review helpfulness and positively affects it. However, few studies have examined the determinants of review length. This study attempts to address this issue
Anime · Helpfulness · Originality · Sample (material · Value (mathematics · Computer Science · Digital Marketing and Social Media · Impact of Technology on Adolescents · Psychology · Social Media and Politics · Social Psychology
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| Highly cited | No |
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