Accordingly, given the networked nature of social interactions on online review websites, it is essential to explore how network structural positions relate to reviewer characteristics in online reviews, as this can help business managers precisely segment online reviewers and devise efficient marketing strategies for targeted segments. In these ways, a reviewer–brand network or a reviewer–reviewer network is formed within online review websites, offering brand managers an opportunity to understand the characteristics of online reviewers through network structural positions, given that the features of individuals’ influences can be inferred from their locations in the network (e.g., Lee et al., 2010 Risselada et al., 2016). For example, reviewers on Yelp can interact with brands via reviews and replies or link with other reviewers by adding friends or peer evaluation votes. Although network structural positions and individual characteristics are not independent of each other (Muller & Peres, 2019), no prior studies have been devoted to integrating the two in the context of online reviews.Īn increasing number of online review websites have incorporated social networking functions (Li et al., 2017). Particularly, several researchers suggest that being an opinion leader or influencer is a significant characteristic that distinguishes individuals in central positions in the network from the rest of the individuals in the network (e.g., Kratzer & Lettl 2009 Litterio et al., 2017 Risselada et al., 2016). In these investigations, individuals’ centrality in a social network has been found to positively correlate with both opinion leadership and susceptibility to interpersonal influence (Lee et al., 2010). Relationships between network structural positions and individual characteristics have been explored in previous studies (Kratzer & Lettl, 2009 Lee et al., 2010 Litterio et al., 2017 Risselada et al., 2016 Van Eck et al., 2011 Zhu et al., 2014). Nonetheless, past research has been limited to the examination of reviewers’ demographic or psychographic features (e.g., Ma et al., 2013 Zhang et al., 2016) and has overlooked reviewer characteristics from the perspective of network structure. Among these studies, reviewer characteristics (e.g., gender, experience, and geographic mobility) have been a significant topic of researcher focus, given that these characteristics not only serve as essential bases of market segmentation in marketing strategies (Kotler & Keller, 2006) but also play an important role in moderating the effects of prior reviews/ratings on subsequent reviews/ratings (Li et al., 2020 Ma et al., 2013). Additionally, several studies have shown concern about the social influence of prior reviews/ratings on subsequent reviews/ratings (e.g., Lee et al., 2015 Li et al., 2020 Ma et al., 2013). Previous studies on online reviews have paid much attention to social influence issues, and most of these studies revealed the significant impacts of various online review characteristics, including images (Zinko et al., 2020), emotional content (Guo et al., 2020), inconsistent reviews (Steur et al., 2022), review quality (Lee & Shin, 2014), information overload (Furner & Zinko, 2017), and information helpfulness (Filieri et al., 2018) on consumer purchase intentions. Online reviews have been a significant topic that has drawn attention from not only business managers but also academic researchers over the past several years. Online reviews provide rich information sources regarding product or service purchase experiences (Ahani et al., 2019 Gao et al., 2018 Li et al., 2017), affecting nearly half of all buying decisions among consumers (Mathwick & Mosteller, 2017). Websites that offer online reviews, such as Yelp, TripAdvisor, Amazon, and Netflix, have been prevalent in recent years. The study has significant theoretical and practical implications for researchers and brand managers who are interested in understanding online review markets. ![]() The results of this study showed that compared to peripheral reviewers, core reviewers exhibited significantly more photos and brands reviewed and included a higher proportion of early reviewers. ![]() The study used multiple data collection and analysis approaches, including web scraping, network analysis, and statistical analysis. Accordingly, using data from Yelp websites as samples, this study attempted to explore the differences in reviewer characteristics by network structural positions. ![]() Nonetheless, past studies have overlooked the role of network structural positions in the characteristics of online reviewers. ![]() With the prevalence of online review websites, understanding online reviewer characteristics has become important, as such an understanding provides brand managers with opportunities to segment their markets, target influencers, and develop effective marketing strategies.
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